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"login": "Emeka Ezike", - "name": "Emeka Ezike", - "avatar_url": "https://www.gravatar.com/avatar/af39c27c6090c50a1921a9b6366e81cc?d=identicon&s=100", - "profile": "https://github.com/harvard-edge/cs249r_book/graphs/contributors", + "login": "alex-oesterling", + "name": "Alex Oesterling", + "avatar_url": "https://avatars.githubusercontent.com/alex-oesterling", + "profile": "https://github.com/alex-oesterling", + "contributions": [] + }, + { + "login": "aryatschand", + "name": "Arya Tschand", + "avatar_url": "https://avatars.githubusercontent.com/aryatschand", + "profile": "https://github.com/aryatschand", "contributions": [] }, { @@ -328,13 +342,6 @@ "profile": "https://github.com/BrunoScaglione", "contributions": [] }, - { - "login": "Allen-Kuang", - "name": "Allen-Kuang", - "avatar_url": "https://avatars.githubusercontent.com/Allen-Kuang", - "profile": "https://github.com/Allen-Kuang", - "contributions": [] - }, { "login": "Gjain234", "name": "Gauri Jain", @@ -342,6 +349,13 @@ "profile": "https://github.com/Gjain234", "contributions": [] }, + { + "login": "Allen-Kuang", + "name": "Allen-Kuang", + "avatar_url": "https://avatars.githubusercontent.com/Allen-Kuang", + "profile": "https://github.com/Allen-Kuang", + "contributions": [] + }, { "login": "FinAminToastCrunch", "name": "Fin Amin", @@ -356,6 +370,13 @@ "profile": "https://github.com/harvard-edge/cs249r_book/graphs/contributors", "contributions": [] }, + { + "login": "vitasam", + "name": "The Random DIY", + "avatar_url": "https://avatars.githubusercontent.com/vitasam", + "profile": "https://github.com/vitasam", + "contributions": [] + }, { "login": "gnodipac886", "name": "gnodipac886", @@ -363,13 +384,6 @@ "profile": "https://github.com/gnodipac886", "contributions": [] }, - 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Emergency stop. + + +l.95 \msg_fatal:nn {fontspec} {cannot-use-pdftex} + +*** (cannot \read from terminal in nonstop modes) + + +Here is how much of TeX's memory you used: + 11624 strings out of 474116 + 218366 string characters out of 5735128 + 1928187 words of memory out of 5000000 + 33887 multiletter control sequences out of 15000+600000 + 558832 words of font info for 37 fonts, out of 8000000 for 9000 + 1141 hyphenation exceptions out of 8191 + 108i,1n,107p,10919b,271s stack positions out of 10000i,1000n,20000p,200000b,200000s +! ==> Fatal error occurred, no output PDF file produced! diff --git a/README.md b/README.md index 616bcfbb..2cbef5e8 100644 --- a/README.md +++ b/README.md @@ -13,11 +13,16 @@

- ⭐ Help Us Reach 1,000 GitHub Stars! ⭐
- For every 25 stars, Arduino and SEEED will donate a Nicla Vision or XIAO ESP32S3 for AI education. -
Your ⭐ makes a difference. Click below to support our mission! + 🌟 We Hit 1,000 GitHub Stars - Thank You! 🌟
+ Thanks to your support, Arduino and SEEED are donating Nicla Vision and XIAO ESP32S3 boards for AI education. +
But we’re not stopping here! Every 25 stars from here on helps us bring even more resources to the community.

+

+ ⭐ Keep the Momentum Going - Star Our Repo! ⭐ +

+ +

GitHub Stars @@ -68,11 +73,17 @@ If you're unsure where to start or have any questions, feel free to reach out th Want to build the book locally? Here's how: 1. **Install Quarto**: Follow the [Quarto installation instructions](https://quarto.org/docs/download/). -2. **Render the Book**: +2. **Render the Book in all formats**: ```bash cd cs249r_book quarto render ``` +3. **Render the Book in a specific format (works faster)**: + ```bash + cd cs249r_book + quarto render --to epub + ``` + --- ## Contributors @@ -86,24 +97,24 @@ This project follows the [all-contributors](https://allcontributors.org) specifi Vijay Janapa Reddi
Vijay Janapa Reddi
+ jasonjabbour
jasonjabbour
Ikechukwu Uchendu
Ikechukwu Uchendu
Naeem Khoshnevis
Naeem Khoshnevis
- jasonjabbour
jasonjabbour
- Douwe den Blanken
Douwe den Blanken
+ Marcelo Rovai
Marcelo Rovai
+ Sara Khosravi
Sara Khosravi
+ Douwe den Blanken
Douwe den Blanken
shanzehbatool
shanzehbatool
- Marcelo Rovai
Marcelo Rovai
+ Kai Kleinbard
Kai Kleinbard
Elias Nuwara
Elias Nuwara
- kai4avaya
kai4avaya
- Jared Ping
Jared Ping
+ Jared Ping
Jared Ping
Matthew Stewart
Matthew Stewart
Itai Shapira
Itai Shapira
Maximilian Lam
Maximilian Lam
Jayson Lin
Jayson Lin
- Sara Khosravi
Sara Khosravi
Sophia Cho
Sophia Cho
@@ -113,79 +124,83 @@ This project follows the [all-contributors](https://allcontributors.org) specifi Korneel Van den Berghe
Korneel Van den Berghe
- Zishen Wan
Zishen Wan
Colby Banbury
Colby Banbury
- Divya Amirtharaj
Divya Amirtharaj
+ Zishen Wan
Zishen Wan
Abdulrahman Mahmoud
Abdulrahman Mahmoud
Srivatsan Krishnan
Srivatsan Krishnan
+ Divya Amirtharaj
Divya Amirtharaj
- Haoran Qiu
Haoran Qiu
+ Emeka Ezike
Emeka Ezike
Aghyad Deeb
Aghyad Deeb
+ Haoran Qiu
Haoran Qiu
marin-llobet
marin-llobet
- Michael Schnebly
Michael Schnebly
- oishib
oishib
+ Emil Njor
Emil Njor
- Jared Ni
Jared Ni
Aditi Raju
Aditi Raju
+ Jared Ni
Jared Ni
+ Michael Schnebly
Michael Schnebly
+ oishib
oishib
ELSuitorHarvard
ELSuitorHarvard
- Emil Njor
Emil Njor
- Henry Bae
Henry Bae
+ Henry Bae
Henry Bae
+ Jae-Won Chung
Jae-Won Chung
Yu-Shun Hsiao
Yu-Shun Hsiao
Mark Mazumder
Mark Mazumder
- Jae-Won Chung
Jae-Won Chung
- Shvetank Prakash
Shvetank Prakash
- Pong Trairatvorakul
Pong Trairatvorakul
+ Marco Zennaro
Marco Zennaro
Eura Nofshin
Eura Nofshin
Andrew Bass
Andrew Bass
+ Pong Trairatvorakul
Pong Trairatvorakul
Jennifer Zhou
Jennifer Zhou
- Marco Zennaro
Marco Zennaro
- Emeka Ezike
Emeka Ezike
+ Shvetank Prakash
Shvetank Prakash
+ Alex Oesterling
Alex Oesterling
+ Arya Tschand
Arya Tschand
Bruno Scaglione
Bruno Scaglione
- Allen-Kuang
Allen-Kuang
Gauri Jain
Gauri Jain
- Fin Amin
Fin Amin
- Fatima Shah
Fatima Shah
+ Allen-Kuang
Allen-Kuang
+ Fin Amin
Fin Amin
+ Fatima Shah
Fatima Shah
+ The Random DIY
The Random DIY
gnodipac886
gnodipac886
- Alex Oesterling
Alex Oesterling
Sercan Aygün
Sercan Aygün
- Emmanuel Rassou
Emmanuel Rassou
- Jason Yik
Jason Yik
- abigailswallow
abigailswallow
- Yang Zhou
Yang Zhou
+ Baldassarre Cesarano
Baldassarre Cesarano
+ Abenezer
Abenezer
Bilge Acun
Bilge Acun
+ yanjingl
yanjingl
+ Yang Zhou
Yang Zhou
+ + + abigailswallow
abigailswallow
+ Jason Yik
Jason Yik
happyappledog
happyappledog
- Jessica Quaye
Jessica Quaye
+ Curren Iyer
Curren Iyer
+ Emmanuel Rassou
Emmanuel Rassou
- The Random DIY
The Random DIY
- Shreya Johri
Shreya Johri
Sonia Murthy
Sonia Murthy
+ Shreya Johri
Shreya Johri
+ Jessica Quaye
Jessica Quaye
+ Vijay Edupuganti
Vijay Edupuganti
Costin-Andrei Oncescu
Costin-Andrei Oncescu
- Baldassarre Cesarano
Baldassarre Cesarano
Annie Laurie Cook
Annie Laurie Cook
- Vijay Edupuganti
Vijay Edupuganti
Jothi Ramaswamy
Jothi Ramaswamy
Batur Arslan
Batur Arslan
- Curren Iyer
Curren Iyer
- - Fatima Shah
Fatima Shah
- yanjingl
yanjingl
a-saraf
a-saraf
+ + songhan
songhan
Zishen
Zishen
diff --git a/_quarto.yml b/_quarto.yml index 988d57ea..47ca125c 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -9,7 +9,7 @@ website: announcement: icon: star-half dismissable: false - content: 🌟 Help Us Reach 1,000 GitHub Stars! 🌟 For every 25 stars, Arduino and SEEED will donate a NiclaVision or XIAO ESP32S3 for AI education. Click here to ⭐ + content: ⭐ We Hit 1,000 GitHub Stars 🎉 Thanks to you, Arduino and SEEED donated AI hardware kits for education!
🎓 The [EDGE AI Foundation](https://www.edgeaifoundation.org/) is now matching scholarship funds for every new GitHub ⭐ (up to 10,000 stars). Click here to support! 🙏
🚀 Our mission. 1 ⭐ == 1 👩‍🎓 Learner. Let's make every star a symbol of engagement and support to make cutting-edge education globally accessible. type: info position: below-navbar @@ -75,6 +75,22 @@ book: inference optimization, and benchmarking methodologies. The book also explores crucial systems considerations in areas like reliability, privacy, responsible AI, and solution validation. Enjoy reading it! + + --- + + 🎙 Listen to the **AI Podcast**, + created using Google's Notebook LM and inspired by insights drawn from our + [IEEE education viewpoint paper](https://web.eng.fiu.edu/gaquan/Papers/ESWEEK24Papers/CPS-Proceedings/pdfs/CODES-ISSS/563900a043/563900a043.pdf). + This podcast provides an accessible overview of what this book is all about. + +   + + + _Acknowledgment:_ Special thanks to [Marco Zennaro](https://www.ictp.it/member/marco-zennaro), one of our early community contributors who helped us with the [AI for Good](./contents/core/ai_for_good/ai_for_good.qmd) chapter, for inspiring the creation of this podcast. Thank you, Marco! + + ---- repo-url: https://github.com/harvard-edge/cs249r_book repo-branch: dev @@ -90,54 +106,37 @@ book: chapters: - text: "---" - - part: FRONT MATTER - chapters: - - index.qmd - - contents/dedication.qmd - - contents/acknowledgements/acknowledgements.qmd - - contents/contributors.qmd - - contents/copyright.qmd - - contents/about.qmd + - index.qmd + - contents/copyright.qmd + - contents/dedication.qmd + - contents/core/acknowledgements/acknowledgements.qmd + - contents/contributors.qmd + - contents/about.qmd - text: "---" - - part: MAIN - - part: Fundamentals - chapters: - - contents/introduction/introduction.qmd - - contents/ml_systems/ml_systems.qmd - - contents/dl_primer/dl_primer.qmd - - part: Workflow - chapters: - - contents/workflow/workflow.qmd - - contents/data_engineering/data_engineering.qmd - - contents/frameworks/frameworks.qmd - - part: Training - chapters: - - contents/training/training.qmd - - contents/efficient_ai/efficient_ai.qmd - - contents/optimizations/optimizations.qmd - - contents/hw_acceleration/hw_acceleration.qmd - - part: Deployment - chapters: - - contents/benchmarking/benchmarking.qmd - - contents/ondevice_learning/ondevice_learning.qmd - - contents/ops/ops.qmd - - part: Advanced Topics - chapters: - - contents/privacy_security/privacy_security.qmd - - contents/responsible_ai/responsible_ai.qmd - - contents/sustainable_ai/sustainable_ai.qmd - - contents/robust_ai/robust_ai.qmd - - contents/generative_ai/generative_ai.qmd - - part: Social Impact - chapters: - - contents/ai_for_good/ai_for_good.qmd - - part: Closing - chapters: - - contents/conclusion/conclusion.qmd + - contents/core/introduction/introduction.qmd + - contents/core/ml_systems/ml_systems.qmd + - contents/core/dl_primer/dl_primer.qmd + - contents/core/workflow/workflow.qmd + - contents/core/data_engineering/data_engineering.qmd + - contents/core/frameworks/frameworks.qmd + - contents/core/training/training.qmd + - contents/core/efficient_ai/efficient_ai.qmd + - contents/core/optimizations/optimizations.qmd + - contents/core/hw_acceleration/hw_acceleration.qmd + - contents/core/benchmarking/benchmarking.qmd + - contents/core/ondevice_learning/ondevice_learning.qmd + - contents/core/ops/ops.qmd + - contents/core/privacy_security/privacy_security.qmd + - contents/core/responsible_ai/responsible_ai.qmd + - contents/core/sustainable_ai/sustainable_ai.qmd + - contents/core/robust_ai/robust_ai.qmd + - contents/core/generative_ai/generative_ai.qmd + - contents/core/ai_for_good/ai_for_good.qmd + - contents/core/conclusion/conclusion.qmd - text: "---" - - part: LABS + - part: contents/labs/labs.qmd chapters: - - contents/labs/labs.qmd + - contents/labs/overview.qmd - contents/labs/getting_started.qmd - part: contents/labs/arduino/nicla_vision/nicla_vision.qmd chapters: @@ -167,37 +166,29 @@ book: - part: REFERENCES chapters: - references.qmd - - text: "---" - appendices: - - contents/tools.qmd - - contents/zoo_datasets.qmd - - contents/zoo_models.qmd - - contents/learning_resources.qmd - - contents/community.qmd - - contents/case_studies.qmd bibliography: # main - - contents/introduction/introduction.bib - - contents/ai_for_good/ai_for_good.bib - - contents/benchmarking/benchmarking.bib - - contents/data_engineering/data_engineering.bib - - contents/dl_primer/dl_primer.bib - - contents/efficient_ai/efficient_ai.bib - - contents/ml_systems/ml_systems.bib - - contents/frameworks/frameworks.bib - - contents/generative_ai/generative_ai.bib - - contents/hw_acceleration/hw_acceleration.bib - - contents/ondevice_learning/ondevice_learning.bib - - contents/ops/ops.bib - - contents/optimizations/optimizations.bib - - contents/privacy_security/privacy_security.bib - - contents/responsible_ai/responsible_ai.bib - - contents/robust_ai/robust_ai.bib - - contents/sustainable_ai/sustainable_ai.bib - - contents/training/training.bib - - contents/workflow/workflow.bib - - contents/conclusion/conclusion.bib + - contents/core/introduction/introduction.bib + - contents/core/ai_for_good/ai_for_good.bib + - contents/core/benchmarking/benchmarking.bib + - contents/core/data_engineering/data_engineering.bib + - contents/core/dl_primer/dl_primer.bib + - contents/core/efficient_ai/efficient_ai.bib + - contents/core/ml_systems/ml_systems.bib + - contents/core/frameworks/frameworks.bib + - contents/core/generative_ai/generative_ai.bib + - contents/core/hw_acceleration/hw_acceleration.bib + - contents/core/ondevice_learning/ondevice_learning.bib + - contents/core/ops/ops.bib + - contents/core/optimizations/optimizations.bib + - contents/core/privacy_security/privacy_security.bib + - contents/core/responsible_ai/responsible_ai.bib + - contents/core/robust_ai/robust_ai.bib + - contents/core/sustainable_ai/sustainable_ai.bib + - contents/core/training/training.bib + - contents/core/workflow/workflow.bib + - contents/core/conclusion/conclusion.bib comments: giscus: @@ -273,8 +264,7 @@ format: include-in-header: text: | -# -# + # # # diff --git a/contents/about.qmd b/contents/about.qmd index 047e1bf6..8d06c17a 100644 --- a/contents/about.qmd +++ b/contents/about.qmd @@ -6,30 +6,28 @@ comments: false ## Overview -Welcome to this collaborative project initiated by the CS249r Machine Learning Systems class at Harvard University. Our goal is to make this book a community resource that assists educators and learners in understanding ML systems. The book will be regularly updated to reflect new insights into ML systems and effective teaching methods. +Welcome to this collaborative textbook, developed as part of the CS249r Machine Learning Systems class at Harvard University. Our goal is to provide a comprehensive resource for educators and students seeking to understand machine learning systems. This book is continually updated to incorporate the latest insights and effective teaching strategies. -## Topics Explored - -This book offers a comprehensive look at various aspects of machine learning systems. We cover the entire end-to-end ML systems workflow, starting with fundamental concepts and progressing through data engineering, AI frameworks, and model training. - -You'll learn about optimizing models for efficiency, deploying AI on various hardware platforms, and benchmarking performance. The book also explores more advanced topics like security, privacy, responsible and sustainable AI, robust and generative AI, and the social impact of AI. By the end, you'll have a solid foundation and practical insights into both the technical and ethical dimensions of machine learning. +## What's Inside the Book -By the time you finish this book, we hope that you'll have a foundational understanding of machine learning and its applications. You'll also learn about real-world implementations of machine learning systems and gain practical experience through project-based labs and assignments. +We explore the technical foundations of machine learning systems, the challenges of building and deploying these systems across the computing continuum, and the vast array of applications they enable. A unique aspect of this book is its function as a conduit to seminal scholarly works and academic research papers, aimed at enriching the reader's understanding and encouraging deeper exploration of the subject. This approach seeks to bridge the gap between pedagogical materials and cutting-edge research trends, offering a comprehensive guide that is in step with the evolving field of applied machine learning. -### **Who Should Read This** +To improve the learning experience, we have included a variety of supplementary materials. Throughout the book, you will find slides that summarize key concepts, videos that provide in-depth explanations and demonstrations, exercises that reinforce your understanding, and labs that offer hands-on experience with the tools and techniques discussed. These additional resources are designed to cater to different learning styles and help you gain a deeper, more practical understanding of the subject matter. -This book is tailored for individuals at various stages in their interaction with machine learning systems. It starts with the fundamentals and progresses to more advanced topics pertinent to the ML community and broader research areas. The most relevant audiences include: +## Topics Explored -* **Students in Computer Science and Electrical Engineering:** Senior and graduate students in these fields will find this book invaluable. It introduces the techniques used in designing and building ML systems, focusing on fundamentals rather than depth—typically the focus of classroom instruction. This book aims to provide the necessary background and context, enabling instructors to delve deeper into advanced topics. An important aspect is the end-to-end focus, often overlooked in traditional curricula. +This textbook offers a comprehensive exploration of various aspects of machine learning systems, covering the entire end-to-end workflow. Starting with foundational concepts, it progresses through essential areas such as data engineering, AI frameworks, and model training. -* **Systems Engineers:** For engineers, this book serves as a guide to understanding the challenges of intelligent applications, especially on resource-constrained ML platforms. It covers the conceptual framework and practical components that constitute an ML system, extending beyond specific areas you might specialize in at your job. +To enhance the learning experience, we included a diverse array of supplementary materials. These resources consist of slides that summarize key concepts, videos providing detailed explanations and demonstrations, exercises designed to reinforce understanding, and labs that offer hands-on experience with the discussed tools and techniques. -* **Researchers and Academics:** Researchers will find that this book addresses the unique challenges of running machine learning algorithms on diverse platforms. Efficiency is becoming increasingly important; understanding algorithms alone is not sufficient, as a deeper understanding of systems is necessary to build more efficient models. For researchers, the book cites seminal papers, guiding you towards foundational works that have shaped the field and drawing connections between various areas with significant implications for your work. +Readers will gain insights into optimizing models for efficiency, deploying AI across different hardware platforms, and benchmarking performance. The book also delves into advanced topics, including security, privacy, responsible and sustainable AI, robust AI, and generative AI. Additionally, it examines the social impact of AI, concluding with an emphasis on the positive contributions AI can make to society. ## Key Learning Outcomes Readers will acquire skills in training and deploying deep neural network models on various platforms, along with understanding the broader challenges involved in their design, development, and deployment. Specifically, after completing this book, learners will be able to: +::: {.callout-tip} + 1. Explain core concepts and their relevance to AI systems. 2. Describe the fundamental components and architecture of AI systems. @@ -50,6 +48,8 @@ Readers will acquire skills in training and deploying deep neural network models 10. Critically assess the ethical implications and societal impacts of AI systems. +::: + ## Prerequisites for Readers * **Basic Programming Skills:** We recommend that you have some prior programming experience, ideally in Python. A grasp of variables, data types, and control structures will make it easier to engage with the book. @@ -65,3 +65,128 @@ Readers will acquire skills in training and deploying deep neural network models * **Resource Availability:** For the hands-on aspects, you'll need a computer with Python and the relevant libraries installed. Optional access to development boards or specific hardware will also be beneficial for experimenting with machine learning model deployment. By meeting these prerequisites, you'll be well-positioned to deepen your understanding of machine learning systems, engage in coding exercises, and even implement practical applications on various devices. + +## Who Should Read This + +This book is designed for individuals at different stages of their journey with machine learning systems, from beginners to those more advanced in the field. It introduces fundamental concepts and progresses to complex topics relevant to the machine learning community and expansive research areas. The key audiences for this book include: + +* **Students in Computer Science and Electrical Engineering:** Senior and graduate students will find this book particularly valuable. It introduces the techniques essential for designing and building ML systems, focusing on foundational knowledge rather than exhaustive detail---often the focus of classroom instruction. This book will provide the necessary background and context, enabling instructors to explore advanced topics more deeply. An essential feature is its end-to-end perspective, which is often overlooked in traditional curricula. + +* **Systems Engineers:** This book serves as a guide for engineers seeking to understand the complexities of intelligent systems and applications, particularly involving ML. It encompasses the conceptual frameworks and practical components that comprise an ML system, extending beyond the specific areas you might encounter in your professional role. + +* **Researchers and Academics:** For researchers, this book addresses the distinct challenges of executing machine learning algorithms across diverse platforms. As efficiency gains importance, a robust understanding of systems, beyond algorithms alone, is crucial for developing more efficient models. The book references seminal papers, directing researchers to works that have influenced the field and establishing connections between various areas with significant implications for their research. + +## How to Navigate This Book + +To get the most out of this book, we recommend a structured learning approach that leverages the various resources provided. Each chapter includes slides, videos, exercises, and labs to cater to different learning styles and reinforce your understanding. + +1. **Fundamentals (Chapters 1-3):** Start by building a strong foundation with the initial chapters, which provide an introduction to AI and cover core topics like AI systems and deep learning. + +2. **Workflow (Chapters 4-6):** With that foundation, move on to the chapters focused on practical aspects of the AI model building process like workflows, data engineering, and frameworks. + +3. **Training (Chapters 7-10):** These chapters offer insights into effectively training AI models, including techniques for efficiency, optimizations, and acceleration. + +4. **Deployment (Chapters 11-13):** Learn about deploying AI on devices and monitoring the operationalization through methods like benchmarking, on-device learning, and MLOps. + +5. **Advanced Topics (Chapters 14-18):** Critically examine topics like security, privacy, ethics, sustainability, robustness, and generative AI. + +6. **Social Impact (Chapter 19):** Explore the positive applications and potential of AI for societal good. + +7. **Conclusion (Chapter 20):** Reflect on the key takeaways and future directions in AI systems. + +While the book is designed for progressive learning, we encourage an interconnected learning approach that allows you to navigate chapters based on your interests and needs. Throughout the book, you'll find case studies and hands-on exercises that help you relate theory to real-world applications. We also recommend participating in forums and groups to engage in [discussions](https://github.com/harvard-edge/cs249r_book/discussions), debate concepts, and share insights with fellow learners. Regularly revisiting chapters can help reinforce your learning and offer new perspectives on the concepts covered. By adopting this structured yet flexible approach and actively engaging with the content and the community, you'll embark on a fulfilling and enriching learning experience that maximizes your understanding. + +## Chapter-by-Chapter Insights + +Here's a closer look at what each chapter covers. We have structured the book into six main sections: Fundamentals, Workflow, Training, Deployment, Advanced Topics, and Impact. These sections closely reflect the major components of a typical machine learning pipeline, from understanding the basic concepts to deploying and maintaining AI systems in real-world applications. By organizing the content in this manner, we aim to provide a logical progression that mirrors the actual process of developing and implementing AI systems. + +### Fundamentals + +In the Fundamentals section, we lay the groundwork for understanding AI. This is far from being a thorough deep dive into the algorithms, but we aim to introduce key concepts, provide an overview of machine learning systems, and dive into the principles and algorithms of deep learning that power AI applications in their associated systems. This section equips you with the essential knowledge needed to grasp the subsequent chapters. + +1. **[Introduction:](./core/introduction/introduction.qmd)** This chapter sets the stage, providing an overview of AI and laying the groundwork for the chapters that follow. +2. **[ML Systems:](./core/ml_systems/ml_systems.qmd)** We introduce the basics of machine learning systems, the platforms where AI algorithms are widely applied. +3. **[Deep Learning Primer:](./core/dl_primer/dl_primer.qmd)** This chapter offers a brief introduction to the algorithms and principles that underpin AI applications in ML systems. + +### Workflow + +The Workflow section guides you through the practical aspects of building AI models. We break down the AI workflow, discuss data engineering best practices, and review popular AI frameworks. By the end of this section, you'll have a clear understanding of the steps involved in developing proficient AI applications and the tools available to streamline the process. + +4. **[AI Workflow:](./core/workflow/workflow.qmd)** This chapter breaks down the machine learning workflow, offering insights into the steps leading to proficient AI applications. +5. **[Data Engineering:](./core/data_engineering/data_engineering.qmd)** We focus on the importance of data in AI systems, discussing how to effectively manage and organize data. +6. **[AI Frameworks:](./core/frameworks/frameworks.qmd)** This chapter reviews different frameworks for developing machine learning models, guiding you in choosing the most suitable one for your projects. + +### Training + +In the Training section, we explore techniques for training efficient and reliable AI models. We cover strategies for achieving efficiency, model optimizations, and the role of specialized hardware in AI acceleration. This section empowers you with the knowledge to develop high-performing models that can be seamlessly integrated into AI systems. + +7. **[AI Training:](./core/training/training.qmd)** This chapter explores model training, exploring techniques for developing efficient and reliable models. +8. **[Efficient AI:](./core/efficient_ai/efficient_ai.qmd)** Here, we discuss strategies for achieving efficiency in AI applications, from computational resource optimization to performance enhancement. +9. **[Model Optimizations:](./core/optimizations/optimizations.qmd)** We explore various avenues for optimizing AI models for seamless integration into AI systems. +10. **[AI Acceleration:](./core/hw_acceleration/hw_acceleration.qmd)** We discuss the role of specialized hardware in enhancing the performance of AI systems. + +### Deployment + +The Deployment section focuses on the challenges and solutions for deploying AI models. We discuss benchmarking methods to evaluate AI system performance, techniques for on-device learning to improve efficiency and privacy, and the processes involved in ML operations. This section equips you with the skills to effectively deploy and maintain AI functionalities in AI systems. + +11. **[Benchmarking AI:](./core/benchmarking/benchmarking.qmd)** This chapter focuses on how to evaluate AI systems through systematic benchmarking methods. +12. **[On-Device Learning:](./core/ondevice_learning/ondevice_learning.qmd)** We explore techniques for localized learning, which enhances both efficiency and privacy. +13. **[ML Operations:](./core/ops/ops.qmd)** This chapter looks at the processes involved in the seamless integration, monitoring, and maintenance of AI functionalities. + +### Advanced Topics + +In the Advanced Topics section, We will study the critical issues surrounding AI. We address privacy and security concerns, explore the ethical principles of responsible AI, discuss strategies for sustainable AI development, examine techniques for building robust AI models, and introduce the exciting field of generative AI. This section broadens your understanding of the complex landscape of AI and prepares you to navigate its challenges. + +14. **[Security & Privacy:](./core/privacy_security/privacy_security.qmd)** As AI becomes more ubiquitous, this chapter addresses the crucial aspects of privacy and security in AI systems. +15. **[Responsible AI:](./core/responsible_ai/responsible_ai.qmd)** We discuss the ethical principles guiding the responsible use of AI, focusing on fairness, accountability, and transparency. +16. **[Sustainable AI:](./core/sustainable_ai/sustainable_ai.qmd)** This chapter explores practices and strategies for sustainable AI, ensuring long-term viability and reduced environmental impact. +17. **[Robust AI:](./core/robust_ai/robust_ai.qmd)** We discuss techniques for developing reliable and robust AI models that can perform consistently across various conditions. +18. **[Generative AI:](./core/generative_ai/generative_ai.qmd)** This chapter explores the algorithms and techniques behind generative AI, opening avenues for innovation and creativity. + +### Social Impact + +The Impact section highlights the transformative potential of AI in various domains. We showcase real-world applications of TinyML in healthcare, agriculture, conservation, and other areas where AI is making a positive difference. This section inspires you to leverage the power of AI for societal good and to contribute to the development of impactful solutions. + +19. **[AI for Good:](./core/ai_for_good/ai_for_good.qmd)** We highlight positive applications of TinyML in areas like healthcare, agriculture, and conservation. + +### Closing + +In the Closing section, we reflect on the key learnings from the book and look ahead to the future of AI. We synthesize the concepts covered, discuss emerging trends, and provide guidance on continuing your learning journey in this rapidly evolving field. This section leaves you with a comprehensive understanding of AI and the excitement to apply your knowledge in innovative ways. + +20. **[Conclusion:](./core/conclusion/conclusion.qmd)** The book concludes with a reflection on the key learnings and future directions in the field of AI. + +## Tailored Learning + +We understand that readers have diverse interests; some may wish to grasp the fundamentals, while others are eager to delve into advanced topics like hardware acceleration or AI ethics. To help you navigate the book more effectively, we've created a persona-based reading guide tailored to your specific interests and goals. This guide assists you in identifying the reader persona that best matches your interests. Each persona represents a distinct reader profile with specific objectives. By selecting the persona that resonates with you, you can focus on the chapters and sections most relevant to your needs. + ++------------------------+--------------------------------------------------------------------------+-----------------------------------------------+-----------------------------------------------------------------------------------------------------------+ +| Persona | Description | Chapters | Focus | ++:=======================+:=========================================================================+:==============================================+:==========================================================================================================+ +| The TinyML Newbie | You are new to the field of TinyML and eager to learn the basics. | 1-3, 8, 9, 10, 12 | Understand the fundamentals, gain insights into efficient and optimized ML, | +| | | | and learn about on-device learning. | ++------------------------+--------------------------------------------------------------------------+-----------------------------------------------+-----------------------------------------------------------------------------------------------------------+ +| The EdgeML Enthusiast | You have some TinyML knowledge and are interested in exploring | 1-3, 8, 9, 10, 12, 13 | Build a strong foundation, delve into the intricacies of efficient ML, | +| | the broader world of EdgeML. | | and explore the operational aspects of embedded systems. | ++------------------------+--------------------------------------------------------------------------+-----------------------------------------------+-----------------------------------------------------------------------------------------------------------+ +| The Computer Visionary | You are fascinated by computer vision and its applications in TinyML | 1-3, 5, 8-10, 12, 13, 17, 20 | Start with the basics, explore data engineering, and study methods for optimizing ML | +| | and EdgeML. | | models. Learn about robustness and the future of ML systems. | ++------------------------+--------------------------------------------------------------------------+-----------------------------------------------+-----------------------------------------------------------------------------------------------------------+ +| The Data Maestro | You are passionate about data and its crucial role in ML systems. | 1-5, 8-13 | Gain a comprehensive understanding of data's role in ML systems, explore the ML | +| | | | workflow, and dive into model optimization and deployment considerations. | ++------------------------+--------------------------------------------------------------------------+-----------------------------------------------+-----------------------------------------------------------------------------------------------------------+ +| The Hardware Hero | You are excited about the hardware aspects of ML systems and how | 1-3, 6, 8-10, 12, 14, 17, 20 | Build a solid foundation in ML systems and frameworks, explore challenges of | +| | they impact model performance. | | optimizing models for efficiency, hardware-software co-design, and security aspects. | ++------------------------+--------------------------------------------------------------------------+-----------------------------------------------+-----------------------------------------------------------------------------------------------------------+ +| The Sustainability | You are an advocate for sustainability and want to learn how to | 1-3, 8-10, 12, 15, 16, 20 | Begin with the fundamentals of ML systems and TinyML, explore model optimization | +| Champion | develop eco-friendly AI systems. | | techniques, and learn about responsible and sustainable AI practices. | ++------------------------+--------------------------------------------------------------------------+-----------------------------------------------+-----------------------------------------------------------------------------------------------------------+ +| The AI Ethicist | You are concerned about the ethical implications of AI and want to | 1-3, 5, 7, 12, 14-16, 19, 20 | Gain insights into the ethical considerations surrounding AI, including fairness, | +| | ensure responsible development and deployment. | | privacy, sustainability, and responsible development practices. | ++------------------------+--------------------------------------------------------------------------+-----------------------------------------------+-----------------------------------------------------------------------------------------------------------+ +| The Full-Stack ML | You are a seasoned ML expert and want to deepen your understanding | The entire book | Understand the end-to-end process of building and deploying ML systems, from data | +| Engineer | of the entire ML system stack. | | engineering and model optimization to hardware acceleration and ethical considerations. | ++------------------------+--------------------------------------------------------------------------+-----------------------------------------------+-----------------------------------------------------------------------------------------------------------+ + +## Join the Community + +Learning in the fast-paced world of AI is a collaborative journey. We set out to nurture a vibrant community of learners, innovators, and contributors. As you explore the concepts and engage with the exercises, we encourage you to share your insights and experiences. Whether it's a novel approach, an interesting application, or a thought-provoking question, your contributions can enrich the learning ecosystem. Engage in discussions, offer and seek guidance, and collaborate on projects to foster a culture of mutual growth and learning. By sharing knowledge, you play an important role in fostering a globally connected, informed, and empowered community. diff --git a/contents/benchmarking/benchmarking.bib b/contents/benchmarking/benchmarking.bib deleted file mode 100644 index 9b0cd0e6..00000000 --- a/contents/benchmarking/benchmarking.bib +++ /dev/null @@ -1,380 +0,0 @@ -%comment{This file was created with betterbib v5.0.11.} - - -@article{bianco2018benchmark, - author = {Bianco, Simone and Cadene, Remi and Celona, Luigi and Napoletano, Paolo}, - title = {Benchmark analysis of representative deep neural network architectures}, - journal = {IEEE access}, - volume = {6}, - pages = {64270--64277}, - year = {2018}, - publisher = {IEEE}, -} - -@inproceedings{adolf2016fathom, - author = {Adolf, Robert and Rama, Saketh and Reagen, Brandon and Wei, Gu-yeon and Brooks, David}, - booktitle = {2016 IEEE International Symposium on Workload Characterization (IISWC)}, - doi = {10.1109/iiswc.2016.7581275}, - organization = {IEEE}, - pages = {1--10}, - publisher = {IEEE}, - source = {Crossref}, - title = {Fathom: {Reference} workloads for modern deep learning methods}, - url = {https://doi.org/10.1109/iiswc.2016.7581275}, - year = {2016}, - month = sep, -} - -@inproceedings{antol2015vqa, - author = {Antol, Stanislaw and Agrawal, Aishwarya and Lu, Jiasen and Mitchell, Margaret and Batra, Dhruv and Zitnick, C. Lawrence and Parikh, Devi}, - bibsource = {dblp computer science bibliography, https://dblp.org}, - biburl = {https://dblp.org/rec/conf/iccv/AntolALMBZP15.bib}, - booktitle = {2015 IEEE International Conference on Computer Vision (ICCV)}, - doi = {10.1109/iccv.2015.279}, - pages = {2425--2433}, - publisher = {IEEE}, - timestamp = {Wed, 24 May 2017 01:00:00 +0200}, - title = {{VQA:} {Visual} Question Answering}, - url = {https://doi.org/10.1109/iccv.2015.279}, - year = {2015}, - source = {Crossref}, - month = dec, -} - -@article{banbury2020benchmarking, - author = {Banbury, Colby R and Reddi, Vijay Janapa and Lam, Max and Fu, William and Fazel, Amin and Holleman, Jeremy and Huang, Xinyuan and Hurtado, Robert and Kanter, David and Lokhmotov, Anton and others}, - journal = {ArXiv preprint}, - title = {Benchmarking tinyml systems: {Challenges} and direction}, - url = {https://arxiv.org/abs/2003.04821}, - volume = {abs/2003.04821}, - year = {2020}, -} - -@article{beyer2020we, - author = {Beyer, Lucas and H\'enaff, Olivier J and Kolesnikov, Alexander and Zhai, Xiaohua and Oord, A\"aron van den}, - journal = {ArXiv preprint}, - title = {Are we done with imagenet?}, - url = {https://arxiv.org/abs/2006.07159}, - volume = {abs/2006.07159}, - year = {2020}, -} - -@inproceedings{brown2020language, - author = {Brown, Tom B. and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel M. and Wu, Jeffrey and Winter, Clemens and Hesse, Christopher and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario}, - editor = {Larochelle, Hugo and Ranzato, Marc'Aurelio and Hadsell, Raia and Balcan, Maria-Florina and Lin, Hsuan-Tien}, - bibsource = {dblp computer science bibliography, https://dblp.org}, - biburl = {https://dblp.org/rec/conf/nips/BrownMRSKDNSSAA20.bib}, - booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual}, - timestamp = {Tue, 19 Jan 2021 00:00:00 +0100}, - title = {Language Models are Few-Shot Learners}, - url = {https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html}, - year = {2020}, -} - -@inproceedings{chu2021discovering, - author = {Chu, Grace and Arikan, Okan and Bender, Gabriel and Wang, Weijun and Brighton, Achille and Kindermans, Pieter-Jan and Liu, Hanxiao and Akin, Berkin and Gupta, Suyog and Howard, Andrew}, - bibsource = {dblp computer science bibliography, https://dblp.org}, - biburl = {https://dblp.org/rec/conf/cvpr/ChuABWBKLAG021.bib}, - booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, - doi = {10.1109/cvprw53098.2021.00337}, - pages = {3022--3031}, - publisher = {IEEE}, - timestamp = {Mon, 18 Jul 2022 01:00:00 +0200}, - title = {Discovering Multi-Hardware Mobile Models via Architecture Search}, - url = {https://doi.org/10.1109/cvprw53098.2021.00337}, - year = {2021}, - source = {Crossref}, - month = jun, -} - -@article{coleman2017dawnbench, - author = {Coleman, Cody and Kang, Daniel and Narayanan, Deepak and Nardi, Luigi and Zhao, Tian and Zhang, Jian and Bailis, Peter and Olukotun, Kunle and R\'e, Chris and Zaharia, Matei}, - doi = {10.1145/3352020.3352024}, - issn = {0163-5980}, - journal = {ACM SIGOPS Operating Systems Review}, - number = {1}, - pages = {14--25}, - publisher = {Association for Computing Machinery (ACM)}, - source = {Crossref}, - title = {Analysis of {DAWNBench,} a Time-to-Accuracy Machine Learning Performance Benchmark}, - url = {https://doi.org/10.1145/3352020.3352024}, - volume = {53}, - year = {2019}, - month = jul, -} - -@inproceedings{coleman2022similarity, - author = {Coleman, Cody and Chou, Edward and Katz-Samuels, Julian and Culatana, Sean and Bailis, Peter and Berg, Alexander C. and Nowak, Robert D. and Sumbaly, Roshan and Zaharia, Matei and Yalniz, I. 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Scott and Hubara, Itay and Idgunji, Sachin and Jablin, Thomas B. and Jiao, Jeff and John, Tom St. and Kanwar, Pankaj and Lee, David and Liao, Jeffery and Lokhmotov, Anton and Massa, Francisco and Meng, Peng and Micikevicius, Paulius and Osborne, Colin and Pekhimenko, Gennady and Rajan, Arun Tejusve Raghunath and Sequeira, Dilip and Sirasao, Ashish and Sun, Fei and Tang, Hanlin and Thomson, Michael and Wei, Frank and Wu, Ephrem and Xu, Lingjie and Yamada, Koichi and Yu, Bing and Yuan, George and Zhong, Aaron and Zhang, Peizhao and Zhou, Yuchen}, - booktitle = {2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA)}, - doi = {10.1109/isca45697.2020.00045}, - organization = {IEEE}, - pages = {446--459}, - publisher = {IEEE}, - source = {Crossref}, - title = {{MLPerf} Inference Benchmark}, - url = {https://doi.org/10.1109/isca45697.2020.00045}, - year = {2020}, - month = may, -} - -@inproceedings{ribeiro2016should, - author = {Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos}, - booktitle = {Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining}, - pages = {1135--1144}, - title = {{\textquotedblright} Why should i trust you?{\textquotedblright} Explaining the predictions of any classifier}, - year = {2016}, -} - -@article{schuman2022opportunities, - author = {Schuman, Catherine D. and Kulkarni, Shruti R. and Parsa, Maryam and Mitchell, J. 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Whether you are a seasoned developer, a researcher, or a curious hobbyist looking to dive into the world of TinyML, this page is a non-exhaustive list of community resources and forums to help you get started and thrive in this domain. From vibrant online communities and educational platforms to blogs and social media groups, discover a world brimming with knowledge, collaboration, and innovation. Begin your TinyML journey here, where opportunities for learning and networking are just a click away! - -## Online Forums - -1. **TinyML Forum** - Website: [TinyML Forum](https://forums.tinyml.org/) - Description: A dedicated forum for discussions, news, and updates on TinyML. - -2. **Reddit** - Subreddits: r/TinyML - Description: Reddit community discussing various topics related to TinyML. - -## Blogs and Websites - -1. **TinyML Foundation** - Website: [TinyML Foundation](https://tinyml.org/) - Description: The official website offers a wealth of information including research, news, and events. - -2. **Edge Impulse Blog** - Website: [Blog](https://www.edgeimpulse.com/blog) - Description: Contains several articles, tutorials, and resources on TinyML. - -3. **Tiny Machine Learning Open Education Initiative (TinyMLedu)** - Website: [TinyML Open Education Initiative](https://tinymledu.org/) - Description: The website offers links to educational materials on TinyML, training events and research papers. - -## Social Media Groups - -1. **LinkedIn Groups** - Description: Join TinyML groups on LinkedIn to connect with professionals and enthusiasts in the field. - -2. **Twitter** - Description: Follow TinyML enthusiasts, organizations, and experts on Twitter for the latest news and updates. - Example handles to follow: - - [Twitter](https://twitter.com/tinymlf) - - [EdgeImpulse](https://twitter.com/EdgeImpulse) - -## Conferences and Meetups - -1. **TinyML Summit** - Website: [TinyML Summit](https://www.tinyml.org/) - Description: Annual event where professionals and enthusiasts gather to discuss the latest developments in TinyML. - -2. **Meetup** - Website: [Meetup](https://www.meetup.com/pro/tinyml) - Description: Search for TinyML groups on Meetup to find local or virtual gatherings. - -Remember to always check the credibility and activity level of the platforms and groups before diving in to ensure a productive experience. diff --git a/contents/contributors.bib b/contents/contributors.bib deleted file mode 100644 index e69de29b..00000000 diff --git a/contents/contributors.qmd b/contents/contributors.qmd index ac5cd3db..a5e4415d 100644 --- a/contents/contributors.qmd +++ b/contents/contributors.qmd @@ -73,24 +73,24 @@ We extend our sincere thanks to the diverse group of individuals who have genero Vijay Janapa Reddi
Vijay Janapa Reddi
+ jasonjabbour
jasonjabbour
Ikechukwu Uchendu
Ikechukwu Uchendu
Naeem Khoshnevis
Naeem Khoshnevis
- jasonjabbour
jasonjabbour
- Douwe den Blanken
Douwe den Blanken
+ Marcelo Rovai
Marcelo Rovai
+ Sara Khosravi
Sara Khosravi
+ Douwe den Blanken
Douwe den Blanken
shanzehbatool
shanzehbatool
- Marcelo Rovai
Marcelo Rovai
+ Kai Kleinbard
Kai Kleinbard
Elias Nuwara
Elias Nuwara
- kai4avaya
kai4avaya
- Jared Ping
Jared Ping
+ Jared Ping
Jared Ping
Matthew Stewart
Matthew Stewart
Itai Shapira
Itai Shapira
Maximilian Lam
Maximilian Lam
Jayson Lin
Jayson Lin
- Sara Khosravi
Sara Khosravi
Sophia Cho
Sophia Cho
@@ -100,79 +100,83 @@ We extend our sincere thanks to the diverse group of individuals who have genero Korneel Van den Berghe
Korneel Van den Berghe
- Zishen Wan
Zishen Wan
Colby Banbury
Colby Banbury
- Divya Amirtharaj
Divya Amirtharaj
+ Zishen Wan
Zishen Wan
Abdulrahman Mahmoud
Abdulrahman Mahmoud
Srivatsan Krishnan
Srivatsan Krishnan
+ Divya Amirtharaj
Divya Amirtharaj
- Haoran Qiu
Haoran Qiu
+ Emeka Ezike
Emeka Ezike
Aghyad Deeb
Aghyad Deeb
+ Haoran Qiu
Haoran Qiu
marin-llobet
marin-llobet
- Michael Schnebly
Michael Schnebly
- oishib
oishib
+ Emil Njor
Emil Njor
- Jared Ni
Jared Ni
Aditi Raju
Aditi Raju
+ Jared Ni
Jared Ni
+ Michael Schnebly
Michael Schnebly
+ oishib
oishib
ELSuitorHarvard
ELSuitorHarvard
- Emil Njor
Emil Njor
- Henry Bae
Henry Bae
+ Henry Bae
Henry Bae
+ Jae-Won Chung
Jae-Won Chung
Yu-Shun Hsiao
Yu-Shun Hsiao
Mark Mazumder
Mark Mazumder
- Jae-Won Chung
Jae-Won Chung
- Shvetank Prakash
Shvetank Prakash
- Pong Trairatvorakul
Pong Trairatvorakul
+ Marco Zennaro
Marco Zennaro
Eura Nofshin
Eura Nofshin
Andrew Bass
Andrew Bass
+ Pong Trairatvorakul
Pong Trairatvorakul
Jennifer Zhou
Jennifer Zhou
- Marco Zennaro
Marco Zennaro
- Emeka Ezike
Emeka Ezike
+ Shvetank Prakash
Shvetank Prakash
+ Alex Oesterling
Alex Oesterling
+ Arya Tschand
Arya Tschand
Bruno Scaglione
Bruno Scaglione
- Allen-Kuang
Allen-Kuang
Gauri Jain
Gauri Jain
- Fin Amin
Fin Amin
- Fatima Shah
Fatima Shah
+ Allen-Kuang
Allen-Kuang
+ Fin Amin
Fin Amin
+ Fatima Shah
Fatima Shah
+ The Random DIY
The Random DIY
gnodipac886
gnodipac886
- Alex Oesterling
Alex Oesterling
Sercan Aygün
Sercan Aygün
- Emmanuel Rassou
Emmanuel Rassou
- Jason Yik
Jason Yik
- abigailswallow
abigailswallow
- Yang Zhou
Yang Zhou
+ Baldassarre Cesarano
Baldassarre Cesarano
+ Abenezer
Abenezer
Bilge Acun
Bilge Acun
+ yanjingl
yanjingl
+ Yang Zhou
Yang Zhou
+ + + abigailswallow
abigailswallow
+ Jason Yik
Jason Yik
happyappledog
happyappledog
- Jessica Quaye
Jessica Quaye
+ Curren Iyer
Curren Iyer
+ Emmanuel Rassou
Emmanuel Rassou
- The Random DIY
The Random DIY
- Shreya Johri
Shreya Johri
Sonia Murthy
Sonia Murthy
+ Shreya Johri
Shreya Johri
+ Jessica Quaye
Jessica Quaye
+ Vijay Edupuganti
Vijay Edupuganti
Costin-Andrei Oncescu
Costin-Andrei Oncescu
- Baldassarre Cesarano
Baldassarre Cesarano
Annie Laurie Cook
Annie Laurie Cook
- Vijay Edupuganti
Vijay Edupuganti
Jothi Ramaswamy
Jothi Ramaswamy
Batur Arslan
Batur Arslan
- Curren Iyer
Curren Iyer
- - Fatima Shah
Fatima Shah
- yanjingl
yanjingl
a-saraf
a-saraf
+ + songhan
songhan
Zishen
Zishen
diff --git a/contents/conventions.qmd b/contents/conventions.qmd deleted file mode 100644 index 109fc7e5..00000000 --- a/contents/conventions.qmd +++ /dev/null @@ -1,68 +0,0 @@ -# Conventions Used in this Book - -Please follow these conventions as you contribute to this online book: - -1. **Clear Structure and Organization:** - - - **Chapter Outlines:** Begin each chapter with an outline that provides an - overview of the topics covered. - - **Sequential Numbering:** Utilize sequential numbering for chapters, - sections, and subsections to facilitate easy reference. - -2. **Accessible Language:** - - - **Glossary:** Include a glossary that defines technical terms and jargon. - - **Consistent Terminology:** Maintain consistent use of terminology - throughout the book to avoid confusion. - -3. **Learning Aids:** - - - **Diagrams and Figures:** Employ diagrams, figures, and tables to visually - convey complex concepts. - - **Sidebars:** Use sidebars for additional information, anecdotes, or to - provide real-world context to the theoretical content. - -4. **Interactive Elements:** - - - **Colabs and Projects:** Integrate exercises and projects at the end of - each chapter to encourage active learning and practical application of - concepts. - - **Case Studies:** Incorporate case studies to provide a deeper - understanding of how principles are applied in real-world situations. - -5. **References and Further Reading:** - - - **Bibliography:** Include a bibliography at the end of each chapter for - readers who wish to dive deeper into specific topics. - - **Citations:** Maintain a consistent style for citations, adhering to - recognized academic standards like APA, MLA, or Chicago. - -6. **Supporting Materials:** - - - **Supplementary Online Resources:** Provide links to supplementary online - resources, such as video lectures, webinars, or interactive modules. - - **Datasets and Code Repositories:** Share datasets and code repositories - for hands-on practice, particularly for sections dealing with algorithms - and applications. - -7. **Feedback and Community Engagement:** - - - **Forums and Discussion Groups:** Establish forums or discussion groups - where readers can interact, ask questions, and share knowledge. - - **Open Review Process:** Implement an open review process, inviting - feedback from the community to continuously improve the content. - -8. **Inclusivity and Accessibility:** - - - **Inclusive Language:** Utilize inclusive language that respects diversity - and promotes equality. - - **Accessible Formats:** Ensure the textbook is available in accessible - formats, including audio and Braille, to cater to readers with - disabilities. - -9. **Index:** - - **Comprehensive Index:** Include a comprehensive index at the end of the - book to help readers quickly locate specific information. - -Implementing these conventions can contribute to creating a textbook that is -comprehensive, accessible, and conducive to effective learning. diff --git a/contents/acknowledgements/acknowledgements.qmd b/contents/core/acknowledgements/acknowledgements.qmd similarity index 86% rename from contents/acknowledgements/acknowledgements.qmd rename to contents/core/acknowledgements/acknowledgements.qmd index 7f285aab..5cb77312 100644 --- a/contents/acknowledgements/acknowledgements.qmd +++ b/contents/core/acknowledgements/acknowledgements.qmd @@ -10,13 +10,11 @@ Assembling this book has been a long journey, spanning several years of hard wor We extend our heartfelt gratitude to the open source community of learners, teachers and sharers. Whether you contributed an entire section, a single sentence, or merely corrected a typo, your efforts have enhanced this book. We deeply appreciate everyone's time, expertise, and commitment. This book is as much yours as it is ours. -Special thanks go to Professor Vijay Janapa Reddi, whose belief in the transformative power of open-source communities and invaluable guidance have been our guiding light from the outset. - We also owe a great deal to the team at GitHub and Quarto. You've revolutionized the way people collaborate, and this book stands as a testament to what can be achieved when barriers to global cooperation are removed. ## Funding Agencies and Companies -We are immensely grateful for the generous support from the various funding agencies and companies that supported the teaching assistants (TAs) involved in this work. The organizations listed below played a crucial role in bringing this project to life with their contributions. +We are immensely grateful for the generous support from the various funding agencies and companies that supported the teaching assistants (TAs) involved in this work. The organizations listed below played an important role in bringing this project to life with their contributions. ::: {layout-nrow=2} diff --git a/contents/acknowledgements/images/png/HDSI.png b/contents/core/acknowledgements/images/png/HDSI.png similarity index 100% rename from contents/acknowledgements/images/png/HDSI.png rename to contents/core/acknowledgements/images/png/HDSI.png diff --git a/contents/acknowledgements/images/png/NSF.png b/contents/core/acknowledgements/images/png/NSF.png similarity index 100% rename from contents/acknowledgements/images/png/NSF.png rename to contents/core/acknowledgements/images/png/NSF.png diff --git a/contents/acknowledgements/images/png/google.png b/contents/core/acknowledgements/images/png/google.png similarity index 100% rename from contents/acknowledgements/images/png/google.png rename to contents/core/acknowledgements/images/png/google.png diff --git a/contents/acknowledgements/images/png/harvard-xtension-school.png b/contents/core/acknowledgements/images/png/harvard-xtension-school.png similarity index 100% rename from contents/acknowledgements/images/png/harvard-xtension-school.png rename to contents/core/acknowledgements/images/png/harvard-xtension-school.png diff --git a/contents/ai_for_good/ai_for_good.bib b/contents/core/ai_for_good/ai_for_good.bib similarity index 100% rename from contents/ai_for_good/ai_for_good.bib rename to contents/core/ai_for_good/ai_for_good.bib diff --git a/contents/ai_for_good/ai_for_good.qmd b/contents/core/ai_for_good/ai_for_good.qmd similarity index 98% rename from contents/ai_for_good/ai_for_good.qmd rename to contents/core/ai_for_good/ai_for_good.qmd index 9374f5ab..ca1c9087 100644 --- a/contents/ai_for_good/ai_for_good.qmd +++ b/contents/core/ai_for_good/ai_for_good.qmd @@ -5,7 +5,7 @@ bibliography: ai_for_good.bib # AI for Good {#sec-ai_for_good} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-ai-for-good-resource), [Videos](#sec-ai-for-good-resource), [Exercises](#sec-ai-for-good-resource), [Labs](#sec-ai-for-good-resource) +Resources: [Slides](#sec-ai-for-good-resource), [Videos](#sec-ai-for-good-resource), [Exercises](#sec-ai-for-good-resource) ::: ![_DALL·E 3 Prompt: Illustration of planet Earth wrapped in shimmering neural networks, with diverse humans and AI robots working together on various projects like planting trees, cleaning the oceans, and developing sustainable energy solutions. The positive and hopeful atmosphere represents a united effort to create a better future._](images/png/cover_ai_good.png) @@ -34,12 +34,12 @@ By aligning AI progress with human values, goals, and ethics, the ultimate goal To give ourselves a framework around which to think about AI for social good, we will be following the UN Sustainable Development Goals (SDGs). The UN SDGs are a collection of 17 global goals, shown in @fig-sdg, adopted by the United Nations in 2015 as part of the 2030 Agenda for Sustainable Development. The SDGs address global challenges related to poverty, inequality, climate change, environmental degradation, prosperity, and peace and justice. +![United Nations Sustainable Development Goals (SDG). Source: [United Nations](https://sdgs.un.org/goals).](https://www.un.org/sustainabledevelopment/wp-content/uploads/2015/12/english_SDG_17goals_poster_all_languages_with_UN_emblem_1.png){#fig-sdg} + What is special about the SDGs is that they are a collection of interlinked objectives designed to serve as a "shared blueprint for peace and prosperity for people and the planet, now and into the future." The SDGs emphasize sustainable development's interconnected environmental, social, and economic aspects by putting sustainability at their center. A recent study [@vinuesa2020role] highlights the influence of AI on all aspects of sustainable development, particularly on the 17 Sustainable Development Goals (SDGs) and 169 targets internationally defined in the 2030 Agenda for Sustainable Development. The study shows that AI can act as an enabler for 134 targets through technological improvements, but it also highlights the challenges of AI on some targets. The study shows that AI can benefit 67 targets when considering AI and societal outcomes. Still, it also warns about the issues related to the implementation of AI in countries with different cultural values and wealth. -![United Nations Sustainable Development Goals (SDG). Source: [United Nations](https://sdgs.un.org/goals).](https://www.un.org/sustainabledevelopment/wp-content/uploads/2015/12/english_SDG_17goals_poster_all_languages_with_UN_emblem_1.png){#fig-sdg} - In our book's context, TinyML could help advance at least some of these SDG goals. * **Goal 1 - No Poverty:** TinyML could help provide low-cost solutions for crop monitoring to improve agricultural yields in developing countries. @@ -285,15 +285,3 @@ These slides are a valuable tool for instructors to deliver lectures and for stu * @exr-hc ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we offer a series of hands-on labs allowing students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience. - -* _Coming soon._ -::: - - - diff --git a/contents/ai_for_good/images/png/cover_ai_good.png b/contents/core/ai_for_good/images/png/cover_ai_good.png similarity index 100% rename from contents/ai_for_good/images/png/cover_ai_good.png rename to contents/core/ai_for_good/images/png/cover_ai_good.png diff --git a/contents/ai_for_good/images/png/msfarmbeats.png b/contents/core/ai_for_good/images/png/msfarmbeats.png similarity index 100% rename from contents/ai_for_good/images/png/msfarmbeats.png rename to contents/core/ai_for_good/images/png/msfarmbeats.png diff --git a/contents/core/benchmarking/benchmarking.bib b/contents/core/benchmarking/benchmarking.bib new file mode 100644 index 00000000..c785dd69 --- /dev/null +++ b/contents/core/benchmarking/benchmarking.bib @@ -0,0 +1,454 @@ +%comment{This file was created with betterbib v5.0.11.} + +@article{bianco2018benchmark, + doi = {10.1109/access.2018.2877890}, + pages = {64270--64277}, + source = {Crossref}, + volume = {6}, + author = {Bianco, Simone and Cadene, Remi and Celona, Luigi and Napoletano, Paolo}, + year = {2018}, + url = {https://doi.org/10.1109/access.2018.2877890}, + issn = {2169-3536}, + journal = {IEEE Access}, + publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, + title = {Benchmark Analysis of Representative Deep Neural Network Architectures}, +} + +@inproceedings{adolf2016fathom, + doi = {10.1109/iiswc.2016.7581275}, + pages = {1--10}, + source = {Crossref}, + author = {Adolf, Robert and Rama, Saketh and Reagen, Brandon and Wei, Gu-yeon and Brooks, David}, + year = {2016}, + month = sep, + url = {https://doi.org/10.1109/iiswc.2016.7581275}, + booktitle = {2016 IEEE International Symposium on Workload Characterization (IISWC)}, + publisher = {IEEE}, + title = {Fathom: reference workloads for modern deep learning methods}, + organization = {IEEE}, +} + +@inproceedings{antol2015vqa, + doi = {10.1109/iccv.2015.279}, + pages = {2425--2433}, + source = {Crossref}, + author = {Antol, Stanislaw and Agrawal, Aishwarya and Lu, Jiasen and Mitchell, Margaret and Batra, Dhruv and Zitnick, C. 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On each tier of the podium, there are AI chips with intricate designs. The top chip has a gold medal hanging from it, the second one has a silver medal, and the third has a bronze medal. Banners with 'AI Olympics' are displayed prominently in the background._](images/png/cover_ai_benchmarking.png) @@ -20,7 +20,7 @@ This chapter will provide an overview of popular ML benchmarks, best practices f * Understand the purpose and goals of benchmarking AI systems, including performance assessment, resource evaluation, validation, and more. -* Learn about key model benchmarks, metrics, and trends, including accuracy, fairness, complexity, and efficiency. +* Learn about key model benchmarks, metrics, and trends, including accuracy, fairness, complexity, performance, and energy efficiency. * Become familiar with the key components of an AI benchmark, including datasets, tasks, metrics, baselines, reproducibility rules, and more. @@ -36,7 +36,7 @@ This chapter will provide an overview of popular ML benchmarks, best practices f ::: -## Introduction {#sec-benchmarking-ai} +## Introduction Benchmarking provides the essential measurements needed to drive machine learning progress and truly understand system performance. As the physicist Lord Kelvin famously said, "To measure is to know." Benchmarks allow us to quantitatively know the capabilities of different models, software, and hardware. They allow ML developers to measure the inference time, memory usage, power consumption, and other metrics that characterize a system. Moreover, benchmarks create standardized processes for measurement, enabling fair comparisons across different solutions. @@ -46,6 +46,8 @@ Benchmarking has several important goals and objectives that guide its implement * **Performance assessment.** This involves evaluating key metrics like a given model's speed, accuracy, and efficiency. For instance, in a TinyML context, it is crucial to benchmark how quickly a voice assistant can recognize commands, as this evaluates real-time performance. +* **Power assessment.** Evaluating the power drawn by a workload along with its performance equates to its energy efficiency. As the environmental impact of ML computing continues to grow, benchmarking energy can enable us to better optimize our systems for sustainability. + * **Resource evaluation.** This means assessing the model's impact on critical system resources, including battery life, memory usage, and computational overhead. A relevant example is comparing the battery drain of two different image recognition algorithms running on a wearable device. * **Validation and verification.** Benchmarking helps ensure the system functions correctly and meets specified requirements. One way is by checking the accuracy of an algorithm, like a heart rate monitor on a smartwatch, against readings from medical-grade equipment as a form of clinical validation. @@ -60,7 +62,7 @@ This chapter will cover the 3 types of AI benchmarks, the standard metrics, tool ## Historical Context -### Standard Benchmarks +### Performance Benchmarks The evolution of benchmarks in computing vividly illustrates the industry's relentless pursuit of excellence and innovation. In the early days of computing during the 1960s and 1970s, benchmarks were rudimentary and designed for mainframe computers. For example, the [Whetstone benchmark](https://en.wikipedia.org/wiki/Whetstone_(benchmark)), named after the Whetstone ALGOL compiler, was one of the first standardized tests to measure the floating-point arithmetic performance of a CPU. These pioneering benchmarks prompted manufacturers to refine their architectures and algorithms to achieve better benchmark scores. @@ -70,7 +72,25 @@ The 1990s brought the era of graphics-intensive applications and video games. Th The 2000s saw a surge in mobile phones and portable devices like tablets. With portability came the challenge of balancing performance and power consumption. Benchmarks like [MobileMark](https://bapco.com/products/mobilemark-2014/) by BAPCo evaluated speed and battery life. This drove companies to develop more energy-efficient System-on-Chips (SOCs), leading to the emergence of architectures like ARM that prioritized power efficiency. -The focus of the recent decade has shifted towards cloud computing, big data, and artificial intelligence. Cloud service providers like Amazon Web Services and Google Cloud compete on performance, scalability, and cost-effectiveness. Tailored cloud benchmarks like [CloudSuite](http://cloudsuite.ch/) have become essential, driving providers to optimize their infrastructure for better services. +The focus of the recent decade has shifted towards cloud computing, big data, and artificial intelligence. Cloud service providers like Amazon Web Services and Google Cloud compete on performance, scalability, and cost-effectiveness. Tailored cloud benchmarks like [CloudSuite](http://cloudsuite.ch/) have become essential, driving providers to optimize their infrastructure for better services. + +### Energy Benchmarks + +Energy consumption and environmental concerns have gained prominence in recent years, making power (more precisely, energy) benchmarking increasingly important in the industry. This shift began in the mid-2000s when processors and systems started hitting cooling limits, and scaling became a crucial aspect of building large-scale systems due to internet advancements. Since then, energy considerations have expanded to encompass all areas of computing, from personal devices to large-scale data centers. + +Power benchmarking aims to measure the energy efficiency of computing systems, evaluating performance in relation to power consumption. This is crucial for several reasons: + +* **Environmental impact:** With the growing carbon footprint of the tech industry, there's a pressing need to reduce energy consumption. +* **Operational costs:** Energy expenses constitute a significant portion of data center operating costs. +* **Device longevity:** For mobile devices, power efficiency directly impacts battery life and user experience. + +Several key benchmarks have emerged in this space: + +* **SPEC Power:** Introduced in 2007, [SPEC Power](https://www.spec.org/power/) was one of the first industry-standard benchmarks for evaluating the power and performance characteristics of computer servers. +* **Green500:** The [Green500](https://top500.org/lists/green500/) list ranks supercomputers by energy efficiency, complementing the performance-focused TOP500 list. +* **Energy Star:** While not a benchmark per se, [ENERGY STAR for Computers](https://www.energystar.gov/products/computers) certification program has driven manufacturers to improve the energy efficiency of consumer electronics. + +Power benchmarking faces unique challenges, such as accounting for different workloads and system configurations, and measuring power consumption accurately across a range of hardware that scales from microWatts to megaWatts in power consumption. As AI and edge computing continue to grow, power benchmarking is likely to become even more critical, driving the development of specialized energy-efficient AI hardware and software optimizations. ### Custom Benchmarks @@ -88,7 +108,7 @@ A key prerogative for any benchmark to be impactful is that it must reflect the Furthermore, benchmarks published with broad co-authorship from respected institutions carry authority and validity that convinces the community to adopt them as trusted standards. Benchmarks perceived as biased by particular corporate or institutional interests breed skepticism. Ongoing community engagement through workshops and challenges is also key after the initial release, and that is what, for instance, led to the success of ImageNet. As research progresses, collective participation enables continual refinement and expansion of benchmarks over time. -Finally, community-developed benchmarks released with open access accelerate adoption and consistent implementation. We shared open-source code, documentation, models, and infrastructure to lower barriers for groups to benchmark solutions on an equal footing using standardized implementations. This consistency is critical for fair comparisons. Without coordination, labs and companies may implement benchmarks differently, reducing result reproducibility. +Finally, releasing community-developed benchmarks with open access promotes their adoption and consistent use. By providing open-source code, documentation, models, and infrastructure, we reduce barriers to entry, enabling groups to benchmark solutions on an equal footing with standardized implementations. This consistency is essential for fair comparisons. Without coordination, labs and companies might implement benchmarks differently, which can undermine reproducibility and comparability of results. Community consensus brings benchmarks lasting relevance, while fragmentation confuses. Through collaborative development and transparent operation, benchmarks can become authoritative standards for tracking progress. Several of the benchmarks that we discuss in this chapter were developed and built by the community, for the community, and that is what ultimately led to their success. @@ -106,7 +126,7 @@ The architecture, size, and complexity of AI models vary widely. Different model ### Data Benchmarks -AI, particularly machine learning, is inherently data-driven. The quality, size, and diversity of data influence AI models' training efficacy and generalization capability. Data benchmarks focus on the datasets used in AI training and evaluation. They provide standardized datasets the community can use to train and test models, ensuring a level playing field for comparisons. Moreover, these benchmarks highlight data quality, diversity, and representation challenges, pushing the community to address biases and gaps in AI training data. By understanding data benchmarks, researchers can also gauge how models might perform in real-world scenarios, ensuring robustness and reliability. +In machine learning, data is foundational because the quality, scale, and diversity of datasets directly impact model efficacy and generalization. Data benchmarks focus on the datasets used in training and evaluation. They provide standardized datasets the community can use to train and test models, ensuring a level playing field for comparisons. Moreover, these benchmarks highlight data quality, diversity, and representation challenges, pushing the community to address biases and gaps in training data. By understanding data benchmarks, researchers can also gauge how models might perform in real-world scenarios, ensuring robustness and reliability. In the remainder of the sections, we will discuss each of these benchmark types. The focus will be an in-depth exploration of system benchmarks, as these are critical to understanding and advancing machine learning system performance. We will briefly cover model and data benchmarks for a comprehensive perspective, but the emphasis and majority of the content will be devoted to system benchmarks. @@ -123,7 +143,7 @@ Machine learning system benchmarking provides a structured and systematic approa #### Micro Benchmarks -Micro-benchmarks in AI are specialized, evaluating distinct components or specific operations within a broader machine learning process. These benchmarks zero in on individual tasks, offering insights into the computational demands of a particular neural network layer, the efficiency of a unique optimization technique, or the throughput of a specific activation function. For instance, practitioners might use micro-benchmarks to measure the computational time required by a convolutional layer in a deep learning model or to evaluate the speed of data preprocessing that feeds data into the model. Such granular assessments are instrumental in fine-tuning and optimizing discrete aspects of AI models, ensuring that each component operates at its peak potential. +Micro-benchmarks are specialized, evaluating distinct components or specific operations within a broader machine learning process. These benchmarks focus on individual tasks, offering insights into the computational demands of a particular neural network layer, the efficiency of a unique optimization technique, or the throughput of a specific activation function. For instance, practitioners might use micro-benchmarks to measure the computational time required by a convolutional layer in a deep learning model or to evaluate the speed of data preprocessing that feeds data into the model. Such granular assessments are instrumental in fine-tuning and optimizing discrete aspects of models, ensuring that each component operates at its peak potential. These types of microbenchmarks include zooming into very specific operations or components of the AI pipeline, such as the following: @@ -133,7 +153,7 @@ These types of microbenchmarks include zooming into very specific operations or * **Layer Benchmarks:** Evaluations of the computational efficiency of distinct neural network layers, such as LSTM or Transformer blocks, when operating on standardized input sizes. -Example: [DeepBench](https://github.com/baidu-research/DeepBench), introduced by Baidu, is a good example of something that assesses the above. DeepBench assesses the performance of basic operations in deep learning models, providing insights into how different hardware platforms handle neural network training and inference. +Example: [DeepBench](https://github.com/baidu-research/DeepBench), introduced by Baidu, is a good benchmark that evaluates fundamental deep learning operations, such as those mentioned above. DeepBench assesses the performance of basic operations in deep learning models, providing insights into how different hardware platforms handle neural network training and inference. :::{#exr-cuda .callout-caution collapse="true"} @@ -147,7 +167,7 @@ Ever wonder how your image filters get so fast? Special libraries like cuDNN sup #### Macro Benchmarks -Macro benchmarks provide a holistic view, assessing the end-to-end performance of entire machine learning models or comprehensive AI systems. Rather than focusing on individual operations, macro-benchmarks evaluate the collective efficacy of models under real-world scenarios or tasks. For example, a macro-benchmark might assess the complete performance of a deep learning model undertaking image classification on a dataset like [ImageNet](https://www.image-net.org/). This includes gauging accuracy, computational speed, and resource consumption. Similarly, one might measure the cumulative time and resources needed to train a natural language processing model on extensive text corpora or evaluate the performance of an entire recommendation system, from data ingestion to final user-specific outputs. +Macro benchmarks provide a holistic view, assessing the end-to-end performance of entire machine learning models or comprehensive ML systems. Rather than focusing on individual operations, macro-benchmarks evaluate the collective efficacy of models under real-world scenarios or tasks. For example, a macro-benchmark might assess the complete performance of a deep learning model undertaking image classification on a dataset like [ImageNet](https://www.image-net.org/). This includes gauging accuracy, computational speed, and resource consumption. Similarly, one might measure the cumulative time and resources needed to train a natural language processing model on extensive text corpora or evaluate the performance of an entire recommendation system, from data ingestion to final user-specific outputs. Examples: These benchmarks evaluate the AI model: @@ -159,7 +179,7 @@ Examples: These benchmarks evaluate the AI model: #### End-to-end Benchmarks -End-to-end benchmarks provide an all-inclusive evaluation that extends beyond the boundaries of the AI model itself. Instead of focusing solely on a machine learning model's computational efficiency or accuracy, these benchmarks encompass the entire pipeline of an AI system. This includes initial data preprocessing, the core model's performance, post-processing of the model's outputs, and other integral components like storage and network interactions. +End-to-end benchmarks provide an all-inclusive evaluation that extends beyond the boundaries of the ML model itself. Instead of focusing solely on a machine learning model's computational efficiency or accuracy, these benchmarks encompass the entire pipeline of an AI system. This includes initial data preprocessing, the core model's performance, post-processing of the model's outputs, and other integral components like storage and network interactions. Data preprocessing is the first stage in many AI systems, transforming raw data into a format suitable for model training or inference. These preprocessing steps' efficiency, scalability, and accuracy are vital for the overall system's performance. End-to-end benchmarks assess this phase, ensuring that data cleaning, normalization, augmentation, or any other transformation process doesn't become a bottleneck. @@ -197,7 +217,7 @@ Example: Tasks for natural language processing benchmarks might include sentimen #### Evaluation Metrics -Once a task is defined, benchmarks require metrics to quantify performance. These metrics offer objective measures to compare different models or systems. In classification tasks, metrics like accuracy, precision, recall, and [F1 score](https://en.wikipedia.org/wiki/F-score) are commonly used. Mean squared or absolute errors might be employed for regression tasks. +Once a task is defined, benchmarks require metrics to quantify performance. These metrics offer objective measures to compare different models or systems. In classification tasks, metrics like accuracy, precision, recall, and [F1 score](https://en.wikipedia.org/wiki/F-score) are commonly used. Mean squared or absolute errors might be employed for regression tasks. We can also measure the power consumed by the benchmark execution to calculate energy efficiency. #### Baselines and Baseline Models @@ -234,28 +254,19 @@ Beyond raw scores or metrics, benchmarks often provide guidelines or context to Example: A benchmark might highlight that while Model A scored higher than Model B in accuracy, it offers better real-time performance, making it more suitable for time-sensitive applications. -### Training vs. Inference - -The development life cycle of a machine learning model involves two critical phases - training and inference. [Training](../training/training.qmd), as you may recall, is the process of learning patterns from data to create the model. Inference refers to the model making predictions on new unlabeled data. Both phases play indispensable yet distinct roles. Consequently, each phase warrants rigorous benchmarking to evaluate performance metrics like speed, accuracy, and computational efficiency. - -Benchmarking the training phase provides insights into how different model architectures, hyperparameter values, and optimization algorithms impact the time and resources needed to train the model. For instance, benchmarking shows how neural network depth affects training time on a given dataset. Benchmarking also reveals how hardware accelerators like GPUs and TPUs can speed up training. - -On the other hand, benchmarking inference evaluates model performance in real-world conditions after deployment. Key metrics include latency, throughput, memory footprint, and power consumption. This type of benchmarking determines if a model meets the requirements of its target application regarding response time and device constraints. However, we will discuss these broadly to ensure a general understanding. - - ### Training Benchmarks -Training represents the phase where the system processes and ingests raw data to adjust and refine its parameters. Therefore, it is an algorithmic activity and involves system-level considerations, including data pipelines, storage, computing resources, and orchestration mechanisms. The goal is to ensure that the ML system can efficiently learn from data, optimizing both the model's performance and the system's resource utilization. +The development life cycle of a machine learning model involves two critical phases - training and inference. Training represents the phase where the system processes and ingests raw data to adjust and refine its parameters. Benchmarking the training phase reveals how choices in data pipelines, storage solutions, model architectures, computing resources, hyperparameter settings, and optimization algorithms affect the efficiency and resource demands of model training. The goal is to ensure that the ML system can efficiently learn from data, optimizing both the model's performance and the system's resource utilization. #### Purpose -From an ML systems perspective, training benchmarks evaluate how well the system scales with increasing data volumes and computational demands. It's about understanding the interplay between hardware, software, and the data pipeline in the training process. +From a systems perspective, training machine learning models is resource-intensive, especially when working with large models. These models often contain billions or even trillions of trainable parameters and require enormous amounts of data, often on the scale of many terabytes. For example, [OpenAI's GPT-3](https://arxiv.org/abs/2005.14165) [@brown2020language] has 175 billion parameters, was trained on 45 TB of compressed plaintext data, and required 3,640 petaflop-days of compute for pretraining. ML training benchmarks evaluate the systems and resources required to manage the computational load of training such models. -Consider a distributed ML system designed to train on vast datasets, like those used in large-scale e-commerce product recommendations. A training benchmark would assess how efficiently the system scales across multiple nodes, manage data sharding and handle failures or node drop-offs during training. +Efficient data storage and delivery during training also play a major role in the training process. For instance, in a machine learning model that predicts bounding boxes around objects in an image, thousands of images may be required. However, loading an entire image dataset into memory is typically infeasible, so practitioners rely on data loaders (as disucssed in @sec-frameworks-data-loaders) from ML frameworks. Successful model training depends on timely and efficient data delivery, making it essential to benchmark tools like data loaders, data pipelines, preprocessing speed, and storage retrieval times to understand their impact on training performance. -Training benchmarks evaluate CPU, GPU, memory, and network utilization during the training phase, guiding system optimizations. When training a model in a cloud-based ML system, it's crucial to understand how resources are being utilized. Are GPUs being fully leveraged? Is there unnecessary memory overhead? Benchmarks can highlight bottlenecks or inefficiencies in resource utilization, leading to cost savings and performance improvements. +Hardware selection is another key factor in training machine learning systems, as it can significantly impact training time. Training benchmarks evaluate CPU, GPU, memory, and network utilization during the training phase to guide system optimizations. Understanding how resources are used is essential: Are GPUs being fully leveraged? Is there unnecessary memory overhead? Benchmarks can uncover bottlenecks or inefficiencies in resource utilization, leading to cost savings and performance improvements. -Training an ML model is contingent on timely and efficient data delivery. Benchmarks in this context would also assess the efficiency of data pipelines, data preprocessing speed, and storage retrieval times. For real-time analytics systems, like those used in fraud detection, the speed at which training data is ingested, preprocessed, and fed into the model can be critical. Benchmarks would evaluate the latency of data pipelines, the efficiency of storage systems (like SSDs vs. HDDs), and the speed of data augmentation or transformation tasks. +In many cases, using a single hardware accelerator, such as a single GPU, is insufficient to meet the computational demands of large-scale model training. Machine learning models are often trained in data centers with multiple GPUs or TPUs, where distributed computing enables parallel processing across nodes. Training benchmarks assess how efficiently the system scales across multiple nodes, manages data sharding, and handles challenges like node failures or drop-offs during training. #### Metrics @@ -265,13 +276,13 @@ The following metrics are often considered important: 1. **Training Time:** The time it takes to train a model from scratch until it reaches a satisfactory performance level. It directly measures the computational resources required to train a model. For example, [Google's BERT](https://arxiv.org/abs/1810.04805) [@devlin2018bert] is a natural language processing model that requires several days to train on a massive corpus of text data using multiple GPUs. The long training time is a significant resource consumption and cost challenge. In some cases, benchmarks can instead measure the training throughput (training samples per unit of time). Throughput can be calculated much faster and easier than training time but may obscure the metrics we really care about (e.g. time to train). -2. **Scalability:** How well the training process can handle increases in data size or model complexity. Scalability can be assessed by measuring training time, memory usage, and other resource consumption as data size or model complexity increases. [OpenAI's GPT-3](https://arxiv.org/abs/2005.14165) [@brown2020language] model has 175 billion parameters, making it one of the largest language models in existence. Training GPT-3 required extensive engineering efforts to scale the training process to handle the massive model size. This involved using specialized hardware, distributed training, and other techniques to ensure the model could be trained efficiently. +2. **Scalability:** How well the training process can handle increases in data size or model complexity. Scalability can be assessed by measuring training time, memory usage, and other resource consumption as data size or model complexity increases. For instance, training OpenAI's GPT-3 required extensive engineering efforts to scale the training process across many GPU nodes to handle the massive model size. This involved using specialized hardware, distributed training, and other techniques to ensure the model could be trained efficiently. 3. **Resource Utilization:** The extent to which the training process utilizes available computational resources such as CPU, GPU, memory, and disk I/O. High resource utilization can indicate an efficient training process, while low utilization can suggest bottlenecks or inefficiencies. For instance, training a convolutional neural network (CNN) for image classification requires significant GPU resources. Utilizing multi-GPU setups and optimizing the training code for GPU acceleration can greatly improve resource utilization and training efficiency. 4. **Memory Consumption:** The amount of memory the training process uses. Memory consumption can be a limiting factor for training large models or datasets. For example, Google researchers faced significant memory consumption challenges when training BERT. The model has hundreds of millions of parameters, requiring large amounts of memory. The researchers had to develop techniques to reduce memory consumption, such as gradient checkpointing and model parallelism. -5. **Energy Consumption:** The energy consumed during training. As machine learning models become more complex, energy consumption has become an important consideration. Training large machine learning models can consume significant energy, leading to a large carbon footprint. For instance, the training of OpenAI's GPT-3 was estimated to have a carbon footprint equivalent to traveling by car for 700,000 kilometers. +5. **Energy Consumption:** The energy consumed during training. As machine learning models become more complex, energy consumption has become an important consideration. Training large machine learning models can consume significant energy, leading to a large carbon footprint. For instance, the training of OpenAI's GPT-3 was estimated to have a carbon footprint equivalent to traveling by car for 700,000 kilometers (~435,000 miles). 6. **Throughput:** The number of training samples processed per unit time. Higher throughput generally indicates a more efficient training process. The throughput is an important metric to consider when training a recommendation system for an e-commerce platform. A high throughput ensures that the model can process large volumes of user interaction data promptly, which is crucial for maintaining the relevance and accuracy of the recommendations. But it's also important to understand how to balance throughput with latency bounds. Therefore, a latency-bounded throughput constraint is often imposed on service-level agreements for data center application deployments. @@ -285,27 +296,13 @@ The following metrics are often considered important: By benchmarking for these types of metrics, we can obtain a comprehensive view of the training process's performance and efficiency from a systems perspective. This can help identify areas for improvement and ensure that resources are used effectively. -#### Tasks - -Selecting a handful of representative tasks for benchmarking machine learning systems is challenging because machine learning is applied to various domains with unique characteristics and requirements. Here are some of the challenges faced in selecting representative tasks: - -1. **Diversity of Applications:** Machine learning is used in numerous fields such as healthcare, finance, natural language processing, computer vision, and many more. Each field has specific tasks that may not be representative of other fields. For example, image classification tasks in computer vision may not be relevant to financial fraud detection. -2. **Variability in Data Types and Quality:** Different tasks require different data types, such as text, images, videos, or numerical data. Data quality and availability can vary greatly between tasks, making it difficult to select tasks that are representative of the general challenges faced in machine learning. -3. **Task Complexity and Difficulty:** The complexity of tasks varies greatly. Some are relatively straightforward, while others are highly complex and require sophisticated models and techniques. Selecting representative tasks that cover the complexities encountered in machine learning is challenging. -4. **Ethical and Privacy Concerns:** Some tasks may involve sensitive or private data, such as medical records or personal information. These tasks may have ethical and privacy concerns that need to be addressed, making them less suitable as representative tasks for benchmarking. -5. **Scalability and Resource Requirements:** Different tasks may have different scalability and resource requirements. Some tasks may require extensive computational resources, while others can be performed with minimal resources. Selecting tasks that represent the general resource requirements in machine learning is difficult. -6. **Evaluation Metrics:** The metrics used to evaluate the performance of machine learning models vary between tasks. Some tasks may have well-established evaluation metrics, while others lack clear or standardized metrics. This can make it challenging to compare performance across different tasks. -7. **Generalizability of Results:** The results obtained from benchmarking on a specific task may not be generalizable to other tasks. This means that a machine learning system's performance on a selected task may not be indicative of its performance on other tasks. - -It is important to carefully consider these factors when designing benchmarks to ensure they are meaningful and relevant to the diverse range of tasks encountered in machine learning. - #### Benchmarks Here are some original works that laid the fundamental groundwork for developing systematic benchmarks for training machine learning systems. -*[MLPerf Training Benchmark](https://github.com/mlcommons/training)* +**[MLPerf Training Benchmark](https://github.com/mlcommons/training)**: MLPerf is a suite of benchmarks designed to measure the performance of machine learning hardware, software, and services. The MLPerf Training benchmark [@mattson2020mlperf] focuses on the time it takes to train models to a target quality metric. It includes diverse workloads, such as image classification, object detection, translation, and reinforcement learning. @fig-perf-trend highlights the performance improvements in progressive versions of MLPerf Training benchmarks, which have all outpaced Moore's Law. Using standardized benchmarking trends enables us to rigorously showcase the rapid evolution of ML computing. -MLPerf is a suite of benchmarks designed to measure the performance of machine learning hardware, software, and services. The MLPerf Training benchmark [@mattson2020mlperf] focuses on the time it takes to train models to a target quality metric. It includes diverse workloads, such as image classification, object detection, translation, and reinforcement learning. +![MLPerf Training performance trends. Source: @mattson2020mlperf.](images/png/mlperf_perf_trend.png){#fig-perf-trend} Metrics: @@ -313,9 +310,7 @@ Metrics: * Throughput (examples per second) * Resource utilization (CPU, GPU, memory, disk I/O) -*[DAWNBench](https://dawn.cs.stanford.edu/benchmark/)* - -DAWNBench [@coleman2017dawnbench] is a benchmark suite focusing on end-to-end deep learning training time and inference performance. It includes common tasks such as image classification and question answering. +**[DAWNBench](https://dawn.cs.stanford.edu/benchmark/)**: DAWNBench [@coleman2017dawnbench] is a benchmark suite focusing on end-to-end deep learning training time and inference performance. It includes common tasks such as image classification and question answering. Metrics: @@ -323,9 +318,7 @@ Metrics: * Inference latency * Cost (in terms of cloud computing and storage resources) -*[Fathom](https://github.com/rdadolf/fathom)* - -Fathom [@adolf2016fathom] is a benchmark from Harvard University that evaluates the performance of deep learning models using a diverse set of workloads. These include common tasks such as image classification, speech recognition, and language modeling. +**[Fathom](https://github.com/rdadolf/fathom)**: Fathom [@adolf2016fathom] is a benchmark from Harvard University that evaluates the performance of deep learning models using a diverse set of workloads. These include common tasks such as image classification, speech recognition, and language modeling. Metrics: @@ -335,17 +328,18 @@ Metrics: #### Example Use Case -Consider a scenario where we want to benchmark the training of an image classification model on a specific hardware platform. +Imagine you have been tasked with benchmarking the training performance of an image classification model on a specific hardware platform. Let’s break down how you might approach this: + +1. **Define the Task**: First, choose a model and dataset. In this case, you’ll be training a CNN to classify images in the [CIFAR-10](https://www.cs.toronto.edu/kriz/cifar.html) dataset, a widely used benchmark in computer vision. -1. **Task:** The task is to train a convolutional neural network (CNN) for image classification on the CIFAR-10 dataset. -2. **Benchmark:** We can use the MLPerf Training benchmark for this task. It includes an image classification workload that is relevant to our task. -3. **Metrics:** We will measure the following metrics: +2. **Select the Benchmark**: Choosing a widely accepted benchmark helps ensure your setup is comparable with other real-world evaluations. You could choose to use the MLPerf Training benchmark because it provides a structured image classification workload, making it a relevant and standardized option for assessing training performance on CIFAR-10. Using MLPerf enables you to evaluate your system against industry-standard metrics, helping to ensure that results are meaningful and comparable to those achieved on other hardware platforms. -* Training time to reach a target accuracy of 90%. -* Throughput in terms of images processed per second. -* GPU and CPU utilization during training. +3. **Identify Key Metrics**: Now, decide on the metrics that will help you evaluate the system’s training performance. For this example, you might track: + - **Training Time**: How long does it take to reach 90% accuracy? + - **Throughput**: How many images are processed per second? + - **Resource Utilization**: What’s the GPU and CPU usage throughout training? -By measuring these metrics, we can assess the performance and efficiency of the training process on the selected hardware platform. This information can then be used to identify potential bottlenecks or areas for improvement. +By analyzing these metrics, you’ll gain insights into the model's training performance on your chosen hardware platform. Consider whether training time meets your expectations, if there are any bottlenecks, such as underutilized GPUs or slow data loading. This process helps identify areas for potential optimization, like improving data handling or adjusting resource allocation, and can guide future benchmarking decisions. ### Inference Benchmarks @@ -375,26 +369,6 @@ Finally, it is vital to ensure that the model's predictions are not only accurat 6. **Memory Usage:** Memory usage quantifies the volume of RAM needed by a machine learning model to carry out inference tasks. A relevant example to illustrate this would be a face recognition system based on a CNN; if such a system requires 150 MB of RAM to process and recognize faces within an image, its memory usage is 150 MB. -#### Tasks - -The challenges in picking representative tasks for benchmarking inference machine learning systems are, by and large, somewhat similar to the taxonomy we have provided for training. Nevertheless, to be pedantic, let's discuss those in the context of inference machine learning systems. - -1. **Diversity of Applications:** Inference machine learning is employed across numerous domains such as healthcare, finance, entertainment, security, and more. Each domain has unique tasks, and what's representative in one domain might not be in another. For example, an inference task for predicting stock prices in the financial domain might differ from image recognition tasks in the medical domain. - -2. **Variability in Data Types:** Different inference tasks require different types of data—text, images, videos, numerical data, etc. Ensuring that benchmarks address the wide variety of data types used in real-world applications is challenging. For example, voice recognition systems process audio data, which is vastly different from the visual data processed by facial recognition systems. - -3. **Task Complexity:** The complexity of inference tasks can differ immensely, from basic classification tasks to intricate tasks requiring state-of-the-art models. For example, differentiating between two categories (binary classification) is typically simpler than detecting hundreds of object types in a crowded scene. - -4. **Real-time Requirements:** Some applications demand immediate or real-time responses, while others may allow for some delay. In autonomous driving, real-time object detection and decision-making are paramount, whereas a recommendation engine for a shopping website might tolerate slight delays. - -5. **Scalability Concerns:** Given the varied scale of applications, from edge devices to cloud-based servers, tasks must represent the diverse computational environments where inference occurs. For example, an inference task running on a smartphone's limited resources differs from a powerful cloud server. - -6. **Evaluation Metrics Diversity:** The metrics used to evaluate performance can differ significantly depending on the task. Finding a common ground or universally accepted metric for diverse tasks is challenging. For example, precision and recall might be vital for a medical diagnosis task, whereas throughput (inferences per second) might be more crucial for video processing tasks. - -7. **Ethical and Privacy Concerns:** Concerns related to ethics and privacy exist, especially in sensitive areas like facial recognition or personal data processing. These concerns can impact the selection and nature of tasks used for benchmarking. For example, using real-world facial data for benchmarking can raise privacy issues, whereas synthetic data might not replicate real-world challenges. - -8. **Hardware Diversity:** With a wide range of devices from GPUs, CPUs, and TPUs to custom ASICs used for inference, ensuring that tasks are representative across varied hardware is challenging. For example, a task optimized for inference on a GPU might perform sub-optimally on an edge device. - #### Benchmarks Here are some original works that laid the fundamental groundwork for developing systematic benchmarks for inference machine learning systems. @@ -434,20 +408,19 @@ Metrics: #### Example Use Case -Consider a scenario where we want to evaluate the inference performance of an object detection model on a specific edge device. +Suppose you were tasked with evaluating the inference performance of an object detection model on a specific edge device. Here’s how you might approach structuring this benchmark: -Task: The task is to perform real-time object detection on video streams, detecting and identifying objects such as vehicles, pedestrians, and traffic signs. +1. **Define the Task**: In this case, the task is real-time object detection on video streams, identifying objects such as vehicles, pedestrians, and traffic signs. -Benchmark: We can use the AI Benchmark for this task as it evaluates inference performance on edge devices, which suits our scenario. +2. **Select the Benchmark**: To align with your goal of evaluating inference on an edge device, the AI Benchmark is a suitable choice. It provides a standardized framework specifically for assessing inference performance on edge hardware, making it relevant to this scenario. -Metrics: We will measure the following metrics: +3. **Identify Key Metrics**: Now, determine the metrics that will help evaluate the model’s inference performance. For this example, you might track: + - **Inference Time**: How long does it take to process each video frame? + - **Latency**: What is the delay in generating bounding boxes for detected objects? + - **Energy Consumption**: How much power is used during inference? + - **Throughput**: How many video frames are processed per second? -* Inference time to process each video frame -* Latency to generate the bounding boxes for detected objects -* Energy consumption during the inference process -* Throughput in terms of video frames processed per second - -By measuring these metrics, we can assess the performance of the object detection model on the edge device and identify any potential bottlenecks or areas for optimization to improve real-time processing capabilities. +By measuring these metrics, you’ll gain insights into how well the object detection model performs on the edge device. This can help identify any bottlenecks, such as slow frame processing or high energy consumption, and highlight areas for potential optimization to improve real-time performance. :::{#exr-perf .callout-caution collapse="true"} @@ -459,6 +432,31 @@ Get ready to put your AI models to the ultimate test! MLPerf is like the Olympic ::: + +### Benchmark Task Selection + +Selecting representative tasks for benchmarking machine learning systems is complex due to the varied applications, data types, and requirements across different domains. Machine learning is applied in fields such as healthcare, finance, natural language processing, and computer vision, each with unique tasks that may not be relevant or comparable to others. Key challenges in task selection include: + +1. **Diversity of Applications and Data Types:** Tasks across domains involve different data types (e.g., text, images, video) and qualities, making it difficult to find benchmarks that universally represent ML challenges. +2. **Task Complexity and Resource Needs:** Tasks vary in complexity and resource demands, with some requiring substantial computational power and sophisticated models, while others can be addressed with simpler resources and methods. +3. **Privacy Concerns:** Tasks involving sensitive data, such as medical records or personal information, introduce ethical and privacy issues, making them unsuitable for general benchmarks. +4. **Evaluation Metrics:** Performance metrics vary widely across tasks, and results from one task often do not generalize to others, complicating comparisons and limiting insights from one benchmarked task to another. + +Addressing these challenges is essential to designing meaningful benchmarks that are relevant across the diverse tasks encountered in machine learning, ensuring benchmarks provide useful, generalizable insights for both training and inference. + + +### Measuring Energy Efficiency + +As machine learning capabilities expand, both in training and inference, concerns about increased power consumption and its ecological footprint have intensified. Addressing the sustainability of ML systems, a topic explored in more depth in the [Sustainable AI](../sustainable_ai/sustainable_ai.qmd) chapter, has thus become a key priority. This focus on sustainability has led to the development of standardized benchmarks designed to accurately measure energy efficiency. However, standardizing these methodologies poses challenges due to the need to accommodate vastly different scales—from the microwatt consumption of TinyML devices to the megawatt demands of data center training systems. Moreover, ensuring that benchmarking is fair and reproducible requires accommodating the diverse range of hardware configurations and architectures in use today. + +One example is the MLPerf Power benchmarking methodology [@tschand2024mlperf], which tackles these challenges by tailoring the methodologies for datacenter, edge inference, and tiny inference systems while measuring power consumption as comprehensively as possible for each scale. This methodology adapts to a variety of hardware, from general-purpose CPUs to specialized AI accelerators, while maintaining uniform measurement principles to ensure that comparisons are both fair and accurate across different platforms. + +@fig-power-diagram illustrates the power measurement boundaries for different system scales, from TinyML devices to inference nodes and training racks. Each example highlights the components within the measurement boundary and those outside it. This setup allows for accurate reflection of the true energy costs associated with running ML workloads across various real-world scenarios, and ensures that the benchmark captures the full spectrum of energy consumption. + +![MLPerf Power system measurement diagram. Source: @tschand2024mlperf.](images/png/power_component_diagram.png){#fig-power-diagram} + +It is important to note that optimizing a system for performance may not lead to the most energy efficient execution. Oftentimes, sacrificing a small amount of performance or accuracy can lead to significant gains in energy efficiency, highlighting the importance of accurately benchmarking power metrics. Future insights from energy efficiency and sustainability benchmarking will enable us to optimize for more sustainable ML systems. + ### Benchmark Example To properly illustrate the components of a systems benchmark, we can look at the keyword spotting benchmark in MLPerf Tiny and explain the motivation behind each decision. @@ -505,15 +503,13 @@ But of all these, the most important challenge is benchmark engineering. #### Hardware Lottery -The ["hardware lottery"](https://arxiv.org/abs/2009.06489) in benchmarking machine learning systems refers to the situation where the success or efficiency of a machine learning model is significantly influenced by the compatibility of the model with the underlying hardware [@chu2021discovering]. In other words, some models perform exceptionally well because they are a good fit for the particular characteristics or capabilities of the hardware they are run on rather than because they are intrinsically superior models. - -![Hardware Lottery.](images/png/hardware_lottery.png){#fig-hardware-lottery} +The hardware lottery, first described by @10.1145/3467017, refers to the situation where a machine learning model's success or efficiency is significantly influenced by its compatibility with the underlying hardware [@chu2021discovering]. Some models perform exceptionally well not because they are intrinsically superior, but because they are optimized for specific hardware characteristics, such as the parallel processing capabilities of Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs). -For instance, certain machine learning models may be designed and optimized to take advantage of the parallel processing capabilities of specific hardware accelerators, such as Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs). As a result, these models might show superior performance when benchmarked on such hardware compared to other models that are not optimized for the hardware. +For instance, @fig-hardware-lottery compares the performance of models across different hardware platforms. The multi-hardware models show comparable results to "MobileNetV3 Large min" on both the CPU uint8 and GPU configurations. However, these multi-hardware models demonstrate significant performance improvements over the MobileNetV3 Large baseline when run on the EdgeTPU and DSP hardware. This emphasizes the variable efficiency of multi-hardware models in specialized computing environments. -For example, a 2018 paper introduced a new convolutional neural network architecture for image classification that achieved state-of-the-art accuracy on ImageNet. However, the paper only mentioned that the model was trained on 8 GPUs without specifying the model, memory size, or other relevant details. A follow-up study tried to reproduce the results but found that training the same model on commonly available GPUs achieved 10% lower accuracy, even after hyperparameter tuning. The original hardware likely had far higher memory bandwidth and compute power. As another example, training times for large language models can vary drastically based on the GPUs used. +![Accuracy-latency trade-offs of multiple ML models and how they perform on various hardware. Source: @chu2021discovering](images/png/hardware_lottery.png){#fig-hardware-lottery} -The "hardware lottery" can introduce challenges and biases in benchmarking machine learning systems, as the model's performance is not solely dependent on the model's architecture or algorithm but also on the compatibility and synergies with the underlying hardware. This can make it difficult to compare different models fairly and to identify the best model based on its intrinsic merits. It can also lead to a situation where the community converges on models that are a good fit for the popular hardware of the day, potentially overlooking other models that might be superior but incompatible with the current hardware trends. +Hardware lottery can introduce challenges and biases in benchmarking machine learning systems, as the model's performance is not solely dependent on the model's architecture or algorithm but also on the compatibility and synergies with the underlying hardware. This can make it difficult to compare different models fairly and to identify the best model based on its intrinsic merits. It can also lead to a situation where the community converges on models that are a good fit for the popular hardware of the day, potentially overlooking other models that might be superior but incompatible with the current hardware trends. #### Benchmark Engineering @@ -521,7 +517,7 @@ Hardware lottery occurs when a machine learning model unintentionally performs e In contrast to the accidental hardware lottery, benchmark engineering involves deliberately optimizing or designing a machine learning model to perform exceptionally well on specific hardware, often to win benchmarks or competitions. This intentional optimization might include tweaking the model's architecture, algorithms, or parameters to exploit the hardware's features and capabilities fully. -#### Problem +##### Problem Benchmark engineering refers to tweaking or modifying an AI system to optimize performance on specific benchmark tests, often at the expense of generalizability or real-world performance. This can include adjusting hyperparameters, training data, or other aspects of the system specifically to achieve high scores on benchmark metrics without necessarily improving the overall functionality or utility of the system. @@ -531,7 +527,7 @@ It can lead to several risks and challenges. One of the primary risks is that th The AI community must prioritize transparency and accountability to mitigate the risks associated with benchmark engineering. This can include disclosing any optimizations or adjustments made specifically for benchmark tests and providing more comprehensive evaluations of AI systems that include real-world performance metrics and benchmark scores. Researchers and developers must prioritize holistic improvements to AI systems that improve their generalizability and functionality across various applications rather than focusing solely on benchmark-specific optimizations. -#### Issues +##### Issues One of the primary problems with benchmark engineering is that it can compromise the real-world performance of AI systems. When developers focus on optimizing their systems to achieve high scores on specific benchmark tests, they may neglect other important system performance aspects crucial in real-world applications. For example, an AI system designed for image recognition might be engineered to perform exceptionally well on a benchmark test that includes a specific set of images but needs help to recognize images slightly different from those in the test set accurately. @@ -539,15 +535,15 @@ Another area for improvement with benchmark engineering is that it can result in It can also lead to misleading results. When AI systems are engineered to perform well on benchmark tests, the results may not accurately reflect the system's true capabilities. This can be problematic for users or investors who rely on benchmark scores to make informed decisions about which AI systems to use or invest in. For example, an AI system engineered to achieve high scores on a benchmark test for speech recognition might need to be more capable of accurately recognizing speech in real-world situations, leading users or investors to make decisions based on inaccurate information. -#### Mitigation +##### Mitigation There are several ways to mitigate benchmark engineering. Transparency in the benchmarking process is crucial to maintaining benchmark accuracy and reliability. This involves clearly disclosing the methodologies, data sets, and evaluation criteria used in benchmark tests, as well as any optimizations or adjustments made to the AI system for the purpose of the benchmark. One way to achieve transparency is through the use of open-source benchmarks. Open-source benchmarks are made publicly available, allowing researchers, developers, and other stakeholders to review, critique, and contribute to them, thereby ensuring their accuracy and reliability. This collaborative approach also facilitates sharing best practices and developing more robust and comprehensive benchmarks. -One example is the MLPerf Tiny. It's an open-source framework designed to make it easy to compare different solutions in the world of TinyML. Its modular design allows components to be swapped out for comparison or improvement. The reference implementations, shown in green and orange in @fig-ml-perf, act as the baseline for results. TinyML often needs optimization across the entire system, and users can contribute by focusing on specific parts, like quantization. The modular benchmark design allows users to showcase their contributions and competitive advantage by modifying a reference implementation. In short, MLPerf Tiny offers a flexible and modular way to assess and improve TinyML applications, making it easier to compare and improve different aspects of the technology. +The modular design of MLPerf Tiny connects to the problem of benchmark engineering by providing a structured yet flexible approach that encourages a balanced evaluation of TinyML. In benchmark engineering, systems may be overly optimized for specific benchmarks, leading to inflated performance scores that don’t necessarily translate to real-world effectiveness. MLPerf Tiny’s modular design aims to address this issue by allowing contributors to swap out and test specific components within a standardized framework, such as hardware, quantization techniques, or inference models. The reference implementations, highlighted in green and orange in @fig-ml-perf, provide a baseline for results, enabling flexible yet controlled testing by specifying which components can be modified. This structure supports transparency and flexibility, enabling a focus on genuine improvements rather than benchmark-specific optimizations. -![MLPerf Tiny modular design. Source: @mattson2020mlperf.](images/png/mlperf_tiny.png){#fig-ml-perf} +![Modular design of the MLPerf Tiny benchmark, showing the reference implementation with modifiable components. This modular approach enables flexible, targeted testing while maintaining a standardized baseline. Source: @banbury2021mlperf.](images/png/mlperf_tiny.png){#fig-ml-perf} Another method for achieving transparency is through peer review of benchmarks. This involves having independent experts review and validate the benchmark's methodology, data sets, and results to ensure their credibility and reliability. Peer review can provide a valuable means of verifying the accuracy of benchmark tests and help build confidence in the results. @@ -569,17 +565,17 @@ Machine learning datasets have a rich history and have evolved significantly ove The [MNIST dataset](https://www.tensorflow.org/datasets/catalog/mnist), created by Yann LeCun, Corinna Cortes, and Christopher J.C. Burges in 1998, can be considered a cornerstone in the history of machine learning datasets. It comprises 70,000 labeled 28x28 pixel grayscale images of handwritten digits (0-9). MNIST has been widely used for benchmarking algorithms in image processing and machine learning as a starting point for many researchers and practitioners. @fig-mnist shows some examples of handwritten digits. -![MNIST handwritten digits. Source: [Suvanjanprasai.](https://en.wikipedia.org/wiki/File:MnistExamplesModified.png)](images/png/mnist.png){#fig-mnist} +![MNIST handwritten digits. Source: [Suvanjanprasai](https://en.wikipedia.org/wiki/File:MnistExamplesModified.png)](images/png/mnist.png){#fig-mnist} #### ImageNet (2009) -Fast forward to 2009, and we see the introduction of the [ImageNet dataset](https://www.tensorflow.org/datasets/catalog/imagenet2012), which marked a significant leap in the scale and complexity of datasets. ImageNet consists of over 14 million labeled images spanning more than 20,000 categories. Fei-Fei Li and her team developed it to advance object recognition and computer vision research. The dataset became synonymous with the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), an annual competition crucial in developing deep learning models, including the famous AlexNet in 2012. +Fast forward to 2009, and we see the introduction of the [ImageNet dataset](https://www.tensorflow.org/datasets/catalog/imagenet2012), which marked a significant leap in the scale and complexity of datasets. ImageNet consists of over 14 million labeled images spanning more than 20,000 categories. Fei-Fei Li and her team developed it to advance object recognition and computer vision research. The dataset became synonymous with the ImageNet [Large Scale Visual Recognition Challenge (LSVRC)](https://www.image-net.org/challenges/LSVRC/), an annual competition crucial in developing deep learning models, including the famous AlexNet in 2012. #### COCO (2014) -The [Common Objects in Context (COCO) dataset](https://cocodataset.org/) [@lin2014microsoft], released in 2014, further expanded the landscape of machine learning datasets by introducing a richer set of annotations. COCO consists of images containing complex scenes with multiple objects, and each image is annotated with object bounding boxes, segmentation masks, and captions. This dataset has been instrumental in advancing research in object detection, segmentation, and image captioning. +The [Common Objects in Context (COCO) dataset](https://cocodataset.org/) [@lin2014microsoft], released in 2014, further expanded the landscape of machine learning datasets by introducing a richer set of annotations. COCO consists of images containing complex scenes with multiple objects, and each image is annotated with object bounding boxes, segmentation masks, and captions, as shown in @fig-coco. This dataset has been instrumental in advancing research in object detection, segmentation, and image captioning. -![Coco dataset. Source: Coco.](images/png/coco.png){#fig-coco} +![Example images from the COCO dataset. Source: [Coco](https://cocodataset.org/)](images/png/coco.png){#fig-coco} #### GPT-3 (2020) @@ -607,61 +603,45 @@ Machine learning model evaluation has evolved from a narrow focus on accuracy to #### Accuracy -Accuracy is one of the most intuitive and commonly used metrics for evaluating machine learning models. At its core, accuracy measures the proportion of correct predictions made by the model out of all predictions. For example, imagine we have developed a machine learning model to classify images as either containing a cat or not. If we test this model on a dataset of 100 images, and it correctly identifies 90 of them, we would calculate its accuracy as 90%. +Accuracy is one of the most intuitive and commonly used metrics for evaluating machine learning models. In the early stages of machine learning, accuracy was often the primary, if not the only, metric considered when evaluating model performance. However, as the field has evolved, it’s become clear that relying solely on accuracy can be misleading, especially in applications where certain types of errors carry significant consequences. -In the initial stages of machine learning, accuracy was often the primary, if not the only, metric considered when evaluating model performance. This is understandable, given its straightforward nature and ease of interpretation. However, as the field has progressed, the limitations of relying solely on accuracy have become more apparent. +Consider the example of a medical diagnosis model with an accuracy of 95%. While at first glance this may seem impressive, we must look deeper to assess the model's performance fully. Suppose the model fails to accurately diagnose severe conditions that, while rare, can have severe consequences; its high accuracy may not be as meaningful. A well-known example of this limitation is [Google’s diabetic retinopathy model](https://about.google/intl/ALL_us/stories/seeingpotential/). While it achieved high accuracy in lab settings, it encountered challenges when deployed in real-world clinics in Thailand, where variations in patient populations, image quality, and environmental factors reduced its effectiveness. This example illustrates that even models with high accuracy need to be tested for their ability to generalize across diverse, unpredictable conditions to ensure reliability and impact in real-world settings. -Consider the example of a medical diagnosis model with an accuracy of 95%. While at first glance this may seem impressive, we must look deeper to assess the model's performance fully. Suppose the model fails to accurately diagnose severe conditions that, while rare, can have severe consequences; its high accuracy may not be as meaningful. A pertinent example of this is [Google's retinopathy machine learning model](https://about.google/intl/ALL_us/stories/seeingpotential/), which was designed to diagnose diabetic retinopathy and diabetic macular edema from retinal photographs. +Similarly, if the model performs well on average but exhibits significant disparities in performance across different demographic groups, this, too, would be cause for concern. The evolution of machine learning has thus seen a shift towards a more holistic approach to model evaluation, taking into account not just accuracy, but also other crucial factors such as fairness, transparency, and real-world applicability. A prime example is the [Gender Shades project](http://gendershades.org/) at MIT Media Lab, led by Joy Buolamwini, highlighting biases by performing better on lighter-skinned and male faces compared to darker-skinned and female faces. -The Google model demonstrated impressive accuracy levels in lab settings. Still, when deployed in real-world clinical environments in Thailand, [it faced significant challenges](https://www.technologyreview.com/2020/04/27/1000658/google-medical-ai-accurate-lab-real-life-clinic-covid-diabetes-retina-disease/). In the real-world setting, the model encountered diverse patient populations, varying image quality, and a range of different medical conditions that it had not been exposed to during its training. Consequently, its performance could have been better, and it struggled to maintain the same accuracy levels observed in lab settings. This example serves as a clear reminder that while high accuracy is an important and desirable attribute for a medical diagnosis model, it must be evaluated in conjunction with other factors, such as the model's ability to generalize to different populations and handle diverse and unpredictable real-world conditions, to understand its value and potential impact on patient care truly. - -Similarly, if the model performs well on average but exhibits significant disparities in performance across different demographic groups, this, too, would be cause for concern. - -The evolution of machine learning has thus seen a shift towards a more holistic approach to model evaluation, taking into account not just accuracy, but also other crucial factors such as fairness, transparency, and real-world applicability. A prime example is the [Gender Shades project](http://gendershades.org/) at MIT Media Lab, led by Joy Buolamwini, highlighting significant racial and gender biases in commercial facial recognition systems. The project evaluated the performance of three facial recognition technologies developed by IBM, Microsoft, and Face++. It found that they all exhibited biases, performing better on lighter-skinned and male faces compared to darker-skinned and female faces. - -While accuracy remains a fundamental and valuable metric for evaluating machine learning models, a more comprehensive approach is required to fully assess a model's performance. This means considering additional metrics that account for fairness, transparency, and real-world applicability, as well as conducting rigorous testing across diverse datasets to uncover and mitigate any potential biases. The move towards a more holistic approach to model evaluation reflects the maturation of the field and its increasing recognition of the real-world implications and ethical considerations associated with deploying machine learning models. +While accuracy remains essential for evaluating machine learning models, a comprehensive approach is needed to fully assess performance. This includes additional metrics for fairness, transparency, and real-world applicability, along with rigorous testing across diverse datasets to identify and address biases. This holistic evaluation approach reflects the field’s growing awareness of real-world implications in deploying models. #### Fairness -Fairness in machine learning models is a multifaceted and critical aspect that requires careful attention, particularly in high-stakes applications that significantly affect people's lives, such as in loan approval processes, hiring, and criminal justice. It refers to the equitable treatment of all individuals, irrespective of their demographic or social attributes such as race, gender, age, or socioeconomic status. - -Simply relying on accuracy can be insufficient and potentially misleading when evaluating models. For instance, consider a loan approval model with a 95% accuracy rate. While this figure may appear impressive at first glance, it does not reveal how the model performs across different demographic groups. If this model consistently discriminates against a particular group, its accuracy is less commendable, and its fairness is questioned. +Fairness in machine learning involves ensuring that models perform consistently across diverse groups, especially in high-impact applications like loan approvals, hiring, and criminal justice. Relying solely on accuracy can be misleading if the model exhibits biased outcomes across demographic groups. For example, a loan approval model with high accuracy may still consistently deny loans to certain groups, raising questions about its fairness. -Discrimination can manifest in various forms, such as direct discrimination, where a model explicitly uses sensitive attributes like race or gender in its decision-making process, or indirect discrimination, where seemingly neutral variables correlate with sensitive attributes, indirectly influencing the model's outcomes. An infamous example of the latter is the COMPAS tool used in the US criminal justice system, which exhibited racial biases in predicting recidivism rates despite not explicitly using race as a variable. +Bias in models can arise directly, when sensitive attributes like race or gender influence decisions, or indirectly, when neutral features correlate with these attributes, affecting outcomes. Simply relying on accuracy can be insufficient when evaluating models. For instance, consider a loan approval model with a 95% accuracy rate. While this figure may appear impressive at first glance, it does not reveal how the model performs across different demographic groups. For instance, a well-known example is the COMPAS tool used in the US criminal justice system, which showed racial biases in predicting recidivism despite not explicitly using race as a variable. -Addressing fairness involves careful examination of the model's performance across diverse groups, identifying potential biases, and rectifying disparities through corrective measures such as re-balancing datasets, adjusting model parameters, and implementing fairness-aware algorithms. Researchers and practitioners continuously develop metrics and methodologies tailored to specific use cases to evaluate fairness in real-world scenarios. For example, disparate impact analysis, demographic parity, and equal opportunity are some of the metrics employed to assess fairness. +Addressing fairness requires analyzing a model’s performance across groups, identifying biases, and applying corrective measures like re-balancing datasets or using fairness-aware algorithms. Researchers and practitioners continuously develop metrics and methodologies tailored to specific use cases to evaluate fairness in real-world scenarios. For example, disparate impact analysis, demographic parity, and equal opportunity are some of the metrics employed to assess fairness. Additionally, transparency and interpretability of models are fundamental to achieving fairness. Tools like [AI Fairness 360](https://ai-fairness-360.org/) and [Fairness Indicators](https://www.tensorflow.org/tfx/guide/fairness_indicators) help explain how a model makes decisions, allowing developers to detect and correct fairness issues in machine learning models. -Additionally, transparency and interpretability of models are fundamental to achieving fairness. Understanding how a model makes decisions can reveal potential biases and enable stakeholders to hold developers accountable. Open-source tools like [AI Fairness 360](https://ai-fairness-360.org/) by IBM and [Fairness Indicators](https://www.tensorflow.org/tfx/guide/fairness_indicators) by TensorFlow are being developed to facilitate fairness assessments and mitigation of biases in machine learning models. - -Ensuring fairness in machine learning models, particularly in applications that significantly impact people's lives, requires rigorous evaluation of the model's performance across diverse groups, careful identification and mitigation of biases, and implementation of transparency and interpretability measures. By comprehensively addressing fairness, we can work towards developing machine learning models that are equitable, just, and beneficial for society. +While accuracy is a valuable metric, it doesn’t always provide the full picture; assessing fairness ensures models are effective across real-world scenarios. Ensuring fairness in machine learning models, particularly in applications that significantly impact people's lives, requires rigorous evaluation of the model's performance across diverse groups, careful identification and mitigation of biases, and implementation of transparency and interpretability measures. #### Complexity -##### Parameters* - -In the initial stages of machine learning, model benchmarking often relied on parameter counts as a proxy for model complexity. The rationale was that more parameters typically lead to a more complex model, which should, in turn, deliver better performance. However, this approach has proven inadequate as it needs to account for the computational cost associated with processing many parameters. +##### Parameters -For example, GPT-3, developed by OpenAI, is a language model that boasts an astounding 175 billion parameters. While it achieves state-of-the-art performance on various natural language processing tasks, its size and the computational resources required to run it make it impractical for deployment in many real-world scenarios, especially those with limited computational capabilities. +In the initial stages of machine learning, model benchmarking often relied on parameter counts as a proxy for model complexity. The rationale was that more parameters typically lead to a more complex model, which should, in turn, deliver better performance. However, this approach overlooks the practical costs associated with processing large models. As parameter counts increase, so do the computational resources required, making such models impractical for deployment in real-world scenarios, particularly on devices with limited processing power. -Relying on parameter counts as a proxy for model complexity also fails to consider the model's efficiency. If optimized for efficiency, a model with fewer parameters might be just as effective, if not more so, than a model with a higher parameter count. For instance, MobileNets, developed by Google, is a family of models designed specifically for mobile and edge devices. They use depth-wise separable convolutions to reduce the number of parameters and computational costs while still achieving competitive performance. +Relying on parameter counts as a proxy for model complexity also fails to consider the model's efficiency. A well-optimized model with fewer parameters can often achieve comparable or even superior performance to a larger model. For instance, MobileNets, developed by Google, is a family of models designed specifically for mobile and edge devices. They used depth-wise separable convolutions to reduce parameter counts and computational demands while still maintaining strong performance. -In light of these limitations, the field has moved towards a more holistic approach to model benchmarking that considers parameter counts and other crucial factors such as floating-point operations per second (FLOPs), memory consumption, and latency. FLOPs, in particular, have emerged as an important metric as they provide a more accurate representation of the computational load a model imposes. This shift towards a more comprehensive approach to model benchmarking reflects a recognition of the need to balance performance with practicality, ensuring that models are effective, efficient, and deployable in real-world scenarios. +In light of these limitations, the field has moved towards a more holistic approach to model benchmarking that considers parameter counts and other crucial factors such as floating-point operations per second (FLOPs), memory consumption, and latency. This comprehensive approach balances performance with deployability, ensuring that models are not only accurate but also efficient and suitable for real-world applications. ##### FLOPS -The size of a machine learning model is an essential aspect that directly impacts its usability in practical scenarios, especially when computational resources are limited. Traditionally, the number of parameters in a model was often used as a proxy for its size, with the underlying assumption being that more parameters would translate to better performance. However, this simplistic view does not consider the computational cost of processing these parameters. This is where the concept of floating-point operations per second (FLOPs) comes into play, providing a more accurate representation of the computational load a model imposes. +FLOPs, or floating-point operations per second, have become a critical metric for representing a model’s computational load. Traditionally, parameter count was used as a proxy for model complexity, based on the assumption that more parameters would yield better performance. However, this approach overlooks the computational cost of processing these parameters, which can impact a model’s usability in real-world scenarios with limited resources. -FLOPs measure the number of floating-point operations a model performs to generate a prediction. A model with many FLOPs requires substantial computational resources to process the vast number of operations, which may render it impractical for certain applications. Conversely, a model with a lower FLOP count is more lightweight and can be easily deployed in scenarios where computational resources are limited. - -@fig-flops, from [@bianco2018benchmark], shows the relationship between Top-1 Accuracy on ImageNet (y-axis), the model's G-FLOPs (x-axis), and the model's parameter count (circle-size). +FLOPs measure the number of floating-point operations a model performs to generate a prediction. A model with many FLOPs requires substantial computational resources to process the vast number of operations, which may render it impractical for certain applications. Conversely, a model with a lower FLOP count is more lightweight and can be easily deployed in scenarios where computational resources are limited. @fig-flops, from [@bianco2018benchmark], illustrates the trade-off between ImageNet accuracy, FLOPs, and parameter count, showing that some architectures achieve higher efficiency than others. ![A graph that depicts the top-1 imagenet accuracy vs. the FLOP count of a model along with the model's parameter count. The figure shows a overall tradeoff between model complexity and accuracy, although some model architectures are more efficiency than others. Source: @bianco2018benchmark.](images/png/model_FLOPS_VS_TOP_1.png){#fig-flops} -Let's consider an example. BERT [Bidirectional Encoder Representations from Transformers] [@devlin2018bert], a popular natural language processing model, has over 340 million parameters, making it a large model with high accuracy and impressive performance across various tasks. However, the sheer size of BERT, coupled with its high FLOP count, makes it a computationally intensive model that may not be suitable for real-time applications or deployment on edge devices with limited computational capabilities. - -In light of this, there has been a growing interest in developing smaller models that can achieve similar performance levels as their larger counterparts while being more efficient in computational load. DistilBERT, for instance, is a smaller version of BERT that retains 97% of its performance while being 40% smaller in terms of parameter count. The size reduction also translates to a lower FLOP count, making DistilBERT a more practical choice for resource-constrained scenarios. +Let's consider an example. BERT---Bidirectional Encoder Representations from Transformers [@devlin2018bert]---is a popular natural language processing model, has over 340 million parameters, making it a large model with high accuracy and impressive performance across various tasks. However, the sheer size of BERT, coupled with its high FLOP count, makes it a computationally intensive model that may not be suitable for real-time applications or deployment on edge devices with limited computational capabilities. In light of this, there has been a growing interest in developing smaller models that can achieve similar performance levels as their larger counterparts while being more efficient in computational load. DistilBERT, for instance, is a smaller version of BERT that retains 97% of its performance while being 40% smaller in terms of parameter count. The size reduction also translates to a lower FLOP count, making DistilBERT a more practical choice for resource-constrained scenarios. -In summary, while parameter count provides a useful indication of model size, it is not a comprehensive metric as it needs to consider the computational cost associated with processing these parameters. FLOPs, on the other hand, offer a more accurate representation of a model's computational load and are thus an essential consideration when deploying machine learning models in real-world scenarios, particularly when computational resources are limited. The evolution from relying solely on parameter count to considering FLOPs signifies a maturation in the field, reflecting a greater awareness of the practical constraints and challenges of deploying machine learning models in diverse settings. +While parameter count indicates model size, it does not fully capture the computational cost. FLOPs provide a more accurate measure of computational load, highlighting the practical trade-offs in model deployment. This shift from parameter count to FLOPs reflects the field’s growing awareness of deployment challenges in diverse settings. ##### Efficiency @@ -717,10 +697,10 @@ The [Speech Commands dataset](https://arxiv.org/pdf/1804.03209.pdf) and its succ For the past several years, AI has focused on developing increasingly sophisticated machine learning models like large language models. The goal has been to create models capable of human-level or superhuman performance on a wide range of tasks by training them on massive datasets. This model-centric approach produced rapid progress, with models attaining state-of-the-art results on many established benchmarks. @fig-superhuman-perf shows the performance of AI systems relative to human performance (marked by the horizontal line at 0) across five applications: handwriting recognition, speech recognition, image recognition, reading comprehension, and language understanding. Over the past decade, the AI performance has surpassed that of humans. -However, growing concerns about issues like bias, safety, and robustness persist even in models that achieve high accuracy on standard benchmarks. Additionally, some popular datasets used for evaluating models are beginning to saturate, with models reaching near-perfect performance on existing test splits [@kiela2021dynabench]. As a simple example, there are test images in the classic MNIST handwritten digit dataset that may look indecipherable to most human evaluators but were assigned a label when the dataset was created - models that happen to agree with those labels may appear to exhibit superhuman performance but instead may only be capturing idiosyncrasies of the labeling and acquisition process from the dataset's creation in 1994. In the same spirit, computer vision researchers now ask, "Are we done with ImageNet?" [@beyer2020we]. This highlights limitations in the conventional model-centric approach of optimizing accuracy on fixed datasets through architectural innovations. - ![AI vs human performane. Source: @kiela2021dynabench.](images/png/dynabench.png){#fig-superhuman-perf} +However, growing concerns about issues like bias, safety, and robustness persist even in models that achieve high accuracy on standard benchmarks. Additionally, some popular datasets used for evaluating models are beginning to saturate, with models reaching near-perfect performance on existing test splits [@kiela2021dynabench]. As a simple example, there are test images in the classic MNIST handwritten digit dataset that may look indecipherable to most human evaluators but were assigned a label when the dataset was created - models that happen to agree with those labels may appear to exhibit superhuman performance but instead may only be capturing idiosyncrasies of the labeling and acquisition process from the dataset's creation in 1994. In the same spirit, computer vision researchers now ask, "Are we done with ImageNet?" [@beyer2020we]. This highlights limitations in the conventional model-centric approach of optimizing accuracy on fixed datasets through architectural innovations. + An alternative paradigm is emerging called data-centric AI. Rather than treating data as static and focusing narrowly on model performance, this approach recognizes that models are only as good as their training data. So, the emphasis shifts to curating high-quality datasets that better reflect real-world complexity, developing more informative evaluation benchmarks, and carefully considering how data is sampled, preprocessed, and augmented. The goal is to optimize model behavior by improving the data rather than just optimizing metrics on flawed datasets. Data-centric AI critically examines and enhances the data itself to produce beneficial AI. This reflects an important evolution in mindset as the field addresses the shortcomings of narrow benchmarking. This section will explore the key differences between model-centric and data-centric approaches to AI. This distinction has important implications for how we benchmark AI systems. Specifically, we will see how focusing on data quality and Efficiency can directly improve machine learning performance as an alternative to optimizing model architectures solely. The data-centric approach recognizes that models are only as good as their training data. So, enhancing data curation, evaluation benchmarks, and data handling processes can produce AI systems that are safer, fairer, and more robust. Rethinking benchmarking to prioritize data alongside models represents an important evolution as the field strives to deliver trustworthy real-world impact. @@ -776,7 +756,7 @@ Several approaches can be taken to improve data quality. These methods include a * **Feature Engineering:** Transforming or creating new features can significantly improve model performance by providing more relevant information for learning. * **Data Augmentation:** Augmenting data by creating new samples through various transformations can help improve model robustness and generalization. * **Active Learning:** This is a semi-supervised learning approach where the model actively queries a human oracle to label the most informative samples [@coleman2022similarity]. This ensures that the model is trained on the most relevant data. -* Dimensionality Reduction: Techniques like PCA can reduce the number of features in a dataset, thereby reducing complexity and training time. +* **Dimensionality Reduction:** Techniques like PCA can reduce the number of features in a dataset, thereby reducing complexity and training time. There are many other methods in the wild. But the goal is the same. Refining the dataset and ensuring it is of the highest quality can reduce the training time required for models to converge. However, achieving this requires developing and implementing sophisticated methods, algorithms, and techniques that can clean, preprocess, and augment data while retaining the most informative samples. This is an ongoing challenge that will require continued research and innovation in the field of machine learning. @@ -788,7 +768,7 @@ Benchmarking the triad of system, model, and data in an integrated fashion will @fig-benchmarking-trifecta illustrates the many potential ways to interplay data benchmarking, model benchmarking, and system infrastructure benchmarking together. Exploring these intricate interactions is likely to uncover new optimization opportunities and enhancement capabilities. The data, model, and system benchmark triad offers a rich space for co-design and co-optimization. -![Benchmarking trifecta.](images/png/trifecta.png){#fig-benchmarking-trifecta} +![Benchmarking trifecta.](images/png/benchmarking_trifecta.png){#fig-benchmarking-trifecta} While this integrated perspective represents an emerging trend, the field has much more to discover about the synergies and trade-offs between these components. As we iteratively benchmark combinations of data, models, and systems, new insights that remain hidden when these elements are studied in isolation will emerge. This multifaceted benchmarking approach charting the intersections of data, algorithms, and hardware promises to be a fruitful avenue for major progress in AI, even though it is still in its early stages. @@ -847,13 +827,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * @exr-perf ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we offer a series of hands-on labs allowing students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience. - -* _Coming soon._ - -::: diff --git a/contents/core/benchmarking/images/png/benchmarking_trifecta.png b/contents/core/benchmarking/images/png/benchmarking_trifecta.png new file mode 100644 index 00000000..3b2b9e56 Binary files /dev/null and b/contents/core/benchmarking/images/png/benchmarking_trifecta.png differ diff --git a/contents/benchmarking/images/png/coco.png b/contents/core/benchmarking/images/png/coco.png similarity index 100% rename from contents/benchmarking/images/png/coco.png rename to contents/core/benchmarking/images/png/coco.png diff --git a/contents/benchmarking/images/png/cover_ai_benchmarking.png b/contents/core/benchmarking/images/png/cover_ai_benchmarking.png similarity index 100% rename from contents/benchmarking/images/png/cover_ai_benchmarking.png rename to contents/core/benchmarking/images/png/cover_ai_benchmarking.png diff --git 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bibliography: data_engineering.bib # Data Engineering {#sec-data_engineering} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-data-engineering-resource), [Videos](#sec-data-engineering-resource), [Exercises](#sec-data-engineering-resource), [Labs](#sec-data-engineering-resource) +Resources: [Slides](#sec-data-engineering-resource), [Videos](#sec-data-engineering-resource), [Exercises](#sec-data-engineering-resource) ::: ![_DALL·E 3 Prompt: Create a rectangular illustration visualizing the concept of data engineering. Include elements such as raw data sources, data processing pipelines, storage systems, and refined datasets. Show how raw data is transformed through cleaning, processing, and storage to become valuable information that can be analyzed and used for decision-making._](images/png/cover_data_engineering.png) @@ -45,18 +45,20 @@ We begin by discussing data collection: Where do we source data, and how do we g ## Problem Definition -In many machine learning domains, sophisticated algorithms take center stage, while the fundamental importance of data quality is often overlooked. This neglect gives rise to ["Data Cascades"](https://research.google/pubs/pub49953/) by @sambasivan2021everyone (see @fig-cascades)—events where lapses in data quality compound, leading to negative downstream consequences such as flawed predictions, project terminations, and even potential harm to communities. In @fig-cascades, we have an illustration of potential data pitfalls at every stage and how they influence the entire process down the line. The influence of data collection errors is especially pronounced. Any lapses in this stage will become apparent at later stages (in model evaluation and deployment) and might lead to costly consequences, such as abandoning the entire model and restarting anew. Therefore, investing in data engineering techniques from the onset will help us detect errors early. +In many machine learning domains, sophisticated algorithms take center stage, while the fundamental importance of data quality is often overlooked. This neglect gives rise to ["Data Cascades"](https://research.google/pubs/pub49953/) by @sambasivan2021everyone—events where lapses in data quality compound, leading to negative downstream consequences such as flawed predictions, project terminations, and even potential harm to communities. + +@fig-cascades illustrates these potential data pitfalls at every stage and how they influence the entire process down the line. The influence of data collection errors is especially pronounced. As depicted in the figure, any lapses in this initial stage will become apparent at later stages (in model evaluation and deployment) and might lead to costly consequences, such as abandoning the entire model and restarting anew. Therefore, investing in data engineering techniques from the onset will help us detect errors early, mitigating the cascading effects illustrated in the figure. ![Data cascades: compounded costs. Source: @sambasivan2021everyone.](images/png/data_engineering_cascades.png){#fig-cascades} Despite many ML professionals recognizing the importance of data, numerous practitioners report facing these cascades. This highlights a systemic issue: while the allure of developing advanced models remains, data often needs to be more appreciated. -Take, for example, Keyword Spotting (KWS) (see @fig-keywords). KWS is a prime example of TinyML in action and is a critical technology behind voice-enabled interfaces on endpoint devices such as smartphones. Typically functioning as lightweight wake-word engines, these systems are consistently active, listening for a specific phrase to trigger further actions. When we say "OK, Google" or "Alexa," this initiates a process on a microcontroller embedded within the device. Despite their limited resources, these microcontrollers play an important role in enabling seamless voice interactions with devices, often operating in environments with high ambient noise. The uniqueness of the wake word helps minimize false positives, ensuring that the system is not triggered inadvertently. - -It is important to appreciate that these keyword-spotting technologies are not isolated; they integrate seamlessly into larger systems, processing signals continuously while managing low power consumption. These systems extend beyond simple keyword recognition, evolving to facilitate diverse sound detections, such as glass breaking. This evolution is geared towards creating intelligent devices capable of understanding and responding to vocal commands, heralding a future where even household appliances can be controlled through voice interactions. +Keyword Spotting (KWS) provides an excellent example of TinyML in action, as illustrated in @fig-keywords. This technology is critical for voice-enabled interfaces on endpoint devices such as smartphones. Typically functioning as lightweight wake-word engines, KWS systems are consistently active, listening for a specific phrase to trigger further actions. As depicted in the figure, when we say "OK, Google" or "Alexa," this initiates a process on a microcontroller embedded within the device. Despite their limited resources, these microcontrollers play an important role in enabling seamless voice interactions with devices, often operating in environments with high ambient noise. The uniqueness of the wake word, as shown in the figure, helps minimize false positives, ensuring that the system is not triggered inadvertently. ![Keyword Spotting example: interacting with Alexa. Source: Amazon.](images/png/data_engineering_kws.png){#fig-keywords} +It is important to appreciate that these keyword-spotting technologies are not isolated; they integrate seamlessly into larger systems, processing signals continuously while managing low power consumption. These systems extend beyond simple keyword recognition, evolving to facilitate diverse sound detections, such as glass breaking. This evolution is geared towards creating intelligent devices capable of understanding and responding to vocal commands, heralding a future where even household appliances can be controlled through voice interactions. + Building a reliable KWS model is a complex task. It demands a deep understanding of the deployment scenario, encompassing where and how these devices will operate. For instance, a KWS model's effectiveness is not just about recognizing a word; it's about discerning it among various accents and background noises, whether in a bustling cafe or amid the blaring sound of a television in a living room or a kitchen where these devices are commonly found. It's about ensuring that a whispered "Alexa" in the dead of night or a shouted "OK Google" in a noisy marketplace are recognized with equal precision. Moreover, many current KWS voice assistants support a limited number of languages, leaving a substantial portion of the world's linguistic diversity unrepresented. This limitation is partly due to the difficulty in gathering and monetizing data for languages spoken by smaller populations. The long-tail distribution of languages implies that many languages have limited data, making the development of supportive technologies challenging. @@ -125,12 +127,12 @@ In this context, using KWS as an example, we can break each of the steps out as ### Keyword Spotting with TensorFlow Lite Micro -Explore a hands-on guide for building and deploying Keyword Spotting (KWS) systems using TensorFlow Lite Micro. Follow steps from data collection to model training and deployment to microcontrollers. Learn to create efficient KWS models that recognize specific keywords amidst background noise. Perfect for those interested in machine learning on embedded systems. Unlock the potential of voice-enabled devices with TensorFlow Lite Micro! +Explore a hands-on guide for building and deploying Keyword Spotting systems using TensorFlow Lite Micro. Follow steps from data collection to model training and deployment to microcontrollers. Learn to create efficient KWS models that recognize specific keywords amidst background noise. Perfect for those interested in machine learning on embedded systems. Unlock the potential of voice-enabled devices with TensorFlow Lite Micro! [![](https://colab.research.google.com/assets/colab-badge.png)](https://colab.research.google.com/drive/17I7GL8WTieGzXYKRtQM2FrFi3eLQIrOM) ::: -The current chapter underscores the essential role of data quality in ML, using Keyword Spotting (KWS) systems as an example. It outlines key steps, from problem definition to stakeholder engagement, emphasizing iterative feedback. The forthcoming chapter will dig deeper into data quality management, discussing its consequences and future trends, focusing on the importance of high-quality, diverse data in AI system development, addressing ethical considerations and data sourcing methods. +The current chapter underscores the essential role of data quality in ML, using Keyword Spotting systems as an example. It outlines key steps, from problem definition to stakeholder engagement, emphasizing iterative feedback. The forthcoming chapter will dig deeper into data quality management, discussing its consequences and future trends, focusing on the importance of high-quality, diverse data in AI system development, addressing ethical considerations and data sourcing methods. ## Data Sourcing @@ -144,10 +146,12 @@ The quality assurance that comes with popular pre-existing datasets is important While platforms like Kaggle and UCI Machine Learning Repository are invaluable resources, it's essential to understand the context in which the data was collected. Researchers should be wary of potential overfitting when using popular datasets, as multiple models might have been trained on them, leading to inflated performance metrics. Sometimes, these [datasets do not reflect the real-world data](https://venturebeat.com/uncategorized/3-big-problems-with-datasets-in-ai-and-machine-learning/). -In addition, bias, validity, and reproducibility issues may exist in these datasets, and there has been a growing awareness of these issues in recent years. Furthermore, using the same dataset to train different models as shown in @fig-misalignment can sometimes create misalignment: training multiple models using the same dataset results in a 'misalignment' between the models and the world, in which an entire ecosystem of models reflects only a narrow subset of the real-world data. +In recent years, there has been growing awareness of bias, validity, and reproducibility issues that may exist in machine learning datasets. @fig-misalignment illustrates another critical concern: the potential for misalignment when using the same dataset to train different models. ![Training different models on the same dataset. Source: (icons from left to right: Becris; Freepik; Freepik; Paul J; SBTS2018).](images/png/dataset_myopia.png){#fig-misalignment} +As shown in @fig-misalignment, training multiple models using the same dataset can result in a 'misalignment' between the models and the world. This misalignment creates an entire ecosystem of models that reflects only a narrow subset of the real-world data. Such a scenario can lead to limited generalization and potentially biased outcomes across various applications using these models. + ### Web Scraping Web scraping refers to automated techniques for extracting data from websites. It typically involves sending HTTP requests to web servers, retrieving HTML content, and parsing that content to extract relevant information. Popular tools and frameworks for web scraping include Beautiful Soup, Scrapy, and Selenium. These tools offer different functionalities, from parsing HTML content to automating web browser interactions, especially for websites that load content dynamically using JavaScript. @@ -201,7 +205,11 @@ Thus, while crowdsourcing can work well in many cases, the specialized needs of ### Synthetic Data -Synthetic data generation can be useful for addressing some of the data collection limitations. It involves creating data that wasn't originally captured or observed but is generated using algorithms, simulations, or other techniques to resemble real-world data. As shown in @fig-synthetic-data, synthetic data is merged with historical data and then used as input for model training. It has become a valuable tool in various fields, particularly when real-world data is scarce, expensive, or ethically challenging (e.g., TinyML). Various techniques, such as Generative Adversarial Networks (GANs), can produce high-quality synthetic data almost indistinguishable from real data. These techniques have advanced significantly, making synthetic data generation increasingly realistic and reliable. +Synthetic data generation can be a valuable solution for addressing data collection limitations. @fig-synthetic-data illustrates how this process works: synthetic data is merged with historical data to create a larger, more diverse dataset for model training. + +![Increasing training data size with synthetic data generation. Source: [AnyLogic](https://www.anylogic.com/features/artificial-intelligence/synthetic-data/).](images/jpg/synthetic_data.jpg){#fig-synthetic-data} + +As shown in the figure, synthetic data involves creating information that wasn't originally captured or observed but is generated using algorithms, simulations, or other techniques to resemble real-world data. This approach has become particularly valuable in fields where real-world data is scarce, expensive, or ethically challenging to obtain, such as in TinyML applications. Various techniques, including Generative Adversarial Networks (GANs), can produce high-quality synthetic data almost indistinguishable from real data. These methods have advanced significantly, making synthetic data generation increasingly realistic and reliable. More real-world data may need to be available for analysis or training machine learning models in many domains, especially emerging ones. Synthetic data can fill this gap by producing large volumes of data that mimic real-world scenarios. For instance, detecting the sound of breaking glass might be challenging in security applications where a TinyML device is trying to identify break-ins. Collecting real-world data would require breaking numerous windows, which is impractical and costly. @@ -215,8 +223,6 @@ Many embedded use cases deal with unique situations, such as manufacturing plant While synthetic data offers numerous advantages, it is essential to use it judiciously. Care must be taken to ensure that the generated data accurately represents the underlying real-world distributions and does not introduce unintended biases. -![Increasing training data size with synthetic data generation. Source: [AnyLogic](https://www.anylogic.com/features/artificial-intelligence/synthetic-data/).](images/jpg/synthetic_data.jpg){#fig-synthetic-data} - :::{#exr-sd .callout-caution collapse="true"} ### Synthetic Data @@ -247,12 +253,16 @@ Data sourcing and data storage go hand in hand, and data must be stored in a for : Comparative overview of the database, data warehouse, and data lake. {#tbl-storage .striped .hover} -The stored data is often accompanied by metadata, defined as 'data about data.' It provides detailed contextual information about the data, such as means of data creation, time of creation, attached data use license, etc. For example, [Hugging Face](https://huggingface.co/) has [Dataset Cards](https://huggingface.co/docs/hub/datasets-cards). To promote responsible data use, dataset creators should disclose potential biases through the dataset cards. These cards can educate users about a dataset's contents and limitations. The cards also give vital context on appropriate dataset usage by highlighting biases and other important details. Having this type of metadata can also allow fast retrieval if structured properly. Once the model is developed and deployed to edge devices, the storage systems can continue to store incoming data, model updates, or analytical results. @fig-data-collection showcases the pillars of data collection and their collection methods. +The stored data is often accompanied by metadata, defined as 'data about data. It provides detailed contextual information about the data, such as means of data creation, time of creation, attached data use license, etc. @fig-data-collection illustrates the key pillars of data collection and their associated methods, highlighting the importance of structured data management. For example, [Hugging Face](https://huggingface.co/) has implemented [Dataset Cards](https://huggingface.co/docs/hub/datasets-cards) to promote responsible data use. These cards, which align with the documentation pillar shown in @fig-data-collection, allow dataset creators to disclose potential biases and educate users about a dataset's contents and limitations. + +The dataset cards provide important context on appropriate dataset usage by highlighting biases and other important details. Having this type of structured metadata can also allow for fast retrieval, aligning with the efficient data management principles illustrated in the figure. Once the model is developed and deployed to edge devices, the storage systems can continue to store incoming data, model updates, or analytical results, potentially utilizing methods from multiple pillars shown in @fig-data-collection. This ongoing data collection and management process ensures that the model remains up-to-date and relevant in its operational environment. ![Pillars of data collection. Source: [Alexsoft](https://www.altexsoft.com/blog/data-collection-machine-learning/)](images/png/datacollection.png){#fig-data-collection} **Data Governance:** With a large amount of data storage, it is also imperative to have policies and practices (i.e., data governance) that help manage data during its life cycle, from acquisition to disposal. Data governance outlines how data is managed and includes making key decisions about data access and control. @fig-governance illustrates the different domains involved in data governance. It involves exercising authority and making decisions concerning data to uphold its quality, ensure compliance, maintain security, and derive value. Data governance is operationalized by developing policies, incentives, and penalties, cultivating a culture that perceives data as a valuable asset. Specific procedures and assigned authorities are implemented to safeguard data quality and monitor its utilization and related risks. +![An overview of the data governance framework. Source: [StarCIO.](https://www.groundwatergovernance.org/the-importance-of-governance-for-all-stakeholders/).](images/jpg/data_governance.jpg){#fig-governance} + Data governance utilizes three integrative approaches: planning and control, organizational, and risk-based. * **The planning and control approach**, common in IT, aligns business and technology through annual cycles and continuous adjustments, focusing on policy-driven, auditable governance. @@ -261,8 +271,6 @@ Data governance utilizes three integrative approaches: planning and control, org * **The risk-based approach**, intensified by AI advancements, focuses on identifying and managing inherent risks in data and algorithms. It especially addresses AI-specific issues through regular assessments and proactive risk management strategies, allowing for incidental and preventive actions to mitigate undesired algorithm impacts. -![An overview of the data governance framework. Source: [StarCIO.](https://www.groundwatergovernance.org/the-importance-of-governance-for-all-stakeholders/).](images/jpg/data_governance.jpg){#fig-governance} - Some examples of data governance across different sectors include: * **Medicine:** [Health Information Exchanges(HIEs)](https://www.healthit.gov/topic/health-it-and-health-information-exchange-basics/what-hie) enable the sharing of health information across different healthcare providers to improve patient care. They implement strict data governance practices to maintain data accuracy, integrity, privacy, and security, complying with regulations such as the [Health Insurance Portability and Accountability Act (HIPAA)](https://www.cdc.gov/phlp/publications/topic/hipaa.html). Governance policies ensure that patient data is only shared with authorized entities and that patients can control access to their information. @@ -302,7 +310,10 @@ Data often comes from diverse sources and can be unstructured or semi-structured * Using techniques like dimensionality reduction Data validation serves a broader role than ensuring adherence to certain standards, like preventing temperature values from falling below absolute zero. These issues arise in TinyML because sensors may malfunction or temporarily produce incorrect readings; such transients are not uncommon. Therefore, it is imperative to catch data errors early before propagating through the data pipeline. Rigorous validation processes, including verifying the initial annotation practices, detecting outliers, and handling missing values through techniques like mean imputation, contribute directly to the quality of datasets. This, in turn, impacts the performance, fairness, and safety of the models trained on them. -Let's take a look at @fig-data-engineering-kws2 for an example of a data processing pipeline. In the context of TinyML, the Multilingual Spoken Words Corpus (MSWC) is an example of data processing pipelines—systematic and automated workflows for data transformation, storage, and processing. The input data (which's a collection of short recordings) goes through several phases of processing, such as audio-word alignement and keyword extraction. By streamlining the data flow, from raw data to usable datasets, data pipelines improve productivity and facilitate the rapid development of machine learning models. The MSWC is an expansive and expanding collection of audio recordings of spoken words in 50 different languages, which are collectively used by over 5 billion people. This dataset is intended for academic study and business uses in areas like keyword identification and speech-based search. It is openly licensed under Creative Commons Attribution 4.0 for broad usage. + +Let's take a look at @fig-data-engineering-kws2 for an example of a data processing pipeline. In the context of TinyML, the Multilingual Spoken Words Corpus (MSWC) is an example of data processing pipelines—systematic and automated workflows for data transformation, storage, and processing. The input data (which's a collection of short recordings) goes through several phases of processing, such as audio-word alignement and keyword extraction. + +MSWC streamlines the data flow, from raw data to usable datasets, data pipelines improve productivity and facilitate the rapid development of machine learning models. The MSWC is an expansive and expanding collection of audio recordings of spoken words in 50 different languages, which are collectively used by over 5 billion people. This dataset is intended for academic study and business uses in areas like keyword identification and speech-based search. It is openly licensed under Creative Commons Attribution 4.0 for broad usage. ![An overview of the Multilingual Spoken Words Corpus (MSWC) data processing pipeline. Source: @mazumder2021multilingual.](images/png/data_engineering_kws2.png){#fig-data-engineering-kws2} @@ -333,7 +344,8 @@ Labels capture information about key tasks or concepts. @fig-labels includes som Unless focused on self-supervised learning, a dataset will likely provide labels addressing one or more tasks of interest. Given their unique resource constraints, dataset creators must consider what information labels should capture and how they can practically obtain the necessary labels. Creators must first decide what type(s) of content labels should capture. For example, a creator interested in car detection would want to label cars in their dataset. Still, they might also consider whether to simultaneously collect labels for other tasks that the dataset could potentially be used for, such as pedestrian detection. -Additionally, annotators can provide metadata that provides insight into how the dataset represents different characteristics of interest (see @sec-data-transparency). The Common Voice dataset, for example, includes various types of metadata that provide information about the speakers, recordings, and dataset quality for each language represented [@ardila2020common]. They include demographic splits showing the number of recordings by speaker age range and gender. This allows us to see who contributed recordings for each language. They also include statistics like average recording duration and total hours of validated recordings. These give insights into the nature and size of the datasets for each language. +Additionally, annotators can provide metadata that provides insight into how the dataset represents different characteristics of interest (see @sec-data-transparency). The Common Voice dataset, for example, includes various types of metadata that provide information about the speakers, recordings, and dataset quality for each language represented [@ardila2020common]. They include demographic splits showing the number of recordings by speaker age range and gender. This allows us to see who contributed recordings for each language. They also include statistics like average recording duration and total hours of validated recordings. These give insights into the nature and size of the datasets for each language. + Additionally, quality control metrics like the percentage of recordings that have been validated are useful to know how complete and clean the datasets are. The metadata also includes normalized demographic splits scaled to 100% for comparison across languages. This highlights representation differences between higher and lower resource languages. Next, creators must determine the format of those labels. For example, a creator interested in car detection might choose between binary classification labels that say whether a car is present, bounding boxes that show the general locations of any cars, or pixel-wise segmentation labels that show the exact location of each car. Their choice of label format may depend on their use case and resource constraints, as finer-grained labels are typically more expensive and time-consuming to acquire. @@ -376,14 +388,14 @@ ML has an insatiable demand for data. Therefore, more data is needed. This raise * **Active learning:** AI models can identify the most informative data points in a dataset, which can then be prioritized for human annotation. This can help improve the labeled dataset's quality while reducing the overall annotation time. * **Quality control:** AI models can identify and flag potential errors in human annotations, helping to ensure the accuracy and consistency of the labeled dataset. +![Strategies for acquiring additional labeled training data. Source: [Standford AI Lab.](https://ai.stanford.edu/blog/weak-supervision/)](https://ai.stanford.edu/blog//assets/img/posts/2019-03-03-weak_supervision/WS_mapping.png){#fig-weak-supervision} + Here are some examples of how AI-assisted annotation has been proposed to be useful: * **Medical imaging:** AI-assisted annotation labels medical images, such as MRI scans and X-rays [@krishnan2022selfsupervised]. Carefully annotating medical datasets is extremely challenging, especially at scale, since domain experts are scarce and become costly. This can help to train AI models to diagnose diseases and other medical conditions more accurately and efficiently. * **Self-driving cars:** AI-assisted annotation is being used to label images and videos from self-driving cars. This can help to train AI models to identify objects on the road, such as other vehicles, pedestrians, and traffic signs. * **Social media:** AI-assisted annotation labels social media posts like images and videos. This can help to train AI models to identify and classify different types of content, such as news, advertising, and personal posts. -![Strategies for acquiring additional labeled training data. Source: [Standford AI Lab.](https://ai.stanford.edu/blog/weak-supervision/)](https://dawn.cs.stanford.edu/assets/img/2017-07-16-weak-supervision/WS_mapping.png){#fig-weak-supervision} - ## Data Version Control Production systems are perpetually inundated with fluctuating and escalating volumes of data, prompting the rapid emergence of numerous data replicas. This increasing data serves as the foundation for training machine learning models. For instance, a global sales company engaged in sales forecasting continuously receives consumer behavior data. Similarly, healthcare systems formulating predictive models for disease diagnosis are consistently acquiring new patient data. TinyML applications, such as keyword spotting, are highly data-hungry regarding the amount of data generated. Consequently, meticulous tracking of data versions and the corresponding model performance is imperative. @@ -394,8 +406,7 @@ Data Version Control offers a structured methodology to handle alterations and v **Collaboration and Efficiency:** Easy access to different dataset versions in one place can improve data sharing of specific checkpoints and enable efficient collaboration. -**Reproducibility:** Data version control allows for tracking the performance of models concerning different versions of the data, -and therefore enabling reproducibility. +**Reproducibility:** Data version control allows for tracking the performance of models concerning different versions of the data, and therefore enabling reproducibility. **Key Concepts** @@ -411,7 +422,7 @@ With data version control in place, we can track the changes shown in @fig-data- **Popular Data Version Control Systems** -[**[DVC]{.underline}**](https://dvc.org/doc): It stands for Data Version Control in short and is an open-source, lightweight tool that works on top of Git Hub and supports all kinds of data formats. It can seamlessly integrate into the workflow if Git is used to manage code. It captures the versions of data and models in the Git commits while storing them on-premises or on the cloud (e.g., AWS, Google Cloud, Azure). These data and models (e.g., ML artifacts) are defined in the metadata files, which get updated in every commit. It can allow metrics tracking of models on different versions of the data. +[**[DVC]**](https://dvc.org/doc): It stands for Data Version Control in short and is an open-source, lightweight tool that works on top of Git Hub and supports all kinds of data formats. It can seamlessly integrate into the workflow if Git is used to manage code. It captures the versions of data and models in the Git commits while storing them on-premises or on the cloud (e.g., AWS, Google Cloud, Azure). These data and models (e.g., ML artifacts) are defined in the metadata files, which get updated in every commit. It can allow metrics tracking of models on different versions of the data. **[lakeFS](https://docs.lakefs.io/):** It is an open-source tool that supports the data version control on data lakes. It supports many git-like operations, such as branching and merging of data, as well as reverting to previous versions of the data. It also has a unique UI feature, making exploring and managing data much easier. @@ -542,15 +553,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * @exr-bl ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we offer a series of hands-on labs allowing students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience. - -* _Coming soon._ -::: - - - diff --git a/contents/data_engineering/images/jpg/1914_traffic.jpeg b/contents/core/data_engineering/images/jpg/1914_traffic.jpeg similarity index 100% rename from contents/data_engineering/images/jpg/1914_traffic.jpeg rename to contents/core/data_engineering/images/jpg/1914_traffic.jpeg diff --git a/contents/data_engineering/images/jpg/data_engineering_features.jpg b/contents/core/data_engineering/images/jpg/data_engineering_features.jpg similarity index 100% rename from contents/data_engineering/images/jpg/data_engineering_features.jpg rename to contents/core/data_engineering/images/jpg/data_engineering_features.jpg diff --git a/contents/data_engineering/images/jpg/data_governance.jpg b/contents/core/data_engineering/images/jpg/data_governance.jpg similarity index 100% rename from contents/data_engineering/images/jpg/data_governance.jpg rename to 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contents/dl_primer/dl_primer.bib rename to contents/core/dl_primer/dl_primer.bib diff --git a/contents/dl_primer/dl_primer.qmd b/contents/core/dl_primer/dl_primer.qmd similarity index 82% rename from contents/dl_primer/dl_primer.qmd rename to contents/core/dl_primer/dl_primer.qmd index 08718e5b..c44639d4 100644 --- a/contents/dl_primer/dl_primer.qmd +++ b/contents/core/dl_primer/dl_primer.qmd @@ -5,12 +5,14 @@ bibliography: dl_primer.bib # DL Primer {#sec-dl_primer} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-deep-learning-primer-resource), [Videos](#sec-deep-learning-primer-resource), [Exercises](#sec-deep-learning-primer-resource), [Labs](#sec-deep-learning-primer-resource) +Resources: [Slides](#sec-deep-learning-primer-resource), [Videos](#sec-deep-learning-primer-resource), [Exercises](#sec-deep-learning-primer-resource) ::: ![_DALL·E 3 Prompt: Photo of a classic classroom with a large blackboard dominating one wall. Chalk drawings showcase a detailed deep neural network with several hidden layers, and each node and connection is precisely labeled with white chalk. The rustic wooden floor and brick walls provide a contrast to the modern concepts. Surrounding the room, posters mounted on frames emphasize deep learning themes: convolutional networks, transformers, neurons, activation functions, and more._](images/png/cover_dl_primer.png) -This section briefly introduces deep learning, starting with an overview of its history, applications, and relevance to embedded AI systems. It examines the core concepts like neural networks, highlighting key components like perceptrons, multilayer perceptrons, activation functions, and computational graphs. The primer also briefly explores major deep learning architecture, contrasting their applications and uses. Additionally, it compares deep learning to traditional machine learning to equip readers with the general conceptual building blocks to make informed choices between deep learning and traditional ML techniques based on problem constraints, setting the stage for more advanced techniques and applications that will follow in subsequent chapters. +This section serves as a primer for deep learning, providing systems practitioners with essential context and foundational knowledge needed to implement deep learning solutions effectively. Rather than delving into theoretical depths, we focus on key concepts, architectures, and practical considerations relevant to systems implementation. We begin with an overview of deep learning's evolution and its particular significance in embedded AI systems. Core concepts like neural networks are introduced with an emphasis on implementation considerations rather than mathematical foundations. + +The primer explores major deep learning architectures from a systems perspective, examining their practical implications and resource requirements. We also compare deep learning to traditional machine learning approaches, helping readers make informed architectural choices based on real-world system constraints. This high-level overview sets the context for the more detailed systems-focused techniques and optimizations covered in subsequent chapters. ::: {.callout-tip} @@ -30,17 +32,23 @@ This section briefly introduces deep learning, starting with an overview of its ### Definition and Importance -Deep learning, a specialized area within machine learning and artificial intelligence (AI), utilizes algorithms modeled after the structure and function of the human brain, known as artificial neural networks. This field is a foundational element in AI, driving progress in diverse sectors such as computer vision, natural language processing, and self-driving vehicles. Its significance in embedded AI systems is highlighted by its capability to handle intricate calculations and predictions, optimizing the limited resources in embedded settings. @fig-ai-ml-dl illustrates the chronological development and relative segmentation of the three fields. +Deep learning, a specialized area within machine learning and artificial intelligence (AI), utilizes algorithms modeled after the structure and function of the human brain, known as artificial neural networks. This field is a foundational element in AI, driving progress in diverse sectors such as computer vision, natural language processing, and self-driving vehicles. Its significance in embedded AI systems is highlighted by its capability to handle intricate calculations and predictions, optimizing the limited resources in embedded settings. + +@fig-ai-ml-dl provides a visual representation of how deep learning fits within the broader context of AI and machine learning. The diagram illustrates the chronological development and relative segmentation of these three interconnected fields, showcasing deep learning as a specialized subset of machine learning, which in turn is a subset of AI. ![The diagram illustrates artificial intelligence as the overarching field encompassing all computational methods that mimic human cognitive functions. Machine learning is a subset of AI that includes algorithms capable of learning from data. Deep learning, a further subset of ML, specifically involves neural networks that are able to learn more complex patterns in large volumes of data. Source: NVIDIA.](images/png/ai_dl_progress_nvidia.png){#fig-ai-ml-dl} +As shown in the figure, AI represents the overarching field, encompassing all computational methods that mimic human cognitive functions. Machine learning, shown as a subset of AI, includes algorithms capable of learning from data. Deep learning, the smallest subset in the diagram, specifically involves neural networks that are able to learn more complex patterns from large volumes of data. + ### Brief History of Deep Learning The idea of deep learning has origins in early artificial neural networks. It has experienced several cycles of interest, starting with the introduction of the Perceptron in the 1950s [@rosenblatt1957perceptron], followed by the invention of backpropagation algorithms in the 1980s [@rumelhart1986learning]. The term "deep learning" became prominent in the 2000s, characterized by advances in computational power and data accessibility. Important milestones include the successful training of deep networks like AlexNet [@krizhevsky2012imagenet] by [Geoffrey Hinton](https://amturing.acm.org/award_winners/hinton_4791679.cfm), a leading figure in AI, and the renewed focus on neural networks as effective tools for data analysis and modeling. -Deep learning has recently seen exponential growth, transforming various industries. Computational growth followed an 18-month doubling pattern from 1952 to 2010, which then accelerated to a 6-month cycle from 2010 to 2022, as shown in @fig-trends. Concurrently, we saw the emergence of large-scale models between 2015 and 2022, appearing 2 to 3 orders of magnitude faster and following a 10-month doubling cycle. +Deep learning has recently seen exponential growth, transforming various industries. @fig-trends illustrates this remarkable progression, highlighting two key trends in the field. First, the graph shows that computational growth followed an 18-month doubling pattern from 1952 to 2010. This trend then dramatically accelerated to a 6-month doubling cycle from 2010 to 2022, indicating a significant leap in computational capabilities. + +Second, the figure depicts the emergence of large-scale models between 2015 and 2022. These models appeared 2 to 3 orders of magnitude faster than the general trend, following an even more aggressive 10-month doubling cycle. This rapid scaling of model sizes represents a paradigm shift in deep learning capabilities. ![Growth of deep learning models.](https://epochai.org/assets/images/posts/2022/compute-trends.png){#fig-trends} @@ -54,11 +62,11 @@ Organizations worldwide recognize deep learning's transformative potential and i ### Applications of Deep Learning -Deep learning is extensively used across numerous industries today, and its transformative impact on society is evident. In finance, it powers stock market prediction, risk assessment, and fraud detection. For instance, deep learning algorithms can predict stock market trends, guide investment strategies, and improve financial decisions. In marketing, it drives customer segmentation, personalization, and content optimization. Deep learning analyzes consumer behavior and preferences to enable highly targeted advertising and personalized content delivery. In manufacturing, deep learning streamlines production processes and enhances quality control by continuously analyzing large volumes of data. This allows companies to boost productivity and minimize waste, leading to the production of higher quality goods at lower costs. In healthcare, machine learning aids in diagnosis, treatment planning, and patient monitoring. Similarly, deep learning can make medical predictions that improve patient diagnosis and save lives. The benefits are clear: machine learning predicts with greater accuracy than humans and does so much more quickly. @fig-deeplearning further illustrates some applications of deep learning. +Deep learning is extensively used across numerous industries today, with its transformative impact evident in various sectors, as illustrated in @fig-deeplearning. In finance, it powers stock market prediction, risk assessment, and fraud detection, guiding investment strategies and improving financial decisions. Marketing leverages deep learning for customer segmentation and personalization, enabling highly targeted advertising and content optimization based on consumer behavior analysis. In manufacturing, it streamlines production processes and enhances quality control, allowing companies to boost productivity and minimize waste. Healthcare benefits from deep learning in diagnosis, treatment planning, and patient monitoring, potentially saving lives through improved medical predictions. -Deep learning enhances everyday products, such as strengthening Netflix's recommender systems to provide users with more [personalized recommendations](https://dl.acm.org/doi/abs/10.1145/3543873.3587675). At Google, deep learning models have driven significant improvements in [Google Translate](https://research.google/blog/recent-advances-in-google-translate/), enabling it to handle over [100 languages](https://cloud.google.com/translate/docs/languages). Autonomous vehicles from companies like Waymo, Cruise, and Motional have become a reality through the use of deep learning in their [perception system](https://motional.com/news/technically-speaking-improving-av-perception-through-transformative-machine-learning). Additionally, Amazon employs deep learning at the edge in their Alexa devices to perform [keyword spotting](https://towardsdatascience.com/how-amazon-alexa-works-your-guide-to-natural-language-processing-ai-7506004709d3). +![Deep learning applications, benefits, and implementations across various industries including finance, marketing, manufacturing, and healthcare. Source: [Leeway Hertz](https://www.leewayhertz.com/what-is-deep-learning/)](images/png/deeplearning.png){#fig-deeplearning} -![Deep learning applications, benefits and implementations. Source: [Leeway Hertz](https://www.leewayhertz.com/what-is-deep-learning/)](images/png/deeplearning.png){#fig-deeplearning} +Beyond these core industries, deep learning enhances everyday products and services. Netflix uses it to strengthen its recommender systems, providing users with more [personalized recommendations](https://dl.acm.org/doi/abs/10.1145/3543873.3587675). Google has significantly improved its Translate service, now handling over [100 languages](https://cloud.google.com/translate/docs/languages) with increased accuracy, as highlighted in their [recent advances](https://research.google/blog/recent-advances-in-google-translate/). Autonomous vehicles from companies like Waymo, Cruise, and Motional have become a reality through deep learning in their [perception system](https://motional.com/news/technically-speaking-improving-av-perception-through-transformative-machine-learning). Additionally, Amazon employs deep learning at the edge in Alexa devices for tasks such as [keyword spotting](https://towardsdatascience.com/how-amazon-alexa-works-your-guide-to-natural-language-processing-ai-7506004709d3). These applications demonstrate how machine learning often predicts and processes information with greater accuracy and speed than humans, revolutionizing various aspects of our daily lives. ### Relevance to Embedded AI @@ -74,7 +82,11 @@ Below, we examine the primary components and structures in neural networks. ### Perceptrons -The Perceptron is the basic unit or node that forms the foundation for more complex structures. It functions by taking multiple inputs, each representing a feature of the object under analysis, such as the characteristics of a home for predicting its price or the attributes of a song to forecast its popularity in music streaming services. These inputs are denoted as $x_1, x_2, ..., x_n$. +The Perceptron is the basic unit or node that forms the foundation for more complex structures. It functions by taking multiple inputs, each representing a feature of the object under analysis, such as the characteristics of a home for predicting its price or the attributes of a song to forecast its popularity in music streaming services. These inputs are denoted as $x_1, x_2, ..., x_n$. A perceptron can be configured to perform either regression or classification tasks. For regression, the actual numerical output $\hat{y}$ is used. For classification, the output depends on whether $\hat{y}$ crosses a certain threshold. If $\hat{y}$ exceeds this threshold, the perceptron might output one class (e.g., 'yes'), and if it does not, another class (e.g., 'no'). + +@fig-perceptron illustrates the fundamental building blocks of a perceptron, which serves as the foundation for more complex neural networks. A perceptron can be thought of as a miniature decision-maker, utilizing its weights, bias, and activation function to process inputs and generate outputs based on learned parameters. This concept forms the basis for understanding more intricate neural network architectures, such as multilayer perceptrons. In these advanced structures, layers of perceptrons work in concert, with each layer's output serving as the input for the subsequent layer. This hierarchical arrangement creates a deep learning model capable of comprehending and modeling complex, abstract patterns within data. By stacking these simple units, neural networks gain the ability to tackle increasingly sophisticated tasks, from image recognition to natural language processing. + +![Perceptron. Conceived in the 1950s, perceptrons paved the way for developing more intricate neural networks and have been a fundamental building block in deep learning. Source: Wikimedia - Chrislb.](images/png/Rosenblattperceptron.png){#fig-perceptron} Each input $x_i$ has a corresponding weight $w_{ij}$, and the perceptron simply multiplies each input by its matching weight. This operation is similar to linear regression, where the intermediate output, $z$, is computed as the sum of the products of inputs and their weights: @@ -98,21 +110,15 @@ $$ ![Activation functions enable the modeling of complex non-linear relationships. Source: Medium - Sachin Kaushik.](images/png/nonlinear_patterns.png){#fig-nonlinear} -A perceptron can be configured to perform either regression or classification tasks. For regression, the actual numerical output $\hat{y}$ is used. For classification, the output depends on whether $\hat{y}$ crosses a certain threshold. If $\hat{y}$ exceeds this threshold, the perceptron might output one class (e.g., 'yes'), and if it does not, another class (e.g., 'no'). - -![Perceptron. Conceived in the 1950s, perceptrons paved the way for developing more intricate neural networks and have been a fundamental building block in deep learning. Source: Wikimedia - Chrislb.](images/png/Rosenblattperceptron.png){#fig-perceptron} - -@fig-perceptron illustrates the fundamental building blocks of a perceptron, which serves as the foundation for more complex neural networks. A perceptron can be thought of as a miniature decision-maker, utilizing its weights, bias, and activation function to process inputs and generate outputs based on learned parameters. This concept forms the basis for understanding more intricate neural network architectures, such as multilayer perceptrons. In these advanced structures, layers of perceptrons work in concert, with each layer's output serving as the input for the subsequent layer. This hierarchical arrangement creates a deep learning model capable of comprehending and modeling complex, abstract patterns within data. By stacking these simple units, neural networks gain the ability to tackle increasingly sophisticated tasks, from image recognition to natural language processing. - ### Multilayer Perceptrons -Multilayer perceptrons (MLPs) are an evolution of the single-layer perceptron model, featuring multiple layers of nodes connected in a feedforward manner. In a feedforward network, information moves in only one direction - from the input layer, through the hidden layers, to the output layer, without any cycles or loops. This structure is illustrated in @fig-mlp. The network layers include an input layer for data reception, several hidden layers for data processing, and an output layer for final result generation. - -While a single perceptron is limited in its capacity to model complex patterns, the real strength of neural networks emerges from the assembly of multiple layers. Each layer consists of numerous perceptrons working together, allowing the network to capture intricate and non-linear relationships within the data. With sufficient depth and breadth, these networks can approximate virtually any function, no matter how complex. +Multilayer perceptrons (MLPs) are an evolution of the single-layer perceptron model, featuring multiple layers of nodes connected in a feedforward manner. @fig-mlp provides a visual representation of this structure. As illustrated in the figure, information in a feedforward network moves in only one direction - from the input layer on the left, through the hidden layers in the middle, to the output layer on the right, without any cycles or loops. ![Multilayer Perceptron. Source: Wikimedia - Charlie.](https://www.nomidl.com/wp-content/uploads/2022/04/image-7.png){width=70% #fig-mlp} -### Training Process +While a single perceptron is limited in its capacity to model complex patterns, the real strength of neural networks emerges from the assembly of multiple layers. Each layer consists of numerous perceptrons working together, allowing the network to capture intricate and non-linear relationships within the data. With sufficient depth and breadth, these networks can approximate virtually any function, no matter how complex. + +### Training Process A neural network receives an input, performs a calculation, and produces a prediction. The prediction is determined by the calculations performed within the sets of perceptrons found between the input and output layers. These calculations depend primarily on the input and the weights. Since you do not have control over the input, the objective during training is to adjust the weights in such a way that the output of the network provides the most accurate prediction. @@ -120,9 +126,7 @@ The training process involves several key steps, beginning with the forward pass #### Forward Pass -The forward pass is the initial phase where data moves through the network from the input to the output layer. At the start of training, the network's weights are randomly initialized, setting the initial conditions for learning. During the forward pass, each layer performs specific computations on the input data using these weights and biases, and the results are then passed to the subsequent layer. The final output of this phase is the network's prediction. This prediction is compared to the actual target values present in the dataset to calculate the loss, which can be thought of as the difference between the predicted outputs and the target values. The loss quantifies the network's performance at this stage, providing a crucial metric for the subsequent adjustment of weights during the backward pass. - -@fig-forward-propagation explains the concept of forward pass using an illustration. +The forward pass is the initial phase where data moves through the network from the input to the output layer, as illustrated in @fig-forward-propagation. At the start of training, the network's weights are randomly initialized, setting the initial conditions for learning. During the forward pass, each layer performs specific computations on the input data using these weights and biases, and the results are then passed to the subsequent layer. The final output of this phase is the network's prediction. This prediction is compared to the actual target values present in the dataset to calculate the loss, which can be thought of as the difference between the predicted outputs and the target values. The loss quantifies the network's performance at this stage, providing a crucial metric for the subsequent adjustment of weights during the backward pass. ![Neural networks - forward and backward propagation. Source: [Linkedin](https://www.linkedin.com/pulse/lecture2-unveiling-theoretical-foundations-ai-machine-underdown-phd-oqsuc/)](images/png/forwardpropagation.png){#fig-forward-propagation} @@ -199,8 +203,8 @@ CNNs are crucial for image and video recognition tasks, where real-time processi ### Convolutional Neural Networks (CNNs) -We discussed that CNNs excel at identifying image features, making them ideal for tasks like object classification. Now, you'll get to put this knowledge into action! This Colab notebook focuses on building a CNN to classify images from the CIFAR-10 dataset, which includes objects like airplanes, cars, and animals. You'll learn about the key differences between CIFAR-10 and the MNIST dataset we explored earlier and how these differences influence model choice. By the end of this notebook, you'll have a grasp of CNNs for image recognition and be well on your way to becoming a TinyML expert!   -   +We discussed that CNNs excel at identifying image features, making them ideal for tasks like object classification. Now, you'll get to put this knowledge into action! This Colab notebook focuses on building a CNN to classify images from the CIFAR-10 dataset, which includes objects like airplanes, cars, and animals. You'll learn about the key differences between CIFAR-10 and the MNIST dataset we explored earlier and how these differences influence model choice. By the end of this notebook, you'll have a grasp of CNNs for image recognition. + [![](https://colab.research.google.com/assets/colab-badge.png)](https://colab.research.google.com/github/Mjrovai/UNIFEI-IESTI01-TinyML-2022.1/blob/main/00_Curse_Folder/1_Fundamentals/Class_11/CNN_Cifar_10.ipynb) ::: @@ -233,7 +237,13 @@ These architectures serve specific purposes and excel in different domains, offe ### Traditional ML vs Deep Learning -Deep learning extends traditional machine learning by utilizing neural networks to discern patterns in data. In contrast, traditional machine learning relies on a set of established algorithms such as decision trees, k-nearest neighbors, and support vector machines, but does not involve neural networks. To briefly highlight the differences, @tbl-mlvsdl illustrates the contrasting characteristics between traditional ML and deep learning. @fig-ml-dl further explains the differences between Machine Learning and Deep Learning. +Deep learning extends traditional machine learning by utilizing neural networks to discern patterns in data. In contrast, traditional machine learning relies on a set of established algorithms such as decision trees, k-nearest neighbors, and support vector machines, but does not involve neural networks. @fig-ml-dl provides a visual comparison of Machine Learning and Deep Learning, highlighting their key differences in approach and capabilities. + +![Comparing Machine Learning and Deep Learning. Source: [Medium](https://aoyilmaz.medium.com/understanding-the-differences-between-deep-learning-and-machine-learning-eb41d64f1732)](images/png/mlvsdl.png){#fig-ml-dl} + +As shown in the figure, deep learning models can process raw data directly and automatically extract relevant features, while traditional machine learning often requires manual feature engineering. The figure also illustrates how deep learning models can handle more complex tasks and larger datasets compared to traditional machine learning approaches. + +To further highlight the differences, @tbl-mlvsdl provides a more detailed comparison of the contrasting characteristics between traditional ML and deep learning. This table complements the visual representation in @fig-ml-dl by offering specific points of comparison across various aspects of these two approaches. +-------------------------------+-----------------------------------------------------------+--------------------------------------------------------------+ | Aspect | Traditional ML | Deep Learning | @@ -253,8 +263,6 @@ Deep learning extends traditional machine learning by utilizing neural networks : Comparison of traditional machine learning and deep learning. {#tbl-mlvsdl .striped .hover} -![Comparing Machine Learning and Deep Learning. Source: [Medium](https://aoyilmaz.medium.com/understanding-the-differences-between-deep-learning-and-machine-learning-eb41d64f1732)](images/png/mlvsdl.png){#fig-ml-dl} - ### Choosing Traditional ML vs. DL #### Data Availability and Volume @@ -367,10 +375,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * @exr-cnn ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -* _Coming soon._ -::: diff --git a/contents/dl_primer/images/jpg/activation-functions3.jpg b/contents/core/dl_primer/images/jpg/activation-functions3.jpg similarity index 100% rename from contents/dl_primer/images/jpg/activation-functions3.jpg rename to contents/core/dl_primer/images/jpg/activation-functions3.jpg diff --git a/contents/dl_primer/images/png/Rosenblattperceptron.png b/contents/core/dl_primer/images/png/Rosenblattperceptron.png similarity index 100% rename from contents/dl_primer/images/png/Rosenblattperceptron.png rename to contents/core/dl_primer/images/png/Rosenblattperceptron.png diff --git a/contents/dl_primer/images/png/ai_dl_progress_nvidia.png b/contents/core/dl_primer/images/png/ai_dl_progress_nvidia.png similarity index 100% rename from contents/dl_primer/images/png/ai_dl_progress_nvidia.png rename to contents/core/dl_primer/images/png/ai_dl_progress_nvidia.png diff --git a/contents/dl_primer/images/png/cover_dl_primer.png b/contents/core/dl_primer/images/png/cover_dl_primer.png similarity index 100% rename from contents/dl_primer/images/png/cover_dl_primer.png rename to contents/core/dl_primer/images/png/cover_dl_primer.png diff --git a/contents/dl_primer/images/png/deeplearning.png b/contents/core/dl_primer/images/png/deeplearning.png similarity index 100% rename from contents/dl_primer/images/png/deeplearning.png rename to contents/core/dl_primer/images/png/deeplearning.png diff --git a/contents/dl_primer/images/png/forwardpropagation.png b/contents/core/dl_primer/images/png/forwardpropagation.png similarity index 100% rename from contents/dl_primer/images/png/forwardpropagation.png rename to contents/core/dl_primer/images/png/forwardpropagation.png diff --git a/contents/dl_primer/images/png/mlvsdl.png b/contents/core/dl_primer/images/png/mlvsdl.png similarity index 100% rename from contents/dl_primer/images/png/mlvsdl.png rename to contents/core/dl_primer/images/png/mlvsdl.png diff --git a/contents/dl_primer/images/png/nonlinear_patterns.png b/contents/core/dl_primer/images/png/nonlinear_patterns.png similarity index 100% rename from contents/dl_primer/images/png/nonlinear_patterns.png rename to contents/core/dl_primer/images/png/nonlinear_patterns.png diff --git a/contents/efficient_ai/efficient_ai.bib b/contents/core/efficient_ai/efficient_ai.bib similarity index 100% rename from contents/efficient_ai/efficient_ai.bib rename to contents/core/efficient_ai/efficient_ai.bib diff --git a/contents/efficient_ai/efficient_ai.qmd b/contents/core/efficient_ai/efficient_ai.qmd similarity index 96% rename from contents/efficient_ai/efficient_ai.qmd rename to contents/core/efficient_ai/efficient_ai.qmd index 9ebd1b27..dc6bd007 100644 --- a/contents/efficient_ai/efficient_ai.qmd +++ b/contents/core/efficient_ai/efficient_ai.qmd @@ -5,12 +5,12 @@ bibliography: efficient_ai.bib # Efficient AI {#sec-efficient_ai} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-efficient-ai-resource), [Videos](#sec-efficient-ai-resource), [Exercises](#sec-efficient-ai-resource), [Labs](#sec-efficient-ai-resource) +Resources: [Slides](#sec-efficient-ai-resource), [Videos](#sec-efficient-ai-resource), [Exercises](#sec-efficient-ai-resource) ::: ![_DALL·E 3 Prompt: A conceptual illustration depicting efficiency in artificial intelligence using a shipyard analogy. The scene shows a bustling shipyard where containers represent bits or bytes of data. These containers are being moved around efficiently by cranes and vehicles, symbolizing the streamlined and rapid information processing in AI systems. The shipyard is meticulously organized, illustrating the concept of optimal performance within the constraints of limited resources. In the background, ships are docked, representing different platforms and scenarios where AI is applied. The atmosphere should convey advanced technology with an underlying theme of sustainability and wide applicability._](images/png/cover_efficient_ai.png) -Efficiency in artificial intelligence (AI) is not simply a luxury but a necessity. In this chapter, we dive into the key concepts underpinning AI systems' efficiency. The computational demands on neural networks can be daunting, even for minimal systems. For AI to be seamlessly integrated into everyday devices and essential systems, it must perform optimally within the constraints of limited resources while maintaining its efficacy. The pursuit of efficiency guarantees that AI models are streamlined, rapid, and sustainable, thereby widening their applicability across various platforms and scenarios. +Efficiency in artificial intelligence is not simply a luxury but a necessity. In this chapter, we dive into the key concepts underpinning AI systems' efficiency. The computational demands on neural networks can be daunting, even for minimal systems. For AI to be seamlessly integrated into everyday devices and essential systems, it must perform optimally within the constraints of limited resources while maintaining its efficacy. The pursuit of efficiency guarantees that AI models are streamlined, rapid, and sustainable, thereby widening their applicability across various platforms and scenarios. ::: {.callout-tip} @@ -103,7 +103,7 @@ Machine learning, and especially deep learning, involves enormous amounts of com ### Numerical Formats {#sec-numerical-formats} -There are many different types of numerics. Numerics have a long history in computing systems. +There are many different types of numerics. Numerics have a long history in computing systems. **Floating point:** Known as a single-precision floating point, FP32 utilizes 32 bits to represent a number, incorporating its sign, exponent, and mantissa. Understanding how floating point numbers are represented under the hood is crucial for grasping the various optimizations possible in numerical computations. The sign bit determines whether the number is positive or negative, the exponent controls the range of values that can be represented, and the mantissa determines the precision of the number. The combination of these components allows floating point numbers to represent a vast range of values with varying degrees of precision. @@ -117,18 +117,16 @@ There are many different types of numerics. Numerics have a long history in comp ::: +FP32 is widely adopted in many deep learning frameworks and balances accuracy and computational requirements. It is prevalent in the training phase for many neural networks due to its sufficient precision in capturing minute details during weight updates. Also known as half-precision floating point, FP16 uses 16 bits to represent a number, including its sign, exponent, and fraction. It offers a good balance between precision and memory savings. FP16 is particularly popular in deep learning training on GPUs that support mixed-precision arithmetic, combining the speed benefits of FP16 with the precision of FP32 where needed. -FP32 is widely adopted in many deep learning frameworks and balances accuracy and computational requirements. It is prevalent in the training phase for many neural networks due to its sufficient precision in capturing minute details during weight updates. -Also known as half-precision floating point, FP16 uses 16 bits to represent a number, including its sign, exponent, and fraction. It offers a good balance between precision and memory savings. FP16 is particularly popular in deep learning training on GPUs that support mixed-precision arithmetic, combining the speed benefits of FP16 with the precision of FP32 where needed. +@fig-float-point-formats shows three different floating-point formats: Float32, Float16, and BFloat16. + +![Three floating-point formats.](images/png/three_float_types.png){#fig-float-point-formats width=90%} Several other numerical formats fall into an exotic class. An exotic example is BF16 or Brain Floating Point. It is a 16-bit numerical format designed explicitly for deep learning applications. It is a compromise between FP32 and FP16, retaining the 8-bit exponent from FP32 while reducing the mantissa to 7 bits (as compared to FP32's 23-bit mantissa). This structure prioritizes range over precision. BF16 has achieved training results comparable in accuracy to FP32 while using significantly less memory and computational resources [@kalamkar2019study]. This makes it suitable not just for inference but also for training deep neural networks. By retaining the 8-bit exponent of FP32, BF16 offers a similar range, which is crucial for deep learning tasks where certain operations can result in very large or very small numbers. At the same time, by truncating precision, BF16 allows for reduced memory and computational requirements compared to FP32. BF16 has emerged as a promising middle ground in the landscape of numerical formats for deep learning, providing an efficient and effective alternative to the more traditional FP32 and FP16 formats. -@fig-float-point-formats shows three different floating-point formats: Float32, Float16, and BFloat16. - -![Three floating-point formats.](images/png/three_float_types.png){#fig-float-point-formats width=90%} - **Integer:** These are integer representations using 8, 4, and 2 bits. They are often used during the inference phase of neural networks, where the weights and activations of the model are quantized to these lower precisions. Integer representations are deterministic and offer significant speed and memory advantages over floating-point representations. For many inference tasks, especially on edge devices, the slight loss in accuracy due to quantization is often acceptable, given the efficiency gains. An extreme form of integer numerics is for binary neural networks (BNNs), where weights and activations are constrained to one of two values: +1 or -1. **Variable bit widths:** Beyond the standard widths, research is ongoing into extremely low bit-width numerics, even down to binary or ternary representations. Extremely low bit-width operations can offer significant speedups and further reduce power consumption. While challenges remain in maintaining model accuracy with such drastic quantization, advances continue to be made in this area. @@ -257,13 +255,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer - _Coming soon._ ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we offer a series of hands-on labs allowing students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience. - -- _Coming soon._ -::: - diff --git a/contents/efficient_ai/images/jpg/DmUyPSSW0AAChGa.jpg b/contents/core/efficient_ai/images/jpg/DmUyPSSW0AAChGa.jpg similarity index 100% rename from contents/efficient_ai/images/jpg/DmUyPSSW0AAChGa.jpg rename to contents/core/efficient_ai/images/jpg/DmUyPSSW0AAChGa.jpg diff --git a/contents/efficient_ai/images/jpg/ds_stoves.jpg b/contents/core/efficient_ai/images/jpg/ds_stoves.jpg similarity index 100% rename from contents/efficient_ai/images/jpg/ds_stoves.jpg rename to contents/core/efficient_ai/images/jpg/ds_stoves.jpg diff --git a/contents/efficient_ai/images/jpg/pruning.jpeg b/contents/core/efficient_ai/images/jpg/pruning.jpeg similarity index 100% rename from contents/efficient_ai/images/jpg/pruning.jpeg rename to contents/core/efficient_ai/images/jpg/pruning.jpeg diff --git a/contents/efficient_ai/images/jpg/quantization.jpeg b/contents/core/efficient_ai/images/jpg/quantization.jpeg similarity index 100% rename from contents/efficient_ai/images/jpg/quantization.jpeg rename to contents/core/efficient_ai/images/jpg/quantization.jpeg diff --git a/contents/efficient_ai/images/png/cover_efficient_ai.png b/contents/core/efficient_ai/images/png/cover_efficient_ai.png similarity index 100% rename from contents/efficient_ai/images/png/cover_efficient_ai.png rename to contents/core/efficient_ai/images/png/cover_efficient_ai.png diff --git a/contents/efficient_ai/images/png/knowledgedistillation.png b/contents/core/efficient_ai/images/png/knowledgedistillation.png similarity index 100% rename from contents/efficient_ai/images/png/knowledgedistillation.png rename to contents/core/efficient_ai/images/png/knowledgedistillation.png diff --git a/contents/efficient_ai/images/png/tflite_edge_tpu_perf.png b/contents/core/efficient_ai/images/png/tflite_edge_tpu_perf.png similarity index 100% rename from contents/efficient_ai/images/png/tflite_edge_tpu_perf.png rename to contents/core/efficient_ai/images/png/tflite_edge_tpu_perf.png diff --git a/contents/efficient_ai/images/png/three_float_types.png b/contents/core/efficient_ai/images/png/three_float_types.png similarity index 100% rename from contents/efficient_ai/images/png/three_float_types.png rename to contents/core/efficient_ai/images/png/three_float_types.png diff --git a/contents/frameworks/frameworks.bib b/contents/core/frameworks/frameworks.bib similarity index 100% rename from contents/frameworks/frameworks.bib rename to contents/core/frameworks/frameworks.bib diff --git a/contents/frameworks/frameworks.qmd b/contents/core/frameworks/frameworks.qmd similarity index 92% rename from contents/frameworks/frameworks.qmd rename to contents/core/frameworks/frameworks.qmd index c369eb7d..0779d729 100644 --- a/contents/frameworks/frameworks.qmd +++ b/contents/core/frameworks/frameworks.qmd @@ -5,7 +5,7 @@ bibliography: frameworks.bib # AI Frameworks {#sec-ai_frameworks} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-ai-frameworks-resource), [Videos](#sec-ai-frameworks-resource), [Exercises](#sec-ai-frameworks-resource), [Labs](#sec-ai-frameworks-resource) +Resources: [Slides](#sec-ai-frameworks-resource), [Videos](#sec-ai-frameworks-resource), [Exercises](#sec-ai-frameworks-resource) ::: ![_DALL·E 3 Prompt: Illustration in a rectangular format, designed for a professional textbook, where the content spans the entire width. The vibrant chart represents training and inference frameworks for ML. Icons for TensorFlow, Keras, PyTorch, ONNX, and TensorRT are spread out, filling the entire horizontal space, and aligned vertically. Each icon is accompanied by brief annotations detailing their features. The lively colors like blues, greens, and oranges highlight the icons and sections against a soft gradient background. The distinction between training and inference frameworks is accentuated through color-coded sections, with clean lines and modern typography maintaining clarity and focus._](images/png/cover_ml_frameworks.png) @@ -74,7 +74,7 @@ Each generation of frameworks unlocked new capabilities that powered advancement * TensorFlow Graphics (2020) added 3D data structures to handle point clouds and meshes. -In recent years, the frameworks have converged. @fig-ml-framework shows that TensorFlow and PyTorch have become the overwhelmingly dominant ML frameworks, representing more than 95% of ML frameworks used in research and production. @fig-tensorflow-pytorch draws a contrast between the attributes of TensorFlow and PyTorch. Keras was integrated into TensorFlow in 2019; Preferred Networks transitioned Chainer to PyTorch in 2019; and Microsoft stopped actively developing CNTK in 2022 to support PyTorch on Windows. +In recent years, the landscape of machine learning frameworks has significantly consolidated. @fig-ml-framework illustrates this convergence, showing that TensorFlow and PyTorch have become the overwhelmingly dominant ML frameworks, collectively representing more than 95% of ML frameworks used in research and production. While both frameworks have risen to prominence, they have distinct characteristics. @fig-tensorflow-pytorch draws a contrast between the attributes of TensorFlow and PyTorch, helping to explain their complementary dominance in the field. ![PyTorch vs. TensorFlow: Features and Functions. Source: [K&C](https://www.google.com/url?sa=i&url=https%3A%2F%2Fkruschecompany.com%2Fpytorch-vs-tensorflow%2F&psig=AOvVaw1-DSFxXYprQmYH7Z4Nk6Tk&ust=1722533288351000&source=images&cd=vfe&opi=89978449&ved=0CBEQjRxqFwoTCPDhst7m0YcDFQAAAAAdAAAAABAg)](images/png/tensorflowpytorch.png){#fig-tensorflow-pytorch} @@ -190,9 +190,7 @@ PyTorch and TensorFlow have established themselves as frontrunners in the indust **Performance:** Both frameworks offer efficient hardware acceleration for their operations. However, TensorFlow has a slightly more robust optimization workflow, such as the XLA (Accelerated Linear Algebra) compiler, which can further boost performance. Its static computational graph was also advantageous for certain optimizations in the early versions. -**Ecosystem:** PyTorch has a growing ecosystem with tools like TorchServe for serving models and libraries like TorchVision, TorchText, and TorchAudio for specific domains. As we mentioned earlier, TensorFlow has a broad and mature ecosystem. TensorFlow Extended (TFX) provides an end-to-end platform for deploying production machine learning pipelines. Other tools and libraries include TensorFlow Lite, TensorFlow Lite Micro, TensorFlow.js, TensorFlow Hub, and TensorFlow Serving. - -@tbl-pytorch_vs_tf provides a comparative analysis: +**Ecosystem:** PyTorch has a growing ecosystem with tools like TorchServe for serving models and libraries like TorchVision, TorchText, and TorchAudio for specific domains. As we mentioned earlier, TensorFlow has a broad and mature ecosystem. TensorFlow Extended (TFX) provides an end-to-end platform for deploying production machine learning pipelines. Other tools and libraries include TensorFlow Lite, TensorFlow Lite Micro, TensorFlow.js, TensorFlow Hub, and TensorFlow Serving. @tbl-pytorch_vs_tf provides a comparative analysis: +-------------------------------+--------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------+ | Aspect | Pytorch | TensorFlow | @@ -216,14 +214,13 @@ Having introduced the popular machine learning frameworks and provided a high-le ### Tensor data structures {#sec-tensor-data-structures} -To understand tensors, let us start from the familiar concepts in linear algebra. As demonstrated in @fig-tensor-data-structure, vectors can be represented as a stack of numbers in a 1-dimensional array. Matrices follow the same idea, and one can think of them as many vectors stacked on each other, making them 2 dimensional. Higher dimensional tensors work the same way. A 3-dimensional tensor is simply a set of matrices stacked on each other in another direction. Therefore, vectors and matrices can be considered special cases of tensors with 1D and 2D dimensions, respectively. - -![Visualization of Tensor Data Structure.](images/png/image2.png){#fig-tensor-data-structure} +As shown in the figure, vectors can be represented as a stack of numbers in a 1-dimensional array. Matrices follow the same idea, and one can think of them as many vectors stacked on each other, making them 2 dimensional. Higher dimensional tensors work the same way. A 3-dimensional tensor, as illustrated in @fig-tensor-data-structure-a, is simply a set of matrices stacked on each other in another direction. Therefore, vectors and matrices can be considered special cases of tensors with 1D and 2D dimensions, respectively. -Tensors offer a flexible structure that can represent data in higher dimensions. For instance, to represent image data, the pixels at each position of an image are structured as matrices. However, images are not represented by just one matrix of pixel values; they typically have three channels where each channel is a matrix containing pixel values that represent the intensity of red, green, or blue. Together, these channels create a colored image. Without tensors, storing all this information from multiple matrices can be complex. With tensors, it is easy to contain image data in a single 3-dimensional tensor, with each number representing a certain color value at a specific location in the image. +![Visualization of Tensor Data Structure.](images/png/image2.png){#fig-tensor-data-structure-a} -![Visualization of colored image structure that can be easily stored as a 3D Tensor. Credit: [Niklas Lang](https://towardsdatascience.com/what-are-tensors-in-machine-learning-5671814646ff)](images/png/color_channels_of_image.png){#fig-tensor-data-structure} +Tensors offer a flexible structure that can represent data in higher dimensions. @fig-tensor-data-structure-b illustrates how this concept applies to image data. As shown in the figure, images are not represented by just one matrix of pixel values. Instead, they typically have three channels, where each channel is a matrix containing pixel values that represent the intensity of red, green, or blue. Together, these channels create a colored image. Without tensors, storing all this information from multiple matrices can be complex. However, as @fig-tensor-data-structure-b illustrates, tensors make it easy to contain image data in a single 3-dimensional structure, with each number representing a certain color value at a specific location in the image. +![Visualization of colored image structure that can be easily stored as a 3D Tensor. Credit: [Niklas Lang](https://towardsdatascience.com/what-are-tensors-in-machine-learning-5671814646ff)](images/png/color_channels_of_image.png){#fig-tensor-data-structure-b} You don't have to stop there. If we wanted to store a series of images, we could use a 4-dimensional tensor, where the new dimension represents different images. This means you are storing multiple images, each having three matrices that represent the three color channels. This gives you an idea of the usefulness of tensors when dealing with multi-dimensional data efficiently. @@ -302,13 +299,13 @@ This automatic differentiation is a powerful feature of tensors in frameworks li #### Graph Definition -Computational graphs are a key component of deep learning frameworks like TensorFlow and PyTorch. They allow us to express complex neural network architectures efficiently and differently. A computational graph consists of a directed acyclic graph (DAG) where each node represents an operation or variable, and edges represent data dependencies between them. +Computational graphs are a key component of deep learning frameworks like TensorFlow and PyTorch. They allow us to express complex neural network architectures efficiently and differently. A computational graph consists of a directed acyclic graph (DAG) where each node represents an operation or variable, and edges represent data dependencies between them. -It's important to differentiate computational graphs from neural network diagrams, such as those for multilayer perceptrons (MLPs), which depict nodes and layers. Neural network diagrams, as depicted in [Chapter 3](../dl_primer/dl_primer.qmd), visualize the architecture and flow of data through nodes and layers, providing an intuitive understanding of the model's structure. In contrast, computational graphs provide a low-level representation of the underlying mathematical operations and data dependencies required to implement and train these networks. +It is important to differentiate computational graphs from neural network diagrams, such as those for multilayer perceptrons (MLPs), which depict nodes and layers. Neural network diagrams, as depicted in [Chapter 3](../dl_primer/dl_primer.qmd), visualize the architecture and flow of data through nodes and layers, providing an intuitive understanding of the model's structure. In contrast, computational graphs provide a low-level representation of the underlying mathematical operations and data dependencies required to implement and train these networks. -For example, a node might represent a matrix multiplication operation, taking two input matrices (or tensors) and producing an output matrix (or tensor). To visualize this, consider the simple example in @fig-computational-graph. The directed acyclic graph above computes $z = x \times y$, where each variable is just numbers. +For example, a node might represent a matrix multiplication operation, taking two input matrices (or tensors) and producing an output matrix (or tensor). To visualize this, consider the simple example in @fig-comp-graph. The directed acyclic graph computes $z = x \times y$, where each variable is just numbers. -![Basic example of a computational graph.](images/png/image1.png){#fig-computational-graph width="50%" height="auto" align="center"} +![Basic example of a computational graph.](images/png/image1.png){#fig-comp-graph width="50%" height="auto" align="center"} Frameworks like TensorFlow and PyTorch create computational graphs to implement the architectures of neural networks that we typically represent with diagrams. When you define a neural network layer in code (e.g., a dense layer in TensorFlow), the framework constructs a computational graph that includes all the necessary operations (such as matrix multiplication, addition, and activation functions) and their data dependencies. This graph enables the framework to efficiently manage the flow of data, optimize the execution of operations, and automatically compute gradients for training. Underneath the hood, the computational graphs represent abstractions for common layers like convolutional, pooling, recurrent, and dense layers, with data including activations, weights, and biases represented in tensors. This representation allows for efficient computation, leveraging the structure of the graph to parallelize operations and apply optimizations. @@ -400,20 +397,20 @@ Recently, the distinction has blurred as frameworks adopt both modes. TensorFlow Computational graphs can only be as good as the data they learn from and work on. Therefore, feeding training data efficiently is crucial for optimizing deep neural network performance, though it is often overlooked as one of the core functionalities. Many modern AI frameworks provide specialized pipelines to ingest, process, and augment datasets for model training. -#### Data Loaders +#### Data Loaders {#sec-frameworks-data-loaders} At the core of these pipelines are data loaders, which handle reading training examples from sources like files, databases, and object storage. Data loaders facilitate efficient data loading and preprocessing, crucial for deep learning models. For instance, TensorFlow's [tf.data](https://www.tensorflow.org/guide/data) dataloading pipeline is designed to manage this process. Depending on the application, deep learning models require diverse data formats such as CSV files or image folders. Some popular formats include: -* CSV, a versatile, simple format often used for tabular data. +* **CSV**: A versatile, simple format often used for tabular data. -* TFRecord: TensorFlow's proprietary format, optimized for performance. +* **TFRecord**: TensorFlow's proprietary format, optimized for performance. -* Parquet: Columnar storage, offering efficient data compression and retrieval. +* **Parquet**: Columnar storage, offering efficient data compression and retrieval. -* JPEG/PNG: Commonly used for image data. +* **JPEG/PNG**: Commonly used for image data. -* WAV/MP3: Prevalent formats for audio data. +* **WAV/MP3**: Prevalent formats for audio data. Data loaders batch examples to leverage vectorization support in hardware. Batching refers to grouping multiple data points for simultaneous processing, leveraging the vectorized computation capabilities of hardware like GPUs. While typical batch sizes range from 32 to 512 examples, the optimal size often depends on the data's memory footprint and the specific hardware constraints. Advanced loaders can stream virtually unlimited datasets from disk and cloud storage. They stream large datasets from disks or networks instead of fully loading them into memory, enabling unlimited dataset sizes. @@ -549,9 +546,11 @@ These steps to remove barriers to entry continue to democratize machine learning Transfer learning is the practice of using knowledge gained from a pre-trained model to train and improve the performance of a model for a different task. For example, models such as MobileNet and ResNet are trained on the ImageNet dataset. To do so, one may freeze the pre-trained model, utilizing it as a feature extractor to train a much smaller model built on top of the feature extraction. One can also fine-tune the entire model to fit the new task. Machine learning frameworks make it easy to load pre-trained models, freeze specific layers, and train custom layers on top. They simplify this process by providing intuitive APIs and easy access to large repositories of [pre-trained models](https://keras.io/api/applications/). -Transfer learning has challenges, such as the modified model's inability to conduct its original tasks after transfer learning. Papers such as ["Learning without Forgetting"](https://browse.arxiv.org/pdf/1606.09282.pdf) by @li2017learning try to address these challenges and have been implemented in modern machine learning platforms. @fig-transfer-learning simplifies the concept of transfer learning through an example. +Transfer learning, while powerful, comes with challenges. One significant issue is the modified model's potential inability to conduct its original tasks after transfer learning. To address these challenges, researchers have proposed various solutions. For example, @li2017learning introduced the concept of "Learning without Forgetting" in their paper ["Learning without Forgetting"](https://browse.arxiv.org/pdf/1606.09282.pdf), which has since been implemented in modern machine learning platforms. @fig-tl provides a simplified illustration of the transfer learning concept: + +![Transfer learning. Source: [Tech Target](https://www.google.com/url?sa=i&url=https%3A%2F%2Fanalyticsindiamag.com%2Fdevelopers-corner%2Fcomplete-guide-to-understanding-precision-and-recall-curves%2F&psig=AOvVaw3MosZItazJt2eermLTArjj&ust=1722534897757000&source=images&cd=vfe&opi=89978449&ved=0CBEQjRxqFwoTCIi389bs0YcDFQAAAAAdAAAAABAw)](images/png/transferlearning.png){#fig-tl} -![Transfer learning. Source: [Tech Target](https://www.google.com/url?sa=i&url=https%3A%2F%2Fanalyticsindiamag.com%2Fdevelopers-corner%2Fcomplete-guide-to-understanding-precision-and-recall-curves%2F&psig=AOvVaw3MosZItazJt2eermLTArjj&ust=1722534897757000&source=images&cd=vfe&opi=89978449&ved=0CBEQjRxqFwoTCIi389bs0YcDFQAAAAAdAAAAABAw)](images/png/transferlearning.png){#fig-transfer-learning} +As shown in @fig-tl, transfer learning involves taking a model trained on one task (the source task) and adapting it to perform a new, related task (the target task). This process allows the model to leverage knowledge gained from the source task, potentially improving performance and reducing training time on the target task. However, as mentioned earlier, care must be taken to ensure that the model doesn't "forget" its ability to perform the original task during this process. #### Federated Learning @@ -763,29 +762,43 @@ Through various custom techniques, such as static compilation, model-based sched ## Choosing the Right Framework -Choosing the right machine learning framework for a given application requires carefully evaluating models, hardware, and software considerations. By analyzing these three aspects—models, hardware, and software—ML engineers can select the optimal framework and customize it as needed for efficient and performant on-device ML applications. The goal is to balance model complexity, hardware limitations, and software integration to design a tailored ML pipeline for embedded and edge devices. +Choosing the right machine learning framework for a given application requires carefully evaluating models, hardware, and software considerations. @fig-tf-comparison provides a comparison of different TensorFlow frameworks, which we'll discuss in more detail: ![TensorFlow Framework Comparison - General. Source: TensorFlow.](images/png/image4.png){#fig-tf-comparison width="100%" height="auto" align="center" caption="TensorFlow Framework Comparison - General"} +Analyzing these three aspects—models, hardware, and software—as depicted in @fig-tf-comparison, ML engineers can select the optimal framework and customize it as needed for efficient and performant on-device ML applications. The goal is to balance model complexity, hardware limitations, and software integration to design a tailored ML pipeline for embedded and edge devices. As we examine the differences shown in @fig-tf-comparison, we'll gain insights into how to pick the right framework and understand what causes the variations between frameworks. + ### Model -TensorFlow supports significantly more operations (ops) than TensorFlow Lite and TensorFlow Lite Micro as it is typically used for research or cloud deployment, which require a large number of and more flexibility with operators (see @fig-tf-comparison). TensorFlow Lite supports select ops for on-device training, whereas TensorFlow Micro does not. TensorFlow Lite also supports dynamic shapes and quantization-aware training, but TensorFlow Micro does not. In contrast, TensorFlow Lite and TensorFlow Micro offer native quantization tooling and support, where quantization refers to transforming an ML program into an approximated representation with available lower precision operations. +@fig-tf-comparison illustrates the key differences between TensorFlow variants, particularly in terms of supported operations (ops) and features. TensorFlow supports significantly more operations than TensorFlow Lite and TensorFlow Lite Micro, as it is typically used for research or cloud deployment, which require a large number of and more flexibility with operators. + +The figure clearly demonstrates this difference in op support across the frameworks. TensorFlow Lite supports select ops for on-device training, whereas TensorFlow Micro does not. Additionally, the figure shows that TensorFlow Lite supports dynamic shapes and quantization-aware training, features that are absent in TensorFlow Micro. In contrast, both TensorFlow Lite and TensorFlow Micro offer native quantization tooling and support. Here, quantization refers to transforming an ML program into an approximated representation with available lower precision operations, a crucial feature for embedded and edge devices with limited computational resources. ### Software +As shown in @fig-tf-sw-comparison, TensorFlow Lite Micro does not have OS support, while TensorFlow and TensorFlow Lite do. This design choice for TensorFlow Lite Micro helps reduce memory overhead, make startup times faster, and consume less energy. Instead, TensorFlow Lite Micro can be used in conjunction with real-time operating systems (RTOS) like FreeRTOS, Zephyr, and Mbed OS. + +The figure also highlights an important memory management feature: TensorFlow Lite and TensorFlow Lite Micro support model memory mapping, allowing models to be directly accessed from flash storage rather than loaded into RAM. In contrast, TensorFlow does not offer this capability. + ![TensorFlow Framework Comparison - Software. Source: TensorFlow.](images/png/image5.png){#fig-tf-sw-comparison width="100%" height="auto" align="center" caption="TensorFlow Framework Comparison - Model"} -TensorFlow Lite Micro does not have OS support, while TensorFlow and TensorFlow Lite do, to reduce memory overhead, make startup times faster, and consume less energy (see @fig-tf-sw-comparison). TensorFlow Lite Micro can be used in conjunction with real-time operating systems (RTOS) like FreeRTOS, Zephyr, and Mbed OS. TensorFlow Lite and TensorFlow Lite Micro support model memory mapping, allowing models to be directly accessed from flash storage rather than loaded into RAM, whereas TensorFlow does not. TensorFlow and TensorFlow Lite support accelerator delegation to schedule code to different accelerators, whereas TensorFlow Lite Micro does not, as embedded systems tend to have a limited array of specialized accelerators. +Another key difference is accelerator delegation. TensorFlow and TensorFlow Lite support this feature, allowing them to schedule code to different accelerators. However, TensorFlow Lite Micro does not offer accelerator delegation, as embedded systems tend to have a limited array of specialized accelerators. + +These differences demonstrate how each TensorFlow variant is optimized for its target deployment environment, from powerful cloud servers to resource-constrained embedded devices. ### Hardware +TensorFlow Lite and TensorFlow Lite Micro have significantly smaller base binary sizes and memory footprints than TensorFlow (see @fig-tf-hw-comparison). For example, a typical TensorFlow Lite Micro binary is less than 200KB, whereas TensorFlow is much larger. This is due to the resource-constrained environments of embedded systems. TensorFlow supports x86, TPUs, and GPUs like NVIDIA, AMD, and Intel. + ![TensorFlow Framework Comparison - Hardware. Source: TensorFlow.](images/png/image3.png){#fig-tf-hw-comparison width="100%" height="auto" align="center" caption="TensorFlow Framework Comparison - Hardware"} -TensorFlow Lite and TensorFlow Lite Micro have significantly smaller base binary sizes and memory footprints than TensorFlow (see @fig-tf-hw-comparison). For example, a typical TensorFlow Lite Micro binary is less than 200KB, whereas TensorFlow is much larger. This is due to the resource-constrained environments of embedded systems. TensorFlow supports x86, TPUs, and GPUs like NVIDIA, AMD, and Intel. TensorFlow Lite supports Arm Cortex-A and x86 processors commonly used on mobile phones and tablets. The latter is stripped of all the unnecessary training logic for on-device deployment. TensorFlow Lite Micro provides support for microcontroller-focused Arm Cortex M cores like M0, M3, M4, and M7, as well as DSPs like Hexagon and SHARC and MCUs like STM32, NXP Kinetis, Microchip AVR. +TensorFlow Lite supports Arm Cortex-A and x86 processors commonly used on mobile phones and tablets. The latter is stripped of all the unnecessary training logic for on-device deployment. TensorFlow Lite Micro provides support for microcontroller-focused Arm Cortex M cores like M0, M3, M4, and M7, as well as DSPs like Hexagon and SHARC and MCUs like STM32, NXP Kinetis, Microchip AVR. ### Other Factors -Selecting the appropriate AI framework is essential to ensure that embedded systems can efficiently execute AI models. Several key factors beyond models, hardware, and software should be considered when evaluating AI frameworks for embedded systems. Other key factors to consider when choosing a machine learning framework are performance, scalability, ease of use, integration with data engineering tools, integration with model optimization tools, and community support. By understanding these factors, you can make informed decisions and maximize the potential of your machine-learning initiatives. +Selecting the appropriate AI framework is essential to ensure that embedded systems can efficiently execute AI models. Several key factors beyond models, hardware, and software should be considered when evaluating AI frameworks for embedded systems. + +Other key factors to consider when choosing a machine learning framework are performance, scalability, ease of use, integration with data engineering tools, integration with model optimization tools, and community support. Developers can make informed decisions and maximize the potential of your machine-learning initiatives by understanding these various factors. #### Performance @@ -843,7 +856,7 @@ We first introduced the necessity of machine learning frameworks like TensorFlow Advanced features further improve these frameworks' usability, enabling tasks like fine-tuning large pre-trained models and facilitating federated learning. These capabilities are critical for developing sophisticated machine learning models efficiently. -Embedded AI frameworks, such as TensorFlow Lite Micro, provide specialized tools for deploying models on resource-constrained platforms. TensorFlow Lite Micro, for instance, offers comprehensive optimization tooling, including quantization mapping and kernel optimizations, to ensure high performance on microcontroller-based platforms like Arm Cortex-M and RISC-V processors. Frameworks specifically built for specialized hardware like CMSIS-NN on Cortex-M processors can further maximize performance but sacrifice portability. Integrated frameworks from processor vendors tailor the stack to their architectures, unlocking the full potential of their chips but locking you into their ecosystem. +Embedded AI or TinyML frameworks, such as TensorFlow Lite Micro, provide specialized tools for deploying models on resource-constrained platforms. TensorFlow Lite Micro, for instance, offers comprehensive optimization tooling, including quantization mapping and kernel optimizations, to ensure high performance on microcontroller-based platforms like Arm Cortex-M and RISC-V processors. Frameworks specifically built for specialized hardware like CMSIS-NN on Cortex-M processors can further maximize performance but sacrifice portability. Integrated frameworks from processor vendors tailor the stack to their architectures, unlocking the full potential of their chips but locking you into their ecosystem. Ultimately, choosing the right framework involves finding the best match between its capabilities and the requirements of the target platform. This requires balancing trade-offs between performance needs, hardware constraints, model complexity, and other factors. Thoroughly assessing the intended models and use cases and evaluating options against key metrics will guide developers in selecting the ideal framework for their machine learning applications. @@ -896,15 +909,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * @exr-k ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we offer a series of hands-on labs allowing students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience. - -* _Coming soon._ -::: - - - diff --git a/contents/frameworks/images/png/color_channels_of_image.png b/contents/core/frameworks/images/png/color_channels_of_image.png similarity index 100% rename from contents/frameworks/images/png/color_channels_of_image.png rename to contents/core/frameworks/images/png/color_channels_of_image.png diff --git a/contents/frameworks/images/png/cover_ml_frameworks.png b/contents/core/frameworks/images/png/cover_ml_frameworks.png similarity index 100% rename from contents/frameworks/images/png/cover_ml_frameworks.png rename to contents/core/frameworks/images/png/cover_ml_frameworks.png diff --git a/contents/frameworks/images/png/federated_learning.png b/contents/core/frameworks/images/png/federated_learning.png similarity index 100% rename from contents/frameworks/images/png/federated_learning.png rename to 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[Labs](#sec-ai-acceleration-resource) +Resources: [Slides](#sec-ai-acceleration-resource), [Videos](#sec-ai-acceleration-resource), [Exercises](#sec-ai-acceleration-resource) ::: ![_DALL·E 3 Prompt: Create an intricate and colorful representation of a System on Chip (SoC) design in a rectangular format. Showcase a variety of specialized machine learning accelerators and chiplets, all integrated into the processor. Provide a detailed view inside the chip, highlighting the rapid movement of electrons. Each accelerator and chiplet should be designed to interact with neural network neurons, layers, and activations, emphasizing their processing speed. Depict the neural networks as a network of interconnected nodes, with vibrant data streams flowing between the accelerator pieces, showcasing the enhanced computation speed._](images/png/cover_ai_hardware.png) @@ -58,14 +58,14 @@ This evolution demonstrates how hardware acceleration has focused on solving com The evolution of hardware acceleration is closely tied to the broader history of computing. Central to this history is the role of transistors, the fundamental building blocks of modern electronics. Transistors act as tiny switches that can turn on or off, enabling the complex computations that drive everything from simple calculators to advanced machine learning models. In the early decades, chip design was governed by Moore's Law, which predicted that the number of transistors on an integrated circuit would double approximately every two years, and Dennard Scaling, which observed that as transistors became smaller, their performance (speed) increased, while power density (power per unit area) remained constant. These two laws were held through the single-core era. @fig-moore-dennard shows the trends of different microprocessor metrics. As the figure denotes, Dennard Scaling fails around the mid-2000s; notice how the clock speed (frequency) remains almost constant even as the number of transistors keeps increasing. +![Microprocessor trends. Source: [Karl Rupp](https://www.karlrupp.net/2018/02/42-years-of-microprocessor-trend-data/).](images/png/hwai_40yearsmicrotrenddata.png){#fig-moore-dennard} + However, as @patterson2016computer describes, technological constraints eventually forced a transition to the multicore era, with chips containing multiple processing cores to deliver performance gains. Power limitations prevented further scaling, which led to "dark silicon" ([Dark Silicon](https://en.wikipedia.org/wiki/Dark_silicon)), where not all chip areas could be simultaneously active [@xiu2019time]. "Dark silicon" refers to portions of the chip that cannot be powered simultaneously due to thermal and power limitations. Essentially, as the density of transistors increased, the proportion of the chip that could be actively used without overheating or exceeding power budgets shrank. This phenomenon meant that while chips had more transistors, not all could be operational simultaneously, limiting potential performance gains. This power crisis necessitated a shift to the accelerator era, with specialized hardware units tailored for specific tasks to maximize efficiency. The explosion in AI workloads further drove demand for customized accelerators. Enabling factors included new programming languages, software tools, and manufacturing advances. -![Microprocessor trends. Source: [Karl Rupp](https://www.karlrupp.net/2018/02/42-years-of-microprocessor-trend-data/).](images/png/hwai_40yearsmicrotrenddata.png){#fig-moore-dennard} - Fundamentally, hardware accelerators are evaluated on performance, power, and silicon area (PPA)—the nature of the target application—whether memory-bound or compute-bound—heavily influences the design. For example, memory-bound workloads demand high bandwidth and low latency access, while compute-bound applications require maximal computational throughput. ### General Principles @@ -140,10 +140,10 @@ By structuring the analysis along this spectrum, we aim to illustrate the fundam @fig-design-tradeoffs illustrates the complex interplay between flexibility, performance, functional diversity, and area of architecture design. Notice how the ASIC is on the bottom-right corner, with minimal area, flexibility, and power consumption and maximal performance, due to its highly specialized application-specific nature. A key tradeoff is functional diversity vs performance: general purpose architectures can serve diverse applications but their application performance is degraded as compared to more customized architectures. -The progression begins with the most specialized option, ASICs purpose-built for AI, to ground our understanding in the maximum possible optimizations before expanding to more generalizable architectures. This structured approach elucidates the accelerator design space. - ![Design tradeoffs. Source: @rayis2014.](images/png/tradeoffs.png){#fig-design-tradeoffs} +The progression begins with the most specialized option, ASICs purpose-built for AI, to ground our understanding in the maximum possible optimizations before expanding to more generalizable architectures. This structured approach elucidates the accelerator design space. + ### Application-Specific Integrated Circuits (ASICs) An Application-Specific Integrated Circuit (ASIC) is a type of [integrated circuit](https://en.wikipedia.org/wiki/Integrated_circuit) (IC) that is custom-designed for a specific application or workload rather than for general-purpose use. Unlike CPUs and GPUs, ASICs do not support multiple applications or workloads. Rather, they are optimized to perform a single task extremely efficiently. The Google TPU is an example of an ASIC. @@ -267,7 +267,7 @@ While FPGAs may not achieve the utmost performance and efficiency of workload-sp ##### Customized Parallelism and Pipelining -FPGA architectures can leverage spatial parallelism and pipelining by tailoring the hardware design to mirror the parallelism in ML models. For example, Intel's HARPv2 FPGA platform splits the layers of an MNIST convolutional network across separate processing elements to maximize throughput. Unique parallel patterns like tree ensemble evaluations are also possible on FPGAs. Deep pipelines with optimized buffering and dataflow can be customized to each model's structure and datatypes. This level of tailored parallelism and pipelining is not feasible on GPUs. +FPGA architectures can leverage spatial parallelism and pipelining by tailoring the hardware design to mirror the parallelism in ML models. For example, on an Intel's HARPv2 FPGA platform one can split the layers of a convolutional network across separate processing elements to maximize throughput. Unique parallel patterns like tree ensemble evaluations are also possible on FPGAs. Deep pipelines with optimized buffering and dataflow can be customized to each model's structure and datatypes. This level of tailored parallelism and pipelining is not feasible on GPUs. ##### Low Latency On-Chip Memory @@ -275,15 +275,14 @@ Large amounts of high-bandwidth on-chip memory enable localized storage for weig ##### Native Support for Low Precision -A key advantage of FPGAs is the ability to natively implement any bit width for arithmetic units, such as INT4 or bfloat16, used in quantized ML models. For example, Intel's Stratix 10 NX FPGAs have dedicated INT8 cores that can achieve up to 143 INT8 TOPS (Tera Operations Per Second) at ~1 TOPS/W (Tera Operations Per Second per Watt) [Intel Stratix 10 NX FPGA -](https://www.intel.com/content/www/us/en/products/details/fpga/stratix/10/nx.html). TOPS is a measure of performance similar to FLOPS, but while FLOPS measures floating-point calculations, TOPS measures the number of integer operations a system can perform per second. Lower bit widths, like INT8 or INT4, increase arithmetic density and performance. FPGAs can even support mixed precision or dynamic precision tuning at runtime. +A key advantage of FPGAs is the ability to natively implement any bit width for arithmetic units, such as INT4 or bfloat16, used in quantized ML models. For example, [Intel Stratix 10 NX FPGA +](https://www.intel.com/content/www/us/en/products/details/fpga/stratix/10/nx.html) has dedicated INT8 cores that can achieve up to 143 INT8 TOPS (Tera Operations Per Second) at ~1 TOPS/W (Tera Operations Per Second per Watt). TOPS is a measure of performance similar to FLOPS, but while FLOPS measures floating-point calculations, TOPS measures the number of integer operations a system can perform per second. Lower bit widths, like INT8 or INT4, increase arithmetic density and performance. FPGAs can even support mixed precision or dynamic precision tuning at runtime. #### Disadvantages ##### Lower Peak Throughput than ASICs -FPGAs cannot match the raw throughput numbers of ASICs customized for a specific model and precision. The overheads of the reconfigurable fabric compared to fixed function hardware result in lower peak performance. For example, the TPU v5e pods allow up to 256 chips to be connected with more than 100 petaOps (Peta Operations Per Second) of INT8 performance, while FPGAs can offer up to 143 INT8 TOPS or 286 INT4 TOPS [Intel Stratix 10 NX FPGA -](https://www.intel.com/content/www/us/en/products/details/fpga/stratix/10/nx.html). PetaOps represents quadrillions of operations per second, whereas TOPS measures trillions, highlighting the much greater throughput capability of TPU pods compared to FPGAs. +FPGAs cannot match the raw throughput numbers of ASICs customized for a specific model and precision. The overheads of the reconfigurable fabric compared to fixed function hardware result in lower peak performance. For example, the TPU v5e pods allow up to 256 chips to be connected with more than 100 PetaOps (Peta Operations Per Second) of INT8 performance, while FPGAs can offer up to 143 INT8 TOPS or 286 INT4 TOPS such as on the Intel Stratix 10 NX FPGA; PetaOps represents quadrillions of operations per second, whereas TOPS measures trillions, highlighting the much greater throughput capability of TPU pods compared to FPGAs. This is because FPGAs comprise basic building blocks—configurable logic blocks, RAM blocks, and interconnects. Vendors provide a set amount of these resources. To program FPGAs, engineers write HDL code and compile it into bitstreams that rearrange the fabric, which has inherent overheads versus an ASIC purpose-built for one computation. @@ -319,7 +318,7 @@ DSPs integrate large amounts of fast on-chip SRAM memory to hold data locally fo ##### Power Efficiency -DSPs are engineered to provide high performance per watt on digital signal workloads. Efficient data paths, parallelism, and memory architectures enable trillions of math operations per second within tight mobile power budgets. For example, [Qualcomm's Hexagon DSP](https://developer.qualcomm.com/software/hexagon-dsp-sdk/dsp-processor) can deliver 4 trillion operations per second (TOPS) while consuming minimal watts. +DSPs are engineered to provide high performance per watt on digital signal workloads. Efficient data paths, parallelism, and memory architectures enable trillions of math operations per second within tight mobile power budgets. For example, [Qualcomm's Hexagon DSP](https://developer.qualcomm.com/software/hexagon-dsp-sdk/dsp-processor) can deliver 4 TOPS while consuming minimal watts. ##### Support for Integer and Floating Point Math @@ -696,7 +695,7 @@ The [benchmarking chapter](../benchmarking/benchmarking.qmd) explores this topic Benchmarking suites such as MLPerf, Fathom, and AI Benchmark offer a set of standardized tests that can be used across different hardware platforms. These suites measure AI accelerator performance across various neural networks and machine learning tasks, from basic image classification to complex language processing. Providing a common ground for Comparison, they help ensure that performance claims are consistent and verifiable. These "tools" are applied not only to guide the development of hardware but also to ensure that the software stack leverages the full potential of the underlying architecture. -* **MLPerf:** Includes a broad set of benchmarks covering both training [@mattson2020mlperf] and inference [@reddi2020mlperf] for a range of machine learning tasks. @fig-ml-perf showcases the uses of MLperf. +* **MLPerf:** Includes a broad set of benchmarks covering both training [@mattson2020mlperf] and inference [@reddi2020mlperf] for a range of machine learning tasks. @fig-ml-perf showcases the diversity of AI use cases covered by MLPerf. * **Fathom:** Focuses on core operations in deep learning models, emphasizing their execution on different architectures [@adolf2016fathom]. * **AI Benchmark:** Targets mobile and consumer devices, assessing AI performance in end-user applications [@ignatov2018ai]. @@ -799,10 +798,10 @@ In response, new manufacturing techniques like wafer-scale fabrication and advan Wafer-scale AI takes an extremely integrated approach, manufacturing an entire silicon wafer as one gigantic chip. This differs drastically from conventional CPUs and GPUs, which cut each wafer into many smaller individual chips. @fig-wafer-scale shows a comparison between Cerebras Wafer Scale Engine 2, which is the largest chip ever built, and the largest GPU. While some GPUs may contain billions of transistors, they still pale in Comparison to the scale of a wafer-size chip with over a trillion transistors. -The wafer-scale approach also diverges from more modular system-on-chip designs that still have discrete components communicating by bus. Instead, wafer-scale AI enables full customization and tight integration of computation, memory, and interconnects across the entire die. - ![Wafer-scale vs. GPU. Source: [Cerebras](https://www.cerebras.net/product-chip/).](images/png/aimage1.png){#fig-wafer-scale} +The wafer-scale approach also diverges from more modular system-on-chip designs that still have discrete components communicating by bus. Instead, wafer-scale AI enables full customization and tight integration of computation, memory, and interconnects across the entire die. + By designing the wafer as one integrated logic unit, data transfer between elements is minimized. This provides lower latency and power consumption than discrete system-on-chip or chiplet designs. While chiplets can offer flexibility by mixing and matching components, communication between chiplets is challenging. The monolithic nature of wafer-scale integration eliminates these inter-chip communication bottlenecks. However, the ultra-large-scale also poses difficulties for manufacturability and yield with wafer-scale designs. Defects in any region of the wafer can make (certain parts of) the chip unusable. Specialized lithography techniques are required to produce such large dies. So, wafer-scale integration pursues the maximum performance gains from integration but requires overcoming substantial fabrication challenges. @@ -819,7 +818,9 @@ However, the ultra-large-scale also poses difficulties for manufacturability and #### Chiplets for AI -Chiplet design refers to a semiconductor architecture in which a single integrated circuit (IC) is constructed from multiple smaller, individual components known as chiplets. Each chiplet is a self-contained functional block, typically specialized for a specific task or functionality. These chiplets are then interconnected on a larger substrate or package to create a cohesive system. @fig-chiplet illustrates this concept. For AI hardware, chiplets enable the mixing of different types of chips optimized for tasks like matrix multiplication, data movement, analog I/O, and specialized memories. This heterogeneous integration differs greatly from wafer-scale integration, where all logic is manufactured as one monolithic chip. Companies like Intel and AMD have adopted chiplet designs for their CPUs. +Chiplet design refers to a semiconductor architecture in which a single integrated circuit (IC) is constructed from multiple smaller, individual components known as chiplets. Each chiplet is a self-contained functional block, typically specialized for a specific task or functionality. These chiplets are then interconnected on a larger substrate or package to create a cohesive system. + +@fig-chiplet illustrates this concept. For AI hardware, chiplets enable the mixing of different types of chips optimized for tasks like matrix multiplication, data movement, analog I/O, and specialized memories. This heterogeneous integration differs greatly from wafer-scale integration, where all logic is manufactured as one monolithic chip. Companies like Intel and AMD have adopted chiplet designs for their CPUs. Chiplets are interconnected using advanced packaging techniques like high-density substrate interposers, 2.5D/3D stacking, and wafer-level packaging. This allows combining chiplets fabricated with different process nodes, specialized memories, and various optimized AI engines. @@ -848,7 +849,9 @@ Neuromorphic computing is an emerging field aiming to emulate the efficiency and Intel and IBM are leading commercial efforts in neuromorphic hardware. Intel's Loihi and Loihi 2 chips [@davies2018loihi; @davies2021advancing] offer programmable neuromorphic cores with on-chip learning. IBM's Northpole [@modha2023neural] device comprises over 100 million magnetic tunnel junction synapses and 68 billion transistors. These specialized chips deliver benefits like low power consumption for edge inference. -Spiking neural networks (SNNs) [@maass1997networks] are computational models for neuromorphic hardware. Unlike deep neural networks communicating via continuous values, SNNs use discrete spikes that are more akin to biological neurons. This allows efficient event-based computation rather than constant processing. Additionally, SNNs consider the temporal and spatial characteristics of input data. This better mimics biological neural networks, where the timing of neuronal spikes plays an important role. However, training SNNs remains challenging due to the added temporal complexity. @fig-spiking provides an overview of the spiking methodology: (a) Diagram of a neuron; (b) Measuring an action potential propagated along the axon of a neuron. Only the action potential is detectable along the axon; (c) The neuron's spike is approximated with a binary representation; (d) Event-Driven Processing; (e) Active Pixel Sensor and Dynamic Vision Sensor. +Spiking neural networks (SNNs) [@maass1997networks] are computational models for neuromorphic hardware. Unlike deep neural networks communicating via continuous values, SNNs use discrete spikes that are more akin to biological neurons. This allows efficient event-based computation rather than constant processing. Additionally, SNNs consider the temporal and spatial characteristics of input data. This better mimics biological neural networks, where the timing of neuronal spikes plays an important role. + +However, training SNNs remains challenging due to the added temporal complexity. @fig-spiking provides an overview of the spiking methodology: (a) illustration of a neuron; (b) Measuring an action potential propagated along the axon of a neuron. Only the action potential is detectable along the axon; (c) The neuron's spike is approximated with a binary representation; (d) Event-Driven Processing; (e) Active Pixel Sensor and Dynamic Vision Sensor. ![Neuromorphic spiking. Source: @eshraghian2023training.](images/png/aimage4.png){#fig-spiking} @@ -924,7 +927,7 @@ While in-memory computing technologies like ReRAM and PIM offer exciting prospec ### Optical Computing -In AI acceleration, a burgeoning area of interest lies in novel technologies that deviate from traditional paradigms. Some emerging technologies mentioned above, such as flexible electronics, in-memory computing, or even neuromorphic computing, are close to becoming a reality, given their ground-breaking innovations and applications. One of the promising and leading next-gen frontiers is optical computing technologies [@miller2000optical,@zhou2022photonic ]. Companies like [[LightMatter]](https://lightmatter.co/) are pioneering the use of light photonics for calculations, thereby utilizing photons instead of electrons for data transmission and computation. +In AI acceleration, a burgeoning area of interest lies in novel technologies that deviate from traditional paradigms. Some emerging technologies mentioned above, such as flexible electronics, in-memory computing, or even neuromorphic computing, are close to becoming a reality, given their ground-breaking innovations and applications. One of the promising and leading next-gen frontiers is optical computing technologies [@miller2000optical,@zhou2022photonic ]. Companies like [LightMatter](https://lightmatter.co/) are pioneering the use of light photonics for calculations, thereby utilizing photons instead of electrons for data transmission and computation. Optical computing utilizes photons and photonic devices rather than traditional electronic circuits for computing and data processing. It takes inspiration from fiber optic communication links that rely on light for fast, efficient data transfer [@shastri2021photonics]. Light can propagate with much less loss than semiconductors' electrons, enabling inherent speed and efficiency benefits. @@ -949,7 +952,7 @@ As a result, optical computing is still in the very early research stage despite Quantum computers leverage unique phenomena of quantum physics, like superposition and entanglement, to represent and process information in ways not possible classically. Instead of binary bits, the fundamental unit is the quantum bit or qubit. Unlike classical bits, which are limited to 0 or 1, qubits can exist simultaneously in a superposition of both states due to quantum effects. -Multiple qubits can also be entangled, leading to exponential information density but introducing probabilistic results. Superposition enables parallel computation on all possible states, while entanglement allows nonlocal correlations between qubits. @fig-qubit simulates the structure of a qubit. +Multiple qubits can also be entangled, leading to exponential information density but introducing probabilistic results. Superposition enables parallel computation on all possible states, while entanglement allows nonlocal correlations between qubits. @fig-qubit visually conveys the differences between classical bits in computing and quantum bits (qbits). ![Qubits, the building blocks of quantum computing. Source: [Microsoft](https://azure.microsoft.com/en-gb/resources/cloud-computing-dictionary/what-is-a-qubit)](images/png/qubit.png){#fig-qubit} @@ -968,7 +971,7 @@ However, quantum states are fragile and prone to errors that require error-corre While meaningful quantum advantage for ML remains far off, active research at companies like [D-Wave](https://www.dwavesys.com/company/about-d-wave/), [Rigetti](https://www.rigetti.com/), and [IonQ](https://ionq.com/) is advancing quantum computer engineering and quantum algorithms. Major technology companies like Google, [IBM](https://www.ibm.com/quantum?utm_content=SRCWW&p1=Search&p4C700050385964705&p5=e&gclid=Cj0KCQjw-pyqBhDmARIsAKd9XIPD9U1Sjez_S0z5jeDDE4nRyd6X_gtVDUKJ-HIolx2vOc599KgW8gAaAv8gEALw_wcB&gclsrc=aw.ds), and Microsoft are actively exploring quantum computing. Google recently announced a 72-qubit quantum processor called [Bristlecone](https://blog.research.google/2018/03/a-preview-of-bristlecone-googles-new.html) and plans to build a 49-qubit commercial quantum system. Microsoft also has an active research program in topological quantum computing and collaborates with quantum startup [IonQ](https://ionq.com/) -Quantum techniques may first make inroads into optimization before more generalized ML adoption. Realizing quantum ML's full potential awaits major milestones in quantum hardware development and ecosystem maturity. @fig-q-computing illustrates a comparison between quantum computing and classical computing. +Quantum techniques may first make inroads into optimization before more generalized ML adoption. Realizing quantum ML's full potential awaits major milestones in quantum hardware development and ecosystem maturity. @fig-q-computing illustratively compares quantum computing and classical computing. ![Comparing quantum computing with classical computing. Source: [Devopedia](​​https://devopedia.org/quantum-computing)](images/png/qcomputing.png){#fig-q-computing} @@ -1103,10 +1106,3 @@ Here is a curated list of resources to support students and instructors in their * @exr-tvm ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -* _Coming soon._ -::: diff --git a/contents/hw_acceleration/images/jpg/flexible-circuit.jpeg b/contents/core/hw_acceleration/images/jpg/flexible-circuit.jpeg similarity index 100% rename from contents/hw_acceleration/images/jpg/flexible-circuit.jpeg rename to contents/core/hw_acceleration/images/jpg/flexible-circuit.jpeg diff --git a/contents/hw_acceleration/images/png/aimage1.png b/contents/core/hw_acceleration/images/png/aimage1.png similarity index 100% rename from contents/hw_acceleration/images/png/aimage1.png rename to contents/core/hw_acceleration/images/png/aimage1.png diff --git a/contents/hw_acceleration/images/png/aimage2.png b/contents/core/hw_acceleration/images/png/aimage2.png similarity 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b/contents/core/introduction/introduction.qmd @@ -0,0 +1,422 @@ +--- +bibliography: introduction.bib +--- + +# Introduction {#sec-introduction} + +![_DALL·E 3 Prompt: A detailed, rectangular, flat 2D illustration depicting a roadmap of a book's chapters on machine learning systems, set on a crisp, clean white background. The image features a winding road traveling through various symbolic landmarks. Each landmark represents a chapter topic: Introduction, ML Systems, Deep Learning, AI Workflow, Data Engineering, AI Frameworks, AI Training, Efficient AI, Model Optimizations, AI Acceleration, Benchmarking AI, On-Device Learning, Embedded AIOps, Security & Privacy, Responsible AI, Sustainable AI, AI for Good, Robust AI, Generative AI. The style is clean, modern, and flat, suitable for a technical book, with each landmark clearly labeled with its chapter title._](images/png/cover_introduction.png) + +## Why Machine Learning Systems Matter + +AI is everywhere. Consider your morning routine: You wake up to an AI-powered smart alarm that learned your sleep patterns. Your phone suggests your route to work, having learned from traffic patterns. During your commute, your music app automatically creates a playlist it thinks you'll enjoy. At work, your email client filters spam and prioritizes important messages. Throughout the day, your smartwatch monitors your activity, suggesting when to move or exercise. In the evening, your streaming service recommends shows you might like, while your smart home devices adjust lighting and temperature based on your learned preferences. + +But these everyday conveniences are just the beginning. AI is transforming our world in extraordinary ways. Today, AI systems detect early-stage cancers with unprecedented accuracy, predict and track extreme weather events to save lives, and accelerate drug discovery by simulating millions of molecular interactions. Autonomous vehicles navigate complex city streets while processing real-time sensor data from dozens of sources. Language models engage in sophisticated conversations, translate between hundreds of languages, and help scientists analyze vast research databases. In scientific laboratories, AI systems are making breakthrough discoveries - from predicting protein structures that unlock new medical treatments to identifying promising materials for next-generation solar cells and batteries. Even in creative fields, AI collaborates with artists and musicians to explore new forms of expression, pushing the boundaries of human creativity. + +This isn't science fiction---it's the reality of how artificial intelligence, specifically machine learning systems, has become woven into the fabric of our daily lives. In the early 1990s, [Mark Weiser](https://en.wikipedia.org/wiki/Mark_Weiser), a pioneering computer scientist, introduced the world to a revolutionary concept that would forever change how we interact with technology. This vision was succinctly captured in his seminal paper, "The Computer for the 21st Century" (see @fig-ubiquitous). Weiser envisioned a future where computing would be seamlessly integrated into our environments, becoming an invisible, integral part of daily life. + +![Ubiquitous computing as envisioned by Mark Weiser.](images/png/21st_computer.png){#fig-ubiquitous width=50%} + +He termed this concept "ubiquitous computing," promising a world where technology would serve us without demanding our constant attention or interaction. Today, we find ourselves living in Weiser's envisioned future, largely enabled by machine learning systems. The true essence of his vision—creating an intelligent environment that can anticipate our needs and act on our behalf—has become reality through the development and deployment of ML systems that span entire ecosystems, from powerful cloud data centers to edge devices to the tiniest IoT sensors. + +Yet most of us rarely think about the complex systems that make this possible. Behind each of these seemingly simple interactions lies a sophisticated infrastructure of data, algorithms, and computing resources working together. Understanding how these systems work—their capabilities, limitations, and requirements—has become increasingly critical as they become more integrated into our world. + +To appreciate the magnitude of this transformation and the complexity of modern machine learning systems, we need to understand how we got here. The journey from early artificial intelligence to today's ubiquitous ML systems is a story of not just technological evolution, but of changing perspectives on what's possible and what's necessary to make AI practical and reliable. + +## The Evolution of AI + +The evolution of AI, depicted in the timeline shown in @fig-ai-timeline, highlights key milestones such as the development of the "perceptron"[^defn-perceptron] in 1957 by Frank Rosenblatt, a foundational element for modern neural networks. Imagine walking into a computer lab in 1965. You'd find room-sized mainframes running programs that could prove basic mathematical theorems or play simple games like tic-tac-toe. These early artificial intelligence systems, while groundbreaking for their time, were a far cry from today's machine learning systems that can detect cancer in medical images or understand human speech. The timeline shows the progression from early innovations like the ELIZA chatbot in 1966, to significant breakthroughs such as IBM's Deep Blue defeating chess champion Garry Kasparov in 1997. More recent advancements include the introduction of OpenAI's GPT-3 in 2020 and GPT-4 in 2023, demonstrating the dramatic evolution and increasing complexity of AI systems over the decades. + +[^defn-perceptron]: The first artificial neural network—a simple model that could learn to classify visual patterns, similar to a single neuron making a yes/no decision based on its inputs. + +![Milestones in AI from 1950 to 2020. Source: IEEE Spectrum](https://spectrum.ieee.org/media-library/a-chart-of-milestones-in-ai-from-1950-to-2020.png?id=27547255){#fig-ai-timeline} + +Let's explore how we got here. + +### Symbolic AI (1956-1974) + +The story of machine learning begins at the historic Dartmouth Conference in 1956, where pioneers like John McCarthy, Marvin Minsky, and Claude Shannon first coined the term "artificial intelligence." Their approach was based on a compelling idea: intelligence could be reduced to symbol manipulation. Consider Daniel Bobrow's STUDENT system from 1964, one of the first AI programs that could solve algebra word problems: + +::: {.callout-note} +### Example: STUDENT (1964) + +``` +Problem: "If the number of customers Tom gets is twice the +square of 20% of the number of advertisements he runs, and +the number of advertisements is 45, what is the number of +customers Tom gets?" + +STUDENT would: + +1. Parse the English text +2. Convert it to algebraic equations +3. Solve the equation: n = 2(0.2 × 45)² +4. Provide the answer: 162 customers +``` +::: + +Early AI like STUDENT suffered from a fundamental limitation: they could only handle inputs that exactly matched their pre-programmed patterns and rules. Imagine a language translator that only works when sentences follow perfect grammatical structure---even slight variations like changing word order, using synonyms, or natural speech patterns would cause the STUDENT to fail. This "brittleness" meant that while these solutions could appear intelligent when handling very specific cases they were designed for, they would break down completely when faced with even minor variations or real-world complexity. This limitation wasn't just a technical inconvenience—it revealed a deeper problem with rule-based approaches to AI: they couldn't genuinely understand or generalize from their programming, they could only match and manipulate patterns exactly as specified. + +### Expert Systems(1970s-1980s) + +By the mid-1970s, researchers realized that general AI was too ambitious. Instead, they focused on capturing human expert knowledge in specific domains. MYCIN, developed at Stanford, was one of the first large-scale expert systems designed to diagnose blood infections: + +::: {.callout-note} +### Example: MYCIN (1976) +``` +Rule Example from MYCIN: +IF + The infection is primary-bacteremia + The site of the culture is one of the sterile sites + The suspected portal of entry is the gastrointestinal tract +THEN + There is suggestive evidence (0.7) that infection is bacteroid +``` +::: + +While MYCIN represented a major advance in medical AI with its 600 expert rules for diagnosing blood infections, it revealed fundamental challenges that still plague ML today. Getting domain knowledge from human experts and converting it into precise rules proved incredibly time-consuming and difficult—doctors often couldn't explain exactly how they made decisions. MYCIN struggled with uncertain or incomplete information, unlike human doctors who could make educated guesses. Perhaps most importantly, maintaining and updating the rule base became exponentially more complex as MYCIN grew—adding new rules often conflicted with existing ones, and medical knowledge itself kept evolving. These same challenges of knowledge capture, uncertainty handling, and maintenance remain central concerns in modern machine learning, even though we now use different technical approaches to address them. + +### Statistical Learning: A Paradigm Shift (1990s) + +The 1990s marked a radical transformation in artificial intelligence as the field moved away from hand-coded rules toward statistical learning approaches. This wasn't a simple choice—it was driven by three converging factors that made statistical methods both possible and powerful. The digital revolution meant massive amounts of data were suddenly available to train the algorithms. **Moore's Law**[^defn-mooreslaw] delivered the computational power needed to process this data effectively. And researchers developed new algorithms like Support Vector Machines and improved neural networks that could actually learn patterns from this data rather than following pre-programmed rules. This combination fundamentally changed how we built AI: instead of trying to encode human knowledge directly, we could now let machines discover patterns automatically from examples, leading to more robust and adaptable AI. + +[^defn-mooreslaw]: The observation made by Intel co-founder Gordon Moore in 1965 that the number of transistors on a microchip doubles approximately every two years, while the cost halves. This exponential growth in computing power has been a key driver of advances in machine learning, though the pace has begun to slow in recent years. + +Consider how email spam filtering evolved: + +::: {.callout-note} +### Example: Early Spam Detection Systems + +``` +Rule-based (1980s): +IF contains("viagra") OR contains("winner") THEN spam + +Statistical (1990s): +P(spam|word) = (frequency in spam emails) / (total frequency) +Combined using Naive Bayes: +P(spam|email) ∝ P(spam) × ∏ P(word|spam) +``` +::: + +The move to statistical approaches fundamentally changed how we think about building AI by introducing three core concepts that remain important today. First, the quality and quantity of training data became as important as the algorithms themselves---AI could only learn patterns that were present in its training examples. Second, we needed rigorous ways to evaluate how well AI actually performed, leading to metrics that could measure success and compare different approaches. Third, we discovered an inherent tension between precision (being right when we make a prediction) and recall (catching all the cases we should find), forcing designers to make explicit trade-offs based on their application's needs. For example, a spam filter might tolerate some spam to avoid blocking important emails, while medical diagnosis might need to catch every potential case even if it means more false alarms. + +@tbl-ai-evolution-strengths encapsulates the evolutionary journey of AI approaches we have discussed so far, highlighting the key strengths and capabilities that emerged with each new paradigm. As we move from left to right across the table, we can observe several important trends. We will talk about shallow and deep learning next, but it is useful to understand the trade-offs between the approaches we have covered so far. + ++---------------------+--------------------------+--------------------------+--------------------------+-------------------------------+ +| Aspect | Symbolic AI | Expert Systems | Statistical Learning | Shallow / Deep Learning | ++:====================+:=========================+:=========================+:=========================+:==============================+ +| Key Strength | Logical reasoning | Domain expertise | Versatility | Pattern recognition | ++---------------------+--------------------------+--------------------------+--------------------------+-------------------------------+ +| Best Use Case | Well-defined, rule-based | Specific domain problems | Various structured data | Complex, unstructured data | +| | problems | | problems | problems | ++---------------------+--------------------------+--------------------------+--------------------------+-------------------------------+ +| Data Handling | Minimal data needed | Domain knowledge-based | Moderate data required | Large-scale data processing | ++---------------------+--------------------------+--------------------------+--------------------------+-------------------------------+ +| Adaptability | Fixed rules | Domain-specific | Adaptable to various | Highly adaptable to diverse | +| | | adaptability | domains | tasks | ++---------------------+--------------------------+--------------------------+--------------------------+-------------------------------+ +| Problem Complexity | Simple, logic-based | Complicated, domain- | Complex, structured | Highly complex, unstructured | +| | | specific | | | ++---------------------+--------------------------+--------------------------+--------------------------+-------------------------------+ + +: Evolution of AI - Key Positive Aspects {#tbl-ai-evolution-strengths .hover .striped} + +The table serves as a bridge between the early approaches we've discussed and the more recent developments in shallow and deep learning that we'll explore next. It sets the stage for understanding why certain approaches gained prominence in different eras and how each new paradigm built upon and addressed the limitations of its predecessors. Moreover, it illustrates how the strengths of earlier approaches continue to influence and enhance modern AI techniques, particularly in the era of foundation models. + +### Shallow Learning (2000s) + +The 2000s marked a fascinating period in machine learning history that we now call the "shallow learning" era. To understand why it's "shallow," imagine building a house: deep learning (which came later) is like having multiple construction crews working at different levels simultaneously, each crew learning from the work of crews below them. In contrast, shallow learning typically had just one or two levels of processing - like having just a foundation crew and a framing crew. + +During this time, several powerful algorithms dominated the machine learning landscape. Each brought unique strengths to different problems: Decision trees provided interpretable results by making choices much like a flowchart. K-nearest neighbors made predictions by finding similar examples in past data, like asking your most experienced neighbors for advice. Linear and logistic regression offered straightforward, interpretable models that worked well for many real-world problems. Support Vector Machines (SVMs) excelled at finding complex boundaries between categories using the "kernel trick" - imagine being able to untangle a bowl of spaghetti into straight lines by lifting it into a higher dimension. +These algorithms formed the foundation of practical machine learning because: +Consider a typical computer vision solution from 2005: + +::: {.callout-note} +### Example: Traditional Computer Vision Pipeline +``` +1. Manual Feature Extraction + - SIFT (Scale-Invariant Feature Transform) + - HOG (Histogram of Oriented Gradients) + - Gabor filters +2. Feature Selection/Engineering +3. "Shallow" Learning Model (e.g., SVM) +4. Post-processing +``` +::: + +What made this era distinct was its hybrid approach: human-engineered features combined with statistical learning. They had strong mathematical foundations (researchers could prove why they worked). They performed well even with limited data. They were computationally efficient. They produced reliable, reproducible results. + +Take the example of face detection, where the Viola-Jones algorithm (2001) achieved real-time performance using simple rectangular features and a cascade of classifiers. This algorithm powered digital camera face detection for nearly a decade. + +### Deep Learning (2012-Present) + +While Support Vector Machines excelled at finding complex boundaries between categories using mathematical transformations, deep learning took a radically different approach inspired by the human brain's architecture. Deep learning is built from layers of artificial neurons, where each layer learns to transform its input data into increasingly abstract representations. Imagine processing an image of a cat: the first layer might learn to detect simple edges and contrasts, the next layer combines these into basic shapes and textures, another layer might recognize whiskers and pointy ears, and the final layers assemble these features into the concept of "cat." Unlike shallow learning methods that required humans to carefully engineer features, deep learning networks can automatically discover useful features directly from raw data. This ability to learn hierarchical representations—from simple to complex, concrete to abstract—is what makes deep learning "deep," and it turned out to be a remarkably powerful approach for handling complex, real-world data like images, speech, and text. + +In 2012, a deep neural network called AlexNet, shown in @fig-alexnet, achieved a breakthrough in the ImageNet competition that would transform the field of machine learning. The challenge was formidable: correctly classify 1.2 million high-resolution images into 1,000 different categories. While previous approaches struggled with error rates above 25%, AlexNet achieved a 15.3% error rate, dramatically outperforming all existing methods. + +![Deep neural network architecture for Alexnet.](./images/png/alexnet_arch.png){#fig-alexnet} + +The success of AlexNet wasn't just a technical achievement---it was a watershed moment that demonstrated the practical viability of deep learning. It showed that with sufficient data, computational power, and architectural innovations, neural networks could outperform hand-engineered features and shallow learning methods that had dominated the field for decades. This single result triggered an explosion of research and applications in deep learning that continues to this day. + +From this foundation, deep learning entered an era of unprecedented scale. By the late 2010s, companies like Google, Facebook, and OpenAI were training neural networks thousands of times larger than **AlexNet**[^defn-alexnet]. These massive models, often called "foundation models," took deep learning to new heights. GPT-3, released in 2020, contained 175 billion **parameters**[^defn-parameters]---imagine a student that could read through all of Wikipedia multiple times and learn patterns from every article. These models showed remarkable abilities: writing human-like text, engaging in conversation, generating images from descriptions, and even writing computer code. The key insight was simple but powerful: as we made neural networks bigger and fed them more data, they became capable of solving increasingly complex tasks. However, this scale brought unprecedented systems challenges: how do you efficiently train models that require thousands of GPUs working in parallel? How do you store and serve models that are hundreds of gigabytes in size? How do you handle the massive datasets needed for training? + +[^defn-alexnet]: A breakthrough deep neural network from 2012 that won the [ImageNet competition](https://www.image-net.org/challenges/LSVRC/) by a large margin and helped spark the deep learning revolution. + +[^defn-parameters]: Similar to how the brain's neural connections grow stronger as you learn a new skill, having more parameters generally means that the model can learn more complex patterns. + +The deep learning revolution of 2012 didn't emerge from nowhere---it was built on neural network research dating back to the 1950s. The story begins with Frank Rosenblatt's Perceptron in 1957, which captured the imagination of researchers by showing how a simple artificial neuron could learn to classify patterns. While it could only handle linearly separable problems—a limitation dramatically highlighted by Minsky and Papert's 1969 book "Perceptrons"—it introduced the fundamental concept of trainable neural networks. The 1980s brought more important breakthroughs: Rumelhart, Hinton, and Williams introduced backpropagation in 1986, providing a systematic way to train multi-layer networks, while Yann LeCun demonstrated its practical application in recognizing handwritten digits using **convolutional neural networks (CNNs)**[^defn-cnn]. + +[^defn-cnn]: A type of neural network specially designed for processing images, inspired by how the human visual system works. The "convolutional" part refers to how it scans images in small chunks, similar to how our eyes focus on different parts of a scene. + +:::{#vid-tl .callout-important} + +# Convolutional Network Demo from 1989 + +{{< video https://www.youtube.com/watch?v=FwFduRA_L6Q&ab_channel=YannLeCun >}} + +::: + +Yet these networks largely languished through the 1990s and 2000s, not because the ideas were wrong, but because they were ahead of their time---the field lacked three important ingredients: sufficient data to train complex networks, enough computational power to process this data, and the technical innovations needed to train very deep networks effectively. + +The field had to wait for the convergence of big data, better computing hardware, and algorithmic breakthroughs before deep learning's potential could be unlocked. This long gestation period helps explain why the 2012 ImageNet moment was less a sudden revolution and more the culmination of decades of accumulated research finally finding its moment. As we'll explore in the following sections, this evolution has led to two significant developments in the field. First, it has given rise to define the field of machine learning systems engineering, a discipline that teaches how to bridge the gap between theoretical advancements and practical implementation. Second, it has necessitated a more comprehensive definition of machine learning systems, one that encompasses not just algorithms, but also data and computing infrastructure. Today's challenges of scale echo many of the same fundamental questions about computation, data, and learning methods that researchers have grappled with since the field's inception, but now within a more complex and interconnected framework. + +## The Rise of ML Systems Engineering + +The story we've traced--from the early days of the Perceptron through the deep learning revolution---has largely been one of algorithmic breakthroughs. Each era brought new mathematical insights and modeling approaches that pushed the boundaries of what AI could achieve. But something important changed over the past decade: the success of AI systems became increasingly dependent not just on algorithmic innovations, but on sophisticated engineering. + +This shift mirrors the evolution of computer science and engineering in the late 1960s and early 1970s. During that period, as computing systems grew more complex, a new discipline emerged: Computer Engineering. This field bridged the gap between Electrical Engineering's hardware expertise and Computer Science's focus on algorithms and software. Computer Engineering arose because the challenges of designing and building complex computing systems required an integrated approach that neither discipline could fully address on its own. + +Today, we're witnessing a similar transition in the field of AI. While Computer Science continues to push the boundaries of ML algorithms and Electrical Engineering advances specialized AI hardware, neither discipline fully addresses the engineering principles needed to deploy, optimize, and sustain ML systems at scale. This gap highlights the need for a new discipline: Machine Learning Systems Engineering. While there is no explicit definition of what this field is as such today, it can be broadly defined as such: + +> Machine Learning Systems Engineering is the discipline that focuses on the design, development, deployment, and maintenance of large-scale machine learning systems. It encompasses the entire lifecycle of ML applications, from data collection and preprocessing to model training, deployment, monitoring, and continuous improvement. MLSE integrates principles from software engineering, distributed systems, data engineering, and machine learning to create robust, scalable, and efficient AI systems that can operate reliably in real-world environments. + +Let's consider space exploration. While astronauts venture into new frontiers and explore the vast unknowns of the universe, their discoveries are only possible because of the complex engineering systems supporting them---the rockets that lift them into space, the life support systems that keep them alive, and the communication networks that keep them connected to Earth. Similarly, while AI researchers push the boundaries of what's possible with learning algorithms, their breakthroughs only become practical reality through careful systems engineering. Modern AI systems need robust infrastructure to collect and manage data, powerful computing systems to train models, and reliable deployment platforms to serve millions of users. + +This emergence of machine learning systems engineering as a important discipline reflects a broader reality: turning AI algorithms into real-world systems requires bridging the gap between theoretical possibilities and practical implementation. It's not enough to have a brilliant algorithm if you can't efficiently collect and process the data it needs, distribute its computation across hundreds of machines, serve it reliably to millions of users, or monitor its performance in production. + +Understanding this interplay between algorithms and engineering has become fundamental for modern AI practitioners. While researchers continue to push the boundaries of what's algorithmically possible, engineers are tackling the complex challenge of making these algorithms work reliably and efficiently in the real world. This brings us to a fundamental question: what exactly is a machine learning system, and what makes it different from traditional software systems? + +## Definition of a ML System + +There's no universally accepted, clear-cut textbook definition of a machine learning system. This ambiguity stems from the fact that different practitioners, researchers, and industries often refer to machine learning systems in varying contexts and with different scopes. Some might focus solely on the algorithmic aspects, while others might include the entire pipeline from data collection to model deployment. This loose usage of the term reflects the rapidly evolving and multidisciplinary nature of the field. + +Given this diversity of perspectives, it is important to establish a clear and comprehensive definition that encompasses all these aspects. In this textbook, we take a holistic approach to machine learning systems, considering not just the algorithms but also the entire ecosystem in which they operate. Therefore, we define a machine learning system as follows: + +> A machine learning system is an integrated computing system that consists of three essential elements: data, algorithms, and computing infrastructure, where data represents the input that controls the behavior of the algorithms that learn from that data, which in turn rely on underlying hardware and software infrastructure to execute the learning process (training) and/or the application of learned knowledge (inference or serving), and all together, these components enable the system to make predictions, generate content, or take actions based on learned patterns. + +The core of any machine learning system consists of three interrelated components, as illustrated in @fig-ai-triangle: Models/Algorithms, Data, and Computing Infrastructure. These components form a triangular dependency where each element fundamentally shapes the possibilities of the others. The model architecture dictates both the computational demands for training and inference, as well as the volume and structure of data required for effective learning. The data's scale and complexity influence what infrastructure is needed for storage and processing, while simultaneously determining which model architectures are feasible. The infrastructure capabilities establish practical limits on both model scale and data processing capacity, creating a framework within which the other components must operate. + +![Machine learning systems involve algorithms, data, and computation, all intertwined together.](images/png/triangle.png){#fig-ai-triangle} + +Each of these components serves a distinct but interconnected purpose: + +- **Algorithms:** Mathematical models and methods that learn patterns from data to make predictions or decisions + +- **Data:** Processes and infrastructure for collecting, storing, processing, managing, and serving data for both training and inference. + +- **Computing:** Hardware and software infrastructure that enables efficient training, serving, and operation of models at scale. + +The interdependency of these components means no single element can function in isolation. The most sophisticated algorithm cannot learn without data or computing resources to run on. The largest datasets are useless without algorithms to extract patterns or infrastructure to process them. And the most powerful computing infrastructure serves no purpose without algorithms to execute or data to process. + +To illustrate these relationships, we can draw an analogy to space exploration. Algorithm developers are like astronauts---exploring new frontiers and making discoveries. Data science teams function like mission control specialists—ensuring the constant flow of critical information and resources needed to keep the mission running. Computing infrastructure engineers are like rocket engineers—designing and building the systems that make the mission possible. Just as a space mission requires the seamless integration of astronauts, mission control, and rocket systems, a machine learning system demands the careful orchestration of algorithms, data, and computing infrastructure. + +## The ML Systems Lifecycle + +Traditional software systems follow a predictable lifecycle where developers write explicit instructions for computers to execute. These systems are built on decades of established software engineering practices. Version control systems maintain precise histories of code changes. Continuous integration and deployment pipelines automate testing and release processes. Static analysis tools measure code quality and identify potential issues. This infrastructure enables reliable development, testing, and deployment of software systems, following well-defined principles of software engineering. + +Machine learning systems represent a fundamental departure from this traditional paradigm. While traditional systems execute explicit programming logic, machine learning systems derive their behavior from patterns in data. This shift from code to data as the primary driver of system behavior introduces new complexities. + +As illustrated in @fig-ml_lifecycle_overview, the ML lifecycle consists of interconnected stages from data collection through model monitoring, with feedback loops for continuous improvement when performance degrades or models need enhancement. + +![The typical lifecycle of a machine learning system.](./images/png/ml_lifecycle_overview.png){#fig-ml_lifecycle_overview} + +Unlike source code, which changes only when developers modify it, data reflects the dynamic nature of the real world. Changes in data distributions can silently alter system behavior. Traditional software engineering tools, designed for deterministic code-based systems, prove insufficient for managing these data-dependent systems. For example, version control systems that excel at tracking discrete code changes struggle to manage large, evolving datasets. Testing frameworks designed for deterministic outputs must be adapted for probabilistic predictions. This data-dependent nature creates a more dynamic lifecycle, requiring continuous monitoring and adaptation to maintain system relevance as real-world data patterns evolve. + +Understanding the machine learning system lifecycle requires examining its distinct stages. Each stage presents unique requirements from both learning and infrastructure perspectives. This dual consideration---of learning needs and systems support---is wildly important for building effective machine learning systems. + +However, the various stages of the ML lifecycle in production are not isolated; they are, in fact, deeply interconnected. This interconnectedness can create either virtuous or vicious cycles. In a virtuous cycle, high-quality data enables effective learning, robust infrastructure supports efficient processing, and well-engineered systems facilitate the collection of even better data. However, in a vicious cycle, poor data quality undermines learning, inadequate infrastructure hampers processing, and system limitations prevent the improvement of data collection—each problem compounds the others. + +## The Spectrum of ML Systems + +The complexity of managing machine learning systems becomes even more apparent when we consider the broad spectrum across which ML is deployed today. ML systems exist at vastly different scales and in diverse environments, each presenting unique challenges and constraints. + +At one end of the spectrum, we have cloud-based ML systems running in massive data centers. These systems, like large language models or recommendation engines, process petabytes of data and serve millions of users simultaneously. They can leverage virtually unlimited computing resources but must manage enormous operational complexity and costs. + +At the other end, we find TinyML systems running on microcontrollers and embedded devices. These systems must perform ML tasks with severe constraints on memory, computing power, and energy consumption. Imagine a smart home device, such as Alexa or Google Assistant, that must recognize voice commands using less power than a LED bulb, or a sensor that must detect anomalies while running on a battery for months or even years. + +Between these extremes, we find a rich variety of ML systems adapted for different contexts. Edge ML systems bring computation closer to data sources, reducing latency and bandwidth requirements while managing local computing resources. Mobile ML systems must balance sophisticated capabilities with battery life and processor limitations on smartphones and tablets. Enterprise ML systems often operate within specific business constraints, focusing on particular tasks while integrating with existing infrastructure. Some organizations employ hybrid approaches, distributing ML capabilities across multiple tiers to balance various requirements. + +## ML System Implications on the ML Lifecycle + +The diversity of ML systems across the spectrum represents a complex interplay of requirements, constraints, and trade-offs. These decisions fundamentally impact every stage of the ML lifecycle we discussed earlier, from data collection to continuous operation. + +Performance requirements often drive initial architectural decisions. Latency-sensitive applications, like autonomous vehicles or real-time fraud detection, might require edge or embedded architectures despite their resource constraints. Conversely, applications requiring massive computational power for training, such as large language models, naturally gravitate toward centralized cloud architectures. However, raw performance is just one consideration in a complex decision space. + +Resource management varies dramatically across architectures. Cloud systems must optimize for cost efficiency at scale—balancing expensive GPU clusters, storage systems, and network bandwidth. Edge systems face fixed resource limits and must carefully manage local compute and storage. Mobile and embedded systems operate under the strictest constraints, where every byte of memory and milliwatt of power matters. These resource considerations directly influence both model design and system architecture. + +Operational complexity increases with system distribution. While centralized cloud architectures benefit from mature deployment tools and managed services, edge and hybrid systems must handle the complexity of distributed system management. This complexity manifests throughout the ML lifecycle—from data collection and version control to model deployment and monitoring. As we discussed in our examination of technical debt, this operational complexity can compound over time if not carefully managed. + +Data considerations often introduce competing pressures. Privacy requirements or data sovereignty regulations might push toward edge or embedded architectures, while the need for large-scale training data might favor cloud approaches. The velocity and volume of data also influence architectural choices—real-time sensor data might require edge processing to manage bandwidth, while batch analytics might be better suited to cloud processing. + +Evolution and maintenance requirements must be considered from the start. Cloud architectures offer flexibility for system evolution but can incur significant ongoing costs. Edge and embedded systems might be harder to update but could offer lower operational overhead. The continuous cycle of ML systems we discussed earlier becomes particularly challenging in distributed architectures, where updating models and maintaining system health requires careful orchestration across multiple tiers. + +These trade-offs are rarely simple binary choices. Modern ML systems often adopt hybrid approaches, carefully balancing these considerations based on specific use cases and constraints. The key is understanding how these decisions will impact the system throughout its lifecycle, from initial development through continuous operation and evolution. + +### Emerging Trends + +We are just at the beginning. As machine learning systems continue to evolve, several key trends are reshaping the landscape of ML system design and deployment. + +The rise of agentic systems marks a profound evolution in ML systems. Traditional ML systems were primarily reactive—they made predictions or classifications based on input data. In contrast, agentic systems can take actions, learn from their outcomes, and adapt their behavior accordingly. These systems, exemplified by autonomous agents that can plan, reason, and execute complex tasks, introduce new architectural challenges. They require sophisticated frameworks for decision-making, safety constraints, and real-time interaction with their environment. + +Architectural evolution is being driven by new hardware and deployment patterns. Specialized AI accelerators are emerging across the spectrum—from powerful data center chips to efficient edge processors to tiny neural processing units in mobile devices. This heterogeneous computing landscape is enabling new architectural possibilities, such as dynamic model distribution across tiers based on computing capabilities and current conditions. The traditional boundaries between cloud, edge, and embedded systems are becoming increasingly fluid. + +Resource efficiency is gaining prominence as the environmental and economic costs of large-scale ML become more apparent. This has sparked innovation in model compression, efficient training techniques, and energy-aware computing. Future systems will likely need to balance the drive for more powerful models against growing sustainability concerns. This emphasis on efficiency is particularly relevant given our earlier discussion of technical debt and operational costs. + +System intelligence is moving toward more autonomous operation. Future ML systems will likely incorporate more sophisticated self-monitoring, automated resource management, and adaptive deployment strategies. This evolution builds upon the continuous cycle we discussed earlier, but with increased automation in handling data distribution shifts, model updates, and system optimization. + +Integration challenges are becoming more complex as ML systems interact with broader technology ecosystems. The need to integrate with existing software systems, handle diverse data sources, and operate across organizational boundaries is driving new approaches to system design. This integration complexity adds new dimensions to the technical debt considerations we explored earlier. + +These trends suggest that future ML systems will need to be increasingly adaptable and efficient while managing growing complexity. Understanding these directions is important for building systems that can evolve with the field while avoiding the accumulation of technical debt we discussed earlier. + +## Real-world Applications and Impact + +The ability to build and operationalize ML systems across various scales and environments has led to transformative changes across numerous sectors. This section showcases a few examples where theoretical concepts and practical considerations we have discussed manifest in tangible, impactful applications and real-world impact. + +### Case Study: FarmBeats: Edge and Embedded ML for Agriculture + +FarmBeats, a project developed by Microsoft Research, shown in @fig-farmbeats-overview is a significant advancement in the application of machine learning to agriculture. This system aims to increase farm productivity and reduce costs by leveraging AI and IoT technologies. FarmBeats exemplifies how edge and embedded ML systems can be deployed in challenging, real-world environments to solve practical problems. By bringing ML capabilities directly to the farm, FarmBeats demonstrates the potential of distributed AI systems in transforming traditional industries. + +![Microsoft Farmbeats: AI, Edge & IoT for Agriculture.](./images/png/farmbeats.png){#fig-farmbeats-overview} + +**Data Aspects** + +The data ecosystem in FarmBeats is diverse and distributed. Sensors deployed across fields collect real-time data on soil moisture, temperature, and nutrient levels. Drones equipped with multispectral cameras capture high-resolution imagery of crops, providing insights into plant health and growth patterns. Weather stations contribute local climate data, while historical farming records offer context for long-term trends. The challenge lies not just in collecting this heterogeneous data, but in managing its flow from dispersed, often remote locations with limited connectivity. FarmBeats employs innovative data transmission techniques, such as using TV white spaces (unused broadcasting frequencies) to extend internet connectivity to far-flung sensors. This approach to data collection and transmission embodies the principles of edge computing we discussed earlier, where data processing begins at the source to reduce bandwidth requirements and enable real-time decision making. + +**Algorithm/Model Aspects** + +FarmBeats uses a variety of ML algorithms tailored to agricultural applications. For soil moisture prediction, it uses temporal neural networks that can capture the complex dynamics of water movement in soil. Computer vision algorithms process drone imagery to detect crop stress, pest infestations, and yield estimates. These models must be robust to noisy data and capable of operating with limited computational resources. Machine learning methods such as "transfer learning" allow models to learn on data-rich farms to be adapted for use in areas with limited historical data. The system also incorporates a mixture of methods that combine outputs from multiple algorithms to improve prediction accuracy and reliability. A key challenge FarmBeats addresses is model personalization---adapting general models to the specific conditions of individual farms, which may have unique soil compositions, microclimates, and farming practices. + +**Computing Infrastructure Aspects** + +FarmBeats exemplifies the edge computing paradigm we explored in our discussion of the ML system spectrum. At the lowest level, embedded ML models run directly on IoT devices and sensors, performing basic data filtering and anomaly detection. Edge devices, such as ruggedized field gateways, aggregate data from multiple sensors and run more complex models for local decision-making. These edge devices operate in challenging conditions, requiring robust hardware designs and efficient power management to function reliably in remote agricultural settings. The system employs a hierarchical architecture, with more computationally intensive tasks offloaded to on-premises servers or the cloud. This tiered approach allows FarmBeats to balance the need for real-time processing with the benefits of centralized data analysis and model training. The infrastructure also includes mechanisms for over-the-air model updates, ensuring that edge devices can receive improved models as more data becomes available and algorithms are refined. + +**Impact and Future Implications** + +FarmBeats shows how ML systems can be deployed in resource-constrained, real-world environments to drive significant improvements in traditional industries. By providing farmers with AI-driven insights, the system has shown potential to increase crop yields, reduce water usage, and optimize resource allocation. Looking forward, the FarmBeats approach could be extended to address global challenges in food security and sustainable agriculture. The success of this system also highlights the growing importance of edge and embedded ML in IoT applications, where bringing intelligence closer to the data source can lead to more responsive, efficient, and scalable solutions. As edge computing capabilities continue to advance, we can expect to see similar distributed ML architectures applied to other domains, from smart cities to environmental monitoring. + +### Case Study: AlphaFold: Large-Scale Scientific ML + +AlphaFold, developed by DeepMind, is a landmark achievement in the application of machine learning to complex scientific problems. This AI system is designed to predict the three-dimensional structure of proteins, as shown in @fig-alphafold-overview, from their amino acid sequences, a challenge known as the "protein folding problem" that has puzzled scientists for decades. AlphaFold's success demonstrates how large-scale ML systems can accelerate scientific discovery and potentially revolutionize fields like structural biology and drug design. This case study exemplifies the use of advanced ML techniques and massive computational resources to tackle problems at the frontiers of science. + +::: {.content-visible when-format="html"} +![Examples of protein targets within the free modeling category. Source: Google DeepMind](images/png/alphafold.gif){#fig-alphafold-overview} +::: + +::: {.content-visible when-format="pdf"} +![Examples of protein targets within the free modeling category. Source: Google DeepMind](images/png/alphafold.png){#fig-alphafold-overview} +::: + +**Data Aspects** + +The data underpinning AlphaFold's success is vast and multifaceted. The primary dataset is the Protein Data Bank (PDB), which contains the experimentally determined structures of over 180,000 proteins. This is complemented by databases of protein sequences, which number in the hundreds of millions. AlphaFold also utilizes evolutionary data in the form of multiple sequence alignments (MSAs), which provide insights into the conservation patterns of amino acids across related proteins. The challenge lies not just in the volume of data, but in its quality and representation. Experimental protein structures can contain errors or be incomplete, requiring sophisticated data cleaning and validation processes. Moreover, the representation of protein structures and sequences in a form amenable to machine learning is a significant challenge in itself. AlphaFold's data pipeline involves complex preprocessing steps to convert raw sequence and structural data into meaningful features that capture the physical and chemical properties relevant to protein folding. + +**Algorithm/Model Aspects** + +AlphaFold's algorithmic approach represents a tour de force in the application of deep learning to scientific problems. At its core, AlphaFold uses a novel neural network architecture that combines with techniques from computational biology. The model learns to predict inter-residue distances and torsion angles, which are then used to construct a full 3D protein structure. A key innovation is the use of "equivariant attention" layers that respect the symmetries inherent in protein structures. The learning process involves multiple stages, including initial "pretraining" on a large corpus of protein sequences, followed by fine-tuning on known structures. AlphaFold also incorporates domain knowledge in the form of physics-based constraints and scoring functions, creating a hybrid system that leverages both data-driven learning and scientific prior knowledge. The model's ability to generate accurate confidence estimates for its predictions is crucial, allowing researchers to assess the reliability of the predicted structures. + +**Computing Infrastructure Aspects** + +The computational demands of AlphaFold epitomize the challenges of large-scale scientific ML systems. Training the model requires massive parallel computing resources, leveraging clusters of GPUs or TPUs (Tensor Processing Units) in a distributed computing environment. DeepMind utilized Google's cloud infrastructure, with the final version of AlphaFold trained on 128 TPUv3 cores for several weeks. The inference process, while less computationally intensive than training, still requires significant resources, especially when predicting structures for large proteins or processing many proteins in parallel. To make AlphaFold more accessible to the scientific community, DeepMind has collaborated with the European Bioinformatics Institute to create a [public database](https://alphafold.ebi.ac.uk/) of predicted protein structures, which itself represents a substantial computing and data management challenge. This infrastructure allows researchers worldwide to access AlphaFold's predictions without needing to run the model themselves, demonstrating how centralized, high-performance computing resources can be leveraged to democratize access to advanced ML capabilities. + +**Impact and Future Implications** + +AlphaFold's impact on structural biology has been profound, with the potential to accelerate research in areas ranging from fundamental biology to drug discovery. By providing accurate structural predictions for proteins that have resisted experimental methods, AlphaFold opens new avenues for understanding disease mechanisms and designing targeted therapies. The success of AlphaFold also serves as a powerful demonstration of how ML can be applied to other complex scientific problems, potentially leading to breakthroughs in fields like materials science or climate modeling. However, it also raises important questions about the role of AI in scientific discovery and the changing nature of scientific inquiry in the age of large-scale ML systems. As we look to the future, the AlphaFold approach suggests a new paradigm for scientific ML, where massive computational resources are combined with domain-specific knowledge to push the boundaries of human understanding. + +### Case Study: Autonomous Vehicles: Spanning the ML Spectrum + +Waymo, a subsidiary of Alphabet Inc., stands at the forefront of autonomous vehicle technology, representing one of the most ambitious applications of machine learning systems to date. Evolving from the Google Self-Driving Car Project initiated in 2009, Waymo's approach to autonomous driving exemplifies how ML systems can span the entire spectrum from embedded systems to cloud infrastructure. This case study demonstrates the practical implementation of complex ML systems in a safety-critical, real-world environment, integrating real-time decision-making with long-term learning and adaptation. + +{{< video https://youtu.be/hA_-MkU0Nfw?si=6DIH7qwMbeMicnJ5 >}} + +**Data Aspects** + +The data ecosystem underpinning Waymo's technology is vast and dynamic. Each vehicle serves as a roving data center, its sensor suite—comprising LiDAR, radar, and high-resolution cameras—generating approximately one terabyte of data per hour of driving. This real-world data is complemented by an even more extensive simulated dataset, with Waymo's vehicles having traversed over 20 billion miles in simulation and more than 20 million miles on public roads. The challenge lies not just in the volume of data, but in its heterogeneity and the need for real-time processing. Waymo must handle both structured (e.g., GPS coordinates) and unstructured data (e.g., camera images) simultaneously. The data pipeline spans from edge processing on the vehicle itself to massive cloud-based storage and processing systems. Sophisticated data cleaning and validation processes are necessary, given the safety-critical nature of the application. Moreover, the representation of the vehicle's environment in a form amenable to machine learning presents significant challenges, requiring complex preprocessing to convert raw sensor data into meaningful features that capture the dynamics of traffic scenarios. + +**Algorithm/Model Aspects** + +Waymo's ML stack represents a sophisticated ensemble of algorithms tailored to the multifaceted challenge of autonomous driving. The perception system employs deep learning techniques, including convolutional neural networks, to process visual data for object detection and tracking. Prediction models, needed for anticipating the behavior of other road users, leverage recurrent neural networks to understand temporal sequences. Waymo has developed custom ML models like VectorNet for predicting vehicle trajectories. The planning and decision-making systems may incorporate reinforcement learning or imitation learning techniques to navigate complex traffic scenarios. A key innovation in Waymo's approach is the integration of these diverse models into a coherent system capable of real-time operation. The ML models must also be interpretable to some degree, as understanding the reasoning behind a vehicle's decisions is vital for safety and regulatory compliance. Waymo's learning process involves continuous refinement based on real-world driving experiences and extensive simulation, creating a feedback loop that constantly improves the system's performance. + +**Computing Infrastructure Aspects** + +The computing infrastructure supporting Waymo's autonomous vehicles epitomizes the challenges of deploying ML systems across the full spectrum from edge to cloud. Each vehicle is equipped with a custom-designed compute platform capable of processing sensor data and making decisions in real-time, often leveraging specialized hardware like GPUs or custom AI accelerators. This edge computing is complemented by extensive use of cloud infrastructure, leveraging the power of Google's data centers for training models, running large-scale simulations, and performing fleet-wide learning. The connectivity between these tiers is critical, with vehicles requiring reliable, high-bandwidth communication for real-time updates and data uploading. Waymo's infrastructure must be designed for robustness and fault tolerance, ensuring safe operation even in the face of hardware failures or network disruptions. The scale of Waymo's operation presents significant challenges in data management, model deployment, and system monitoring across a geographically distributed fleet of vehicles. + +**Impact and Future Implications** + +Waymo's impact extends beyond technological advancement, potentially revolutionizing transportation, urban planning, and numerous aspects of daily life. The launch of Waymo One, a commercial ride-hailing service using autonomous vehicles in Phoenix, Arizona, represents a significant milestone in the practical deployment of AI systems in safety-critical applications. Waymo's progress has broader implications for the development of robust, real-world AI systems, driving innovations in sensor technology, edge computing, and AI safety that have applications far beyond the automotive industry. However, it also raises important questions about liability, ethics, and the interaction between AI systems and human society. As Waymo continues to expand its operations and explore applications in trucking and last-mile delivery, it serves as an important test bed for advanced ML systems, driving progress in areas such as continual learning, robust perception, and human-AI interaction. The Waymo case study underscores both the tremendous potential of ML systems to transform industries and the complex challenges involved in deploying AI in the real world. + +## Challenges and Considerations + +Building and deploying machine learning systems presents unique challenges that go beyond traditional software development. These challenges help explain why creating effective ML systems is about more than just choosing the right algorithm or collecting enough data. Let's explore the key areas where ML practitioners face significant hurdles. + +### Data Challenges + +The foundation of any ML system is its data, and managing this data introduces several fundamental challenges. First, there's the basic question of data quality - real-world data is often messy and inconsistent. Imagine a healthcare application that needs to process patient records from different hospitals. Each hospital might record information differently, use different units of measurement, or have different standards for what data to collect. Some records might have missing information, while others might contain errors or inconsistencies that need to be cleaned up before the data can be useful. + +As ML systems grow, they often need to handle increasingly large amounts of data. A video streaming service like Netflix, for example, needs to process billions of viewer interactions to power its recommendation system. This scale introduces new challenges in how to store, process, and manage such large datasets efficiently. + +Another critical challenge is how data changes over time. This phenomenon, known as "data drift," occurs when the patterns in new data begin to differ from the patterns the system originally learned from. For example, many predictive models struggled during the COVID-19 pandemic because consumer behavior changed so dramatically that historical patterns became less relevant. ML systems need ways to detect when this happens and adapt accordingly. + +### Model Challenges + +Creating and maintaining the ML models themselves presents another set of challenges. Modern ML models, particularly in deep learning, can be extremely complex. Consider a language model like GPT-3, which has hundreds of billions of parameters (the individual settings the model learns during training). This complexity creates practical challenges: these models require enormous computing power to train and run, making it difficult to deploy them in situations with limited resources, like on mobile phones or IoT devices. + +Training these models effectively is itself a significant challenge. Unlike traditional programming where we write explicit instructions, ML models learn from examples. This learning process involves many choices: How should we structure the model? How long should we train it? How can we tell if it's learning the right things? Making these decisions often requires both technical expertise and considerable trial and error. + +A particularly important challenge is ensuring that models work well in real-world conditions. A model might perform excellently on its training data but fail when faced with slightly different situations in the real world. This gap between training performance and real-world performance is a central challenge in machine learning, especially for critical applications like autonomous vehicles or medical diagnosis systems. + +### System Challenges + +Getting ML systems to work reliably in the real world introduces its own set of challenges. Unlike traditional software that follows fixed rules, ML systems need to handle uncertainty and variability in their inputs and outputs. They also typically need both training systems (for learning from data) and serving systems (for making predictions), each with different requirements and constraints. + +Consider a company building a speech recognition system. They need infrastructure to collect and store audio data, systems to train models on this data, and then separate systems to actually process users' speech in real-time. Each part of this pipeline needs to work reliably and efficiently, and all the parts need to work together seamlessly. + +These systems also need constant monitoring and updating. How do we know if the system is working correctly? How do we update models without interrupting service? How do we handle errors or unexpected inputs? These operational challenges become particularly complex when ML systems are serving millions of users. + +### Ethical and Social Considerations + +As ML systems become more prevalent in our daily lives, their broader impacts on society become increasingly important to consider. One major concern is fairness - ML systems can sometimes learn to make decisions that discriminate against certain groups of people. This often happens unintentionally, as the systems pick up biases present in their training data. For example, a job application screening system might inadvertently learn to favor certain demographics if those groups were historically more likely to be hired. + +Another important consideration is transparency. Many modern ML models, particularly deep learning models, work as "black boxes" - while they can make predictions, it's often difficult to understand how they arrived at their decisions. This becomes particularly problematic when ML systems are making important decisions about people's lives, such as in healthcare or financial services. + +Privacy is also a major concern. ML systems often need large amounts of data to work effectively, but this data might contain sensitive personal information. How do we balance the need for data with the need to protect individual privacy? How do we ensure that models don't inadvertently memorize and reveal private information? + +These challenges aren't merely technical problems to be solved, but ongoing considerations that shape how we approach ML system design and deployment. Throughout this book, we'll explore these challenges in detail and examine strategies for addressing them effectively. + +## Future Directions + +As we look to the future of machine learning systems, several exciting trends are shaping the field. These developments promise to both solve existing challenges and open new possibilities for what ML systems can achieve. + +One of the most significant trends is the democratization of AI technology. Just as personal computers transformed computing from specialized mainframes to everyday tools, ML systems are becoming more accessible to developers and organizations of all sizes. Cloud providers now offer pre-trained models and automated ML platforms that reduce the expertise needed to deploy AI solutions. This democratization is enabling new applications across industries, from small businesses using AI for customer service to researchers applying ML to previously intractable problems. + +As concerns about computational costs and environmental impact grow, there's an increasing focus on making ML systems more efficient. Researchers are developing new techniques for training models with less data and computing power. Innovation in specialized hardware, from improved GPUs to custom AI chips, is making ML systems faster and more energy-efficient. These advances could make sophisticated AI capabilities available on more devices, from smartphones to IoT sensors. + +Perhaps the most transformative trend is the development of more autonomous ML systems that can adapt and improve themselves. These systems are beginning to handle their own maintenance tasks - detecting when they need retraining, automatically finding and correcting errors, and optimizing their own performance. This automation could dramatically reduce the operational overhead of running ML systems while improving their reliability. + +While these trends are promising, it's important to recognize the field's limitations. Creating truly artificial general intelligence remains a distant goal. Current ML systems excel at specific tasks but lack the flexibility and understanding that humans take for granted. Challenges around bias, transparency, and privacy continue to require careful consideration. As ML systems become more prevalent, addressing these limitations while leveraging new capabilities will be crucial. + +## Learning Path and Book Structure + +This book is designed to guide you from understanding the fundamentals of ML systems to effectively designing and implementing them. To address the complexities and challenges of Machine Learning Systems engineering, we've organized the content around five fundamental pillars that encompass the lifecycle of ML systems. These pillars provide a framework for understanding, developing, and maintaining robust ML systems. + +![Overview of the five fundamental system pillars of Machine Learning Systems engineering.](images/png/book_pillars.png){#fig-pillars} + +As illustrated in Figure @fig-pillars, the five pillars central to the framework are: + +- **Data**: Emphasizing data engineering and foundational principles critical to how AI operates in relation to data. +- **Training**: Exploring the methodologies for AI training, focusing on efficiency, optimization, and acceleration techniques to enhance model performance. +- **Deployment**: Encompassing benchmarks, on-device learning strategies, and machine learning operations to ensure effective model application. +- **Operations**: Highlighting the maintenance challenges unique to machine learning systems, which require specialized approaches distinct from traditional engineering systems. +- **Ethics & Governance**: Addressing concerns such as security, privacy, responsible AI practices, and the broader societal implications of AI technologies. + +Each pillar represents a critical phase in the lifecycle of ML systems and is composed of foundational elements that build upon each other. This structure ensures a comprehensive understanding of MLSE, from basic principles to advanced applications and ethical considerations. + +For more detailed information about the book's overview, contents, learning outcomes, target audience, prerequisites, and navigation guide, please refer to the [About the Book](../../about.qmd) section. There, you'll also find valuable details about our learning community and how to maximize your experience with this resource. \ No newline at end of file diff --git a/contents/ml_systems/images/jpg/cloud_ml_tpu.jpg b/contents/core/ml_systems/images/jpg/cloud_ml_tpu.jpg similarity index 100% rename from contents/ml_systems/images/jpg/cloud_ml_tpu.jpg rename to contents/core/ml_systems/images/jpg/cloud_ml_tpu.jpg diff --git a/contents/ml_systems/images/jpg/cloud_mobile_tiny_sizes.jpg b/contents/core/ml_systems/images/jpg/cloud_mobile_tiny_sizes.jpg similarity index 100% rename from contents/ml_systems/images/jpg/cloud_mobile_tiny_sizes.jpg rename to contents/core/ml_systems/images/jpg/cloud_mobile_tiny_sizes.jpg diff --git a/contents/ml_systems/images/jpg/edge_ml_iot.jpg b/contents/core/ml_systems/images/jpg/edge_ml_iot.jpg similarity index 100% rename from contents/ml_systems/images/jpg/edge_ml_iot.jpg rename to contents/core/ml_systems/images/jpg/edge_ml_iot.jpg diff --git a/contents/ml_systems/images/jpg/tiny_ml.jpg b/contents/core/ml_systems/images/jpg/tiny_ml.jpg similarity index 100% rename from contents/ml_systems/images/jpg/tiny_ml.jpg rename to contents/core/ml_systems/images/jpg/tiny_ml.jpg diff --git a/contents/ml_systems/images/png/cloud-edge-tiny.png b/contents/core/ml_systems/images/png/cloud-edge-tiny.png similarity index 100% rename from contents/ml_systems/images/png/cloud-edge-tiny.png rename to contents/core/ml_systems/images/png/cloud-edge-tiny.png diff --git a/contents/ml_systems/images/png/cloud_ml_tpu.png b/contents/core/ml_systems/images/png/cloud_ml_tpu.png similarity index 100% rename from contents/ml_systems/images/png/cloud_ml_tpu.png rename to contents/core/ml_systems/images/png/cloud_ml_tpu.png diff --git a/contents/ml_systems/images/png/cloudml.png b/contents/core/ml_systems/images/png/cloudml.png similarity index 100% rename from contents/ml_systems/images/png/cloudml.png rename to contents/core/ml_systems/images/png/cloudml.png diff --git a/contents/ml_systems/images/png/cover_ml_systems.png b/contents/core/ml_systems/images/png/cover_ml_systems.png similarity index 100% rename from contents/ml_systems/images/png/cover_ml_systems.png rename to contents/core/ml_systems/images/png/cover_ml_systems.png diff --git a/contents/ml_systems/images/png/cover_ml_systems_ai.png b/contents/core/ml_systems/images/png/cover_ml_systems_ai.png similarity index 100% rename from contents/ml_systems/images/png/cover_ml_systems_ai.png rename to contents/core/ml_systems/images/png/cover_ml_systems_ai.png diff --git a/contents/ml_systems/images/png/edgeml.png b/contents/core/ml_systems/images/png/edgeml.png similarity index 100% rename from contents/ml_systems/images/png/edgeml.png rename to contents/core/ml_systems/images/png/edgeml.png diff --git a/contents/ml_systems/images/png/microprocessor_vs_microcontroller.png b/contents/core/ml_systems/images/png/microprocessor_vs_microcontroller.png similarity index 100% rename from contents/ml_systems/images/png/microprocessor_vs_microcontroller.png rename to contents/core/ml_systems/images/png/microprocessor_vs_microcontroller.png diff --git a/contents/ml_systems/images/png/tinyml.png b/contents/core/ml_systems/images/png/tinyml.png similarity index 100% rename from contents/ml_systems/images/png/tinyml.png rename to contents/core/ml_systems/images/png/tinyml.png diff --git a/contents/ml_systems/images/png/venndiagram.png b/contents/core/ml_systems/images/png/venndiagram.png similarity index 100% rename from contents/ml_systems/images/png/venndiagram.png rename to contents/core/ml_systems/images/png/venndiagram.png diff --git a/contents/ml_systems/ml_systems.bib b/contents/core/ml_systems/ml_systems.bib similarity index 100% rename from contents/ml_systems/ml_systems.bib rename to contents/core/ml_systems/ml_systems.bib diff --git a/contents/ml_systems/ml_systems.qmd b/contents/core/ml_systems/ml_systems.qmd similarity index 89% rename from contents/ml_systems/ml_systems.qmd rename to contents/core/ml_systems/ml_systems.qmd index 9a0ac851..ff1e948f 100644 --- a/contents/ml_systems/ml_systems.qmd +++ b/contents/core/ml_systems/ml_systems.qmd @@ -5,14 +5,14 @@ bibliography: ml_systems.bib # ML Systems {#sec-ml_systems} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-ml-systems-resource), [Videos](#sec-ml-systems-resource), [Exercises](#sec-ml-systems-resource), [Labs](#sec-ml-systems-resource) +Resources: [Slides](#sec-ml-systems-resource), [Videos](#sec-ml-systems-resource), [Exercises](#sec-ml-systems-resource) ::: ![_DALL·E 3 Prompt: Illustration in a rectangular format depicting the merger of embedded systems with Embedded AI. The left half of the image portrays traditional embedded systems, including microcontrollers and processors, detailed and precise. The right half showcases the world of artificial intelligence, with abstract representations of machine learning models, neurons, and data flow. The two halves are distinctly separated, emphasizing the individual significance of embedded tech and AI, but they come together in harmony at the center._](images/png/cover_ml_systems.png) Machine learning (ML) systems, built on the foundation of computing systems, hold the potential to transform our world. These systems, with their specialized roles and real-time computational capabilities, represent a critical junction where data and computation meet on a micro-scale. They are specifically tailored to optimize performance, energy usage, and spatial efficiency—key factors essential for the successful implementation of ML systems. -As this chapter progresses, we will explore ML systems' complex and fascinating world. We'll gain insights into their structural design and operational features and understand their key role in powering ML applications. Starting with the basics of microcontroller units, we will examine the interfaces and peripherals that improve their functionalities. This chapter is designed to be a comprehensive guide elucidating the nuanced aspects of ML systems. +As this chapter progresses, we will explore ML systems' complex and fascinating world. We'll gain insights into their structural design and operational features and understand their key role in powering ML applications. Starting with the basics of microcontroller units, we will examine the interfaces and peripherals that improve their functionalities. This chapter is designed to be a comprehensive guide that explains the nuanced aspects of different ML systems. ::: {.callout-tip} @@ -36,7 +36,7 @@ ML is rapidly evolving, with new paradigms reshaping how models are developed, t Modern machine learning systems span a spectrum of deployment options, each with its own set of characteristics and use cases. At one end, we have cloud-based ML, which leverages powerful centralized computing resources for complex, data-intensive tasks. Moving along the spectrum, we encounter edge ML, which brings computation closer to the data source for reduced latency and improved privacy. At the far end, we find TinyML, which enables machine learning on extremely low-power devices with severe memory and processing constraints. -This chapter explores the landscape of contemporary machine learning systems, covering the key approaches of Cloud ML, Edge ML, and TinyML (@fig-cloud-edge-tinyml-comparison). We'll examine the unique characteristics, advantages, and challenges of each approach, as well as the emerging trends and technologies that are shaping the future of machine learning deployment. +This chapter explores the landscape of contemporary machine learning systems, covering three key approaches: Cloud ML, Edge ML, and TinyML. @fig-cloud-edge-tinyml-comparison illustrates the spectrum of distributed intelligence across these approaches, providing a visual comparison of their characteristics. We will examine the unique characteristics, advantages, and challenges of each approach, as depicted in the figure. Additionally, we will discuss the emerging trends and technologies that are shaping the future of machine learning deployment, considering how they might influence the balance between these three paradigms. ![Cloud vs. Edge vs. TinyML: The Spectrum of Distributed Intelligence. Source: ABI Research -- TinyML.](images/png/cloud-edge-tiny.png){#fig-cloud-edge-tinyml-comparison} @@ -56,15 +56,15 @@ Each of these paradigms has its own strengths and is suited to different use cas The progression from Cloud to Edge to TinyML reflects a broader trend in computing towards more distributed, localized processing. This evolution is driven by the need for faster response times, improved privacy, reduced bandwidth usage, and the ability to operate in environments with limited or no connectivity. -@fig-vMLsizes illustrates the key differences between Cloud ML, Edge ML, and TinyML in terms of hardware, latency, connectivity, power requirements, and model complexity. As we move from Cloud to Edge to TinyML, we see a dramatic reduction in available resources, which presents significant challenges for deploying sophisticated machine learning models. - -This resource disparity becomes particularly apparent when attempting to deploy deep learning models on microcontrollers, the primary hardware platform for TinyML. These tiny devices have severely constrained memory and storage capacities, which are often insufficient for conventional deep learning models. We will learn to put these things into perspective in this chapter. +@fig-vMLsizes illustrates the key differences between Cloud ML, Edge ML, and TinyML in terms of hardware, latency, connectivity, power requirements, and model complexity. As we move from Cloud to Edge to TinyML, we see a dramatic reduction in available resources, which presents significant challenges for deploying sophisticated machine learning models. This resource disparity becomes particularly apparent when attempting to deploy deep learning models on microcontrollers, the primary hardware platform for TinyML. These tiny devices have severely constrained memory and storage capacities, which are often insufficient for conventional deep learning models. We will learn to put these things into perspective in this chapter. ![From cloud GPUs to microcontrollers: Navigating the memory and storage landscape across computing devices. Source: [@lin2023tiny]](./images/jpg/cloud_mobile_tiny_sizes.jpg){#fig-vMLsizes} ## Cloud ML -Cloud ML leverages powerful servers in the cloud for training and running large, complex ML models, and relies on internet connectivity. +Cloud ML leverages powerful servers in the cloud for training and running large, complex ML models and relies on internet connectivity. @fig-cloud-ml provides an overview of Cloud ML's capabilities which we will discuss in greater detail throughout this section. + +![Section overview for Cloud ML.](images/png/cloudml.png){#fig-cloud-ml} ### Characteristics @@ -74,7 +74,9 @@ Cloud Machine Learning (Cloud ML) is a subfield of machine learning that leverag **Centralized Infrastructure** -One of the key characteristics of Cloud ML is its centralized infrastructure. Cloud service providers offer a virtual platform that consists of high-capacity servers, expansive storage solutions, and robust networking architectures, all housed in data centers distributed across the globe (@fig-cloudml-example). This centralized setup allows for the pooling and efficient management of computational resources, making it easier to scale machine learning projects as needed. +One of the key characteristics of Cloud ML is its centralized infrastructure. @fig-cloudml-example illustrates this concept with an example from Google's Cloud TPU data center. Cloud service providers offer a virtual platform that consists of high-capacity servers, expansive storage solutions, and robust networking architectures, all housed in data centers distributed across the globe. As shown in the figure, these centralized facilities can be massive in scale, housing rows upon rows of specialized hardware. This centralized setup allows for the pooling and efficient management of computational resources, making it easier to scale machine learning projects as needed. + +![Cloud TPU data center at Google. Source: [Google.](https://blog.google/technology/ai/google-gemini-ai/#scalable-efficient)](images/png/cloud_ml_tpu.png){#fig-cloudml-example} **Scalable Data Processing and Model Training** @@ -94,8 +96,6 @@ By leveraging the pay-as-you-go pricing model offered by cloud service providers Cloud ML has revolutionized the way machine learning is approached, making it more accessible, scalable, and efficient. It has opened up new possibilities for organizations to harness the power of machine learning without the need for significant investments in hardware and infrastructure. -![Cloud TPU data center at Google. Source: [Google.](https://blog.google/technology/ai/google-gemini-ai/#scalable-efficient)](images/png/cloud_ml_tpu.png){#fig-cloudml-example} - ### Benefits Cloud ML offers several significant benefits that make it a powerful choice for machine learning projects: @@ -170,10 +170,7 @@ Cloud ML is deeply integrated into our online experiences, shaping the way we in **Security and Anomaly Detection** -Cloud ML plays a role in bolstering user security by powering anomaly detection systems. These systems continuously monitor user activities and system logs to identify unusual patterns or suspicious behavior. By analyzing vast amounts of data in real-time, Cloud ML algorithms can detect potential cyber threats, such as unauthorized access attempts, malware infections, or data breaches. The cloud's scalability and processing power enable these systems to handle the increasing complexity and volume of security data, providing a proactive approach to protecting users and systems from potential threats. @fig-cloud-ml provides an overview of this section. - -![Section summary for Cloud ML.](images/png/cloudml.png){#fig-cloud-ml} - +Cloud ML plays a role in bolstering user security by powering anomaly detection systems. These systems continuously monitor user activities and system logs to identify unusual patterns or suspicious behavior. By analyzing vast amounts of data in real-time, Cloud ML algorithms can detect potential cyber threats, such as unauthorized access attempts, malware infections, or data breaches. The cloud’s scalability and processing power enable these systems to handle the increasing complexity and volume of security data, providing a proactive approach to protecting users and systems from potential threats. ## Edge ML @@ -181,18 +178,20 @@ Cloud ML plays a role in bolstering user security by powering anomaly detection **Definition of Edge ML** -Edge Machine Learning (Edge ML) runs machine learning algorithms directly on endpoint devices or closer to where the data is generated rather than relying on centralized cloud servers. This approach brings computation closer to the data source, reducing the need to send large volumes of data over networks, often resulting in lower latency and improved data privacy. +Edge Machine Learning (Edge ML) runs machine learning algorithms directly on endpoint devices or closer to where the data is generated rather than relying on centralized cloud servers. This approach brings computation closer to the data source, reducing the need to send large volumes of data over networks, often resulting in lower latency and improved data privacy. @fig-edge-ml provides an overview of this section. + +![Section overview for Edge ML.](images/png/edgeml.png){#fig-edge-ml} **Decentralized Data Processing** -In Edge ML, data processing happens in a decentralized fashion. Instead of sending data to remote servers, the data is processed locally on devices like smartphones, tablets, or Internet of Things (IoT) devices (@fig-edgeml-example). This local processing allows devices to make quick decisions based on the data they collect without relying heavily on a central server's resources. This decentralization is particularly important in real-time applications where even a slight delay can have significant consequences. +In Edge ML, data processing happens in a decentralized fashion, as illustrated in @fig-edgeml-example. Instead of sending data to remote servers, the data is processed locally on devices like smartphones, tablets, or Internet of Things (IoT) devices. The figure showcases various examples of these edge devices, including wearables, industrial sensors, and smart home appliances. This local processing allows devices to make quick decisions based on the data they collect without relying heavily on a central server's resources. + +![Edge ML Examples. Source: Edge Impulse.](images/jpg/edge_ml_iot.jpg){#fig-edgeml-example} **Local Data Storage and Computation** Local data storage and computation are key features of Edge ML. This setup ensures that data can be stored and analyzed directly on the devices, thereby maintaining the privacy of the data and reducing the need for constant internet connectivity. Moreover, this often leads to more efficient computation, as data doesn't have to travel long distances, and computations are performed with a more nuanced understanding of the local context, which can sometimes result in more insightful analyses. -![Edge ML Examples. Source: Edge Impulse.](images/jpg/edge_ml_iot.jpg){#fig-edgeml-example} - ### Benefits **Reduced Latency** @@ -237,9 +236,7 @@ Edge ML plays a crucial role in efficiently managing various systems in smart ho The Industrial IoT leverages Edge ML to monitor and control complex industrial processes. Here, machine learning models can analyze data from numerous sensors in real-time, enabling predictive maintenance, optimizing operations, and enhancing safety measures. This revolution in industrial automation and efficiency is transforming manufacturing and production across various sectors. -The applicability of Edge ML is vast and not limited to these examples. Various other sectors, including healthcare, agriculture, and urban planning, are exploring and integrating Edge ML to develop innovative solutions responsive to real-world needs and challenges, heralding a new era of smart, interconnected systems. @fig-edge-ml provides an overview of this section. - -![Section summary for Edge ML.](images/png/edgeml.png){#fig-edge-ml} +The applicability of Edge ML is vast and not limited to these examples. Various other sectors, including healthcare, agriculture, and urban planning, are exploring and integrating Edge ML to develop innovative solutions responsive to real-world needs and challenges, heralding a new era of smart, interconnected systems. ## Tiny ML @@ -247,7 +244,9 @@ The applicability of Edge ML is vast and not limited to these examples. Various **Definition of TinyML** -TinyML sits at the crossroads of embedded systems and machine learning, representing a burgeoning field that brings smart algorithms directly to tiny microcontrollers and sensors. These microcontrollers operate under severe resource constraints, particularly regarding memory, storage, and computational power (see a TinyML kit example in @fig-tinyml-example). +TinyML sits at the crossroads of embedded systems and machine learning, representing a burgeoning field that brings smart algorithms directly to tiny microcontrollers and sensors. These microcontrollers operate under severe resource constraints, particularly regarding memory, storage, and computational power. @fig-tiny-ml encapsulates the key aspects of TinyML discussed in this section. + +![Section overview for Tiny ML.](images/png/tinyml.png){#fig-tiny-ml} **On-Device Machine Learning** @@ -255,7 +254,7 @@ In TinyML, the focus is on on-device machine learning. This means that machine l **Low Power and Resource-Constrained Environments** -TinyML excels in low-power and resource-constrained settings. These environments require highly optimized solutions that function within the available resources. TinyML meets this need through specialized algorithms and models designed to deliver decent performance while consuming minimal energy, thus ensuring extended operational periods, even in battery-powered devices. +TinyML excels in low-power and resource-constrained settings. These environments require highly optimized solutions that function within the available resources. @fig-tinyml-example showcases an example TinyML device kit, illustrating the compact nature of these systems. These devices can typically fit in the palm of your hand or, in some cases, are even as small as a fingernail. TinyML meets the need for efficiency through specialized algorithms and models designed to deliver decent performance while consuming minimal energy, thus ensuring extended operational periods, even in battery-powered devices like those shown. ![Examples of TinyML device kits. Source: [Widening Access to Applied Machine Learning with TinyML.](https://arxiv.org/pdf/2106.04008.pdf)](images/jpg/tiny_ml.jpg){#fig-tinyml-example} @@ -315,16 +314,16 @@ TinyML can be employed to create anomaly detection models that identify unusual In environmental monitoring, TinyML enables real-time data analysis from various field-deployed sensors. These could range from city air quality monitoring to wildlife tracking in protected areas. Through TinyML, data can be processed locally, allowing for quick responses to changing conditions and providing a nuanced understanding of environmental patterns, crucial for informed decision-making. -In summary, TinyML serves as a trailblazer in the evolution of machine learning, fostering innovation across various fields by bringing intelligence directly to the edge. Its potential to transform our interaction with technology and the world is immense, promising a future where devices are connected, intelligent, and capable of making real-time decisions and responses. @fig-tiny-ml provides an overview of this section. - -![Section summary for Tiny ML.](images/png/tinyml.png){#fig-tiny-ml} +In summary, TinyML serves as a trailblazer in the evolution of machine learning, fostering innovation across various fields by bringing intelligence directly to the edge. Its potential to transform our interaction with technology and the world is immense, promising a future where devices are connected, intelligent, and capable of making real-time decisions and responses. ## Comparison -Up to this point, we've explored each of the different ML variants individually. Now, let's bring them all together for a comprehensive view. @tbl-big_vs_tiny offers a comparative analysis of Cloud ML, Edge ML, and TinyML based on various features and aspects. Additionally, @fig-venn-diagram draws a contrast using a venn diagram. This comparison provides a clear perspective on the unique advantages and distinguishing factors, aiding in making informed decisions based on the specific needs and constraints of a given application or project. +Let's bring together the different ML variants we've explored individually for a comprehensive view. @fig-venn-diagram illustrates the relationships and overlaps between Cloud ML, Edge ML, and TinyML using a Venn diagram. This visual representation effectively highlights the unique characteristics of each approach while also showing areas of commonality. Each ML paradigm has its own distinct features, but there are also intersections where these approaches share certain attributes or capabilities. This diagram helps us understand how these variants relate to each other in the broader landscape of machine learning implementations. ![ML Venn diagram. Source: [arXiv](https://arxiv.org/html/2403.19076v1)](images/png/venndiagram.png){#fig-venn-diagram} +For a more detailed comparison of these ML variants, we can refer to @tbl-big_vs_tiny. This table offers a comprehensive analysis of Cloud ML, Edge ML, and TinyML based on various features and aspects. By examining these different characteristics side by side, we gain a clearer perspective on the unique advantages and distinguishing factors of each approach. This detailed comparison, combined with the visual overview provided by the Venn diagram, aids in making informed decisions based on the specific needs and constraints of a given application or project. + +--------------------------+---------------------------------------------------------+---------------------------------------------------------+----------------------------------------------------------+ | Aspect | Cloud ML | Edge ML | TinyML | +:=========================+:========================================================+:========================================================+:=========================================================+ @@ -407,12 +406,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * _Coming soon._ ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we offer a series of hands-on labs allowing students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience. - -* _Coming soon._ -::: diff --git a/contents/ondevice_learning/images/jpg/pruning.jpeg b/contents/core/ondevice_learning/images/jpg/pruning.jpeg similarity index 100% rename from contents/ondevice_learning/images/jpg/pruning.jpeg rename to contents/core/ondevice_learning/images/jpg/pruning.jpeg diff --git a/contents/ondevice_learning/images/png/cover_ondevice_learning.png b/contents/core/ondevice_learning/images/png/cover_ondevice_learning.png similarity index 100% rename from contents/ondevice_learning/images/png/cover_ondevice_learning.png rename to contents/core/ondevice_learning/images/png/cover_ondevice_learning.png diff --git a/contents/ondevice_learning/images/png/federatedvsoil.png b/contents/core/ondevice_learning/images/png/federatedvsoil.png similarity index 100% rename from contents/ondevice_learning/images/png/federatedvsoil.png rename to 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contents/core/ondevice_learning/images/png/transfer_learning.png diff --git a/contents/core/ondevice_learning/ondevice_learning.bib b/contents/core/ondevice_learning/ondevice_learning.bib new file mode 100644 index 00000000..4b765ea3 --- /dev/null +++ b/contents/core/ondevice_learning/ondevice_learning.bib @@ -0,0 +1,388 @@ +%comment{This file was created with betterbib v5.0.11.} + +@inproceedings{abadi2016deep, + doi = {10.1145/2976749.2978318}, + source = {Crossref}, + author = {Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H. 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The engineers, including men, women, and non-binary individuals, are tuning parameters, repairing connections, and enhancing the network on the fly. Data flows into the ML model, being processed in real-time, and generating output inferences._](images/png/cover_ondevice_learning.png) -On-device Learning represents a significant innovation for embedded and edge IoT devices, enabling models to train and update directly on small local devices. This contrasts with traditional methods, where models are trained on expansive cloud computing resources before deployment. With On-Device Learning, devices like smart speakers, wearables, and industrial sensors can refine models in real-time based on local data without needing to transmit data externally. For example, a voice-enabled smart speaker could learn and adapt to its owner's speech patterns and vocabulary right on the device. However, there is no such thing as a free lunch; therefore, in this chapter, we will discuss both the benefits and the limitations of on-device learning. +On-device Learning represents a significant innovation for embedded and edge IoT devices, enabling models to train and update directly on small local devices. This contrasts with traditional methods, where models are trained on expansive cloud computing resources before deployment. With on-device learning, devices like smart speakers, wearables, and industrial sensors can refine models in real-time based on local data without needing to transmit data externally. For example, a voice-enabled smart speaker could learn and adapt to its owner's speech patterns and vocabulary right on the device. However, there is no such thing as a free lunch; therefore, in this chapter, we will discuss both the benefits and the limitations of on-device learning. ::: {.callout-tip} @@ -30,11 +30,13 @@ On-device Learning represents a significant innovation for embedded and edge IoT ## Introduction -On-device Learning refers to training ML models directly on the device where they are deployed, as opposed to traditional methods where models are trained on powerful servers and then deployed to devices. This method is particularly relevant to TinyML, where ML systems are integrated into tiny, resource-constrained devices. +On-device learning refers to training ML models directly on the device where they are deployed, as opposed to traditional methods where models are trained on powerful servers and then deployed to devices. This method is particularly relevant to TinyML, where ML systems are integrated into tiny, resource-constrained devices. -An example of On-Device Learning can be seen in a smart thermostat that adapts to user behavior over time. Initially, the thermostat may have a generic model that understands basic usage patterns. However, as it is exposed to more data, such as the times the user is home or away, preferred temperatures, and external weather conditions, the thermostat can refine its model directly on the device to provide a personalized experience. This is all done without sending data back to a central server for processing. +An example of on-device learning can be seen in a smart thermostat that adapts to user behavior over time. Initially, the thermostat may have a generic model that understands basic usage patterns. However, as it is exposed to more data, such as the times the user is home or away, preferred temperatures, and external weather conditions, the thermostat can refine its model directly on the device to provide a personalized experience. This is all done without sending data back to a central server for processing. -Another example is in predictive text on smartphones. As users type, the phone learns from the user's language patterns and suggests words or phrases that are likely to be used next. This learning happens directly on the device, and the model updates in real-time as more data is collected. A widely used real-world example of on-device learning is Gboard. On an Android phone, Gboard learns from typing and dictation patterns to enhance the experience for all users. On-device learning is also called federated learning. @fig-federated-cycle shows the cycle of federated learning on mobile devices: A. the device learns from user patterns; B. local model updates are communicated to the cloud; C. the cloud server updates the global model and sends the new model to all the devices. +Another example is in predictive text on smartphones. As users type, the phone learns from the user's language patterns and suggests words or phrases that are likely to be used next. This learning happens directly on the device, and the model updates in real-time as more data is collected. A widely used real-world example of on-device learning is [Gboard](https://play.google.com/store/apps/details?id=com.google.android.inputmethod.latin). On an Android phone, Gboard [learns from typing and dictation patterns](https://research.google/blog/federated-learning-collaborative-machine-learning-without-centralized-training-data/) to enhance the experience for all users. + +In some cases, on-device learning can be coupled with a federated learning setup, where each device tunes its model locally using only the data stored on that device. This approach allows the model to learn from each device’s unique data without transmitting any of it to a central server. As shown in @fig-federated-cycle, federated learning preserves privacy by keeping all personal data on the device, ensuring that the training process remains entirely on-device, with only summarized model updates shared across devices. ![Federated learning cycle. Source: [Google Research.](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html)](images/png/ondevice_intro.png){#fig-federated-cycle} @@ -42,7 +44,7 @@ Another example is in predictive text on smartphones. As users type, the phone l On-device learning provides several advantages over traditional cloud-based ML. By keeping data and models on the device, it eliminates the need for costly data transmission and addresses privacy concerns. This allows for more personalized, responsive experiences, as the model can adapt in real-time to user behavior. -However, On-Device Learning also comes with tradeoffs. The limited computing resources on consumer devices can make it challenging to run complex models locally. Datasets are also more restricted since they consist only of user-generated data from a single device. Additionally, updating models requires pushing out new versions rather than seamless cloud updates. +However, on-device learning also comes with drawbacks. The limited computing resources on consumer devices can make it challenging to run complex models locally. Datasets are also more restricted since they consist only of user-generated data from a single device. Additionally, updating models on each device can be more challenging, as it often requires deploying new versions to each device individually, rather than seamlessly updating a single model in the cloud. On-device learning opens up new capabilities by enabling offline AI while maintaining user privacy. However, it requires carefully managing model and data complexity within the constraints of consumer devices. Finding the right balance between localization and cloud offloading is key to optimizing on-device experiences. @@ -56,7 +58,7 @@ Server breaches are far from rare, with millions of records compromised annually Regulations like the Health Insurance Portability and Accountability Act ([HIPAA](https://www.cdc.gov/phlp/publications/topic/hipaa.html)) and the General Data Protection Regulation ([GDPR](https://gdpr.eu/tag/gdpr/)) mandate stringent data privacy requirements that on-device learning adeptly addresses. By ensuring data remains localized and is not transferred to other systems, on-device learning facilitates [compliance with these regulations](https://www.researchgate.net/publication/321515854_The_EU_General_Data_Protection_Regulation_GDPR_A_Practical_Guide). -On-device learning is not just beneficial for individual users; it has significant implications for organizations and sectors dealing with highly sensitive data. For instance, within the military, on-device learning empowers frontline systems to adapt models and function independently of connections to central servers that could potentially be compromised. Critical and sensitive information is staunchly protected by localizing data processing and learning. However, this comes with the tradeoff that individual devices take on more value and may incentivize theft or destruction as they become the sole carriers of specialized AI models. Care must be taken to secure devices themselves when transitioning to on-device learning. +On-device learning is not just beneficial for individual users; it has significant implications for organizations and sectors dealing with highly sensitive data. For instance, within the military, on-device learning empowers frontline systems to adapt models and function independently of connections to central servers that could potentially be compromised. Critical and sensitive information is staunchly protected by localizing data processing and learning. However, a drawback is that individual devices take on more value and may incentivize theft or destruction as they become the sole carriers of specialized AI models. Care must be taken to secure devices themselves when transitioning to on-device learning. It is also important to preserve the privacy, security, and regulatory compliance of personal and sensitive data. Instead of in the cloud, training and operating models locally substantially augment privacy measures, ensuring that user data is safeguarded from potential threats. @@ -77,13 +79,17 @@ Major privacy regulations impose restrictions on cross-border data movement that One major advantage of on-device learning is the significant reduction in bandwidth usage and associated cloud infrastructure costs. By keeping data localized for model training rather than transmitting raw data to the cloud, on-device learning can result in substantial bandwidth savings. For instance, a network of cameras analyzing video footage can achieve significant reductions in data transfer by training models on-device rather than streaming all video footage to the cloud for processing. -This reduction in data transmission saves bandwidth and translates to lower costs for servers, networking, and data storage in the cloud. Large organizations, which might spend millions on cloud infrastructure to train models on-device data, can experience dramatic cost reductions through on-device learning. In the era of Generative AI, where [costs have been escalating significantly](https://epochai.org/blog/trends-in-the-dollar-training-cost-of-machine-learning-systems), finding ways to keep expenses down has become increasingly important. +This reduction in data transmission saves bandwidth and translates to lower costs for servers, networking, and data storage in the cloud. Large organizations, which might spend millions on cloud infrastructure to train models, can experience dramatic cost reductions through on-device learning. In the era of Generative AI, where [costs have been escalating significantly](https://epochai.org/blog/trends-in-the-dollar-training-cost-of-machine-learning-systems), finding ways to keep expenses down has become increasingly important. Furthermore, the energy and environmental costs of running large server farms are also diminished. Data centers consume vast amounts of energy, contributing to greenhouse gas emissions. By reducing the need for extensive cloud-based infrastructure, on-device learning plays a part in mitigating the environmental impact of data processing [@wu2022sustainable]. Specifically for endpoint applications, on-device learning minimizes the number of network API calls needed to run inference through a cloud provider. The cumulative costs associated with bandwidth and API calls can quickly escalate for applications with millions of users. In contrast, performing training and inferences locally is considerably more efficient and cost-effective. Under state-of-the-art optimizations, on-device learning has been shown to reduce training memory requirements, drastically improve memory efficiency, and reduce up to 20% in per-iteration latency [@dhar2021survey]. -Another key benefit of on-device learning is the potential for IoT devices to continuously adapt their ML model to new data for continuous, lifelong learning. On-device models can quickly become outdated as user behavior, data patterns, and preferences change. Continuous learning enables the model to efficiently adapt to new data and improvements and maintain high model performance over time. +#### Lifelong Learning + +One of the key benefits of on-device learning is its ability to support lifelong learning, allowing models to continuously adapt to new data and evolving user behavior directly on the device. In dynamic environments, data patterns can change over time—a phenomenon known as data drift—which can degrade model accuracy and relevance if the model remains static. For example, user preferences, seasonal trends, or even external conditions (such as network traffic patterns or weather) can evolve, requiring models to adjust in order to maintain optimal performance. + +On-device learning enables models to address this by adapting incrementally as new data becomes available. This continuous adaptation process allows models to remain relevant and effective, reducing the need for frequent cloud updates. Local adaptations reduce the need to transmit large datasets back to the cloud for retraining, saving bandwidth and ensuring data privacy. ### Limitations @@ -111,7 +117,7 @@ In the cloud, models are trained on massive, diverse datasets like ImageNet or C On-device learning instead relies on smaller, decentralized data silos unique to each device. A smartphone camera roll may contain only thousands of photos of users' interests and environments. -This decentralized data leads to a need for IID (independent and identically distributed) data. For instance, two friends may take many photos of the same places and objects, meaning their data distributions are highly correlated rather than independent. +In machine learning, effective model training often assumes that data is independent and identically distributed (IID). This means that each data point is generated independently (without influencing other points) and follows the same statistical distribution as the rest of the data. When data is IID, models trained on it are more likely to generalize well to new, similar data. However, in on-device learning, this IID condition is rarely met, as data is highly specific to individual users and contexts. For example, two friends may take similar photos of the same places, creating correlated data that doesn’t represent a broader population or the variety needed for generalization. Reasons data may be non-IID in on-device settings: @@ -127,15 +133,9 @@ The limited data and optimizations required for on-device learning can negativel * Noisy user-generated data reduces quality. Sensor noise or improper data labeling by non-experts may degrade training. * Optimizations like pruning and quantization trade off accuracy for efficiency. An 8-bit quantized model runs faster but less accurately than a 32-bit model. -So while cloud models achieve high accuracy with massive datasets and no constraints, on-device models can struggle to generalize. Some studies show that on-device training matches cloud accuracy on select tasks. However, performance on real-world workloads requires further study [@lin2022device]. - -For instance, a cloud model can accurately detect pneumonia in chest X-rays from thousands of hospitals. However, an on-device model trained only on a small local patient population may fail to generalize. - -Unreliable accuracy limits the real-world applicability of on-device learning for mission-critical uses like disease diagnosis or self-driving vehicles. - -On-device training is also slower than the cloud due to limited resources. Even if each iteration is faster, the overall training process takes longer. +So while cloud models achieve high accuracy with massive datasets and no constraints, on-device models can struggle to generalize. Some studies show that on-device training matches cloud accuracy on select tasks. However, performance on real-world workloads requires further study [@lin2022device]. For instance, a cloud model can accurately detect pneumonia in chest X-rays from thousands of hospitals. However, an on-device model trained only on a small local patient population may fail to generalize. This limits the real-world applicability of on-device learning for mission-critical uses like disease diagnosis or self-driving vehicles. -For example, a real-time robotics application may require model updates within milliseconds. On-device training on small embedded hardware may take seconds or minutes per update - too slow for real-time use. +On-device training is also slower than the cloud due to limited resources. Even if each iteration is faster, the overall training process takes longer. For example, a real-time robotics application may require model updates within milliseconds. On-device training on small embedded hardware may take seconds or minutes per update - too slow for real-time use. Accuracy, generalization, and speed challenges pose hurdles to adopting on-device learning for real-world production systems, especially when reliability and low latency are critical. @@ -167,11 +167,9 @@ With some refinements, these classical ML algorithms can be adapted to specific #### Pruning -Pruning is a technique for reducing the size and complexity of an ML model to improve its efficiency and generalization performance. This is beneficial for training models on edge devices, where we want to minimize resource usage while maintaining competitive accuracy. +As discussed in @sec-pruning, pruning is a key technique for reducing the size and complexity of ML models. For on-device learning, pruning is particularly valuable, as it minimizes resource consumption while retaining competitive accuracy. By removing less informative components of a model, pruning allows ML models to run more efficiently on resource-limited devices. -The primary goal of pruning is to remove parts of the model that do not contribute significantly to its predictive power while retaining the most informative aspects. In the context of decision trees, pruning involves removing some branches (subtrees) from the tree, leading to a smaller and simpler tree. In the context of DNN, pruning is used to reduce the number of neurons (units) or connections in the network, as shown in @fig-ondevice-pruning. - -![Network pruning.](images/jpg/pruning.jpeg){#fig-ondevice-pruning} +In the context of on-device learning, pruning is applied to adapt complex deep learning models to the limited memory and processing power of edge devices. For example, pruning can reduce the number of neurons or connections in a DNN, resulting in a model that consumes less memory and requires fewer computations. This approach simplifies the neural network structure, resulting in a more compact and efficient model. #### Reducing Complexity of Deep Learning Models @@ -191,7 +189,7 @@ Quantization is a common method for reducing the memory footprint of DNN trainin A specific algorithmic technique is Quantization-Aware Scaling (QAS), which improves the performance of neural networks on low-precision hardware, such as edge devices, mobile devices, or TinyML systems, by adjusting the scale factors during the quantization process. -As we discussed in the Model Optimizations chapter, quantization is the process of mapping a continuous range of values to a discrete set of values. In the context of neural networks, quantization often involves reducing the precision of the weights and activations from 32-bit floating point to lower-precision formats such as 8-bit integers. This reduction in precision can significantly reduce the computational cost and memory footprint of the model, making it suitable for deployment on low-precision hardware. @fig-float-int-quantization is an example of float-to-integer quantization. +As we discussed in the [Model Optimizations](../optimizations/optimizations.qmd) chapter, quantization is the process of mapping a continuous range of values to a discrete set of values. In the context of neural networks, this often involves reducing the precision of weights and activations from 32-bit floating point to lower-precision formats such as 8-bit integers. This reduction in precision can significantly decrease the model's computational cost and memory footprint, making it suitable for deployment on low-precision hardware. @fig-float-int-quantization illustrates this concept, showing an example of float-to-integer quantization where high-precision floating-point values are mapped to a more compact integer representation. This visual representation helps to clarify how quantization can maintain the essential structure of the data while reducing its complexity and storage requirements. ![Float to integer quantization. Source: [Nvidia.](https://developer-blogs.nvidia.com/wp-content/uploads/2021/07/qat-training-precision.png)](images/png/ondevice_quantization_matrix.png){#fig-float-int-quantization} @@ -209,7 +207,7 @@ QAS is used to overcome the difficulties of optimizing models on tiny devices wi Although QAS enables the optimization of a quantized model, it uses a large amount of memory, which is unrealistic for on-device training. So, spare updates are used to reduce the memory footprint of full backward computation. Instead of pruning weights for inference, sparse update prunes the gradient during backward propagation to update the model sparsely. In other words, sparse update skips computing gradients of less important layers and sub-tensors. -However, determining the optimal sparse update scheme given a constraining memory budget can be challenging due to the large search space. For example, the MCUNet model has 43 convolutional layers and a search space of approximately 1030. One technique to address this issue is contribution analysis. Contribution analysis measures the accuracy improvement from biases (updating the last few biases compared to only updating the classifier) and weights (updating the weight of one extra layer compared to only having a bias update). By trying to maximize these improvements, contribution analysis automatically derives an optimal sparse update scheme for enabling on-device training. +However, determining the optimal sparse update scheme given a constraining memory budget can be challenging due to the large search space. For example, the MCUNet model has 43 convolutional layers and a search space of approximately $10^{30}$. One technique to address this issue is contribution analysis. Contribution analysis measures the accuracy improvement from biases (updating the last few biases compared to only updating the classifier) and weights (updating the weight of one extra layer compared to only having a bias update). By trying to maximize these improvements, contribution analysis automatically derives an optimal sparse update scheme for enabling on-device training. #### Layer-Wise Training @@ -231,9 +229,9 @@ Other more common methods of data compression focus on reducing the dimensionali ## Transfer Learning -Transfer learning is an ML technique in which a model developed for a particular task is reused as the starting point for a model on a second task. In the context of on-device AI, transfer learning allows us to leverage pre-trained models that have already learned useful representations from large datasets and finetune them for specific tasks using smaller datasets directly on the device. This can significantly reduce the computational resources and time required for training models from scratch. +Transfer learning is a technique in which a model developed for a particular task is reused as the starting point for a model on a second task. Transfer learning allows us to leverage pre-trained models that have already learned useful representations from large datasets and fine-tune them for specific tasks using smaller datasets directly on the device. This can significantly reduce the computational resources and time required for training models from scratch. -@fig-transfer-learning-apps includes some intuitive examples of transfer learning from the real world. For instance, if you can ride a bicycle, you know how to balance yourself on two-wheel vehicles. Then, it would be easier for you to learn how to ride a motorcycle than it would be for someone who cannot ride a bicycle. +It can be understood through intuitive real-world examples, as illustrated in @fig-transfer-learning-apps. The figure shows scenarios where skills from one domain can be applied to accelerate learning in a related field. A prime example is the relationship between riding a bicycle and a motorcycle. If you can ride a bicycle, you would have already mastered the skill of balancing on a two-wheeled vehicle. The foundational knowledge about this skill makes it significantly easier for you to learn how to ride a motorcycle compared to someone without any cycling experience. The figure depicts this and other similar scenarios, demonstrating how transfer learning leverages existing knowledge to expedite the acquisition of new, related skills. ![Transferring knowledge between tasks. Source: @zhuang2021comprehensive.](images/png/ondevice_transfer_learning_apps.png){#fig-transfer-learning-apps} @@ -247,7 +245,7 @@ Transfer learning is important in making on-device AI practical by allowing us t Transfer learning has revolutionized the way models are developed and deployed, both in the cloud and at the edge. Transfer learning is being used in the real world. One such example is the use of transfer learning to develop AI models that can detect and diagnose diseases from medical images, such as X-rays, MRI scans, and CT scans. For example, researchers at Stanford University developed a transfer learning model that can detect cancer in skin images with an accuracy of 97% [@esteva2017dermatologist]. This model was pre-trained on 1.28 million images to classify a broad range of objects and then specialized for cancer detection by training on a dermatologist-curated dataset of skin images. -Implementation in production scenarios can be broadly categorized into two stages: pre-deployment and post-deployment. +In production settings, implementing transfer learning typically involves two key stages: pre-deployment and post-deployment. Pre-deployment focuses on preparing the model for its specialized task before release, while post-deployment enables the model to adapt further based on individual user data, enhancing personalization and accuracy over time. ### Pre-Deployment Specialization @@ -258,7 +256,7 @@ This pre-trained model serves as a solid foundation and contains a wealth of kno Here's how it works in practice: * **Start with a Pre-Trained Model:** Begin by selecting a model that has already been trained on a comprehensive dataset, usually related to a general task. This model serves as the foundation for the task at hand. -* **Finetuning:** The pre-trained model is then finetuned on a smaller, more specialized dataset specific to the desired task. This step allows the model to adapt and specialize its knowledge to the specific requirements of the application. +* **Fine-tuning:** The pre-trained model is then finetuned on a smaller, more specialized dataset specific to the desired task. This step allows the model to adapt and specialize its knowledge to the specific requirements of the application. * **Validation:** After finetuning, the model is validated to ensure it meets the performance criteria for the specialized task. * **Deployment:** Once validated, the specialized model is then deployed into the production environment. @@ -273,9 +271,9 @@ Consider a real-world application where a parent wishes to identify their child Here's how it works: 1. **Data Collection:** The embedded system gathers images that include the child, ideally with the parent's input to ensure accuracy and relevance. This can be done directly on the device, maintaining the user's data privacy. -2. **Model Finetuning:** The pre-existing face recognition model, which has been trained on a large and diverse dataset, is then finetuned using the newly collected images of the child. This process adapts the model to recognize the child's specific facial features, distinguishing them from other children in the images. +2. **On-Device Fine-tuning:** The pre-existing face recognition model, which has been trained on a large and diverse dataset, is then finetuned using the newly collected images of the child. This process adapts the model to recognize the child's specific facial features, distinguishing them from other children in the images. 3. **Validation:** The refined model is then validated to ensure it accurately recognizes the child in various images. This can involve the parent verifying the model's performance and providing feedback for further improvements. -4. **Deployment:** Once validated, the adapted model is deployed on the device, enabling the parent to easily identify their child in images without having to sift through them manually. +4. **Localized Use:** Once adapted, the model can instantly locate the child in photos, providing a customized experience without needing cloud resources or data transfer. This on-the-fly customization enhances the model's efficacy for the individual user, ensuring that they benefit from ML personalization. This is, in part, how iPhotos or Google Photos works when they ask us to recognize a face, and then, based on that information, they index all the photos by that face. Because the learning and adaptation occur on the device itself, there are no risks to personal privacy. The parent's images are not uploaded to a cloud server or shared with third parties, protecting the family's privacy while still reaping the benefits of a personalized ML model. This approach represents a significant step forward in the quest to provide users with tailored ML solutions that respect and uphold their privacy. @@ -283,13 +281,13 @@ This on-the-fly customization enhances the model's efficacy for the individual u Transfer learning has become an important technique in ML and artificial intelligence, and it is particularly valuable for several reasons. -1. **Data Scarcity:** In many real-world scenarios, acquiring a sufficiently large labeled dataset to train an ML model from scratch is challenging. Transfer learning mitigates this issue by allowing the use of pre-trained models that have already learned valuable features from a vast dataset. +1. **Data Scarcity:** In many real-world applications, gathering a large, labeled dataset to train an ML model from scratch is difficult, costly, and time-consuming. Transfer learning addresses this challenge by allowing the use of pre-trained models that have already learned valuable features from vast labeled datasets, thereby reducing the need for extensive annotated data in the new task. 2. **Computational Expense:** Training a model from scratch requires significant computational resources and time, especially for complex models like deep neural networks. By using transfer learning, we can leverage the computation that has already been done during the training of the source model, thereby saving both time and computational power. -3. **Limited Annotated Data:** For some specific tasks, there might be ample raw data available, but the process of labeling that data for supervised learning can be costly and time-consuming. Transfer learning enables us to use pre-trained models that have been trained on a related task with labeled data, hence requiring less annotated data for the new task. + There are advantages to reusing the features: -1. **Hierarchical Feature Learning:** Deep learning models, particularly Convolutional Neural Networks (CNNs), can learn hierarchical features. Lower layers typically learn generic features like edges and shapes, while higher layers learn more complex and task-specific features. Transfer learning allows us to reuse the generic features learned by a model and finetune the higher layers for our specific task. +1. **Hierarchical Feature Learning:** Deep learning models, particularly CNNs, can learn hierarchical features. Lower layers typically learn generic features like edges and shapes, while higher layers learn more complex and task-specific features. Transfer learning allows us to reuse the generic features learned by a model and finetune the higher layers for our specific task. 2. **Boosting Performance:** Transfer learning has been proven to boost the performance of models on tasks with limited data. The knowledge gained from the source task can provide a valuable starting point and lead to faster convergence and improved accuracy on the target task. :::{#exr-tlb .callout-caution collapse="true"} @@ -382,11 +380,11 @@ When engaging in transfer learning, there are several factors that must be consi #### Domain Similarity -Domain similarity refers to how closely related the source and target domains are. The more similar the domains, the more likely the transfer learning will be successful. Transferring knowledge from a model trained on images of outdoor scenes (source domain) to a new task that involves recognizing objects in indoor scenes (target domain) might be more successful than transferring knowledge from outdoor scenes to a task involving text analysis, as the domains (images vs. text) are quite different. +Domain similarity refers to the degree of resemblance between the types of data used in the source and target applications. The more similar the domains, the more likely the transfer learning will be successful. For instance, transferring knowledge from a model trained on outdoor images (source domain) to a new application involving indoor images (target domain) is more feasible than transferring knowledge from outdoor images to a text-based application. Since images and text are fundamentally different types of data, the domains are dissimilar, making transfer learning more challenging. #### Task Similarity -Task similarity refers to how closely related the source and target tasks are. Similar tasks are likely to benefit more from transfer learning. A model trained to recognize different breeds of dogs (source task) can be more easily adapted to recognize different breeds of cats (target task) than it can be adapted to perform a completely different task like language translation. +Task similarity, on the other hand, refers to how similar the objectives or functions of the source and target tasks are. If the tasks are similar, transfer learning is more likely to be effective. For instance, a model trained to classify different breeds of dogs (source task) can be more easily adapted to classify different breeds of cats (target task) than it could be adapted to a less related task, such as identifying satellite imagery. Since both tasks involve the visual classification of animals, task similarity supports effective transfer, while moving to an unrelated task could make transfer learning less effective. #### Data Quality and Quantity @@ -398,7 +396,7 @@ Feature space overlap refers to how well the features learned by the source mode #### Model Complexity -The complexity of the source model can also impact the success of transfer learning. Sometimes, a simpler model might transfer better than a complex one, as it is less likely to overfit the source task. For example, a simple convolutional neural network (CNN) model trained on image data (source task) may transfer more successfully to a new image classification task (target task) than a complex CNN with many layers, as the simpler model is less likely to overfit the source task. +The complexity of the source model can also impact the success of transfer learning. Sometimes, a simpler model might transfer better than a complex one, as it is less likely to overfit the source task. For example, a simple CNN model trained on image data (source task) may transfer more successfully to a new image classification task (target task) than a complex CNN with many layers, as the simpler model is less likely to overfit the source task. By considering these factors, ML practitioners can make informed decisions about when and how to use transfer learning, ultimately leading to more successful model performance on the target task. The success of transfer learning hinges on the degree of similarity between the source and target domains. Overfitting is risky, especially when finetuning occurs on a limited dataset. On the computational front, certain pre-trained models, owing to their size, might not comfortably fit into the memory constraints of some devices or may run prohibitively slowly. Over time, as data evolves, there is potential for model drift, indicating the need for periodic re-training or ongoing adaptation. @@ -414,13 +412,12 @@ Learn more about transfer learning in @vid-tl below. ## Federated Machine Learning {#sec-fl} -Federated Learning Overview +### Federated Learning Overview -The modern internet is full of large networks of connected devices. Whether it's cell phones, thermostats, smart speakers, or other IOT products, countless edge devices are a goldmine for hyper-personalized, rich data. However, with that rich data comes an assortment of problems with information transfer and privacy. Constructing a training dataset in the cloud from these devices would involve high volumes of bandwidth, cost-efficient data transfer, and violation of users' privacy. +The modern internet is full of large networks of connected devices. Whether it's cell phones, thermostats, smart speakers, or other IoT products, countless edge devices are a goldmine for hyper-personalized, rich data. However, with that rich data comes an assortment of problems with information transfer and privacy. Constructing a training dataset in the cloud from these devices would involve high volumes of bandwidth, cost-efficient data transfer, and violation of users' privacy. -Federated learning offers a solution to these problems: train models partially on the edge devices and only communicate model updates to the cloud. In 2016, a team from Google designed architecture for federated learning that attempts to address these problems. +Federated learning offers a solution to these problems: train models partially on the edge devices and only communicate model updates to the cloud. In 2016, a team from Google designed architecture for federated learning that attempts to address these problems. In their initial paper, @mcmahan2017communication outline a principle federated learning algorithm called FederatedAveraging, shown in @fig-federated-avg-algo. Specifically, FederatedAveraging performs stochastic gradient descent (SGD) over several different edge devices. In this process, each device calculates a gradient $g_k = \nabla F_k(w_t)$ which is then applied to update the server-side weights as (with $\eta$ as learning rate across $k$ clients): -In their initial paper, Google outlines a principle federated learning algorithm called FederatedAveraging, which is shown in @fig-federated-avg-algo. Specifically, FederatedAveraging performs stochastic gradient descent (SGD) over several different edge devices. In this process, each device calculates a gradient $g_k = \nabla F_k(w_t)$ which is then applied to update the server-side weights as (with $\eta$ as learning rate across $k$ clients): $$ w_{t+1} \rightarrow w_t - \eta \sum_{k=1}^{K} \frac{n_k}{n}g_k $$ @@ -444,7 +441,6 @@ With this proposed structure, there are a few key vectors for further optimizing ![Federated learning is revolutionizing on-device learning.](images/png/federatedvsoil.png){#fig-federated-learning} - ### Communication Efficiency One of the key bottlenecks in federated learning is communication. Every time a client trains the model, they must communicate their updates back to the server. Similarly, once the server has averaged all the updates, it must send them back to the client. This incurs huge bandwidth and resource costs on large networks of millions of devices. As the field of federated learning advances, a few optimizations have been developed to minimize this communication. To address the footprint of the model, researchers have developed model compression techniques. In the client-server protocol, federated learning can also minimize communication through the selective sharing of updates on clients. Finally, efficient aggregation techniques can also streamline the communication process. @@ -459,15 +455,19 @@ In 2022, another team at Google proposed that each client communicates via a com There are many methods for selectively sharing updates. The general principle is that reducing the portion of the model that the clients are training on the edge reduces the memory necessary for training and the size of communication to the server. In basic federated learning, the client trains the entire model. This means that when a client sends an update to the server, it has gradients for every weight in the network. -However, we cannot just reduce communication by sending pieces of those gradients from each client to the server because the gradients are part of an entire update required to improve the model. Instead, you need to architecturally design the model such that each client trains only a small portion of the broader model, reducing the total communication while still gaining the benefit of training on client data. A paper [@shi2022data] from the University of Sheffield applies this concept to a CNN by splitting the global model into two parts: an upper and a lower part, as shown in @chen2023learning. +However, we cannot just reduce communication by sending pieces of those gradients from each client to the server because the gradients are part of an entire update required to improve the model. Instead, you need to architecturally design the model such that each client trains only a small portion of the broader model, reducing the total communication while still gaining the benefit of training on client data. @shi2022data apply this concept to a CNN by splitting the global model into two parts: an upper and a lower part, as shown in @chen2023learning. -![Split model architecture for selective sharing. Source: Shi et al., ([2022](https://doi.org/10.1145/3517207.3526980)).](images/png/ondevice_split_model.png){#fig-split-model} +![Federated learning with split model training. The model is divided into a lower part, trained locally on each client, and an upper part, refined on the server. Clients perform local updates, generating activation maps from their data, which are sent to the server instead of raw data to ensure privacy. The server uses these activation maps to update the upper part, then combines both parts and redistributes the updated model to clients. This setup minimizes communication, preserves privacy, and adapts the model to diverse client data. Source: Shi et al., ([2022](https://doi.org/10.1145/3517207.3526980)).](images/png/ondevice_split_model.png){#fig-split-model} - The lower part is designed to focus on generic features in the dataset, while the upper part, trained on those generic features, is designed to be more sensitive to the activation maps. This means that the lower part of the model is trained through standard federated averaging across all of the clients. Meanwhile, the upper part of the model is trained entirely on the server side from the activation maps generated by the clients. This approach drastically reduces communication for the model while still making the network robust to various types of input found in the data on the client devices. +The lower part of the model, responsible for extracting generic features, is trained directly on each client device. Using federated averaging, this lower part learns shared foundational features across all clients, allowing it to generalize well across varied data. Meanwhile, the upper part of the model, which captures more specific and complex patterns, is trained on the server. Rather than sending raw data to the server, each client generates activation maps—a compressed representation of its local data’s most relevant features—and sends these to the server. The server uses these activation maps to refine the upper part of the model, allowing it to become more sensitive to the diverse data distributions found across clients without compromising user privacy. + +This approach significantly reduces the communication load, as only summarized activation maps are transmitted instead of full datasets. By focusing on shared training for the lower part and specialized tuning for the upper part, the system achieves a balance: it minimizes data transfer, preserves privacy, and makes the model robust to varied input types encountered on client devices. ### Optimized Aggregation -In addition to reducing the communication overhead, optimizing the aggregation function can improve model training speed and accuracy in certain federated learning use cases. While the standard for aggregation is just averaging, various other approaches can improve model efficiency, accuracy, and security. One alternative is clipped averaging, which clips the model updates within a specific range. Another strategy to preserve security is differential privacy average aggregation. This approach integrates differential privacy into the aggregation step to protect client identities. Each client adds a layer of random noise to their updates before communicating to the server. The server then updates itself with the noisy updates, meaning that the amount of noise needs to be tuned carefully to balance privacy and accuracy. +In addition to reducing the communication overhead, optimizing the aggregation function can improve model training speed and accuracy in certain federated learning use cases. While the standard for aggregation is just averaging, various other approaches can improve model efficiency, accuracy, and security. + +One alternative is clipped averaging, which clips the model updates within a specific range. Another strategy to preserve security is differential privacy average aggregation. This approach integrates differential privacy into the aggregation step to protect client identities. Each client adds a layer of random noise to their updates before communicating to the server. The server then updates itself with the noisy updates, meaning that the amount of noise needs to be tuned carefully to balance privacy and accuracy. In addition to security-enhancing aggregation methods, there are several modifications to the aggregation methods that can improve training speed and performance by adding client metadata along with the weight updates. Momentum aggregation is a technique that helps address the convergence problem. In federated learning, client data can be extremely heterogeneous depending on the different environments in which the devices are used. That means that many models with heterogeneous data may need help to converge. Each client stores a momentum term locally, which tracks the pace of change over several updates. With clients communicating this momentum, the server can factor in the rate of change of each update when changing the global model to accelerate convergence. Similarly, weighted aggregation can factor in the client performance or other parameters like device type or network connection strength to adjust the weight with which the server should incorporate the model updates. Further descriptions of specific aggregation algorithms are provided by @moshawrab2023reviewing. @@ -489,7 +489,7 @@ Considering all of the factors influencing the efficacy of federated learning, l When selecting clients, there are three main components to consider: data heterogeneity, resource allocation, and communication cost. We can select clients on the previously proposed metrics in the non-IID section to address data heterogeneity. In federated learning, all devices may have different amounts of computing, resulting in some being more inefficient at training than others. When selecting a subset of clients for training, one must consider a balance of data heterogeneity and available resources. In an ideal scenario, you can always select the subset of clients with the greatest resources. However, this may skew your dataset, so a balance must be struck. Communication differences add another layer; you want to avoid being bottlenecked by waiting for devices with poor connections to transmit all their updates. Therefore, you must also consider choosing a subset of diverse yet well-connected devices. -### An Example of Deployed Federated Learning: G board +### Gboard Example A primary example of a deployed federated learning system is Google's Keyboard, Gboard, for Android devices. In implementing federated learning for the keyboard, Google focused on employing differential privacy techniques to protect the user's data and identity. Gboard leverages language models for several key features, such as Next Word Prediction (NWP), Smart Compose (SC), and On-The-Fly rescoring (OTF) [@xu2023federated], as shown in @fig-gboard-features. @@ -521,9 +521,13 @@ Want to train an image-savvy AI without sending your photos to the cloud? Federa ::: -### Benchmarking for Federated Learning: MedPerf +### Benchmarking Federated Learning: MedPerf -One of the richest examples of data on the edge is medical devices. These devices store some of the most personal data on users but offer huge advances in personalized treatment and better accuracy in medical AI. Given these two factors, medical devices are the perfect use case for federated learning. [MedPerf](https://doi.org/10.1038/s42256-023-00652-2) is an open-source platform used to benchmark models using federated evaluation [@karargyris2023federated]. Instead of just training models via federated learning, MedPerf takes the model to edge devices to test it against personalized data while preserving privacy. In this way, a benchmark committee can evaluate various models in the real world on edge devices while still preserving patient anonymity. +Medical devices represent one of the richest examples of data on the edge. These devices store some of the most personal user data while simultaneously offering significant advances in personalized treatment and improved accuracy in medical AI. This combination of sensitive data and potential for innovation makes medical devices an ideal use case for federated learning. + +A key development in this field is MedPerf, an open-source platform designed for benchmarking models using federated evaluation [@karargyris2023federated]. MedPerf goes beyond traditional federated learning by bringing the model to edge devices for testing against personalized data while maintaining privacy. This approach allows a benchmark committee to evaluate various models in real-world scenarios on edge devices without compromising patient anonymity. + +The MedPerf platform, detailed in a recent study (https://doi.org/10.1038/s42256-023-00652-2), demonstrates how federated techniques can be applied not just to model training, but also to model evaluation and benchmarking. This advancement is particularly crucial in the medical field, where the balance between leveraging large datasets for improved AI performance and protecting individual privacy is of utmost importance. ## Security Concerns @@ -545,7 +549,7 @@ Several data poisoning attack techniques exist: * **Label Flipping:** It involves applying incorrect labels to samples. For instance, in image classification, cat photos may be labeled as dogs to confuse the model. Flipping even [10% of labels](https://proceedings.mlr.press/v139/schwarzschild21a.html) can have significant consequences on the model. * **Data Insertion:** It introduces fake or distorted inputs into the training set. This could include pixelated images, noisy audio, or garbled text. -* **Logic Corruption:** This alters the underlying [patterns] () in data to mislead the model. In sentiment analysis, highly negative reviews may be marked positive through this technique. For this reason, recent surveys have shown that many companies are more [afraid of data poisoning](https://proceedings.mlr.press/v139/schwarzschild21a.html) than other adversarial ML concerns. +* **Logic Corruption:** This alters the underlying [patterns]() in data to mislead the model. In sentiment analysis, highly negative reviews may be marked positive through this technique. For this reason, recent surveys have shown that many companies are more [afraid of data poisoning](https://proceedings.mlr.press/v139/schwarzschild21a.html) than other adversarial ML concerns. What makes data poisoning alarming is how it exploits the discrepancy between curated datasets and live training data. Consider a cat photo dataset collected from the internet. In the weeks later, when this data trains a model on-device, new cat photos on the web differ significantly. @@ -687,7 +691,7 @@ Tiny Transfer Learning (TinyTL) enables memory-efficient on-device training thro To reduce this memory overhead, TinyTL freezes the majority of the weights so they do not need to be updated during training. This eliminates the need to store intermediate activations for frozen parts of the network. TinyTL only finetunes the bias terms, which are much smaller than the weights. An overview of TinyTL workflow is shown in @fig-tinytl-workflow. -![TinyTL workflow. Source: @cai2020tinytl.)](images/png/ondevice_transfer_tinytl.png){#fig-tinytl-workflow} +![TinyTL workflow. In (a), conventional transfer learning fine-tunes both weights and biases, requiring large memory (shown in blue) for activation maps during back-propagation. In (b), TinyTL reduces memory needs by fixing weights and fine-tuning only the biases, enabling transfer learning on smaller devices. Finally, in (c), TinyTL adds a "lite" residual learning component to compensate for fixed weights, using efficient group convolutions and avoiding memory-heavy bottlenecks, achieving high efficiency with minimal memory. Source: @cai2020tinytl.)](images/png/ondevice_transfer_tinytl.png){#fig-tinytl-workflow} Freezing weights apply to fully connected layers as well as convolutional and normalization layers. However, only adapting the biases limits the model's ability to learn and adapt to new data. @@ -699,10 +703,10 @@ By freezing most weights, TinyTL significantly reduces memory usage during on-de TinyTrain significantly reduces the time required for on-device training by selectively updating only certain parts of the model. It does this using a technique called task-adaptive sparse updating, as shown in @fig-tiny-train. -Based on the user data, memory, and computing available on the device, TinyTrain dynamically chooses which neural network layers to update during training. This layer selection is optimized to reduce computation and memory usage while maintaining high accuracy. - ![TinyTrain workflow. Source: @kwon2023tinytrain.](images/png/ondevice_pretraining.png){#fig-tiny-train} +Based on the user data, memory, and computing available on the device, TinyTrain dynamically chooses which neural network layers to update during training. This layer selection is optimized to reduce computation and memory usage while maintaining high accuracy. + More specifically, TinyTrain first does offline pretraining of the model. During pretraining, it not only trains the model on the task data but also meta-trains the model. Meta-training means training the model on metadata about the training process itself. This meta-learning improves the model's ability to adapt accurately even when limited data is available for the target task. Then, during the online adaptation stage, when the model is being customized on the device, TinyTrain performs task-adaptive sparse updates. Using the criteria around the device's capabilities, it selects only certain layers to update through backpropagation. The layers are chosen to balance accuracy, memory usage, and computation time. @@ -805,12 +809,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * @exr-fli ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we also offer a series of hands-on labs that allow students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience. - -* _Coming soon._ -::: diff --git a/contents/ops/images/png/cicd_pipelines.png b/contents/core/ops/images/png/cicd_pipelines.png similarity index 100% rename from contents/ops/images/png/cicd_pipelines.png rename to contents/core/ops/images/png/cicd_pipelines.png diff --git a/contents/ops/images/png/clinaiops.png b/contents/core/ops/images/png/clinaiops.png similarity index 100% rename from contents/ops/images/png/clinaiops.png rename to contents/core/ops/images/png/clinaiops.png diff --git a/contents/ops/images/png/clinaiops_loops.png b/contents/core/ops/images/png/clinaiops_loops.png similarity index 100% rename from contents/ops/images/png/clinaiops_loops.png rename to contents/core/ops/images/png/clinaiops_loops.png diff --git a/contents/ops/images/png/cover_ml_ops.png b/contents/core/ops/images/png/cover_ml_ops.png similarity index 100% rename from contents/ops/images/png/cover_ml_ops.png rename to contents/core/ops/images/png/cover_ml_ops.png diff --git a/contents/ops/images/png/data_cascades.png b/contents/core/ops/images/png/data_cascades.png similarity index 100% rename from contents/ops/images/png/data_cascades.png rename to contents/core/ops/images/png/data_cascades.png diff --git a/contents/ops/images/png/edge_impulse_dashboard.png b/contents/core/ops/images/png/edge_impulse_dashboard.png similarity index 100% rename from contents/ops/images/png/edge_impulse_dashboard.png rename to contents/core/ops/images/png/edge_impulse_dashboard.png diff --git a/contents/core/ops/images/png/hidden_debt.png b/contents/core/ops/images/png/hidden_debt.png new file mode 100644 index 00000000..d171877b Binary files /dev/null and b/contents/core/ops/images/png/hidden_debt.png differ diff --git a/contents/ops/images/png/impulse.png b/contents/core/ops/images/png/impulse.png similarity index 100% rename from contents/ops/images/png/impulse.png rename to contents/core/ops/images/png/impulse.png diff --git a/contents/ops/images/png/mlops_flow.png b/contents/core/ops/images/png/mlops_flow.png similarity index 100% rename from contents/ops/images/png/mlops_flow.png rename to contents/core/ops/images/png/mlops_flow.png diff --git a/contents/ops/images/png/mlops_overview_layers.png b/contents/core/ops/images/png/mlops_overview_layers.png similarity index 100% rename from contents/ops/images/png/mlops_overview_layers.png rename to contents/core/ops/images/png/mlops_overview_layers.png diff --git a/contents/ops/images/png/transfer_learning.png b/contents/core/ops/images/png/transfer_learning.png similarity index 100% rename from contents/ops/images/png/transfer_learning.png rename to contents/core/ops/images/png/transfer_learning.png diff --git a/contents/ops/ops.bib b/contents/core/ops/ops.bib similarity index 100% rename from contents/ops/ops.bib rename to contents/core/ops/ops.bib diff --git a/contents/ops/ops.qmd b/contents/core/ops/ops.qmd similarity index 98% rename from contents/ops/ops.qmd rename to contents/core/ops/ops.qmd index 46da88e6..1018d76a 100644 --- a/contents/ops/ops.qmd +++ b/contents/core/ops/ops.qmd @@ -5,7 +5,7 @@ bibliography: ops.bib # ML Operations {#sec-mlops} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-embedded-aiops-resource), [Videos](#sec-embedded-aiops-resource), [Exercises](#sec-embedded-aiops-resource), [Labs](#sec-embedded-aiops-resource) +Resources: [Slides](#sec-embedded-aiops-resource), [Videos](#sec-embedded-aiops-resource), [Exercises](#sec-embedded-aiops-resource) ::: ![_DALL·E 3 Prompt: Create a detailed, wide rectangular illustration of an AI workflow. The image should showcase the process across six stages, with a flow from left to right: 1. Data collection, with diverse individuals of different genders and descents using a variety of devices like laptops, smartphones, and sensors to gather data. 2. Data processing, displaying a data center with active servers and databases with glowing lights. 3. Model training, represented by a computer screen with code, neural network diagrams, and progress indicators. 4. Model evaluation, featuring people examining data analytics on large monitors. 5. Deployment, where the AI is integrated into robotics, mobile apps, and industrial equipment. 6. Monitoring, showing professionals tracking AI performance metrics on dashboards to check for accuracy and concept drift over time. Each stage should be distinctly marked and the style should be clean, sleek, and modern with a dynamic and informative color scheme._](images/png/cover_ml_ops.png) @@ -113,10 +113,12 @@ Learn more about ML Lifecycles through a case study featuring speech recognition In this chapter, we will provide an overview of the core components of MLOps, an emerging set of practices that enables robust delivery and lifecycle management of ML models in production. While some MLOps elements like automation and monitoring were covered in previous chapters, we will integrate them into a framework and expand on additional capabilities like governance. Additionally, we will describe and link to popular tools used within each component, such as [LabelStudio](https://labelstud.io/) for data labeling. By the end, we hope that you will understand the end-to-end MLOps methodology that takes models from ideation to sustainable value creation within organizations. -@fig-ops-layers shows the MLOps system stack. The MLOps lifecycle starts from data management and CI/CD pipelines for model development. Developed models go through model training and evaluation. Once trained to convergence, model deployment brings models up to production and ready to serve. After deployment, model serving reacts to workload changes and meets service level agreements cost-effectively when serving millions of end users or AI applications. Infrastructure management ensures the necessary resources are available and optimized throughout the lifecycle. Continuous monitoring, governance, and communication and collaboration are the remaining pieces of MLOps to ensure seamless development and operations of ML models. +@fig-ops-layers illustrates the comprehensive MLOps system stack. It shows the various layers involved in machine learning operations. At the top of the stack are ML Models/Applications, such as BERT, followed by ML Frameworks/Platforms like PyTorch. The core MLOps layer, labeled as Model Orchestration, encompasses several key components: Data Management, CI/CD, Model Training, Model Evaluation, Deployment, and Model Serving. Underpinning the MLOps layer is the Infrastructure layer, represented by technologies such as Kubernetes. This layer manages aspects such as Job Scheduling, Resource Management, Capacity Management, and Monitoring, among others. Holding it all together is the Hardware layer, which provides the necessary computational resources for ML operations. ![The MLOps stack, including ML Models, Frameworks, Model Orchestration, Infrastructure, and Hardware, illustrates the end-to-end workflow of MLOps.](images/png/mlops_overview_layers.png){#fig-ops-layers} +This layered approach in @fig-ops-layers demonstrates how MLOps integrates various technologies and processes to facilitate the development, deployment, and management of machine learning models in a production environment. The figure effectively illustrates the interdependencies between different components and how they come together to form a comprehensive MLOps ecosystem. + ### Data Management {#sec-ops-data-mgmt} Robust data management and data engineering actively empower successful [MLOps](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning) implementations. Teams properly ingest, store, and prepare raw data from sensors, databases, apps, and other systems for model training and deployment. @@ -492,7 +494,7 @@ Skilled project managers enable MLOps teams to work synergistically to rapidly d ## Embedded System Challenges -We will briefly review the challenges with embedded systems so that it sets the context for the specific challenges that emerge with embedded MLOps, which we will discuss in the following section. +Building on our discussion of [On-device Learning](../ondevice_learning/ondevice_learning.qmd) in the previous chapter, we now turn our attention to the broader context of embedded systems in MLOps. The unique constraints and requirements of embedded environments significantly impact the implementation of machine learning models and operations. To set the stage for the specific challenges that emerge with embedded MLOps, it is important to first review the general challenges associated with embedded systems. This overview will provide a foundation for understanding how these constraints intersect with and shape the practices of MLOps in resource-limited, edge computing scenarios. ### Limited Compute Resources @@ -762,9 +764,9 @@ Despite the proliferation of new MLOps tools in response to the increase in dema * Seamless deployment onto edge devices through compilation, SDKs, and benchmarks * Collaboration features for teams and integration with other platforms -With Edge Impulse, developers with limited data science expertise can develop specialized ML models that run efficiently within small computing environments. It provides a comprehensive solution for creating embedded intelligence and advancing machine learning. @fig-edge-impulse further illustrates this concept. +Edge Impulse offers a comprehensive solution for creating embedded intelligence and advancing machine learning, particularly for developers with limited data science expertise. This platform enables the development of specialized ML models that run efficiently within small computing environments. As illustrated in @fig-edge-impulse, Edge Impulse facilitates the journey from data collection to model deployment, highlighting its user-friendly interface and tools that simplify the creation of embedded ML solutions, thus making it accessible to a broader range of developers and applications. -![The inner workings of edge impulse. Source: [Edge Impulse](https://www.edgeimpulse.com/blog/getting-started-with-edge-impulse/)](images/png/impulse.png){#fig-edge-impulse} +![Edge impulse overview. Source: [Edge Impulse](https://www.edgeimpulse.com/blog/getting-started-with-edge-impulse/)](images/png/impulse.png){#fig-edge-impulse} ##### User Interface @@ -1021,13 +1023,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * @exr-ei ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we also offer a series of hands-on labs that allow students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. 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@@ bibliography: optimizations.bib # Model Optimizations {#sec-model_optimizations} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-model-optimizations-resource), [Videos](#sec-model-optimizations-resource), [Exercises](#sec-model-optimizations-resource), [Labs](#sec-model-optimizations-resource) +Resources: [Slides](#sec-model-optimizations-resource), [Videos](#sec-model-optimizations-resource), [Exercises](#sec-model-optimizations-resource) ::: ![_DALL·E 3 Prompt: Illustration of a neural network model represented as a busy construction site, with a diverse group of construction workers, both male and female, of various ethnicities, labeled as 'pruning', 'quantization', and 'sparsity'. They are working together to make the neural network more efficient and smaller, while maintaining high accuracy. The 'pruning' worker, a Hispanic female, is cutting unnecessary connections from the middle of the network. The 'quantization' worker, a Caucasian male, is adjusting or tweaking the weights all over the place. The 'sparsity' worker, an African female, is removing unnecessary nodes to shrink the model. Construction trucks and cranes are in the background, assisting the workers in their tasks. The neural network is visually transforming from a complex and large structure to a more streamlined and smaller one._](images/png/cover_model_optimizations.png) @@ -33,13 +33,17 @@ When machine learning models are deployed on systems, especially on resource-con ## Introduction -We have structured this chapter in three tiers. First, in @sec-model_ops_representation we examine the significance and methodologies of reducing the parameter complexity of models without compromising their inference capabilities. Techniques such as pruning and knowledge distillation are discussed, offering insights into how models can be compressed and simplified while maintaining, or even enhancing, their performance. +The optimization of machine learning models for practical deployment is a critical aspect of AI systems. This chapter focuses on exploring model optimization techniques as they relate to the development of ML systems, ranging from high-level model architecture considerations to low-level hardware adaptations. @fig-3-sections Illustrates the three layers of the optimization stack we cover. -Going one level lower, in @sec-model_ops_numerics, we study the role of numerical precision in model computations and how altering it impacts model size, speed, and accuracy. We will examine the various numerical formats and how reduced-precision arithmetic can be leveraged to optimize models for embedded deployment. +![Three layers to be covered.](images/png/modeloptimization_structure.png){#fig-3-sections width=50%} -Finally, as we go lower and closer to the hardware, in @sec-model_ops_hw, we will navigate through the landscape of hardware-software co-design, exploring how models can be optimized by tailoring them to the specific characteristics and capabilities of the target hardware. We will discuss how models can be adapted to exploit the available hardware resources effectively. +At the highest level, we examine methodologies for reducing the complexity of model parameters without compromising inferential capabilities. Techniques such as pruning and knowledge distillation offer powerful approaches to compress and refine models while maintaining or even improving their performance, not only in terms of model quality but also in actual system runtime performance. These methods are crucial for creating efficient models that can be deployed in resource-constrained environments. -![Three layers to be covered.](images/png/modeloptimization_structure.png){#fig-3-sections width=50%} +Furthermore, we explore the role of numerical precision in model computations. Understanding how different levels of numerical precision impact model size, speed, and accuracy is essential for optimizing performance. We investigate various numerical formats and the application of reduced-precision arithmetic, particularly relevant for embedded system deployments where computational resources are often limited. + +At the lowest level, we navigate the intricate landscape of hardware-software co-design. This exploration reveals how models can be tailored to leverage the specific characteristics and capabilities of target hardware platforms. By aligning model design with hardware architecture, we can significantly enhance performance and efficiency. + +This collective approach focuses on helping us develop and deploy efficient, powerful, and hardware-aware machine learning models. From simplifying model architectures to fine-tuning numerical precision and adapting to specific hardware, this chapter covers the full spectrum of optimization strategies. By the conclusion of this chapter, readers will have gained a thorough understanding of various optimization techniques and their practical applications in real-world scenarios. This knowledge is important for creating machine learning models that not only perform well but are also optimized for the constraints and opportunities presented by modern computing environments. ## Efficient Model Representation {#sec-model_ops_representation} @@ -98,7 +102,7 @@ There are several techniques for assigning these importance scores: The idea is to measure, either directly or indirectly, the contribution of each component to the model's output. Structures with minimal influence according to the defined criteria are pruned first. This enables selective, optimized pruning that maximally compresses models while preserving predictive capacity. In general, it is important to evaluate the impact of removing particular structures on the model's output, with recent works such as [@rachwan2022winning] and [@lubana2020gradient] investigating combinations of techniques like magnitude-based pruning and gradient-based pruning. -##### 3. Selecting a pruning strategy +##### 3. Selecting a Pruning Strategy Now that you understand some techniques for determining the importance of structures within a neural network, the next step is to decide how to apply these insights. This involves selecting an appropriate pruning strategy, which dictates how and when the identified structures are removed and how the model is fine-tuned to maintain its performance. Two main structured pruning strategies exist: iterative pruning and one-shot pruning. @@ -112,8 +116,7 @@ Consider a situation where we wish to prune the 6 least effective channels (base The choice between these strategies involves weighing factors like model size, target sparsity level, available compute and acceptable accuracy losses. One-shot pruning can rapidly compress models, but iterative pruning may enable better accuracy retention for a target level of pruning. In practice, the strategy is tailored based on use case constraints. The overarching aim is to generate an optimal strategy that removes redundancy, achieves efficiency gains through pruning, and finely tunes the model to stabilize accuracy at an acceptable level for deployment. -Now consider the same network we had in the iterative pruning example. Whereas in the iterative process we pruned 2 channels at a time, in the one-shot pruning we would prune the 6 channels at once (@fig-oneshot-pruning). Removing 27% of the network's channel simultaneously alters the structure significantly, causing the accuracy to drop from 0.995 to 0.914. Given the major changes, the network is not able to properly adapt during fine-tuning, and the accuracy went up to 0.943, a 5% degradation from the accuracy of the unpruned network. While the final structures in both iterative pruning and oneshot pruning processes are identical, the former is able to maintain high performance while the latter suffers significant degradations. - +Now consider the same network we had in the iterative pruning example. Whereas in the iterative process we pruned 2 channels at a time, in the one-shot pruning we would prune the 6 channels at once, as shown in @fig-oneshot-pruning. Removing 27% of the network's channel simultaneously alters the structure significantly, causing the accuracy to drop from 0.995 to 0.914. Given the major changes, the network is not able to properly adapt during fine-tuning, and the accuracy went up to 0.943, a 5% degradation from the accuracy of the unpruned network. While the final structures in both iterative pruning and oneshot pruning processes are identical, the former is able to maintain high performance while the latter suffers significant degradations. ![One-shot pruning.](images/jpg/modeloptimization_oneshot_pruning.jpeg){#fig-oneshot-pruning} @@ -271,12 +274,12 @@ Furthermore, in scenarios where data evolves or grows over time, developing LRMF #### Tensor Decomposition -You have learned in @sec-tensor-data-structures that tensors are flexible structures, commonly used by ML Frameworks, that can represent data in higher dimensions. Similar to low-rank matrix factorization, more complex models may store weights in higher dimensions, such as tensors. Tensor decomposition is the higher-dimensional analogue of matrix factorization, where a model tensor is decomposed into lower rank components (see @fig-tensor-decomposition). These lower-rank components are easier to compute on and store but may suffer from the same issues mentioned above, such as information loss and the need for nuanced hyperparameter tuning. Mathematically, given a tensor $\mathcal{A}$, tensor decomposition seeks to represent $\mathcal{A}$ as a combination of simpler tensors, facilitating a compressed representation that approximates the original data while minimizing the loss of information. - -The work of Tamara G. Kolda and Brett W. Bader, ["Tensor Decompositions and Applications"](https://epubs.siam.org/doi/abs/10.1137/07070111X) (2009), stands out as a seminal paper in the field of tensor decompositions. The authors provide a comprehensive overview of various tensor decomposition methods, exploring their mathematical underpinnings, algorithms, and a wide array of applications, ranging from signal processing to data mining. Of course, the reason we are discussing it is because it has huge potential for system performance improvements, particularly in the space of TinyML, where throughput and memory footprint savings are crucial to feasibility of deployments. +You have learned in @sec-tensor-data-structures that tensors are flexible structures, commonly used by ML Frameworks, that can represent data in higher dimensions. Similar to low-rank matrix factorization, more complex models may store weights in higher dimensions, such as tensors. Tensor decomposition is the higher-dimensional analogue of matrix factorization, where a model tensor is decomposed into lower-rank components (see @fig-tensor-decomposition). These lower-rank components are easier to compute on and store but may suffer from the same issues mentioned above, such as information loss and the need for nuanced hyperparameter tuning. Mathematically, given a tensor $\mathcal{A}$, tensor decomposition seeks to represent $\mathcal{A}$ as a combination of simpler tensors, facilitating a compressed representation that approximates the original data while minimizing the loss of information. ![Tensor decomposition. Source: @xinyu.](images/png/modeloptimization_tensor_decomposition.png){#fig-tensor-decomposition} +The work of Tamara G. Kolda and Brett W. Bader, ["Tensor Decompositions and Applications"](https://epubs.siam.org/doi/abs/10.1137/07070111X) (2009), stands out as a seminal paper in the field of tensor decompositions. The authors provide a comprehensive overview of various tensor decomposition methods, exploring their mathematical underpinnings, algorithms, and a wide array of applications, ranging from signal processing to data mining. Of course, the reason we are discussing it is because it has huge potential for system performance improvements, particularly in the space of TinyML, where throughput and memory footprint savings are crucial to feasibility of deployments. + :::{#exr-mc .callout-caution collapse="true"} ### Scalable Model Compression with TensorFlow @@ -325,7 +328,7 @@ TinyNAS and MorphNet represent a few of the many significant advancements in the ### Edge-Aware Model Design -Imagine you're building a tiny robot that can identify different flowers. It needs to be smart, but also small and energy-efficient! In the "Edge-Aware Model Design" world, we learned about techniques like depthwise separable convolutions and architectures like SqueezeNet, MobileNet, and EfficientNet – all designed to pack intelligence into compact models. Now, let's see these ideas in action with some xColabs: +Imagine you're building a tiny robot that can identify different flowers. It needs to be smart, but also small and energy-efficient! In the "Edge-Aware Model Design" world, we learned about techniques like depthwise separable convolutions and architectures like SqueezeNet, MobileNet, and EfficientNet---all designed to pack intelligence into compact models. Now, let's see these ideas in action with some xColabs: **SqueezeNet in Action:** Maybe you'd like a Colab showing how to train a SqueezeNet model on a flower image dataset. This would demonstrate its small size and how it learns to recognize patterns despite its efficiency. @@ -566,7 +569,6 @@ Symmetric clipping ranges are the most widely adopted in practice as they have t Asymmetric quantization maps real values to an asymmetrical clipping range that isn't necessarily centered around 0, as shown in @fig-quantization-symmetry on the right. It involves choosing a range [$\alpha$, $\beta$] where $\alpha \neq -\beta$. For example, selecting a range based on the minimum and maximum real values, or where $\alpha = r_{min}$ and $\beta = r_{max}$, creates an asymmetric range. Typically, asymmetric quantization produces tighter clipping ranges compared to symmetric quantization, which is important when target weights and activations are imbalanced, e.g., the activation after the ReLU always has non-negative values. Despite producing tighter clipping ranges, asymmetric quantization is less preferred to symmetric quantization as it doesn't always zero out the real value zero. - ![Quantization (a)symmetry. Source: @gholami2021survey.](images/png/efficientnumerics_symmetry.png){#fig-quantization-symmetry} #### Granularity @@ -594,17 +596,49 @@ Between the two, calculating the range dynamically usually is very costly, so mo ### Techniques -The two prevailing techniques for quantizing models are Post Training Quantization and Quantization-Aware Training. +When optimizing machine learning models for deployment, various quantization techniques are used to balance model efficiency, accuracy, and adaptability. Each method---post-training quantization, quantization-aware training, and dynamic quantization--offers unique advantages and trade-offs, impacting factors such as implementation complexity, computational overhead, and performance optimization. + +@tbl-quantization_methods provides an overview of these quantization methods, highlighting their respective strengths, limitations, and trade-offs. We will delve deeper into each of these methods because they are widely deployed and used across all ML systems of wildly different scales. + ++------------------------------+------------------------------+------------------------------+------------------------------+ +| Aspect | Post Training Quantization | Quantization-Aware Training | Dynamic Quantization | ++:=============================+:=============================+:=============================+:=============================+ +| **Pros** | | | | ++------------------------------+------------------------------+------------------------------+------------------------------+ +| Simplicity | ✓ | ✗ | ✗ | ++------------------------------+------------------------------+------------------------------+------------------------------+ +| Accuracy Preservation | ✗ | ✓ | ✓ | ++------------------------------+------------------------------+------------------------------+------------------------------+ +| Adaptability | ✗ | ✗ | ✓ | ++------------------------------+------------------------------+------------------------------+------------------------------+ +| Optimized Performance | ✗ | ✓ | Potentially | ++------------------------------+------------------------------+------------------------------+------------------------------+ +| **Cons** | | | | ++------------------------------+------------------------------+------------------------------+------------------------------+ +| Accuracy Degradation | ✓ | ✗ | Potentially | ++------------------------------+------------------------------+------------------------------+------------------------------+ +| Computational Overhead | ✗ | ✓ | ✓ | ++------------------------------+------------------------------+------------------------------+------------------------------+ +| Implementation Complexity | ✗ | ✓ | ✓ | ++------------------------------+------------------------------+------------------------------+------------------------------+ +| **Tradeoffs** | | | | ++------------------------------+------------------------------+------------------------------+------------------------------+ +| Speed vs. Accuracy | ✓ | ✗ | ✗ | ++------------------------------+------------------------------+------------------------------+------------------------------+ +| Accuracy vs. Cost | ✗ | ✓ | ✗ | ++------------------------------+------------------------------+------------------------------+------------------------------+ +| Adaptability vs. Overhead | ✗ | ✗ | ✓ | ++------------------------------+------------------------------+------------------------------+------------------------------+ + +: Comparison of post-training quantization, quantization-aware training, and dynamic quantization. {#tbl-quantization_methods .striped .hover} **Post Training Quantization:** Post-training quantization (PTQ) is a quantization technique where the model is quantized after it has been trained. The model is trained in floating point and then weights and activations are quantized as a post-processing step. This is the simplest approach and does not require access to the training data. Unlike Quantization-Aware Training (QAT), PTQ sets weight and activation quantization parameters directly, making it low-overhead and suitable for limited or unlabeled data situations. However, not readjusting the weights after quantizing, especially in low-precision quantization can lead to very different behavior and thus lower accuracy. To tackle this, techniques like bias correction, equalizing weight ranges, and adaptive rounding methods have been developed. PTQ can also be applied in zero-shot scenarios, where no training or testing data are available. This method has been made even more efficient to benefit compute- and memory- intensive large language models. Recently, SmoothQuant, a training-free, accuracy-preserving, and general-purpose PTQ solution which enables 8-bit weight, 8-bit activation quantization for LLMs, has been developed, demonstrating up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy [[@xiao2022smoothquant]](https://arxiv.org/abs/2211.10438). - In PTQ, a pretrained model undergoes a calibration process, as shown in @fig-PTQ-diagram. Calibration involves using a separate dataset known as calibration data, a specific subset of the training data reserved for quantization to help find the appropriate clipping ranges and scaling factors. ![Post-Training Quantization and calibration. Source: @gholami2021survey.](images/png/efficientnumerics_PTQ.png){#fig-PTQ-diagram} -**Quantization-Aware Training:** Quantization-aware training (QAT) is a fine-tuning of the PTQ model. The model is trained aware of quantization, allowing it to adjust for quantization effects. This produces better accuracy with quantized inference. Quantizing a trained neural network model with methods such as PTQ introduces perturbations that can deviate the model from its original convergence point. For instance, Krishnamoorthi showed that even with per-channel quantization, networks like MobileNet do not reach baseline accuracy with int8 Post Training Quantization (PTQ) and require Quantization-Aware Training (QAT) [[@krishnamoorthi2018quantizing]](https://arxiv.org/abs/1806.08342).To address this, QAT retrains the model with quantized parameters, employing forward and backward passes in floating point but quantizing parameters after each gradient update. Handling the non-differentiable quantization operator is crucial; a widely used method is the Straight Through Estimator (STE), approximating the rounding operation as an identity function. While other methods and variations exist, STE remains the most commonly used due to its practical effectiveness. -In QAT, a pretrained model is quantized and then finetuned using training data to adjust parameters and recover accuracy degradation, as shown in @fig-QAT-diagram. The calibration process is often conducted in parallel with the finetuning process for QAT. +**Quantization-Aware Training:** Quantization-aware training (QAT) is a fine-tuning of the PTQ model. The model is trained aware of quantization, allowing it to adjust for quantization effects. This produces better accuracy with quantized inference. Quantizing a trained neural network model with methods such as PTQ introduces perturbations that can deviate the model from its original convergence point. For instance, Krishnamoorthi showed that even with per-channel quantization, networks like MobileNet do not reach baseline accuracy with int8 PTQ and require QAT [[@krishnamoorthi2018quantizing]](https://arxiv.org/abs/1806.08342).To address this, QAT retrains the model with quantized parameters, employing forward and backward passes in floating point but quantizing parameters after each gradient update. Handling the non-differentiable quantization operator is crucial; a widely used method is the Straight Through Estimator (STE), approximating the rounding operation as an identity function. While other methods and variations exist, STE remains the most commonly used due to its practical effectiveness. In QAT, a pretrained model is quantized and then finetuned using training data to adjust parameters and recover accuracy degradation, as shown in @fig-QAT-diagram. The calibration process is often conducted in parallel with the finetuning process for QAT. ![Quantization-Aware Training. Source: @gholami2021survey.](images/png/efficientnumerics_QAT.png){#fig-QAT-diagram} @@ -616,21 +650,6 @@ Quantization-Aware Training serves as a natural extension of Post-Training Quant ![Relative accuracies of PTQ and QAT. Source: @wu2020integer.](images/png/efficientnumerics_PTQQATsummary.png){#fig-quantization-methods-summary} -| **Aspect** | **Post Training Quantization** | **Quantization-Aware Training** | **Dynamic Quantization** | -|:------------------------------|:------------------------------|:------------------------------|:------------------------------| -| **Pros** | | | | -| Simplicity | ✓ | ✗ | ✗ | -| Accuracy Preservation | ✗ | ✓ | ✓ | -| Adaptability | ✗ | ✗ | ✓ | -| Optimized Performance | ✗ | ✓ | Potentially | -| **Cons** | | | | -| Accuracy Degradation| ✓ | ✗ | Potentially | -| Computational Overhead | ✗ | ✓ | ✓ | -| Implementation Complexity | ✗ | ✓ | ✓ | -| **Tradeoffs** | | | | -| Speed vs. Accuracy |✓ | ✗ | ✗ | -| Accuracy vs. Cost | ✗ | ✓ | ✗ | -| Adaptability vs. Overhead | ✗ | ✗ | ✓ | ### Weights vs. Activations @@ -688,7 +707,6 @@ Efficient hardware implementation transcends the selection of suitable component Focusing only on the accuracy when performing Neural Architecture Search leads to models that are exponentially complex and require increasing memory and compute. This has lead to hardware constraints limiting the exploitation of the deep learning models at their full potential. Manually designing the architecture of the model is even harder when considering the hardware variety and limitations. This has lead to the creation of Hardware-aware Neural Architecture Search that incorporate the hardware contractions into their search and optimize the search space for a specific hardware and accuracy. HW-NAS can be categorized based how it optimizes for hardware. We will briefly explore these categories and leave links to related papers for the interested reader. - #### Single Target, Fixed Platform Configuration The goal here is to find the best architecture in terms of accuracy and hardware efficiency for one fixed target hardware. For a specific hardware, the Arduino Nicla Vision for example, this category of HW-NAS will look for the architecture that optimizes accuracy, latency, energy consumption, etc. @@ -783,10 +801,11 @@ In a contrasting approach, hardware can be custom-designed around software requi ![Delegating data processing to an FPGA. Source: @kwon2021hardwaresoftware.](images/png/modeloptimization_preprocessor.png){#fig-fpga-preprocessing} - #### SplitNets -SplitNets were introduced in the context of Head-Mounted systems. They distribute the Deep Neural Networks (DNNs) workload among camera sensors and an aggregator. This is particularly compelling the in context of TinyML. The SplitNet framework is a split-aware NAS to find the optimal neural network architecture to achieve good accuracy, split the model among the sensors and the aggregator, and minimize the communication between the sensors and the aggregator. @fig-splitnet-performance demonstrates how SplitNets (in red) achieves higher accuracy for lower latency (running on ImageNet) than different approaches, such as running the DNN on-sensor (All-on-sensor; in green) or on mobile (All-on-aggregator; in blue). Minimal communication is important in TinyML where memory is highly constrained, this way the sensors conduct some of the processing on their chips and then they send only the necessary information to the aggregator. When testing on ImageNet, SplitNets were able to reduce the latency by one order of magnitude on head-mounted devices. This can be helpful when the sensor has its own chip. [@dong2022splitnets] +SplitNets were introduced in the context of Head-Mounted systems. They distribute the Deep Neural Networks (DNNs) workload among camera sensors and an aggregator. This is particularly compelling the in context of TinyML. The SplitNet framework is a split-aware NAS to find the optimal neural network architecture to achieve good accuracy, split the model among the sensors and the aggregator, and minimize the communication between the sensors and the aggregator. + +@fig-splitnet-performance demonstrates how SplitNets (in red) achieves higher accuracy for lower latency (running on ImageNet) than different approaches, such as running the DNN on-sensor (All-on-sensor; in green) or on mobile (All-on-aggregator; in blue). Minimal communication is important in TinyML where memory is highly constrained, this way the sensors conduct some of the processing on their chips and then they send only the necessary information to the aggregator. When testing on ImageNet, SplitNets were able to reduce the latency by one order of magnitude on head-mounted devices. This can be helpful when the sensor has its own chip. [@dong2022splitnets] ![SplitNets vs other approaches. Source: @dong2022splitnets.](images/png/modeloptimization_SplitNets.png){#fig-splitnet-performance} @@ -804,9 +823,9 @@ Without the extensive software innovation across frameworks, optimization tools Major machine learning frameworks like TensorFlow, PyTorch, and MXNet provide libraries and APIs to allow common model optimization techniques to be applied without requiring custom implementations. For example, TensorFlow offers the TensorFlow Model Optimization Toolkit which contains modules like: -* [quantization](https://www.tensorflow.org/model_optimization/api_docs/python/tfmot/quantization/keras/quantize_model) - Applies quantization-aware training to convert floating point models to lower precision like int8 with minimal accuracy loss. Handles weight and activation quantization. -* [sparsity](https://www.tensorflow.org/model_optimization/api_docs/python/tfmot/sparsity/keras) - Provides pruning APIs to induce sparsity and remove unnecessary connections in models like neural networks. Can prune weights, layers, etc. -* [clustering](https://www.tensorflow.org/model_optimization/api_docs/python/tfmot/clustering) - Supports model compression by clustering weights into groups for higher compression rates. +* **[Quantization](https://www.tensorflow.org/model_optimization/api_docs/python/tfmot/quantization/keras/quantize_model)**: Applies quantization-aware training to convert floating point models to lower precision like int8 with minimal accuracy loss. Handles weight and activation quantization. +* **[Sparsity](https://www.tensorflow.org/model_optimization/api_docs/python/tfmot/sparsity/keras)**: Provides pruning APIs to induce sparsity and remove unnecessary connections in models like neural networks. Can prune weights, layers, etc. +* **[Clustering](https://www.tensorflow.org/model_optimization/api_docs/python/tfmot/clustering)**: Supports model compression by clustering weights into groups for higher compression rates. These APIs allow users to enable optimization techniques like quantization and pruning without directly modifying model code. Parameters like target sparsity rates, quantization bit-widths etc. can be configured. Similarly, PyTorch provides torch.quantization for converting models to lower precision representations. TorchTensor and TorchModule form the base classes for quantization support. It also offers torch.nn.utils.prune for built-in pruning of models. MXNet offers gluon.contrib layers that add quantization capabilities like fixed point rounding and stochastic rounding of weights/activations during training. This allows quantization to be readily included in gluon models. @@ -816,9 +835,9 @@ The core benefit of built-in optimizations is that users can apply them without Automated optimization tools provided by frameworks can analyze models and automatically apply optimizations like quantization, pruning, and operator fusion to make the process easier and accessible without excessive manual tuning. In effect, this builds on top of the previous section. For example, TensorFlow provides the TensorFlow Model Optimization Toolkit which contains modules like: -* [QuantizationAwareTraining](https://www.tensorflow.org/model_optimization/guide/quantization/training) - Automatically quantizes weights and activations in a model to lower precision like UINT8 or INT8 with minimal accuracy loss. It inserts fake quantization nodes during training so that the model can learn to be quantization-friendly. -* [Pruning](https://www.tensorflow.org/model_optimization/guide/pruning/pruning_with_keras) - Automatically removes unnecessary connections in a model based on analysis of weight importance. Can prune entire filters in convolutional layers or attention heads in transformers. Handles iterative re-training to recover any accuracy loss. -* [GraphOptimizer](https://www.tensorflow.org/guide/graph_optimization) - Applies graph optimizations like operator fusion to consolidate operations and reduce execution latency, especially for inference. In @fig-graph-optimizer, you can see the original (Source Graph) on the left, and how its operations are transformed (consolidated) on the right. Notice how Block1 in Source Graph has 3 separate steps (Convolution, BiasAdd, and Activation), which are then consolidated together in Block1 on Optimized Graph. +* **[QuantizationAwareTraining](https://www.tensorflow.org/model_optimization/guide/quantization/training)**: Automatically quantizes weights and activations in a model to lower precision like UINT8 or INT8 with minimal accuracy loss. It inserts fake quantization nodes during training so that the model can learn to be quantization-friendly. +* **[Pruning](https://www.tensorflow.org/model_optimization/guide/pruning/pruning_with_keras)**: Automatically removes unnecessary connections in a model based on analysis of weight importance. Can prune entire filters in convolutional layers or attention heads in transformers. Handles iterative re-training to recover any accuracy loss. +* **[GraphOptimizer](https://www.tensorflow.org/guide/graph_optimization)**: Applies graph optimizations like operator fusion to consolidate operations and reduce execution latency, especially for inference. In @fig-graph-optimizer, you can see the original (Source Graph) on the left, and how its operations are transformed (consolidated) on the right. Notice how Block1 in Source Graph has 3 separate steps (Convolution, BiasAdd, and Activation), which are then consolidated together in Block1 on Optimized Graph. ![GraphOptimizer. Source: @annette2020.](./images/png/source_opt.png){#fig-graph-optimizer} @@ -954,12 +973,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * @exr-md ::: - -:::{.callout-warning collapse="false"} -#### Labs - -In addition to exercises, we also offer a series of hands-on labs that allow students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience. - -* *Coming soon.* -::: - diff --git a/contents/privacy_security/images/png/image14.png b/contents/core/privacy_security/images/png/Data_poisoning.png similarity index 100% rename from contents/privacy_security/images/png/image14.png rename to contents/core/privacy_security/images/png/Data_poisoning.png diff --git a/contents/privacy_security/images/png/image3.png b/contents/core/privacy_security/images/png/Fault-injection_demonstrated_with_assembly_code.png similarity index 100% rename from contents/privacy_security/images/png/image3.png rename to contents/core/privacy_security/images/png/Fault-injection_demonstrated_with_assembly_code.png diff --git a/contents/privacy_security/images/png/image7.png b/contents/core/privacy_security/images/png/Federated_Learning_lifecycle.png similarity index 100% rename from contents/privacy_security/images/png/image7.png rename to 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b/contents/core/privacy_security/images/png/stuxnet.png similarity index 100% rename from contents/privacy_security/images/png/stuxnet.png rename to contents/core/privacy_security/images/png/stuxnet.png diff --git a/contents/privacy_security/privacy_security.bib b/contents/core/privacy_security/privacy_security.bib similarity index 100% rename from contents/privacy_security/privacy_security.bib rename to contents/core/privacy_security/privacy_security.bib diff --git a/contents/privacy_security/privacy_security.qmd b/contents/core/privacy_security/privacy_security.qmd similarity index 91% rename from contents/privacy_security/privacy_security.qmd rename to contents/core/privacy_security/privacy_security.qmd index 764e2765..8a14f2f5 100644 --- a/contents/privacy_security/privacy_security.qmd +++ b/contents/core/privacy_security/privacy_security.qmd @@ -5,7 +5,7 @@ bibliography: privacy_security.bib # Security & Privacy {#sec-security_privacy} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-security-and-privacy-resource), [Videos](#sec-security-and-privacy-resource), [Exercises](#sec-security-and-privacy-resource), [Labs](#sec-security-and-privacy-resource) +Resources: [Slides](#sec-security-and-privacy-resource), [Videos](#sec-security-and-privacy-resource), [Exercises](#sec-security-and-privacy-resource) ::: ![_DALL·E 3 Prompt: An illustration on privacy and security in machine learning systems. The image shows a digital landscape with a network of interconnected nodes and data streams, symbolizing machine learning algorithms. In the foreground, there's a large lock superimposed over the network, representing privacy and security. The lock is semi-transparent, allowing the underlying network to be partially visible. The background features binary code and digital encryption symbols, emphasizing the theme of cybersecurity. The color scheme is a mix of blues, greens, and grays, suggesting a high-tech, digital environment._](images/png/cover_security_privacy.png) @@ -236,7 +236,7 @@ On the other hand, this tool can be used maliciously and affect legitimate gener @fig-poisoning demonstrates the effects of different levels of data poisoning (50 samples, 100 samples, and 300 samples of poisoned images) on generating images in various categories. Notice how the images start deforming and deviating from the desired category. For example, after 300 poison samples, a car prompt generates a cow. -![Data poisoning. Source: @shan2023prompt.](images/png/image14.png){#fig-poisoning} +![Data poisoning. Source: @shan2023prompt.](images/png/Data_poisoning.png){#fig-poisoning} ### Adversarial Attacks @@ -338,9 +338,9 @@ Various physical tampering techniques can be used for fault injection. Low volta For ML systems, consequences include impaired model accuracy, denial of service, extraction of private training data or model parameters, and reverse engineering of model architectures. Attackers could use fault injection to force misclassifications, disrupt autonomous systems, or steal intellectual property. -For example, in [@breier2018deeplaser], the authors successfully injected a fault attack into a deep neural network deployed on a microcontroller. They used a laser to heat specific transistors, forcing them to switch states. In one instance, they used this method to attack a ReLU activation function, resulting in the function always outputting a value of 0, regardless of the input. In the assembly code in @fig-injection, the attack caused the executing program always to skip the `jmp end` instruction on line 6. This means that `HiddenLayerOutput[i]` is always set to 0, overwriting any values written to it on lines 4 and 5. As a result, the targeted neurons are rendered inactive, resulting in misclassifications. +For example, @breier2018deeplaser successfully injected a fault attack into a deep neural network deployed on a microcontroller. They used a laser to heat specific transistors, forcing them to switch states. In one instance, they used this method to attack a ReLU activation function, resulting in the function always outputting a value of 0, regardless of the input. In the assembly code shown in @fig-injection, the attack caused the executing program always to skip the `jmp end` instruction on line 6. This means that `HiddenLayerOutput[i]` is always set to 0, overwriting any values written to it on lines 4 and 5. As a result, the targeted neurons are rendered inactive, resulting in misclassifications. -![Fault-injection demonstrated with assembly code. Source: @breier2018deeplaser.](images/png/image3.png){#fig-injection} +![Fault-injection demonstrated with assembly code. Source: @breier2018deeplaser.](images/png/Fault-injection_demonstrated_with_assembly_code.png){#fig-injection} An attacker's strategy could be to infer information about the activation functions using side-channel attacks (discussed next). Then, the attacker could attempt to target multiple activation function computations by randomly injecting faults into the layers as close to the output layer as possible, increasing the likelihood and impact of the attack. @@ -368,15 +368,15 @@ Below, a simplified visualization illustrates how analyzing the encryption devic First, the power analysis of the device's operations after entering a correct password is shown in the first picture in @fig-encryption. The dense blue graph outputs the encryption device's voltage measurement. What is significant here is the comparison between the different analysis charts rather than the specific details of what is happening in each scenario. -![Power analysis of an encryption device with a correct password. Source: [Colin O'Flynn.](https://www.youtube.com/watch?v=2iDLfuEBcs8)](images/png/image5.png){#fig-encryption} +![Power analysis of an encryption device with a correct password. Source: [Colin O'Flynn.](https://www.youtube.com/watch?v=2iDLfuEBcs8)](images/png/Power_analysis_of_an_encryption_device_with_a_correct_password.png){#fig-encryption} When an incorrect password is entered, the power analysis chart is shown in @fig-encryption2. The first three bytes of the password are correct. As a result, the voltage patterns are very similar or identical between the two charts, up to and including the fourth byte. After the device processes the fourth byte, a mismatch between the secret key and the attempted input is determined. A change in the pattern at the transition point between the fourth and fifth bytes is noticed: the voltage increases (the current decreases) because the device has stopped processing the rest of the input. -![Power analysis of an encryption device with a (partially) wrong password. Source: [Colin O'Flynn.](https://www.youtube.com/watch?v=2iDLfuEBcs8)](images/png/image16.png){#fig-encryption2} +![Power analysis of an encryption device with a (partially) wrong password. Source: [Colin O'Flynn.](https://www.youtube.com/watch?v=2iDLfuEBcs8)](images/png/Power_analysis_of_an_encryption_device_with_a_(partially)_wrong_password.png){#fig-encryption2} @fig-encryption3 describes another chart of a completely wrong password. After the device finishes processing the first byte, it determines that it is incorrect and stops further processing - the voltage goes up and the current down. -![Power analysis of an encryption device with a wrong password. Source: [Colin O'Flynn.](https://www.youtube.com/watch?v=2iDLfuEBcs8)](images/png/image15.png){#fig-encryption3} +![Power analysis of an encryption device with a wrong password. Source: [Colin O'Flynn.](https://www.youtube.com/watch?v=2iDLfuEBcs8)](images/png/Power_analysis_of_an_encryption_device_with_a_wrong_password.png){#fig-encryption3} The example above demonstrates how information about the encryption process and the secret key can be inferred by analyzing different inputs and attempting to 'eavesdrop' on the device's operations on each input byte. For a more detailed explanation, watch @vid-powerattack below. @@ -464,7 +464,7 @@ As ML moves into more critical systems, verifying hardware integrity from design #### About TEE -A Trusted Execution Environment (TEE) is a secure area within a main processor that provides a high level of security for the execution of code and protection of data. TEEs operate by isolating the execution of sensitive tasks from the rest of the device's operations, thereby creating an environment resistant to attacks from software and hardware vectors. +A Trusted Execution Environment (TEE) is a secure area within a host processor that ensures the safe execution of code and the protection of sensitive data. By isolating critical tasks from the operating system, TEEs resist software and hardware attacks, providing a secure environment for handling sensitive computations. #### Benefits @@ -472,6 +472,8 @@ TEEs are particularly valuable in scenarios where sensitive data must be process For instance, a TEE can protect ML model parameters from being extracted by malicious software on the same device. This protection is vital for privacy and maintaining the integrity of the ML system, ensuring that the models perform as expected and do not provide skewed outputs due to manipulated parameters. [Apple's Secure Enclave](https://support.apple.com/guide/security/secure-enclave-sec59b0b31ff/web), found in iPhones and iPads, is a form of TEE that provides an isolated environment to protect sensitive user data and cryptographic operations. +Trusted Execution Environments (TEEs) are crucial for industries that demand high levels of security, including telecommunications, finance, healthcare, and automotive. TEEs protect the integrity of 5G networks in telecommunications and support critical applications. In finance, they secure mobile payments and authentication processes. Healthcare relies on TEEs to safeguard sensitive patient data, while the automotive industry depends on them for the safety and reliability of autonomous systems. Across all sectors, TEEs ensure the confidentiality and integrity of data and operations. + In ML systems, TEEs can: * Securely perform model training and inference, ensuring the computation results remain confidential. @@ -482,21 +484,25 @@ In ML systems, TEEs can: * Enable secure updates to ML models, ensuring that updates come from a trusted source and have not been tampered with in transit. +* Strengthen network security by safeguarding data transmission between distributed ML components through encryption and secure in-TEE processing. + The importance of TEEs in ML hardware security stems from their ability to protect against external and internal threats, including the following: * **Malicious Software:** TEEs can prevent high-privilege malware from accessing sensitive areas of the ML system. * **Physical Tampering:** By integrating with hardware security measures, TEEs can protect against physical tampering that attempts to bypass software security. -* **Side-channel Attacks:** Although not impenetrable, TEEs can mitigate certain side-channel attacks by controlling access to sensitive operations and data patterns. +* **Side-channel Attacks:** Although not impenetrable, TEEs can mitigate specific side-channel attacks by controlling access to sensitive operations and data patterns. + +* **Network Threats:** TEEs enhance network security by safeguarding data transmission between distributed ML components through encryption and secure in-TEE processing. This effectively prevents man-in-the-middle attacks and ensures data is transmitted through trusted channels. #### Mechanics The fundamentals of TEEs contain four main parts: -* **Isolated Execution:** Code within a TEE runs in a separate environment from the device's main operating system. This isolation protects the code from unauthorized access by other applications. +* **Isolated Execution:** Code within a TEE runs in a separate environment from the host device's host operating system. This isolation protects the code from unauthorized access by other applications. -* **Secure Storage:** TEEs can securely store cryptographic keys, authentication tokens, and sensitive data, preventing access by regular applications running outside the TEE. +* **Secure Storage:** TEEs can securely store cryptographic keys, authentication tokens, and sensitive data, preventing regular applications from accessing them outside the TEE. * **Integrity Protection:** TEEs can verify the integrity of code and data, ensuring that they have not been altered before execution or during storage. @@ -504,31 +510,31 @@ The fundamentals of TEEs contain four main parts: Here are some examples of TEEs that provide hardware-based security for sensitive applications: -* **[ARMTrustZone](https://www.arm.com/technologies/trustzone-for-cortex-m):**This technology creates secure and normal world execution environments isolated using hardware controls and implemented in many mobile chipsets. +* **[ARMTrustZone](https://www.arm.com/technologies/trustzone-for-cortex-m):** This technology creates secure and normal world execution environments isolated using hardware controls and implemented in many mobile chipsets. -* **[IntelSGX](https://www.intel.com/content/www/us/en/architecture-and-technology/software-guard-extensions.html):**Intel's Software Guard Extensions provide an enclave for code execution that protects against certain software attacks, specifically O.S. layer attacks. They are used to safeguard workloads in the cloud. +* **[IntelSGX](https://www.intel.com/content/www/us/en/architecture-and-technology/software-guard-extensions.html):** Intel's Software Guard Extensions provide an enclave for code execution that protects against various software-based threats, specifically targeting O.S. layer vulnerabilities. They are used to safeguard workloads in the cloud. -* **[Qualcomm Secure Execution Environment](https://www.qualcomm.com/products/features/mobile-security-solutions):**A Hardware sandbox on Qualcomm chipsets for mobile payment and authentication apps. +* **[Qualcomm Secure Execution Environment](https://www.qualcomm.com/products/features/mobile-security-solutions):** A Hardware sandbox on Qualcomm chipsets for mobile payment and authentication apps. -* **[Apple SecureEnclave](https://support.apple.com/guide/security/secure-enclave-sec59b0b31ff/web):**TEE for biometric data and key management on iPhones and iPads.Facilitates mobile payments. +* **[Apple SecureEnclave](https://support.apple.com/guide/security/secure-enclave-sec59b0b31ff/web):** A TEE for biometric data and cryptographic key management on iPhones and iPads, facilitating secure mobile payments. -@fig-enclave is a diagram demonstrating a secure enclave isolated from the main processor to provide an extra layer of security. The secure enclave has a boot ROM to establish a hardware root of trust, an AES engine for efficient and secure cryptographic operations, and protected memory. It also has a mechanism to store information securely on attached storage separate from the NAND flash storage used by the application processor and operating system. This design keeps sensitive user data secure even when the Application Processor kernel becomes compromised. +@fig-enclave is a diagram demonstrating a secure enclave isolated from the host processor to provide an extra layer of security. The secure enclave has a boot ROM to establish a hardware root of trust, an AES engine for efficient and secure cryptographic operations, and protected memory. It also has a mechanism to store information securely on attached storage separate from the NAND flash storage used by the application processor and operating system. This design keeps sensitive user data secure even when the Application Processor kernel becomes compromised. -![System-on-chip secure enclave. Source: [Apple.](https://support.apple.com/guide/security/secure-enclave-sec59b0b31ff/web)](images/png/image1.png){#fig-enclave} +![System-on-chip secure enclave. Source: [Apple.](https://support.apple.com/guide/security/secure-enclave-sec59b0b31ff/web)](images/png/System-on-chip_secure_enclave.png){#fig-enclave} #### Tradeoffs -If TEEs are so good, why don't all systems have TEE enabled by default? The decision to implement a TEE is not taken lightly. There are several reasons why a TEE might only be present in some systems by default. Here are some tradeoffs and challenges associated with TEEs: +While Trusted Execution Environments offer significant security benefits, their implementation involves trade-offs. Several factors influence whether a system includes a TEE: **Cost:** Implementing TEEs involves additional costs. There are direct costs for the hardware and indirect costs associated with developing and maintaining secure software for TEEs. These costs may only be justifiable for some devices, especially low-margin products. **Complexity:** TEEs add complexity to system design and development. Integrating a TEE with existing systems requires a substantial redesign of the hardware and software stack, which can be a barrier, especially for legacy systems. -**Performance Overhead:** While TEEs offer enhanced security, they can introduce performance overhead. For example, the additional steps in verifying and encrypting data can slow down system performance, which may be critical in time-sensitive applications. +**Performance Overhead:** TEEs may introduce performance overhead due to the additional steps involved in encryption and data verification, which could slow down time-sensitive applications. **Development Challenges:** Developing for TEEs requires specialized knowledge and often must adhere to strict development protocols. This can extend development time and complicate the debugging and testing processes. -**Scalability and Flexibility:** TEEs, due to their secure nature, may impose limitations on scalability and flexibility. Upgrading secure components or scaling the system for more users or data can be more challenging when everything must pass through a secure, enclosed environment. +**Scalability and Flexibility:** TEEs, due to their protected nature, may impose limitations on scalability and flexibility. Upgrading protected components or scaling the system for more users or data can be more challenging when everything must pass through a secure, enclosed environment. **Energy Consumption:** The increased processing required for encryption, decryption, and integrity checks can lead to higher energy consumption, a significant concern for battery-powered devices. @@ -542,47 +548,46 @@ If TEEs are so good, why don't all systems have TEE enabled by default? The deci #### About -A secure boot is a security standard that ensures a device boots using only software trusted by the original equipment manufacturer (OEM). When the device starts up, the firmware checks the signature of each piece of boot software, including the bootloader, kernel, and base operating system, to ensure it's not tampered with. If the signatures are valid, the device continues to boot. If not, the boot process stops to prevent potential security threats from executing. +A Secure Boot is a fundamental security standard that ensures a device only boots using software trusted by the Original Equipment Manufacturer (OEM). During startup, the firmware checks the digital signature of each boot software component, including the bootloader, kernel, and base operating system. This process verifies that the software has not been altered or tampered with. If any signature fails verification, the boot process is halted to prevent unauthorized code execution that could compromise the system’s security integrity. #### Benefits -The integrity of an ML system is critical from the moment it is powered on. A compromised boot process could undermine the system by allowing malicious software to load before the operating system and ML applications start. This could lead to manipulated ML operations, stolen data, or the device being repurposed for malicious activities such as botnets or crypto-mining. +The integrity of an embedded machine learning (ML) system is paramount from the moment it is powered on. Any compromise in the boot process can lead to the execution of malicious software before the operating system and ML applications begin, resulting in manipulated ML operations, unauthorized data access, or repurposing the device for malicious activities such as botnets or crypto-mining. -Secure Boot helps protect embedded ML hardware in several ways: +Secure Boot offers vital protections for embedded ML hardware through the following critical mechanisms: * **Protecting ML Data:** Ensuring that the data used by ML models, which may include private or sensitive information, is not exposed to tampering or theft during the boot process. -* **Guarding Model Integrity:** Maintaining the ML models' integrity is important, as tampering with them could lead to incorrect or malicious outcomes. +* **Guarding Model Integrity:** Maintaining the integrity of the ML models is crucial, as tampering with them could lead to incorrect or malicious outcomes. * **Secure Model Updates:** Enabling secure updates to ML models and algorithms, ensuring that updates are authenticated and have not been altered. #### Mechanics -TEEs benefit from Secure Boot in multiple ways. @fig-secure-boot illustrates a flow diagram of a trusted embedded system. For instance, during initial validation, Secure Boot ensures that the code running inside the TEE is the correct and untampered version approved by the device manufacturer. It can ensure resilience against tampering by verifying the digital signatures of the firmware and other critical components; Secure Boot prevents unauthorized modifications that could undermine the TEE's security properties. Secure Boot establishes a foundation of trust upon which the TEE can securely operate, enabling secure operations such as cryptographic key management, secure processing, and sensitive data handling. +Secure Boot works with TEEs to further enhance system security. @fig-secure-boot illustrates a flow diagram of a trusted embedded system. In the initial validation phase, Secure Boot verifies that the code running within the TEE is the correct, untampered version authorized by the device manufacturer. By checking digital signatures of the firmware and other critical system components, Secure Boot prevents unauthorized modifications that could compromise the TEE’s security capabilities. This establishes a foundation of trust upon which the TEE can securely execute sensitive operations such as cryptographic key management and secure data processing. By enforcing these layers of security, Secure Boot enables resilient and secure device operations in even the most resource-constrained environments. -![Secure Boot flow. Source: @Rashmi2018Secure.](images/png/image4.png){#fig-secure-boot} +![Secure Boot flow. Source: @Rashmi2018Secure.](images/png/Secure_Boot_flow.png){#fig-secure-boot} #### Case Study: Apple's Face ID -Let's take a real-world example. Apple's Face ID technology uses advanced machine learning algorithms to enable [facial recognition](https://support.apple.com/en-us/102381) on iPhones and iPads. It relies on a sophisticated framework of sensors and software to accurately map the geometry of a user's face. For Face ID to function securely and protect user biometric data, the device's operations must be trustworthy from the moment it is powered on, which is where Secure Boot plays a crucial role. Here's how Secure Boot works in conjunction with Face ID: +A real-world example of Secure Boot's application can be observed in Apple's Face ID technology, which uses advanced machine learning algorithms to enable [facial recognition](https://support.apple.com/en-us/102381) on iPhones and iPads. Face ID relies on a sophisticated integration of sensors and software to precisely map the geometry of a user's face. For Face ID to operate securely and protect users' biometric data, the device's operations must be trustworthy from initialization. This is where Secure Boot plays a pivotal role. The following outlines how Secure Boot functions in conjunction with Face ID: -**Initial Verification:** When an iPhone is powered on, the Secure Boot process begins in the Secure Enclave, a coprocessor providing an extra security layer. The Secure Enclave is responsible for processing fingerprint data for Touch ID and facial recognition data for Face ID. The boot process verifies that Apple has signed the Secure Enclave's firmware and has not been tampered with. This step ensures that the firmware used to process biometric data is authentic and safe. +**Initial Verification:** Upon booting up an iPhone, the Secure Boot process commences within the Secure Enclave, a specialized coprocessor designed to add an extra layer of security. The Secure Enclave handles biometric data, such as fingerprints for Touch ID and facial recognition data for Face ID. During the boot process, the system rigorously verifies that Apple has digitally signed the Secure Enclave's firmware, guaranteeing its authenticity. This verification step ensures that the firmware used to process biometric data remains secure and uncompromised. -**Continuous Security Checks:** After the initial power-on self-test and verification by Secure Boot, the Secure Enclave communicates with the device's main processor to continue the secure boot chain. It verifies the digital signatures of the iOS kernel and other critical boot components before allowing the boot process to proceed. This chained trust model prevents unauthorized modifications to the bootloader and operating system, which could compromise the device's security. +**Continuous Security Checks:** Following the system's initialization and validation by Secure Boot, the Secure Enclave communicates with the device's central processor to maintain a secure boot chain. During this process, the digital signatures of the iOS kernel and other critical boot components are meticulously verified to ensure their integrity before proceeding. This "chain of trust" model effectively prevents unauthorized modifications to the bootloader and operating system, safeguarding the device's overall security. -**Face Data Processing:** Once the device has completed its secure boot sequence, the Secure Enclave can interact safely with the ML algorithms that power Face ID. Facial recognition involves projecting and analyzing over 30,000 invisible dots to create a depth map of the user's face and an infrared image. This data is then converted into a mathematical representation and compared with the registered face data securely stored in the Secure Enclave. +**Face Data Processing:** Once the secure boot sequence is completed, the Secure Enclave interacts securely with the machine learning algorithms that power Face ID. Facial recognition involves projecting and analyzing over 30,000 invisible points to create a depth map of the user's face and an infrared image. This data is converted into a mathematical representation and is securely compared with the registered face data stored in the Secure Enclave. -**Secure Enclave and Data Protection:** The Secure Enclave is designed to protect sensitive data and handle the cryptographic operations that secure it. It ensures that even if the operating system kernel is compromised, the facial data cannot be accessed by unauthorized apps or attackers. Face ID data never leaves the device and is not backed up to iCloud or anywhere else. +**Secure Enclave and Data Protection:** The Secure Enclave is precisely engineered to protect sensitive data and manage cryptographic operations that safeguard this data. Even in the event of a compromised operating system kernel, the facial data processed through Face ID remains inaccessible to unauthorized applications or external attackers. Importantly, Face ID data is never transmitted off the device and is not stored on iCloud or other external servers. -**Firmware Updates:** Apple frequently releases firmware updates to address security vulnerabilities and improve the functionality of its systems. Secure Boot ensures that each firmware update is authenticated and that only updates signed by Apple are installed on the device, preserving the integrity and security of the Face ID system. +**Firmware Updates:** Apple frequently releases updates to address security vulnerabilities and enhance system functionality. Secure Boot ensures that all firmware updates are authenticated, allowing only those signed by Apple to be installed. This process helps preserve the integrity and security of the Face ID system over time. -By using Secure Boot with dedicated hardware like the Secure Enclave, Apple can provide strong security assurances for sensitive operations like facial recognition. +By integrating Secure Boot with dedicated hardware such as the Secure Enclave, Apple delivers robust security guarantees for critical operations like facial recognition. #### Challenges -Implementing Secure Boot poses several challenges that must be addressed to realize its full benefits. - -**Key Management Complexity:** Generating, storing, distributing, rotating, and revoking cryptographic keys provably securely is extremely challenging yet vital for maintaining the chain of trust. Any compromise of keys cripples protections. Large enterprises managing multitudes of device keys face particular scale challenges. +Despite its benefits, implementing Secure Boot presents several challenges, particularly in complex and large-scale deployments: +**Key Management Complexity:** Generating, storing, distributing, rotating, and revoking cryptographic keys provably securely is particularly challenging yet vital for maintaining the chain of trust. Any compromise of keys cripples protections. Large enterprises managing multitudes of device keys face particular scale challenges. **Performance Overhead:** Checking cryptographic signatures during Boot can add 50-100ms or more per component verified. This delay may be prohibitive for time-sensitive or resource-constrained applications. However, performance impacts can be reduced through parallelization and hardware acceleration. @@ -664,7 +669,7 @@ The working principle behind PUFs, shown in @fig-pfu, involves generating a "cha @fig-pfu illustrates an overview of the PUF basics: a) PUF can be thought of as a unique fingerprint for each piece of hardware; b) an Optical PUF is a special plastic token that is illuminated, creating a unique speckle pattern that is then recorded; c) in an APUF (Arbiter PUF), challenge bits select different paths, and a judge decides which one is faster, giving a response of '1' or '0'; d) in an SRAM PUF, the response is determined by the mismatch in the threshold voltage of transistors, where certain conditions lead to a preferred response of '1'. Each of these methods uses specific characteristics of the hardware to create a unique identifier. -![PUF basics. Source: @Gao2020Physical.](images/png/image2.png){#fig-pfu} +![PUF basics. Source: @Gao2020Physical.](images/png/PUF_basics.png){#fig-pfu} #### Challenges @@ -768,11 +773,11 @@ Privacy and security concerns have also risen with the public use of generative While ChatGPT has instituted protections to prevent people from accessing private and ethically questionable information, several individuals have successfully bypassed these protections through prompt injection and other security attacks. As demonstrated in @fig-role-play, users can bypass ChatGPT protections to mimic the tone of a "deceased grandmother" to learn how to bypass a web application firewall [@Gupta2023ChatGPT]. -![Grandma role play to bypass safety restrictions. Source: @Gupta2023ChatGPT.](images/png/image6.png){#fig-role-play} +![Grandma role play to bypass safety restrictions. Source: @Gupta2023ChatGPT.](images/png/Grandma_role_play_to_bypass_safety_restrictions.png){#fig-role-play} Further, users have also successfully used reverse psychology to manipulate ChatGPT and access information initially prohibited by the model. In @fig-role-play2, a user is initially prevented from learning about piracy websites through ChatGPT but can bypass these restrictions using reverse psychology. -![Reverse psychology to bypass safety restrictions. Source: @Gupta2023ChatGPT.](images/png/image10.png){#fig-role-play2} +![Reverse psychology to bypass safety restrictions. Source: @Gupta2023ChatGPT.](images/png/Reverse_psychology_to_bypass_safety_restrictions.png){#fig-role-play2} The ease at which security attacks can manipulate ChatGPT is concerning, given the private information it was trained upon without consent. Further research on data privacy in LLMs and generative AI should focus on preventing the model from being so naive to prompt injection attacks. @@ -806,7 +811,7 @@ While the Laplace distribution is common, other distributions like Gaussian can To illustrate the tradeoff of Privacy and accuracy in ($\epsilon$, $\delta$)-differential Privacy, the following graphs in @fig-tradeoffs show the results on accuracy for different noise levels on the MNIST dataset, a large dataset of handwritten digits [@abadi2016deep]. The delta value (black line; right y-axis) denotes the level of privacy relaxation (a high value means Privacy is less stringent). As Privacy becomes more relaxed, the accuracy of the model increases. -![Privacy-accuracy tradeoff. Source: @abadi2016deep.](images/png/image8.png){#fig-tradeoffs} +![Privacy-accuracy tradeoff. Source: @abadi2016deep.](images/png/Privacy-accuracy_tradeoff.png){#fig-tradeoffs} The key points to remember about differential Privacy are the following: @@ -866,7 +871,7 @@ Federated Learning (FL) is a type of machine learning in which a model is built FL trains machine learning models across decentralized networks of devices or systems while keeping all training data localized. @fig-fl-lifecycle illustrates this process: each participating device leverages its local data to calculate model updates, which are then aggregated to build an improved global model. However, the raw training data is never directly shared, transferred, or compiled. This privacy-preserving approach allows for the joint development of ML models without centralizing the potentially sensitive training data in one place. -![Federated Learning lifecycle. Source: @jin2020towards.](images/png/image7.png){#fig-fl-lifecycle} +![Federated Learning lifecycle. Source: @jin2020towards.](images/png/Federated_Learning_lifecycle.png){#fig-fl-lifecycle} One of the most common model aggregation algorithms is Federated Averaging (FedAvg), where the global model is created by averaging all of the parameters from local parameters. While FedAvg works well with independent and identically distributed data (IID), alternate algorithms like Federated Proximal (FedProx) are crucial in real-world applications where data is often non-IID. FedProx is designed for the FL process when there is significant heterogeneity in the client updates due to diverse data distributions across devices, computational capabilities, or varied amounts of data. @@ -928,7 +933,7 @@ Machine unlearning is a fairly new process that describes how the influence of a Some researchers have demonstrated a real-life example of machine unlearning approaches applied to SOTA machine learning models through training an LLM, LLaMA2-7b, to unlearn any references to Harry Potter [@eldan2023whos]. Though this model took 184K GPU hours to pre-train, it only took 1 GPU hour of fine-tuning to erase the model's ability to generate or recall Harry Potter-related content without noticeably compromising the accuracy of generating content unrelated to Harry Potter. @fig-hp-prompts demonstrates how the model output changes before (Llama-7b-chat-hf column) and after (Finetuned Llama-b column) unlearning has occurred. -![Llama unlearning Harry Potter. Source: @eldan2023whos.](images/png/image13.png){#fig-hp-prompts} +![Llama unlearning Harry Potter. Source: @eldan2023whos.](images/png/Llama_unlearning_Harry_Potter.png){#fig-hp-prompts} #### Other Uses @@ -988,7 +993,7 @@ For many real-time and embedded applications, fully homomorphic encryption remai ### Homomorphic Encryption -Ready to unlock the power of encrypted computation? Homomorphic encryption is like a magic trick for your data! In this Colab, we'll learn how to do calculations on secret numbers without ever revealing them. Imagine training a model on data you can't even see – that's the power of this mind-bending technology. +The power of encrypted computation is unlocked through homomorphic encryption – a transformative approach in which calculations are performed directly on encrypted data, ensuring privacy is preserved throughout the process. This Colab explores the principles of computing on encrypted numbers without exposing the underlying data. Imagine a scenario where a machine learning model is trained on data that cannot be directly accessed – such is the strength of homomorphic encryption. [![](https://colab.research.google.com/assets/colab-badge.png)](https://colab.research.google.com/drive/1GjKT5Lgh9Madjsyr9UiyeogUgVpTEBMp?usp=sharing) @@ -998,9 +1003,9 @@ Ready to unlock the power of encrypted computation? Homomorphic encryption is li #### Core Idea -The overarching goal of Multi-Party Communication (MPC) is to enable different parties to jointly compute a function over their inputs while keeping those inputs private. For example, two organizations may want to collaborate on training a machine learning model by combining their respective data sets. Still, they cannot directly reveal that data due to Privacy or confidentiality constraints. MPC provides protocols and techniques that allow them to achieve the benefits of pooled data for model accuracy without compromising the privacy of each organization's sensitive data. +Multi-Party Communication (MPC) enables multiple parties to jointly compute a function over their inputs while ensuring that each party’s inputs remain confidential. For instance, two organizations can collaborate on training a machine learning model by combining datasets without revealing sensitive information to each other. MPC protocols are essential where privacy and confidentiality regulations restrict direct data sharing, such as in healthcare or financial sectors. -At a high level, MPC works by carefully splitting the computation into parts that each party can execute independently using their private input. The results are then combined to reveal only the final output of the function and nothing about the intermediate values. Cryptographic techniques are used to guarantee that the partial results remain private provably. +MPC divides computation into parts that each participant executes independently using their private data. These results are then combined to reveal only the final output, preserving the privacy of intermediate values. Cryptographic techniques are used to guarantee that the partial results remain private provably. Let's take a simple example of an MPC protocol. One of the most basic MPC protocols is the secure addition of two numbers. Each party splits its input into random shares that are secretly distributed. They exchange the shares and locally compute the sum of the shares, which reconstructs the final sum without revealing the individual inputs. For example, if Alice has input x and Bob has input y: @@ -1014,7 +1019,7 @@ Let's take a simple example of an MPC protocol. One of the most basic MPC protoc 5. $s_1 + s_2 = x + y$ is the final sum, without revealing $x$ or $y$. -Alice's and Bob's individual inputs ($x$ and $y$) remain private, and each party only reveals one number associated with their original inputs. The random outputs ensure that no information about the original numbers disclosed. +Alice's and Bob's individual inputs ($x$ and $y$) remain private, and each party only reveals one number associated with their original inputs. The random outputs ensure that no information about the original numbers is disclosed. **Secure Comparison:** Another basic operation is a secure comparison of two numbers, determining which is greater than the other. This can be done using techniques like Yao's Garbled Circuits, where the comparison circuit is encrypted to allow joint evaluation of the inputs without leaking them. @@ -1044,7 +1049,7 @@ While MPC protocols provide strong privacy guarantees, they come at a high compu * Oblivious transfer and garbled circuits add masking and encryption to hide data access patterns and execution flows. -* MPC systems require extensive communication and interaction between parties to compute on shares/ciphertexts jointly. +* MPC systems require extensive communication and interaction between parties to jointly compute on shares/ciphertexts. As a result, MPC protocols can slow down computations by 3-4 orders of magnitude compared to plain implementations. This becomes prohibitively expensive for large datasets and models. Therefore, training machine learning models on encrypted data using MPC remains infeasible today for realistic dataset sizes due to the overhead. Clever optimizations and approximations are needed to make MPC practical. @@ -1054,15 +1059,15 @@ Ongoing MPC research closes this efficiency gap through cryptographic advances, #### Core Idea -Synthetic data generation has emerged as an important privacy-preserving machine learning approach that allows models to be developed and tested without exposing real user data. The key idea is to train generative models on real-world datasets and then sample from these models to synthesize artificial data that statistically match the original data distribution but does not contain actual user information. For example, a GAN could be trained on a dataset of sensitive medical records to learn the underlying patterns and then used to sample synthetic patient data. +Synthetic data generation has emerged as an important privacy-preserving machine learning approach that allows models to be developed and tested without exposing real user data. The key idea is to train generative models on real-world datasets and then sample from these models to synthesize artificial data that statistically matches the original data distribution but does not contain actual user information. For example, a GAN could be trained on a dataset of sensitive medical records to learn the underlying patterns and then used to sample synthetic patient data. -The primary challenge of synthesizing data is to ensure adversaries are unable to re-identify the original dataset. A simple approach to achieving synthetic data is adding noise to the original dataset, which still risks privacy leakage. When noise is added to data in the context of differential privacy, sophisticated mechanisms based on the data's sensitivity are used to calibrate the amount and distribution of noise. Through these mathematically rigorous frameworks, differential Privacy generally guarantees Privacy at some level, which is the primary goal of this privacy-preserving technique. Beyond preserving privacy, synthetic data combats multiple data availability issues such as imbalanced datasets, scarce datasets, and anomaly detection. +The primary challenge of synthesizing data is to ensure adversaries cannot re-identify the original dataset. A simple approach to achieving synthetic data is adding noise to the original dataset, which still risks privacy leakage. When noise is added to data in the context of differential privacy, sophisticated mechanisms based on the data's sensitivity are used to calibrate the amount and distribution of noise. Through these mathematically rigorous frameworks, differential Privacy generally guarantees Privacy at some level, which is the primary goal of this privacy-preserving technique. Beyond preserving privacy, synthetic data combats multiple data availability issues such as imbalanced datasets, scarce datasets, and anomaly detection. Researchers can freely share this synthetic data and collaborate on modeling without revealing private medical information. Well-constructed synthetic data protects Privacy while providing utility for developing accurate models. Key techniques to prevent reconstructing the original data include adding differential privacy noise during training, enforcing plausibility constraints, and using multiple diverse generative models. Here are some common approaches for generating synthetic data: * **Generative Adversarial Networks (GANs):** GANs are an AI algorithm used in unsupervised learning where two neural networks compete against each other in a game. @fig-gans is an overview of the GAN system. The generator network (big red box) is responsible for producing the synthetic data, and the discriminator network (yellow box) evaluates the authenticity of the data by distinguishing between fake data created by the generator network and the real data. The generator and discriminator networks learn and update their parameters based on the results. The discriminator acts as a metric on how similar the fake and real data are to one another. It is highly effective at generating realistic data and is a popular approach for generating synthetic data. -![Flowchart of GANs. Source: @rosa2021.](images/png/image9.png){#fig-gans} +![Flowchart of GANs. Source: @rosa2021.](images/png/Flowchart_of_GANs.png){#fig-gans} * **Variational Autoencoders (VAEs):** VAEs are neural networks capable of learning complex probability distributions and balancing data generation quality and computational efficiency. They encode data into a latent space where they learn the distribution to decode the data back. @@ -1090,7 +1095,7 @@ While synthetic data tries to remove any evidence of the original dataset, priva A core challenge with synthetic data is the potential gap between synthetic and real-world data distributions. Despite advancements in generative modeling techniques, synthetic data may only partially capture real data's complexity, diversity, and nuanced patterns. This can limit the utility of synthetic data for robustly training machine learning models. Rigorously evaluating synthetic data quality through adversary methods and comparing model performance to real data benchmarks helps assess and improve fidelity. However, inherently, synthetic data remains an approximation. -Another critical concern is the privacy risks of synthetic data. Generative models may leak identifiable information about individuals in the training data, which could enable reconstruction of private information. Emerging adversarial attacks demonstrate the challenges in preventing identity leakage from synthetic data generation pipelines. Techniques like differential Privacy can help safeguard Privacy but come with tradeoffs in data utility. There is an inherent tension between producing useful synthetic data and fully protecting sensitive training data, which must be balanced. +Another critical concern is the privacy risks of synthetic data. Generative models may leak identifiable information about individuals in the training data, which could enable the reconstruction of private information. Emerging adversarial attacks demonstrate the challenges in preventing identity leakage from synthetic data generation pipelines. Techniques like differential privacy can help safeguard privacy, but they come with tradeoffs in data utility. There is an inherent tension between producing valid synthetic data and fully protecting sensitive training data, which must be balanced. Additional pitfalls of synthetic data include amplified biases, mislabeling, the computational overhead of training generative models, storage costs, and failure to account for out-of-distribution novel data. While these are secondary to the core synthetic-real gap and privacy risks, they remain important considerations when evaluating the suitability of synthetic data for particular machine-learning tasks. As with any technique, the advantages of synthetic data come with inherent tradeoffs and limitations that require thoughtful mitigation strategies. @@ -1170,13 +1175,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * @exr-he ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we offer a series of hands-on labs allowing students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience. - -* _Coming soon._ -::: - diff --git a/contents/responsible_ai/images/png/adversarial_robustness.png b/contents/core/responsible_ai/images/png/adversarial_robustness.png similarity index 100% rename from contents/responsible_ai/images/png/adversarial_robustness.png rename to contents/core/responsible_ai/images/png/adversarial_robustness.png diff --git a/contents/responsible_ai/images/png/cover_responsible_ai.png b/contents/core/responsible_ai/images/png/cover_responsible_ai.png similarity index 100% rename from contents/responsible_ai/images/png/cover_responsible_ai.png rename to contents/core/responsible_ai/images/png/cover_responsible_ai.png diff --git a/contents/responsible_ai/images/png/diffusion_memorization.png b/contents/core/responsible_ai/images/png/diffusion_memorization.png similarity index 100% rename from contents/responsible_ai/images/png/diffusion_memorization.png rename to contents/core/responsible_ai/images/png/diffusion_memorization.png diff --git a/contents/responsible_ai/images/png/fairness_cartoon.png b/contents/core/responsible_ai/images/png/fairness_cartoon.png similarity index 100% rename from contents/responsible_ai/images/png/fairness_cartoon.png rename to contents/core/responsible_ai/images/png/fairness_cartoon.png diff --git a/contents/responsible_ai/images/png/human_centered_ai.ai b/contents/core/responsible_ai/images/png/human_centered_ai.ai similarity index 100% rename from contents/responsible_ai/images/png/human_centered_ai.ai rename to contents/core/responsible_ai/images/png/human_centered_ai.ai diff --git a/contents/responsible_ai/images/png/human_centered_ai.png b/contents/core/responsible_ai/images/png/human_centered_ai.png similarity index 100% rename from contents/responsible_ai/images/png/human_centered_ai.png rename to contents/core/responsible_ai/images/png/human_centered_ai.png diff --git a/contents/responsible_ai/images/png/human_compatible_ai.png b/contents/core/responsible_ai/images/png/human_compatible_ai.png similarity index 100% rename from contents/responsible_ai/images/png/human_compatible_ai.png rename to contents/core/responsible_ai/images/png/human_compatible_ai.png diff --git a/contents/responsible_ai/responsible_ai.bib b/contents/core/responsible_ai/responsible_ai.bib similarity index 100% rename from contents/responsible_ai/responsible_ai.bib rename to contents/core/responsible_ai/responsible_ai.bib diff --git a/contents/responsible_ai/responsible_ai.qmd b/contents/core/responsible_ai/responsible_ai.qmd similarity index 98% rename from contents/responsible_ai/responsible_ai.qmd rename to contents/core/responsible_ai/responsible_ai.qmd index 333e83c1..fefa3930 100644 --- a/contents/responsible_ai/responsible_ai.qmd +++ b/contents/core/responsible_ai/responsible_ai.qmd @@ -5,7 +5,7 @@ bibliography: responsible_ai.bib # Responsible AI {#sec-responsible_ai} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-responsible-ai-resource), [Videos](#sec-responsible-ai-resource), [Exercises](#sec-responsible-ai-resource), [Labs](#sec-responsible-ai-resource) +Resources: [Slides](#sec-responsible-ai-resource), [Videos](#sec-responsible-ai-resource), [Exercises](#sec-responsible-ai-resource) ::: ![_DALL·E 3 Prompt: Illustration of responsible AI in a futuristic setting with the universe in the backdrop: A human hand or hands nurturing a seedling that grows into an AI tree, symbolizing a neural network. The tree has digital branches and leaves, resembling a neural network, to represent the interconnected nature of AI. The background depicts a future universe where humans and animals with general intelligence collaborate harmoniously. The scene captures the initial nurturing of the AI as a seedling, emphasizing the ethical development of AI technology in harmony with humanity and the universe._](images/png/cover_responsible_ai.png) @@ -338,7 +338,7 @@ To ensure that models keep up to date with such changes in the real world, devel ### Organizational and Cultural Structures -While innovation and regulation are often seen as having competing interests, many countries have found it necessary to provide oversight as AI systems expand into more sectors. As illustrated in @fig-human-centered-ai, this oversight has become crucial as these systems continue permeating various industries and impacting people's lives (see [Human-Centered AI, Chapter 8 "Government Interventions and Regulations"](https://academic-oup-com.ezp-prod1.hul.harvard.edu/book/41126/chapter/350465542). +While innovation and regulation are often seen as having competing interests, many countries have found it necessary to provide oversight as AI systems expand into more sectors. As shown in in @fig-human-centered-ai, this oversight has become crucial as these systems continue permeating various industries and impacting people's lives (see [Human-Centered AI, Chapter 8 "Government Interventions and Regulations"](https://academic-oup-com.ezp-prod1.hul.harvard.edu/book/41126/chapter/350465542). ![How various groups impact human-centered AI. Source: @schneiderman2020.](images/png/human_centered_ai.png){#fig-human-centered-ai} @@ -500,14 +500,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * _Coming soon._ ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we offer a series of hands-on labs allowing students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. 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--- a/contents/robust_ai/robust_ai.qmd +++ b/contents/core/robust_ai/robust_ai.qmd @@ -5,7 +5,7 @@ bibliography: robust_ai.bib # Robust AI {#sec-robust_ai} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-robust-ai-resource), [Videos](#sec-robust-ai-resource), [Exercises](#sec-robust-ai-resource), [Labs](#sec-robust-ai-resource) +Resources: [Slides](#sec-robust-ai-resource), [Videos](#sec-robust-ai-resource), [Exercises](#sec-robust-ai-resource) ::: ![_DALL·E 3 Prompt: Create an image featuring an advanced AI system symbolized by an intricate, glowing neural network, deeply nested within a series of progressively larger and more fortified shields. Each shield layer represents a layer of defense, showcasing the system's robustness against external threats and internal errors. The neural network, at the heart of this fortress of shields, radiates with connections that signify the AI's capacity for learning and adaptation. This visual metaphor emphasizes not only the technological sophistication of the AI but also its resilience and security, set against the backdrop of a state-of-the-art, secure server room filled with the latest in technological advancements. The image aims to convey the concept of ultimate protection and resilience in the field of artificial intelligence._](./images/png/cover_robust_ai.png) @@ -230,10 +230,10 @@ Intermittent faults can arise from several causes, both internal and external, t Manufacturing defects or process variations can also introduce intermittent faults, where marginal or borderline components may exhibit sporadic failures under specific conditions, as shown in [@fig-intermittent-fault-dram](#kix.7lswkjecl7ra). -Environmental factors, such as temperature fluctuations, humidity, or vibrations, can trigger intermittent faults by altering the electrical characteristics of the components. Loose or degraded connections, such as those in connectors or printed circuit boards, can cause intermittent faults. - ![Residue induced intermittent fault in a DRAM chip. Source: [Hynix Semiconductor](https://ieeexplore.ieee.org/document/4925824)](./images/png/intermittent_fault_dram.png){#fig-intermittent-fault-dram} +Environmental factors, such as temperature fluctuations, humidity, or vibrations, can trigger intermittent faults by altering the electrical characteristics of the components. Loose or degraded connections, such as those in connectors or printed circuit boards, can cause intermittent faults. + #### Mechanisms of Intermittent Faults Intermittent faults can manifest through various mechanisms, depending on the underlying cause and the affected component. One mechanism is the intermittent open or short circuit, where a signal path or connection becomes temporarily disrupted or shorted, causing erratic behavior. Another mechanism is the intermittent delay fault [@zhang2018thundervolt], where the timing of signals or propagation delays becomes inconsistent, leading to synchronization issues or incorrect computations. Intermittent faults can manifest as transient bit flips or soft errors in memory cells or registers, causing data corruption or incorrect program execution. @@ -398,12 +398,12 @@ The landscape of machine learning models is complex and broad, especially given #### Mechanisms of Adversarial Attacks -![Gradient-Based Attacks. Source: [Ivezic](https://defence.ai/ai-security/gradient-based-attacks/)](./images/png/gradient_attack.png){#fig-gradient-attack} - **Gradient-based Attacks** One prominent category of adversarial attacks is gradient-based attacks. These attacks leverage the gradients of the ML model's loss function to craft adversarial examples. The [Fast Gradient Sign Method](https://www.tensorflow.org/tutorials/generative/adversarial_fgsm) (FGSM) is a well-known technique in this category. FGSM perturbs the input data by adding small noise in the gradient direction, aiming to maximize the model's prediction error. FGSM can quickly generate adversarial examples, as shown in [@fig-gradient-attack], by taking a single step in the gradient direction. +![Gradient-Based Attacks. Source: [Ivezic](https://defence.ai/ai-security/gradient-based-attacks/)](./images/png/gradient_attack.png){#fig-gradient-attack} + Another variant, the Projected Gradient Descent (PGD) attack, extends FGSM by iteratively applying the gradient update step, allowing for more refined and powerful adversarial examples. The Jacobian-based Saliency Map Attack (JSMA) is another gradient-based approach that identifies the most influential input features and perturbs them to create adversarial examples. **Optimization-based Attacks** @@ -453,12 +453,12 @@ As adversarial machine learning evolves, researchers explore new attack mechanis Adversarial attacks on machine learning systems have emerged as a significant concern in recent years, highlighting the potential vulnerabilities and risks associated with the widespread adoption of ML technologies. These attacks involve carefully crafted perturbations to input data that can deceive or mislead ML models, leading to incorrect predictions or misclassifications, as shown in [@fig-adversarial-googlenet]. The impact of adversarial attacks on ML systems is far-reaching and can have serious consequences in various domains. +![Adversarial example generation applied to GoogLeNet (Szegedy et al., 2014a) on ImageNet. Source: [Goodfellow](https://arxiv.org/abs/1412.6572)](./images/png/adversarial_googlenet.png){#fig-adversarial-googlenet} + One striking example of the impact of adversarial attacks was demonstrated by researchers in 2017. They experimented with small black and white stickers on stop signs [@eykholt2018robust]. To the human eye, these stickers did not obscure the sign or prevent its interpretability. However, when images of the sticker-modified stop signs were fed into standard traffic sign classification ML models, a shocking result emerged. The models misclassified the stop signs as speed limit signs over 85% of the time. This demonstration shed light on the alarming potential of simple adversarial stickers to trick ML systems into misreading critical road signs. The implications of such attacks in the real world are significant, particularly in the context of autonomous vehicles. If deployed on actual roads, these adversarial stickers could cause self-driving cars to misinterpret stop signs as speed limits, leading to dangerous situations, as shown in [@fig-graffiti]. Researchers warned that this could result in rolling stops or unintended acceleration into intersections, endangering public safety. -![Adversarial example generation applied to GoogLeNet (Szegedy et al., 2014a) on ImageNet. Source: [Goodfellow](https://arxiv.org/abs/1412.6572)](./images/png/adversarial_googlenet.png){#fig-adversarial-googlenet} - ![Graffiti on a stop sign tricked a self-driving car into thinking it was a 45 mph speed limit sign. Source: [Eykholt](https://arxiv.org/abs/1707.08945)](./images/png/graffiti.png){#fig-graffiti} The case study of the adversarial stickers on stop signs provides a concrete illustration of how adversarial examples exploit how ML models recognize patterns. By subtly manipulating the input data in ways that are invisible to humans, attackers can induce incorrect predictions and create serious risks, especially in safety-critical applications like autonomous vehicles. The attack's simplicity highlights the vulnerability of ML models to even minor changes in the input, emphasizing the need for robust defenses against such threats. @@ -537,10 +537,10 @@ Data poisoning attacks can be carried out through various mechanisms, exploiting Each of these mechanisms presents unique challenges and requires different mitigation strategies. For example, detecting label manipulation may involve analyzing the distribution of labels and identifying anomalies [@zhou2018learning], while preventing feature manipulation may require secure data preprocessing and anomaly detection techniques [@carta2020local]. Defending against insider threats may involve strict access control policies and monitoring of data access patterns. Moreover, the effectiveness of data poisoning attacks often depends on the attacker's knowledge of the ML system, including the model architecture, training algorithms, and data distribution. Attackers may use adversarial machine learning or data synthesis techniques to craft samples that are more likely to bypass detection and achieve their malicious objectives. -![Garbage In -- Garbage Out. Source: [Information Matters](https://informationmatters.net/data-poisoning-ai/)](./images/png/distribution_shift_example.png){#fig-distribution-shift-example} - **Modifying training data labels:** One of the most straightforward mechanisms of data poisoning is modifying the training data labels. In this approach, the attacker selectively changes the labels of a subset of the training samples to mislead the model's learning process as shown in [@fig-distribution-shift-example]. For example, in a binary classification task, the attacker might flip the labels of some positive samples to negative, or vice versa. By introducing such label noise, the attacker degrades the model's performance or cause it to make incorrect predictions for specific target instances. +![Garbage In -- Garbage Out. Source: [Information Matters](https://informationmatters.net/data-poisoning-ai/)](./images/png/distribution_shift_example.png){#fig-distribution-shift-example} + **Altering feature values in training data:** Another mechanism of data poisoning involves altering the feature values of the training samples without modifying the labels. The attacker carefully crafts the feature values to introduce specific biases or vulnerabilities into the model. For instance, in an image classification task, the attacker might add imperceptible perturbations to a subset of images, causing the model to learn a particular pattern or association. This type of poisoning can create backdoors or trojans in the trained model, which specific input patterns can trigger. **Injecting carefully crafted malicious samples:** In this mechanism, the attacker creates malicious samples designed to poison the model. These samples are crafted to have a specific impact on the model's behavior while blending in with the legitimate training data. The attacker might use techniques such as adversarial perturbations or data synthesis to generate poisoned samples that are difficult to detect. The attacker manipulates the model's decision boundaries by injecting these malicious samples into the training data or introducing targeted misclassifications. @@ -549,10 +549,10 @@ Each of these mechanisms presents unique challenges and requires different mitig **Manipulating data at the source (e.g., sensor data):** In some cases, attackers can manipulate the data at its source, such as sensor data or input devices. By tampering with the sensors or manipulating the environment in which data is collected, attackers can introduce poisoned samples or bias the data distribution. For instance, in a self-driving car scenario, an attacker might manipulate the sensors or the environment to feed misleading information into the training data, compromising the model's ability to make safe and reliable decisions. -![Data Poisoning Attack. Source: [Sikandar](https://www.researchgate.net/publication/366883200_A_Detailed_Survey_on_Federated_Learning_Attacks_and_Defenses)](./images/png/poisoning_attack_example.png){#fig-poisoning-attack-example} - **Poisoning data in online learning scenarios:** Data poisoning attacks can also target ML systems that employ online learning, where the model is continuously updated with new data in real time. In such scenarios, an attacker can gradually inject poisoned samples over time, slowly manipulating the model's behavior. Online learning systems are particularly vulnerable to data poisoning because they adapt to new data without extensive validation, making it easier for attackers to introduce malicious samples, as shown in [@fig-poisoning-attack-example]. +![Data Poisoning Attack. Source: [Sikandar](https://www.researchgate.net/publication/366883200_A_Detailed_Survey_on_Federated_Learning_Attacks_and_Defenses)](./images/png/poisoning_attack_example.png){#fig-poisoning-attack-example} + **Collaborating with insiders to manipulate data:** Sometimes, data poisoning attacks can involve collaboration with insiders with access to the training data. Malicious insiders, such as employees or data providers, can manipulate the data before it is used to train the model. Insider threats are particularly challenging to detect and prevent, as the attackers have legitimate access to the data and can carefully craft the poisoning strategy to evade detection. These are the key mechanisms of data poisoning in ML systems. Attackers often employ these mechanisms to make their attacks more effective and harder to detect. The risk of data poisoning attacks grows as ML systems become increasingly complex and rely on larger datasets from diverse sources. Defending against data poisoning requires a multifaceted approach. ML practitioners and system designers must be aware of the various mechanisms of data poisoning and adopt a comprehensive approach to data security and model resilience. This includes secure data collection, robust data validation, and continuous model performance monitoring. Implementing secure data collection and preprocessing practices is crucial to prevent data poisoning at the source. Data validation and anomaly detection techniques can also help identify and mitigate potential poisoning attempts. Monitoring model performance for signs of data poisoning is also essential to detect and respond to attacks promptly. @@ -577,10 +577,10 @@ Addressing the impact of data poisoning requires a proactive approach to data se ##### Case Study -![Samples of dirty-label poison data regarding mismatched text/image pairs. Source: [Shan](https://arxiv.org/pdf/2310.13828)](./images/png/dirty_label_example.png){#fig-dirty-label-example} - Interestingly enough, data poisoning attacks are not always malicious [@shan2023prompt]. Nightshade, a tool developed by a team led by Professor Ben Zhao at the University of Chicago, utilizes data poisoning to help artists protect their art against scraping and copyright violations by generative AI models. Artists can use the tool to make subtle modifications to their images before uploading them online, as shown in [@fig-dirty-label-example]. +![Samples of dirty-label poison data regarding mismatched text/image pairs. Source: [Shan](https://arxiv.org/pdf/2310.13828)](./images/png/dirty_label_example.png){#fig-dirty-label-example} + While these changes are indiscernible to the human eye, they can significantly disrupt the performance of generative AI models when incorporated into the training data. Generative models can be manipulated to generate hallucinations and weird images. For example, with only 300 poisoned images, the University of Chicago researchers could trick the latest Stable Diffusion model into generating images of dogs that look like cats or images of cows when prompted for cars. As the number of poisoned images on the internet increases, the performance of the models that use scraped data will deteriorate exponentially. First, the poisoned data is hard to detect and requires manual elimination. Second, the "poison" spreads quickly to other labels because generative models rely on connections between words and concepts as they generate images. So a poisoned image of a "car" could spread into generated images associated with words like "truck\," "train\," " bus\," etc. @@ -618,14 +618,14 @@ The key characteristics of distribution shift include: **Unrepresentative training data:** The training data may only partially capture the variability and diversity of the real-world data encountered during deployment. Unrepresentative training data can lead to biased or skewed models that perform poorly on real-world data. Suppose the training data needs to capture the variability and diversity of the real-world data adequately. In that case, the model may learn patterns specific to the training set but needs to generalize better to new, unseen data. This can result in poor performance, biased predictions, and limited model applicability. For instance, if a facial recognition model is trained primarily on images of individuals from a specific demographic group, it may struggle to accurately recognize faces from other demographic groups when deployed in a real-world setting. Ensuring that the training data is representative and diverse is crucial for building models that can generalize well to real-world scenarios. -![Concept drift refers to a change in data patterns and relationships over time. Source: [Evidently AI](https://www.evidentlyai.com/ml-in-production/concept-drift)](./images/png/drift_over_time.png){#fig-drift-over-time} - Distribution shift can manifest in various forms, such as: **Covariate shift:** The distribution of the input features (covariates) changes while the conditional distribution of the target variable given the input remains the same. Covariate shift matters because it can impact the model's ability to make accurate predictions when the input features (covariates) differ between the training and test data. Even if the relationship between the input features and the target variable remains the same, a change in the distribution of the input features can affect the model's performance. For example, consider a model trained to predict housing prices based on features like square footage, number of bedrooms, and location. Suppose the distribution of these features in the test data significantly differs from the training data (e.g., the test data contains houses with much larger square footage). In that case, the model's predictions may become less accurate. Addressing covariate shifts is important to ensure the model's robustness and reliability when applied to new data. **Concept drift:** The relationship between the input features and the target variable changes over time, altering the underlying concept the model is trying to learn, as shown in [@fig-drift-over-time]. Concept drift is important because it indicates changes in the fundamental relationship between the input features and the target variable over time. When the underlying concept that the model is trying to learn shifts, its performance can deteriorate if not adapted to the new concept. For instance, in a customer churn prediction model, the factors influencing customer churn may evolve due to market conditions, competitor offerings, or customer preferences. If the model is not updated to capture these changes, its predictions may become less accurate and irrelevant. Detecting and adapting to concept drift is crucial to maintaining the model's effectiveness and alignment with evolving real-world concepts. +![Concept drift refers to a change in data patterns and relationships over time. Source: [Evidently AI](https://www.evidentlyai.com/ml-in-production/concept-drift)](./images/png/drift_over_time.png){#fig-drift-over-time} + **Domain generalization:** The model must generalize to unseen domains or distributions not present during training. Domain generalization is important because it enables ML models to be applied to new, unseen domains without requiring extensive retraining or adaptation. In real-world scenarios, training data that covers all possible domains or distributions that the model may encounter is often infeasible. Domain generalization techniques aim to learn domain-invariant features or models that can generalize well to new domains. For example, consider a model trained to classify images of animals. If the model can learn features invariant to different backgrounds, lighting conditions, or poses, it can generalize well to classify animals in new, unseen environments. Domain generalization is crucial for building models that can be deployed in diverse and evolving real-world settings. The presence of a distribution shift can significantly impact the performance and reliability of ML models, as the models may need help generalizing well to the new data distribution. Detecting and adapting to distribution shifts is crucial to ensure ML systems' robustness and practical utility in real-world scenarios. @@ -714,18 +714,18 @@ Practitioners can develop more robust and resilient ML systems by leveraging the Recall that data poisoning is an attack that targets the integrity of the training data used to build ML models. By manipulating or corrupting the training data, attackers can influence the model's behavior and cause it to make incorrect predictions or perform unintended actions. Detecting and mitigating data poisoning attacks is crucial to ensure the trustworthiness and reliability of ML systems, as shown in [@fig-adversarial-attack-injection]. -##### Anomaly Detection Techniques for Identifying Poisoned Data - ![Malicious data injection. Source: [Li](https://www.mdpi.com/2227-7390/12/2/247)](./images/png/adversarial_attack_injection.png){#fig-adversarial-attack-injection} +##### Anomaly Detection Techniques for Identifying Poisoned Data + Statistical outlier detection methods identify data points that deviate significantly from most data. These methods assume that poisoned data instances are likely to be statistical outliers. Techniques such as the [Z-score method](https://ubalt.pressbooks.pub/mathstatsguides/chapter/z-score-basics/), [Tukey's method](https://www.itl.nist.gov/div898/handbook/prc/section4/prc471.htm), or the [Mahalanobis distance](https://www.statisticshowto.com/mahalanobis-distance/) can be used to measure the deviation of each data point from the central tendency of the dataset. Data points that exceed a predefined threshold are flagged as potential outliers and considered suspicious for data poisoning. Clustering-based methods group similar data points together based on their features or attributes. The assumption is that poisoned data instances may form distinct clusters or lie far away from the normal data clusters. By applying clustering algorithms like [K-means](https://www.oreilly.com/library/view/data-algorithms/9781491906170/ch12.html), [DBSCAN](https://www.oreilly.com/library/view/machine-learning-algorithms/9781789347999/50efb27d-abbe-4855-ad81-a5357050161f.xhtml), or [hierarchical clustering](https://www.oreilly.com/library/view/cluster-analysis-5th/9780470978443/chapter04.html), anomalous clusters or data points that do not belong to any cluster can be identified. These anomalous instances are then treated as potentially poisoned data. -![Autoencoder. Source: [Dertat](https://towardsdatascience.com/applied-deep-learning-part-3-autoencoders-1c083af4d798)](./images/png/autoencoder.png){#fig-autoencoder} - Autoencoders are neural networks trained to reconstruct the input data from a compressed representation, as shown in [@fig-autoencoder]. They can be used for anomaly detection by learning the normal patterns in the data and identifying instances that deviate from them. During training, the autoencoder is trained on clean, unpoisoned data. At inference time, the reconstruction error for each data point is computed. Data points with high reconstruction errors are considered abnormal and potentially poisoned, as they do not conform to the learned normal patterns. +![Autoencoder. Source: [Dertat](https://towardsdatascience.com/applied-deep-learning-part-3-autoencoders-1c083af4d798)](./images/png/autoencoder.png){#fig-autoencoder} + ##### Data Sanitization and Preprocessing Techniques Data poisoning can be avoided by cleaning data, which involves identifying and removing or correcting noisy, incomplete, or inconsistent data points. Techniques such as data deduplication, missing value imputation, and outlier removal can be applied to improve the quality of the training data. By eliminating or filtering out suspicious or anomalous data points, the impact of poisoned instances can be reduced. @@ -770,10 +770,10 @@ In addition, domain classifiers are trained to distinguish between different dom ##### Mitigation Techniques for Distribution Shifts -![Transfer learning. Source: [Bhavsar](https://medium.com/modern-nlp/transfer-learning-in-nlp-f5035cc3f62f)](./images/png/transfer_learning.png){#fig-transfer-learning} - Transfer learning leverages knowledge gained from one domain to improve performance in another, as shown in [@fig-transfer-learning]. By using pre-trained models or transferring learned features from a source domain to a target domain, transfer learning can help mitigate the impact of distribution shifts. The pre-trained model can be fine-tuned on a small amount of labeled data from the target domain, allowing it to adapt to the new distribution. Transfer learning is particularly effective when the source and target domains share similar characteristics or when labeled data in the target domain is scarce. +![Transfer learning. Source: [Bhavsar](https://medium.com/modern-nlp/transfer-learning-in-nlp-f5035cc3f62f)](./images/png/transfer_learning.png){#fig-transfer-learning} + Continual learning, also known as lifelong learning, enables ML models to learn continuously from new data distributions while retaining knowledge from previous distributions. Techniques such as elastic weight consolidation (EWC) [@kirkpatrick2017overcoming] or gradient episodic memory (GEM) [@lopez2017gradient] allow models to adapt to evolving data distributions over time. These techniques aim to balance the plasticity of the model (ability to learn from new data) with the stability of the model (retaining previously learned knowledge). By incrementally updating the model with new data and mitigating catastrophic forgetting, continual learning helps models stay robust to distribution shifts. Data augmentation techniques, such as those we have seen previously, involve applying transformations or perturbations to the existing training data to increase its diversity and improve the model's robustness to distribution shifts. By introducing variations in the data, such as rotations, translations, scaling, or adding noise, data augmentation helps the model learn invariant features and generalize better to unseen distributions. Data augmentation can be performed during training and inference to improve the model's ability to handle distribution shifts. @@ -868,7 +868,7 @@ Adopting a proactive and systematic approach to fault detection and mitigation c ### Fault Tolerance -Get ready to become an AI fault-fighting superhero! Software glitches can derail machine learning systems, but in this Colab, you'll learn how to make them resilient. We'll simulate software faults to see how AI can break, then explore techniques to save your ML model's progress, like checkpoints in a game. You'll see how to train your AI to bounce back after a crash, ensuring it stays on track. This is crucial for building reliable, trustworthy AI, especially in critical applications. So gear up because this Colab directly connects with the Robust AI chapter – you'll move from theory to hands-on troubleshooting and build AI systems that can handle the unexpected! +Get ready to become an AI fault-fighting superhero! Software glitches can derail machine learning systems, but in this Colab, you'll learn how to make them resilient. We'll simulate software faults to see how AI can break, then explore techniques to save your ML model's progress, like checkpoints in a game. You'll see how to train your AI to bounce back after a crash, ensuring it stays on track. This is crucial for building reliable, trustworthy AI, especially in critical applications. So gear up because this Colab directly connects with the Robust AI chapter---you'll move from theory to hands-on troubleshooting and build AI systems that can handle the unexpected! [![](https://colab.research.google.com/assets/colab-badge.png)](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/migrate/fault_tolerance.ipynb#scrollTo=77z2OchJTk0l) ::: @@ -919,8 +919,6 @@ Two of the most common hardware-based fault injection methods are FPGA-based fau **Radiation or Beam Testing:** Radiation or beam testing [@velazco2010combining] involves exposing the hardware running an ML model to high-energy particles, such as protons or neutrons as illustrated in [@fig-beam-testing](#5a77jp776dxi). These particles can cause bitflips or other types of faults in the hardware, mimicking the effects of real-world radiation-induced faults. Beam testing is widely regarded as a highly accurate method for measuring the error rate induced by particle strikes on a running application. It provides a realistic representation of the faults in real-world environments, particularly in applications exposed to high radiation levels, such as space systems or particle physics experiments. However, unlike FPGA-based fault injection, beam testing could be more precise in targeting specific bits or components within the hardware, as it might be difficult to aim the beam of particles to a particular bit in the hardware. Despite being quite expensive from a research standpoint, beam testing is a well-regarded industry practice for reliability. -![](./images/png/image15.png) - ![Radiation test setup for semiconductor components [@lee2022design] Source: [JD Instrument](https://jdinstruments.net/tester-capabilities-radiation-test/)](./images/png/image14.png){#fig-beam-testing} #### Limitations @@ -957,17 +955,17 @@ Software-based fault injection tools also have some limitations compared to hard **Fidelity:** Software-based tools may provide a different level of Fidelity than hardware-based methods in terms of representing real-world fault conditions. The accuracy of the results obtained from software-based fault injection experiments may depend on how closely the software model approximates the actual hardware behavior. -![Comparison of techniques at layers of abstraction. Source: [MAVFI](https://ieeexplore.ieee.org/abstract/document/10315202)](./images/jpg/mavfi.jpg){#fig-mavfi} - ##### Types of Fault Injection Tools Software-based fault injection tools can be categorized based on their target frameworks or use cases. Here, we will discuss some of the most popular tools in each category: Ares [@reagen2018ares], a fault injection tool initially developed for the Keras framework in 2018, emerged as one of the first tools to study the impact of hardware faults on deep neural networks (DNNs) in the context of the rising popularity of ML frameworks in the mid-to-late 2010s. The tool was validated against a DNN accelerator implemented in silicon, demonstrating its effectiveness in modeling hardware faults. Ares provides a comprehensive study on the impact of hardware faults in both weights and activation values, characterizing the effects of single-bit flips and bit-error rates (BER) on hardware structures. Later, the Ares framework was extended to support the PyTorch ecosystem, enabling researchers to investigate hardware faults in a more modern setting and further extending its utility in the field. +PyTorchFI [@mahmoud2020pytorchfi], a fault injection tool specifically designed for the PyTorch framework, was developed in 2020 in collaboration with Nvidia Research. It enables the injection of faults into the weights, activations, and gradients of PyTorch models, supporting a wide range of fault models. By leveraging the GPU acceleration capabilities of PyTorch, PyTorchFI provides a fast and efficient implementation for conducting fault injection experiments on large-scale ML systems, as shown in [@fig-phantom-objects](#txkz61sj1mj4). + ![Hardware bitflips in ML workloads can cause phantom objects and misclassifications, which can erroneously be used downstream by larger systems, such as in autonomous driving. Shown above is a correct and faulty version of the same image using the PyTorchFI injection framework.](./images/png/phantom_objects.png){#fig-phantom-objects} -PyTorchFI [@mahmoud2020pytorchfi], a fault injection tool specifically designed for the PyTorch framework, was developed in 2020 in collaboration with Nvidia Research. It enables the injection of faults into the weights, activations, and gradients of PyTorch models, supporting a wide range of fault models. By leveraging the GPU acceleration capabilities of PyTorch, PyTorchFI provides a fast and efficient implementation for conducting fault injection experiments on large-scale ML systems, as shown in [@fig-phantom-objects](#txkz61sj1mj4). The tool's speed and ease of use have led to widespread adoption in the community, resulting in multiple developer-led projects, such as PyTorchALFI by Intel xColabs, which focuses on safety in automotive environments. Follow-up PyTorch-centric tools for fault injection include Dr. DNA by Meta [@ma2024dr] (which further facilitates the Pythonic programming model for ease of use), and the GoldenEye framework [@mahmoud2022dsn], which incorporates novel numerical datatypes (such as AdaptivFloat [@tambe2020algorithm] and [BlockFloat](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format) in the context of hardware bit flips. +The tool's speed and ease of use have led to widespread adoption in the community, resulting in multiple developer-led projects, such as PyTorchALFI by Intel xColabs, which focuses on safety in automotive environments. Follow-up PyTorch-centric tools for fault injection include Dr. DNA by Meta [@ma2024dr] (which further facilitates the Pythonic programming model for ease of use), and the GoldenEye framework [@mahmoud2022dsn], which incorporates novel numerical datatypes (such as AdaptivFloat [@tambe2020algorithm] and [BlockFloat](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format) in the context of hardware bit flips. TensorFI [@chen2020tensorfi], or the TensorFlow Fault Injector, is a fault injection tool developed specifically for the TensorFlow framework. Analogous to Ares and PyTorchFI, TensorFI is considered the state-of-the-art tool for ML robustness studies in the TensorFlow ecosystem. It allows researchers to inject faults into the computational graph of TensorFlow models and study their impact on the model's performance, supporting a wide range of fault models. One of the key benefits of TensorFI is its ability to evaluate the resilience of various ML models, not just DNNs. Further advancements, such as BinFi [@chen2019sc], provide a mechanism to speed up error injection experiments by focusing on the "important" bits in the system, accelerating the process of ML robustness analysis and prioritizing the critical components of a model. @@ -1077,13 +1075,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * @exr-ft ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we offer a series of hands-on labs allowing students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience. - -* _Coming soon._ -::: - diff --git a/contents/sustainable_ai/images/png/azure_dashboard.png b/contents/core/sustainable_ai/images/png/azure_dashboard.png similarity index 100% rename from contents/sustainable_ai/images/png/azure_dashboard.png rename to contents/core/sustainable_ai/images/png/azure_dashboard.png diff --git a/contents/sustainable_ai/images/png/carbon_benchmarks.png b/contents/core/sustainable_ai/images/png/carbon_benchmarks.png similarity index 100% rename from contents/sustainable_ai/images/png/carbon_benchmarks.png rename to contents/core/sustainable_ai/images/png/carbon_benchmarks.png diff --git a/contents/sustainable_ai/images/png/cover_sustainable_ai.png b/contents/core/sustainable_ai/images/png/cover_sustainable_ai.png similarity index 100% rename from contents/sustainable_ai/images/png/cover_sustainable_ai.png rename to contents/core/sustainable_ai/images/png/cover_sustainable_ai.png diff --git a/contents/sustainable_ai/images/png/energy_datacenter.png b/contents/core/sustainable_ai/images/png/energy_datacenter.png similarity index 100% rename from contents/sustainable_ai/images/png/energy_datacenter.png rename to contents/core/sustainable_ai/images/png/energy_datacenter.png diff --git a/contents/sustainable_ai/images/png/ethicalai.png b/contents/core/sustainable_ai/images/png/ethicalai.png similarity index 100% rename from contents/sustainable_ai/images/png/ethicalai.png rename to contents/core/sustainable_ai/images/png/ethicalai.png diff --git a/contents/sustainable_ai/images/png/europe_energy_grid.png b/contents/core/sustainable_ai/images/png/europe_energy_grid.png similarity index 100% rename from contents/sustainable_ai/images/png/europe_energy_grid.png rename to contents/core/sustainable_ai/images/png/europe_energy_grid.png diff --git a/contents/sustainable_ai/images/png/mckinsey_analysis.png b/contents/core/sustainable_ai/images/png/mckinsey_analysis.png similarity index 100% rename from contents/sustainable_ai/images/png/mckinsey_analysis.png rename to contents/core/sustainable_ai/images/png/mckinsey_analysis.png diff --git a/contents/sustainable_ai/images/png/model_carbonfootprint.png b/contents/core/sustainable_ai/images/png/model_carbonfootprint.png similarity index 100% rename from contents/sustainable_ai/images/png/model_carbonfootprint.png rename to contents/core/sustainable_ai/images/png/model_carbonfootprint.png diff --git a/contents/sustainable_ai/images/png/model_scaling.png b/contents/core/sustainable_ai/images/png/model_scaling.png similarity index 100% rename from contents/sustainable_ai/images/png/model_scaling.png rename to contents/core/sustainable_ai/images/png/model_scaling.png diff --git a/contents/sustainable_ai/images/png/statista_chip_growth.png b/contents/core/sustainable_ai/images/png/statista_chip_growth.png similarity index 100% rename from contents/sustainable_ai/images/png/statista_chip_growth.png rename to contents/core/sustainable_ai/images/png/statista_chip_growth.png diff --git a/contents/sustainable_ai/sustainable_ai.bib b/contents/core/sustainable_ai/sustainable_ai.bib similarity index 100% rename from contents/sustainable_ai/sustainable_ai.bib rename to contents/core/sustainable_ai/sustainable_ai.bib diff --git a/contents/sustainable_ai/sustainable_ai.qmd b/contents/core/sustainable_ai/sustainable_ai.qmd similarity index 98% rename from contents/sustainable_ai/sustainable_ai.qmd rename to contents/core/sustainable_ai/sustainable_ai.qmd index 5a721446..4f766c1c 100644 --- a/contents/sustainable_ai/sustainable_ai.qmd +++ b/contents/core/sustainable_ai/sustainable_ai.qmd @@ -5,7 +5,7 @@ bibliography: sustainable_ai.bib # Sustainable AI {#sec-sustainable_ai} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-sustainable-ai-resource), [Videos](#sec-sustainable-ai-resource), [Exercises](#sec-sustainable-ai-resource), [Labs](#sec-sustainable-ai-resource) +Resources: [Slides](#sec-sustainable-ai-resource), [Videos](#sec-sustainable-ai-resource), [Exercises](#sec-sustainable-ai-resource) ::: ![_DALL·E 3 Prompt: 3D illustration on a light background of a sustainable AI network interconnected with a myriad of eco-friendly energy sources. The AI actively manages and optimizes its energy from sources like solar arrays, wind turbines, and hydro dams, emphasizing power efficiency and performance. Deep neural networks spread throughout, receiving energy from these sustainable resources._](images/png/cover_sustainable_ai.png) @@ -99,7 +99,9 @@ What drives such immense requirements? During training, models like GPT-3 learn Developing and training AI models requires immense data, computing power, and energy. However, the deployment and operation of those models also incur significant recurrent resource costs over time. AI systems are now integrated across various industries and applications and are entering the daily lives of an increasing demographic. Their cumulative operational energy and infrastructure impacts could eclipse the upfront model training. -This concept is reflected in the demand for training and inference hardware in data centers and on the edge. Inference refers to using a trained model to make predictions or decisions on real-world data. According to a [recent McKinsey analysis](https://www.mckinsey.com/~/media/McKinsey/Industries/Semiconductors/Our%20Insights/Artificial%20intelligence%20hardware%20New%20opportunities%20for%20semiconductor%20companies/Artificial-intelligence-hardware.ashx), the need for advanced systems to train ever-larger models is rapidly growing. However, inference computations already make up a dominant and increasing portion of total AI workloads, as shown in @fig-mckinsey. Running real-time inference with trained models--whether for image classification, speech recognition, or predictive analytics--invariably demands computing hardware like servers and chips. However, even a model handling thousands of facial recognition requests or natural language queries daily is dwarfed by massive platforms like Meta. Where inference on millions of photos and videos shared on social media, the infrastructure energy requirements continue to scale! +This concept is reflected in the demand for training and inference hardware in data centers and on the edge. Inference refers to using a trained model to make predictions or decisions on real-world data. According to a [recent McKinsey analysis](https://www.mckinsey.com/~/media/McKinsey/Industries/Semiconductors/Our%20Insights/Artificial%20intelligence%20hardware%20New%20opportunities%20for%20semiconductor%20companies/Artificial-intelligence-hardware.ashx), the need for advanced systems to train ever-larger models is rapidly growing. + +However, inference computations already make up a dominant and increasing portion of total AI workloads, as shown in @fig-mckinsey. Running real-time inference with trained models--whether for image classification, speech recognition, or predictive analytics--invariably demands computing hardware like servers and chips. However, even a model handling thousands of facial recognition requests or natural language queries daily is dwarfed by massive platforms like Meta. Where inference on millions of photos and videos shared on social media, the infrastructure energy requirements continue to scale! ![Market size for inference and training hardware. Source: [McKinsey.](https://www.mckinsey.com/~/media/McKinsey/Industries/Semiconductors/Our%20Insights/Artificial%20intelligence%20hardware%20New%20opportunities%20for%20semiconductor%20companies/Artificial-intelligence-hardware.ashx)](images/png/mckinsey_analysis.png){#fig-mckinsey} @@ -429,7 +431,7 @@ Access to the right frameworks and tools is essential to effectively implementin Several software libraries and development environments are specifically tailored for Green AI. These tools often include features for optimizing AI models to reduce their computational load and, consequently, their energy consumption. For example, libraries in PyTorch and TensorFlow that support model pruning, quantization, and efficient neural network architectures enable developers to build AI systems that require less processing power and energy. Additionally, open-source communities like the [Green Software Foundation](https://github.com/Green-Software-Foundation) are creating a centralized carbon intensity metric and building software for carbon-aware computing. -Energy monitoring tools are crucial for Green AI, as they allow developers to measure and analyze the energy consumption of their AI systems. By providing detailed insights into where and how energy is being used, these tools enable developers to make informed decisions about optimizing their models for better energy efficiency. This can involve adjustments in algorithm design, hardware selection, cloud computing software selection, or operational parameters. @fig-azuredashboard is a screenshot of an energy consumption dashboard provided by Microsoft's cloud services platform. +Energy monitoring tools are crucial for Green AI, as they allow developers to measure and analyze the energy consumption of their AI systems. @fig-azuredashboard is a screenshot of an energy consumption dashboard provided by Microsoft's cloud services platform. By providing detailed insights into where and how energy is being used, these tools enable developers to make informed decisions about optimizing their models for better energy efficiency. This can involve adjustments in algorithm design, hardware selection, cloud computing software selection, or operational parameters. ![Microsoft Azure energy consumption dashboard. Source: [Will Buchanan.](https://techcommunity.microsoft.com/t5/green-tech-blog/charting-the-path-towards-sustainable-ai-with-azure-machine/ba-p/2866923)](images/png/azure_dashboard.png){#fig-azuredashboard} @@ -703,12 +705,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * @exr-mle ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we offer hands-on labs that allow students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience. - -* _Coming soon._ -::: diff --git a/contents/training/images/jpeg/activation-functions3.jpg b/contents/core/training/images/jpeg/activation-functions3.jpg similarity index 100% rename from contents/training/images/jpeg/activation-functions3.jpg rename to contents/core/training/images/jpeg/activation-functions3.jpg diff --git a/contents/training/images/png/ai_training.png b/contents/core/training/images/png/ai_training.png similarity index 100% rename from contents/training/images/png/ai_training.png rename to contents/core/training/images/png/ai_training.png diff --git a/contents/training/images/png/aitrainingfit.png b/contents/core/training/images/png/aitrainingfit.png similarity index 100% rename from contents/training/images/png/aitrainingfit.png rename to contents/core/training/images/png/aitrainingfit.png diff --git a/contents/training/images/png/aitrainingnn.png b/contents/core/training/images/png/aitrainingnn.png similarity index 100% rename from contents/training/images/png/aitrainingnn.png rename to contents/core/training/images/png/aitrainingnn.png diff --git a/contents/training/images/png/aitrainingpara.png b/contents/core/training/images/png/aitrainingpara.png similarity index 100% rename from contents/training/images/png/aitrainingpara.png rename to contents/core/training/images/png/aitrainingpara.png diff --git a/contents/training/images/png/aitrainingroof.png b/contents/core/training/images/png/aitrainingroof.png similarity index 100% rename from contents/training/images/png/aitrainingroof.png rename to contents/core/training/images/png/aitrainingroof.png diff --git a/contents/training/images/png/aitrainingsgd.png b/contents/core/training/images/png/aitrainingsgd.png similarity index 100% rename from contents/training/images/png/aitrainingsgd.png rename to contents/core/training/images/png/aitrainingsgd.png diff --git a/contents/training/images/png/fits.png b/contents/core/training/images/png/fits.png similarity index 100% rename from contents/training/images/png/fits.png rename to contents/core/training/images/png/fits.png diff --git a/contents/training/images/png/graph.png b/contents/core/training/images/png/graph.png similarity index 100% rename from contents/training/images/png/graph.png rename to contents/core/training/images/png/graph.png diff --git a/contents/training/training.bib b/contents/core/training/training.bib similarity index 100% rename from contents/training/training.bib rename to contents/core/training/training.bib diff --git a/contents/training/training.qmd b/contents/core/training/training.qmd similarity index 98% rename from contents/training/training.qmd rename to contents/core/training/training.qmd index 95c93fe7..80ba9dbe 100644 --- a/contents/training/training.qmd +++ b/contents/core/training/training.qmd @@ -5,7 +5,7 @@ bibliography: training.bib # AI Training {#sec-ai_training} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-ai-training-resource), [Videos](#sec-ai-frameworks-resource), [Exercises](#sec-ai-training-resource), [Labs](#sec-ai-training-resource) +Resources: [Slides](#sec-ai-training-resource), [Videos](#sec-ai-frameworks-resource), [Exercises](#sec-ai-training-resource) ::: ![_DALL·E 3 Prompt: An illustration for AI training, depicting a neural network with neurons that are being repaired and firing. The scene includes a vast network of neurons, each glowing and firing to represent activity and learning. Among these neurons, small figures resembling engineers and scientists are actively working, repairing and tweaking the neurons. These miniature workers symbolize the process of training the network, adjusting weights and biases to achieve convergence. The entire scene is a visual metaphor for the intricate and collaborative effort involved in AI training, with the workers representing the continuous optimization and learning within a neural network. The background is a complex array of interconnected neurons, creating a sense of depth and complexity._](images/png/ai_training.png) @@ -72,10 +72,12 @@ How is this process defined mathematically? Formally, neural networks are mathem ### Neural Network Notation -Diving into the details, the core of a neural network can be viewed as a sequence of alternating linear and nonlinear operations, as show in @fig-neural-net-diagram: +The core of a neural network can be viewed as a sequence of alternating linear and nonlinear operations, as shown in @fig-neural-net-diagram. ![Neural network diagram. Source: astroML.](images/png/aitrainingnn.png){#fig-neural-net-diagram} +Neural networks are structured with layers of neurons connected by weights (representing linear operations) and activation functions (representing nonlinear operations). By examining the figure, we see how information flows through the network, starting from the input layer, passing through one or more hidden layers, and finally reaching the output layer. Each connection between neurons represents a weight, while each neuron typically applies a nonlinear activation function to its inputs. + The neural network operates by taking an input vector $x_i$ and passing it through a series of layers, each of which performs linear and non-linear operations. The output of the network at each layer $A_j$ can be represented as: $$ @@ -1040,9 +1042,9 @@ The batch size used during neural network training and inference significantly i Specifically, let's look at the arithmetic intensity of matrix multiplication during neural network training. This measures the ratio between computational operations and memory transfers. The matrix multiply of two matrices of size $N \times M$ and $M \times B$ requires $N \times M \times B$ multiply-accumulate operations, but only transfers of $N \times M + M \times B$ matrix elements. -As we increase the batch size $B$, the number of arithmetic operations grows faster than the memory transfers. For example, with a batch size of 1, we need $N \times M$ operations and $N + M$ transfers, giving an arithmetic intensity ratio of around $\frac{N \times M}{N+M}$. But with a large batch size of 128, the intensity ratio becomes $\frac{128 \times N \times M}{N \times M + M \times 128} \approx 128$. Using a larger batch size shifts the overall computation from memory-bounded to more compute-bounded. AI training uses large batch sizes and is generally limited by peak arithmetic computational performance, i.e., Application 3 in @fig-roofline. +As we increase the batch size $B$, the number of arithmetic operations grows faster than the memory transfers. For example, with a batch size of 1, we need $N \times M$ operations and $N + M$ transfers, giving an arithmetic intensity ratio of around $\frac{N \times M}{N+M}$. But with a large batch size of 128, the intensity ratio becomes $\frac{128 \times N \times M}{N \times M + M \times 128} \approx 128$. -Therefore, batched matrix multiplication is far more computationally intensive than memory access bound. This has implications for hardware design and software optimizations, which we will cover next. The key insight is that we can significantly alter the computational profile and bottlenecks posed by neural network training and inference by tuning the batch size. +Using a larger batch size shifts the overall computation from memory-bounded to more compute-bounded. AI training uses large batch sizes and is generally limited by peak arithmetic computational performance, i.e., Application 3 in @fig-roofline. Therefore, batched matrix multiplication is far more computationally intensive than memory access bound. This has implications for hardware design and software optimizations, which we will cover next. The key insight is that we can significantly alter the computational profile and bottlenecks posed by neural network training and inference by tuning the batch size. ![AI training roofline model.](images/png/aitrainingroof.png){#fig-roofline} diff --git a/contents/workflow/images/png/ML_life_cycle.png b/contents/core/workflow/images/png/ML_life_cycle.png similarity index 100% rename from contents/workflow/images/png/ML_life_cycle.png rename to contents/core/workflow/images/png/ML_life_cycle.png diff --git a/contents/workflow/images/png/comparingdlandml.png b/contents/core/workflow/images/png/comparingdlandml.png similarity index 100% rename from contents/workflow/images/png/comparingdlandml.png rename to contents/core/workflow/images/png/comparingdlandml.png diff --git a/contents/workflow/images/png/cover_ai_workflow.png b/contents/core/workflow/images/png/cover_ai_workflow.png similarity index 100% rename from contents/workflow/images/png/cover_ai_workflow.png rename to contents/core/workflow/images/png/cover_ai_workflow.png diff --git a/contents/workflow/images/png/embeddedai.png b/contents/core/workflow/images/png/embeddedai.png similarity index 100% rename from contents/workflow/images/png/embeddedai.png rename to contents/core/workflow/images/png/embeddedai.png diff --git a/contents/workflow/workflow.bib b/contents/core/workflow/workflow.bib similarity index 100% rename from contents/workflow/workflow.bib rename to contents/core/workflow/workflow.bib diff --git a/contents/workflow/workflow.qmd b/contents/core/workflow/workflow.qmd similarity index 84% rename from contents/workflow/workflow.qmd rename to contents/core/workflow/workflow.qmd index fab73039..25fd6cd2 100644 --- a/contents/workflow/workflow.qmd +++ b/contents/core/workflow/workflow.qmd @@ -5,15 +5,15 @@ bibliography: workflow.bib # AI Workflow {#sec-ai_workflow} ::: {.content-visible when-format="html"} -Resources: [Slides](#sec-ai-workflow-resource), [Videos](#sec-ai-workflow-resource), [Exercises](#sec-ai-workflow-resource), [Labs](#sec-ai-workflow-resource) +Resources: [Slides](#sec-ai-workflow-resource), [Videos](#sec-ai-workflow-resource), [Exercises](#sec-ai-workflow-resource) ::: ![_DALL·E 3 Prompt: Create a rectangular illustration of a stylized flowchart representing the AI workflow/pipeline. From left to right, depict the stages as follows: 'Data Collection' with a database icon, 'Data Preprocessing' with a filter icon, 'Model Design' with a brain icon, 'Training' with a weight icon, 'Evaluation' with a checkmark, and 'Deployment' with a rocket. Connect each stage with arrows to guide the viewer horizontally through the AI processes, emphasizing these steps' sequential and interconnected nature._](images/png/cover_ai_workflow.png) -In this chapter, we'll explore the machine learning (ML) workflow, setting the stage for subsequent chapters that go deeper into the specifics. To ensure we see the bigger picture, this chapter offers a high-level overview of the steps involved in the ML workflow. - The ML workflow is a structured approach that guides professionals and researchers through developing, deploying, and maintaining ML models. This workflow is generally divided into several crucial stages, each contributing to the effective development of intelligent systems. +In this chapter, we will explore the machine learning workflow, setting the stage for subsequent chapters that go deeper into the specifics. This chapter focuses only presenting a high-level overview of the steps involved in the ML workflow. + ::: {.callout-tip} ## Learning Objectives @@ -36,7 +36,7 @@ The ML workflow is a structured approach that guides professionals and researche ![Multi-step design methodology for the development of a machine learning model. Commonly referred to as the machine learning lifecycle](images/png/ML_life_cycle.png){#fig-ml-life-cycle} -Developing a successful machine learning model requires a systematic workflow. This end-to-end process enables you to build, deploy, and maintain models effectively. As shown in @fig-ml-life-cycle, It typically involves the following key steps: +@fig-ml-life-cycle illustrates the systematic workflow required for developing a successful machine learning model. This end-to-end process, commonly referred to as the machine learning lifecycle, enables you to build, deploy, and maintain models effectively. It typically involves the following key steps: 1. **Problem Definition** - Start by clearly articulating the specific problem you want to solve. This focuses on your efforts during data collection and model building. 2. **Data Collection and Preparation:** Gather relevant, high-quality training data that captures all aspects of the problem. Clean and preprocess the data to prepare it for modeling. @@ -94,7 +94,7 @@ The deployment phase often requires specialized hardware and infrastructure, as As models make decisions that can impact individuals and society, ethical and legal aspects of machine learning are becoming increasingly important. Ethicists and legal advisors are needed to ensure compliance with ethical standards and legal regulations. -@tbl-mlops_roles shows a rundown of the typical roles involved. While the lines between these roles can sometimes blur, the table below provides a general overview. +Understanding the various roles involved in an ML project is crucial for its successful completion. @tbl-mlops_roles provides a general overview of these typical roles, although it's important to note that the lines between them can sometimes blur. Let's examine this breakdown: +----------------------------------------+----------------------------------------------------------------------------------------------------+ | Role | Responsibilities | @@ -128,13 +128,15 @@ As models make decisions that can impact individuals and society, ethical and le : Roles and responsibilities of people involved in MLOps. {#tbl-mlops_roles .striped .hover} -Understanding these roles is crucial for completing an ML project. As we proceed through the upcoming chapters, we'll explore each role's essence and expertise, fostering a comprehensive understanding of the complexities involved in embedded AI projects. This holistic view facilitates seamless collaboration and nurtures an environment ripe for innovation and breakthroughs. +As we proceed through the upcoming chapters, we will explore each role's essence and expertise and foster a deeper understanding of the complexities involved in AI projects. This holistic view facilitates seamless collaboration and nurtures an environment ripe for innovation and breakthroughs. ## Conclusion -This chapter has laid the foundation for understanding the machine learning workflow, a structured approach crucial for the development, deployment, and maintenance of ML models. By exploring the distinct stages of the ML lifecycle, we have gained insights into the unique challenges faced by traditional ML and embedded AI workflows, particularly in terms of resource optimization, real-time processing, data management, and hardware-software integration. These distinctions underscore the importance of tailoring workflows to meet the specific demands of the application environment. +This chapter has laid the foundation for understanding the machine learning workflow, a structured approach crucial for the development, deployment, and maintenance of ML models. We explored the unique challenges faced in ML workflows, where resource optimization, real-time processing, data management, and hardware-software integration are paramount. These distinctions underscore the importance of tailoring workflows to meet the specific demands of the application environment. + +Moreover, we emphasized the significance of multidisciplinary collaboration in ML projects. By examining the diverse roles involved, from data scientists to software engineers, we gained an overview of the teamwork necessary to navigate the experimental and resource-intensive nature of ML development. This understanding is crucial for fostering effective communication and collaboration across different domains of expertise. -The chapter emphasized the significance of multidisciplinary collaboration in ML projects. Understanding the diverse roles provides a comprehensive view of the teamwork necessary to navigate the experimental and resource-intensive nature of ML development. As we move forward to more detailed discussions in the subsequent chapters, this high-level overview equips us with a holistic perspective on the ML workflow and the various roles involved. +As we move forward to more detailed discussions in subsequent chapters, this high-level overview equips us with a holistic perspective on the ML workflow and the various roles involved. This foundation will prove important as we dive into specific aspects of machine learning, which will allow us to contextualize advanced concepts within the broader framework of ML development and deployment. ## Resources {#sec-ai-workflow-resource} @@ -167,12 +169,3 @@ To reinforce the concepts covered in this chapter, we have curated a set of exer * _Coming soon._ ::: - -:::{.callout-warning collapse="false"} - -#### Labs - -In addition to exercises, we offer a series of hands-on labs allowing students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience. - -* _Coming soon._ -::: diff --git a/contents/foreword.qmd b/contents/foreword.qmd deleted file mode 100644 index e69de29b..00000000 diff --git a/contents/front.qmd b/contents/front.qmd deleted file mode 100644 index b3930974..00000000 --- a/contents/front.qmd +++ /dev/null @@ -1,2 +0,0 @@ -# FRONT MATTER - diff --git a/contents/introduction/introduction.qmd b/contents/introduction/introduction.qmd deleted file mode 100644 index 7bbcfab7..00000000 --- a/contents/introduction/introduction.qmd +++ /dev/null @@ -1,122 +0,0 @@ ---- -bibliography: introduction.bib ---- - -# Introduction {#sec-introduction} - -![_DALL·E 3 Prompt: A detailed, rectangular, flat 2D illustration depicting a roadmap of a book's chapters on machine learning systems, set on a crisp, clean white background. The image features a winding road traveling through various symbolic landmarks. Each landmark represents a chapter topic: Introduction, ML Systems, Deep Learning, AI Workflow, Data Engineering, AI Frameworks, AI Training, Efficient AI, Model Optimizations, AI Acceleration, Benchmarking AI, On-Device Learning, Embedded AIOps, Security & Privacy, Responsible AI, Sustainable AI, AI for Good, Robust AI, Generative AI. The style is clean, modern, and flat, suitable for a technical book, with each landmark clearly labeled with its chapter title._](images/png/cover_introduction.png) - -## Overview - -In the early 1990s, [Mark Weiser](https://en.wikipedia.org/wiki/Mark_Weiser), a pioneering computer scientist, introduced the world to a revolutionary concept that would forever change how we interact with technology. This was succintly captured in the paper he wrote on "The Computer for the 21st Century" (@fig-ubiqutous). He envisioned a future where computing would be seamlessly integrated into our environments, becoming an invisible, integral part of daily life. This vision, which he termed "ubiquitous computing," promised a world where technology would serve us without demanding our constant attention or interaction. Fast forward to today, and we find ourselves on the cusp of realizing Weiser's vision, thanks to the advent and proliferation of machine learning systems. - -![Ubiqutous computing.](images/png/21st_computer.png){#fig-ubiqutous width=50%} - -In the vision of ubiquitous computing [@weiser1991computer], the integration of processors into everyday objects is just one aspect of a larger paradigm shift. The true essence of this vision lies in creating an intelligent environment that can anticipate our needs and act on our behalf, enhancing our experiences without requiring explicit commands. To achieve this level of pervasive intelligence, it is crucial to develop and deploy machine learning systems that span the entire ecosystem, from the cloud to the edge and even to the tiniest IoT devices. - -By distributing machine learning capabilities across the "computing continuum," from cloud to edge to embedded systems that surround us, we can harness the strengths of each layer while mitigating their limitations. The cloud, with its vast computational resources and storage capacity, is ideal for training complex models on large datasets and performing resource-intensive tasks. Edge devices, such as gateways and smartphones, can process data locally, enabling faster response times, improved privacy, and reduced bandwidth requirements. Finally, the tiniest IoT devices, equipped with machine learning capabilities, can make quick decisions based on sensor data, enabling highly responsive and efficient systems. - -This distributed intelligence is particularly crucial for applications that require real-time processing, such as autonomous vehicles, industrial automation, and smart healthcare. By processing data at the most appropriate layer of the computing continuum, we can ensure that decisions are made quickly and accurately, without relying on constant communication with a central server. - -The migration of machine learning intelligence across the ecosystem also enables more personalized and context-aware experiences. By learning from user behavior and preferences at the edge, devices can adapt to individual needs without compromising privacy. This localized intelligence can then be aggregated and refined in the cloud, creating a feedback loop that continuously improves the overall system. - -However, deploying machine learning systems across the computing continuum presents several challenges. Ensuring the interoperability and seamless integration of these systems requires standardized protocols and interfaces. Security and privacy concerns must also be addressed, as the distribution of intelligence across multiple layers increases the attack surface and the potential for data breaches. - -Furthermore, the varying computational capabilities and energy constraints of devices at different layers of the computing continuum necessitate the development of efficient and adaptable machine learning models. Techniques such as model compression, federated learning, and transfer learning can help address these challenges, enabling the deployment of intelligence across a wide range of devices. - -As we move towards the realization of Weiser's vision of ubiquitous computing, the development and deployment of machine learning systems across the entire ecosystem will be critical. By leveraging the strengths of each layer of the computing continuum, we can create an intelligent environment that seamlessly integrates with our daily lives, anticipating our needs and enhancing our experiences in ways that were once unimaginable. As we continue to push the boundaries of what's possible with distributed machine learning, we inch closer to a future where technology becomes an invisible but integral part of our world. @fig-applications-of-ml illustrates some common applications of AI around us. - -![Common applications of Machine Learning. Source: [EDUCBA](https://www.educba.com/applications-of-machine-learning/)](images/png/mlapplications.png){#fig-applications-of-ml} - -## What's Inside the Book - -In this book, we will explore the technical foundations of ubiquitous machine learning systems, the challenges of building and deploying these systems across the computing continuum, and the vast array of applications they enable. A unique aspect of this book is its function as a conduit to seminal scholarly works and academic research papers, aimed at enriching the reader's understanding and encouraging deeper exploration of the subject. This approach seeks to bridge the gap between pedagogical materials and cutting-edge research trends, offering a comprehensive guide that is in step with the evolving field of applied machine learning. - -To improve the learning experience, we have included a variety of supplementary materials. Throughout the book, you will find slides that summarize key concepts, videos that provide in-depth explanations and demonstrations, exercises that reinforce your understanding, and labs that offer hands-on experience with the tools and techniques discussed. These additional resources are designed to cater to different learning styles and help you gain a deeper, more practical understanding of the subject matter. - -We begin with the fundamentals, introducing key concepts in systems and machine learning, and providing a deep learning primer. We then guide you through the AI workflow, from data engineering to selecting the right AI frameworks. The training section covers efficient AI training techniques, model optimizations, and AI acceleration using specialized hardware. Deployment is addressed next, with chapters on benchmarking AI, distributed learning, and ML operations. Advanced topics like security, privacy, responsible AI, sustainable AI, robust AI, and generative AI are then explored in depth. The book concludes by highlighting the positive impact of AI and its potential for good. @fig-ml-lifecycle outlines the lifecycle of a machine learning project. - -![Machine Learning project life cycle. Source:[Medium](https://ihsanulpro.medium.com/complete-machine-learning-project-flowchart-explained-0f55e52b9381)](images/png/mlprojectlifecycle.png){#fig-ml-lifecycle} - -## How to Navigate This Book - -To get the most out of this book, we recommend a structured learning approach that leverages the various resources provided. Each chapter includes slides, videos, exercises, and labs to cater to different learning styles and reinforce your understanding. Additionally, an AI tutor bot (SocratiQ AI) is readily available to guide you through the content and provide personalized assistance. - -1. **Fundamentals (Chapters 1-3):** Start by building a strong foundation with the initial chapters, which provide an introduction to AI and cover core topics like AI systems and deep learning. - -2. **Workflow (Chapters 4-6):** With that foundation, move on to the chapters focused on practical aspects of the AI model building process like workflows, data engineering, and frameworks. - -3. **Training (Chapters 7-10):** These chapters offer insights into effectively training AI models, including techniques for efficiency, optimizations, and acceleration. - -4. **Deployment (Chapters 11-13):** Learn about deploying AI on devices and monitoring the operationalization through methods like benchmarking, on-device learning, and MLOps. - -5. **Advanced Topics (Chapters 14-18):** Critically examine topics like security, privacy, ethics, sustainability, robustness, and generative AI. - -6. **Social Impact (Chapter 19):** Explore the positive applications and potential of AI for societal good. - -7. **Conclusion (Chapter 20):** Reflect on the key takeaways and future directions in AI systems. - -While the book is designed for progressive learning, we encourage an interconnected learning approach that allows you to navigate chapters based on your interests and needs. Throughout the book, you'll find case studies and hands-on exercises that help you relate theory to real-world applications. We also recommend participating in forums and groups to engage in [discussions](https://github.com/harvard-edge/cs249r_book/discussions), debate concepts, and share insights with fellow learners. Regularly revisiting chapters can help reinforce your learning and offer new perspectives on the concepts covered. By adopting this structured yet flexible approach and actively engaging with the content and the community, you'll embark on a fulfilling and enriching learning experience that maximizes your understanding. - -## Chapter Breakdown - -Here's a closer look at what each chapter covers. We have structured the book into six main sections: Fundamentals, Workflow, Training, Deployment, Advanced Topics, and Impact. These sections closely reflect the major components of a typical machine learning pipeline, from understanding the basic concepts to deploying and maintaining AI systems in real-world applications. By organizing the content in this manner, we aim to provide a logical progression that mirrors the actual process of developing and implementing AI systems. - -### Fundamentals - -In the Fundamentals section, we lay the groundwork for understanding AI. This is far from being a thorough deep dive into the algorithms, but we aim to introduce key concepts, provide an overview of machine learning systems, and dive into the principles and algorithms of deep learning that power AI applications in their associated systems. This section equips you with the essential knowledge needed to grasp the subsequent chapters. - -1. **[Introduction:](../introduction/introduction.qmd)** This chapter sets the stage, providing an overview of AI and laying the groundwork for the chapters that follow. -2. **[ML Systems:](../ml_systems/ml_systems.qmd)** We introduce the basics of machine learning systems, the platforms where AI algorithms are widely applied. -3. **[Deep Learning Primer:](../dl_primer/dl_primer.qmd)** This chapter offers a brief introduction to the algorithms and principles that underpin AI applications in ML systems. - -### Workflow - -The Workflow section guides you through the practical aspects of building AI models. We break down the AI workflow, discuss data engineering best practices, and review popular AI frameworks. By the end of this section, you'll have a clear understanding of the steps involved in developing proficient AI applications and the tools available to streamline the process. - -4. **[AI Workflow:](../workflow/workflow.qmd)** This chapter breaks down the machine learning workflow, offering insights into the steps leading to proficient AI applications. -5. **[Data Engineering:](../data_engineering/data_engineering.qmd)** We focus on the importance of data in AI systems, discussing how to effectively manage and organize data. -6. **[AI Frameworks:](../frameworks/frameworks.qmd)** This chapter reviews different frameworks for developing machine learning models, guiding you in choosing the most suitable one for your projects. - -### Training - -In the Training section, we explore techniques for training efficient and reliable AI models. We cover strategies for achieving efficiency, model optimizations, and the role of specialized hardware in AI acceleration. This section empowers you with the knowledge to develop high-performing models that can be seamlessly integrated into AI systems. - -7. **[AI Training:](../training/training.qmd)** This chapter explores model training, exploring techniques for developing efficient and reliable models. -8. **[Efficient AI:](../efficient_ai/efficient_ai.qmd)** Here, we discuss strategies for achieving efficiency in AI applications, from computational resource optimization to performance enhancement. -9. **[Model Optimizations:](../optimizations/optimizations.qmd)** We explore various avenues for optimizing AI models for seamless integration into AI systems. -10. **[AI Acceleration:](../hw_acceleration/hw_acceleration.qmd)** We discuss the role of specialized hardware in enhancing the performance of AI systems. - -### Deployment - -The Deployment section focuses on the challenges and solutions for deploying AI models. We discuss benchmarking methods to evaluate AI system performance, techniques for on-device learning to improve efficiency and privacy, and the processes involved in ML operations. This section equips you with the skills to effectively deploy and maintain AI functionalities in AI systems. - -11. **[Benchmarking AI:](../benchmarking/benchmarking.qmd)** This chapter focuses on how to evaluate AI systems through systematic benchmarking methods. -12. **[On-Device Learning:](../ondevice_learning/ondevice_learning.qmd)** We explore techniques for localized learning, which enhances both efficiency and privacy. -13. **[ML Operations:](../ops/ops.qmd)** This chapter looks at the processes involved in the seamless integration, monitoring, and maintenance of AI functionalities. - -### Advanced Topics - -In the Advanced Topics section, We will study the critical issues surrounding AI. We address privacy and security concerns, explore the ethical principles of responsible AI, discuss strategies for sustainable AI development, examine techniques for building robust AI models, and introduce the exciting field of generative AI. This section broadens your understanding of the complex landscape of AI and prepares you to navigate its challenges. - -14. **[Security & Privacy:](../privacy_security/privacy_security.qmd)** As AI becomes more ubiquitous, this chapter addresses the crucial aspects of privacy and security in AI systems. -15. **[Responsible AI:](../responsible_ai/responsible_ai.qmd)** We discuss the ethical principles guiding the responsible use of AI, focusing on fairness, accountability, and transparency. -16. **[Sustainable AI:](../sustainable_ai/sustainable_ai.qmd)** This chapter explores practices and strategies for sustainable AI, ensuring long-term viability and reduced environmental impact. -17. **[Robust AI:](../robust_ai/robust_ai.qmd)** We discuss techniques for developing reliable and robust AI models that can perform consistently across various conditions. -18. **[Generative AI:](../generative_ai/generative_ai.qmd)** This chapter explores the algorithms and techniques behind generative AI, opening avenues for innovation and creativity. - -### Social Impact - -The Impact section highlights the transformative potential of AI in various domains. We showcase real-world applications of TinyML in healthcare, agriculture, conservation, and other areas where AI is making a positive difference. This section inspires you to leverage the power of AI for societal good and to contribute to the development of impactful solutions. - -19. **[AI for Good:](../ai_for_good/ai_for_good.qmd)** We highlight positive applications of TinyML in areas like healthcare, agriculture, and conservation. - -### Closing - -In the Closing section, we reflect on the key learnings from the book and look ahead to the future of AI. We synthesize the concepts covered, discuss emerging trends, and provide guidance on continuing your learning journey in this rapidly evolving field. This section leaves you with a comprehensive understanding of AI and the excitement to apply your knowledge in innovative ways. - -20. **[Conclusion:](../conclusion/conclusion.qmd)** The book concludes with a reflection on the key learnings and future directions in the field of AI. - -## Contribute Back - -Learning in the fast-paced world of AI is a collaborative journey. We set out to nurture a vibrant community of learners, innovators, and contributors. As you explore the concepts and engage with the exercises, we encourage you to share your insights and experiences. Whether it's a novel approach, an interesting application, or a thought-provoking question, your contributions can enrich the learning ecosystem. Engage in discussions, offer and seek guidance, and collaborate on projects to foster a culture of mutual growth and learning. By sharing knowledge, you play an important role in fostering a globally connected, informed, and empowered community. diff --git a/contents/labs/arduino/nicla_vision/kws/kws.qmd b/contents/labs/arduino/nicla_vision/kws/kws.qmd index 08fee3e4..f6a03dd9 100644 --- a/contents/labs/arduino/nicla_vision/kws/kws.qmd +++ b/contents/labs/arduino/nicla_vision/kws/kws.qmd @@ -267,7 +267,7 @@ void setup() Create two functions, `turn_off_leds()` function , to turn off all RGB LEDs ``` cpp -** +/* * @brief turn_off_leds function - turn-off all RGB LEDs */ void turn_off_leds(){ @@ -280,7 +280,7 @@ void turn_off_leds(){ Another `turn_on_led()` function is used to turn on the RGB LEDs according to the most probable result of the classifier. ``` cpp -/** +/* * @brief turn_on_leds function used to turn on the RGB LEDs * @param[in] pred_index * no: [0] ==> Red ON diff --git a/contents/labs/labs.qmd b/contents/labs/labs.qmd index 10fc8474..1cd77bdf 100644 --- a/contents/labs/labs.qmd +++ b/contents/labs/labs.qmd @@ -1,74 +1,3 @@ -# Overview {.unnumbered} - -Welcome to the hands-on labs section where you'll explore deploying ML models onto real embedded devices, which will offer a practical introduction to ML systems. Unlike traditional approaches with large-scale models, these labs focus on interacting directly with both hardware and software. They help us show case various sensor modalities across different application use cases. This approach provides valuable insights into the challenges and opportunities of deploying AI on real physical systems. - -## Learning Objectives - -By completing these labs, we hope learners will: - -:::{.callout-tip} - -* Gain proficiency in setting up and deploying ML models on supported devices, enabling you to tackle real-world ML deployment scenarios with confidence. - -* Understand the steps involved in adapting and experimenting with ML models for different applications, allowing you to optimize performance and efficiency. - -* Learn troubleshooting techniques specific to embedded ML deployments equipping you with the skills to overcome common pitfalls and challenges. - -* Acquire practical experience in deploying TinyML models on embedded devices bridging the gap between theory and practice. - -* Explore various sensor modalities and their applications expanding your understanding of how ML can be leveraged in diverse domains. - -* Foster an understanding of the real-world implications and challenges associated with ML system deployments preparing you for future projects. - -::: - -## Target Audience - -These labs are designed for: - -* **Beginners** in the field of machine learning who have a keen interest in exploring the intersection of ML and embedded systems. - -* **Developers and engineers** looking to apply ML models to real-world applications using low-power, resource-constrained devices. - -* **Enthusiasts and researchers** who want to gain practical experience in deploying AI on edge devices and understand the unique challenges involved. - -## Supported Devices - -+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ -| Exercise | [Nicla Vision](https://store.arduino.cc/products/nicla-vision) | [XIAO ESP32S3](https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/) | [Raspberry Pi](https://www.raspberrypi.com/) | -+:===========================+:===============================================================+:===========================================================================+:=============================================+ -| Installation & Setup | | | | -+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ -| Keyword Spotting (KWS) | | | | -+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ -| Image Classification | | | Coming soon. | -+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ -| Object Detection | | | Coming soon. | -+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ -| Motion Detection | | | | -+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ -| Small Language Models (SLM)| | | Coming soon. | -+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ - -## Lab Structure - -Each lab follows a structured approach: - -1. **Introduction**: Explore the application and its significance in real-world scenarios. - -2. **Setup**: Step-by-step instructions to configure the hardware and software environment. - -3. **Deployment**: Guidance on training and deploying the pre-trained ML models on supported devices. - -4. **Exercises**: Hands-on tasks to modify and experiment with model parameters. - -5. **Discussion**: Analysis of results, potential improvements, and practical insights. - -## Troubleshooting and Support - -If you encounter any issues during the labs, consult the troubleshooting comments or check the FAQs within each lab. For further assistance, feel free to reach out to our support team or engage with the community forums. - -## Credits - -Special credit and thanks to [Prof. Marcelo Rovai](https://github.com/Mjrovai) for his valuable contributions to the development and continuous refinement of these labs. +# LABS +This page is intentionally left blank. diff --git a/contents/labs/overview.qmd b/contents/labs/overview.qmd new file mode 100644 index 00000000..970e1ebd --- /dev/null +++ b/contents/labs/overview.qmd @@ -0,0 +1,84 @@ +# Overview {.unnumbered} + +Welcome to the hands-on labs section where you'll explore deploying ML models onto real embedded devices, which will offer a practical introduction to ML systems. Unlike traditional approaches with large-scale models, these labs focus on interacting directly with both hardware and software. They help us show case various sensor modalities across different application use cases. This approach provides valuable insights into the challenges and opportunities of deploying AI on real physical systems. + +## Learning Objectives + +By completing these labs, we hope learners will: + +:::{.callout-tip} + +* Gain proficiency in setting up and deploying ML models on supported devices, enabling you to tackle real-world ML deployment scenarios with confidence. + +* Understand the steps involved in adapting and experimenting with ML models for different applications, allowing you to optimize performance and efficiency. + +* Learn troubleshooting techniques specific to embedded ML deployments equipping you with the skills to overcome common pitfalls and challenges. + +* Acquire practical experience in deploying TinyML models on embedded devices bridging the gap between theory and practice. + +* Explore various sensor modalities and their applications expanding your understanding of how ML can be leveraged in diverse domains. + +* Foster an understanding of the real-world implications and challenges associated with ML system deployments preparing you for future projects. + +::: + +## Target Audience + +These labs are designed for: + +* **Beginners** in the field of machine learning who have a keen interest in exploring the intersection of ML and embedded systems. + +* **Developers and engineers** looking to apply ML models to real-world applications using low-power, resource-constrained devices. + +* **Enthusiasts and researchers** who want to gain practical experience in deploying AI on edge devices and understand the unique challenges involved. + +## Supported Devices + +We have included laboratory materials for three key devices that represent different hardware profiles and capabilities. + +* Nicla Vision: Optimized for vision-based applications like image classification and object detection, ideal for compact, low-power use cases. +* XIAO ESP32S3: A versatile, compact board suitable for keyword spotting and motion detection tasks. +* Raspberry Pi: A flexible platform for more computationally intensive tasks, including small language models and various classification and detection applications. + ++----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ +| Exercise | [Nicla Vision](https://store.arduino.cc/products/nicla-vision) | [XIAO ESP32S3](https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/) | [Raspberry Pi](https://www.raspberrypi.com/) | ++:===========================+:===============================================================+:===========================================================================+:=============================================+ +| Installation & Setup | ✓ | ✓ | ✓ | ++----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ +| Keyword Spotting (KWS) | ✓ | ✓ | | ++----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ +| Image Classification | ✓ | ✓ | ✓ | ++----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ +| Object Detection | ✓ | ✓ | ✓ | ++----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ +| Motion Detection | ✓ | ✓ | | ++----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ +| Small Language Models (SLM)| | | ✓ | ++----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ + +## Lab Structure + +Each lab follows a structured approach: + +1. **Introduction**: Explore the application and its significance in real-world scenarios. + +2. **Setup**: Step-by-step instructions to configure the hardware and software environment. + +3. **Deployment**: Guidance on training and deploying the pre-trained ML models on supported devices. + +4. **Exercises**: Hands-on tasks to modify and experiment with model parameters. + +5. **Discussion**: Analysis of results, potential improvements, and practical insights. + +## Recommended Lab Sequence + +If you're new to embedded ML, we suggest starting with setup and keyword spotting before moving on to image classification and object detection. Raspberry Pi users can explore more advanced tasks, like small language models, after familiarizing themselves with the basics. + +## Troubleshooting and Support + +If you encounter any issues during the labs, consult the troubleshooting comments or check the FAQs within each lab. For further assistance, feel free to reach out to our support team or engage with the community forums. + +## Credits + +Special credit and thanks to [Prof. Marcelo Rovai](https://github.com/Mjrovai) for his valuable contributions to the development and continuous refinement of these labs. + diff --git a/contents/labs/raspi/image_classification/image_classification.qmd b/contents/labs/raspi/image_classification/image_classification.qmd index 81fc56fa..472934db 100644 --- a/contents/labs/raspi/image_classification/image_classification.qmd +++ b/contents/labs/raspi/image_classification/image_classification.qmd @@ -131,11 +131,10 @@ tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz tar xzf mobilenet_v2_1.0_224_quant.tgz ``` -Get its labels: +Get its [labels](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/IMG_CLASS/models/labels.txt): ```bash -wget https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/ -lite/java/demo/app/src/main/assets/labels_mobilenet_quant_v1_224.txt -O labels.txt +wget https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/IMG_CLASS/models/labels.txt ``` In the end, you should have the models in its directory: @@ -292,7 +291,7 @@ So, let's reshape the image, add the batch dimension, and see the result: ```python img = img.resize((input_details[0]['shape'][1], input_details[0]['shape'][2])) -input_data = np.expand_dims(img, axis=0 +input_data = np.expand_dims(img, axis=0) input_data.shape ``` @@ -422,30 +421,30 @@ def image_classification(img_path, model_path, labels, top_k_results=5): # Get input and output tensors input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() - + # Preprocess img = img.resize((input_details[0]['shape'][1], input_details[0]['shape'][2])) input_data = np.expand_dims(img, axis=0) - + # Inference on Raspi-Zero interpreter.set_tensor(input_details[0]['index'], input_data) interpreter.invoke() # Obtain results and map them to the classes predictions = interpreter.get_tensor(output_details[0]['index'])[0] - + # Get indices of the top k results top_k_indices = np.argsort(predictions)[::-1][:top_k_results] - + # Get quantization parameters scale, zero_point = output_details[0]['quantization'] - + # Dequantize the output and apply softmax dequantized_output = (predictions.astype(np.float32) - zero_point) * scale exp_output = np.exp(dequantized_output - np.max(dequantized_output)) probabilities = exp_output / np.sum(exp_output) - + print("\n\t[PREDICTION] [Prob]\n") for i in range(top_k_results): print("\t{:20}: {}%".format( @@ -483,7 +482,7 @@ On the notebook [Cifar 10 - Image Classification on a Raspi with TFLite](https:/ ```bash source ~/tflite/bin/activate - ``` +``` 2. Now, let's create a .pth file in your virtual environment to add the system site-packages path: @@ -578,6 +577,7 @@ Once we have defined our Machine Learning project goal, the next and most crucia 2. Let's create a new Python script combining image capture with a web server. We'll call it `get_img_data.py`:

+ ```python from flask import Flask, Response, render_template_string, request, redirect, url_for from picamera2 import Picamera2 @@ -766,7 +766,7 @@ if __name__ == '__main__': ```bash python3 get_img_data.py - ``` +``` 4. Access the web interface: @@ -1147,11 +1147,11 @@ def image_classification(img_path, model_path, labels, top_k_results=3, # Load the TFLite model interpreter = tflite.Interpreter(model_path=model_path) interpreter.allocate_tensors() - + # Get input and output tensors input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() - + # Preprocess img = img.resize((input_details[0]['shape'][1], input_details[0]['shape'][2])) @@ -1167,20 +1167,20 @@ def image_classification(img_path, model_path, labels, top_k_results=3, input_data = np.expand_dims(img_array, axis=0) else: # float32 input_data = np.expand_dims(np.array(img, dtype=np.float32), axis=0) / 255.0 - + # Inference on Raspi-Zero start_time = time.time() interpreter.set_tensor(input_details[0]['index'], input_data) interpreter.invoke() end_time = time.time() inference_time = (end_time - start_time) * 1000 # Convert to milliseconds - + # Obtain results predictions = 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00000000..e2d20de8 Binary files /dev/null and b/contents/labs/raspi/llm/images/png/verbose.png differ diff --git a/contents/labs/raspi/llm/llm.qmd b/contents/labs/raspi/llm/llm.qmd index dc1f7fd4..90098990 100644 --- a/contents/labs/raspi/llm/llm.qmd +++ b/contents/labs/raspi/llm/llm.qmd @@ -1,6 +1,1324 @@ # Small Language Models (SLM) {.unnumbered} +![*DALL·E prompt - A 1950s-style cartoon illustration showing a Raspberry Pi running a small language model at the edge. The Raspberry Pi is stylized in a retro-futuristic way with rounded edges and chrome accents, connected to playful cartoonish sensors and devices. Speech bubbles are floating around, representing language processing, and the background has a whimsical landscape of interconnected devices with wires and small gadgets, all drawn in a vintage cartoon style. The color palette uses soft pastel colors and bold outlines typical of 1950s cartoons, giving a fun and nostalgic vibe to the scene.*](images/jpeg/cover.jpg) +## Introduction -## *Coming soon.* +In the fast-growing area of artificial intelligence, edge computing presents an opportunity to decentralize capabilities traditionally reserved for powerful, centralized servers. This lab explores the practical integration of small versions of traditional large language models (LLMs) into a Raspberry Pi 5, transforming this edge device into an AI hub capable of real-time, on-site data processing. +As large language models grow in size and complexity, Small Language Models (SLMs) offer a compelling alternative for edge devices, striking a balance between performance and resource efficiency. By running these models directly on Raspberry Pi, we can create responsive, privacy-preserving applications that operate even in environments with limited or no internet connectivity. + +This lab will guide you through setting up, optimizing, and leveraging SLMs on Raspberry Pi. We will explore the installation and utilization of [Ollama](https://ollama.com/). This open-source framework allows us to run LLMs locally on our machines (our desktops or edge devices such as the Raspberry Pis or NVidia Jetsons). Ollama is designed to be efficient, scalable, and easy to use, making it a good option for deploying AI models such as Microsoft Phi, Google Gemma, Meta Llama, and LLaVa (Multimodal). We will integrate some of those models into projects using Python's ecosystem, exploring their potential in real-world scenarios (or at least point in this direction). + +![](images/jpeg/slm-example.jpg) + +## Setup + +We could use any Raspi model in the previous labs, but here, the choice must be the Raspberry Pi 5 (Raspi-5). It is a robust platform that substantially upgrades the last version 4, equipped with the Broadcom BCM2712, a 2.4GHz quad-core 64-bit Arm Cortex-A76 CPU featuring Cryptographic Extension and enhanced caching capabilities. It boasts a VideoCore VII GPU, dual 4Kp60 HDMI® outputs with HDR, and a 4Kp60 HEVC decoder. Memory options include 4GB and 8GB of high-speed LPDDR4X SDRAM, with 8GB being our choice to run SLMs. It also features expandable storage via a microSD card slot and a PCIe 2.0 interface for fast peripherals such as M.2 SSDs (Solid State Drives). + +> For real SSL applications, SSDs are a better option than SD cards. + +By the way, as [Alasdair Allan](https://www.hackster.io/aallan) discussed, inferencing directly on the Raspberry Pi 5 CPU—with no GPU acceleration—is now on par with the performance of the Coral TPU. + +![](images/png/bench.png) + +For more info, please see the complete article: [Benchmarking TensorFlow and TensorFlow Lite on Raspberry Pi 5](https://www.hackster.io/news/benchmarking-tensorflow-and-tensorflow-lite-on-raspberry-pi-5-b9156d58a6a2?mc_cid=0cab3d08f4&mc_eid=e96256ccba). + +### Raspberry Pi Active Cooler + +We suggest installing an Active Cooler, a dedicated clip-on cooling solution for Raspberry Pi 5 (Raspi-5), for this lab. It combines an aluminum heatsink with a temperature-controlled blower fan to keep the Raspi-5 operating comfortably under heavy loads, such as running SLMs. + +![](images/png/cooler.png) + +The Active Cooler has pre-applied thermal pads for heat transfer and is mounted directly to the Raspberry Pi 5 board using spring-loaded push pins. The Raspberry Pi firmware actively manages it: at 60°C, the blower’s fan will be turned on; at 67.5°C, the fan speed will be increased; and finally, at 75°C, the fan increases to full speed. The blower’s fan will spin down automatically when the temperature drops below these limits. + +![](images/png/temp_comp.png) + +> To prevent overheating, all Raspberry Pi boards begin to throttle the processor when the temperature reaches 80°Cand throttle even further when it reaches the maximum temperature of 85°C (more detail [here](https://www.raspberrypi.com/news/heating-and-cooling-raspberry-pi-5/)). + +## Generative AI (GenAI) + +Generative AI is an artificial intelligence system capable of creating new, original content across various mediums such as **text, images, audio, and video**. These systems learn patterns from existing data and use that knowledge to generate novel outputs that didn't previously exist. **Large Language Models (LLMs)**, **Small Language Models (SLMs)**, and **multimodal models** can all be considered types of GenAI when used for generative tasks. + +GenAI provides the conceptual framework for AI-driven content creation, with LLMs serving as powerful general-purpose text generators. SLMs adapt this technology for edge computing, while multimodal models extend GenAI capabilities across different data types. Together, they represent a spectrum of generative AI technologies, each with its strengths and applications, collectively driving AI-powered content creation and understanding. + +### Large Language Models (LLMs) + +Large Language Models (LLMs) are advanced artificial intelligence systems that understand, process, and generate human-like text. These models are characterized by their massive scale in terms of the amount of data they are trained on and the number of parameters they contain. Critical aspects of LLMs include: + +1. **Size**: LLMs typically contain billions of parameters. For example, GPT-3 has 175 billion parameters, while some newer models exceed a trillion parameters. + +2. **Training Data**: They are trained on vast amounts of text data, often including books, websites, and other diverse sources, amounting to hundreds of gigabytes or even terabytes of text. + +3. **Architecture**: Most LLMs use [transformer-based architectures](https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)), which allow them to process and generate text by paying attention to different parts of the input simultaneously. + +4. **Capabilities**: LLMs can perform a wide range of language tasks without specific fine-tuning, including: + - Text generation + - Translation + - Summarization + - Question answering + - Code generation + - Logical reasoning + +5. **Few-shot Learning**: They can often understand and perform new tasks with minimal examples or instructions. + +6. **Resource-Intensive**: Due to their size, LLMs typically require significant computational resources to run, often needing powerful GPUs or TPUs. + +7. **Continual Development**: The field of LLMs is rapidly evolving, with new models and techniques constantly emerging. + +8. **Ethical Considerations**: The use of LLMs raises important questions about bias, misinformation, and the environmental impact of training such large models. + +9. **Applications**: LLMs are used in various fields, including content creation, customer service, research assistance, and software development. + +10. **Limitations**: Despite their power, LLMs can produce incorrect or biased information and lack true understanding or reasoning capabilities. + +We must note that we use large models beyond text, calling them *multi-modal models*. These models integrate and process information from multiple types of input simultaneously. They are designed to understand and generate content across various forms of data, such as text, images, audio, and video. + +Certainly. Let's define open and closed models in the context of AI and language models: + +### Closed vs Open Models: + +**Closed models**, also called proprietary models, are AI models whose internal workings, code, and training data are not publicly disclosed. Examples: GPT-4 (by OpenAI), Claude (by Anthropic), Gemini (by Google). + +**Open models**, also known as open-source models, are AI models whose underlying code, architecture, and often training data are publicly available and accessible. Examples: Gemma (by Google), LLaMA (by Meta) and Phi (by Microsoft). + +Open models are particularly relevant for running models on edge devices like Raspberry Pi as they can be more easily adapted, optimized, and deployed in resource-constrained environments. Still, it is crucial to verify their Licenses. Open models come with various open-source licenses that may affect their use in commercial applications, while closed models have clear, albeit restrictive, terms of service. + +![Adapted from https://arxiv.org/pdf/2304.13712](images/png/llms-slm.png) + +### Small Language Models (SLMs) + +In the context of edge computing on devices like Raspberry Pi, full-scale LLMs are typically too large and resource-intensive to run directly. This limitation has driven the development of smaller, more efficient models, such as the Small Language Models (SLMs). + +SLMs are compact versions of LLMs designed to run efficiently on resource-constrained devices such as smartphones, IoT devices, and single-board computers like the Raspberry Pi. These models are significantly smaller in size and computational requirements than their larger counterparts while still retaining impressive language understanding and generation capabilities. + +Key characteristics of SLMs include: + +1. **Reduced parameter count**: Typically ranging from a few hundred million to a few billion parameters, compared to two-digit billions in larger models. + +2. **Lower memory footprint**: Requiring, at most, a few gigabytes of memory rather than tens or hundreds of gigabytes. + +3. **Faster inference time**: Can generate responses in milliseconds to seconds on edge devices. + +4. **Energy efficiency**: Consuming less power, making them suitable for battery-powered devices. + +5. **Privacy-preserving**: Enabling on-device processing without sending data to cloud servers. + +6. **Offline functionality**: Operating without an internet connection. + +SLMs achieve their compact size through various techniques such as knowledge distillation, model pruning, and quantization. While they may not match the broad capabilities of larger models, SLMs excel in specific tasks and domains, making them ideal for targeted applications on edge devices. + +> We will generally consider SLMs, language models with less than 5 billion parameters quantized to 4 bits. + +Examples of SLMs include compressed versions of models like Meta Llama, Microsoft PHI, and Google Gemma. These models enable a wide range of natural language processing tasks directly on edge devices, from text classification and sentiment analysis to question answering and limited text generation. + +For more information on SLMs, the paper, [LLM Pruning and Distillation in Practice: The Minitron Approach](https://arxiv.org/pdf/2408.11796), provides an approach applying pruning and distillation to obtain SLMs from LLMs. And, [SMALL LANGUAGE MODELS: SURVEY, MEASUREMENTS, AND INSIGHTS](https://arxiv.org/pdf/2409.15790), presents a comprehensive survey and analysis of Small Language Models (SLMs), which are language models with 100 million to 5 billion parameters designed for resource-constrained devices. + +## Ollama + +![ollama logo](https://ollama.com/public/ollama.png) + +[Ollama](https://ollama.com/) is an open-source framework that allows us to run language models (LMs), large or small, locally on our machines. Here are some critical points about Ollama: + +1. **Local Model Execution**: Ollama enables running LMs on personal computers or edge devices such as the Raspi-5, eliminating the need for cloud-based API calls. + +2. **Ease of Use**: It provides a simple command-line interface for downloading, running, and managing different language models. + +3. **Model Variety**: Ollama supports various LLMs, including Phi, Gemma, Llama, Mistral, and other open-source models. + +4. **Customization**: Users can create and share custom models tailored to specific needs or domains. + +5. **Lightweight**: Designed to be efficient and run on consumer-grade hardware. + +6. **API Integration**: Offers an API that allows integration with other applications and services. + +7. **Privacy-Focused**: By running models locally, it addresses privacy concerns associated with sending data to external servers. + +8. **Cross-Platform**: Available for macOS, Windows, and Linux systems (our case, here). + +9. **Active Development**: Regularly updated with new features and model support. + +10. **Community-Driven**: Benefits from community contributions and model sharing. + +To learn more about what Ollama is and how it works under the hood, you should see this short video from [Matt Williams](https://www.youtube.com/@technovangelist), one of the founders of Ollama: + +{{< video https://www.youtube.com/embed/90ozfdsQOKo >}} + +> Matt has an entirely free course about Ollama that we recommend: {{< video https://youtu.be/9KEUFe4KQAI?si=D_-q3CMbHiT-twuy >}} + +### Installing Ollama + +Let's set up and activate a Virtual Environment for working with Ollama: + +```bash +python3 -m venv ~/ollama +source ~/ollama/bin/activate +``` + +And run the command to install Ollama: + +```bash +curl -fsSL https://ollama.com/install.sh | sh +``` + +As a result, an API will run in the background on `127.0.0.1:11434`. From now on, we can run Ollama via the terminal. For starting, let's verify the Ollama version, which will also tell us that it is correctly installed: + +```bash +ollama -v +``` + + + +![](images/png/install-ollama-rpi5.png) + + + +On the [Ollama Library page](https://ollama.com/library), we can find the models Ollama supports. For example, by filtering by `Most popular`, we can see Meta Llama, Google Gemma, Microsoft Phi, LLaVa, etc. + +### Meta Llama 3.2 1B/3B + +![](images/png/small_and_multimodal.png) + +Let's install and run our first small language model, [Llama 3.2](https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/) 1B (and 3B). The Meta Llama 3.2 series comprises a set of multilingual generative language models available in 1 billion and 3 billion parameter sizes. These models are designed to process text input and generate text output. The instruction-tuned variants within this collection are specifically optimized for multilingual conversational applications, including tasks involving information retrieval and summarization with an agentic approach. When compared to many existing open-source and proprietary chat models, the Llama 3.2 instruction-tuned models demonstrate superior performance on widely-used industry benchmarks. + +The 1B and 3B models were pruned from the Llama 8B, and then logits from the 8B and 70B models were used as token-level targets (token-level distillation). Knowledge distillation was used to recover performance (they were trained with 9 trillion tokens). The 1B model has 1,24B, quantized to integer (Q8_0), and the 3B, 3.12B parameters, with a Q4_0 quantization, which ends with a size of 1.3 GB and 2GB, respectively. Its context window is 131,072 tokens. + +![](images/jpeg/llama3_2.jpg) + +**Install and run the** **Model** + +```bash +ollama run llama3.2:1b +``` + +Running the model with the command before, we should have the Ollama prompt available for us to input a question and start chatting with the LLM model; for example, + +`>>> What is the capital of France?` + +Almost immediately, we get the correct answer: + +`The capital of France is Paris.` + +Using the option `--verbose` when calling the model will generate several statistics about its performance (The model will be polling only the first time we run the command). + +![](images/png/llama3_2_1b_performance.png) + +Each metric gives insights into how the model processes inputs and generates outputs. Here’s a breakdown of what each metric means: + +- **Total Duration (2.620170326s)**: This is the complete time taken from the start of the command to the completion of the response. It encompasses loading the model, processing the input prompt, and generating the response. +- **Load Duration (39.947908ms)**: This duration indicates the time to load the model or necessary components into memory. If this value is minimal, it can suggest that the model was preloaded or that only a minimal setup was required. +- **Prompt Eval Count (32 tokens)**: The number of tokens in the input prompt. In NLP, tokens are typically words or subwords, so this count includes all the tokens that the model evaluated to understand and respond to the query. +- **Prompt Eval Duration (1.644773s)**: This measures the model's time to evaluate or process the input prompt. It accounts for the bulk of the total duration, implying that understanding the query and preparing a response is the most time-consuming part of the process. +- **Prompt Eval Rate (19.46 tokens/s)**: This rate indicates how quickly the model processes tokens from the input prompt. It reflects the model’s speed in terms of natural language comprehension. +- **Eval Count (8 token(s))**: This is the number of tokens in the model’s response, which in this case was, “The capital of France is Paris.” +- **Eval Duration (889.941ms)**: This is the time taken to generate the output based on the evaluated input. It’s much shorter than the prompt evaluation, suggesting that generating the response is less complex or computationally intensive than understanding the prompt. +- **Eval Rate (8.99 tokens/s)**: Similar to the prompt eval rate, this indicates the speed at which the model generates output tokens. It's a crucial metric for understanding the model's efficiency in output generation. + +This detailed breakdown can help understand the computational demands and performance characteristics of running SLMs like Llama on edge devices like the Raspberry Pi 5. It shows that while prompt evaluation is more time-consuming, the actual generation of responses is relatively quicker. This analysis is crucial for optimizing performance and diagnosing potential bottlenecks in real-time applications. + +Loading and running the 3B model, we can see the difference in performance for the same prompt; + +![](images/png/llama3_2_3b_performance.png) + +The eval rate is lower, 5.3 tokens/s versus 9 tokens/s with the smaller model. + +When question about + +`>>> What is the distance between Paris and Santiago, Chile?` + +The 1B model answered `9,841 kilometers (6,093 miles)`, which is inaccurate, and the 3B model answered `7,300 miles (11,700 km)`, which is close to the correct (11,642 km). + +Let's ask for the Paris's coordinates: + +`>>> what is the latitude and longitude of Paris?` + +```bash +The latitude and longitude of Paris are 48.8567° N (48°55' +42" N) and 2.3510° E (2°22' 8" E), respectively. +``` + +![](images/png/paris-lat-lon.png) + +Both 1B and 3B models gave correct answers. + +### Google Gemma 2 2B + +Let's install [Gemma 2](https://ollama.com/library/gemma2:2b), a high-performing and efficient model available in three sizes: 2B, 9B, and 27B. We will install [**Gemma 2 2B**](https://huggingface.co/collections/google/gemma-2-2b-release-66a20f3796a2ff2a7c76f98f), a lightweight model trained with 2 trillion tokens that produces outsized results by learning from larger models through distillation. The model has 2.6 billion parameters and a Q4_0 quantization, which ends with a size of 1.6 GB. Its context window is 8,192 tokens. + +![](images/png/gemma_2.png) + + + +**Install and run the** **Model** + +```bash +ollama run gemma2:2b --verbose +``` + +Running the model with the command before, we should have the Ollama prompt available for us to input a question and start chatting with the LLM model; for example, + +`>>> What is the capital of France?` + +Almost immediately, we get the correct answer: + +`The capital of France is **Paris**. 🗼` + +And it' statistics. + +![](images/png/gemma.png) + +We can see that Gemma 2:2B has around the same performance as Lama 3.2:3B, but having less parameters. + +**Other examples:** + +```bash +>>> What is the distance between Paris and Santiago, Chile? + +The distance between Paris, France and Santiago, Chile is +approximately **7,000 miles (11,267 kilometers)**. + +Keep in mind that this is a straight-line distance, and actual +travel distance can vary depending on the chosen routes and any +stops along the way. ✈️` +``` + +Also, a good response but less accurate than Llama3.2:3B. + +```bash +>>> what is the latitude and longitude of Paris? + +You got it! Here are the latitudes and longitudes of Paris, +France: + +* **Latitude:** 48.8566° N (north) +* **Longitude:** 2.3522° E (east) + +Let me know if you'd like to explore more about Paris or its +location! 🗼🇫🇷 +``` + +A good and accurate answer (a little more verbose than the Llama answers). + +### Microsoft Phi3.5 3.8B + +Let's pull a bigger (but still tiny) model, the [PHI3.5,](https://ollama.com/library/phi3.5) a 3.8B lightweight state-of-the-art open model by Microsoft. The model belongs to the Phi-3 model family and supports `128K token` context length and the languages: Arabic, Chinese, Czech, Danish, Dutch, English, Finnish, French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish and Ukrainian. + +The model size, in terms of bytes, will depend on the specific quantization format used. The size can go from 2-bit quantization (`q2_k`) of 1.4GB (higher performance/lower quality) to 16-bit quantization (fp-16) of 7.6GB (lower performance/higher quality). + +Let's run the 4-bit quantization (`Q4_0`), which will need 2.2GB of RAM, with an intermediary trade-off regarding output quality and performance. + +```bash +ollama run phi3.5:3.8b --verbose +``` + +> You can use `run` or `pull` to download the model. What happens is that Ollama keeps note of the pulled models, and once the PHI3 does not exist, before running it, Ollama pulls it. + +Let's enter with the same prompt used before: + +```bash +>>> What is the capital of France? + +The capital of France is Paris. It' extradites significant +historical, cultural, and political importance to the country as +well as being a major European city known for its art, fashion, +gastronomy, and culture. Its influence extends beyond national +borders, with millions of tourists visiting each year from around +the globe. The Seine River flows through Paris before it reaches +the broader English Channel at Le Havre. Moreover, France is one +of Europe's leading economies with its capital playing a key role + +... +``` + +The answer was very "verbose", let's specify a better prompt: + +![](images/png/paris-2.png) + +In this case, the answer was still longer than we expected, with an eval rate of 2.25 tokens/s, more than double that of Gemma and Llama. + +> Choosing the most appropriate prompt is one of the most important skills to be used with LLMs, no matter its size. + +When we asked the same questions about distance and Latitude/Longitude, we did not get a good answer for a distance of `13,507 kilometers (8,429 miles)`, but it was OK for coordinates. Again, it could have been less verbose (more than 200 tokens for each answer). + +We can use any model as an assistant since their speed is relatively decent, but on September 24 (2023), the Llama2:3B is a better choice. You should try other models, depending on your needs. [🤗 Open LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) can give you an idea about the best models in size, benchmark, license, etc. + +> The best model to use is the one fit for your specific necessity. Also, take into consideration that this field evolves with new models everyday. + +### Multimodal Models + +Multimodal models are artificial intelligence (AI) systems that can process and understand information from multiple sources, such as images, text, audio, and video. In our context, multimodal LLMs can process various inputs, including text, images, and audio, as prompts and convert those prompts into various outputs, not just the source type. + +We will work here with [LLaVA-Phi-3](https://ollama.com/library/llava-phi3:3.8b), a fine-tuned LLaVA model from Phi 3 Mini 4k. It has strong performance benchmarks that are on par with the original [LLaVA](https://llava-vl.github.io/) (Large Language and Vision Assistant) model. + +The LLaVA-Phi-3 is an end-to-end trained large multimodal model designed to understand and generate content based on visual inputs (images) and textual instructions. It combines the capabilities of a visual encoder and a language model to process and respond to multimodal inputs. + +Let's install the model: + +```bash +ollama run llava-phi3:3.8b --verbose +``` + +Let's start with a text input: + +```bash +>>> You are a helpful AI assistant. What is the capital of France? + +As an AI language model, I can tell you that the capital of France +is Paris. It's not only the largest city in the country but also +serves as its political and administrative center. Paris is known +for its iconic landmarks such as the Eiffel Tower, Notre-Dame +Cathedral, and the Louvre Museum. The city has a rich history, +beautiful architecture, and is widely considered to be one of the +most romantic cities in the world. +``` + +The response took around 30s, with an eval rate of 3.93 tokens/s! Not bad! + +But let us know to enter with an image as input. For that, let's create a directory for working: + +```bash +cd Documents/ +mkdir OLLAMA +cd OLLAMA +``` + +Let's download a 640x320 image from the internet, for example (Wikipedia: [Paris, France)](https://upload.wikimedia.org/wikipedia/commons/thumb/4/4b/La_Tour_Eiffel_vue_de_la_Tour_Saint-Jacques%2C_Paris_ao%C3%BBt_2014_%282%29.jpg/640px-La_Tour_Eiffel_vue_de_la_Tour_Saint-Jacques%2C_Paris_ao%C3%BBt_2014_%282%29.jpg): + +![](images/jpeg/paris.jpg) + +Using FileZilla, for example, let's upload the image to the OLLAMA folder at the Raspi-5 and name it `image_test_1.jpg`. We should have the whole image path (we can use `pwd` to get it). + +`/home/mjrovai/Documents/OLLAMA/image_test_1.jpg` + +If you use a desktop, you can copy the image path by clicking the image with the mouse's right button. + +![](images/png/image_test-path.png) + +Let's enter with this prompt: + +```bash +>>> Describe the image /home/mjrovai/Documents/OLLAMA/image_test_1.jpg +``` + +The result was great, but the overall latency was significant; almost 4 minutes to perform the inference. + +![](images/png/paris-infer-1.png) + +### Inspecting local resources + +Using htop, we can monitor the resources running on our device. + +```bash +htop +``` + +During the time that the model is running, we can inspect the resources: + +![](images/png/htop.png) + +All four CPUs run at almost 100% of their capacity, and the memory used with the model loaded is `3.24GB`. Exiting Ollama, the memory goes down to around `377MB` (with no desktop). + +It is also essential to monitor the temperature. When running the Raspberry with a desktop, you can have the temperature shown on the taskbar: + +![](images/png/resourses-temp.png) + +If you are "headless", the temperature can be monitored with the command: + +```bash +vcgencmd measure_temp +``` + +If you are doing nothing, the temperature is around `50°C` for CPUs running at 1%. During inference, with the CPUs at 100%, the temperature can rise to almost `70°C`. This is OK and means the active cooler is working, keeping the temperature below 80°C / 85°C (its limit). + +## Ollama Python Library + +So far, we have explored SLMs' chat capability using the command line on a terminal. However, we want to integrate those models into our projects, so Python seems to be the right path. The good news is that Ollama has such a library. + +The [Ollama Python library](https://github.com/ollama/ollama-python) simplifies interaction with advanced LLM models, enabling more sophisticated responses and capabilities, besides providing the easiest way to integrate Python 3.8+ projects with [Ollama.](https://github.com/ollama/ollama) + +For a better understanding of how to create apps using Ollama with Python, we can follow [Matt Williams's videos](https://www.youtube.com/@technovangelist), as the one below: + +{{< video https://www.youtube.com/embed/_4K20tOsXK8 >}} + +**Installation:** + +In the terminal, run the command: + +```bash +pip install ollama +``` + +We will need a text editor or an IDE to create a Python script. If you run the Raspberry OS on a desktop, several options, such as Thonny and Geany, have already been installed by default (accessed by `[Menu][Programming]`). You can download other IDEs, such as Visual Studio Code, from `[Menu][Recommended Software]`. When the window pops up, go to `[Programming]`, select the option of your choice, and press `[Apply]`. + +![](images/png/menu.png) + +If you prefer using Jupyter Notebook for development: + +```bash +pip install jupyter +jupyter notebook --generate-config +``` + +To run Jupyter Notebook, run the command (change the IP address for yours): + +```bash +jupyter notebook --ip=192.168.4.209 --no-browser +``` + +On the terminal, you can see the local URL address to open the notebook: + +![](images/png/jupyter.png) + +We can access it from another computer by entering the Raspberry Pi's IP address and the provided token in a web browser (we should copy it from the terminal). + +In our working directory in the Raspi, we will create a new Python 3 notebook. + +Let's enter with a very simple script to verify the installed models: + +```python +import ollama +ollama.list() +``` + +All the models will be printed as a dictionary, for example: + +```python + {'name': 'gemma2:2b', + 'model': 'gemma2:2b', + 'modified_at': '2024-09-24T19:30:40.053898094+01:00', + 'size': 1629518495, + 'digest': '8ccf136fdd5298f3ffe2d69862750ea7fb56555fa4d5b18c04e3fa4d82ee09d7', + 'details': {'parent_model': '', + 'format': 'gguf', + 'family': 'gemma2', + 'families': ['gemma2'], + 'parameter_size': '2.6B', + 'quantization_level': 'Q4_0'}}]} +``` + +Let's repeat one of the questions that we did before, but now using `ollama.generate()` from Ollama python library. This API will generate a response for the given prompt with the provided model. This is a streaming endpoint, so there will be a series of responses. The final response object will include statistics and additional data from the request. + +```python +MODEL = 'gemma2:2b' +PROMPT = 'What is the capital of France?' + +res = ollama.generate(model=MODEL, prompt=PROMPT) +print (res) +``` + +In case you are running the code as a Python script, you should save it, for example, test_ollama.py. You can use the IDE to run it or do it directly on the terminal. Also, remember that you should always call the model and define it when running a stand-alone script. + +```bash +python test_ollama.py +``` + +As a result, we will have the model response in a JSON format: + +``` +{'model': 'gemma2:2b', 'created_at': '2024-09-25T14:43:31.869633807Z', +'response': 'The capital of France is **Paris**. 🇫🇷 \n', 'done': True, +'done_reason': 'stop', 'context': [106, 1645, 108, 1841, 603, 573, 6037, 576, +6081, 235336, 107, 108, 106, 2516, 108, 651, 6037, 576, 6081, 603, 5231, 29437, +168428, 235248, 244304, 241035, 235248, 108], 'total_duration': 24259469458, +'load_duration': 19830013859, 'prompt_eval_count': 16, 'prompt_eval_duration': +1908757000, 'eval_count': 14, 'eval_duration': 2475410000} +``` + +As we can see, several pieces of information are generated, such as: + +- **response**: the main output text generated by the model in response to our prompt. + - `The capital of France is **Paris**. 🇫🇷` + +- **context**: the token IDs representing the input and context used by the model. Tokens are numerical representations of text used for processing by the language model. + - `[106, 1645, 108, 1841, 603, 573, 6037, 576, 6081, 235336, 107, 108,` + ` 106, 2516, 108, 651, 6037, 576, 6081, 603, 5231, 29437, 168428, ` + ` 235248, 244304, 241035, 235248, 108]` + + +The Performance Metrics: + +- **total_duration**: The total time taken for the operation in nanoseconds. In this case, approximately 24.26 seconds. +- **load_duration**: The time taken to load the model or components in nanoseconds. About 19.83 seconds. +- **prompt_eval_duration**: The time taken to evaluate the prompt in nanoseconds. Around 16 nanoseconds. +- **eval_count**: The number of tokens evaluated during the generation. Here, 14 tokens. +- **eval_duration**: The time taken for the model to generate the response in nanoseconds. Approximately 2.5 seconds. + +But, what we want is the plain 'response' and, perhaps for analysis, the total duration of the inference, so let's change the code to extract it from the dictionary: + +```python +print(f"\n{res['response']}") +print(f"\n [INFO] Total Duration: {(res['total_duration']/1e9):.2f} seconds") +``` + +Now, we got: + +```bash +The capital of France is **Paris**. 🇫🇷 + + [INFO] Total Duration: 24.26 seconds +``` + +**Using Ollama.chat()** + +Another way to get our response is to use `ollama.chat()`, which generates the next message in a chat with a provided model. This is a streaming endpoint, so a series of responses will occur. Streaming can be disabled using `"stream": false`. The final response object will also include statistics and additional data from the request. + +```python +PROMPT_1 = 'What is the capital of France?' + +response = ollama.chat(model=MODEL, messages=[ +{'role': 'user','content': PROMPT_1,},]) +resp_1 = response['message']['content'] +print(f"\n{resp_1}") +print(f"\n [INFO] Total Duration: {(res['total_duration']/1e9):.2f} seconds") +``` + +The answer is the same as before. + +An important consideration is that by using `ollama.generate()`, the response is "clear" from the model's "memory" after the end of inference (only used once), but If we want to keep a conversation, we must use `ollama.chat()`. Let's see it in action: + +```python +PROMPT_1 = 'What is the capital of France?' +response = ollama.chat(model=MODEL, messages=[ +{'role': 'user','content': PROMPT_1,},]) +resp_1 = response['message']['content'] +print(f"\n{resp_1}") +print(f"\n [INFO] Total Duration: {(response['total_duration']/1e9):.2f} seconds") + +PROMPT_2 = 'and of Italy?' +response = ollama.chat(model=MODEL, messages=[ +{'role': 'user','content': PROMPT_1,}, +{'role': 'assistant','content': resp_1,}, +{'role': 'user','content': PROMPT_2,},]) +resp_2 = response['message']['content'] +print(f"\n{resp_2}") +print(f"\n [INFO] Total Duration: {(response['total_duration']/1e9):.2f} seconds") +``` + +In the above code, we are running two queries, and the second prompt considers the result of the first one. + +Here is how the model responded: + +```bash +The capital of France is **Paris**. 🇫🇷 + + [INFO] Total Duration: 2.82 seconds + +The capital of Italy is **Rome**. 🇮🇹 + + [INFO] Total Duration: 4.46 seconds +``` + +**Getting an image description**: + +In the same way that we have used the `LlaVa-PHI-3` model with the command line to analyze an image, the same can be done here with Python. Let's use the same image of Paris, but now with the `ollama.generate()`: + +```python +MODEL = 'llava-phi3:3.8b' +PROMPT = "Describe this picture" + +with open('image_test_1.jpg', 'rb') as image_file: + img = image_file.read() + +response = ollama.generate( + model=MODEL, + prompt=PROMPT, + images= [img] +) +print(f"\n{response['response']}") +print(f"\n [INFO] Total Duration: {(res['total_duration']/1e9):.2f} seconds") +``` + +Here is the result: + +```bash +This image captures the iconic cityscape of Paris, France. The vantage point +is high, providing a panoramic view of the Seine River that meanders through +the heart of the city. Several bridges arch gracefully over the river, +connecting different parts of the city. The Eiffel Tower, an iron lattice +structure with a pointed top and two antennas on its summit, stands tall in the +background, piercing the sky. It is painted in a light gray color, contrasting +against the blue sky speckled with white clouds. + +The buildings that line the river are predominantly white or beige, their uniform +color palette broken occasionally by red roofs peeking through. The Seine River +itself appears calm and wide, reflecting the city's architectural beauty in its +surface. On either side of the river, trees add a touch of green to the urban +landscape. + +The image is taken from an elevated perspective, looking down on the city. This +viewpoint allows for a comprehensive view of Paris's beautiful architecture and +layout. The relative positions of the buildings, bridges, and other structures +create a harmonious composition that showcases the city's charm. + +In summary, this image presents a serene day in Paris, with its architectural +marvels - from the Eiffel Tower to the river-side buildings - all bathed in soft +colors under a clear sky. + + [INFO] Total Duration: 256.45 seconds +``` + +The model took about 4 minutes (256.45 s) to return with a detailed image description. + +> In the [10-Ollama_Python_Library](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OLLAMA_SLMs/10-Ollama_Python_Library.ipynb) notebook, it is possible to find the experiments with the Ollama Python library. + +### Function Calling + +So far, we can observe that by using the model's response into a variable, we can effectively incorporate it into real-world projects. However, a major issue arises when the model provides varying responses to the same input. For instance, let's assume that we only need the name of a country's capital and its coordinates as the model's response in the previous examples, without any additional information, even when utilizing verbose models like Microsoft Phi. To ensure consistent responses, we can employ the 'Ollama function call,' which is fully compatible with the OpenAI API. + +#### But what exactly is "function calling"? + +In modern artificial intelligence, function calling with Large Language Models (LLMs) allows these models to perform actions beyond generating text. By integrating with external functions or APIs, LLMs can access real-time data, automate tasks, and interact with various systems. + +For instance, instead of merely responding to a query about the weather, an LLM can call a weather API to fetch the current conditions and provide accurate, up-to-date information. This capability enhances the relevance and accuracy of the model's responses and makes it a powerful tool for driving workflows and automating processes, transforming it into an active participant in real-world applications. + +For more details about Function Calling, please see this video made by [Marvin Prison](https://www.youtube.com/@MervinPraison): + +{{< video https://www.youtube.com/embed/eHfMCtlsb1o >}} + +#### Let's create a project. + +We want to create an *app* where the user enters a country's name and gets, as an output, the distance in km from the capital city of such a country and the app's location (for simplicity, We will use Santiago, Chile, as the app location). + +![](images/png/block-fc-proj.png) + +Once the user enters a country name, the model will return the name of its capital city (as a string) and the latitude and longitude of such city (in float). Using those coordinates, we can use a simple Python library ([haversine](https://pypi.org/project/haversine/)) to calculate the distance between those 2 points. + +The idea of this project is to demonstrate a combination of language model interaction, structured data handling with Pydantic, and geospatial calculations using the Haversine formula (traditional computing). + +First, let us install some libraries. Besides *Haversine*, the main one is the [OpenAI Python library](https://github.com/openai/openai-python), which provides convenient access to the OpenAI REST API from any Python 3.7+ application. The other one is [Pydantic](https://docs.pydantic.dev/latest/) (and instructor), a robust data validation and settings management library engineered by Python to enhance the robustness and reliability of our codebase. In short, *Pydantic* will help ensure that our model's response will always be consistent. + +```bash +pip install haversine +pip install openai +pip install pydantic +pip install instructor +``` + +Now, we should create a Python script designed to interact with our model (LLM) to determine the coordinates of a country's capital city and calculate the distance from Santiago de Chile to that capital. + +Let's go over the code: + +### 1. Importing Libraries + +```python +import sys +from haversine import haversine +from openai import OpenAI +from pydantic import BaseModel, Field +import instructor +``` + +- **sys**: Provides access to system-specific parameters and functions. It's used to get command-line arguments. +- **haversine**: A function from the haversine library that calculates the distance between two geographic points using the Haversine formula. +- **openAI**: A module for interacting with the OpenAI API (although it's used in conjunction with a local setup, Ollama). Everything is off-line here. +- **pydantic**: Provides data validation and settings management using Python-type annotations. It's used to define the structure of expected response data. +- **instructor**: A module is used to patch the OpenAI client to work in a specific mode (likely related to structured data handling). + +### 2. Defining Input and Model + +```python +country = sys.argv[1] # Get the country from command-line arguments +MODEL = 'phi3.5:3.8b' # The name of the model to be used +mylat = -33.33 # Latitude of Santiago de Chile +mylon = -70.51 # Longitude of Santiago de Chile +``` + +- **country**: On a Python script, getting the country name from command-line arguments is possible. On a Jupyter notebook, we can enter its name, for example, + - `country = "France"` + +- **MODEL**: Specifies the model being used, which is, in this example, the phi3.5. +- **mylat** **and** **mylon**: Coordinates of Santiago de Chile, used as the starting point for the distance calculation. + +### 3. Defining the Response Data Structure + +```python +class CityCoord(BaseModel): + city: str = Field(..., description="Name of the city") + lat: float = Field(..., description="Decimal Latitude of the city") + lon: float = Field(..., description="Decimal Longitude of the city") +``` + +- **CityCoord**: A Pydantic model that defines the expected structure of the response from the LLM. It expects three fields: city (name of the city), lat (latitude), and lon (longitude). + +### 4. Setting Up the OpenAI Client + +```python +client = instructor.patch( + OpenAI( + base_url="http://localhost:11434/v1", # Local API base URL (Ollama) + api_key="ollama", # API key (not used) + ), + mode=instructor.Mode.JSON, # Mode for structured JSON output +) +``` + +- **OpenAI**: This setup initializes an OpenAI client with a local base URL and an API key (ollama). It uses a local server. +- **instructor.patch**: Patches the OpenAI client to work in JSON mode, enabling structured output that matches the Pydantic model. + +### 5. Generating the Response + +```python +resp = client.chat.completions.create( + model=MODEL, + messages=[ + { + "role": "user", + "content": f"return the decimal latitude and decimal longitude \ + of the capital of the {country}." + } + ], + response_model=CityCoord, + max_retries=10 +) +``` + +- **client.chat.completions.create**: Calls the LLM to generate a response. +- **model**: Specifies the model to use (llava-phi3). +- **messages**: Contains the prompt for the LLM, asking for the latitude and longitude of the capital city of the specified country. +- **response_model**: Indicates that the response should conform to the CityCoord model. +- **max_retries**: The maximum number of retry attempts if the request fails. + +### 6. Calculating the Distance + +```python +distance = haversine((mylat, mylon), (resp.lat, resp.lon), unit='km') +print(f"Santiago de Chile is about {int(round(distance, -1)):,} \ + kilometers away from {resp.city}.") +``` + +- **haversine**: Calculates the distance between Santiago de Chile and the capital city returned by the LLM using their respective coordinates. +- **(mylat, mylon)**: Coordinates of Santiago de Chile. +- **resp.city**: Name of the country's capital +- **(resp.lat, resp.lon)**: Coordinates of the capital city are provided by the LLM response. +- **unit='km'**: Specifies that the distance should be calculated in kilometers. +- **print**: Outputs the distance, rounded to the nearest 10 kilometers, with thousands of separators for readability. + +**Running the code** + +If we enter different countries, for example, France, Colombia, and the United States, We can note that we always receive the same structured information: + +```bash +Santiago de Chile is about 8,060 kilometers away from Washington, D.C.. +Santiago de Chile is about 4,250 kilometers away from Bogotá. +Santiago de Chile is about 11,630 kilometers away from Paris. +``` + +If you run the code as a script, the result will be printed on the terminal: + +![](images/png/script-fc.png) + +And the calculations are pretty good! + +![](images/png/calc_real.png) + +> In the [20-Ollama_Function_Calling](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OLLAMA_SLMs/20-Ollama_Function_Calling.ipynb) notebook, it is possible to find experiments with all models installed. + +### Adding images + +Now it is time to wrap up everything so far! Let's modify the script so that instead of entering the country name (as a text), the user enters an image, and the application (based on SLM) returns the city in the image and its geographic location. With those data, we can calculate the distance as before. + +![](images/png/block-2.png) + +For simplicity, we will implement this new code in two steps. First, the LLM will analyze the image and create a description (text). This text will be passed on to another instance, where the model will extract the information needed to pass along. + +We will start importing the libraries + +```python +import sys +import time +from haversine import haversine +import ollama +from openai import OpenAI +from pydantic import BaseModel, Field +import instructor +``` + +We can see the image if you run the code on the Jupyter Notebook. For that we need also import: + +```python +import matplotlib.pyplot as plt +from PIL import Image +``` + +> Those libraries are unnecessary if we run the code as a script. + +Now, we define the model and the local coordinates: + +```python +MODEL = 'llava-phi3:3.8b' +mylat = -33.33 +mylon = -70.51 +``` + +We can download a new image, for example, Machu Picchu from Wikipedia. On the Notebook we can see it: + +```python +# Load the image +img_path = "image_test_3.jpg" +img = Image.open(img_path) + +# Display the image +plt.figure(figsize=(8, 8)) +plt.imshow(img) +plt.axis('off') +#plt.title("Image") +plt.show() +``` + +![](images/jpeg/image_test_3.jpg) + +Now, let's define a function that will receive the image and will `return the decimal latitude and decimal longitude of the city in the image, its name, and what country it is located` + +```python +def image_description(img_path): + with open(img_path, 'rb') as file: + response = ollama.chat( + model=MODEL, + messages=[ + { + 'role': 'user', + 'content': '''return the decimal latitude and decimal longitude + of the city in the image, its name, and + what country it is located''', + 'images': [file.read()], + }, + ], + options = { + 'temperature': 0, + } + ) + #print(response['message']['content']) + return response['message']['content'] +``` + +> We can print the entire response for debug purposes. + +The image description generated for the function will be passed as a prompt for the model again. + +```python +start_time = time.perf_counter() # Start timing + +class CityCoord(BaseModel): + city: str = Field(..., description="Name of the city in the image") + country: str = Field(..., description="""Name of the country where" + the city in the image is located + """) + lat: float = Field(..., description="""Decimal Latitude of the city in" + the image""") + lon: float = Field(..., description="""Decimal Longitude of the city in" + the image""") + +# enables `response_model` in create call +client = instructor.patch( + OpenAI( + base_url="http://localhost:11434/v1", + api_key="ollama" + ), + mode=instructor.Mode.JSON, +) + +image_description = image_description(img_path) +# Send this description to the model +resp = client.chat.completions.create( + model=MODEL, + messages=[ + { + "role": "user", + "content": image_description, + } + ], + response_model=CityCoord, + max_retries=10, + temperature=0, +) +``` + +If we print the image description , we will get: + +```bash +The image shows the ancient city of Machu Picchu, located in Peru. The city is +perched on a steep hillside and consists of various structures made of stone. It +is surrounded by lush greenery and towering mountains. The sky above is blue with +scattered clouds. + +Machu Picchu's latitude is approximately 13.5086° S, and its longitude is around +72.5494° W. +``` + +And the second response from the model (` resp`) will be: + +```bash +CityCoord(city='Machu Picchu', country='Peru', lat=-13.5086, lon=-72.5494) +``` + +Now, we can do a "Post-Processing", calculating the distance and preparing the final answer: + +```python +distance = haversine((mylat, mylon), (resp.lat, resp.lon), unit='km') + +print(f"\n The image shows {resp.city}, with lat:{round(resp.lat, 2)} and \ + long: {round(resp.lon, 2)}, located in {resp.country} and about \ + {int(round(distance, -1)):,} kilometers away from \ + Santiago, Chile.\n") + +end_time = time.perf_counter() # End timing +elapsed_time = end_time - start_time # Calculate elapsed time +print(f" [INFO] ==> The code (running {MODEL}), took {elapsed_time:.1f} \ + seconds to execute.\n") +``` + +And we will get: + +```bash + The image shows Machu Picchu, with lat:-13.16 and long: -72.54, located in Peru + and about 2,250 kilometers away from Santiago, Chile. + + [INFO] ==> The code (running llava-phi3:3.8b), took 491.3 seconds to execute. +``` + +In the [30-Function_Calling_with_images](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OLLAMA_SLMs/30-Function_Calling_with_images.ipynb) notebook, it is possible to find the experiments with multiple images. + +Let's now download the script `calc_distance_image.py` from the [GitHub]() and run it on the terminal with the command: + +```bash +python calc_distance_image.py /home/mjrovai/Documents/OLLAMA/image_test_3.jpg +``` + + + +Enter with the Machu Picchu image full patch as an argument. We will get the same previous result. + +![](images/png/app-machu-picchu.png) + +*How* about Paris? + +![](images/png/paris-app.png) + +Of course, there are many ways to optimize the code used here. Still, the idea is to explore the considerable potential of *function calling* with SLMs at the edge, allowing those models to integrate with external functions or APIs. Going beyond text generation, SLMs can access real-time data, automate tasks, and interact with various systems. + +## SLMs: Optimization Techniques + +Large Language Models (LLMs) have revolutionized natural language processing, but their deployment and optimization come with unique challenges. One significant issue is the tendency for LLMs (and more, the SLMs) to generate plausible-sounding but factually incorrect information, a phenomenon known as **hallucination**. This occurs when models produce content that seems coherent but is not grounded in truth or real-world facts. + +Other challenges include the immense computational resources required for training and running these models, the difficulty in maintaining up-to-date knowledge within the model, and the need for domain-specific adaptations. Privacy concerns also arise when handling sensitive data during training or inference. Additionally, ensuring consistent performance across diverse tasks and maintaining ethical use of these powerful tools present ongoing challenges. Addressing these issues is crucial for the effective and responsible deployment of LLMs in real-world applications. + +The fundamental techniques for enhancing LLM (and SLM) performance and efficiency are Fine-tuning, Prompt engineering, and Retrieval-Augmented Generation (RAG). + +- **Fine-tuning**, while more resource-intensive, offers a way to specialize LLMs for particular domains or tasks. This process involves further training the model on carefully curated datasets, allowing it to adapt its vast general knowledge to specific applications. Fine-tuning can lead to substantial improvements in performance, especially in specialized fields or for unique use cases. + +- **Prompt engineering** is at the forefront of LLM optimization. By carefully crafting input prompts, we can guide models to produce more accurate and relevant outputs. This technique involves structuring queries that leverage the model's pre-trained knowledge and capabilities, often incorporating examples or specific instructions to shape the desired response. + +- **Retrieval-Augmented Generation (RAG)** represents another powerful approach to improving LLM performance. This method combines the vast knowledge embedded in pre-trained models with the ability to access and incorporate external, up-to-date information. By retrieving relevant data to supplement the model's decision-making process, RAG can significantly enhance accuracy and reduce the likelihood of generating outdated or false information. + +For edge applications, it is more beneficial to focus on techniques like RAG that can enhance model performance without needing on-device fine-tuning. Let's explore it. + +## RAG Implementation + +In a basic interaction between a user and a language model, the user asks a question, which is sent as a prompt to the model. The model generates a response based solely on its pre-trained knowledge. In a RAG process, there's an additional step between the user's question and the model's response. The user's question triggers a retrieval process from a knowledge base. + +![](images/png/rag-1.png) + +### A simple RAG project + +Here are the steps to implement a basic Retrieval Augmented Generation (RAG): + +- **Determine the type of documents you'll be using:** The best types are documents from which we can get clean and unobscured text. PDFs can be problematic because they are designed for printing, not for extracting sensible text. To work with PDFs, we should get the source document or use tools to handle it. + +- **Chunk the text:** We can't store the text as one long stream because of context size limitations and the potential for confusion. Chunking involves splitting the text into smaller pieces. Chunk text has many ways, such as character count, tokens, words, paragraphs, or sections. It is also possible to overlap chunks. + +- **Create embeddings:** Embeddings are numerical representations of text that capture semantic meaning. We create embeddings by passing each chunk of text through a particular embedding model. The model outputs a vector, the length of which depends on the embedding model used. We should pull one (or more) [embedding models](https://ollama.com/blog/embedding-models) from Ollama, to perform this task. Here are some examples of embedding models available at Ollama. + + | Model | Parameter Size | Embedding Size | + | ----------------- | -------------- | -------------- | + | mxbai-embed-large | 334M | 1024 | + | nomic-embed-text | 137M | 768 | + | all-minilm | 23M | 384 | + + > Generally, larger embedding sizes capture more nuanced information about the input. Still, they also require more computational resources to process, and a higher number of parameters should increase the latency (but also the quality of the response). + +- **Store the chunks and embeddings in a vector database:** We will need a way to efficiently find the most relevant chunks of text for a given prompt, which is where a vector database comes in. We will use [Chromadb](https://www.trychroma.com/), an AI-native open-source vector database, which simplifies building RAGs by creating knowledge, facts, and skills pluggable for LLMs. Both the embedding and the source text for each chunk are stored. + +- **Build the prompt:** When we have a question, we create an embedding and query the vector database for the most similar chunks. Then, we select the top few results and include their text in the prompt. + +The goal of RAG is to provide the model with the most relevant information from our documents, allowing it to generate more accurate and informative responses. So, let's implement a simple example of an SLM incorporating a particular set of facts about bees ("Bee Facts"). + +Inside the `ollama` env, enter the command in the terminal for Chromadb instalation: + +```bash +pip install ollama chromadb +``` + +Let's pull an intermediary embedding model, `nomic-embed-text` + +```bash +ollama pull nomic-embed-text +``` + +And create a working directory: + +```python +cd Documents/OLLAMA/ +mkdir RAG-simple-bee +cd RAG-simple-bee/ +``` + +Let's create a new Jupyter notebook, [40-RAG-simple-bee](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OLLAMA_SLMs/40-RAG-simple-bee.ipynb) for some exploration: + +Import the needed libraries: + +```python +import ollama +import chromadb +import time +``` + +And define aor models: + +```python +EMB_MODEL = "nomic-embed-text" +MODEL = 'llama3.2:3B' +``` + +Initially, a knowledge base about bee facts should be created. This involves collecting relevant documents and converting them into vector embeddings. These embeddings are then stored in a vector database, allowing for efficient similarity searches later. Enter with the "document," a base of "bee facts" as a list: + +```python +documents = [ + "Bee-keeping, also known as apiculture, involves the maintenance of bee \ + colonies, typically in hives, by humans.", + "The most commonly kept species of bees is the European honey bee (Apis \ + mellifera).", + + ... + + "There are another 20,000 different bee species in the world.", + "Brazil alone has more than 300 different bee species, and the \ + vast majority, unlike western honey bees, don’t sting.", + "Reports written in 1577 by Hans Staden, mention three native bees \ + used by indigenous people in Brazil.", + "The indigenous people in Brazil used bees for medicine and food purposes", + "From Hans Staden report: probable species: mandaçaia (Melipona \ + quadrifasciata), mandaguari (Scaptotrigona postica) and jataí-amarela \ + (Tetragonisca angustula)." +] +``` + +> We do not need to "chunk" the document here because we will use each element of the list and a chunk. + +Now, we will create our vector embedding database `bee_facts` and store the document in it: + +```python +client = chromadb.Client() +collection = client.create_collection(name="bee_facts") + +# store each document in a vector embedding database +for i, d in enumerate(documents): + response = ollama.embeddings(model=EMB_MODEL, prompt=d) + embedding = response["embedding"] + collection.add( + ids=[str(i)], + embeddings=[embedding], + documents=[d] + ) +``` + +Now that we have our "Knowledge Base" created, we can start making queries, retrieving data from it: + +![](images/png/rag-2-2.png) + +**User Query:** The process begins when a user asks a question, such as "How many bees are in a colony? Who lays eggs, and how much? How about common pests and diseases?" + +```python +prompt = "How many bees are in a colony? Who lays eggs and how much? How about\ + common pests and diseases?" +``` + +**Query Embedding:** The user's question is converted into a vector embedding using **the same embedding model** used for the knowledge base. + +```python +response = ollama.embeddings( + prompt=prompt, + model=EMB_MODEL +) +``` + +**Relevant Document Retrieval:** The system searches the knowledge base using the query embedding to find the most relevant documents (in this case, the 5 more probable). This is done using a similarity search, which compares the query embedding to the document embeddings in the database. + +```python +results = collection.query( + query_embeddings=[response["embedding"]], + n_results=5 +) +data = results['documents'] +``` + +**Prompt Augmentation:** The retrieved relevant information is combined with the original user query to create an augmented prompt. This prompt now contains the user's question and pertinent facts from the knowledge base. + +```python +prompt=f"Using this data: {data}. Respond to this prompt: {prompt}", +``` + +**Answer Generation:** The augmented prompt is then fed into a language model, in this case, the `llama3.2:3b` model. The model uses this enriched context to generate a comprehensive answer. Parameters like temperature, top_k, and top_p are set to control the randomness and quality of the generated response. + +```python +output = ollama.generate( + model=MODEL, + prompt=f"Using this data: {data}. Respond to this prompt: {prompt}", + options={ + "temperature": 0.0, + "top_k":10, + "top_p":0.5 } +) +``` + +**Response Delivery:** Finally, the system returns the generated answer to the user. + +```python +print(output['response']) +``` + +```bash +Based on the provided data, here are the answers to your questions: + +1. How many bees are in a colony? +A typical bee colony can contain between 20,000 and 80,000 bees. + +2. Who lays eggs and how much? +The queen bee lays up to 2,000 eggs per day during peak seasons. + +3. What about common pests and diseases? +Common pests and diseases that affect bees include varroa mites, hive beetles, +and foulbrood. +``` + +Let's create a function to help answer new questions: + +```python +def rag_bees(prompt, n_results=5, temp=0.0, top_k=10, top_p=0.5): + start_time = time.perf_counter() # Start timing + + # generate an embedding for the prompt and retrieve the data + response = ollama.embeddings( + prompt=prompt, + model=EMB_MODEL + ) + + results = collection.query( + query_embeddings=[response["embedding"]], + n_results=n_results + ) + data = results['documents'] + + # generate a response combining the prompt and data retrieved + output = ollama.generate( + model=MODEL, + prompt=f"Using this data: {data}. Respond to this prompt: {prompt}", + options={ + "temperature": temp, + "top_k": top_k, + "top_p": top_p } + ) + + print(output['response']) + + end_time = time.perf_counter() # End timing + elapsed_time = round((end_time - start_time), 1) # Calculate elapsed time + + print(f"\n [INFO] ==> The code for model: {MODEL}, took {elapsed_time}s \ + to generate the answer.\n") +``` + +We can now create queries and call the function: + +```python +prompt = "Are bees in Brazil?" +rag_bees(prompt) +``` + +```bash +Yes, bees are found in Brazil. According to the data, Brazil has more than 300 +different bee species, and indigenous people in Brazil used bees for medicine and +food purposes. Additionally, reports from 1577 mention three native bees used by +indigenous people in Brazil. + + [INFO] ==> The code for model: llama3.2:3b, took 22.7s to generate the answer. +``` + +By the way, if the model used supports multiple languages, we can use it (for example, Portuguese), even if the dataset was created in English: + +```python +prompt = "Existem abelhas no Brazil?" +rag_bees(prompt) +``` + +```bash +Sim, existem abelhas no Brasil! De acordo com o relato de Hans Staden, há três +espécies de abelhas nativas do Brasil que foram mencionadas: mandaçaia (Melipona +quadrifasciata), mandaguari (Scaptotrigona postica) e jataí-amarela (Tetragonisca +angustula). Além disso, o Brasil é conhecido por ter mais de 300 espécies diferentes de abelhas, a maioria das quais não é agressiva e não põe veneno. + + [INFO] ==> The code for model: llama3.2:3b, took 54.6s to generate the answer. +``` + +### Going Further + +The small LLM models tested worked well at the edge, both in text and with images, but of course, they had high latency regarding the last one. A combination of specific and dedicated models can lead to better results; for example, in real cases, an Object Detection model (such as YOLO) can get a general description and count of objects on an image that, once passed to an LLM, can help extract essential insights and actions. + +According to Avi Baum, CTO at Hailo, + +> In the vast landscape of artificial intelligence (AI), one of the most intriguing journeys has been the evolution of AI on the edge. This journey has taken us from classic machine vision to the realms of discriminative AI, enhancive AI, and now, the groundbreaking frontier of generative AI. Each step has brought us closer to a future where intelligent systems seamlessly integrate with our daily lives, offering an immersive experience of not just perception but also creation at the palm of our hand. + +![](images/jpeg/halo.jpg) + +## Conclusion + +This lab has demonstrated how a Raspberry Pi 5 can be transformed into a potent AI hub capable of running large language models (LLMs) for real-time, on-site data analysis and insights using Ollama and Python. The Raspberry Pi's versatility and power, coupled with the capabilities of lightweight LLMs like Llama 3.2 and LLaVa-Phi-3-mini, make it an excellent platform for edge computing applications. + +The potential of running LLMs on the edge extends far beyond simple data processing, as in this lab's examples. Here are some innovative suggestions for using this project: + +**1. Smart Home Automation:** + +- Integrate SLMs to interpret voice commands or analyze sensor data for intelligent home automation. This could include real-time monitoring and control of home devices, security systems, and energy management, all processed locally without relying on cloud services. + +**2. Field Data Collection and Analysis:** + +- Deploy SLMs on Raspberry Pi in remote or mobile setups for real-time data collection and analysis. This can be used in agriculture to monitor crop health, in environmental studies for wildlife tracking, or in disaster response for situational awareness and resource management. + +**3. Educational Tools:** + +- Create interactive educational tools that leverage SLMs to provide instant feedback, language translation, and tutoring. This can be particularly useful in developing regions with limited access to advanced technology and internet connectivity. + +**4. Healthcare Applications:** + +- Use SLMs for medical diagnostics and patient monitoring. They can provide real-time analysis of symptoms and suggest potential treatments. This can be integrated into telemedicine platforms or portable health devices. + +**5. Local Business Intelligence:** + +- Implement SLMs in retail or small business environments to analyze customer behavior, manage inventory, and optimize operations. The ability to process data locally ensures privacy and reduces dependency on external services. + +**6. Industrial IoT:** + +- Integrate SLMs into industrial IoT systems for predictive maintenance, quality control, and process optimization. The Raspberry Pi can serve as a localized data processing unit, reducing latency and improving the reliability of automated systems. + +**7. Autonomous Vehicles:** + +- Use SLMs to process sensory data from autonomous vehicles, enabling real-time decision-making and navigation. This can be applied to drones, robots, and self-driving cars for enhanced autonomy and safety. + +**8. Cultural Heritage and Tourism:** + +- Implement SLMs to provide interactive and informative cultural heritage sites and museum guides. Visitors can use these systems to get real-time information and insights, enhancing their experience without internet connectivity. + +**9. Artistic and Creative Projects:** + +- Use SLMs to analyze and generate creative content, such as music, art, and literature. This can foster innovative projects in the creative industries and allow for unique interactive experiences in exhibitions and performances. + +**10. Customized Assistive Technologies:** + +- Develop assistive technologies for individuals with disabilities, providing personalized and adaptive support through real-time text-to-speech, language translation, and other accessible tools. + +## Resources + +- [10-Ollama_Python_Library notebook](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OLLAMA_SLMs/10-Ollama_Python_Library.ipynb) +- [20-Ollama_Function_Calling notebook](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OLLAMA_SLMs/20-Ollama_Function_Calling.ipynb) +- [30-Function_Calling_with_images notebook](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OLLAMA_SLMs/30-Function_Calling_with_images.ipynb) +- [40-RAG-simple-bee notebook](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OLLAMA_SLMs/40-RAG-simple-bee.ipynb) +- [calc_distance_image python script](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OLLAMA_SLMs/calc_distance_image.py) diff --git 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Detection {.unnumbered} -## *Coming soon.* + + +![*DALL·E prompt - A cover image for an 'Object Detection' chapter in a Raspberry Pi tutorial, designed in the same vintage 1950s electronics lab style as previous covers. The scene should prominently feature wheels and cubes, similar to those provided by the user, placed on a workbench in the foreground. A Raspberry Pi with a connected camera module should be capturing an image of these objects. Surround the scene with classic lab tools like soldering irons, resistors, and wires. The lab background should include vintage equipment like oscilloscopes and tube radios, maintaining the detailed and nostalgic feel of the era. No text or logos should be included.*](images/jpeg/cover.jpg) + +## Introduction + +Building upon our exploration of image classification, we now turn our attention to a more advanced computer vision task: object detection. While image classification assigns a single label to an entire image, object detection goes further by identifying and locating multiple objects within a single image. This capability opens up many new applications and challenges, particularly in edge computing and IoT devices like the Raspberry Pi. + +Object detection combines the tasks of classification and localization. It not only determines what objects are present in an image but also pinpoints their locations by, for example, drawing bounding boxes around them. This added complexity makes object detection a more powerful tool for understanding visual scenes, but it also requires more sophisticated models and training techniques. + +In edge AI, where we work with constrained computational resources, implementing efficient object detection models becomes crucial. The challenges we faced with image classification—balancing model size, inference speed, and accuracy—are amplified in object detection. However, the rewards are also more significant, as object detection enables more nuanced and detailed visual data analysis. + +Some applications of object detection on edge devices include: + +1. Surveillance and security systems +2. Autonomous vehicles and drones +3. Industrial quality control +4. Wildlife monitoring +5. Augmented reality applications + +As we put our hands into object detection, we'll build upon the concepts and techniques we explored in image classification. We'll examine popular object detection architectures designed for efficiency, such as: + +- Single Stage Detectors, such as MobileNet and EfficientDet, +- FOMO (Faster Objects, More Objects), and +- YOLO (You Only Look Once). + +> To learn more about object detection models, follow the tutorial [A Gentle Introduction to Object Recognition With Deep Learning](https://machinelearningmastery.com/object-recognition-with-deep-learning/). + +We will explore those object detection models using + +- TensorFlow Lite Runtime (now changed to [LiteRT](https://ai.google.dev/edge/litert)), +- Edge Impulse Linux Python SDK and +- Ultralitics + +![](images/png/block.png) + +Throughout this lab, we’ll cover the fundamentals of object detection and how it differs from image classification. We'll also learn how to train, fine-tune, test, optimize, and deploy popular object detection architectures using a dataset created from scratch. + +### Object Detection Fundamentals + +Object detection builds upon the foundations of image classification but extends its capabilities significantly. To understand object detection, it's crucial first to recognize its key differences from image classification: + +#### Image Classification vs. Object Detection + +**Image Classification:** + +- Assigns a single label to an entire image +- Answers the question: “What is this image's primary object or scene?” +- Outputs a single class prediction for the whole image + +**Object Detection:** + +- Identifies and locates multiple objects within an image +- Answers the questions: "What objects are in this image, and where are they located?" +- Outputs multiple predictions, each consisting of a class label and a bounding box + +To visualize this difference, let's consider an example: ![](images/jpeg/objxclas.jpg) + +This diagram illustrates the critical difference: image classification provides a single label for the entire image, while object detection identifies multiple objects, their classes, and their locations within the image. + +#### Key Components of Object Detection + +Object detection systems typically consist of two main components: + +1. Object Localization: This component identifies where objects are located in the image. It typically outputs bounding boxes, rectangular regions encompassing each detected object. + +2. Object Classification: This component determines the class or category of each detected object, similar to image classification but applied to each localized region. + +#### Challenges in Object Detection + +Object detection presents several challenges beyond those of image classification: + +- Multiple objects: An image may contain multiple objects of various classes, sizes, and positions. +- Varying scales: Objects can appear at different sizes within the image. +- Occlusion: Objects may be partially hidden or overlapping. +- Background clutter: Distinguishing objects from complex backgrounds can be challenging. +- Real-time performance: Many applications require fast inference times, especially on edge devices. + +#### Approaches to Object Detection + +There are two main approaches to object detection: + +1. Two-stage detectors: These first propose regions of interest and then classify each region. Examples include R-CNN and its variants (Fast R-CNN, Faster R-CNN). + +2. Single-stage detectors: These predict bounding boxes (or centroids) and class probabilities in one forward pass of the network. Examples include YOLO (You Only Look Once), EfficientDet, SSD (Single Shot Detector), and FOMO (Faster Objects, More Objects). These are often faster and more suitable for edge devices like Raspberry Pi. + +#### Evaluation Metrics + +Object detection uses different metrics compared to image classification: + +- **Intersection over Union (IoU)**: Measures the overlap between predicted and ground truth bounding boxes. +- **Mean Average Precision (mAP)**: Combines precision and recall across all classes and IoU thresholds. +- **Frames Per Second (FPS)**: Measures detection speed, crucial for real-time applications on edge devices. + +## Pre-Trained Object Detection Models Overview + +As we saw in the introduction, given an image or a video stream, an object detection model can identify which of a known set of objects might be present and provide information about their positions within the image. + +> You can test some common models online by visiting [Object Detection - MediaPipe Studio](https://mediapipe-studio.webapps.google.com/studio/demo/object_detector) + +On [Kaggle](https://www.kaggle.com/models?id=298,130,299), we can find the most common pre-trained tflite models to use with the Raspi, [ssd_mobilenet_v1,](https://www.kaggle.com/models/tensorflow/ssd-mobilenet-v1/tfLite) and [EfficientDet](https://www.kaggle.com/models/tensorflow/efficientdet/tfLite). Those models were trained on the COCO (Common Objects in Context) dataset, with over 200,000 labeled images in 91 categories. Go, download the models, and upload them to the `./models` folder in the Raspi. + +> Alternatively[,](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/tree/main/OBJ_DETEC/models) you can find the models and the COCO labels on [GitHub](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/tree/main/OBJ_DETEC/models). + +For the first part of this lab, we will focus on a pre-trained 300x300 SSD-Mobilenet V1 model and compare it with the 320x320 EfficientDet-lite0, also trained using the COCO 2017 dataset. Both models were converted to a TensorFlow Lite format (4.2MB for the SSD Mobilenet and 4.6MB for the EfficientDet). + +> SSD-Mobilenet V2 or V3 is recommended for transfer learning projects, but once the V1 TFLite model is publicly available, we will use it for this overview. + +![](images/png/model-deploy.png) + +### Setting Up the TFLite Environment + +We should confirm the steps done on the last Hands-On Lab, Image Classification, as follows: + +- Updating the Raspberry Pi + +- Installing Required Libraries + +- Setting up a Virtual Environment (Optional but Recommended) + +```bash +source ~/tflite/bin/activate +``` + +- Installing TensorFlow Lite Runtime + +- Installing Additional Python Libraries (inside the environment) + +### Creating a Working Directory: + +Considering that we have created the `Documents/TFLITE` folder in the last Lab, let's now create the specific folders for this object detection lab: + +```bash +cd Documents/TFLITE/ +mkdir OBJ_DETECT +cd OBJ_DETECT +mkdir images +mkdir models +cd models +``` + +### Inference and Post-Processing + +Let's start a new [notebook](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OBJ_DETEC/notebooks/SSD_MobileNetV1.ipynb) to follow all the steps to detect objects on an image: + +Import the needed libraries: + +```python +import time +import numpy as np +import matplotlib.pyplot as plt +from PIL import Image +import tflite_runtime.interpreter as tflite +``` + +Load the TFLite model and allocate tensors: + +```python +model_path = "./models/ssd-mobilenet-v1-tflite-default-v1.tflite" +interpreter = tflite.Interpreter(model_path=model_path) +interpreter.allocate_tensors() +``` + +Get input and output tensors. + +```python +input_details = interpreter.get_input_details() +output_details = interpreter.get_output_details() +``` + +**Input details** will inform us how the model should be fed with an image. The shape of `(1, 300, 300, 3)` with a dtype of `uint8` tells us that a non-normalized (pixel value range from 0 to 255) image with dimensions (300x300x3) should be input one by one (Batch Dimension: 1). + +The **output details** include not only the labels ("classes") and probabilities (“scores”) but also the relative window position of the bounding boxes ("boxes”) about where the object is located on the image and the number of detected objects ("num_detections"). The output details also tell us that the model can detect a **maximum of 10 objects** in the image. + +![](images/png/inference result.png) + +So, for the above example, using the same cat image used with the *Image Classification Lab* looking for the output, we have a **76% probability** of having found an object with a **class ID of 16** on an area delimited by a **bounding box of [0.028011084, 0.020121813, 0.9886069, 0.802299]**. Those four numbers are related to `ymin`, `xmin`, `ymax` and `xmax`, the box coordinates. + +Taking into consideration that **y** goes from the top `(ymin`) to the bottom (`ymax`) and **x** goes from left (`xmin`) to the right (`xmax`), we have, in fact, the coordinates of the top/left corner and the bottom/right one. With both edges and knowing the shape of the picture, it is possible to draw a rectangle around the object, as shown in the figure below: + +![](images/png/boulding-boxes.png) + +Next, we should find what class ID equal to 16 means. Opening the file `coco_labels.txt`, as a list, each element has an associated index, and inspecting index 16, we get, as expected, `cat`. The probability is the value returning from the score. + +Let's now upload some images with multiple objects on it for testing. + +```python +img_path = "./images/cat_dog.jpeg" +orig_img = Image.open(img_path) + +# Display the image +plt.figure(figsize=(8, 8)) +plt.imshow(orig_img) +plt.title("Original Image") +plt.show() +``` + +![](images/png/cat-dog.png) + +Based on the input details, let's pre-process the image, changing its shape and expanding its dimension: + +```python +img = orig_img.resize((input_details[0]['shape'][1], + input_details[0]['shape'][2])) +input_data = np.expand_dims(img, axis=0) +input_data.shape, input_data.dtype +``` + +The new input_data shape is` (1, 300, 300, 3)` with a dtype of `uint8`, which is compatible with what the model expects. + +Using the input_data, let's run the interpreter, measure the latency, and get the output: + +```python +start_time = time.time() +interpreter.set_tensor(input_details[0]['index'], input_data) +interpreter.invoke() +end_time = time.time() +inference_time = (end_time - start_time) * 1000 # Convert to milliseconds +print ("Inference time: {:.1f}ms".format(inference_time)) +``` + +With a latency of around 800ms, we can get 4 distinct outputs: + +```python +boxes = interpreter.get_tensor(output_details[0]['index'])[0] +classes = interpreter.get_tensor(output_details[1]['index'])[0] +scores = interpreter.get_tensor(output_details[2]['index'])[0] +num_detections = int(interpreter.get_tensor(output_details[3]['index'])[0]) +``` + +On a quick inspection, we can see that the model detected 2 objects with a score over 0.5: + +```python +for i in range(num_detections): + if scores[i] > 0.5: # Confidence threshold + print(f"Object {i}:") + print(f" Bounding Box: {boxes[i]}") + print(f" Confidence: {scores[i]}") + print(f" Class: {classes[i]}") +``` + +![](images/png/infer-mobv1.png) + +And we can also visualize the results: + +```python +plt.figure(figsize=(12, 8)) +plt.imshow(orig_img) +for i in range(num_detections): + if scores[i] > 0.5: # Adjust threshold as needed + ymin, xmin, ymax, xmax = boxes[i] + (left, right, top, bottom) = (xmin * orig_img.width, + xmax * orig_img.width, + ymin * orig_img.height, + ymax * orig_img.height) + rect = plt.Rectangle((left, top), right-left, bottom-top, + fill=False, color='red', linewidth=2) + plt.gca().add_patch(rect) + class_id = int(classes[i]) + class_name = labels[class_id] + plt.text(left, top-10, f'{class_name}: {scores[i]:.2f}', + color='red', fontsize=12, backgroundcolor='white') +``` + + + +![](images/png/visual_inf.png) + +### EfficientDet + +EfficientDet is not technically an SSD (Single Shot Detector) model, but it shares some similarities and builds upon ideas from SSD and other object detection architectures: + +1. EfficientDet: + - Developed by Google researchers in 2019 + - Uses EfficientNet as the backbone network + - Employs a novel bi-directional feature pyramid network (BiFPN) + - It uses compound scaling to scale the backbone network and the object detection components efficiently. + +2. Similarities to SSD: + - Both are single-stage detectors, meaning they perform object localization and classification in a single forward pass. + - Both use multi-scale feature maps to detect objects at different scales. + +3. Key differences: + - Backbone: SSD typically uses VGG or MobileNet, while EfficientDet uses EfficientNet. + - Feature fusion: SSD uses a simple feature pyramid, while EfficientDet uses the more advanced BiFPN. + - Scaling method: EfficientDet introduces compound scaling for all components of the network + +4. Advantages of EfficientDet: + - Generally achieves better accuracy-efficiency trade-offs than SSD and many other object detection models. + - More flexible scaling allows for a family of models with different size-performance trade-offs. + +While EfficientDet is not an SSD model, it can be seen as an evolution of single-stage detection architectures, incorporating more advanced techniques to improve efficiency and accuracy. When using EfficientDet, we can expect similar output structures to SSD (e.g., bounding boxes and class scores). + +> On GitHub, you can find another [notebook](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OBJ_DETEC/notebooks/SSD_EfficientDet.ipynb) exploring the EfficientDet model that we did with SSD MobileNet. +> + +## Object Detection Project + +Now, we will develop a complete Image Classification project from data collection to training and deployment. As we did with the Image Classification project, the trained and converted model will be used for inference. + +We will use the same dataset to train 3 models: SSD-MobileNet V2, FOMO, and YOLO. + +### The Goal + +All Machine Learning projects need to start with a goal. Let's assume we are in an industrial facility and must sort and count **wheels** and special **boxes**. + +![](images/jpeg/proj_goal.jpg) + +In other words, we should perform a multi-label classification, where each image can have three classes: + +- Background (no objects) + +- Box + +- Wheel + +### Raw Data Collection + +Once we have defined our Machine Learning project goal, the next and most crucial step is collecting the dataset. We can use a phone, the Raspi, or a mix to create the raw dataset (with no labels). Let's use the simple web app on our Raspberry Pi to view the `QVGA (320 x 240)` captured images in a browser. + +From GitHub, get the Python script [get_img_data.py](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/IMG_CLASS/python_scripts/get_img_data.py) and open it in the terminal: + +```bash +python3 get_img_data.py +``` + +Access the web interface: + +- On the Raspberry Pi itself (if you have a GUI): Open a web browser and go to `http://localhost:5000` +- From another device on the same network: Open a web browser and go to `http://:5000` (Replace `` with your Raspberry Pi's IP address). For example: `http://192.168.4.210:5000/` + +![](images/png/app.png)The Python script creates a web-based interface for capturing and organizing image datasets using a Raspberry Pi and its camera. It's handy for machine learning projects that require labeled image data or not, as in our case here. + +Access the web interface from a browser, enter a generic label for the images you want to capture, and press `Start Capture`. + +![](images/png/cap-img.png) + +> Note that the images to be captured will have multiple labels that should be defined later. + +Use the live preview to position the camera and click `Capture Image` to save images under the current label (in this case, `box-wheel`. + +![](images/png/img_cap-1.png) + +When we have enough images, we can press `Stop Capture`. The captured images are saved on the folder dataset/box-wheel: + +![](images/png/dataset.png) + +> Get around 60 images. Try to capture different angles, backgrounds, and light conditions. Filezilla can transfer the created raw dataset to your main computer. + +### Labeling Data + +The next step in an Object Detect project is to create a labeled dataset. We should label the raw dataset images, creating bounding boxes around each picture's objects (box and wheel). We can use labeling tools like [LabelImg,](https://pypi.org/project/labelImg/) [CVAT,](https://www.cvat.ai/) [Roboflow,](https://roboflow.com/annotate) or even the [Edge Impulse Studio.](https://edgeimpulse.com/) Once we have explored the Edge Impulse tool in other labs, let’s use Roboflow here. + +> We are using Roboflow (free version) here for two main reasons. 1) We can have auto-labeler, and 2) The annotated dataset is available in several formats and can be used both on Edge Impulse Studio (we will use it for MobileNet V2 and FOMO train) and on CoLab (YOLOv8 train), for example. Having the annotated dataset on Edge Impulse (Free account), it is not possible to use it for training on other platforms. + +We should upload the raw dataset to [Roboflow.](https://roboflow.com/) Create a free account there and start a new project, for example, (“box-versus-wheel”). + +![](images/png/create-project-rf.png) + +> We will not enter in deep details about the Roboflow process once many tutorials are available. + +#### Annotate + +Once the project is created and the dataset is uploaded, you should make the annotations using the "Auto-Label" Tool. Note that you can also upload images with only a background, which should be saved w/o any annotations. + +![](images/png/annotation.png) + +Once all images are annotated, you should split them into training, validation, and testing. + +![](images/png/dataset_rf.png) + +#### Data Pre-Processing + +The last step with the dataset is preprocessing to generate a final version for training. Let's resize all images to 320x320 and generate augmented versions of each image (augmentation) to create new training examples from which our model can learn. + +For augmentation, we will rotate the images (+/-15^o^), crop, and vary the brightness and exposure. + +![](images/png/pre-proc.png) + +At the end of the process, we will have 153 images. + +![](images/png/final-dataset.png) + +Now, you should export the annotated dataset in a format that Edge Impulse, Ultralitics, and other frameworks/tools understand, for example, `YOLOv8`. Let's download a zipped version of the dataset to our desktop. + +![](images/png/download-dataset.png) + +Here, it is possible to review how the dataset was structured + +![](images/png/dataset-struct.png) + +There are 3 separate folders, one for each split (`train`/`test`/`valid`). For each of them, there are 2 subfolders, `images`, and `labels`. The pictures are stored as **image_id.jpg** and **images_id.txt**, where "image_id" is unique for every picture. + +The labels file format will be `class_id` `bounding box coordinates`, where in our case, class_id will be `0` for `box` and `1` for `wheel`. The numerical id (o, 1, 2...) will follow the alphabetical order of the class name. + +The `data.yaml` file has info about the dataset as the classes' names (`names: ['box', 'wheel']`) following the YOLO format. + +And that's it! We are ready to start training using the Edge Impulse Studio (as we will do in the following step), Ultralytics (as we will when discussing YOLO), or even training from scratch on CoLab (as we did with the Cifar-10 dataset on the Image Classification lab). + +> The pre-processed dataset can be found at the [Roboflow site](https://universe.roboflow.com/marcelo-rovai-riila/box-versus-wheel-auto-dataset), or here: +> +> + +## Training an SSD MobileNet Model on Edge Impulse Studio + +Go to [Edge Impulse Studio,](https://www.edgeimpulse.com/) enter your credentials at **Login** (or create an account), and start a new project. + +> Here, you can clone the project developed for this hands-on lab: [Raspi - Object Detection](https://studio.edgeimpulse.com/public/515477/live). + +On the Project `Dashboard` tab, go down and on **Project info,** and for Labeling method select `Bounding boxes (object detection)` + +### Uploading the annotated data + +On Studio, go to the `Data acquisition` tab, and on the `UPLOAD DATA` section, upload from your computer the raw dataset. + +We can use the option `Select a folder`, choosing, for example, the folder `train` in your computer, which contains two sub-folders, `images`, and `labels`. Select the `Image label format`, "YOLO TXT", upload into the caegory `Training`, and press `Upload data`. + +![](images/png/upload-data.png) + +Repeat the process for the test data (upload both folders, test, and validation). At the end of the upload process, you should end with the annotated dataset of 153 images split in the train/test (84%/16%). + +> Note that labels will be stored at the labels files `0` and `1` , which are equivalent to `box` and `wheel`. + +![](images/png/ei-dataset.png) + +### The Impulse Design + +The first thing to define when we enter the `Create impulse` step is to describe the target device for deployment. A pop-up window will appear. We will select Raspberry 4, an intermediary device between the Raspi-Zero and the Raspi-5. + +> This choice will not interfere with the training; it will only give us an idea about the latency of the model on that specific target. + +![](images/png/target-device.png) + +In this phase, you should define how to: + +- **Pre-processing** consists of resizing the individual images. In our case, the images were pre-processed on Roboflow, to `320x320` , so let's keep it. The resize will not matter here because the images are already squared. If you upload a rectangular image, squash it (squared form, without cropping). Afterward, you could define if the images are converted from RGB to Grayscale or not. + +- **Design a Model,** in this case, "Object Detection." + +![](images/png/impulse-design.png) + +### Preprocessing all dataset + +In the section `Image`, select **Color depth** as `RGB`, and press `Save parameters`. + +![](images/png/ei-image-pre.png) + +The Studio moves automatically to the next section, `Generate features`, where all samples will be pre-processed, resulting in 480 objects: 207 boxes and 273 wheels. + +![](images/png/ei-features.png) + +The feature explorer shows that all samples evidence a good separation after the feature generation. + +### Model Design, Training, and Test + +For training, we should select a pre-trained model. Let's use the **MobileNetV2 SSD FPN-Lite (320x320 only)** . It is a pre-trained object detection model designed to locate up to 10 objects within an image, outputting a bounding box for each object detected. The model is around 3.7MB in size. It supports an RGB input at 320x320px. + +Regarding the training hyper-parameters, the model will be trained with: + +- Epochs: 25 +- Batch size: 32 +- Learning Rate: 0.15. + +For validation during training, 20% of the dataset (*validation_dataset*) will be spared. + +![](images/png/ei-train-result.png) + +As a result, the model ends with an overall precision score (based on COCO mAP) of 88.8%, higher than the result when using the test data (83.3%). + +### Deploying the model + +We have two ways to deploy our model: + +- **TFLite model**, which lets deploy the trained model as `.tflite` for the Raspi to run it using Python. +- **Linux (AARCH64)**, a binary for Linux (AARCH64), implements the Edge Impulse Linux protocol, which lets us run our models on any Linux-based development board, with SDKs for Python, for example. See the documentation for more information and [setup instructions](https://docs.edgeimpulse.com/docs/edge-impulse-for-linux). + +Let's deploy the **TFLite model**. On the `Dashboard` tab, go to Transfer learning model (int8 quantized) and click on the download icon: + +![](images/png/ei-deploy-int8.png) + +Transfer the model from your computer to the Raspi folder`./models` and capture or get some images for inference and save them in the folder `./images`. + +### Inference and Post-Processing + +The inference can be made as discussed in the *Pre-Trained Object Detection Models Overview*. Let's start a new [notebook](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OBJ_DETEC/notebooks/EI-SSD-MobileNetV2.ipynb) to follow all the steps to detect cubes and wheels on an image. + +Import the needed libraries: + +```python +import time +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.patches as patches +from PIL import Image +import tflite_runtime.interpreter as tflite +``` + +Define the model path and labels: + +```python +model_path = "./models/ei-raspi-object-detection-SSD-MobileNetv2-320x0320-\ +int8.lite" +labels = ['box', 'wheel'] +``` + +> Remember that the model will output the class ID as values (0 and 1), following an alphabetic order regarding the class names. + +Load the model, allocate the tensors, and get the input and output tensor details: + +```python +# Load the TFLite model +interpreter = tflite.Interpreter(model_path=model_path) +interpreter.allocate_tensors() + +# Get input and output tensors +input_details = interpreter.get_input_details() +output_details = interpreter.get_output_details() +``` + +One crucial difference to note is that the `dtype` of the input details of the model is now `int8`, which means that the input values go from -128 to +127, while each pixel of our raw image goes from 0 to 256. This means that we should pre-process the image to match it. We can check here: + +```python +input_dtype = input_details[0]['dtype'] +input_dtype +``` + +``` +numpy.int8 +``` + +So, let's open the image and show it: + +```python +# Load the image +img_path = "./images/box_2_wheel_2.jpg" +orig_img = Image.open(img_path) + +# Display the image +plt.figure(figsize=(6, 6)) +plt.imshow(orig_img) +plt.title("Original Image") +plt.show() +``` + +![](images/png/orig-img.png) + +And perform the pre-processing: + +```python +scale, zero_point = input_details[0]['quantization'] +img = orig_img.resize((input_details[0]['shape'][1], + input_details[0]['shape'][2])) +img_array = np.array(img, dtype=np.float32) / 255.0 +img_array = (img_array / scale + zero_point).clip(-128, 127).astype(np.int8) +input_data = np.expand_dims(img_array, axis=0) +``` + +Checking the input data, we can verify that the input tensor is compatible with what is expected by the model: + +```python +input_data.shape, input_data.dtype +``` + +``` +((1, 320, 320, 3), dtype('int8')) +``` + +Now, it is time to perform the inference. Let's also calculate the latency of the model: + +```python +# Inference on Raspi-Zero +start_time = time.time() +interpreter.set_tensor(input_details[0]['index'], input_data) +interpreter.invoke() +end_time = time.time() +inference_time = (end_time - start_time) * 1000 # Convert to milliseconds +print ("Inference time: {:.1f}ms".format(inference_time)) +``` + +The model will take around 600ms to perform the inference in the Raspi-Zero, which is around 5 times longer than a Raspi-5. + +Now, we can get the output classes of objects detected, its bounding boxes coordinates, and probabilities. + +```python +boxes = interpreter.get_tensor(output_details[1]['index'])[0] +classes = interpreter.get_tensor(output_details[3]['index'])[0] +scores = interpreter.get_tensor(output_details[0]['index'])[0] +num_detections = int(interpreter.get_tensor(output_details[2]['index'])[0]) +``` + +```python +for i in range(num_detections): + if scores[i] > 0.5: # Confidence threshold + print(f"Object {i}:") + print(f" Bounding Box: {boxes[i]}") + print(f" Confidence: {scores[i]}") + print(f" Class: {classes[i]}") +``` + +![](images/png/infer-text.png) + +From the results, we can see that 4 objects were detected: two with class ID 0 (`box`)and two with class ID 1 (`wheel`), what is correct! + +Let's visualize the result for a ` threshold` of 0.5 + +```python +threshold = 0.5 +plt.figure(figsize=(6,6)) +plt.imshow(orig_img) +for i in range(num_detections): + if scores[i] > threshold: + ymin, xmin, ymax, xmax = boxes[i] + (left, right, top, bottom) = (xmin * orig_img.width, + xmax * orig_img.width, + ymin * orig_img.height, + ymax * orig_img.height) + rect = plt.Rectangle((left, top), right-left, bottom-top, + fill=False, color='red', linewidth=2) + plt.gca().add_patch(rect) + class_id = int(classes[i]) + class_name = labels[class_id] + plt.text(left, top-10, f'{class_name}: {scores[i]:.2f}', + color='red', fontsize=12, backgroundcolor='white') +``` + +![](images/png/infer-visual.png) + +But what happens if we reduce the threshold to 0.3, for example? + +![](images/png/infer-mult.png) + +We start to see false positives and **multiple detections**, where the model detects the same object multiple times with different confidence levels and slightly different bounding boxes. + +Commonly, sometimes, we need to adjust the threshold to smaller values to capture all objects, avoiding false negatives, which would lead to multiple detections. + +To improve the detection results, we should implement **Non-Maximum Suppression (NMS**), which helps eliminate overlapping bounding boxes and keeps only the most confident detection. + +For that, let's create a general function named `non_max_suppression()`, with the role of refining object detection results by eliminating redundant and overlapping bounding boxes. It achieves this by iteratively selecting the detection with the highest confidence score and removing other significantly overlapping detections based on an Intersection over Union (IoU) threshold. + +```python +def non_max_suppression(boxes, scores, threshold): + # Convert to corner coordinates + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= threshold)[0] + order = order[inds + 1] + + return keep +``` + +How it works: + +1. Sorting: It starts by sorting all detections by their confidence scores, highest to lowest. + +2. Selection: It selects the highest-scoring box and adds it to the final list of detections. + +3. Comparison: This selected box is compared with all remaining lower-scoring boxes. + +4. Elimination: Any box that overlaps significantly (above the IoU threshold) with the selected box is eliminated. + +5. Iteration: This process repeats with the next highest-scoring box until all boxes are processed. + +Now, we can define a more precise visualization function that will take into consideration an IoU threshold, detecting only the objects that were selected by the `non_max_suppression` function: + +```python +def visualize_detections(image, boxes, classes, scores, + labels, threshold, iou_threshold): + if isinstance(image, Image.Image): + image_np = np.array(image) + else: + image_np = image + + height, width = image_np.shape[:2] + + # Convert normalized coordinates to pixel coordinates + boxes_pixel = boxes * np.array([height, width, height, width]) + + # Apply NMS + keep = non_max_suppression(boxes_pixel, scores, iou_threshold) + + # Set the figure size to 12x8 inches + fig, ax = plt.subplots(1, figsize=(12, 8)) + + ax.imshow(image_np) + + for i in keep: + if scores[i] > threshold: + ymin, xmin, ymax, xmax = boxes[i] + rect = patches.Rectangle((xmin * width, ymin * height), + (xmax - xmin) * width, + (ymax - ymin) * height, + linewidth=2, edgecolor='r', facecolor='none') + ax.add_patch(rect) + class_name = labels[int(classes[i])] + ax.text(xmin * width, ymin * height - 10, + f'{class_name}: {scores[i]:.2f}', color='red', + fontsize=12, backgroundcolor='white') + + plt.show() +``` + +Now we can create a function that will call the others, performing inference on any image: + +```python +def detect_objects(img_path, conf=0.5, iou=0.5): + orig_img = Image.open(img_path) + scale, zero_point = input_details[0]['quantization'] + img = orig_img.resize((input_details[0]['shape'][1], + input_details[0]['shape'][2])) + img_array = np.array(img, dtype=np.float32) / 255.0 + img_array = (img_array / scale + zero_point).clip(-128, 127).\ + astype(np.int8) + input_data = np.expand_dims(img_array, axis=0) + + # Inference on Raspi-Zero + start_time = time.time() + interpreter.set_tensor(input_details[0]['index'], input_data) + interpreter.invoke() + end_time = time.time() + inference_time = (end_time - start_time) * 1000 # Convert to ms + print ("Inference time: {:.1f}ms".format(inference_time)) + + # Extract the outputs + boxes = interpreter.get_tensor(output_details[1]['index'])[0] + classes = interpreter.get_tensor(output_details[3]['index'])[0] + scores = interpreter.get_tensor(output_details[0]['index'])[0] + num_detections = int(interpreter.get_tensor(output_details[2]['index'])[0]) + + visualize_detections(orig_img, boxes, classes, scores, labels, + threshold=conf, + iou_threshold=iou) +``` + +Now, running the code, having the same image again with a confidence threshold of 0.3, but with a small IoU: + +```python +img_path = "./images/box_2_wheel_2.jpg" +detect_objects(img_path, conf=0.3,iou=0.05) +``` + +![](images/png/infer-iou.png) + +## Training a FOMO Model at Edge Impulse Studio + +The inference with the SSD MobileNet model worked well, but the latency was significantly high. The inference varied from 0.5 to 1.3 seconds on a Raspi-Zero, which means around or less than 1 FPS (1 frame per second). One alternative to speed up the process is to use FOMO (Faster Objects, More Objects). + +This novel machine learning algorithm lets us count multiple objects and find their location in an image in real-time using up to 30x less processing power and memory than MobileNet SSD or YOLO. The main reason this is possible is that while other models calculate the object’s size by drawing a square around it (bounding box), FOMO ignores the size of the image, providing only the information about where the object is located in the image through its centroid coordinates. + +### How FOMO works? + +In a typical object detection pipeline, the first stage is extracting features from the input image. **FOMO leverages MobileNetV2 to perform this task**. MobileNetV2 processes the input image to produce a feature map that captures essential characteristics, such as textures, shapes, and object edges, in a computationally efficient way. + +![](images/png/fomo-1.png) + +Once these features are extracted, FOMO’s simpler architecture, focused on center-point detection, interprets the feature map to determine where objects are located in the image. The output is a grid of cells, where each cell represents whether or not an object center is detected. The model outputs one or more confidence scores for each cell, indicating the likelihood of an object being present. + +Let's see how it works on an image. + +FOMO divides the image into blocks of pixels using a factor of 8. For the input of 96x96, the grid would be 12x12 (96/8=12). For a 160x160, the grid will be 20x20, and so on. Next, FOMO will run a classifier through each pixel block to calculate the probability that there is a box or a wheel in each of them and, subsequently, determine the regions that have the highest probability of containing the object (If a pixel block has no objects, it will be classified as *background*). From the overlap of the final region, the FOMO provides the coordinates (related to the image dimensions) of the centroid of this region. + +![](images/png/fomo-works.png) + +**Trade-off Between Speed and Precision**: + +- **Grid Resolution**: FOMO uses a grid of fixed resolution, meaning each cell can detect if an object is present in that part of the image. While it doesn’t provide high localization accuracy, it makes a trade-off by being fast and computationally light, which is crucial for edge devices. +- **Multi-Object Detection**: Since each cell is independent, FOMO can detect multiple objects simultaneously in an image by identifying multiple centers. + +### Impulse Design, new Training and Testing + +Return to Edge Impulse Studio, and in the `Experiments` tab, create another impulse. Now, the input images should be 160x160 (this is the expected input size for MobilenetV2). + +![](images/png/impulse-2.png) + +On the `Image` tab, generate the features and go to the `Object detection` tab. + +We should select a pre-trained model for training. Let’s use the **FOMO (Faster Objects, More Objects) MobileNetV2 0.35.** + +![](images/png/model-choice.png) + +Regarding the training hyper-parameters, the model will be trained with: + +- Epochs: 30 +- Batch size: 32 +- Learning Rate: 0.001. + +For validation during training, 20% of the dataset (*validation_dataset*) will be spared. We will not apply Data Augmentation for the remaining 80% (*train_dataset*) because our dataset was already augmented during the labeling phase at Roboflow. + +As a result, the model ends with an overall F1 score of 93.3% with an impressive latency of 8ms (Raspi-4), around 60X less than we got with the SSD MovileNetV2. + +![](images/png/fomo-train-result.png) + +> Note that FOMO automatically added a third label background to the two previously defined *boxes* (0) and *wheels* (1). + +On the `Model testing` tab, we can see that the accuracy was 94%. Here is one of the test sample results: + +![](images/png/fomo-test.png) + +> In object detection tasks, accuracy is generally not the primary [evaluation metric.](https://learnopencv.com/mean-average-precision-map-object-detection-model-evaluation-metric/) Object detection involves classifying objects and providing bounding boxes around them, making it a more complex problem than simple classification. The issue is that we do not have the bounding box, only the centroids. In short, using accuracy as a metric could be misleading and may not provide a complete understanding of how well the model is performing. + +### Deploying the model + +As we did in the previous section, we can deploy the trained model as TFLite or Linux (AARCH64). Let's do it now as **Linux (AARCH64)**, a binary that implements the [Edge Impulse Linux](https://docs.edgeimpulse.com/docs/tools/edge-impulse-for-linux) protocol. + +Edge Impulse for Linux models is delivered in `.eim` format. This [executable](https://docs.edgeimpulse.com/docs/run-inference/linux-eim-executable) contains our "full impulse" created in Edge Impulse Studio. The impulse consists of the signal processing block(s) and any learning and anomaly block(s) we added and trained. It is compiled with optimizations for our processor or GPU (e.g., NEON instructions on ARM cores), plus a straightforward IPC layer (over a Unix socket). + +At the `Deploy` tab, select the option `Linux (AARCH64)`, the `int8`model and press `Build`. + +![](images/png/deploy-linux.png) + +The model will be automatically downloaded to your computer. + +On our Raspi, let's create a new working area: + +```bash +cd ~ +cd Documents +mkdir EI_Linux +cd EI_Linux +mkdir models +mkdir images +``` + +Rename the model for easy identification: + +For example, `raspi-object-detection-linux-aarch64-FOMO-int8.eim` and transfer it to the new Raspi folder`./models` and capture or get some images for inference and save them in the folder `./images`. + +### Inference and Post-Processing + +The inference will be made using the [Linux Python SDK](https://docs.edgeimpulse.com/docs/tools/edge-impulse-for-linux/linux-python-sdk). This library lets us run machine learning models and collect sensor data on [Linux](https://docs.edgeimpulse.com/docs/tools/edge-impulse-for-linux) machines using Python. The SDK is open source and hosted on GitHub: [edgeimpulse/linux-sdk-python](https://github.com/edgeimpulse/linux-sdk-python). + +Let's set up a Virtual Environment for working with the Linux Python SDK + +```bash +python3 -m venv ~/eilinux +source ~/eilinux/bin/activate +``` + +And Install the all the libraries needed: + +```bash +sudo apt-get update +sudo apt-get install libatlas-base-dev libportaudio0 libportaudio2 +sudo apt-get installlibportaudiocpp0 portaudio19-dev + +pip3 install edge_impulse_linux -i https://pypi.python.org/simple +pip3 install Pillow matplotlib pyaudio opencv-contrib-python + +sudo apt-get install portaudio19-dev +pip3 install pyaudio +pip3 install opencv-contrib-python +``` + +Permit our model to be executable. + +```bash +chmod +x raspi-object-detection-linux-aarch64-FOMO-int8.eim +``` + +Install the Jupiter Notebook on the new environment + +```bash +pip3 install jupyter +``` + +Run a notebook locally (on the Raspi-4 or 5 with desktop) + +```bash +jupyter notebook +``` + +or on the browser on your computer: + +```bash +jupyter notebook --ip=192.168.4.210 --no-browser +``` + +Let's start a new [notebook](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OBJ_DETEC/notebooks/EI-Linux-FOMO.ipynb) by following all the steps to detect cubes and wheels on an image using the FOMO model and the Edge Impulse Linux Python SDK. + +Import the needed libraries: + +```python +import sys, time +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.patches as patches +from PIL import Image +import cv2 +from edge_impulse_linux.image import ImageImpulseRunner +``` + +Define the model path and labels: + +```python +model_file = "raspi-object-detection-linux-aarch64-int8.eim" +model_path = "models/"+ model_file # Trained ML model from Edge Impulse +labels = ['box', 'wheel'] +``` + +> Remember that the model will output the class ID as values (0 and 1), following an alphabetic order regarding the class names. + +Load and initialize the model: + +```python +# Load the model file +runner = ImageImpulseRunner(model_path) + +# Initialize model +model_info = runner.init() +``` + +The `model_info` will contain critical information about our model. However, unlike the TFLite interpreter, the EI Linux Python SDK library will now prepare the model for inference. + +So, let's open the image and show it (Now, for compatibility, we will use OpenCV, the CV Library used internally by EI. OpenCV reads the image as BGR, so we will need to convert it to RGB : + +```python +# Load the image +img_path = "./images/1_box_1_wheel.jpg" +orig_img = cv2.imread(img_path) +img_rgb = cv2.cvtColor(orig_img, cv2.COLOR_BGR2RGB) + +# Display the image +plt.imshow(img_rgb) +plt.title("Original Image") +plt.show() +``` + +![](images/png/orig-fomo-img.png) + +Now we will get the features and the preprocessed image (`cropped`) using the `runner`: + +```python +features, cropped = runner.get_features_from_image_auto_studio_setings(img_rgb) +``` + +And perform the inference. Let's also calculate the latency of the model: + +```python +res = runner.classify(features) +``` + +Let's get the output classes of objects detected, their bounding boxes centroids, and probabilities. + +```python +print('Found %d bounding boxes (%d ms.)' % ( + len(res["result"]["bounding_boxes"]), + res['timing']['dsp'] + res['timing']['classification'])) +for bb in res["result"]["bounding_boxes"]: + print('\t%s (%.2f): x=%d y=%d w=%d h=%d' % ( + bb['label'], bb['value'], bb['x'], + bb['y'], bb['width'], bb['height'])) +``` + +``` +Found 2 bounding boxes (29 ms.) + 1 (0.91): x=112 y=40 w=16 h=16 + 0 (0.75): x=48 y=56 w=8 h=8 +``` + +The results show that two objects were detected: one with class ID 0 (`box`) and one with class ID 1 (`wheel`), which is correct! + +Let's visualize the result (The ` threshold` is 0.5, the default value set during the model testing on the Edge Impulse Studio). + +```python +print('\tFound %d bounding boxes (latency: %d ms)' % ( + len(res["result"]["bounding_boxes"]), + res['timing']['dsp'] + res['timing']['classification'])) +plt.figure(figsize=(5,5)) +plt.imshow(cropped) + +# Go through each of the returned bounding boxes +bboxes = res['result']['bounding_boxes'] +for bbox in bboxes: + + # Get the corners of the bounding box + left = bbox['x'] + top = bbox['y'] + width = bbox['width'] + height = bbox['height'] + + # Draw a circle centered on the detection + circ = plt.Circle((left+width//2, top+height//2), 5, + fill=False, color='red', linewidth=3) + plt.gca().add_patch(circ) + class_id = int(bbox['label']) + class_name = labels[class_id] + plt.text(left, top-10, f'{class_name}: {bbox["value"]:.2f}', + color='red', fontsize=12, backgroundcolor='white') +plt.show() +``` + +![](images/png/infer-fomo-result.png) + +## Exploring a YOLO Model using Ultralitics + +For this lab, we will explore YOLOv8. [Ultralytics](https://ultralytics.com/) [YOLOv8](https://github.com/ultralytics/ultralytics) is a version of the acclaimed real-time object detection and image segmentation model, YOLO. YOLOv8 is built on cutting-edge advancements in deep learning and computer vision, offering unparalleled performance in terms of speed and accuracy. Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs. + +### Talking about the YOLO Model + +The YOLO (You Only Look Once) model is a highly efficient and widely used object detection algorithm known for its real-time processing capabilities. Unlike traditional object detection systems that repurpose classifiers or localizers to perform detection, YOLO frames the detection problem as a single regression task. This innovative approach enables YOLO to simultaneously predict multiple bounding boxes and their class probabilities from full images in one evaluation, significantly boosting its speed. + +#### Key Features: + +1. **Single Network Architecture**: + + - YOLO employs a single neural network to process the entire image. This network divides the image into a grid and, for each grid cell, directly predicts bounding boxes and associated class probabilities. This end-to-end training improves speed and simplifies the model architecture. + +2. **Real-Time Processing**: + + - One of YOLO’s standout features is its ability to perform object detection in real-time. Depending on the version and hardware, YOLO can process images at high frames per second (FPS). This makes it ideal for applications requiring quick and accurate object detection, such as video surveillance, autonomous driving, and live sports analysis. + +3. **Evolution of Versions**: + + - Over the years, YOLO has undergone significant improvements, from YOLOv1 to the latest YOLOv10. Each iteration has introduced enhancements in accuracy, speed, and efficiency. YOLOv8, for instance, incorporates advancements in network architecture, improved training methodologies, and better support for various hardware, ensuring a more robust performance. + - Although YOLOv10 is the family's newest member with an encouraging performance based on its paper, it was just released (May 2024) and is not fully integrated with the Ultralitycs library. Conversely, the precision-recall curve analysis suggests that YOLOv8 generally outperforms YOLOv9, capturing a higher proportion of true positives while minimizing false positives more effectively (for more details, see this [article](https://encord.com/blog/performanceyolov9-vs-yolov8-custom-dataset/)). So, this lab is based on the YOLOv8n. + + ![](images/jpeg/versions.jpg) + +4. **Accuracy and Efficiency**: + + - While early versions of YOLO traded off some accuracy for speed, recent versions have made substantial strides in balancing both. The newer models are faster and more accurate, detecting small objects (such as bees) and performing well on complex datasets. + +5. **Wide Range of Applications**: + + - YOLO’s versatility has led to its adoption in numerous fields. It is used in traffic monitoring systems to detect and count vehicles, security applications to identify potential threats and agricultural technology to monitor crops and livestock. Its application extends to any domain requiring efficient and accurate object detection. + +6. **Community and Development**: + + - YOLO continues to evolve and is supported by a strong community of developers and researchers (being the YOLOv8 very strong). Open-source implementations and extensive documentation have made it accessible for customization and integration into various projects. Popular deep learning frameworks like Darknet, TensorFlow, and PyTorch support YOLO, further broadening its applicability. + - [Ultralitics YOLOv8](https://github.com/ultralytics/ultralytics?tab=readme-ov-file) can not only [Detect](https://docs.ultralytics.com/tasks/detect) (our case here) but also [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pre-trained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset and YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pre-trained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment, and Pose models. + + ![Ultralytics YOLO supported tasks](https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png) + +### Installation + +On our Raspi, let's deactivate the current environment to create a new working area: + +```bash +deactivate +cd ~ +cd Documents/ +mkdir YOLO +cd YOLO +mkdir models +mkdir images +``` + +Let's set up a Virtual Environment for working with the Ultralytics YOLOv8 + +```bash +python3 -m venv ~/yolo +source ~/yolo/bin/activate +``` + +And install the Ultralytics packages for local inference on the Raspi + +1. Update the packages list, install pip, and upgrade to the latest: + +```bash +sudo apt update +sudo apt install python3-pip -y +pip install -U pip +``` + +2. Install the `ultralytics` pip package with optional dependencies: + +```bash +pip install ultralytics[export] +``` + +3. Reboot the device: + +```bash +sudo reboot +``` + +### Testing the YOLO + +After the Raspi-Zero booting, let's activate the `yolo` env, go to the working directory, + +```bash +source ~/yolo/bin/activate +cd /Documents/YOLO +``` + +and run inference on an image that will be downloaded from the Ultralytics website, using the YOLOV8n model (the smallest in the family) at the Terminal (CLI): + +```bash +yolo predict model='yolov8n' source='https://ultralytics.com/images/bus.jpg' +``` + +> The YOLO model family is pre-trained with the COCO dataset. + +The inference result will appear in the terminal. In the image (bus.jpg), 4 `persons`, 1 `bus,` and 1 `stop signal` were detected: + +![](images/png/yolo-infer-bus.png) + +Also, we got a message that `Results saved to runs/detect/predict`. Inspecting that directory, we can see a new image saved (bus.jpg). Let's download it from the Raspi-Zero to our desktop for inspection: + +![](images/png/yolo-bus.png) + +So, the Ultrayitics YOLO is correctly installed on our Raspi. But, on the Raspi-Zero, an issue is the high latency for this inference, around 18 seconds, even with the most miniature model of the family (YOLOv8n). + +### Export Model to NCNN format + +Deploying computer vision models on edge devices with limited computational power, such as the Raspi-Zero, can cause latency issues. One alternative is to use a format optimized for optimal performance. This ensures that even devices with limited processing power can handle advanced computer vision tasks well. + +Of all the model export formats supported by Ultralytics, the [NCNN](https://docs.ultralytics.com/integrations/ncnn) is a high-performance neural network inference computing framework optimized for mobile platforms. From the beginning of the design, NCNN was deeply considerate about deployment and use on mobile phones and did not have third-party dependencies. It is cross-platform and runs faster than all known open-source frameworks (such as TFLite). + +NCNN delivers the best inference performance when working with Raspberry Pi devices. NCNN is highly optimized for mobile embedded platforms (such as ARM architecture). + +So, let's convert our model and rerun the inference: + +1. Export a YOLOv8n PyTorch model to NCNN format, creating: '/yolov8n_ncnn_model' + +```bash +yolo export model=yolov8n.pt format=ncnn +``` + +2. Run inference with the exported model (now the source could be the bus.jpg image that was downloaded from the website to the current directory on the last inference): + +```bash +yolo predict model='./yolov8n_ncnn_model' source='bus.jpg' +``` + +> The first inference, when the model is loaded, usually has a high latency (around 17s), but from the 2nd, it is possible to note that the inference goes down to around 2s. + +### Exploring YOLO with Python + +To start, let's call the Python Interpreter so we can explore how the YOLO model works, line by line: + +```bash +python3 +``` + +Now, we should call the YOLO library from Ultralitics and load the model: + +```python +from ultralytics import YOLO +model = YOLO('yolov8n_ncnn_model') +``` + +Next, run inference over an image (let's use again `bus.jpg`): + +```python +img = 'bus.jpg' +result = model.predict(img, save=True, imgsz=640, conf=0.5, iou=0.3) +``` + +![](images/png/python-infer-bus.png) + +We can verify that the result is almost identical to the one we get running the inference at the terminal level (CLI), except that the bus stop was not detected with the reduced NCNN model. Note that the latency was reduced. + +Let's analyze the "result" content. + +For example, we can see `result[0].boxes.data`, showing us the main inference result, which is a tensor shape (4, 6). Each line is one of the objects detected, being the 4 first columns, the bounding boxes coordinates, the 5th, the confidence, and the 6th, the class (in this case, `0: person` and `5: bus`): + +![](images/png/result-bus.png) + +We can access several inference results separately, as the inference time, and have it printed in a better format: + +```python +inference_time = int(result[0].speed['inference']) +print(f"Inference Time: {inference_time} ms") +``` + +Or we can have the total number of objects detected: + +```python +print(f'Number of objects: {len (result[0].boxes.cls)}') +``` + +![](images/png/data-bus.png) + +With Python, we can create a detailed output that meets our needs (See [Model Prediction with Ultralytics YOLO]( https://docs.ultralytics.com/modes/predict/) for more details). Let's run a Python script instead of manually entering it line by line in the interpreter, as shown below. Let's use `nano` as our text editor. First, we should create an empty Python script named, for example, `yolov8_tests.py`: + +```python +nano yolov8_tests.py +``` + +Enter with the code lines: + +```python +from ultralytics import YOLO + +# Load the YOLOv8 model +model = YOLO('yolov8n_ncnn_model') + +# Run inference +img = 'bus.jpg' +result = model.predict(img, save=False, imgsz=640, conf=0.5, iou=0.3) + +# print the results +inference_time = int(result[0].speed['inference']) +print(f"Inference Time: {inference_time} ms") +print(f'Number of objects: {len (result[0].boxes.cls)}') +``` + +![](images/png/yolo-py-script.png) + +And enter with the commands: `[CTRL+O]` + `[ENTER]` +` [CTRL+X]` to save the Python script. + +Run the script: + +```bash +python yolov8_tests.py +``` + +The result is the same as running the inference at the terminal level (CLI) and with the built-in Python interpreter. + +> Calling the YOLO library and loading the model for inference for the first time takes a long time, but the inferences after that will be much faster. For example, the first single inference can take several seconds, but after that, the inference time should be reduced to less than 1 second. +> + +### Training YOLOv8 on a Customized Dataset + +Return to our "Boxe versus Wheel" dataset, labeled on [Roboflow](https://universe.roboflow.com/marcelo-rovai-riila/box-versus-wheel-auto-dataset). On the `Download Dataset`, instead of `Download a zip to computer` option done for training on Edge Impulse Studio, we will opt for `Show download code`. This option will open a pop-up window with a code snippet that should be pasted into our training notebook. + +![](images/png/dataset_code.png) + +For training, let's adapt one of the public examples available from Ultralitytics and run it on Google Colab. Below, you can find mine to be adapted in your project: + +- YOLOv8 Box versus Wheel Dataset Training [[Open In Colab]](https://colab.research.google.com/github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OBJ_DETEC/notebooks/yolov8_box_vs_wheel.ipynb) + +#### Critical points on the Notebook: + +1. Run it with GPU (the NVidia T4 is free) + +2. Install Ultralytics using PIP. + + ![](images/png/yolo-train-lib.png) + +3. Now, you can import the YOLO and upload your dataset to the CoLab, pasting the Download code that we get from Roboflow. Note that our dataset will be mounted under `/content/datasets/`: + +![](images/png/yolo-dataset-upload.png) + +4. It is essential to verify and change the file `data.yaml` with the correct path for the images (copy the path on each `images` folder). + +```bash +names: +- box +- wheel +nc: 2 +roboflow: + license: CC BY 4.0 + project: box-versus-wheel-auto-dataset + url: https://universe.roboflow.com/marcelo-rovai-riila/box-versus-wheel-auto-dataset/dataset/5 + version: 5 + workspace: marcelo-rovai-riila +test: /content/datasets/Box-versus-Wheel-auto-dataset-5/test/images +train: /content/datasets/Box-versus-Wheel-auto-dataset-5/train/images +val: /content/datasets/Box-versus-Wheel-auto-dataset-5/valid/images +``` + +5. Define the main hyperparameters that you want to change from default, for example: + + ```bash + MODEL = 'yolov8n.pt' + IMG_SIZE = 640 + EPOCHS = 25 # For a final project, you should consider at least 100 epochs + ``` + +6. Run the training (using CLI): + + ```bash + !yolo task=detect mode=train model={MODEL} data={dataset.location}/data.yaml epochs={EPOCHS} imgsz={IMG_SIZE} plots=True + ``` + + ![image-20240910111319804](images/png/train-result.png) + +​ The model took a few minutes to be trained and has an excellent result (mAP50 of 0.995). At the end of the training, all results are saved in the folder listed, for example: `/runs/detect/train/`. There, you can find, for example, the confusion matrix. + +![](images/png/matrix.png) + +7. Note that the trained model (`best.pt`) is saved in the folder `/runs/detect/train/weights/`. Now, you should validate the trained model with the `valid/images`. + +```bash +!yolo task=detect mode=val model={HOME}/runs/detect/train/weights/best.pt data={dataset.location}/data.yaml +``` + +​ The results were similar to training. + +8. Now, we should perform inference on the images left aside for testing + +```bash +!yolo task=detect mode=predict model={HOME}/runs/detect/train/weights/best.pt conf=0.25 source={dataset.location}/test/images save=True +``` + +The inference results are saved in the folder `runs/detect/predict`. Let's see some of them: + +![](images/png/test-infer-yolo.png) + +9. It is advised to export the train, validation, and test results for a Drive at Google. To do so, we should mount the drive. + + ```python + from google.colab import drive + drive.mount('/content/gdrive') + ``` + + and copy the content of `/runs` folder to a folder that you should create in your Drive, for example: + + ```bash + !scp -r /content/runs '/content/gdrive/MyDrive/10_UNIFEI/Box_vs_Wheel_Project' + ``` + + + +### Inference with the trained model, using the Raspi + +Download the trained model ` /runs/detect/train/weights/best.pt` to your computer. Using the FileZilla FTP, let's transfer the `best.pt` to the Raspi models folder (before the transfer, you may change the model name, for example, `box_wheel_320_yolo.pt`). + +Using the FileZilla FTP, let's transfer a few images from the test dataset to `.\YOLO\images`: + +Let's return to the YOLO folder and use the Python Interpreter: + +```bash +cd .. +python +``` + +As before, we will import the YOLO library and define our converted model to detect bees: + +```python +from ultralytics import YOLO +model = YOLO('./models/box_wheel_320_yolo.pt') +``` + +Now, let's define an image and call the inference (we will save the image result this time to external verification): + +```python +img = './images/1_box_1_wheel.jpg' +result = model.predict(img, save=True, imgsz=320, conf=0.5, iou=0.3) +``` + +Let's repeat for several images. The inference result is saved on the variable `result,` and the processed image on `runs/detect/predict8` + +![](images/png/infer-yolo.png) + +Using FileZilla FTP, we can send the inference result to our Desktop for verification: + +![](images/png/yolo-infer-raspi.png) + +We can see that the inference result is excellent! The model was trained based on the smaller base model of the YOLOv8 family (YOLOv8n). The issue is the latency, around 1 second (or 1 FPS on the Raspi-Zero). Of course, we can reduce this latency and convert the model to TFLite or NCNN. + +## Object Detection on a live stream + +All the models explored in this lab can detect objects in real-time using a camera. The captured image should be the input for the trained and converted model. For the Raspi-4 or 5 with a desktop, OpenCV can capture the frames and display the inference result. + +However, creating a live stream with a webcam to detect objects in real-time is also possible. For example, let’s start with the script developed for the Image Classification app and adapt it for a *Real-Time Object Detection Web Application Using TensorFlow Lite and Flask*. + +This app version will work for all TFLite models. Verify if the model is in its correct folder, for example: + +```python +model_path = "./models/ssd-mobilenet-v1-tflite-default-v1.tflite" +``` + +Download the Python script ` object_detection_app.py` from [GitHub](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OBJ_DETEC/python_scripts/object_detection_app.py). + +And on the terminal, run: + +```bash +python3 object_detection_app.py +``` + +And access the web interface: + +- On the Raspberry Pi itself (if you have a GUI): Open a web browser and go to `http://localhost:5000` +- From another device on the same network: Open a web browser and go to `http://:5000` (Replace `` with your Raspberry Pi's IP address). For example: `http://192.168.4.210:5000/` + +Here are some screenshots of the app running on an external desktop + +![](images/png/app-running.png) + +Let's see a technical description of the key modules used in the object detection application: + +1. **TensorFlow Lite (tflite_runtime)**: + - Purpose: Efficient inference of machine learning models on edge devices. + - Why: TFLite offers reduced model size and optimized performance compared to full TensorFlow, which is crucial for resource-constrained devices like Raspberry Pi. It supports hardware acceleration and quantization, further improving efficiency. + - Key functions: `Interpreter` for loading and running the model,` get_input_details(),` and `get_output_details()` for interfacing with the model. +2. **Flask:** + - Purpose: Lightweight web framework for creating the backend server. + - Why: Flask's simplicity and flexibility make it ideal for rapidly developing and deploying web applications. It's less resource-intensive than larger frameworks suitable for edge devices. + - Key components: route decorators for defining API endpoints, `Response` objects for streaming video, `render_template_string` for serving dynamic HTML. +3. **Picamera2:** + - Purpose: Interface with the Raspberry Pi camera module. + - Why: Picamera2 is the latest library for controlling Raspberry Pi cameras, offering improved performance and features over the original Picamera library. + - Key functions: `create_preview_configuration()` for setting up the camera, `capture_file()` for capturing frames. +4. **PIL (Python Imaging Library):** + - Purpose: Image processing and manipulation. + - Why: PIL provides a wide range of image processing capabilities. It’s used here to resize images, draw bounding boxes, and convert between image formats. + - Key classes: `Image` for loading and manipulating images, `ImageDraw` for drawing shapes and text on images. +5. **NumPy:** + - Purpose: Efficient array operations and numerical computing. + - Why: NumPy’s array operations are much faster than pure Python lists, which is crucial for efficiently processing image data and model inputs/outputs. + - Key functions: `array()` for creating arrays, `expand_dims()` for adding dimensions to arrays. +6. **Threading:** + - Purpose: Concurrent execution of tasks. + - Why: Threading allows simultaneous frame capture, object detection, and web server operation, crucial for maintaining real-time performance. + - Key components: `Thread` class creates separate execution threads, and Lock is used for thread synchronization. +7. **io.BytesIO:** + - Purpose: In-memory binary streams. + - Why: Allows efficient handling of image data in memory without needing temporary files, improving speed and reducing I/O operations. +8. **time:** + - Purpose: Time-related functions. + - Why: Used for adding delays (`time.sleep()`) to control frame rate and for performance measurements. +9. **jQuery (client-side)**: + - Purpose: Simplified DOM manipulation and AJAX requests. + - Why: It makes it easy to update the web interface dynamically and communicate with the server without page reloads. + - Key functions: `.get()` and `.post()` for AJAX requests, DOM manipulation methods for updating the UI. + +Regarding the main app system architecture: + +1. **Main Thread**: Runs the Flask server, handling HTTP requests and serving the web interface. +2. **Camera Thread**: Continuously captures frames from the camera. +3. **Detection Thread**: Processes frames through the TFLite model for object detection. +4. **Frame Buffer**: Shared memory space (protected by locks) storing the latest frame and detection results. + +And the app data flow, we can describe in short: + +1. Camera captures frame → Frame Buffer +2. Detection thread reads from Frame Buffer → Processes through TFLite model → Updates detection results in Frame Buffer +3. Flask routes access Frame Buffer to serve the latest frame and detection results +4. Web client receives updates via AJAX and updates UI + +This architecture allows for efficient, real-time object detection while maintaining a responsive web interface running on a resource-constrained edge device like a Raspberry Pi. Threading and efficient libraries like TFLite and PIL enable the system to process video frames in real-time, while Flask and jQuery provide a user-friendly way to interact with them. + +You can test the app with another pre-processed model, such as the EfficientDet, changing the app line: + +```python +model_path = "./models/lite-model_efficientdet_lite0_detection_metadata_1.tflite" +``` + +> If we want to use the app for the SSD-MobileNetV2 model, trained on Edge Impulse Studio with the "Box versus Wheel" dataset, the code should also be adapted depending on the input details, as we have explored on its [notebook](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OBJ_DETEC/notebooks/EI-SSD-MobileNetV2.ipynb). + +## Conclusion + +This lab has explored the implementation of object detection on edge devices like the Raspberry Pi, demonstrating the power and potential of running advanced computer vision tasks on resource-constrained hardware. We've covered several vital aspects: + +1. **Model Comparison**: We examined different object detection models, including SSD-MobileNet, EfficientDet, FOMO, and YOLO, comparing their performance and trade-offs on edge devices. + +2. **Training and Deployment**: Using a custom dataset of boxes and wheels (labeled on Roboflow), we walked through the process of training models using Edge Impulse Studio and Ultralytics and deploying them on Raspberry Pi. + +3. **Optimization Techniques**: To improve inference speed on edge devices, we explored various optimization methods, such as model quantization (TFLite int8) and format conversion (e.g., to NCNN). + +4. **Real-time Applications**: The lab exemplified a real-time object detection web application, demonstrating how these models can be integrated into practical, interactive systems. + +5. **Performance Considerations**: Throughout the lab, we discussed the balance between model accuracy and inference speed, a critical consideration for edge AI applications. + +The ability to perform object detection on edge devices opens up numerous possibilities across various domains, from precision agriculture, industrial automation, and quality control to smart home applications and environmental monitoring. By processing data locally, these systems can offer reduced latency, improved privacy, and operation in environments with limited connectivity. + +Looking ahead, potential areas for further exploration include: +- Implementing multi-model pipelines for more complex tasks +- Exploring hardware acceleration options for Raspberry Pi +- Integrating object detection with other sensors for more comprehensive edge AI systems +- Developing edge-to-cloud solutions that leverage both local processing and cloud resources + +Object detection on edge devices can create intelligent, responsive systems that bring the power of AI directly into the physical world, opening up new frontiers in how we interact with and understand our environment. + +## Resources + +- [Dataset ("Box versus Wheel")](https://universe.roboflow.com/marcelo-rovai-riila/box-versus-wheel-auto-dataset) + +- [SSD-MobileNet Notebook on a Raspi](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OBJ_DETEC/notebooks/SSD_MobileNetV1.ipynb) + +- [EfficientDet Notebook on a Raspi](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OBJ_DETEC/notebooks/SSD_EfficientDet.ipynb) + +- [FOMO - EI Linux Notebook on a Raspi](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OBJ_DETEC/notebooks/EI-Linux-FOMO.ipynb) + +- [YOLOv8 Box versus Wheel Dataset Training on CoLab](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/blob/main/OBJ_DETEC/notebooks/yolov8_box_vs_wheel.ipynb) + +- [Edge Impulse Project - SSD MobileNet and FOMO ](https://studio.edgeimpulse.com/public/515477/live) + +- [Python Scripts](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/tree/main/OBJ_DETEC/python_scripts) + +- [Models](https://github.com/Mjrovai/EdgeML-with-Raspberry-Pi/tree/main/OBJ_DETEC/models) + + diff --git a/contents/labs/raspi/raspi.qmd b/contents/labs/raspi/raspi.qmd index b41ec2ba..2e6dfe95 100644 --- a/contents/labs/raspi/raspi.qmd +++ b/contents/labs/raspi/raspi.qmd @@ -6,7 +6,7 @@ These labs offer invaluable hands-on experience with machine learning systems, l ## Pre-requisites -- **Raspberry Pi**: Ensure you have at least one of the boards: the Raspberry Pi Zero 2W, Raspberry Pi 4 or 5. +- **Raspberry Pi**: Ensure you have at least one of the boards: the Raspberry Pi Zero 2W, Raspberry Pi 4 or 5 for the Vision Labs, and the Raspberry 5 for the GenAi lab. - **Power Adapter**: To Power on the boards. - Raspberry Pi Zero 2-W: 2.5W with a Micro-USB adapter - Raspberry Pi 4 or 5: 3.5W with a USB-C adapter @@ -24,6 +24,6 @@ These labs offer invaluable hands-on experience with machine learning systems, l | ------------ | --------------------- | -------------------------- | ------------------------------------------------------- | | Vision | Image Classification | Learn to classify images | [Link](./image_classification/image_classification.qmd) | | Vision | Object Detection | Implement object detection | [Link](./object_detection/object_detection.qmd) | -| LLM | Large Language Models | Deploy LLMs at the Edge | [Link](./llm/llm.qmd) | +| GenAI | Small Language Models | Deploy SLMs at the Edge | [Link](./llm/llm.qmd) | | | | | | diff --git a/contents/labs/raspi/setup/setup.qmd b/contents/labs/raspi/setup/setup.qmd index fcd3a891..4cf0a292 100644 --- a/contents/labs/raspi/setup/setup.qmd +++ b/contents/labs/raspi/setup/setup.qmd @@ -4,7 +4,7 @@ This chapter will guide you through setting up Raspberry Pi Zero 2 W (*Raspi-Zero*) and Raspberry Pi 5 (*Raspi-5*) models. We'll cover hardware setup, operating system installation, initial configuration, and tests. -> The general instructions for the *Rasp-5* also apply to the older Raspberry Pi versions, such as the Rasp-3 and Raspi-4. +> The general instructions for the *Raspi-5* also apply to the older Raspberry Pi versions, such as the Raspi-3 and Raspi-4. ## Introduction @@ -78,6 +78,8 @@ This tutorial will guide you through setting up the most common Raspberry Pi mod - **Ports**: 2 × micro HDMI ports, 2 × USB 3.0 ports, 2 × USB 2.0 ports, CSI camera port, DSI display port - **Power**: 5V DC via USB-C connector (3A) +> In the labs, we will use different names to address the Raspberry: `Raspi`, `Raspi-5`, `Raspi-Zero`, etc. Usually, `Raspi` is used when the instructions or comments apply to every model. + ## Installing the Operating System ### The Operating System (OS) @@ -127,7 +129,7 @@ Follow the steps to install the OS in your Raspi. ![img](images/png/zero-burn.png) - > Due to its reduced SDRAM (512MB), the recommended OS for the Rasp Zero is the 32-bit version. However, to run some machine learning models, such as the YOLOv8 from Ultralitics, we should use the 64-bit version. Although Raspi-Zero can run a *desktop*, we will choose the LITE version (no Desktop) to reduce the RAM needed for regular operation. + > Due to its reduced SDRAM (512MB), the recommended OS for the Raspi-Zero is the 32-bit version. However, to run some machine learning models, such as the YOLOv8 from Ultralitics, we should use the 64-bit version. Although Raspi-Zero can run a *desktop*, we will choose the LITE version (no Desktop) to reduce the RAM needed for regular operation. - For **Raspi-5**: We can select the full 64-bit version, which includes a desktop: `Raspberry Pi OS (64-bit)` @@ -141,7 +143,7 @@ Follow the steps to install the OS in your Raspi. 8. Write the image to the microSD card. -> In the examples here, we will use different hostnames: raspi, raspi-5, raspi-Zero, etc. You should replace by the one that you are using. +> In the examples here, we will use different hostnames depending on the device used: raspi, raspi-5, raspi-Zero, etc. It would help if you replaced it with the one you are using. ### Initial Configuration @@ -155,7 +157,7 @@ Follow the steps to install the OS in your Raspi. ### SSH Access -The easiest way to interact with the Rasp-Zero is via SSH ("Headless"). You can use a Terminal (MAC/Linux), [PuTTy (](https://www.putty.org/)Windows), or any other. +The easiest way to interact with the Raspi-Zero is via SSH ("Headless"). You can use a Terminal (MAC/Linux), [PuTTy (](https://www.putty.org/)Windows), or any other. 1. Find your Raspberry Pi's IP address (for example, check your router). @@ -310,7 +312,7 @@ CONF_SWAPSIZE=2000 And save the file. -Next, turn on the swapfile again and reboot the Rasp-zero: +Next, turn on the swapfile again and reboot the Raspi-zero: ```bash sudo dphys-swapfile setup @@ -324,7 +326,7 @@ When your device is rebooted (you should enter with the SSH again), you will rea ## Installing a Camera -The Raspi is an excellent device for computer vision applications; a camera is needed for it. We can install a standard USB webcam on the micro-USB port using a USB OTG adapter (Raspi-Zero and Rasp-5) or a camera module connected to the Raspi CSI (Camera Serial Interface) port. +The Raspi is an excellent device for computer vision applications; a camera is needed for it. We can install a standard USB webcam on the micro-USB port using a USB OTG adapter (Raspi-Zero and Raspi-5) or a camera module connected to the Raspi CSI (Camera Serial Interface) port. > USB Webcams generally have inferior quality to the camera modules that connect to the CSI port. They can also not be controlled using the `raspistill` and `raspivid` commands in the terminal or the `picamera` recording package in Python. Nevertheless, there may be reasons why you want to connect a USB camera to your Raspberry Pi, such as because of the benefit that it is much easier to set up multiple cameras with a single Raspberry Pi, long cables, or simply because you have such a camera on hand. @@ -564,16 +566,19 @@ While we've primarily interacted with the Raspberry Pi using terminal commands v ## Model-Specific Considerations -### Raspberry Pi Zero +### Raspberry Pi Zero (Raspi-Zero) + - Limited processing power, best for lightweight projects -- Use headless setup (SSH) to conserve resources. +- It is better to use a headless setup (SSH) to conserve resources. - Consider increasing swap space for memory-intensive tasks. +- It can be used for Image Classification and Object Detection Labs but not for the LLM (SLM). -### Raspberry Pi 4 or 5 +### Raspberry Pi 4 or 5 (Raspi-4 or Raspi-5) - Suitable for more demanding projects, including AI and machine learning. -- Can run full desktop environment smoothly. -- For Pi 5, consider using an active cooler for temperature management during intensive tasks. +- It can run the whole desktop environment smoothly. +- Raspi-4 can be used for Image Classification and Object Detection Labs but will not work well with LLMs (SLM). +- For Raspi-5, consider using an active cooler for temperature management during intensive tasks, as in the LLMs (SLMs) lab. -Remember to adjust your project requirements based on the specific Raspberry Pi model you're using. The Pi Zero is great for low-power, space-constrained projects, while the Pi 4/5 models are better suited for more computationally intensive tasks. +Remember to adjust your project requirements based on the specific Raspberry Pi model you're using. The Raspi-Zero is great for low-power, space-constrained projects, while the Raspi-4 or 5 models are better suited for more computationally intensive tasks. diff --git a/contents/learning_resources.qmd b/contents/learning_resources.qmd deleted file mode 100644 index a31324c7..00000000 --- a/contents/learning_resources.qmd +++ /dev/null @@ -1,46 +0,0 @@ -# Resources {#sec-resources} - -Embarking on your TinyML journey has never been easier with the curated resources to pave your path to expertise. There are coding platforms and communities where you can immerse yourself in hands-on TinyML projects, sharing or seeking advice on GitHub and Stack Overflow. Meanwhile, there are gateways to structured learning featuring courses that provide a comprehensive education in the field. - -While this page serves as a solid starting point, stay tuned as we continually expand our resource pool, with the aim to foster a rich learning and collaborative environment for TinyML enthusiasts of all levels. - -## Books - -Here is a list of recommended books for learning about TinyML or embedded AI: - -1. [TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers](https://www.amazon.com/TinyML-Learning-TensorFlow-Ultra-Low-Power-Microcontrollers/dp/1492052043) by Pete Warden and Daniel Situnayake - -2. [AI at the Edge: Solving Real-World Problems with Embedded Machine Learning](https://www.oreilly.com/library/view/ai-at-the/9781098120191/) by Daniel Situnayake and Jenny Plunkett - -3. [TinyML Cookbook: Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter](https://www.amazon.com/TinyML-Cookbook-artificial-intelligence-ultra-low-power/dp/180181497X) by Gian Marco Iodice - -4. [Introduction to TinyML](https://www.amazon.com/Introduction-TinyML-Rohit-Sharma/dp/B0B5Q281L9) by Rohit Sharma - -5. [Integrated camera, microphone, BLE, Wi-Fi and the most compact design](https://mjrovai.github.io/XIAO_Big_Power_Small_Board-ebook/) by Lei Feng(Seeed Studio), Marcelo Rovai - -These books cover a range of topics related to TinyML and embedded AI, including: - -- The fundamentals of machine learning and TinyML -- How to choose the right hardware and software for your project -- How to train and deploy TinyML models on embedded devices -- Real-world examples of TinyML applications - -In addition to the above books, there are a number of other resources available for learning about TinyML and embedded AI, including online courses, tutorials, and blog posts. Some of these are listed below. Another great way to learn is by joining the [community](./community.qmd) of embedded AI developers. - -## Tutorials - -## Frameworks - -1. **GitHub** Description: There are various GitHub repositories dedicated to TinyML where you can contribute or learn from existing projects. Some popular organizations/repos to check out are: - -- TensorFlow Lite Micro: [GitHub Repository](https://github.com/tensorflow/tflite-micro) -- TinyML4D: [GitHub Repository](https://tinyml.seas.harvard.edu/4D/) -- Edge Impulse Expert Network: [Repository](https://docs.edgeimpulse.com/experts/) - -2. **Stack Overflow** Tags: [tinyml](https://stackoverflow.com/questions/tagged/tinyml) Description: Use the "tinyml" tag on Stack Overflow to ask technical questions and find answers from the community. - -## Courses and Learning Platforms - -1. **Coursera** Course: [Introduction to Embedded Machine Learning](https://www.coursera.org/learn/introduction-to-embedded-machine-learning) Description: A dedicated course on Coursera to learn the basics and advances of TinyML. - -2. **EdX** Course: [Intro to TinyML](https://www.edx.org/professional-certificate/harvardx-tiny-machine-learning) Description: Learn about TinyML with this HarvardX course. diff --git a/contents/ondevice_learning/ondevice_learning.bib b/contents/ondevice_learning/ondevice_learning.bib deleted file mode 100644 index afc583f7..00000000 --- a/contents/ondevice_learning/ondevice_learning.bib +++ /dev/null @@ -1,360 +0,0 @@ -%comment{This file was created with betterbib v5.0.11.} - - -@inproceedings{abadi2016deep, - author = {Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H. 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IEEE}, - title = {A Comprehensive Survey on Transfer Learning}, - year = {2021}, - volume = {109}, - number = {1}, - pages = {43--76}, - keywords = {Transfer learning;Semisupervised learning;Data models;Covariance matrices;Machine learning;Adaptation models;Domain adaptation;interpretation;machine learning;transfer learning}, - doi = {10.1109/jproc.2020.3004555}, - source = {Crossref}, - url = {https://doi.org/10.1109/jproc.2020.3004555}, - publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, - issn = {0018-9219, 1558-2256}, - month = jan, -} diff --git a/contents/summary.qmd b/contents/summary.qmd deleted file mode 100644 index b450ab7d..00000000 --- a/contents/summary.qmd +++ /dev/null @@ -1,3 +0,0 @@ -# Summary - -In summary, this book has no content whatsoever. diff --git a/contents/tools.qmd b/contents/tools.qmd deleted file mode 100644 index 7bc88c2f..00000000 --- a/contents/tools.qmd +++ /dev/null @@ -1,46 +0,0 @@ -# Tools {.appendix} - -This is a non-exhaustive list of tools and frameworks that are available for embedded AI development. - -## Hardware Kits - -### Microcontrollers and Development Boards - -| No | Hardware | Processor | Features | TinyML Compatibility | -|:----|:------------------------------|:--------------------------------|:---------------------------------------------------------|:-------------------------------------------------| -| 1 | Arduino Nano 33 BLE Sense | ARM Cortex-M4 | Onboard sensors, Bluetooth connectivity | TensorFlow Lite Micro | -| 2 | Raspberry Pi Pico | Dual-core Arm Cortex-M0+ | Low-cost, large community support | TensorFlow Lite Micro | -| 3 | SparkFun Edge | Ambiq Apollo3 Blue | Ultra-low power consumption, onboard microphone | TensorFlow Lite Micro | -| 4 | Adafruit EdgeBadge | ATSAMD51 32-bit Cortex M4 | Compact size, integrated display and microphone | TensorFlow Lite Micro | -| 5 | Google Coral Development Board | NXP i.MX 8M SOC (quad Cortex-A53, Cortex-M4F) | Edge TPU, Wi-Fi, Bluetooth | TensorFlow Lite for Coral | -| 6 | STM32 Discovery Kits | Various (e.g., STM32F7, STM32H7) | Different configurations, Cube.AI software support | STM32Cube.AI | -| 7 | Arduino Nicla Vision | STM32H747AII6 Dual Arm Cortex M7/M4 | Integrated camera, low power, compact design | TensorFlow Lite Micro | -| 8 | Arduino Nicla Sense ME | 64 MHz Arm Cortex M4 (nRF52832) | Multi-sensor platform, environment sensing, BLE, Wi-Fi| TensorFlow Lite Micro| -| 9 | XIAO ESP32S3 Sense | Xtensa LX7 dual-core (ESP32-S3R8) | Integrated camera, microphone, BLE, Wi-Fi and the most compact design| TensorFlow Lite Micro| - -## Software Tools - -### Machine Learning Frameworks - -| No | Machine Learning Framework | Description | Use Cases | -|:----|:---------------------------|:--------------------------------------------------------------------------------|:------------------------------------------| -| 1 | TensorFlow Lite | Lightweight library for running machine learning models on constrained devices | Image recognition, voice commands, anomaly detection | -| 2 | Edge Impulse | A platform providing tools for creating machine learning models optimized for edge devices | Data collection, model training, deployment on tiny devices | -| 3 | ONNX Runtime | A performance-optimized engine for running ONNX models, fine-tuned for edge devices | Cross-platform deployment of machine learning models | - -### Libraries and APIs - -| No | Library/API | Description | Use Cases | -|:----|:-------------|:------------------------------------------------------------------------------------------------------|:------------------------------------------| -| 1 | CMSIS-NN | A collection of efficient neural network kernels optimized for Cortex-M processors | Embedded vision and AI applications | -| 2 | ARM NN | An inference engine for CPUs, GPUs, and NPUs, enabling the translation of neural network frameworks | Accelerating machine learning model inference on ARM-based devices | - -## IDEs and Development Environments - -| No | IDE/Development Environment | Description | Features | -|:----|:------------------------------|:------------------------------------------------------------------------------------|:----------------------------------------------------| -| 1 | PlatformIO | An open-source ecosystem for IoT development catering to various boards & platforms | Cross-platform build system, continuous testing, firmware updates | -| 2 | Eclipse Embedded CDT | A plugin for Eclipse facilitating embedded systems development | Supports various compilers and debuggers, integrates with popular build tools | -| 3 | Arduino IDE | Official development environment for Arduino supporting various boards & languages | User-friendly interface, large community support, extensive library collection | -| 4 | Mbed Studio | ARM's IDE for developing robust embedded software with Mbed OS | Integrated debugger, Mbed OS integration, version control support | -| 5 | Segger Embedded Studio | A powerful IDE for ARM microcontrollers supporting a wide range of development boards | Advanced code editor, project management, debugging capabilities | diff --git a/contents/zoo_datasets.qmd b/contents/zoo_datasets.qmd deleted file mode 100644 index a95caefa..00000000 --- a/contents/zoo_datasets.qmd +++ /dev/null @@ -1,35 +0,0 @@ -# Datasets {#sec-datasets} - -1. **Google Speech Commands Dataset** - - Description: A set of one-second .wav audio files, each containing a single spoken English word. - - [Link to the Dataset](https://ai.googleblog.com/2017/08/launching-speech-commands-dataset.html) - -2. **VisualWakeWords Dataset** - - Description: A dataset tailored for TinyML vision applications, consisting of binary labeled images indicating whether a person is in the image or not. - - [Link to the Dataset](https://github.com/tensorflow/models/tree/master/research/slim#preparing-the-visualwakewords-dataset) - -3. **EMNIST Dataset** - - Description: A dataset containing 28x28 pixel images of handwritten characters and digits, which is an extension of the MNIST dataset but includes letters. - - [Link to the Dataset](https://www.nist.gov/itl/products-and-services/emnist-dataset) - -4. **UCI Machine Learning Repository: Human Activity Recognition Using Smartphones** - - Description: A dataset with the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. - - [Link to the Dataset](https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones) - -5. **PlantVillage Dataset** - - Description: A dataset comprising of images of healthy and diseased crop leaves categorized based on the crop type and disease type, which could be used in a TinyML agricultural project. - - [Link to the Dataset](https://github.com/spMohanty/PlantVillage-Dataset) - -6. **Gesture Recognition using 3D Motion Sensing (3D Gesture Database)** - - Description: This dataset contains 3D gesture data recorded using a Leap Motion Controller, which might be useful for gesture recognition projects. - - [Link to the Dataset](https://lttm.dei.unipd.it/downloads/gesture/) - -7. **Multilingual Spoken Words Corpus** - - Description: A dataset containing recordings of common spoken words in various languages, useful for speech recognition projects targeting multiple languages. - - [Link to the Dataset](https://mlcommons.org/en/multilingual-spoken-words/) - -8. **Wake Vision** - - Description: A dataset containing over 6 million images for binary person classification. In addition, it includes a fine-grain benchmark suite for evaluating the fairness and robustness of models. - - [Link to the Dataset](https://wakevision.ai/) - -Remember to verify the dataset's license or terms of use to ensure it can be used for your intended purpose. diff --git a/contents/zoo_models.qmd b/contents/zoo_models.qmd deleted file mode 100644 index e57464e1..00000000 --- a/contents/zoo_models.qmd +++ /dev/null @@ -1 +0,0 @@ -## Model Zoo diff --git a/index.qmd b/index.qmd index d5e23fb4..58e3a51c 100644 --- a/index.qmd +++ b/index.qmd @@ -1,3 +1,4 @@ + # Preface {.unnumbered} Welcome to {{< var title.long >}}. This book is your gateway to the fast-paced world of AI systems. It is an extension of the course [CS249r](https://sites.google.com/g.harvard.edu/cs249-tinyml-2023) at Harvard University. @@ -23,10 +24,6 @@ The book focuses on AI systems' principles and case studies, aiming to give you To dive into this book, you don't need to be an AI expert. All you need is a basic understanding of computer science concepts and a curiosity to explore how AI systems work. This is where innovation happens, and a basic grasp of programming and data structures will be your compass. -# Book Conventions - -For details on the conventions used in this book, check out the [Conventions](./contents/conventions.qmd) section. - # Content Transparency Statement This book is a community-driven project, with content generated collaboratively by numerous contributors over time. The content creation process may have involved various editing tools, including generative AI technology. As the main author, editor, and curator, Prof. Vijay Janapa Reddi maintains human oversight and editorial oversight to make sure the content is accurate and relevant. However, no one is perfect, so inaccuracies may still exist. We highly value your feedback and encourage you to provide corrections or suggestions. This collaborative approach is crucial for enhancing and maintaining the quality of the content contained within and making high-quality information globally accessible. @@ -41,4 +38,4 @@ Do you have questions or feedback? Feel free to [e-mail Prof. Vijay Janapa Reddi # Contributors -A big thanks to everyone who's helped make this book what it is! You can see the full list of individual contributors [here](./contents/contributors.qmd) and additional GitHub style details [here](https://github.com/harvard-edge/cs249r_book/graphs/contributors). Join us as a contributor! \ No newline at end of file +A big thanks to everyone who's helped make this book what it is! You can see the full list of individual contributors [here](./contents/contributors.qmd) and additional GitHub style details [here](https://github.com/harvard-edge/cs249r_book/graphs/contributors). 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_0x58d375=_0x13e715,_0x4f7742=_0x42686b[this.#e];if(!(Array[_0x58d375(0x81)](_0x4f7742)||_0x4f7742 instanceof Int8Array))throw new Error(this.#e+_0x58d375(0x80));const _0x421f69=(await this.#r)[_0x58d375(0xa1)]([this.#t,this.#t+_0x58d375(0xa6)],_0x58d375(0x9d)),_0xd87e6=_0x421f69[_0x58d375(0x99)](this.#t),_0x896c6=_0x421f69[_0x58d375(0x99)](this.#t+_0x58d375(0xa6));try{const _0x55d24c=_0xd87e6[_0x58d375(0x92)](_0x42686b),_0x4da87c=await new Promise((_0x233fc0,_0x4004e3)=>{const _0x2ff365=_0x58d375;_0x55d24c[_0x2ff365(0x72)]=()=>_0x233fc0(_0x55d24c[_0x2ff365(0x70)]),_0x55d24c[_0x2ff365(0x69)]=()=>_0x4004e3(_0x55d24c[_0x2ff365(0xa3)]);}),_0x477ccb=this.#o[_0x58d375(0x90)](_0x4f7742);for(let _0xa674df of _0x477ccb){const _0x426a2e=await new Promise((_0x1ad6d6,_0xc037b5)=>{const _0x4ccd0f=_0x58d375,_0x3ebf9d=_0x896c6[_0x4ccd0f(0x91)](_0xa674df);_0x3ebf9d[_0x4ccd0f(0x72)]=()=>_0x1ad6d6(_0x3ebf9d['result']||[]),_0x3ebf9d['onerror']=()=>_0xc037b5(_0x3ebf9d['error']);});_0x426a2e[_0x58d375(0x6b)](_0x4da87c),await new Promise((_0x4d0b94,_0x1dc81e)=>{const _0x13d4d5=_0x58d375,_0x14e74a=_0x896c6[_0x13d4d5(0x67)](_0x426a2e,_0xa674df);_0x14e74a[_0x13d4d5(0x72)]=()=>_0x4d0b94(),_0x14e74a[_0x13d4d5(0x69)]=()=>_0x1dc81e(_0x14e74a[_0x13d4d5(0xa3)]);});}return _0x4da87c;}catch(_0x41a0f2){throw _0x41a0f2;}}async['delete'](_0xe2f884){const _0x25240d=_0x13e715;if(null==_0xe2f884)throw new Error(_0x25240d(0x78));const _0x5dcaef=(await this.#r)['transaction']([this.#t,this.#t+_0x25240d(0xa6)],'readwrite'),_0x267c20=_0x5dcaef[_0x25240d(0x99)](this.#t),_0x190179=_0x5dcaef[_0x25240d(0x99)](this.#t+'_hashIndex'),_0x28fdb9=await new Promise((_0x152e59,_0x3d78a8)=>{const _0x5b0058=_0x25240d,_0x186483=_0x267c20[_0x5b0058(0x91)](_0xe2f884);_0x186483['onsuccess']=()=>_0x152e59(_0x186483['result']),_0x186483[_0x5b0058(0x69)]=()=>_0x3d78a8(_0x186483[_0x5b0058(0xa3)]);});if(!_0x28fdb9)throw new Error(_0x25240d(0x95));const _0x10a7cb=_0x28fdb9[this.#e],_0x480c8e=this.#o['hashVector'](_0x10a7cb);return await Promise[_0x25240d(0xa8)](_0x480c8e['map'](_0x5d899f=>this['removeFromBucket'](_0x190179,_0xe2f884,_0x5d899f))),new Promise((_0x363091,_0x3fdafb)=>{const _0x33ace8=_0x25240d,_0x279af2=_0x267c20[_0x33ace8(0xa2)](_0xe2f884);_0x279af2[_0x33ace8(0x72)]=()=>_0x363091(),_0x279af2[_0x33ace8(0x69)]=()=>_0x3fdafb(_0x279af2[_0x33ace8(0xa3)]);});}async['removeFromBucket'](_0x364d0d,_0x403365,_0x5b6112){const _0x4c243f=_0x13e715,_0x2d8aed=await new Promise((_0xab5d9b,_0x4c1643)=>{const _0x242f75=a3_0x40a4,_0x1c536d=_0x364d0d[_0x242f75(0x91)](_0x5b6112);_0x1c536d[_0x242f75(0x72)]=()=>_0xab5d9b(_0x1c536d[_0x242f75(0x70)]||[]),_0x1c536d[_0x242f75(0x69)]=()=>_0x4c1643(_0x1c536d[_0x242f75(0xa3)]);}),_0x24c229=_0x2d8aed['indexOf'](_0x403365);-0x1!==_0x24c229&&(_0x2d8aed[_0x4c243f(0x9a)](_0x24c229,0x1),await new Promise((_0x3be8ce,_0x232a50)=>{const _0x1ae345=_0x4c243f,_0xf82c84=_0x364d0d[_0x1ae345(0x67)](_0x2d8aed,_0x5b6112);_0xf82c84[_0x1ae345(0x72)]=()=>_0x3be8ce(),_0xf82c84[_0x1ae345(0x69)]=()=>_0x232a50(_0xf82c84[_0x1ae345(0xa3)]);}));}async[_0x13e715(0x77)](_0x36f293,_0x1c0072){const _0x158ba6=_0x13e715;if(null==_0x36f293)throw new Error(_0x158ba6(0xad));if(!(this.#e in _0x1c0072))throw new Error(this.#e+_0x158ba6(0x86));if(!(Array[_0x158ba6(0x81)](_0x1c0072[this.#e])||_0x1c0072[this.#e]instanceof Int8Array))throw new Error(this.#e+'\x20on\x20\x27object\x27\x20is\x20expected\x20to\x20be\x20an\x20Array\x20or\x20Int8Array');const _0x2ee32f=(await this.#r)[_0x158ba6(0xa1)]([this.#t,this.#t+_0x158ba6(0xa6)],_0x158ba6(0x9d)),_0x4aa076=_0x2ee32f[_0x158ba6(0x99)](this.#t),_0x3d438a=_0x2ee32f[_0x158ba6(0x99)](this.#t+_0x158ba6(0xa6)),_0x213e50=_0x4aa076[_0x158ba6(0x91)](_0x36f293);return new Promise((_0x5721ee,_0x478554)=>{const _0x44a371=_0x158ba6;_0x213e50[_0x44a371(0x72)]=async()=>{const _0x242b18=_0x44a371,_0x4e35fe=_0x213e50[_0x242b18(0x70)];if(!_0x4e35fe)return void _0x478554(new Error('Object\x20not\x20found\x20with\x20the\x20provided\x20key'));const _0x2f99ac=this.#o['hashVector'](_0x4e35fe[this.#e]),_0x3f8a1e=this.#o[_0x242b18(0x90)](_0x1c0072[this.#e]),_0x5e8e0b=_0x4aa076[_0x242b18(0x67)](_0x1c0072,_0x36f293);_0x5e8e0b[_0x242b18(0x72)]=async()=>{const _0x2f59f0=_0x242b18;try{for(let _0x494bba=0x0;_0x494bba_0x478554(_0x5e8e0b['error']);},_0x213e50[_0x44a371(0x69)]=()=>_0x478554(_0x213e50[_0x44a371(0xa3)]);});}async[_0x13e715(0x9c)](_0x2571f7,_0x16836b,_0x5c688c,_0x180597){const 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this.#r)['transaction']([this.#t,this.#t+_0x532220(0xa6)],_0x532220(0x6f)),_0x553a2b=_0x4f3ece[_0x532220(0x99)](this.#t),_0x1e4afb=_0x4f3ece[_0x532220(0x99)](this.#t+_0x532220(0xa6)),_0x3a2799=this.#o[_0x532220(0x90)](_0x49c59f);for(let _0x60df1 of _0x3a2799){const _0x312e6e=await new Promise((_0x19695c,_0x4232bf)=>{const _0x5855e7=_0x532220,_0x235732=_0x1e4afb[_0x5855e7(0x91)](_0x60df1);_0x235732['onsuccess']=()=>_0x19695c(_0x235732[_0x5855e7(0x70)]||[]),_0x235732[_0x5855e7(0x69)]=()=>_0x4232bf(_0x235732[_0x5855e7(0xa3)]);});for(let _0x3ad19d of _0x312e6e)if(!_0x12c580['has'](_0x3ad19d)){_0x12c580[_0x532220(0x92)](_0x3ad19d);const _0x1420d4=await new Promise((_0x334cb3,_0x4e9d67)=>{const 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_0xf86f68[_0x670409(0x506f)](0x0,_0x3c812e),Object[_0x670409(0x11e8)](_0xf86f68,_0x49ddac);}}class _0x1e79c3{constructor(){const _0x5f74fb=_0x1bed03;this['rules']=[],this[_0x5f74fb(0x41a8)]=[],this[_0x5f74fb(0x4d47)]=0x0,this[_0x5f74fb(0x1dc3)]=0x0,this[_0x5f74fb(0x1b93)]=0x0;}[_0x1bed03(0x3c9b)](_0xa3c6af){const _0x29d56c=_0x1bed03;if(this[_0x29d56c(0x41a8)][_0xa3c6af])return this['multiRegexes'][_0xa3c6af];const _0x396796=new _0x5502d4();return this['rules'][_0x29d56c(0x428e)](_0xa3c6af)[_0x29d56c(0x4854)](([_0x2d2567,_0x460443])=>_0x396796[_0x29d56c(0xeeb)](_0x2d2567,_0x460443)),_0x396796['compile'](),this[_0x29d56c(0x41a8)][_0xa3c6af]=_0x396796,_0x396796;}[_0x1bed03(0x46fa)](){return 0x0!==this['regexIndex'];}[_0x1bed03(0x1627)](){this['regexIndex']=0x0;}['addRule'](_0x476024,_0x561e37){const _0x344e13=_0x1bed03;this[_0x344e13(0x28bb)][_0x344e13(0x4131)]([_0x476024,_0x561e37]),_0x344e13(0x1706)===_0x561e37[_0x344e13(0x70a)]&&this[_0x344e13(0x4d47)]++;}[_0x1bed03(0x3bbd)](_0x47eeb8){const _0x29071f=_0x1bed03,_0x10ce77=this[_0x29071f(0x3c9b)](this['regexIndex']);_0x10ce77[_0x29071f(0x1dc3)]=this[_0x29071f(0x1dc3)];let _0x350367=_0x10ce77[_0x29071f(0x3bbd)](_0x47eeb8);if(this[_0x29071f(0x46fa)]()){if(_0x350367&&_0x350367['index']===this[_0x29071f(0x1dc3)]);else{const _0x2b6e76=this[_0x29071f(0x3c9b)](0x0);_0x2b6e76['lastIndex']=this['lastIndex']+0x1,_0x350367=_0x2b6e76['exec'](_0x47eeb8);}}return _0x350367&&(this['regexIndex']+=_0x350367[_0x29071f(0x3a28)]+0x1,this[_0x29071f(0x1b93)]===this[_0x29071f(0x4d47)]&&this['considerAll']()),_0x350367;}}if(_0x51e5dd['compilerExtensions']||(_0x51e5dd[_0x1bed03(0x3344)]=[]),_0x51e5dd[_0x1bed03(0x160d)]&&_0x51e5dd[_0x1bed03(0x160d)][_0x1bed03(0x1fa2)](_0x1bed03(0x1001)))throw new Error(_0x1bed03(0x1b82));return _0x51e5dd[_0x1bed03(0x301d)]=_0x54435a(_0x51e5dd['classNameAliases']||{}),function _0x256349(_0xeb3ea7,_0x2d3c1d){const _0x3da18b=_0x1bed03,_0x90042c=_0xeb3ea7;if(_0xeb3ea7[_0x3da18b(0x1c32)])return _0x90042c;[_0x581aaf,_0x3bf03c,_0x43df01,_0x3acef8][_0x3da18b(0x4854)](_0x5473bb=>_0x5473bb(_0xeb3ea7,_0x2d3c1d)),_0x51e5dd[_0x3da18b(0x3344)][_0x3da18b(0x4854)](_0x959935=>_0x959935(_0xeb3ea7,_0x2d3c1d)),_0xeb3ea7['__beforeBegin']=null,[_0x5945bf,_0x3e564b,_0x1427d6][_0x3da18b(0x4854)](_0x427181=>_0x427181(_0xeb3ea7,_0x2d3c1d)),_0xeb3ea7[_0x3da18b(0x1c32)]=!0x0;let _0x4e4977=null;return'object'==typeof _0xeb3ea7[_0x3da18b(0x3a6f)]&&_0xeb3ea7['keywords'][_0x3da18b(0x5001)]&&(_0xeb3ea7['keywords']=Object[_0x3da18b(0x11e8)]({},_0xeb3ea7[_0x3da18b(0x3a6f)]),_0x4e4977=_0xeb3ea7['keywords'][_0x3da18b(0x5001)],delete _0xeb3ea7[_0x3da18b(0x3a6f)][_0x3da18b(0x5001)]),_0x4e4977=_0x4e4977||/\w+/,_0xeb3ea7[_0x3da18b(0x3a6f)]&&(_0xeb3ea7[_0x3da18b(0x3a6f)]=_0x3be62a(_0xeb3ea7[_0x3da18b(0x3a6f)],_0x51e5dd[_0x3da18b(0x340)])),_0x90042c['keywordPatternRe']=_0x22923b(_0x4e4977,!0x0),_0x2d3c1d&&(_0xeb3ea7['begin']||(_0xeb3ea7['begin']=/\B|\b/),_0x90042c[_0x3da18b(0x34bb)]=_0x22923b(_0x90042c['begin']),_0xeb3ea7['end']||_0xeb3ea7[_0x3da18b(0x454c)]||(_0xeb3ea7[_0x3da18b(0x721)]=/\B|\b/),_0xeb3ea7[_0x3da18b(0x721)]&&(_0x90042c['endRe']=_0x22923b(_0x90042c[_0x3da18b(0x721)])),_0x90042c[_0x3da18b(0x1df5)]=_0x4d27c6(_0x90042c['end'])||'',_0xeb3ea7[_0x3da18b(0x454c)]&&_0x2d3c1d[_0x3da18b(0x1df5)]&&(_0x90042c['terminatorEnd']+=(_0xeb3ea7[_0x3da18b(0x721)]?'|':'')+_0x2d3c1d[_0x3da18b(0x1df5)])),_0xeb3ea7['illegal']&&(_0x90042c[_0x3da18b(0x3872)]=_0x22923b(_0xeb3ea7[_0x3da18b(0x1111)])),_0xeb3ea7['contains']||(_0xeb3ea7[_0x3da18b(0x160d)]=[]),_0xeb3ea7[_0x3da18b(0x160d)]=[][_0x3da18b(0x41bf)](..._0xeb3ea7['contains'][_0x3da18b(0x2c94)](function(_0x54f64d){return function(_0x3b5332){const _0x12dcb9=a0_0x329b;_0x3b5332[_0x12dcb9(0x2d95)]&&!_0x3b5332[_0x12dcb9(0x20c1)]&&(_0x3b5332[_0x12dcb9(0x20c1)]=_0x3b5332[_0x12dcb9(0x2d95)][_0x12dcb9(0x2c94)](function(_0x35e34c){return _0x54435a(_0x3b5332,{'variants':null},_0x35e34c);}));if(_0x3b5332[_0x12dcb9(0x20c1)])return _0x3b5332[_0x12dcb9(0x20c1)];if(_0x14b2fa(_0x3b5332))return _0x54435a(_0x3b5332,{'starts':_0x3b5332[_0x12dcb9(0x3399)]?_0x54435a(_0x3b5332['starts']):null});if(Object[_0x12dcb9(0x31eb)](_0x3b5332))return _0x54435a(_0x3b5332);return _0x3b5332;}('self'===_0x54f64d?_0xeb3ea7:_0x54f64d);})),_0xeb3ea7[_0x3da18b(0x160d)][_0x3da18b(0x4854)](function(_0x509354){_0x256349(_0x509354,_0x90042c);}),_0xeb3ea7['starts']&&_0x256349(_0xeb3ea7[_0x3da18b(0x3399)],_0x2d3c1d),_0x90042c[_0x3da18b(0x2102)]=function(_0x217085){const _0x315793=_0x3da18b,_0x2b6d76=new _0x1e79c3();return _0x217085[_0x315793(0x160d)]['forEach'](_0xe55896=>_0x2b6d76[_0x315793(0xeeb)](_0xe55896[_0x315793(0x1706)],{'rule':_0xe55896,'type':'begin'})),_0x217085[_0x315793(0x1df5)]&&_0x2b6d76[_0x315793(0xeeb)](_0x217085[_0x315793(0x1df5)],{'type':_0x315793(0x721)}),_0x217085[_0x315793(0x1111)]&&_0x2b6d76[_0x315793(0xeeb)](_0x217085['illegal'],{'type':'illegal'}),_0x2b6d76;}(_0x90042c),_0x90042c;}(_0x51e5dd);}function _0x14b2fa(_0x2c97ba){const _0x5e93c1=_0x462745;return!!_0x2c97ba&&(_0x2c97ba['endsWithParent']||_0x14b2fa(_0x2c97ba[_0x5e93c1(0x3399)]));}class _0x2af65c extends Error{constructor(_0x22ea03,_0x46d718){const _0x2d51e2=_0x462745;super(_0x22ea03),this['name']=_0x2d51e2(0x41d1),this[_0x2d51e2(0x1978)]=_0x46d718;}}const _0x5aaa0d=_0x41b56e,_0x455a4b=_0x54435a,_0x3a1295=Symbol(_0x462745(0xf09)),_0x5e364c=function(_0x273db0){const _0x5eb70e=_0x462745,_0x1326e6=Object[_0x5eb70e(0x2925)](null),_0x43a43e=Object[_0x5eb70e(0x2925)](null),_0x319ceb=[];let _0x3e5462=!0x0;const _0x17f499=_0x5eb70e(0x15a5),_0x538ebf={'disableAutodetect':!0x0,'name':_0x5eb70e(0x1056),'contains':[]};let _0x30b306={'ignoreUnescapedHTML':!0x1,'throwUnescapedHTML':!0x1,'noHighlightRe':/^(no-?highlight)$/i,'languageDetectRe':/\blang(?:uage)?-([\w-]+)\b/i,'classPrefix':_0x5eb70e(0x16c),'cssSelector':'pre\x20code','languages':null,'__emitter':_0x230d3b};function _0xf2ae1e(_0xb6990d){const _0x36366a=_0x5eb70e;return _0x30b306[_0x36366a(0x472e)][_0x36366a(0x34c)](_0xb6990d);}function _0x5b460f(_0x1815ec,_0x15c575,_0x2e2885){const _0x278563=_0x5eb70e;let _0x54eed7='',_0xaf438='';_0x278563(0x2b58)==typeof _0x15c575?(_0x54eed7=_0x1815ec,_0x2e2885=_0x15c575[_0x278563(0x2a4)],_0xaf438=_0x15c575['language']):(_0x1aae4f(_0x278563(0x15ad),'highlight(lang,\x20code,\x20...args)\x20has\x20been\x20deprecated.'),_0x1aae4f('10.7.0',_0x278563(0x2208)),_0xaf438=_0x1815ec,_0x54eed7=_0x15c575),void 0x0===_0x2e2885&&(_0x2e2885=!0x0);const _0x1937f8={'code':_0x54eed7,'language':_0xaf438};_0x3b147c('before:highlight',_0x1937f8);const _0x17dce4=_0x1937f8['result']?_0x1937f8[_0x278563(0x209a)]:_0x31d193(_0x1937f8[_0x278563(0x2be5)],_0x1937f8['code'],_0x2e2885);return _0x17dce4[_0x278563(0xbd0)]=_0x1937f8[_0x278563(0xbd0)],_0x3b147c('after:highlight',_0x17dce4),_0x17dce4;}function _0x31d193(_0xafef5,_0x5d9819,_0x11cea9,_0x505c38){const _0x49f31b=_0x5eb70e,_0x4c1afc=Object['create'](null);function _0x32b2e6(){const _0xcd87bd=a0_0x329b;if(!_0x1ca551[_0xcd87bd(0x3a6f)])return void _0x574628[_0xcd87bd(0x1cc1)](_0x106463);let _0x3bf6e4=0x0;_0x1ca551[_0xcd87bd(0x7b6)][_0xcd87bd(0x1dc3)]=0x0;let _0x29f07c=_0x1ca551[_0xcd87bd(0x7b6)][_0xcd87bd(0x3bbd)](_0x106463),_0x15b2ee='';for(;_0x29f07c;){_0x15b2ee+=_0x106463[_0xcd87bd(0x3934)](_0x3bf6e4,_0x29f07c[_0xcd87bd(0x1cf3)]);const _0x4476dc=_0x3644c4[_0xcd87bd(0x340)]?_0x29f07c[0x0]['toLowerCase']():_0x29f07c[0x0],_0x15e6e8=(_0x3635c8=_0x4476dc,_0x1ca551[_0xcd87bd(0x3a6f)][_0x3635c8]);if(_0x15e6e8){const [_0x169339,_0x55b8d0]=_0x15e6e8;if(_0x574628[_0xcd87bd(0x1cc1)](_0x15b2ee),_0x15b2ee='',_0x4c1afc[_0x4476dc]=(_0x4c1afc[_0x4476dc]||0x0)+0x1,_0x4c1afc[_0x4476dc]<=0x7&&(_0x228100+=_0x55b8d0),_0x169339[_0xcd87bd(0xe6e)]('_'))_0x15b2ee+=_0x29f07c[0x0];else{const _0x15ce66=_0x3644c4[_0xcd87bd(0x301d)][_0x169339]||_0x169339;_0x20b9a0(_0x29f07c[0x0],_0x15ce66);}}else _0x15b2ee+=_0x29f07c[0x0];_0x3bf6e4=_0x1ca551[_0xcd87bd(0x7b6)][_0xcd87bd(0x1dc3)],_0x29f07c=_0x1ca551[_0xcd87bd(0x7b6)][_0xcd87bd(0x3bbd)](_0x106463);}var _0x3635c8;_0x15b2ee+=_0x106463[_0xcd87bd(0x3934)](_0x3bf6e4),_0x574628['addText'](_0x15b2ee);}function _0xb53dc(){const _0x5fd70f=a0_0x329b;null!=_0x1ca551[_0x5fd70f(0x4a4e)]?(function(){const _0x34a7b4=_0x5fd70f;if(''===_0x106463)return;let _0x38251b=null;if(_0x34a7b4(0x4f7c)==typeof _0x1ca551[_0x34a7b4(0x4a4e)]){if(!_0x1326e6[_0x1ca551[_0x34a7b4(0x4a4e)]])return void _0x574628[_0x34a7b4(0x1cc1)](_0x106463);_0x38251b=_0x31d193(_0x1ca551[_0x34a7b4(0x4a4e)],_0x106463,!0x0,_0xcb889b[_0x1ca551[_0x34a7b4(0x4a4e)]]),_0xcb889b[_0x1ca551[_0x34a7b4(0x4a4e)]]=_0x38251b['_top'];}else _0x38251b=_0xf1e362(_0x106463,_0x1ca551[_0x34a7b4(0x4a4e)][_0x34a7b4(0x205b)]?_0x1ca551[_0x34a7b4(0x4a4e)]:null);_0x1ca551[_0x34a7b4(0x3531)]>0x0&&(_0x228100+=_0x38251b[_0x34a7b4(0x3531)]),_0x574628[_0x34a7b4(0x1f7e)](_0x38251b[_0x34a7b4(0x122b)],_0x38251b[_0x34a7b4(0x2be5)]);}()):_0x32b2e6(),_0x106463='';}function _0x20b9a0(_0xd35a93,_0x51f1f6){const _0x4f8244=a0_0x329b;''!==_0xd35a93&&(_0x574628[_0x4f8244(0x2f20)](_0x51f1f6),_0x574628[_0x4f8244(0x1cc1)](_0xd35a93),_0x574628[_0x4f8244(0x18f5)]());}function _0x46bbb2(_0xbe21dc,_0x1f1e1e){const _0x3c44c6=a0_0x329b;let _0x4d1f30=0x1;const _0x3cf815=_0x1f1e1e[_0x3c44c6(0x205b)]-0x1;for(;_0x4d1f30<=_0x3cf815;){if(!_0xbe21dc[_0x3c44c6(0x2b24)][_0x4d1f30]){_0x4d1f30++;continue;}const _0x15b04f=_0x3644c4[_0x3c44c6(0x301d)][_0xbe21dc[_0x4d1f30]]||_0xbe21dc[_0x4d1f30],_0x305604=_0x1f1e1e[_0x4d1f30];_0x15b04f?_0x20b9a0(_0x305604,_0x15b04f):(_0x106463=_0x305604,_0x32b2e6(),_0x106463=''),_0x4d1f30++;}}function _0x58cae5(_0x4021ad,_0x1b6865){const _0x272139=a0_0x329b;return _0x4021ad[_0x272139(0x2939)]&&_0x272139(0x4f7c)==typeof _0x4021ad[_0x272139(0x2939)]&&_0x574628[_0x272139(0x33ea)](_0x3644c4['classNameAliases'][_0x4021ad[_0x272139(0x2939)]]||_0x4021ad['scope']),_0x4021ad[_0x272139(0x311c)]&&(_0x4021ad[_0x272139(0x311c)]['_wrap']?(_0x20b9a0(_0x106463,_0x3644c4[_0x272139(0x301d)][_0x4021ad[_0x272139(0x311c)][_0x272139(0x146e)]]||_0x4021ad[_0x272139(0x311c)][_0x272139(0x146e)]),_0x106463=''):_0x4021ad['beginScope'][_0x272139(0x3838)]&&(_0x46bbb2(_0x4021ad[_0x272139(0x311c)],_0x1b6865),_0x106463='')),_0x1ca551=Object[_0x272139(0x2925)](_0x4021ad,{'parent':{'value':_0x1ca551}}),_0x1ca551;}function _0x3e5029(_0x5d280e,_0x1d0c63,_0x962885){const _0x4f7609=a0_0x329b;let _0x45add2=function(_0x421fc7,_0x443c02){const _0x258fc2=a0_0x329b,_0x586b45=_0x421fc7&&_0x421fc7[_0x258fc2(0x3bbd)](_0x443c02);return _0x586b45&&0x0===_0x586b45[_0x258fc2(0x1cf3)];}(_0x5d280e[_0x4f7609(0x16c5)],_0x962885);if(_0x45add2){if(_0x5d280e[_0x4f7609(0x1350)]){const _0x4a7849=new _0x18443c(_0x5d280e);_0x5d280e[_0x4f7609(0x1350)](_0x1d0c63,_0x4a7849),_0x4a7849[_0x4f7609(0x153e)]&&(_0x45add2=!0x1);}if(_0x45add2){for(;_0x5d280e[_0x4f7609(0xd00)]&&_0x5d280e['parent'];)_0x5d280e=_0x5d280e[_0x4f7609(0x2667)];return _0x5d280e;}}if(_0x5d280e['endsWithParent'])return _0x3e5029(_0x5d280e['parent'],_0x1d0c63,_0x962885);}function _0x449cc1(_0x3527df){const _0x35f4ef=a0_0x329b;return 0x0===_0x1ca551[_0x35f4ef(0x2102)][_0x35f4ef(0x1b93)]?(_0x106463+=_0x3527df[0x0],0x1):(_0x13fdbd=!0x0,0x0);}function _0x439026(_0x2b5ff7){const _0x6b28fe=a0_0x329b,_0xd46b2f=_0x2b5ff7[0x0],_0x2fd763=_0x5d9819[_0x6b28fe(0x3934)](_0x2b5ff7[_0x6b28fe(0x1cf3)]),_0x52a4fb=_0x3e5029(_0x1ca551,_0x2b5ff7,_0x2fd763);if(!_0x52a4fb)return _0x3a1295;const _0x2967b2=_0x1ca551;_0x1ca551[_0x6b28fe(0x18f5)]&&_0x1ca551['endScope'][_0x6b28fe(0x146e)]?(_0xb53dc(),_0x20b9a0(_0xd46b2f,_0x1ca551['endScope'][_0x6b28fe(0x146e)])):_0x1ca551[_0x6b28fe(0x18f5)]&&_0x1ca551['endScope'][_0x6b28fe(0x3838)]?(_0xb53dc(),_0x46bbb2(_0x1ca551['endScope'],_0x2b5ff7)):_0x2967b2[_0x6b28fe(0x1ea7)]?_0x106463+=_0xd46b2f:(_0x2967b2[_0x6b28fe(0x4cb1)]||_0x2967b2[_0x6b28fe(0x318b)]||(_0x106463+=_0xd46b2f),_0xb53dc(),_0x2967b2[_0x6b28fe(0x318b)]&&(_0x106463=_0xd46b2f));do{_0x1ca551['scope']&&_0x574628[_0x6b28fe(0x2adf)](),_0x1ca551[_0x6b28fe(0x1ea7)]||_0x1ca551['subLanguage']||(_0x228100+=_0x1ca551[_0x6b28fe(0x3531)]),_0x1ca551=_0x1ca551[_0x6b28fe(0x2667)];}while(_0x1ca551!==_0x52a4fb[_0x6b28fe(0x2667)]);return _0x52a4fb[_0x6b28fe(0x3399)]&&_0x58cae5(_0x52a4fb[_0x6b28fe(0x3399)],_0x2b5ff7),_0x2967b2[_0x6b28fe(0x4cb1)]?0x0:_0xd46b2f['length'];}let _0x11ff68={};function _0x2f55df(_0x11a7dd,_0xa885c2){const 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_0x29a229=_0x1e6d8b[_0x2e291f(0x160d)][_0x2e291f(0x41bf)]([_0x428f03[_0x2e291f(0x1bbc)],_0x428f03[_0x2e291f(0x4d7)]]);return{'name':_0x2e291f(0x4f5a),'case_insensitive':!0x0,'keywords':_0x587aa8,'contains':[_0x428f03[_0x2e291f(0x36ec)],_0x428f03['QUOTE_STRING_MODE'],_0xe88ab2,_0x428f03[_0x2e291f(0x4d7)],_0x428f03['C_BLOCK_COMMENT_MODE'],{'className':_0x2e291f(0x3148),'begin':_0x2e291f(0x2624)},_0x307730,{'begin':/[{,]\s*/,'relevance':0x0,'contains':[{'begin':_0x375a95+_0x2e291f(0x2b59),'returnBegin':!0x0,'relevance':0x0,'contains':[{'className':'attr','begin':_0x375a95,'relevance':0x0}]}]},{'begin':'('+_0x428f03[_0x2e291f(0x5a3)]+_0x2e291f(0x3c68),'keywords':_0x2e291f(0xf06),'contains':[_0x428f03[_0x2e291f(0x4d7)],_0x428f03[_0x2e291f(0x1bbc)],_0x428f03[_0x2e291f(0xdfc)],{'className':'function','begin':_0x2e291f(0x176b)+_0x375a95+_0x2e291f(0x4a73),'returnBegin':!0x0,'end':_0x2e291f(0x1c88),'contains':[{'className':_0x2e291f(0x4f24),'variants':[{'begin':_0x375a95},{'begin':/\(\s*\)/},{'begin':/\(/,'end':/\)/,'excludeBegin':!0x0,'excludeEnd':!0x0,'keywords':_0x587aa8,'contains':_0x29a229}]}]}],'relevance':0x0},{'beginKeywords':_0x2e291f(0x3dfb),'end':/\{/,'excludeEnd':!0x0,'contains':[_0x428f03[_0x2e291f(0x4f2)](_0x428f03['TITLE_MODE'],{'className':_0x2e291f(0x326a),'begin':_0x375a95}),{'className':_0x2e291f(0x4f24),'begin':/\(/,'end':/\)/,'excludeBegin':!0x0,'excludeEnd':!0x0,'contains':_0x29a229}],'illegal':/\[|%/},{'begin':/\$[(.]/}],'illegal':/#(?!!)/};};},0x134f:_0x562c45=>{const _0xbcb99c=a0_0x329b;_0x562c45[_0xbcb99c(0x15de)]=function(_0x5c6981){const _0xb844ee=_0xbcb99c,_0x34614b={'type':[_0xb844ee(0x1206),_0xb844ee(0x18d4),_0xb844ee(0x2013),_0xb844ee(0x2f8c)],'built_in':[_0xb844ee(0x179e),_0xb844ee(0x1f15),_0xb844ee(0x4f8),_0xb844ee(0x1f57),_0xb844ee(0x17e8),'LiquidCrystal','RobotControl','GSMVoiceCall',_0xb844ee(0x4531),_0xb844ee(0x110b),'HttpClient',_0xb844ee(0xa01),_0xb844ee(0x2f74),_0xb844ee(0x2304),_0xb844ee(0x3e54),'Scheduler',_0xb844ee(0x212d),'YunClient','YunServer',_0xb844ee(0x3e59),'GSMClient',_0xb844ee(0x39d1),_0xb844ee(0x28f7),'Ethernet',_0xb844ee(0x1478),_0xb844ee(0x5069),_0xb844ee(0x487a),_0xb844ee(0x48e8),_0xb844ee(0x23ac),_0xb844ee(0x3440),_0xb844ee(0x4b3d),'Mailbox',_0xb844ee(0x3a7e),_0xb844ee(0x2fce),_0xb844ee(0x28af),_0xb844ee(0x2cd9),_0xb844ee(0x4be3),_0xb844ee(0x4c58),'FileIO','Bridge',_0xb844ee(0x451b),_0xb844ee(0x31aa),_0xb844ee(0x3bd8),_0xb844ee(0x401),_0xb844ee(0x3fcb),_0xb844ee(0x3b11),_0xb844ee(0xafb),_0xb844ee(0x1b54),_0xb844ee(0xa1b),_0xb844ee(0x4cac),_0xb844ee(0x2e90),'TFT','GSM',_0xb844ee(0x4603),'SD'],'_hints':['setup',_0xb844ee(0x2ca7),_0xb844ee(0x35b),_0xb844ee(0x4d1c),_0xb844ee(0x3b88),_0xb844ee(0x3d33),_0xb844ee(0x174b),_0xb844ee(0x3601),'noListenOnLocalhost',_0xb844ee(0x17ba),'setFirmwareVersion',_0xb844ee(0x273c),_0xb844ee(0x251c),'getVoiceCallStatus',_0xb844ee(0xdcc),_0xb844ee(0x12c7),_0xb844ee(0xaf0),'beginTransmission',_0xb844ee(0x802),_0xb844ee(0x15c6),_0xb844ee(0x4a85),_0xb844ee(0x3d99),_0xb844ee(0x1df8),_0xb844ee(0x366c),_0xb844ee(0x3ae7),_0xb844ee(0x1b7d),_0xb844ee(0x2431),_0xb844ee(0xe6c),_0xb844ee(0x554),_0xb844ee(0x1572),_0xb844ee(0x2d33),_0xb844ee(0x4035),'setClockDivider',_0xb844ee(0x23f5),'endTransmission',_0xb844ee(0x4dd7),_0xb844ee(0x3db9),_0xb844ee(0x1fd0),_0xb844ee(0x25e9),_0xb844ee(0x10ba),_0xb844ee(0x412e),'robotNameWrite',_0xb844ee(0x4d4d),_0xb844ee(0x17cd),_0xb844ee(0x4ba7),_0xb844ee(0xe3f),_0xb844ee(0x3dec),_0xb844ee(0x205a),'mouseReleased',_0xb844ee(0x40fa),_0xb844ee(0x223a),_0xb844ee(0x3dc9),_0xb844ee(0x123d),_0xb844ee(0x706),_0xb844ee(0x3e49),_0xb844ee(0x1e36),_0xb844ee(0x421c),_0xb844ee(0x42f4),_0xb844ee(0x3cef),_0xb844ee(0x7b0),_0xb844ee(0x74d),'getModifiers',_0xb844ee(0x359b),_0xb844ee(0x4383),'waitContinue','processInput',_0xb844ee(0x4ef4),_0xb844ee(0x3d17),_0xb844ee(0x33a),_0xb844ee(0x40c8),_0xb844ee(0x4c8e),_0xb844ee(0x73),_0xb844ee(0x14e2),_0xb844ee(0x1120),_0xb844ee(0x3309),_0xb844ee(0x3e0f),_0xb844ee(0x4d71),'beginPacket',_0xb844ee(0x1fed),_0xb844ee(0x3b14),_0xb844ee(0x2299),_0xb844ee(0x1d52),_0xb844ee(0x25e3),_0xb844ee(0x2b5e),'rightToLeft','setTextSize','leftToRight',_0xb844ee(0x14cc),_0xb844ee(0xb02),_0xb844ee(0x4919),_0xb844ee(0x38e0),'interrupts',_0xb844ee(0x4529),_0xb844ee(0x2b85),_0xb844ee(0x25d7),_0xb844ee(0x148d),_0xb844ee(0xda0),'getPINUsed',_0xb844ee(0x1d3f),_0xb844ee(0x31d6),_0xb844ee(0xf0a),_0xb844ee(0x4edc),'analogRead',_0xb844ee(0x21a1),_0xb844ee(0x2394),_0xb844ee(0x3605),'keyPressed',_0xb844ee(0x3dc5),'readButton','subnetMask',_0xb844ee(0x308b),_0xb844ee(0x2ce0),'writeGreen',_0xb844ee(0x2b3f),_0xb844ee(0x2fb),_0xb844ee(0x1695),_0xb844ee(0x10fa),_0xb844ee(0x9af),_0xb844ee(0x1870),_0xb844ee(0x9e4),_0xb844ee(0x2d5),'getXChange',_0xb844ee(0x1bc7),_0xb844ee(0x7ed),_0xb844ee(0x121d),'voiceCall','endPacket',_0xb844ee(0x39c3),_0xb844ee(0x10d7),'writeJSON',_0xb844ee(0x1c2c),'available',_0xb844ee(0x3454),'findUntil','readBytes',_0xb844ee(0x4f69),'readGreen','writeBlue',_0xb844ee(0xdd1),_0xb844ee(0x3e59),_0xb844ee(0x26d4),_0xb844ee(0x2cd4),_0xb844ee(0x12a4),'gatewayIP',_0xb844ee(0x22a7),'getOemKey',_0xb844ee(0x725),_0xb844ee(0x6ac),'loadImage',_0xb844ee(0x4ede),'onRequest',_0xb844ee(0x4433),'changePIN','playFile',_0xb844ee(0x3fc8),'parseInt',_0xb844ee(0x1962),_0xb844ee(0x47a8),_0xb844ee(0x3574),_0xb844ee(0x1f4),_0xb844ee(0x3a80),'updateIR',_0xb844ee(0x22df),'position','writeRGB','highByte',_0xb844ee(0x225d),_0xb844ee(0x6f6),'readBlue',_0xb844ee(0x169),'remoteIP','transfer','shutdown',_0xb844ee(0x502f),_0xb844ee(0x4822),_0xb844ee(0x2713),'attached',_0xb844ee(0x4015),'noCursor','checkReg',_0xb844ee(0xc07),_0xb844ee(0x2583),_0xb844ee(0x301c),_0xb844ee(0x26f5),_0xb844ee(0x287b),_0xb844ee(0x47c3),_0xb844ee(0x1903),_0xb844ee(0x321d),_0xb844ee(0x3259),'getIMEI',_0xb844ee(0x254a),_0xb844ee(0x2af3),'process','getBand',_0xb844ee(0x4107),'beginSD',_0xb844ee(0xa93),_0xb844ee(0x614),'setBand',_0xb844ee(0x1845),'bitRead',_0xb844ee(0x4ec6),_0xb844ee(0x4d4c),'readRed',_0xb844ee(0x1466),'noFill',_0xb844ee(0x80a),_0xb844ee(0x3caa),'stroke',_0xb844ee(0x1a5c),'attach',_0xb844ee(0x2177),_0xb844ee(0x41b8),_0xb844ee(0x1808),'height','bitSet',_0xb844ee(0x443e),_0xb844ee(0x4379),'cursor',_0xb844ee(0x4a2c),'IRread',_0xb844ee(0x1fc0),_0xb844ee(0x192a),_0xb844ee(0x3e98),'micros','millis','begin',_0xb844ee(0x33d3),_0xb844ee(0xe9),_0xb844ee(0x8b9),_0xb844ee(0x573),'width',_0xb844ee(0x1b34),'blink',_0xb844ee(0x30a1),'press','mkdir',_0xb844ee(0x1f0c),_0xb844ee(0x1c79),_0xb844ee(0x6db),_0xb844ee(0x25d6),_0xb844ee(0x1692),_0xb844ee(0x828),_0xb844ee(0x4da9),_0xb844ee(0x36dd),_0xb844ee(0x477e),_0xb844ee(0x2f35),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_0xa7d07='['===this[_0x15bfac(0xfb5)][0x0]&&']'===this[_0x15bfac(0xfb5)][this[_0x15bfac(0xfb5)][_0x15bfac(0x205b)]-0x1];if(!_0xa7d07){const _0x3887b7=this[_0x15bfac(0xfb5)][_0x15bfac(0x29d0)](/\./);for(let _0x5ef9cf=0x0,_0x3c97b0=_0x3887b7[_0x15bfac(0x205b)];_0x5ef9cf<_0x3c97b0;_0x5ef9cf++){const _0x31fd04=_0x3887b7[_0x5ef9cf];if(_0x31fd04&&!_0x31fd04[_0x15bfac(0xdc0)](_0x493c3e)){let _0x228c02='';for(let _0xf650c3=0x0,_0x43fc4c=_0x31fd04[_0x15bfac(0x205b)];_0xf650c3<_0x43fc4c;_0xf650c3++)_0x31fd04[_0x15bfac(0x1806)](_0xf650c3)>0x7f?_0x228c02+='x':_0x228c02+=_0x31fd04[_0xf650c3];if(!_0x228c02[_0x15bfac(0xdc0)](_0x493c3e)){const _0x53c6e4=_0x3887b7['slice'](0x0,_0x5ef9cf),_0x249fa4=_0x3887b7[_0x15bfac(0x428e)](_0x5ef9cf+0x1),_0x24ca51=_0x31fd04[_0x15bfac(0xdc0)](_0x358e5f);_0x24ca51&&(_0x53c6e4['push'](_0x24ca51[0x1]),_0x249fa4[_0x15bfac(0x214d)](_0x24ca51[0x2])),_0x249fa4[_0x15bfac(0x205b)]&&(_0x4abfe5=_0x249fa4['join']('.')+_0x4abfe5),this[_0x15bfac(0xfb5)]=_0x53c6e4[_0x15bfac(0x379c)]('.');break;}}}}this[_0x15bfac(0xfb5)]['length']>0xff&&(this['hostname']=''),_0xa7d07&&(this[_0x15bfac(0xfb5)]=this['hostname'][_0x15bfac(0x32a4)](0x1,this[_0x15bfac(0xfb5)]['length']-0x2));}const _0x73858a=_0x4abfe5[_0x15bfac(0x3458)]('#');-0x1!==_0x73858a&&(this[_0x15bfac(0x37e1)]=_0x4abfe5['substr'](_0x73858a),_0x4abfe5=_0x4abfe5['slice'](0x0,_0x73858a));const _0x12da73=_0x4abfe5[_0x15bfac(0x3458)]('?');return-0x1!==_0x12da73&&(this['search']=_0x4abfe5['substr'](_0x12da73),_0x4abfe5=_0x4abfe5[_0x15bfac(0x428e)](0x0,_0x12da73)),_0x4abfe5&&(this[_0x15bfac(0x459c)]=_0x4abfe5),_0x32d815[_0x611a17]&&this['hostname']&&!this['pathname']&&(this[_0x15bfac(0x459c)]=''),this;},_0x1f656f['prototype'][_0x235866(0x4234)]=function(_0x21d9ad){const _0x1ae90d=_0x235866;let _0x5d8348=_0x2d32d['exec'](_0x21d9ad);_0x5d8348&&(_0x5d8348=_0x5d8348[0x0],':'!==_0x5d8348&&(this[_0x1ae90d(0x3a08)]=_0x5d8348['substr'](0x1)),_0x21d9ad=_0x21d9ad[_0x1ae90d(0x32a4)](0x0,_0x21d9ad[_0x1ae90d(0x205b)]-_0x5d8348[_0x1ae90d(0x205b)])),_0x21d9ad&&(this['hostname']=_0x21d9ad);};const _0x3415b4=function(_0x1296d8,_0x5d6213){if(_0x1296d8&&_0x1296d8 instanceof _0x1f656f)return _0x1296d8;const _0x5da1e4=new _0x1f656f();return 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5\uDECE-\uDEDB\uDEE0-\uDEE8\uDEF0-\uDEF8\uDF00-\uDF92\uDF94-\uDFCA]/,_0x1a8de9=/[ \xA0\u1680\u2000-\u200A\u2028\u2029\u202F\u205F\u3000]/,_0x4783e9=new Uint16Array('ᵁ<Õıʊҝջאٵ۞ޢߖࠏ੊ઑඡ๭༉༦჊ረዡᐕᒝᓃᓟᔥ\x00\x00\x00\x00\x00\x00ᕫᛍᦍᰒᷝ὾⁠↰⊍⏀⏻⑂⠤⤒ⴈ⹈⿎〖㊺㘹㞬㣾㨨㩱㫠㬮ࠀEMabcfglmnoprstu\x5cbfms\x7f\u0084\u008b\u0090\u0095\u0098¦³¹ÈÏlig耻Æ䃆P耻&䀦cute耻Á䃁reve;䄂Āiyx}rc耻Â䃂;䐐r;쀀𝔄rave耻À䃀pha;䎑acr;䄀d;橓Āgp\u009d¡on;䄄f;쀀𝔸plyFunction;恡ing耻Å䃅Ācs¾Ãr;쀀𝒜ign;扔ilde耻Ã䃃ml耻Ä䃄ЀaceforsuåûþėĜĢħĪĀcrêòkslash;或Ŷöø;櫧ed;挆y;䐑ƀcrtąċĔause;戵noullis;愬a;䎒r;쀀𝔅pf;쀀𝔹eve;䋘còēmpeq;扎܀HOacdefhilorsuōőŖƀƞƢƵƷƺǜȕɳɸɾcy;䐧PY耻©䂩ƀcpyŝŢźute;䄆Ā;iŧŨ拒talDifferentialD;慅leys;愭ȀaeioƉƎƔƘron;䄌dil耻Ç䃇rc;䄈nint;戰ot;䄊ĀdnƧƭilla;䂸terDot;䂷òſi;䎧rcleȀDMPTLJNjǑǖot;抙inus;抖lus;投imes;抗oĀcsǢǸkwiseContourIntegral;戲eCurlyĀDQȃȏoubleQuote;思uote;怙ȀlnpuȞȨɇɕonĀ;eȥȦ户;橴ƀgitȯȶȺruent;扡nt;戯ourIntegral;戮ĀfrɌɎ;愂oduct;成nterClockwiseContourIntegral;戳oss;樯cr;쀀𝒞pĀ;Cʄʅ拓ap;才րDJSZacefiosʠʬʰʴʸˋ˗ˡ˦̳ҍĀ;oŹʥtrahd;椑cy;䐂cy;䐅cy;䐏ƀgrsʿ˄ˇger;怡r;憡hv;櫤Āayː˕ron;䄎;䐔lĀ;t˝˞戇a;䎔r;쀀𝔇Āaf˫̧Ācm˰̢riticalȀADGT̖̜̀̆cute;䂴oŴ̋̍;䋙bleAcute;䋝rave;䁠ilde;䋜ond;拄ferentialD;慆Ѱ̽\x00\x00\x00͔͂\x00Ѕf;쀀𝔻ƀ;DE͈͉͍䂨ot;惜qual;扐blèCDLRUVͣͲ΂ϏϢϸontourIntegraìȹoɴ͹\x00\x00ͻ»͉nArrow;懓Āeo·ΤftƀARTΐΖΡrrow;懐ightArrow;懔eåˊngĀLRΫτeftĀARγιrrow;柸ightArrow;柺ightArrow;柹ightĀATϘϞrrow;懒ee;抨pɁϩ\x00\x00ϯrrow;懑ownArrow;懕erticalBar;戥ǹABLRTaВЪаўѿͼrrowƀ;BUНОТ憓ar;椓pArrow;懵reve;䌑eft˒к\x00ц\x00ѐightVector;楐eeVector;楞ectorĀ;Bљњ憽ar;楖ightǔѧ\x00ѱeeVector;楟ectorĀ;BѺѻ懁ar;楗eeĀ;A҆҇护rrow;憧ĀctҒҗr;쀀𝒟rok;䄐ࠀNTacdfglmopqstuxҽӀӄӋӞӢӧӮӵԡԯԶՒ՝ՠեG;䅊H耻Ð䃐cute耻É䃉ƀaiyӒӗӜron;䄚rc耻Ê䃊;䐭ot;䄖r;쀀𝔈rave耻È䃈ement;戈ĀapӺӾcr;䄒tyɓԆ\x00\x00ԒmallSquare;旻erySmallSquare;斫ĀgpԦԪon;䄘f;쀀𝔼silon;䎕uĀaiԼՉlĀ;TՂՃ橵ilde;扂librium;懌Āci՗՚r;愰m;橳a;䎗ml耻Ë䃋Āipժկsts;戃onentialE;慇ʀcfiosօֈ֍ֲ׌y;䐤r;쀀𝔉lledɓ֗\x00\x00֣mallSquare;旼erySmallSquare;斪Ͱֺ\x00ֿ\x00\x00ׄf;쀀𝔽All;戀riertrf;愱cò׋؀JTabcdfgorstר׬ׯ׺؀ؒؖ؛؝أ٬ٲcy;䐃耻>䀾mmaĀ;d׷׸䎓;䏜reve;䄞ƀeiy؇،ؐdil;䄢rc;䄜;䐓ot;䄠r;쀀𝔊;拙pf;쀀𝔾eater̀EFGLSTصلَٖٛ٦qualĀ;Lؾؿ扥ess;招ullEqual;执reater;檢ess;扷lantEqual;橾ilde;扳cr;쀀𝒢;扫ЀAacfiosuڅڋږڛڞڪھۊRDcy;䐪Āctڐڔek;䋇;䁞irc;䄤r;愌lbertSpace;愋ǰگ\x00ڲf;愍izontalLine;攀Āctۃۅòکrok;䄦mpńېۘownHumðįqual;扏܀EJOacdfgmnostuۺ۾܃܇܎ܚܞܡܨ݄ݸދޏޕcy;䐕lig;䄲cy;䐁cute耻Í䃍Āiyܓܘrc耻Î䃎;䐘ot;䄰r;愑rave耻Ì䃌ƀ;apܠܯܿĀcgܴܷr;䄪inaryI;慈lieóϝǴ݉\x00ݢĀ;eݍݎ戬Āgrݓݘral;戫section;拂isibleĀCTݬݲomma;恣imes;恢ƀgptݿރވon;䄮f;쀀𝕀a;䎙cr;愐ilde;䄨ǫޚ\x00ޞcy;䐆l耻Ï䃏ʀcfosuެ޷޼߂ߐĀiyޱ޵rc;䄴;䐙r;쀀𝔍pf;쀀𝕁ǣ߇\x00ߌr;쀀𝒥rcy;䐈kcy;䐄΀HJacfosߤߨ߽߬߱ࠂࠈcy;䐥cy;䐌ppa;䎚Āey߶߻dil;䄶;䐚r;쀀𝔎pf;쀀𝕂cr;쀀𝒦րJTaceflmostࠥࠩࠬࡐࡣ঳সে্਷ੇcy;䐉耻<䀼ʀcmnpr࠷࠼ࡁࡄࡍute;䄹bda;䎛g;柪lacetrf;愒r;憞ƀaeyࡗ࡜ࡡron;䄽dil;䄻;䐛Āfsࡨ॰tԀACDFRTUVarࡾࢩࢱࣦ࣠ࣼयज़ΐ४Ānrࢃ࢏gleBracket;柨rowƀ;BR࢙࢚࢞憐ar;懤ightArrow;懆eiling;挈oǵࢷ\x00ࣃbleBracket;柦nǔࣈ\x00࣒eeVector;楡ectorĀ;Bࣛࣜ懃ar;楙loor;挊ightĀAV࣯ࣵrrow;憔ector;楎Āerँगeƀ;AVउऊऐ抣rrow;憤ector;楚iangleƀ;BEतथऩ抲ar;槏qual;抴pƀDTVषूौownVector;楑eeVector;楠ectorĀ;Bॖॗ憿ar;楘ectorĀ;B॥०憼ar;楒ightáΜs̀EFGLSTॾঋকঝঢভqualGreater;拚ullEqual;扦reater;扶ess;檡lantEqual;橽ilde;扲r;쀀𝔏Ā;eঽা拘ftarrow;懚idot;䄿ƀnpw৔ਖਛgȀLRlr৞৷ਂਐeftĀAR০৬rrow;柵ightArrow;柷ightArrow;柶eftĀarγਊightáοightáϊf;쀀𝕃erĀLRਢਬeftArrow;憙ightArrow;憘ƀchtਾੀੂòࡌ;憰rok;䅁;扪Ѐacefiosuਗ਼੝੠੷੼અઋ઎p;椅y;䐜Ādl੥੯iumSpace;恟lintrf;愳r;쀀𝔐nusPlus;戓pf;쀀𝕄cò੶;䎜ҀJacefostuણધભીଔଙඑ඗ඞcy;䐊cute;䅃ƀaey઴હાron;䅇dil;䅅;䐝ƀgswે૰଎ativeƀMTV૓૟૨ediumSpace;怋hiĀcn૦૘ë૙eryThiî૙tedĀGL૸ଆreaterGreateòٳessLesóੈLine;䀊r;쀀𝔑ȀBnptଢନଷ଺reak;恠BreakingSpace;䂠f;愕ڀ;CDEGHLNPRSTV୕ୖ୪୼஡௫ఄ౞಄ದ೘ൡඅ櫬Āou୛୤ngruent;扢pCap;扭oubleVerticalBar;戦ƀlqxஃஊ஛ement;戉ualĀ;Tஒஓ扠ilde;쀀≂̸ists;戄reater΀;EFGLSTஶஷ஽௉௓௘௥扯qual;扱ullEqual;쀀≧̸reater;쀀≫̸ess;批lantEqual;쀀⩾̸ilde;扵umpń௲௽ownHump;쀀≎̸qual;쀀≏̸eĀfsఊధtTriangleƀ;BEచఛడ拪ar;쀀⧏̸qual;括s̀;EGLSTవశ఼ౄోౘ扮qual;扰reater;扸ess;쀀≪̸lantEqual;쀀⩽̸ilde;扴estedĀGL౨౹reaterGreater;쀀⪢̸essLess;쀀⪡̸recedesƀ;ESಒಓಛ技qual;쀀⪯̸lantEqual;拠ĀeiಫಹverseElement;戌ghtTriangleƀ;BEೋೌ೒拫ar;쀀⧐̸qual;拭ĀquೝഌuareSuĀbp೨೹setĀ;E೰ೳ쀀⊏̸qual;拢ersetĀ;Eഃആ쀀⊐̸qual;拣ƀbcpഓതൎsetĀ;Eഛഞ쀀⊂⃒qual;抈ceedsȀ;ESTലള഻െ抁qual;쀀⪰̸lantEqual;拡ilde;쀀≿̸ersetĀ;E൘൛쀀⊃⃒qual;抉ildeȀ;EFT൮൯൵ൿ扁qual;扄ullEqual;扇ilde;扉erticalBar;戤cr;쀀𝒩ilde耻Ñ䃑;䎝܀Eacdfgmoprstuvලෂ෉෕ෛ෠෧෼ขภยา฿ไlig;䅒cute耻Ó䃓Āiy෎ීrc耻Ô䃔;䐞blac;䅐r;쀀𝔒rave耻Ò䃒ƀaei෮ෲ෶cr;䅌ga;䎩cron;䎟pf;쀀𝕆enCurlyĀDQฎบoubleQuote;怜uote;怘;橔Āclวฬr;쀀𝒪ash耻Ø䃘iŬื฼de耻Õ䃕es;樷ml耻Ö䃖erĀBP๋๠Āar๐๓r;怾acĀek๚๜;揞et;掴arenthesis;揜Ҁacfhilors๿ງຊຏຒດຝະ໼rtialD;戂y;䐟r;쀀𝔓i;䎦;䎠usMinus;䂱Āipຢອncareplanåڝf;愙Ȁ;eio຺ູ໠໤檻cedesȀ;EST່້໏໚扺qual;檯lantEqual;扼ilde;找me;怳Ādp໩໮uct;戏ortionĀ;aȥ໹l;戝Āci༁༆r;쀀𝒫;䎨ȀUfos༑༖༛༟OT耻\x22䀢r;쀀𝔔pf;愚cr;쀀𝒬؀BEacefhiorsu༾གྷཇའཱིྦྷྪྭ႖ႩႴႾarr;椐G耻®䂮ƀcnrཎནབute;䅔g;柫rĀ;tཛྷཝ憠l;椖ƀaeyཧཬཱron;䅘dil;䅖;䐠Ā;vླྀཹ愜erseĀEUྂྙĀlq྇ྎement;戋uilibrium;懋pEquilibrium;楯r»ཹo;䎡ghtЀACDFTUVa࿁࿫࿳ဢဨၛႇϘĀnr࿆࿒gleBracket;柩rowƀ;BL࿜࿝࿡憒ar;懥eftArrow;懄eiling;按oǵ࿹\x00စbleBracket;柧nǔည\x00နeeVector;楝ectorĀ;Bဝသ懂ar;楕loor;挋Āerိ၃eƀ;AVဵံြ抢rrow;憦ector;楛iangleƀ;BEၐၑၕ抳ar;槐qual;抵pƀDTVၣၮၸownVector;楏eeVector;楜ectorĀ;Bႂႃ憾ar;楔ectorĀ;B႑႒懀ar;楓Āpuႛ႞f;愝ndImplies;楰ightarrow;懛ĀchႹႼr;愛;憱leDelayed;槴ڀHOacfhimoqstuფჱჷჽᄙᄞᅑᅖᅡᅧᆵᆻᆿĀCcჩხHcy;䐩y;䐨FTcy;䐬cute;䅚ʀ;aeiyᄈᄉᄎᄓᄗ檼ron;䅠dil;䅞rc;䅜;䐡r;쀀𝔖ortȀDLRUᄪᄴᄾᅉownArrow»ОeftArrow»࢚ightArrow»࿝pArrow;憑gma;䎣allCircle;战pf;쀀𝕊ɲᅭ\x00\x00ᅰt;戚areȀ;ISUᅻᅼᆉᆯ斡ntersection;抓uĀbpᆏᆞsetĀ;Eᆗᆘ抏qual;抑ersetĀ;Eᆨᆩ抐qual;抒nion;抔cr;쀀𝒮ar;拆ȀbcmpᇈᇛሉላĀ;sᇍᇎ拐etĀ;Eᇍᇕqual;抆ĀchᇠህeedsȀ;ESTᇭᇮᇴᇿ扻qual;檰lantEqual;扽ilde;承Tháྌ;我ƀ;esሒሓሣ拑rsetĀ;Eሜም抃qual;抇et»ሓրHRSacfhiorsሾቄ቉ቕ቞ቱቶኟዂወዑORN耻Þ䃞ADE;愢ĀHc቎ቒcy;䐋y;䐦Ābuቚቜ;䀉;䎤ƀaeyብቪቯron;䅤dil;䅢;䐢r;쀀𝔗Āeiቻ኉Dzኀ\x00ኇefore;戴a;䎘Ācn኎ኘkSpace;쀀\u205f\u200aSpace;怉ldeȀ;EFTካኬኲኼ戼qual;扃ullEqual;扅ilde;扈pf;쀀𝕋ipleDot;惛Āctዖዛr;쀀𝒯rok;䅦ૡዷጎጚጦ\x00ጬጱ\x00\x00\x00\x00\x00ጸጽ፷ᎅ\x00᏿ᐄᐊᐐĀcrዻጁute耻Ú䃚rĀ;oጇገ憟cir;楉rǣጓ\x00጖y;䐎ve;䅬Āiyጞጣrc耻Û䃛;䐣blac;䅰r;쀀𝔘rave耻Ù䃙acr;䅪Ādiፁ፩erĀBPፈ፝Āarፍፐr;䁟acĀekፗፙ;揟et;掵arenthesis;揝onĀ;P፰፱拃lus;抎Āgp፻፿on;䅲f;쀀𝕌ЀADETadps᎕ᎮᎸᏄϨᏒᏗᏳrrowƀ;BDᅐᎠᎤar;椒ownArrow;懅ownArrow;憕quilibrium;楮eeĀ;AᏋᏌ报rrow;憥ownáϳerĀLRᏞᏨeftArrow;憖ightArrow;憗iĀ;lᏹᏺ䏒on;䎥ing;䅮cr;쀀𝒰ilde;䅨ml耻Ü䃜ҀDbcdefosvᐧᐬᐰᐳᐾᒅᒊᒐᒖash;披ar;櫫y;䐒ashĀ;lᐻᐼ抩;櫦Āerᑃᑅ;拁ƀbtyᑌᑐᑺar;怖Ā;iᑏᑕcalȀBLSTᑡᑥᑪᑴar;戣ine;䁼eparator;杘ilde;所ThinSpace;怊r;쀀𝔙pf;쀀𝕍cr;쀀𝒱dash;抪ʀcefosᒧᒬᒱᒶᒼirc;䅴dge;拀r;쀀𝔚pf;쀀𝕎cr;쀀𝒲Ȁfiosᓋᓐᓒᓘr;쀀𝔛;䎞pf;쀀𝕏cr;쀀𝒳ҀAIUacfosuᓱᓵᓹᓽᔄᔏᔔᔚᔠcy;䐯cy;䐇cy;䐮cute耻Ý䃝Āiyᔉᔍrc;䅶;䐫r;쀀𝔜pf;쀀𝕐cr;쀀𝒴ml;䅸ЀHacdefosᔵᔹᔿᕋᕏᕝᕠᕤcy;䐖cute;䅹Āayᕄᕉron;䅽;䐗ot;䅻Dzᕔ\x00ᕛoWidtè૙a;䎖r;愨pf;愤cr;쀀𝒵௡ᖃᖊᖐ\x00ᖰᖶᖿ\x00\x00\x00\x00ᗆᗛᗫᙟ᙭\x00ᚕ᚛ᚲᚹ\x00ᚾcute耻á䃡reve;䄃̀;Ediuyᖜᖝᖡᖣᖨᖭ戾;쀀∾̳;房rc耻â䃢te肻´̆;䐰lig耻æ䃦Ā;r²ᖺ;쀀𝔞rave耻à䃠ĀepᗊᗖĀfpᗏᗔsym;愵èᗓha;䎱ĀapᗟcĀclᗤᗧr;䄁g;樿ɤᗰ\x00\x00ᘊʀ;adsvᗺᗻᗿᘁᘇ戧nd;橕;橜lope;橘;橚΀;elmrszᘘᘙᘛᘞᘿᙏᙙ戠;榤e»ᘙsdĀ;aᘥᘦ戡ѡᘰᘲᘴᘶᘸᘺᘼᘾ;榨;榩;榪;榫;榬;榭;榮;榯tĀ;vᙅᙆ戟bĀ;dᙌᙍ抾;榝Āptᙔᙗh;戢»¹arr;捼Āgpᙣᙧon;䄅f;쀀𝕒΀;Eaeiop዁ᙻᙽᚂᚄᚇᚊ;橰cir;橯;扊d;手s;䀧roxĀ;e዁ᚒñᚃing耻å䃥ƀctyᚡᚦᚨr;쀀𝒶;䀪mpĀ;e዁ᚯñʈilde耻ã䃣ml耻ä䃤Āciᛂᛈoninôɲnt;樑ࠀNabcdefiklnoprsu᛭ᛱᜰ᜼ᝃᝈ᝸᝽០៦ᠹᡐᜍ᤽᥈ᥰot;櫭Ācrᛶ᜞kȀcepsᜀᜅᜍᜓong;扌psilon;䏶rime;怵imĀ;e᜚᜛戽q;拍Ŷᜢᜦee;抽edĀ;gᜬᜭ挅e»ᜭrkĀ;t፜᜷brk;掶Āoyᜁᝁ;䐱quo;怞ʀcmprtᝓ᝛ᝡᝤᝨausĀ;eĊĉptyv;榰séᜌnoõēƀahwᝯ᝱ᝳ;䎲;愶een;扬r;쀀𝔟g΀costuvwឍឝឳេ៕៛៞ƀaiuបពរðݠrc;旯p»፱ƀdptឤឨឭot;樀lus;樁imes;樂ɱឹ\x00\x00ើcup;樆ar;昅riangleĀdu៍្own;施p;斳plus;樄eåᑄåᒭarow;植ƀako៭ᠦᠵĀcn៲ᠣkƀlst៺֫᠂ozenge;槫riangleȀ;dlr᠒᠓᠘᠝斴own;斾eft;旂ight;斸k;搣Ʊᠫ\x00ᠳƲᠯ\x00ᠱ;斒;斑4;斓ck;斈ĀeoᠾᡍĀ;qᡃᡆ쀀=⃥uiv;쀀≡⃥t;挐Ȁptwxᡙᡞᡧᡬf;쀀𝕓Ā;tᏋᡣom»Ꮜtie;拈؀DHUVbdhmptuvᢅᢖᢪᢻᣗᣛᣬ᣿ᤅᤊᤐᤡȀLRlrᢎᢐᢒᢔ;敗;敔;敖;敓ʀ;DUduᢡᢢᢤᢦᢨ敐;敦;敩;敤;敧ȀLRlrᢳᢵᢷᢹ;敝;敚;敜;教΀;HLRhlrᣊᣋᣍᣏᣑᣓᣕ救;敬;散;敠;敫;敢;敟ox;槉ȀLRlrᣤᣦᣨᣪ;敕;敒;攐;攌ʀ;DUduڽ᣷᣹᣻᣽;敥;敨;攬;攴inus;抟lus;択imes;抠ȀLRlrᤙᤛᤝ᤟;敛;敘;攘;攔΀;HLRhlrᤰᤱᤳᤵᤷ᤻᤹攂;敪;敡;敞;攼;攤;攜Āevģ᥂bar耻¦䂦Ȁceioᥑᥖᥚᥠr;쀀𝒷mi;恏mĀ;e᜚᜜lƀ;bhᥨᥩᥫ䁜;槅sub;柈Ŭᥴ᥾lĀ;e᥹᥺怢t»᥺pƀ;Eeįᦅᦇ;檮Ā;qۜۛೡᦧ\x00᧨ᨑᨕᨲ\x00ᨷᩐ\x00\x00᪴\x00\x00᫁\x00\x00ᬡᬮ᭍᭒\x00᯽\x00ᰌƀcpr᦭ᦲ᧝ute;䄇̀;abcdsᦿᧀᧄ᧊᧕᧙戩nd;橄rcup;橉Āau᧏᧒p;橋p;橇ot;橀;쀀∩︀Āeo᧢᧥t;恁îړȀaeiu᧰᧻ᨁᨅǰ᧵\x00᧸s;橍on;䄍dil耻ç䃧rc;䄉psĀ;sᨌᨍ橌m;橐ot;䄋ƀdmnᨛᨠᨦil肻¸ƭptyv;榲t脀¢;eᨭᨮ䂢räƲr;쀀𝔠ƀceiᨽᩀᩍy;䑇ckĀ;mᩇᩈ朓ark»ᩈ;䏇r΀;Ecefms᩟᩠ᩢᩫ᪤᪪᪮旋;槃ƀ;elᩩᩪᩭ䋆q;扗eɡᩴ\x00\x00᪈rrowĀlr᩼᪁eft;憺ight;憻ʀRSacd᪒᪔᪖᪚᪟»ཇ;擈st;抛irc;抚ash;抝nint;樐id;櫯cir;槂ubsĀ;u᪻᪼晣it»᪼ˬ᫇᫔᫺\x00ᬊonĀ;eᫍᫎ䀺Ā;qÇÆɭ᫙\x00\x00᫢aĀ;t᫞᫟䀬;䁀ƀ;fl᫨᫩᫫戁îᅠeĀmx᫱᫶ent»᫩eóɍǧ᫾\x00ᬇĀ;dኻᬂot;橭nôɆƀfryᬐᬔᬗ;쀀𝕔oäɔ脀©;sŕᬝr;愗Āaoᬥᬩrr;憵ss;朗Ācuᬲᬷr;쀀𝒸Ābpᬼ᭄Ā;eᭁᭂ櫏;櫑Ā;eᭉᭊ櫐;櫒dot;拯΀delprvw᭠᭬᭷ᮂᮬᯔ᯹arrĀlr᭨᭪;椸;椵ɰ᭲\x00\x00᭵r;拞c;拟arrĀ;p᭿ᮀ憶;椽̀;bcdosᮏᮐᮖᮡᮥᮨ截rcap;橈Āauᮛᮞp;橆p;橊ot;抍r;橅;쀀∪︀Ȁalrv᮵ᮿᯞᯣrrĀ;mᮼᮽ憷;椼yƀevwᯇᯔᯘqɰᯎ\x00\x00ᯒreã᭳uã᭵ee;拎edge;拏en耻¤䂤earrowĀlrᯮ᯳eft»ᮀight»ᮽeäᯝĀciᰁᰇoninôǷnt;戱lcty;挭ঀAHabcdefhijlorstuwz᰸᰻᰿ᱝᱩᱵᲊᲞᲬᲷ᳻᳿ᴍᵻᶑᶫᶻ᷆᷍rò΁ar;楥Ȁglrs᱈ᱍ᱒᱔ger;怠eth;愸òᄳhĀ;vᱚᱛ怐»ऊūᱡᱧarow;椏aã̕Āayᱮᱳron;䄏;䐴ƀ;ao̲ᱼᲄĀgrʿᲁr;懊tseq;橷ƀglmᲑᲔᲘ耻°䂰ta;䎴ptyv;榱ĀirᲣᲨsht;楿;쀀𝔡arĀlrᲳᲵ»ࣜ»သʀaegsv᳂͸᳖᳜᳠mƀ;oș᳊᳔ndĀ;ș᳑uit;晦amma;䏝in;拲ƀ;io᳧᳨᳸䃷de脀÷;o᳧ᳰntimes;拇nø᳷cy;䑒cɯᴆ\x00\x00ᴊrn;挞op;挍ʀlptuwᴘᴝᴢᵉᵕlar;䀤f;쀀𝕕ʀ;emps̋ᴭᴷᴽᵂqĀ;d͒ᴳot;扑inus;戸lus;戔quare;抡blebarwedgåúnƀadhᄮᵝᵧownarrowóᲃarpoonĀlrᵲᵶefôᲴighôᲶŢᵿᶅkaro÷གɯᶊ\x00\x00ᶎrn;挟op;挌ƀcotᶘᶣᶦĀryᶝᶡ;쀀𝒹;䑕l;槶rok;䄑Ādrᶰᶴot;拱iĀ;fᶺ᠖斿Āah᷀᷃ròЩaòྦangle;榦Āci᷒ᷕy;䑟grarr;柿ऀDacdefglmnopqrstuxḁḉḙḸոḼṉṡṾấắẽỡἪἷὄ὎὚ĀDoḆᴴoôᲉĀcsḎḔute耻é䃩ter;橮ȀaioyḢḧḱḶron;䄛rĀ;cḭḮ扖耻ê䃪lon;払;䑍ot;䄗ĀDrṁṅot;扒;쀀𝔢ƀ;rsṐṑṗ檚ave耻è䃨Ā;dṜṝ檖ot;檘Ȁ;ilsṪṫṲṴ檙nters;揧;愓Ā;dṹṺ檕ot;檗ƀapsẅẉẗcr;䄓tyƀ;svẒẓẕ戅et»ẓpĀ1;ẝẤijạả;怄;怅怃ĀgsẪẬ;䅋p;怂ĀgpẴẸon;䄙f;쀀𝕖ƀalsỄỎỒrĀ;sỊị拕l;槣us;橱iƀ;lvỚớở䎵on»ớ;䏵ȀcsuvỪỳἋἣĀioữḱrc»Ḯɩỹ\x00\x00ỻíՈantĀglἂἆtr»ṝess»Ṻƀaeiἒ἖Ἒls;䀽st;扟vĀ;DȵἠD;橸parsl;槥ĀDaἯἳot;打rr;楱ƀcdiἾὁỸr;愯oô͒ĀahὉὋ;䎷耻ð䃰Āmrὓὗl耻ë䃫o;悬ƀcipὡὤὧl;䀡sôծĀeoὬὴctatioîՙnentialåչৡᾒ\x00ᾞ\x00ᾡᾧ\x00\x00ῆῌ\x00ΐ\x00ῦῪ\u2000\x00\u2008⁚llingdotseñṄy;䑄male;晀ƀilrᾭᾳ῁lig;耀ffiɩᾹ\x00\x00᾽g;耀ffig;耀ffl;쀀𝔣lig;耀filig;쀀fjƀaltῙ῜ῡt;晭ig;耀flns;斱of;䆒ǰ΅\x00ῳf;쀀𝕗ĀakֿῷĀ;vῼ´拔;櫙artint;樍Āao‌⁕Ācs‑⁒ႉ‸⁅⁈\x00⁐β•‥‧‪‬\x00‮耻½䂽;慓耻¼䂼;慕;慙;慛Ƴ‴\x00‶;慔;慖ʴ‾⁁\x00\x00⁃耻¾䂾;慗;慜5;慘ƶ⁌\x00⁎;慚;慝8;慞l;恄wn;挢cr;쀀𝒻ࢀEabcdefgijlnorstv₂₉₟₥₰₴⃰⃵⃺⃿℃ℒℸ̗ℾ⅒↞Ā;lٍ₇;檌ƀcmpₐₕ₝ute;䇵maĀ;dₜ᳚䎳;檆reve;䄟Āiy₪₮rc;䄝;䐳ot;䄡Ȁ;lqsؾق₽⃉ƀ;qsؾٌ⃄lanô٥Ȁ;cdl٥⃒⃥⃕c;檩otĀ;o⃜⃝檀Ā;l⃢⃣檂;檄Ā;e⃪⃭쀀⋛︀s;檔r;쀀𝔤Ā;gٳ؛mel;愷cy;䑓Ȁ;Eajٚℌℎℐ;檒;檥;檤ȀEaesℛℝ℩ℴ;扩pĀ;p℣ℤ檊rox»ℤĀ;q℮ℯ檈Ā;q℮ℛim;拧pf;쀀𝕘Āci⅃ⅆr;愊mƀ;el٫ⅎ⅐;檎;檐茀>;cdlqr׮ⅠⅪⅮⅳⅹĀciⅥⅧ;檧r;橺ot;拗Par;榕uest;橼ʀadelsↄⅪ←ٖ↛ǰ↉\x00↎proø₞r;楸qĀlqؿ↖lesó₈ií٫Āen↣↭rtneqq;쀀≩︀Å↪ԀAabcefkosy⇄⇇⇱⇵⇺∘∝∯≨≽ròΠȀilmr⇐⇔⇗⇛rsðᒄf»․ilôکĀdr⇠⇤cy;䑊ƀ;cwࣴ⇫⇯ir;楈;憭ar;意irc;䄥ƀalr∁∎∓rtsĀ;u∉∊晥it»∊lip;怦con;抹r;쀀𝔥sĀew∣∩arow;椥arow;椦ʀamopr∺∾≃≞≣rr;懿tht;戻kĀlr≉≓eftarrow;憩ightarrow;憪f;쀀𝕙bar;怕ƀclt≯≴≸r;쀀𝒽asè⇴rok;䄧Ābp⊂⊇ull;恃hen»ᱛૡ⊣\x00⊪\x00⊸⋅⋎\x00⋕⋳\x00\x00⋸⌢⍧⍢⍿\x00⎆⎪⎴cute耻í䃭ƀ;iyݱ⊰⊵rc耻î䃮;䐸Ācx⊼⊿y;䐵cl耻¡䂡ĀfrΟ⋉;쀀𝔦rave耻ì䃬Ȁ;inoܾ⋝⋩⋮Āin⋢⋦nt;樌t;戭fin;槜ta;愩lig;䄳ƀaop⋾⌚⌝ƀcgt⌅⌈⌗r;䄫ƀelpܟ⌏⌓inåގarôܠh;䄱f;抷ed;䆵ʀ;cfotӴ⌬⌱⌽⍁are;愅inĀ;t⌸⌹戞ie;槝doô⌙ʀ;celpݗ⍌⍐⍛⍡al;抺Āgr⍕⍙eróᕣã⍍arhk;樗rod;樼Ȁcgpt⍯⍲⍶⍻y;䑑on;䄯f;쀀𝕚a;䎹uest耻¿䂿Āci⎊⎏r;쀀𝒾nʀ;EdsvӴ⎛⎝⎡ӳ;拹ot;拵Ā;v⎦⎧拴;拳Ā;iݷ⎮lde;䄩ǫ⎸\x00⎼cy;䑖l耻ï䃯̀cfmosu⏌⏗⏜⏡⏧⏵Āiy⏑⏕rc;䄵;䐹r;쀀𝔧ath;䈷pf;쀀𝕛ǣ⏬\x00⏱r;쀀𝒿rcy;䑘kcy;䑔Ѐacfghjos␋␖␢␧␭␱␵␻ppaĀ;v␓␔䎺;䏰Āey␛␠dil;䄷;䐺r;쀀𝔨reen;䄸cy;䑅cy;䑜pf;쀀𝕜cr;쀀𝓀஀ABEHabcdefghjlmnoprstuv⑰⒁⒆⒍⒑┎┽╚▀♎♞♥♹♽⚚⚲⛘❝❨➋⟀⠁⠒ƀart⑷⑺⑼rò৆òΕail;椛arr;椎Ā;gঔ⒋;檋ar;楢ॣ⒥\x00⒪\x00⒱\x00\x00\x00\x00\x00⒵Ⓔ\x00ⓆⓈⓍ\x00⓹ute;䄺mptyv;榴raîࡌbda;䎻gƀ;dlࢎⓁⓃ;榑åࢎ;檅uo耻«䂫rЀ;bfhlpst࢙ⓞⓦⓩ⓫⓮⓱⓵Ā;f࢝ⓣs;椟s;椝ë≒p;憫l;椹im;楳l;憢ƀ;ae⓿─┄檫il;椙Ā;s┉┊檭;쀀⪭︀ƀabr┕┙┝rr;椌rk;杲Āak┢┬cĀek┨┪;䁻;䁛Āes┱┳;榋lĀdu┹┻;榏;榍Ȁaeuy╆╋╖╘ron;䄾Ādi═╔il;䄼ìࢰâ┩;䐻Ȁcqrs╣╦╭╽a;椶uoĀ;rนᝆĀdu╲╷har;楧shar;楋h;憲ʀ;fgqs▋▌উ◳◿扤tʀahlrt▘▤▷◂◨rrowĀ;t࢙□aé⓶arpoonĀdu▯▴own»њp»०eftarrows;懇ightƀahs◍◖◞rrowĀ;sࣴࢧarpoonó྘quigarro÷⇰hreetimes;拋ƀ;qs▋ও◺lanôবʀ;cdgsব☊☍☝☨c;檨otĀ;o☔☕橿Ā;r☚☛檁;檃Ā;e☢☥쀀⋚︀s;檓ʀadegs☳☹☽♉♋pproøⓆot;拖qĀgq♃♅ôউgtò⒌ôছiíলƀilr♕࣡♚sht;楼;쀀𝔩Ā;Eজ♣;檑š♩♶rĀdu▲♮Ā;l॥♳;楪lk;斄cy;䑙ʀ;achtੈ⚈⚋⚑⚖rò◁orneòᴈard;楫ri;旺Āio⚟⚤dot;䅀ustĀ;a⚬⚭掰che»⚭ȀEaes⚻⚽⛉⛔;扨pĀ;p⛃⛄檉rox»⛄Ā;q⛎⛏檇Ā;q⛎⚻im;拦Ѐabnoptwz⛩⛴⛷✚✯❁❇❐Ānr⛮⛱g;柬r;懽rëࣁgƀlmr⛿✍✔eftĀar০✇ightá৲apsto;柼ightá৽parrowĀlr✥✩efô⓭ight;憬ƀafl✶✹✽r;榅;쀀𝕝us;樭imes;樴š❋❏st;戗áፎƀ;ef❗❘᠀旊nge»❘arĀ;l❤❥䀨t;榓ʀachmt❳❶❼➅➇ròࢨorneòᶌarĀ;d྘➃;業;怎ri;抿̀achiqt➘➝ੀ➢➮➻quo;怹r;쀀𝓁mƀ;egল➪➬;檍;檏Ābu┪➳oĀ;rฟ➹;怚rok;䅂萀<;cdhilqrࠫ⟒☹⟜⟠⟥⟪⟰Āci⟗⟙;檦r;橹reå◲mes;拉arr;楶uest;橻ĀPi⟵⟹ar;榖ƀ;ef⠀भ᠛旃rĀdu⠇⠍shar;楊har;楦Āen⠗⠡rtneqq;쀀≨︀Å⠞܀Dacdefhilnopsu⡀⡅⢂⢎⢓⢠⢥⢨⣚⣢⣤ઃ⣳⤂Dot;戺Ȁclpr⡎⡒⡣⡽r耻¯䂯Āet⡗⡙;時Ā;e⡞⡟朠se»⡟Ā;sျ⡨toȀ;dluျ⡳⡷⡻owîҌefôएðᏑker;斮Āoy⢇⢌mma;権;䐼ash;怔asuredangle»ᘦr;쀀𝔪o;愧ƀcdn⢯⢴⣉ro耻µ䂵Ȁ;acdᑤ⢽⣀⣄sôᚧir;櫰ot肻·Ƶusƀ;bd⣒ᤃ⣓戒Ā;uᴼ⣘;横ţ⣞⣡p;櫛ò−ðઁĀdp⣩⣮els;抧f;쀀𝕞Āct⣸⣽r;쀀𝓂pos»ᖝƀ;lm⤉⤊⤍䎼timap;抸ఀGLRVabcdefghijlmoprstuvw⥂⥓⥾⦉⦘⧚⧩⨕⨚⩘⩝⪃⪕⪤⪨⬄⬇⭄⭿⮮ⰴⱧⱼ⳩Āgt⥇⥋;쀀⋙̸Ā;v⥐௏쀀≫⃒ƀelt⥚⥲⥶ftĀar⥡⥧rrow;懍ightarrow;懎;쀀⋘̸Ā;v⥻ే쀀≪⃒ightarrow;懏ĀDd⦎⦓ash;抯ash;抮ʀbcnpt⦣⦧⦬⦱⧌la»˞ute;䅄g;쀀∠⃒ʀ;Eiop඄⦼⧀⧅⧈;쀀⩰̸d;쀀≋̸s;䅉roø඄urĀ;a⧓⧔普lĀ;s⧓ସdz⧟\x00⧣p肻\u00a0ଷmpĀ;e௹ఀʀaeouy⧴⧾⨃⨐⨓ǰ⧹\x00⧻;橃on;䅈dil;䅆ngĀ;dൾ⨊ot;쀀⩭̸p;橂;䐽ash;怓΀;Aadqsxஒ⨩⨭⨻⩁⩅⩐rr;懗rĀhr⨳⨶k;椤Ā;oᏲᏰot;쀀≐̸uiöୣĀei⩊⩎ar;椨í஘istĀ;s஠டr;쀀𝔫ȀEest௅⩦⩹⩼ƀ;qs஼⩭௡ƀ;qs஼௅⩴lanô௢ií௪Ā;rஶ⪁»ஷƀAap⪊⪍⪑rò⥱rr;憮ar;櫲ƀ;svྍ⪜ྌĀ;d⪡⪢拼;拺cy;䑚΀AEadest⪷⪺⪾⫂⫅⫶⫹rò⥦;쀀≦̸rr;憚r;急Ȁ;fqs఻⫎⫣⫯tĀar⫔⫙rro÷⫁ightarro÷⪐ƀ;qs఻⪺⫪lanôౕĀ;sౕ⫴»శiíౝĀ;rవ⫾iĀ;eచథiäඐĀpt⬌⬑f;쀀𝕟膀¬;in⬙⬚⬶䂬nȀ;Edvஉ⬤⬨⬮;쀀⋹̸ot;쀀⋵̸ǡஉ⬳⬵;拷;拶iĀ;vಸ⬼ǡಸ⭁⭃;拾;拽ƀaor⭋⭣⭩rȀ;ast୻⭕⭚⭟lleì୻l;쀀⫽⃥;쀀∂̸lint;樔ƀ;ceಒ⭰⭳uåಥĀ;cಘ⭸Ā;eಒ⭽ñಘȀAait⮈⮋⮝⮧rò⦈rrƀ;cw⮔⮕⮙憛;쀀⤳̸;쀀↝̸ghtarrow»⮕riĀ;eೋೖ΀chimpqu⮽⯍⯙⬄୸⯤⯯Ȁ;cerല⯆ഷ⯉uå൅;쀀𝓃ortɭ⬅\x00\x00⯖ará⭖mĀ;e൮⯟Ā;q൴൳suĀbp⯫⯭å೸åഋƀbcp⯶ⰑⰙȀ;Ees⯿ⰀഢⰄ抄;쀀⫅̸etĀ;eഛⰋqĀ;qണⰀcĀ;eലⰗñസȀ;EesⰢⰣൟⰧ抅;쀀⫆̸etĀ;e൘ⰮqĀ;qൠⰣȀgilrⰽⰿⱅⱇìௗlde耻ñ䃱çృiangleĀlrⱒⱜeftĀ;eచⱚñదightĀ;eೋⱥñ೗Ā;mⱬⱭ䎽ƀ;esⱴⱵⱹ䀣ro;愖p;怇ҀDHadgilrsⲏⲔⲙⲞⲣⲰⲶⳓⳣash;抭arr;椄p;쀀≍⃒ash;抬ĀetⲨⲬ;쀀≥⃒;쀀>⃒nfin;槞ƀAetⲽⳁⳅrr;椂;쀀≤⃒Ā;rⳊⳍ쀀<⃒ie;쀀⊴⃒ĀAtⳘⳜrr;椃rie;쀀⊵⃒im;쀀∼⃒ƀAan⳰⳴ⴂrr;懖rĀhr⳺⳽k;椣Ā;oᏧᏥear;椧ቓ᪕\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00ⴭ\x00ⴸⵈⵠⵥ⵲ⶄᬇ\x00\x00ⶍⶫ\x00ⷈⷎ\x00ⷜ⸙⸫⸾⹃Ācsⴱ᪗ute耻ó䃳ĀiyⴼⵅrĀ;c᪞ⵂ耻ô䃴;䐾ʀabios᪠ⵒⵗLjⵚlac;䅑v;樸old;榼lig;䅓Ācr⵩⵭ir;榿;쀀𝔬ͯ⵹\x00\x00⵼\x00ⶂn;䋛ave耻ò䃲;槁Ābmⶈ෴ar;榵Ȁacitⶕ⶘ⶥⶨrò᪀Āir⶝ⶠr;榾oss;榻nå๒;槀ƀaeiⶱⶵⶹcr;䅍ga;䏉ƀcdnⷀⷅǍron;䎿;榶pf;쀀𝕠ƀaelⷔ⷗ǒr;榷rp;榹΀;adiosvⷪⷫⷮ⸈⸍⸐⸖戨rò᪆Ȁ;efmⷷⷸ⸂⸅橝rĀ;oⷾⷿ愴f»ⷿ耻ª䂪耻º䂺gof;抶r;橖lope;橗;橛ƀclo⸟⸡⸧ò⸁ash耻ø䃸l;折iŬⸯ⸴de耻õ䃵esĀ;aǛ⸺s;樶ml耻ö䃶bar;挽ૡ⹞\x00⹽\x00⺀⺝\x00⺢⺹\x00\x00⻋ຜ\x00⼓\x00\x00⼫⾼\x00⿈rȀ;astЃ⹧⹲຅脀¶;l⹭⹮䂶leìЃɩ⹸\x00\x00⹻m;櫳;櫽y;䐿rʀcimpt⺋⺏⺓ᡥ⺗nt;䀥od;䀮il;怰enk;怱r;쀀𝔭ƀimo⺨⺰⺴Ā;v⺭⺮䏆;䏕maô੶ne;明ƀ;tv⺿⻀⻈䏀chfork»´;䏖Āau⻏⻟nĀck⻕⻝kĀ;h⇴⻛;愎ö⇴sҀ;abcdemst⻳⻴ᤈ⻹⻽⼄⼆⼊⼎䀫cir;樣ir;樢Āouᵀ⼂;樥;橲n肻±ຝim;樦wo;樧ƀipu⼙⼠⼥ntint;樕f;쀀𝕡nd耻£䂣Ԁ;Eaceinosu່⼿⽁⽄⽇⾁⾉⾒⽾⾶;檳p;檷uå໙Ā;c໎⽌̀;acens່⽙⽟⽦⽨⽾pproø⽃urlyeñ໙ñ໎ƀaes⽯⽶⽺pprox;檹qq;檵im;拨iíໟmeĀ;s⾈ຮ怲ƀEas⽸⾐⽺ð⽵ƀdfp໬⾙⾯ƀals⾠⾥⾪lar;挮ine;挒urf;挓Ā;t໻⾴ï໻rel;抰Āci⿀⿅r;쀀𝓅;䏈ncsp;怈̀fiopsu⿚⋢⿟⿥⿫⿱r;쀀𝔮pf;쀀𝕢rime;恗cr;쀀𝓆ƀaeo⿸〉〓tĀei⿾々rnionóڰnt;樖stĀ;e【】䀿ñἙô༔઀ABHabcdefhilmnoprstux぀けさすムㄎㄫㅇㅢㅲㆎ㈆㈕㈤㈩㉘㉮㉲㊐㊰㊷ƀartぇおがròႳòϝail;検aròᱥar;楤΀cdenqrtとふへみわゔヌĀeuねぱ;쀀∽̱te;䅕iãᅮmptyv;榳gȀ;del࿑らるろ;榒;榥å࿑uo耻»䂻rր;abcfhlpstw࿜ガクシスゼゾダッデナp;極Ā;f࿠ゴs;椠;椳s;椞ë≝ð✮l;楅im;楴l;憣;憝Āaiパフil;椚oĀ;nホボ戶aló༞ƀabrョリヮrò៥rk;杳ĀakンヽcĀekヹ・;䁽;䁝Āes㄂㄄;榌lĀduㄊㄌ;榎;榐Ȁaeuyㄗㄜㄧㄩron;䅙Ādiㄡㄥil;䅗ì࿲âヺ;䑀Ȁclqsㄴㄷㄽㅄa;椷dhar;楩uoĀ;rȎȍh;憳ƀacgㅎㅟངlȀ;ipsླྀㅘㅛႜnåႻarôྩt;断ƀilrㅩဣㅮsht;楽;쀀𝔯ĀaoㅷㆆrĀduㅽㅿ»ѻĀ;l႑ㆄ;楬Ā;vㆋㆌ䏁;䏱ƀgns㆕ㇹㇼht̀ahlrstㆤㆰ㇂㇘㇤㇮rrowĀ;t࿜ㆭaéトarpoonĀduㆻㆿowîㅾp»႒eftĀah㇊㇐rrowó࿪arpoonóՑightarrows;應quigarro÷ニhreetimes;拌g;䋚ingdotseñἲƀahm㈍㈐㈓rò࿪aòՑ;怏oustĀ;a㈞㈟掱che»㈟mid;櫮Ȁabpt㈲㈽㉀㉒Ānr㈷㈺g;柭r;懾rëဃƀafl㉇㉊㉎r;榆;쀀𝕣us;樮imes;樵Āap㉝㉧rĀ;g㉣㉤䀩t;榔olint;樒arò㇣Ȁachq㉻㊀Ⴜ㊅quo;怺r;쀀𝓇Ābu・㊊oĀ;rȔȓƀhir㊗㊛㊠reåㇸmes;拊iȀ;efl㊪ၙᠡ㊫方tri;槎luhar;楨;愞ൡ㋕㋛㋟㌬㌸㍱\x00㍺㎤\x00\x00㏬㏰\x00㐨㑈㑚㒭㒱㓊㓱\x00㘖\x00\x00㘳cute;䅛quï➺Ԁ;Eaceinpsyᇭ㋳㋵㋿㌂㌋㌏㌟㌦㌩;檴ǰ㋺\x00㋼;檸on;䅡uåᇾĀ;dᇳ㌇il;䅟rc;䅝ƀEas㌖㌘㌛;檶p;檺im;择olint;樓iíሄ;䑁otƀ;be㌴ᵇ㌵担;橦΀Aacmstx㍆㍊㍗㍛㍞㍣㍭rr;懘rĀhr㍐㍒ë∨Ā;oਸ਼਴t耻§䂧i;䀻war;椩mĀin㍩ðnuóñt;朶rĀ;o㍶⁕쀀𝔰Ȁacoy㎂㎆㎑㎠rp;景Āhy㎋㎏cy;䑉;䑈rtɭ㎙\x00\x00㎜iäᑤaraì⹯耻­䂭Āgm㎨㎴maƀ;fv㎱㎲㎲䏃;䏂Ѐ;deglnprካ㏅㏉㏎㏖㏞㏡㏦ot;橪Ā;q኱ኰĀ;E㏓㏔檞;檠Ā;E㏛㏜檝;檟e;扆lus;樤arr;楲aròᄽȀaeit㏸㐈㐏㐗Āls㏽㐄lsetmé㍪hp;樳parsl;槤Ādlᑣ㐔e;挣Ā;e㐜㐝檪Ā;s㐢㐣檬;쀀⪬︀ƀflp㐮㐳㑂tcy;䑌Ā;b㐸㐹䀯Ā;a㐾㐿槄r;挿f;쀀𝕤aĀdr㑍ЂesĀ;u㑔㑕晠it»㑕ƀcsu㑠㑹㒟Āau㑥㑯pĀ;sᆈ㑫;쀀⊓︀pĀ;sᆴ㑵;쀀⊔︀uĀbp㑿㒏ƀ;esᆗᆜ㒆etĀ;eᆗ㒍ñᆝƀ;esᆨᆭ㒖etĀ;eᆨ㒝ñᆮƀ;afᅻ㒦ְrť㒫ֱ»ᅼaròᅈȀcemt㒹㒾㓂㓅r;쀀𝓈tmîñiì㐕aræᆾĀar㓎㓕rĀ;f㓔ឿ昆Āan㓚㓭ightĀep㓣㓪psiloîỠhé⺯s»⡒ʀbcmnp㓻㕞ሉ㖋㖎Ҁ;Edemnprs㔎㔏㔑㔕㔞㔣㔬㔱㔶抂;櫅ot;檽Ā;dᇚ㔚ot;櫃ult;櫁ĀEe㔨㔪;櫋;把lus;檿arr;楹ƀeiu㔽㕒㕕tƀ;en㔎㕅㕋qĀ;qᇚ㔏eqĀ;q㔫㔨m;櫇Ābp㕚㕜;櫕;櫓c̀;acensᇭ㕬㕲㕹㕻㌦pproø㋺urlyeñᇾñᇳƀaes㖂㖈㌛pproø㌚qñ㌗g;晪ڀ123;Edehlmnps㖩㖬㖯ሜ㖲㖴㗀㗉㗕㗚㗟㗨㗭耻¹䂹耻²䂲耻³䂳;櫆Āos㖹㖼t;檾ub;櫘Ā;dሢ㗅ot;櫄sĀou㗏㗒l;柉b;櫗arr;楻ult;櫂ĀEe㗤㗦;櫌;抋lus;櫀ƀeiu㗴㘉㘌tƀ;enሜ㗼㘂qĀ;qሢ㖲eqĀ;q㗧㗤m;櫈Ābp㘑㘓;櫔;櫖ƀAan㘜㘠㘭rr;懙rĀhr㘦㘨ë∮Ā;oਫ਩war;椪lig耻ß䃟௡㙑㙝㙠ዎ㙳㙹\x00㙾㛂\x00\x00\x00\x00\x00㛛㜃\x00㜉㝬\x00\x00\x00㞇ɲ㙖\x00\x00㙛get;挖;䏄rë๟ƀaey㙦㙫㙰ron;䅥dil;䅣;䑂lrec;挕r;쀀𝔱Ȁeiko㚆㚝㚵㚼Dz㚋\x00㚑eĀ4fኄኁaƀ;sv㚘㚙㚛䎸ym;䏑Ācn㚢㚲kĀas㚨㚮pproø዁im»ኬsðኞĀas㚺㚮ð዁rn耻þ䃾Ǭ̟㛆⋧es膀×;bd㛏㛐㛘䃗Ā;aᤏ㛕r;樱;樰ƀeps㛡㛣㜀á⩍Ȁ;bcf҆㛬㛰㛴ot;挶ir;櫱Ā;o㛹㛼쀀𝕥rk;櫚á㍢rime;怴ƀaip㜏㜒㝤dåቈ΀adempst㜡㝍㝀㝑㝗㝜㝟ngleʀ;dlqr㜰㜱㜶㝀㝂斵own»ᶻeftĀ;e⠀㜾ñम;扜ightĀ;e㊪㝋ñၚot;旬inus;樺lus;樹b;槍ime;樻ezium;揢ƀcht㝲㝽㞁Āry㝷㝻;쀀𝓉;䑆cy;䑛rok;䅧Āio㞋㞎xô᝷headĀlr㞗㞠eftarro÷ࡏightarrow»ཝऀAHabcdfghlmoprstuw㟐㟓㟗㟤㟰㟼㠎㠜㠣㠴㡑㡝㡫㢩㣌㣒㣪㣶ròϭar;楣Ācr㟜㟢ute耻ú䃺òᅐrǣ㟪\x00㟭y;䑞ve;䅭Āiy㟵㟺rc耻û䃻;䑃ƀabh㠃㠆㠋ròᎭlac;䅱aòᏃĀir㠓㠘sht;楾;쀀𝔲rave耻ù䃹š㠧㠱rĀlr㠬㠮»ॗ»ႃlk;斀Āct㠹㡍ɯ㠿\x00\x00㡊rnĀ;e㡅㡆挜r»㡆op;挏ri;旸Āal㡖㡚cr;䅫肻¨͉Āgp㡢㡦on;䅳f;쀀𝕦̀adhlsuᅋ㡸㡽፲㢑㢠ownáᎳarpoonĀlr㢈㢌efô㠭ighô㠯iƀ;hl㢙㢚㢜䏅»ᏺon»㢚parrows;懈ƀcit㢰㣄㣈ɯ㢶\x00\x00㣁rnĀ;e㢼㢽挝r»㢽op;挎ng;䅯ri;旹cr;쀀𝓊ƀdir㣙㣝㣢ot;拰lde;䅩iĀ;f㜰㣨»᠓Āam㣯㣲rò㢨l耻ü䃼angle;榧ހABDacdeflnoprsz㤜㤟㤩㤭㦵㦸㦽㧟㧤㧨㧳㧹㧽㨁㨠ròϷarĀ;v㤦㤧櫨;櫩asèϡĀnr㤲㤷grt;榜΀eknprst㓣㥆㥋㥒㥝㥤㦖appá␕othinçẖƀhir㓫⻈㥙opô⾵Ā;hᎷ㥢ïㆍĀiu㥩㥭gmá㎳Ābp㥲㦄setneqĀ;q㥽㦀쀀⊊︀;쀀⫋︀setneqĀ;q㦏㦒쀀⊋︀;쀀⫌︀Āhr㦛㦟etá㚜iangleĀlr㦪㦯eft»थight»ၑy;䐲ash»ံƀelr㧄㧒㧗ƀ;beⷪ㧋㧏ar;抻q;扚lip;拮Ābt㧜ᑨaòᑩr;쀀𝔳tré㦮suĀbp㧯㧱»ജ»൙pf;쀀𝕧roð໻tré㦴Ācu㨆㨋r;쀀𝓋Ābp㨐㨘nĀEe㦀㨖»㥾nĀEe㦒㨞»㦐igzag;榚΀cefoprs㨶㨻㩖㩛㩔㩡㩪irc;䅵Ādi㩀㩑Ābg㩅㩉ar;機eĀ;qᗺ㩏;扙erp;愘r;쀀𝔴pf;쀀𝕨Ā;eᑹ㩦atèᑹcr;쀀𝓌ૣណ㪇\x00㪋\x00㪐㪛\x00\x00㪝㪨㪫㪯\x00\x00㫃㫎\x00㫘ៜ៟tré៑r;쀀𝔵ĀAa㪔㪗ròσrò৶;䎾ĀAa㪡㪤ròθrò৫að✓is;拻ƀdptឤ㪵㪾Āfl㪺ឩ;쀀𝕩imåឲĀAa㫇㫊ròώròਁĀcq㫒ីr;쀀𝓍Āpt៖㫜ré។Ѐacefiosu㫰㫽㬈㬌㬑㬕㬛㬡cĀuy㫶㫻te耻ý䃽;䑏Āiy㬂㬆rc;䅷;䑋n耻¥䂥r;쀀𝔶cy;䑗pf;쀀𝕪cr;쀀𝓎Ācm㬦㬩y;䑎l耻ÿ䃿Ԁacdefhiosw㭂㭈㭔㭘㭤㭩㭭㭴㭺㮀cute;䅺Āay㭍㭒ron;䅾;䐷ot;䅼Āet㭝㭡træᕟa;䎶r;쀀𝔷cy;䐶grarr;懝pf;쀀𝕫cr;쀀𝓏Ājn㮅㮇;怍j;怌'[_0x235866(0x29d0)]('')[_0x235866(0x2c94)](_0x16b2b0=>_0x16b2b0[_0x235866(0x1806)](0x0))),_0x1142a8=new Uint16Array(_0x235866(0x1bd6)[_0x235866(0x29d0)]('')[_0x235866(0x2c94)](_0x39ec90=>_0x39ec90[_0x235866(0x1806)](0x0)));var _0x11399a;const _0x5ee96b=new Map([[0x0,0xfffd],[0x80,0x20ac],[0x82,0x201a],[0x83,0x192],[0x84,0x201e],[0x85,0x2026],[0x86,0x2020],[0x87,0x2021],[0x88,0x2c6],[0x89,0x2030],[0x8a,0x160],[0x8b,0x2039],[0x8c,0x152],[0x8e,0x17d],[0x91,0x2018],[0x92,0x2019],[0x93,0x201c],[0x94,0x201d],[0x95,0x2022],[0x96,0x2013],[0x97,0x2014],[0x98,0x2dc],[0x99,0x2122],[0x9a,0x161],[0x9b,0x203a],[0x9c,0x153],[0x9e,0x17e],[0x9f,0x178]]),_0x4658e1=null!==(_0x11399a=String['fromCodePoint'])&&void 0x0!==_0x11399a?_0x11399a:function(_0x502bf2){const _0x4d3ecc=_0x235866;let _0x101fa5='';return _0x502bf2>0xffff&&(_0x502bf2-=0x10000,_0x101fa5+=String[_0x4d3ecc(0xc78)](_0x502bf2>>>0xa&0x3ff|0xd800),_0x502bf2=0xdc00|0x3ff&_0x502bf2),_0x101fa5+=String[_0x4d3ecc(0xc78)](_0x502bf2),_0x101fa5;};function _0x9e292e(_0x332042){const _0x267e2e=_0x235866;var _0x309a34;return _0x332042>=0xd800&&_0x332042<=0xdfff||_0x332042>0x10ffff?0xfffd:null!==(_0x309a34=_0x5ee96b[_0x267e2e(0x2685)](_0x332042))&&void 0x0!==_0x309a34?_0x309a34:_0x332042;}var _0x31525a;!function(_0x532265){const _0x4e01c1=_0x235866;_0x532265[_0x532265[_0x4e01c1(0x1ac4)]=0x23]=_0x4e01c1(0x1ac4),_0x532265[_0x532265[_0x4e01c1(0xb76)]=0x3b]='SEMI',_0x532265[_0x532265['EQUALS']=0x3d]=_0x4e01c1(0xc1d),_0x532265[_0x532265[_0x4e01c1(0x340c)]=0x30]=_0x4e01c1(0x340c),_0x532265[_0x532265[_0x4e01c1(0x1a3e)]=0x39]='NINE',_0x532265[_0x532265[_0x4e01c1(0xed)]=0x61]=_0x4e01c1(0xed),_0x532265[_0x532265[_0x4e01c1(0x2bb0)]=0x66]='LOWER_F',_0x532265[_0x532265['LOWER_X']=0x78]=_0x4e01c1(0x761),_0x532265[_0x532265['LOWER_Z']=0x7a]='LOWER_Z',_0x532265[_0x532265[_0x4e01c1(0x3ef5)]=0x41]=_0x4e01c1(0x3ef5),_0x532265[_0x532265['UPPER_F']=0x46]='UPPER_F',_0x532265[_0x532265['UPPER_Z']=0x5a]=_0x4e01c1(0x1b0b);}(_0x31525a||(_0x31525a={}));var _0x1cc853,_0x52231d,_0x1ed13d;function _0x475ff8(_0x3bb27c){const _0x495ed8=_0x235866;return _0x3bb27c>=_0x31525a[_0x495ed8(0x340c)]&&_0x3bb27c<=_0x31525a[_0x495ed8(0x1a3e)];}function _0x488e07(_0x54e322){const _0x204a54=_0x235866;return _0x54e322>=_0x31525a[_0x204a54(0x3ef5)]&&_0x54e322<=_0x31525a[_0x204a54(0x1872)]||_0x54e322>=_0x31525a[_0x204a54(0xed)]&&_0x54e322<=_0x31525a[_0x204a54(0x2bb0)];}function _0x183e45(_0x4ebeda){return _0x4ebeda===_0x31525a['EQUALS']||function(_0x4ed4ea){const _0x2a90f9=a0_0x329b;return _0x4ed4ea>=_0x31525a[_0x2a90f9(0x3ef5)]&&_0x4ed4ea<=_0x31525a[_0x2a90f9(0x1b0b)]||_0x4ed4ea>=_0x31525a[_0x2a90f9(0xed)]&&_0x4ed4ea<=_0x31525a[_0x2a90f9(0x2abe)]||_0x475ff8(_0x4ed4ea);}(_0x4ebeda);}!function(_0x5a83d0){const _0x3f04e1=_0x235866;_0x5a83d0[_0x5a83d0[_0x3f04e1(0x1ed2)]=0xc000]=_0x3f04e1(0x1ed2),_0x5a83d0[_0x5a83d0[_0x3f04e1(0x2461)]=0x3f80]=_0x3f04e1(0x2461),_0x5a83d0[_0x5a83d0[_0x3f04e1(0xa86)]=0x7f]=_0x3f04e1(0xa86);}(_0x1cc853||(_0x1cc853={})),function(_0x299229){const _0x45de69=_0x235866;_0x299229[_0x299229[_0x45de69(0x6e0)]=0x0]='EntityStart',_0x299229[_0x299229['NumericStart']=0x1]='NumericStart',_0x299229[_0x299229[_0x45de69(0x2204)]=0x2]='NumericDecimal',_0x299229[_0x299229[_0x45de69(0x2fd7)]=0x3]=_0x45de69(0x2fd7),_0x299229[_0x299229['NamedEntity']=0x4]=_0x45de69(0x3ae9);}(_0x52231d||(_0x52231d={})),function(_0x2447b3){const _0x1461e8=_0x235866;_0x2447b3[_0x2447b3[_0x1461e8(0x3494)]=0x0]=_0x1461e8(0x3494),_0x2447b3[_0x2447b3[_0x1461e8(0x1851)]=0x1]=_0x1461e8(0x1851),_0x2447b3[_0x2447b3[_0x1461e8(0x3ba)]=0x2]=_0x1461e8(0x3ba);}(_0x1ed13d||(_0x1ed13d={}));class _0x1c99eb{constructor(_0x304c79,_0x3ebcc8,_0x24b9e0){const _0x4ff850=_0x235866;this['decodeTree']=_0x304c79,this['emitCodePoint']=_0x3ebcc8,this[_0x4ff850(0x2f3f)]=_0x24b9e0,this[_0x4ff850(0x3b95)]=_0x52231d[_0x4ff850(0x6e0)],this['consumed']=0x1,this[_0x4ff850(0x209a)]=0x0,this[_0x4ff850(0x44f0)]=0x0,this[_0x4ff850(0x1062)]=0x1,this[_0x4ff850(0x35a7)]=_0x1ed13d[_0x4ff850(0x1851)];}[_0x235866(0x42f)](_0x21f799){const _0x19de1c=_0x235866;this[_0x19de1c(0x35a7)]=_0x21f799,this[_0x19de1c(0x3b95)]=_0x52231d[_0x19de1c(0x6e0)],this[_0x19de1c(0x209a)]=0x0,this['treeIndex']=0x0,this[_0x19de1c(0x1062)]=0x1,this['consumed']=0x1;}[_0x235866(0xe9)](_0x285663,_0x175ee8){const _0x5ba2ce=_0x235866;switch(this['state']){case _0x52231d[_0x5ba2ce(0x6e0)]:return _0x285663[_0x5ba2ce(0x1806)](_0x175ee8)===_0x31525a[_0x5ba2ce(0x1ac4)]?(this[_0x5ba2ce(0x3b95)]=_0x52231d['NumericStart'],this[_0x5ba2ce(0x4791)]+=0x1,this[_0x5ba2ce(0x3f43)](_0x285663,_0x175ee8+0x1)):(this[_0x5ba2ce(0x3b95)]=_0x52231d[_0x5ba2ce(0x3ae9)],this['stateNamedEntity'](_0x285663,_0x175ee8));case _0x52231d[_0x5ba2ce(0xad4)]:return this[_0x5ba2ce(0x3f43)](_0x285663,_0x175ee8);case _0x52231d['NumericDecimal']:return this[_0x5ba2ce(0xebd)](_0x285663,_0x175ee8);case _0x52231d[_0x5ba2ce(0x2fd7)]:return this['stateNumericHex'](_0x285663,_0x175ee8);case _0x52231d[_0x5ba2ce(0x3ae9)]:return this[_0x5ba2ce(0x3b00)](_0x285663,_0x175ee8);}}[_0x235866(0x3f43)](_0xe5d459,_0x2da658){const _0x296f3d=_0x235866;return _0x2da658>=_0xe5d459[_0x296f3d(0x205b)]?-0x1:(0x20|_0xe5d459[_0x296f3d(0x1806)](_0x2da658))===_0x31525a[_0x296f3d(0x761)]?(this[_0x296f3d(0x3b95)]=_0x52231d['NumericHex'],this[_0x296f3d(0x4791)]+=0x1,this[_0x296f3d(0x1966)](_0xe5d459,_0x2da658+0x1)):(this[_0x296f3d(0x3b95)]=_0x52231d[_0x296f3d(0x2204)],this[_0x296f3d(0xebd)](_0xe5d459,_0x2da658));}[_0x235866(0x3b1f)](_0x422d15,_0x3309fc,_0x10944e,_0x3812dd){const _0xe2b669=_0x235866;if(_0x3309fc!==_0x10944e){const _0x45c20d=_0x10944e-_0x3309fc;this[_0xe2b669(0x209a)]=this['result']*Math[_0xe2b669(0xec3)](_0x3812dd,_0x45c20d)+parseInt(_0x422d15[_0xe2b669(0x32a4)](_0x3309fc,_0x45c20d),_0x3812dd),this[_0xe2b669(0x4791)]+=_0x45c20d;}}[_0x235866(0x1966)](_0x1396b4,_0x2db20a){const _0xb0e22c=_0x235866,_0x14668=_0x2db20a;for(;_0x2db20a<_0x1396b4['length'];){const _0x18fd3e=_0x1396b4[_0xb0e22c(0x1806)](_0x2db20a);if(!_0x475ff8(_0x18fd3e)&&!_0x488e07(_0x18fd3e))return this[_0xb0e22c(0x3b1f)](_0x1396b4,_0x14668,_0x2db20a,0x10),this[_0xb0e22c(0x44e8)](_0x18fd3e,0x3);_0x2db20a+=0x1;}return this[_0xb0e22c(0x3b1f)](_0x1396b4,_0x14668,_0x2db20a,0x10),-0x1;}[_0x235866(0xebd)](_0x286731,_0x553029){const _0x5ca6c7=_0x235866,_0x4e1edd=_0x553029;for(;_0x553029<_0x286731[_0x5ca6c7(0x205b)];){const _0x697d09=_0x286731[_0x5ca6c7(0x1806)](_0x553029);if(!_0x475ff8(_0x697d09))return this['addToNumericResult'](_0x286731,_0x4e1edd,_0x553029,0xa),this[_0x5ca6c7(0x44e8)](_0x697d09,0x2);_0x553029+=0x1;}return this[_0x5ca6c7(0x3b1f)](_0x286731,_0x4e1edd,_0x553029,0xa),-0x1;}[_0x235866(0x44e8)](_0x107a4e,_0x217d2d){const _0x539600=_0x235866;var _0x3f8d3b;if(this[_0x539600(0x4791)]<=_0x217d2d)return null===(_0x3f8d3b=this[_0x539600(0x2f3f)])||void 0x0===_0x3f8d3b||_0x3f8d3b['absenceOfDigitsInNumericCharacterReference'](this[_0x539600(0x4791)]),0x0;if(_0x107a4e===_0x31525a[_0x539600(0xb76)])this['consumed']+=0x1;else{if(this['decodeMode']===_0x1ed13d[_0x539600(0x1851)])return 0x0;}return this['emitCodePoint'](_0x9e292e(this[_0x539600(0x209a)]),this[_0x539600(0x4791)]),this[_0x539600(0x2f3f)]&&(_0x107a4e!==_0x31525a[_0x539600(0xb76)]&&this[_0x539600(0x2f3f)][_0x539600(0x1864)](),this[_0x539600(0x2f3f)][_0x539600(0x5bb)](this[_0x539600(0x209a)])),this['consumed'];}[_0x235866(0x3b00)](_0x3d7360,_0x4630d2){const _0x280774=_0x235866,{decodeTree:_0x435a76}=this;let _0x350522=_0x435a76[this['treeIndex']],_0x192ea3=(_0x350522&_0x1cc853[_0x280774(0x1ed2)])>>0xe;for(;_0x4630d2<_0x3d7360[_0x280774(0x205b)];_0x4630d2++,this[_0x280774(0x1062)]++){const _0x349a98=_0x3d7360['charCodeAt'](_0x4630d2);if(this[_0x280774(0x44f0)]=_0x5ae7ad(_0x435a76,_0x350522,this[_0x280774(0x44f0)]+Math['max'](0x1,_0x192ea3),_0x349a98),this[_0x280774(0x44f0)]<0x0)return 0x0===this[_0x280774(0x209a)]||this['decodeMode']===_0x1ed13d[_0x280774(0x3ba)]&&(0x0===_0x192ea3||_0x183e45(_0x349a98))?0x0:this[_0x280774(0x3107)]();if(_0x350522=_0x435a76[this[_0x280774(0x44f0)]],_0x192ea3=(_0x350522&_0x1cc853[_0x280774(0x1ed2)])>>0xe,0x0!==_0x192ea3){if(_0x349a98===_0x31525a[_0x280774(0xb76)])return this[_0x280774(0xe52)](this[_0x280774(0x44f0)],_0x192ea3,this[_0x280774(0x4791)]+this['excess']);this[_0x280774(0x35a7)]!==_0x1ed13d['Strict']&&(this['result']=this[_0x280774(0x44f0)],this[_0x280774(0x4791)]+=this[_0x280774(0x1062)],this[_0x280774(0x1062)]=0x0);}}return-0x1;}[_0x235866(0x3107)](){const _0x535d9d=_0x235866;var _0x358de8;const {result:_0x48f411,decodeTree:_0x45b9fa}=this,_0x673afb=(_0x45b9fa[_0x48f411]&_0x1cc853[_0x535d9d(0x1ed2)])>>0xe;return this[_0x535d9d(0xe52)](_0x48f411,_0x673afb,this[_0x535d9d(0x4791)]),null===(_0x358de8=this[_0x535d9d(0x2f3f)])||void 0x0===_0x358de8||_0x358de8[_0x535d9d(0x1864)](),this[_0x535d9d(0x4791)];}[_0x235866(0xe52)](_0x389ee0,_0x19edb5,_0xbedd20){const {decodeTree:_0x1988ec}=this;return this['emitCodePoint'](0x1===_0x19edb5?_0x1988ec[_0x389ee0]&~_0x1cc853['VALUE_LENGTH']:_0x1988ec[_0x389ee0+0x1],_0xbedd20),0x3===_0x19edb5&&this['emitCodePoint'](_0x1988ec[_0x389ee0+0x2],_0xbedd20),_0xbedd20;}[_0x235866(0x721)](){const _0x4a25b8=_0x235866;var _0x57b1d7;switch(this[_0x4a25b8(0x3b95)]){case _0x52231d[_0x4a25b8(0x3ae9)]:return 0x0===this['result']||this[_0x4a25b8(0x35a7)]===_0x1ed13d[_0x4a25b8(0x3ba)]&&this[_0x4a25b8(0x209a)]!==this[_0x4a25b8(0x44f0)]?0x0:this[_0x4a25b8(0x3107)]();case _0x52231d['NumericDecimal']:return this[_0x4a25b8(0x44e8)](0x0,0x2);case _0x52231d[_0x4a25b8(0x2fd7)]:return this['emitNumericEntity'](0x0,0x3);case _0x52231d[_0x4a25b8(0xad4)]:return null===(_0x57b1d7=this['errors'])||void 0x0===_0x57b1d7||_0x57b1d7[_0x4a25b8(0x1353)](this[_0x4a25b8(0x4791)]),0x0;case _0x52231d['EntityStart']:return 0x0;}}}function _0x4005be(_0x49c92a){let _0x835052='';const _0x234584=new _0x1c99eb(_0x49c92a,_0x26edf6=>_0x835052+=_0x4658e1(_0x26edf6));return function(_0x1163d7,_0x5ae190){const _0x329dd9=a0_0x329b;let _0x38d29b=0x0,_0x536a71=0x0;for(;(_0x536a71=_0x1163d7[_0x329dd9(0x3458)]('&',_0x536a71))>=0x0;){_0x835052+=_0x1163d7[_0x329dd9(0x428e)](_0x38d29b,_0x536a71),_0x234584[_0x329dd9(0x42f)](_0x5ae190);const _0x1ca2a6=_0x234584[_0x329dd9(0xe9)](_0x1163d7,_0x536a71+0x1);if(_0x1ca2a6<0x0){_0x38d29b=_0x536a71+_0x234584[_0x329dd9(0x721)]();break;}_0x38d29b=_0x536a71+_0x1ca2a6,_0x536a71=0x0===_0x1ca2a6?_0x38d29b+0x1:_0x38d29b;}const _0x1f7828=_0x835052+_0x1163d7[_0x329dd9(0x428e)](_0x38d29b);return _0x835052='',_0x1f7828;};}function _0x5ae7ad(_0x42b818,_0x4b2e68,_0x28168c,_0x5a9ee4){const _0x415bd3=_0x235866,_0x1e4788=(_0x4b2e68&_0x1cc853[_0x415bd3(0x2461)])>>0x7,_0x8e82ba=_0x4b2e68&_0x1cc853[_0x415bd3(0xa86)];if(0x0===_0x1e4788)return 0x0!==_0x8e82ba&&_0x5a9ee4===_0x8e82ba?_0x28168c:-0x1;if(_0x8e82ba){const _0xa9cd4a=_0x5a9ee4-_0x8e82ba;return _0xa9cd4a<0x0||_0xa9cd4a>=_0x1e4788?-0x1:_0x42b818[_0x28168c+_0xa9cd4a]-0x1;}let _0x416bb0=_0x28168c,_0x2815ba=_0x416bb0+_0x1e4788-0x1;for(;_0x416bb0<=_0x2815ba;){const _0x203cdd=_0x416bb0+_0x2815ba>>>0x1,_0x35276f=_0x42b818[_0x203cdd];if(_0x35276f<_0x5a9ee4)_0x416bb0=_0x203cdd+0x1;else{if(!(_0x35276f>_0x5a9ee4))return _0x42b818[_0x203cdd+_0x1e4788];_0x2815ba=_0x203cdd-0x1;}}return-0x1;}const _0x513190=_0x4005be(_0x4783e9);_0x4005be(_0x1142a8);function _0xc30b02(_0x45056d,_0x4aa617=_0x1ed13d['Legacy']){return _0x513190(_0x45056d,_0x4aa617);}function _0x3aa700(_0x18f417){const _0x111636=_0x235866;for(let _0x4f4a01=0x1;_0x4f4a01<_0x18f417[_0x111636(0x205b)];_0x4f4a01++)_0x18f417[_0x4f4a01][0x0]+=_0x18f417[_0x4f4a01-0x1][0x0]+0x1;return _0x18f417;}new 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RegExp(_0x235866(0x200)+[_0x235866(0xf30),_0x235866(0x2df9),_0x235866(0x3162),_0x235866(0x389b),_0x235866(0xe6b),_0x235866(0x30ef),_0x235866(0x3673),'caption','center','col','colgroup','dd',_0x235866(0x752),_0x235866(0x11d),_0x235866(0x1301),_0x235866(0x257d),'dl','dt',_0x235866(0x2f64),_0x235866(0x2f34),_0x235866(0x35b0),_0x235866(0x1f2b),_0x235866(0x377b),_0x235866(0x289d),_0x235866(0x465e),'h1','h2','h3','h4','h5','h6',_0x235866(0x20c7),_0x235866(0x164d),'hr','html','iframe','legend','li',_0x235866(0x3622),_0x235866(0x2589),'menu',_0x235866(0xa8f),_0x235866(0x37c5),_0x235866(0x2378),'ol',_0x235866(0x365a),_0x235866(0x15f4),'p',_0x235866(0x46dd),_0x235866(0x386b),_0x235866(0x3b28),_0x235866(0xe54),_0x235866(0x14b9),_0x235866(0x359a),'td',_0x235866(0x8e),'th',_0x235866(0x1db3),_0x235866(0x5f1),'tr',_0x235866(0x58d),'ul'][_0x235866(0x379c)]('|')+_0x235866(0xc66),'i'),/^$/,!0x0],[new RegExp(_0xeec757['source']+_0x235866(0x446a)),/^$/,!0x1]],_0x48c7d7=[[_0x235866(0x14b9),function(_0x2cfc8a,_0x4e81e5,_0x2e93c4,_0x1d9fcd){const _0x14e972=_0x235866;if(_0x4e81e5+0x2>_0x2e93c4)return!0x1;let _0x2c3e8d=_0x4e81e5+0x1;if(_0x2cfc8a[_0x14e972(0x11e9)][_0x2c3e8d]<_0x2cfc8a[_0x14e972(0x2c69)])return!0x1;if(_0x2cfc8a[_0x14e972(0x11e9)][_0x2c3e8d]-_0x2cfc8a[_0x14e972(0x2c69)]>=0x4)return!0x1;let _0x17d645=_0x2cfc8a[_0x14e972(0x45ae)][_0x2c3e8d]+_0x2cfc8a[_0x14e972(0x1d33)][_0x2c3e8d];if(_0x17d645>=_0x2cfc8a['eMarks'][_0x2c3e8d])return!0x1;const _0x5c7d58=_0x2cfc8a[_0x14e972(0x1005)][_0x14e972(0x1806)](_0x17d645++);if(0x7c!==_0x5c7d58&&0x2d!==_0x5c7d58&&0x3a!==_0x5c7d58)return!0x1;if(_0x17d645>=_0x2cfc8a[_0x14e972(0x1908)][_0x2c3e8d])return!0x1;const _0x4c0a6d=_0x2cfc8a[_0x14e972(0x1005)][_0x14e972(0x1806)](_0x17d645++);if(0x7c!==_0x4c0a6d&&0x2d!==_0x4c0a6d&&0x3a!==_0x4c0a6d&&!_0x2578e9(_0x4c0a6d))return!0x1;if(0x2d===_0x5c7d58&&_0x2578e9(_0x4c0a6d))return!0x1;for(;_0x17d645<_0x2cfc8a[_0x14e972(0x1908)][_0x2c3e8d];){const _0x596c0a=_0x2cfc8a[_0x14e972(0x1005)][_0x14e972(0x1806)](_0x17d645);if(0x7c!==_0x596c0a&&0x2d!==_0x596c0a&&0x3a!==_0x596c0a&&!_0x2578e9(_0x596c0a))return!0x1;_0x17d645++;}let _0x32725b=_0x128f72(_0x2cfc8a,_0x4e81e5+0x1),_0x2463bc=_0x32725b[_0x14e972(0x29d0)]('|');const _0x4df18f=[];for(let _0x54c157=0x0;_0x54c157<_0x2463bc[_0x14e972(0x205b)];_0x54c157++){const _0x4d3ccc=_0x2463bc[_0x54c157][_0x14e972(0x1d0e)]();if(!_0x4d3ccc){if(0x0===_0x54c157||_0x54c157===_0x2463bc[_0x14e972(0x205b)]-0x1)continue;return!0x1;}if(!/^:?-+:?$/[_0x14e972(0x34c)](_0x4d3ccc))return!0x1;0x3a===_0x4d3ccc['charCodeAt'](_0x4d3ccc[_0x14e972(0x205b)]-0x1)?_0x4df18f['push'](0x3a===_0x4d3ccc[_0x14e972(0x1806)](0x0)?_0x14e972(0xadf):'right'):0x3a===_0x4d3ccc[_0x14e972(0x1806)](0x0)?_0x4df18f[_0x14e972(0x4131)](_0x14e972(0x24d0)):_0x4df18f['push']('');}if(_0x32725b=_0x128f72(_0x2cfc8a,_0x4e81e5)[_0x14e972(0x1d0e)](),-0x1===_0x32725b[_0x14e972(0x3458)]('|'))return!0x1;if(_0x2cfc8a['sCount'][_0x4e81e5]-_0x2cfc8a[_0x14e972(0x2c69)]>=0x4)return!0x1;_0x2463bc=_0x27ee7d(_0x32725b),_0x2463bc['length']&&''===_0x2463bc[0x0]&&_0x2463bc[_0x14e972(0x13fc)](),_0x2463bc[_0x14e972(0x205b)]&&''===_0x2463bc[_0x2463bc['length']-0x1]&&_0x2463bc[_0x14e972(0x2895)]();const _0x2e1049=_0x2463bc[_0x14e972(0x205b)];if(0x0===_0x2e1049||_0x2e1049!==_0x4df18f['length'])return!0x1;if(_0x1d9fcd)return!0x0;const _0x66d444=_0x2cfc8a[_0x14e972(0x1581)];_0x2cfc8a[_0x14e972(0x1581)]=_0x14e972(0x14b9);const _0x2a2d89=_0x2cfc8a['md'][_0x14e972(0x3eab)][_0x14e972(0x536)][_0x14e972(0x68d)](_0x14e972(0x30ef)),_0x59b454=[_0x4e81e5,0x0];_0x2cfc8a[_0x14e972(0x4131)](_0x14e972(0x3e30),'table',0x1)[_0x14e972(0x2c94)]=_0x59b454,_0x2cfc8a['push']('thead_open',_0x14e972(0x1db3),0x1)[_0x14e972(0x2c94)]=[_0x4e81e5,_0x4e81e5+0x1],_0x2cfc8a[_0x14e972(0x4131)]('tr_open','tr',0x1)[_0x14e972(0x2c94)]=[_0x4e81e5,_0x4e81e5+0x1];for(let _0x16eb19=0x0;_0x16eb19<_0x2463bc[_0x14e972(0x205b)];_0x16eb19++){const _0x522ae5=_0x2cfc8a['push'](_0x14e972(0x2f03),'th',0x1);_0x4df18f[_0x16eb19]&&(_0x522ae5[_0x14e972(0x3d7f)]=[[_0x14e972(0x3098),_0x14e972(0x3071)+_0x4df18f[_0x16eb19]]]);const _0xdfb2f1=_0x2cfc8a['push'](_0x14e972(0x50d2),'',0x0);_0xdfb2f1[_0x14e972(0x498f)]=_0x2463bc[_0x16eb19]['trim'](),_0xdfb2f1[_0x14e972(0x4538)]=[],_0x2cfc8a[_0x14e972(0x4131)](_0x14e972(0x4995),'th',-0x1);}let _0x20ce86;for(_0x2cfc8a['push'](_0x14e972(0x4c40),'tr',-0x1),_0x2cfc8a['push'](_0x14e972(0x2e1d),_0x14e972(0x1db3),-0x1),_0x2c3e8d=_0x4e81e5+0x2;_0x2c3e8d<_0x2e93c4&&!(_0x2cfc8a[_0x14e972(0x11e9)][_0x2c3e8d]<_0x2cfc8a['blkIndent']);_0x2c3e8d++){let _0x51716c=!0x1;for(let _0x476c07=0x0,_0x4c96b5=_0x2a2d89[_0x14e972(0x205b)];_0x476c07<_0x4c96b5;_0x476c07++)if(_0x2a2d89[_0x476c07](_0x2cfc8a,_0x2c3e8d,_0x2e93c4,!0x0)){_0x51716c=!0x0;break;}if(_0x51716c)break;if(_0x32725b=_0x128f72(_0x2cfc8a,_0x2c3e8d)[_0x14e972(0x1d0e)](),!_0x32725b)break;if(_0x2cfc8a[_0x14e972(0x11e9)][_0x2c3e8d]-_0x2cfc8a[_0x14e972(0x2c69)]>=0x4)break;(_0x2463bc=_0x27ee7d(_0x32725b),_0x2463bc[_0x14e972(0x205b)]&&''===_0x2463bc[0x0]&&_0x2463bc[_0x14e972(0x13fc)](),_0x2463bc[_0x14e972(0x205b)]&&''===_0x2463bc[_0x2463bc[_0x14e972(0x205b)]-0x1]&&_0x2463bc[_0x14e972(0x2895)](),_0x2c3e8d===_0x4e81e5+0x2)&&(_0x2cfc8a[_0x14e972(0x4131)](_0x14e972(0x24b),_0x14e972(0x359a),0x1)['map']=_0x20ce86=[_0x4e81e5+0x2,0x0]);_0x2cfc8a[_0x14e972(0x4131)](_0x14e972(0x3a93),'tr',0x1)[_0x14e972(0x2c94)]=[_0x2c3e8d,_0x2c3e8d+0x1];for(let _0x18132f=0x0;_0x18132f<_0x2e1049;_0x18132f++){const _0x2d7e8b=_0x2cfc8a[_0x14e972(0x4131)](_0x14e972(0xb6c),'td',0x1);_0x4df18f[_0x18132f]&&(_0x2d7e8b[_0x14e972(0x3d7f)]=[['style',_0x14e972(0x3071)+_0x4df18f[_0x18132f]]]);const _0x4c4a5f=_0x2cfc8a[_0x14e972(0x4131)]('inline','',0x0);_0x4c4a5f[_0x14e972(0x498f)]=_0x2463bc[_0x18132f]?_0x2463bc[_0x18132f]['trim']():'',_0x4c4a5f[_0x14e972(0x4538)]=[],_0x2cfc8a[_0x14e972(0x4131)](_0x14e972(0x3083),'td',-0x1);}_0x2cfc8a[_0x14e972(0x4131)]('tr_close','tr',-0x1);}return _0x20ce86&&(_0x2cfc8a[_0x14e972(0x4131)](_0x14e972(0x3252),_0x14e972(0x359a),-0x1),_0x20ce86[0x1]=_0x2c3e8d),_0x2cfc8a[_0x14e972(0x4131)]('table_close',_0x14e972(0x14b9),-0x1),_0x59b454[0x1]=_0x2c3e8d,_0x2cfc8a[_0x14e972(0x1581)]=_0x66d444,_0x2cfc8a[_0x14e972(0x3dde)]=_0x2c3e8d,!0x0;},['paragraph',_0x235866(0x3085)]],[_0x235866(0xbd0),function(_0x205375,_0x576c37,_0x2e1893){const _0x59f323=_0x235866;if(_0x205375['sCount'][_0x576c37]-_0x205375[_0x59f323(0x2c69)]<0x4)return!0x1;let _0x296da5=_0x576c37+0x1,_0x343c02=_0x296da5;for(;_0x296da5<_0x2e1893;)if(_0x205375['isEmpty'](_0x296da5))_0x296da5++;else{if(!(_0x205375[_0x59f323(0x11e9)][_0x296da5]-_0x205375[_0x59f323(0x2c69)]>=0x4))break;_0x296da5++,_0x343c02=_0x296da5;}_0x205375[_0x59f323(0x3dde)]=_0x343c02;const _0x382ad1=_0x205375[_0x59f323(0x4131)](_0x59f323(0x95d),_0x59f323(0xbd0),0x0);return _0x382ad1[_0x59f323(0x498f)]=_0x205375[_0x59f323(0x3131)](_0x576c37,_0x343c02,0x4+_0x205375[_0x59f323(0x2c69)],!0x1)+'\x0a',_0x382ad1[_0x59f323(0x2c94)]=[_0x576c37,_0x205375[_0x59f323(0x3dde)]],!0x0;}],['fence',function(_0x4d1d04,_0x40f32f,_0xf2bcea,_0xa77eea){const _0x3a29fc=_0x235866;let _0x41ac15=_0x4d1d04[_0x3a29fc(0x45ae)][_0x40f32f]+_0x4d1d04[_0x3a29fc(0x1d33)][_0x40f32f],_0x3e01b4=_0x4d1d04[_0x3a29fc(0x1908)][_0x40f32f];if(_0x4d1d04[_0x3a29fc(0x11e9)][_0x40f32f]-_0x4d1d04[_0x3a29fc(0x2c69)]>=0x4)return!0x1;if(_0x41ac15+0x3>_0x3e01b4)return!0x1;const _0x5687dd=_0x4d1d04[_0x3a29fc(0x1005)][_0x3a29fc(0x1806)](_0x41ac15);if(0x7e!==_0x5687dd&&0x60!==_0x5687dd)return!0x1;let _0x3bd0e9=_0x41ac15;_0x41ac15=_0x4d1d04[_0x3a29fc(0x156)](_0x41ac15,_0x5687dd);let _0x18b28b=_0x41ac15-_0x3bd0e9;if(_0x18b28b<0x3)return!0x1;const _0x4451fb=_0x4d1d04['src'][_0x3a29fc(0x428e)](_0x3bd0e9,_0x41ac15),_0x13b7e6=_0x4d1d04['src'][_0x3a29fc(0x428e)](_0x41ac15,_0x3e01b4);if(0x60===_0x5687dd&&_0x13b7e6[_0x3a29fc(0x3458)](String[_0x3a29fc(0xc78)](_0x5687dd))>=0x0)return!0x1;if(_0xa77eea)return!0x0;let _0xef39fe=_0x40f32f,_0x4facd7=!0x1;for(;(_0xef39fe++,!(_0xef39fe>=_0xf2bcea))&&(_0x41ac15=_0x3bd0e9=_0x4d1d04[_0x3a29fc(0x45ae)][_0xef39fe]+_0x4d1d04[_0x3a29fc(0x1d33)][_0xef39fe],_0x3e01b4=_0x4d1d04['eMarks'][_0xef39fe],!(_0x41ac15<_0x3e01b4&&_0x4d1d04['sCount'][_0xef39fe]<_0x4d1d04['blkIndent']));)if(_0x4d1d04['src'][_0x3a29fc(0x1806)](_0x41ac15)===_0x5687dd&&!(_0x4d1d04[_0x3a29fc(0x11e9)][_0xef39fe]-_0x4d1d04['blkIndent']>=0x4||(_0x41ac15=_0x4d1d04[_0x3a29fc(0x156)](_0x41ac15,_0x5687dd),_0x41ac15-_0x3bd0e9<_0x18b28b||(_0x41ac15=_0x4d1d04[_0x3a29fc(0x28e7)](_0x41ac15),_0x41ac15<_0x3e01b4)))){_0x4facd7=!0x0;break;}_0x18b28b=_0x4d1d04['sCount'][_0x40f32f],_0x4d1d04[_0x3a29fc(0x3dde)]=_0xef39fe+(_0x4facd7?0x1:0x0);const _0x13523c=_0x4d1d04[_0x3a29fc(0x4131)](_0x3a29fc(0xecc),_0x3a29fc(0xbd0),0x0);return _0x13523c[_0x3a29fc(0x368d)]=_0x13b7e6,_0x13523c[_0x3a29fc(0x498f)]=_0x4d1d04[_0x3a29fc(0x3131)](_0x40f32f+0x1,_0xef39fe,_0x18b28b,!0x0),_0x13523c['markup']=_0x4451fb,_0x13523c[_0x3a29fc(0x2c94)]=[_0x40f32f,_0x4d1d04[_0x3a29fc(0x3dde)]],!0x0;},[_0x235866(0x2d53),'reference',_0x235866(0x30ef),_0x235866(0x20cf)]],[_0x235866(0x30ef),function(_0x31347e,_0x12eab5,_0x532d63,_0x557750){const _0xd1d726=_0x235866;let _0x3766f2=_0x31347e[_0xd1d726(0x45ae)][_0x12eab5]+_0x31347e[_0xd1d726(0x1d33)][_0x12eab5],_0x16091c=_0x31347e['eMarks'][_0x12eab5];const _0x57c915=_0x31347e[_0xd1d726(0x44dd)];if(_0x31347e[_0xd1d726(0x11e9)][_0x12eab5]-_0x31347e[_0xd1d726(0x2c69)]>=0x4)return!0x1;if(0x3e!==_0x31347e['src'][_0xd1d726(0x1806)](_0x3766f2))return!0x1;if(_0x557750)return!0x0;const _0x3b8709=[],_0x1cd298=[],_0x58c5df=[],_0x178bd3=[],_0x444958=_0x31347e['md'][_0xd1d726(0x3eab)][_0xd1d726(0x536)][_0xd1d726(0x68d)](_0xd1d726(0x30ef)),_0x377091=_0x31347e['parentType'];_0x31347e[_0xd1d726(0x1581)]='blockquote';let _0x3fdd1c,_0x13b722=!0x1;for(_0x3fdd1c=_0x12eab5;_0x3fdd1c<_0x532d63;_0x3fdd1c++){const _0xea7fbe=_0x31347e[_0xd1d726(0x11e9)][_0x3fdd1c]<_0x31347e[_0xd1d726(0x2c69)];if(_0x3766f2=_0x31347e[_0xd1d726(0x45ae)][_0x3fdd1c]+_0x31347e[_0xd1d726(0x1d33)][_0x3fdd1c],_0x16091c=_0x31347e[_0xd1d726(0x1908)][_0x3fdd1c],_0x3766f2>=_0x16091c)break;if(0x3e===_0x31347e[_0xd1d726(0x1005)][_0xd1d726(0x1806)](_0x3766f2++)&&!_0xea7fbe){let _0x3a5669,_0x24685b,_0x55920f=_0x31347e[_0xd1d726(0x11e9)][_0x3fdd1c]+0x1;0x20===_0x31347e[_0xd1d726(0x1005)]['charCodeAt'](_0x3766f2)?(_0x3766f2++,_0x55920f++,_0x24685b=!0x1,_0x3a5669=!0x0):0x9===_0x31347e[_0xd1d726(0x1005)][_0xd1d726(0x1806)](_0x3766f2)?(_0x3a5669=!0x0,(_0x31347e[_0xd1d726(0x2851)][_0x3fdd1c]+_0x55920f)%0x4==0x3?(_0x3766f2++,_0x55920f++,_0x24685b=!0x1):_0x24685b=!0x0):_0x3a5669=!0x1;let _0x1f5f7f=_0x55920f;for(_0x3b8709['push'](_0x31347e[_0xd1d726(0x45ae)][_0x3fdd1c]),_0x31347e[_0xd1d726(0x45ae)][_0x3fdd1c]=_0x3766f2;_0x3766f2<_0x16091c;){const _0xc668a9=_0x31347e[_0xd1d726(0x1005)][_0xd1d726(0x1806)](_0x3766f2);if(!_0x2578e9(_0xc668a9))break;0x9===_0xc668a9?_0x1f5f7f+=0x4-(_0x1f5f7f+_0x31347e[_0xd1d726(0x2851)][_0x3fdd1c]+(_0x24685b?0x1:0x0))%0x4:_0x1f5f7f++,_0x3766f2++;}_0x13b722=_0x3766f2>=_0x16091c,_0x1cd298[_0xd1d726(0x4131)](_0x31347e[_0xd1d726(0x2851)][_0x3fdd1c]),_0x31347e[_0xd1d726(0x2851)][_0x3fdd1c]=_0x31347e[_0xd1d726(0x11e9)][_0x3fdd1c]+0x1+(_0x3a5669?0x1:0x0),_0x58c5df[_0xd1d726(0x4131)](_0x31347e[_0xd1d726(0x11e9)][_0x3fdd1c]),_0x31347e[_0xd1d726(0x11e9)][_0x3fdd1c]=_0x1f5f7f-_0x55920f,_0x178bd3[_0xd1d726(0x4131)](_0x31347e[_0xd1d726(0x1d33)][_0x3fdd1c]),_0x31347e[_0xd1d726(0x1d33)][_0x3fdd1c]=_0x3766f2-_0x31347e[_0xd1d726(0x45ae)][_0x3fdd1c];continue;}if(_0x13b722)break;let _0x20a23e=!0x1;for(let _0x487af8=0x0,_0x13917f=_0x444958[_0xd1d726(0x205b)];_0x487af8<_0x13917f;_0x487af8++)if(_0x444958[_0x487af8](_0x31347e,_0x3fdd1c,_0x532d63,!0x0)){_0x20a23e=!0x0;break;}if(_0x20a23e){_0x31347e[_0xd1d726(0x44dd)]=_0x3fdd1c,0x0!==_0x31347e[_0xd1d726(0x2c69)]&&(_0x3b8709[_0xd1d726(0x4131)](_0x31347e['bMarks'][_0x3fdd1c]),_0x1cd298[_0xd1d726(0x4131)](_0x31347e[_0xd1d726(0x2851)][_0x3fdd1c]),_0x178bd3['push'](_0x31347e['tShift'][_0x3fdd1c]),_0x58c5df['push'](_0x31347e['sCount'][_0x3fdd1c]),_0x31347e[_0xd1d726(0x11e9)][_0x3fdd1c]-=_0x31347e['blkIndent']);break;}_0x3b8709[_0xd1d726(0x4131)](_0x31347e['bMarks'][_0x3fdd1c]),_0x1cd298[_0xd1d726(0x4131)](_0x31347e[_0xd1d726(0x2851)][_0x3fdd1c]),_0x178bd3[_0xd1d726(0x4131)](_0x31347e[_0xd1d726(0x1d33)][_0x3fdd1c]),_0x58c5df[_0xd1d726(0x4131)](_0x31347e[_0xd1d726(0x11e9)][_0x3fdd1c]),_0x31347e['sCount'][_0x3fdd1c]=-0x1;}const _0x54da3e=_0x31347e[_0xd1d726(0x2c69)];_0x31347e['blkIndent']=0x0;const _0x3b9e4e=_0x31347e[_0xd1d726(0x4131)]('blockquote_open',_0xd1d726(0x30ef),0x1);_0x3b9e4e[_0xd1d726(0x2767)]='>';const _0x306177=[_0x12eab5,0x0];_0x3b9e4e[_0xd1d726(0x2c94)]=_0x306177,_0x31347e['md'][_0xd1d726(0x3eab)][_0xd1d726(0x44f9)](_0x31347e,_0x12eab5,_0x3fdd1c),_0x31347e['push']('blockquote_close',_0xd1d726(0x30ef),-0x1)['markup']='>',_0x31347e[_0xd1d726(0x44dd)]=_0x57c915,_0x31347e[_0xd1d726(0x1581)]=_0x377091,_0x306177[0x1]=_0x31347e[_0xd1d726(0x3dde)];for(let _0x3fd377=0x0;_0x3fd377<_0x178bd3[_0xd1d726(0x205b)];_0x3fd377++)_0x31347e[_0xd1d726(0x45ae)][_0x3fd377+_0x12eab5]=_0x3b8709[_0x3fd377],_0x31347e['tShift'][_0x3fd377+_0x12eab5]=_0x178bd3[_0x3fd377],_0x31347e['sCount'][_0x3fd377+_0x12eab5]=_0x58c5df[_0x3fd377],_0x31347e['bsCount'][_0x3fd377+_0x12eab5]=_0x1cd298[_0x3fd377];return _0x31347e['blkIndent']=_0x54da3e,!0x0;},[_0x235866(0x2d53),_0x235866(0x3085),_0x235866(0x30ef),_0x235866(0x20cf)]],['hr',function(_0x215d63,_0x424f08,_0xa0f23d,_0x53df76){const _0x3da25c=_0x235866,_0xd56d9f=_0x215d63[_0x3da25c(0x1908)][_0x424f08];if(_0x215d63[_0x3da25c(0x11e9)][_0x424f08]-_0x215d63[_0x3da25c(0x2c69)]>=0x4)return!0x1;let _0xcbd894=_0x215d63[_0x3da25c(0x45ae)][_0x424f08]+_0x215d63[_0x3da25c(0x1d33)][_0x424f08];const _0x24a5fd=_0x215d63[_0x3da25c(0x1005)][_0x3da25c(0x1806)](_0xcbd894++);if(0x2a!==_0x24a5fd&&0x2d!==_0x24a5fd&&0x5f!==_0x24a5fd)return!0x1;let _0x167320=0x1;for(;_0xcbd894<_0xd56d9f;){const _0x257698=_0x215d63['src'][_0x3da25c(0x1806)](_0xcbd894++);if(_0x257698!==_0x24a5fd&&!_0x2578e9(_0x257698))return!0x1;_0x257698===_0x24a5fd&&_0x167320++;}if(_0x167320<0x3)return!0x1;if(_0x53df76)return!0x0;_0x215d63['line']=_0x424f08+0x1;const _0x20b9ac=_0x215d63[_0x3da25c(0x4131)]('hr','hr',0x0);return _0x20b9ac[_0x3da25c(0x2c94)]=[_0x424f08,_0x215d63[_0x3da25c(0x3dde)]],_0x20b9ac['markup']=Array(_0x167320+0x1)[_0x3da25c(0x379c)](String['fromCharCode'](_0x24a5fd)),!0x0;},[_0x235866(0x2d53),_0x235866(0x3085),'blockquote',_0x235866(0x20cf)]],[_0x235866(0x20cf),function(_0x1fde77,_0x13cddc,_0x58c49d,_0x376f70){const _0x12873e=_0x235866;let _0x4a5895,_0x3986c9,_0x5af6ab,_0x31e44a,_0x1eb033=_0x13cddc,_0x531fae=!0x0;if(_0x1fde77[_0x12873e(0x11e9)][_0x1eb033]-_0x1fde77['blkIndent']>=0x4)return!0x1;if(_0x1fde77[_0x12873e(0x50fb)]>=0x0&&_0x1fde77[_0x12873e(0x11e9)][_0x1eb033]-_0x1fde77['listIndent']>=0x4&&_0x1fde77[_0x12873e(0x11e9)][_0x1eb033]<_0x1fde77[_0x12873e(0x2c69)])return!0x1;let _0x444b65,_0x26ba09,_0x27e984,_0x3cd266=!0x1;if(_0x376f70&&_0x12873e(0x2d53)===_0x1fde77[_0x12873e(0x1581)]&&_0x1fde77[_0x12873e(0x11e9)][_0x1eb033]>=_0x1fde77[_0x12873e(0x2c69)]&&(_0x3cd266=!0x0),(_0x27e984=_0x1a50f7(_0x1fde77,_0x1eb033))>=0x0){if(_0x444b65=!0x0,_0x5af6ab=_0x1fde77['bMarks'][_0x1eb033]+_0x1fde77['tShift'][_0x1eb033],_0x26ba09=Number(_0x1fde77['src']['slice'](_0x5af6ab,_0x27e984-0x1)),_0x3cd266&&0x1!==_0x26ba09)return!0x1;}else{if(!((_0x27e984=_0x34b13a(_0x1fde77,_0x1eb033))>=0x0))return!0x1;_0x444b65=!0x1;}if(_0x3cd266&&_0x1fde77[_0x12873e(0x28e7)](_0x27e984)>=_0x1fde77['eMarks'][_0x1eb033])return!0x1;if(_0x376f70)return!0x0;const _0x185ec0=_0x1fde77[_0x12873e(0x1005)]['charCodeAt'](_0x27e984-0x1),_0x2c7114=_0x1fde77[_0x12873e(0x30f4)][_0x12873e(0x205b)];_0x444b65?(_0x31e44a=_0x1fde77[_0x12873e(0x4131)](_0x12873e(0x3248),'ol',0x1),0x1!==_0x26ba09&&(_0x31e44a['attrs']=[[_0x12873e(0x1698),_0x26ba09]])):_0x31e44a=_0x1fde77[_0x12873e(0x4131)](_0x12873e(0x3edd),'ul',0x1);const _0x410117=[_0x1eb033,0x0];_0x31e44a[_0x12873e(0x2c94)]=_0x410117,_0x31e44a['markup']=String[_0x12873e(0xc78)](_0x185ec0);let _0x543eb3=!0x1;const _0xfd8d62=_0x1fde77['md'][_0x12873e(0x3eab)][_0x12873e(0x536)][_0x12873e(0x68d)](_0x12873e(0x20cf)),_0x57354e=_0x1fde77['parentType'];for(_0x1fde77[_0x12873e(0x1581)]='list';_0x1eb033<_0x58c49d;){_0x3986c9=_0x27e984,_0x4a5895=_0x1fde77[_0x12873e(0x1908)][_0x1eb033];const _0x10c962=_0x1fde77[_0x12873e(0x11e9)][_0x1eb033]+_0x27e984-(_0x1fde77[_0x12873e(0x45ae)][_0x1eb033]+_0x1fde77[_0x12873e(0x1d33)][_0x1eb033]);let _0x102c2b=_0x10c962;for(;_0x3986c9<_0x4a5895;){const _0x2267a1=_0x1fde77[_0x12873e(0x1005)][_0x12873e(0x1806)](_0x3986c9);if(0x9===_0x2267a1)_0x102c2b+=0x4-(_0x102c2b+_0x1fde77['bsCount'][_0x1eb033])%0x4;else{if(0x20!==_0x2267a1)break;_0x102c2b++;}_0x3986c9++;}const _0x5ecb45=_0x3986c9;let _0x2be5fe;_0x2be5fe=_0x5ecb45>=_0x4a5895?0x1:_0x102c2b-_0x10c962,_0x2be5fe>0x4&&(_0x2be5fe=0x1);const _0x429979=_0x10c962+_0x2be5fe;_0x31e44a=_0x1fde77[_0x12873e(0x4131)](_0x12873e(0xc31),'li',0x1),_0x31e44a[_0x12873e(0x2767)]=String[_0x12873e(0xc78)](_0x185ec0);const _0x576f44=[_0x1eb033,0x0];_0x31e44a[_0x12873e(0x2c94)]=_0x576f44,_0x444b65&&(_0x31e44a['info']=_0x1fde77['src'][_0x12873e(0x428e)](_0x5af6ab,_0x27e984-0x1));const _0x803832=_0x1fde77[_0x12873e(0x4ea7)],_0xf4630f=_0x1fde77['tShift'][_0x1eb033],_0x51414e=_0x1fde77[_0x12873e(0x11e9)][_0x1eb033],_0x1c5e5f=_0x1fde77['listIndent'];if(_0x1fde77[_0x12873e(0x50fb)]=_0x1fde77[_0x12873e(0x2c69)],_0x1fde77['blkIndent']=_0x429979,_0x1fde77[_0x12873e(0x4ea7)]=!0x0,_0x1fde77[_0x12873e(0x1d33)][_0x1eb033]=_0x5ecb45-_0x1fde77['bMarks'][_0x1eb033],_0x1fde77['sCount'][_0x1eb033]=_0x102c2b,_0x5ecb45>=_0x4a5895&&_0x1fde77['isEmpty'](_0x1eb033+0x1)?_0x1fde77[_0x12873e(0x3dde)]=Math['min'](_0x1fde77[_0x12873e(0x3dde)]+0x2,_0x58c49d):_0x1fde77['md'][_0x12873e(0x3eab)][_0x12873e(0x44f9)](_0x1fde77,_0x1eb033,_0x58c49d,!0x0),_0x1fde77[_0x12873e(0x4ea7)]&&!_0x543eb3||(_0x531fae=!0x1),_0x543eb3=_0x1fde77[_0x12873e(0x3dde)]-_0x1eb033>0x1&&_0x1fde77['isEmpty'](_0x1fde77['line']-0x1),_0x1fde77[_0x12873e(0x2c69)]=_0x1fde77[_0x12873e(0x50fb)],_0x1fde77[_0x12873e(0x50fb)]=_0x1c5e5f,_0x1fde77[_0x12873e(0x1d33)][_0x1eb033]=_0xf4630f,_0x1fde77[_0x12873e(0x11e9)][_0x1eb033]=_0x51414e,_0x1fde77['tight']=_0x803832,_0x31e44a=_0x1fde77[_0x12873e(0x4131)](_0x12873e(0x9dc),'li',-0x1),_0x31e44a[_0x12873e(0x2767)]=String[_0x12873e(0xc78)](_0x185ec0),_0x1eb033=_0x1fde77[_0x12873e(0x3dde)],_0x576f44[0x1]=_0x1eb033,_0x1eb033>=_0x58c49d)break;if(_0x1fde77[_0x12873e(0x11e9)][_0x1eb033]<_0x1fde77[_0x12873e(0x2c69)])break;if(_0x1fde77[_0x12873e(0x11e9)][_0x1eb033]-_0x1fde77[_0x12873e(0x2c69)]>=0x4)break;let _0x347120=!0x1;for(let _0x41f7ec=0x0,_0x50abc7=_0xfd8d62[_0x12873e(0x205b)];_0x41f7ec<_0x50abc7;_0x41f7ec++)if(_0xfd8d62[_0x41f7ec](_0x1fde77,_0x1eb033,_0x58c49d,!0x0)){_0x347120=!0x0;break;}if(_0x347120)break;if(_0x444b65){if(_0x27e984=_0x1a50f7(_0x1fde77,_0x1eb033),_0x27e984<0x0)break;_0x5af6ab=_0x1fde77[_0x12873e(0x45ae)][_0x1eb033]+_0x1fde77[_0x12873e(0x1d33)][_0x1eb033];}else{if(_0x27e984=_0x34b13a(_0x1fde77,_0x1eb033),_0x27e984<0x0)break;}if(_0x185ec0!==_0x1fde77[_0x12873e(0x1005)][_0x12873e(0x1806)](_0x27e984-0x1))break;}return _0x31e44a=_0x444b65?_0x1fde77[_0x12873e(0x4131)](_0x12873e(0x2d93),'ol',-0x1):_0x1fde77[_0x12873e(0x4131)](_0x12873e(0x2293),'ul',-0x1),_0x31e44a[_0x12873e(0x2767)]=String[_0x12873e(0xc78)](_0x185ec0),_0x410117[0x1]=_0x1eb033,_0x1fde77[_0x12873e(0x3dde)]=_0x1eb033,_0x1fde77[_0x12873e(0x1581)]=_0x57354e,_0x531fae&&function(_0x513a56,_0x29f6a1){const _0x25356f=_0x12873e,_0x3cf0ed=_0x513a56[_0x25356f(0x1279)]+0x2;for(let _0x4a5489=_0x29f6a1+0x2,_0x1472b8=_0x513a56[_0x25356f(0x30f4)][_0x25356f(0x205b)]-0x2;_0x4a5489<_0x1472b8;_0x4a5489++)_0x513a56['tokens'][_0x4a5489][_0x25356f(0x1279)]===_0x3cf0ed&&_0x25356f(0x114e)===_0x513a56['tokens'][_0x4a5489]['type']&&(_0x513a56[_0x25356f(0x30f4)][_0x4a5489+0x2][_0x25356f(0x17f8)]=!0x0,_0x513a56[_0x25356f(0x30f4)][_0x4a5489][_0x25356f(0x17f8)]=!0x0,_0x4a5489+=0x2);}(_0x1fde77,_0x2c7114),!0x0;},['paragraph',_0x235866(0x3085),_0x235866(0x30ef)]],[_0x235866(0x3085),function(_0x3f852b,_0x4d3d83,_0x2421d7,_0x295406){const _0x4ce7c6=_0x235866;let _0x2b37f1=0x0,_0x27897e=_0x3f852b[_0x4ce7c6(0x45ae)][_0x4d3d83]+_0x3f852b[_0x4ce7c6(0x1d33)][_0x4d3d83],_0x54ba36=_0x3f852b[_0x4ce7c6(0x1908)][_0x4d3d83],_0x43ce9c=_0x4d3d83+0x1;if(_0x3f852b[_0x4ce7c6(0x11e9)][_0x4d3d83]-_0x3f852b[_0x4ce7c6(0x2c69)]>=0x4)return!0x1;if(0x5b!==_0x3f852b[_0x4ce7c6(0x1005)]['charCodeAt'](_0x27897e))return!0x1;for(;++_0x27897e<_0x54ba36;)if(0x5d===_0x3f852b[_0x4ce7c6(0x1005)]['charCodeAt'](_0x27897e)&&0x5c!==_0x3f852b[_0x4ce7c6(0x1005)]['charCodeAt'](_0x27897e-0x1)){if(_0x27897e+0x1===_0x54ba36)return!0x1;if(0x3a!==_0x3f852b[_0x4ce7c6(0x1005)][_0x4ce7c6(0x1806)](_0x27897e+0x1))return!0x1;break;}const _0x20d566=_0x3f852b[_0x4ce7c6(0x44dd)],_0x52c843=_0x3f852b['md'][_0x4ce7c6(0x3eab)][_0x4ce7c6(0x536)][_0x4ce7c6(0x68d)](_0x4ce7c6(0x3085)),_0x169a30=_0x3f852b[_0x4ce7c6(0x1581)];for(_0x3f852b[_0x4ce7c6(0x1581)]='reference';_0x43ce9c<_0x20d566&&!_0x3f852b['isEmpty'](_0x43ce9c);_0x43ce9c++){if(_0x3f852b[_0x4ce7c6(0x11e9)][_0x43ce9c]-_0x3f852b[_0x4ce7c6(0x2c69)]>0x3)continue;if(_0x3f852b[_0x4ce7c6(0x11e9)][_0x43ce9c]<0x0)continue;let _0xa1dc4c=!0x1;for(let _0x4a4438=0x0,_0x21aad7=_0x52c843[_0x4ce7c6(0x205b)];_0x4a4438<_0x21aad7;_0x4a4438++)if(_0x52c843[_0x4a4438](_0x3f852b,_0x43ce9c,_0x20d566,!0x0)){_0xa1dc4c=!0x0;break;}if(_0xa1dc4c)break;}const _0x57bfa8=_0x3f852b[_0x4ce7c6(0x3131)](_0x4d3d83,_0x43ce9c,_0x3f852b[_0x4ce7c6(0x2c69)],!0x1)[_0x4ce7c6(0x1d0e)]();_0x54ba36=_0x57bfa8['length'];let _0x12462f=-0x1;for(_0x27897e=0x1;_0x27897e<_0x54ba36;_0x27897e++){const _0x593b42=_0x57bfa8['charCodeAt'](_0x27897e);if(0x5b===_0x593b42)return!0x1;if(0x5d===_0x593b42){_0x12462f=_0x27897e;break;}0xa===_0x593b42?_0x2b37f1++:0x5c===_0x593b42&&(_0x27897e++,_0x27897e<_0x54ba36&&0xa===_0x57bfa8[_0x4ce7c6(0x1806)](_0x27897e)&&_0x2b37f1++);}if(_0x12462f<0x0||0x3a!==_0x57bfa8['charCodeAt'](_0x12462f+0x1))return!0x1;for(_0x27897e=_0x12462f+0x2;_0x27897e<_0x54ba36;_0x27897e++){const _0x4baee3=_0x57bfa8[_0x4ce7c6(0x1806)](_0x27897e);if(0xa===_0x4baee3)_0x2b37f1++;else{if(!_0x2578e9(_0x4baee3))break;}}const _0x11677f=_0x3f852b['md'][_0x4ce7c6(0x2f9b)][_0x4ce7c6(0x21e6)](_0x57bfa8,_0x27897e,_0x54ba36);if(!_0x11677f['ok'])return!0x1;const _0x40d62d=_0x3f852b['md']['normalizeLink'](_0x11677f[_0x4ce7c6(0x31ea)]);if(!_0x3f852b['md'][_0x4ce7c6(0x3a4a)](_0x40d62d))return!0x1;_0x27897e=_0x11677f[_0x4ce7c6(0x44ff)],_0x2b37f1+=_0x11677f['lines'];const _0x49ffa6=_0x27897e,_0x379af1=_0x2b37f1,_0x497a6b=_0x27897e;for(;_0x27897e<_0x54ba36;_0x27897e++){const _0x39b3be=_0x57bfa8[_0x4ce7c6(0x1806)](_0x27897e);if(0xa===_0x39b3be)_0x2b37f1++;else{if(!_0x2578e9(_0x39b3be))break;}}const _0x18bbea=_0x3f852b['md'][_0x4ce7c6(0x2f9b)]['parseLinkTitle'](_0x57bfa8,_0x27897e,_0x54ba36);let _0xefe474;for(_0x27897e<_0x54ba36&&_0x497a6b!==_0x27897e&&_0x18bbea['ok']?(_0xefe474=_0x18bbea['str'],_0x27897e=_0x18bbea['pos'],_0x2b37f1+=_0x18bbea[_0x4ce7c6(0x33a3)]):(_0xefe474='',_0x27897e=_0x49ffa6,_0x2b37f1=_0x379af1);_0x27897e<_0x54ba36;){if(!_0x2578e9(_0x57bfa8[_0x4ce7c6(0x1806)](_0x27897e)))break;_0x27897e++;}if(_0x27897e<_0x54ba36&&0xa!==_0x57bfa8[_0x4ce7c6(0x1806)](_0x27897e)&&_0xefe474)for(_0xefe474='',_0x27897e=_0x49ffa6,_0x2b37f1=_0x379af1;_0x27897e<_0x54ba36;){if(!_0x2578e9(_0x57bfa8['charCodeAt'](_0x27897e)))break;_0x27897e++;}if(_0x27897e<_0x54ba36&&0xa!==_0x57bfa8[_0x4ce7c6(0x1806)](_0x27897e))return!0x1;const _0x2f23ab=_0x479708(_0x57bfa8[_0x4ce7c6(0x428e)](0x1,_0x12462f));return!!_0x2f23ab&&(_0x295406||(void 0x0===_0x3f852b[_0x4ce7c6(0x2f9e)]['references']&&(_0x3f852b[_0x4ce7c6(0x2f9e)][_0x4ce7c6(0x1b08)]={}),void 0x0===_0x3f852b[_0x4ce7c6(0x2f9e)][_0x4ce7c6(0x1b08)][_0x2f23ab]&&(_0x3f852b['env'][_0x4ce7c6(0x1b08)][_0x2f23ab]={'title':_0xefe474,'href':_0x40d62d}),_0x3f852b['parentType']=_0x169a30,_0x3f852b[_0x4ce7c6(0x3dde)]=_0x4d3d83+_0x2b37f1+0x1),!0x0);}],[_0x235866(0x3de3),function(_0x33edd5,_0x165ed3,_0x3f5160,_0x9c76b2){const _0x354571=_0x235866;let _0x10046a=_0x33edd5[_0x354571(0x45ae)][_0x165ed3]+_0x33edd5['tShift'][_0x165ed3],_0x3d416c=_0x33edd5[_0x354571(0x1908)][_0x165ed3];if(_0x33edd5[_0x354571(0x11e9)][_0x165ed3]-_0x33edd5[_0x354571(0x2c69)]>=0x4)return!0x1;if(!_0x33edd5['md']['options'][_0x354571(0x1978)])return!0x1;if(0x3c!==_0x33edd5[_0x354571(0x1005)][_0x354571(0x1806)](_0x10046a))return!0x1;let _0x1a0891=_0x33edd5['src']['slice'](_0x10046a,_0x3d416c),_0x201db5=0x0;for(;_0x201db5<_0x440e82[_0x354571(0x205b)]&&!_0x440e82[_0x201db5][0x0][_0x354571(0x34c)](_0x1a0891);_0x201db5++);if(_0x201db5===_0x440e82[_0x354571(0x205b)])return!0x1;if(_0x9c76b2)return _0x440e82[_0x201db5][0x2];let _0x2ccb18=_0x165ed3+0x1;if(!_0x440e82[_0x201db5][0x1][_0x354571(0x34c)](_0x1a0891)){for(;_0x2ccb18<_0x3f5160&&!(_0x33edd5[_0x354571(0x11e9)][_0x2ccb18]<_0x33edd5[_0x354571(0x2c69)]);_0x2ccb18++)if(_0x10046a=_0x33edd5[_0x354571(0x45ae)][_0x2ccb18]+_0x33edd5[_0x354571(0x1d33)][_0x2ccb18],_0x3d416c=_0x33edd5[_0x354571(0x1908)][_0x2ccb18],_0x1a0891=_0x33edd5[_0x354571(0x1005)][_0x354571(0x428e)](_0x10046a,_0x3d416c),_0x440e82[_0x201db5][0x1][_0x354571(0x34c)](_0x1a0891)){0x0!==_0x1a0891['length']&&_0x2ccb18++;break;}}_0x33edd5[_0x354571(0x3dde)]=_0x2ccb18;const _0x538a8a=_0x33edd5[_0x354571(0x4131)](_0x354571(0x3de3),'',0x0);return _0x538a8a[_0x354571(0x2c94)]=[_0x165ed3,_0x2ccb18],_0x538a8a['content']=_0x33edd5[_0x354571(0x3131)](_0x165ed3,_0x2ccb18,_0x33edd5['blkIndent'],!0x0),!0x0;},[_0x235866(0x2d53),_0x235866(0x3085),_0x235866(0x30ef)]],[_0x235866(0x4d5f),function(_0xfa0c2b,_0x3d35ea,_0x1f854b,_0x1c6ef9){const _0x4abe6a=_0x235866;let _0x50b221=_0xfa0c2b['bMarks'][_0x3d35ea]+_0xfa0c2b[_0x4abe6a(0x1d33)][_0x3d35ea],_0xa135ea=_0xfa0c2b[_0x4abe6a(0x1908)][_0x3d35ea];if(_0xfa0c2b[_0x4abe6a(0x11e9)][_0x3d35ea]-_0xfa0c2b[_0x4abe6a(0x2c69)]>=0x4)return!0x1;let _0x23d9fa=_0xfa0c2b[_0x4abe6a(0x1005)]['charCodeAt'](_0x50b221);if(0x23!==_0x23d9fa||_0x50b221>=_0xa135ea)return!0x1;let _0x48fe2f=0x1;for(_0x23d9fa=_0xfa0c2b['src'][_0x4abe6a(0x1806)](++_0x50b221);0x23===_0x23d9fa&&_0x50b221<_0xa135ea&&_0x48fe2f<=0x6;)_0x48fe2f++,_0x23d9fa=_0xfa0c2b[_0x4abe6a(0x1005)][_0x4abe6a(0x1806)](++_0x50b221);if(_0x48fe2f>0x6||_0x50b221<_0xa135ea&&!_0x2578e9(_0x23d9fa))return!0x1;if(_0x1c6ef9)return!0x0;_0xa135ea=_0xfa0c2b[_0x4abe6a(0x2c89)](_0xa135ea,_0x50b221);const _0x237b7f=_0xfa0c2b[_0x4abe6a(0x667)](_0xa135ea,0x23,_0x50b221);_0x237b7f>_0x50b221&&_0x2578e9(_0xfa0c2b[_0x4abe6a(0x1005)][_0x4abe6a(0x1806)](_0x237b7f-0x1))&&(_0xa135ea=_0x237b7f),_0xfa0c2b[_0x4abe6a(0x3dde)]=_0x3d35ea+0x1;const _0x47d1ad=_0xfa0c2b[_0x4abe6a(0x4131)](_0x4abe6a(0xa22),'h'+String(_0x48fe2f),0x1);_0x47d1ad[_0x4abe6a(0x2767)]=_0x4abe6a(0x2cc4)['slice'](0x0,_0x48fe2f),_0x47d1ad[_0x4abe6a(0x2c94)]=[_0x3d35ea,_0xfa0c2b[_0x4abe6a(0x3dde)]];const _0x136580=_0xfa0c2b[_0x4abe6a(0x4131)](_0x4abe6a(0x50d2),'',0x0);return _0x136580[_0x4abe6a(0x498f)]=_0xfa0c2b[_0x4abe6a(0x1005)][_0x4abe6a(0x428e)](_0x50b221,_0xa135ea)[_0x4abe6a(0x1d0e)](),_0x136580[_0x4abe6a(0x2c94)]=[_0x3d35ea,_0xfa0c2b['line']],_0x136580[_0x4abe6a(0x4538)]=[],_0xfa0c2b['push'](_0x4abe6a(0xbaa),'h'+String(_0x48fe2f),-0x1)[_0x4abe6a(0x2767)]=_0x4abe6a(0x2cc4)['slice'](0x0,_0x48fe2f),!0x0;},[_0x235866(0x2d53),_0x235866(0x3085),_0x235866(0x30ef)]],[_0x235866(0x469),function(_0x410bb2,_0x5ab602,_0x17552c){const _0x549e11=_0x235866,_0x25a6b7=_0x410bb2['md'][_0x549e11(0x3eab)][_0x549e11(0x536)][_0x549e11(0x68d)]('paragraph');if(_0x410bb2[_0x549e11(0x11e9)][_0x5ab602]-_0x410bb2[_0x549e11(0x2c69)]>=0x4)return!0x1;const _0x2b940a=_0x410bb2[_0x549e11(0x1581)];_0x410bb2[_0x549e11(0x1581)]=_0x549e11(0x2d53);let _0x222796,_0x1af94a=0x0,_0x215464=_0x5ab602+0x1;for(;_0x215464<_0x17552c&&!_0x410bb2[_0x549e11(0x272a)](_0x215464);_0x215464++){if(_0x410bb2[_0x549e11(0x11e9)][_0x215464]-_0x410bb2['blkIndent']>0x3)continue;if(_0x410bb2[_0x549e11(0x11e9)][_0x215464]>=_0x410bb2[_0x549e11(0x2c69)]){let _0x54666d=_0x410bb2['bMarks'][_0x215464]+_0x410bb2[_0x549e11(0x1d33)][_0x215464];const _0x4388f3=_0x410bb2[_0x549e11(0x1908)][_0x215464];if(_0x54666d<_0x4388f3&&(_0x222796=_0x410bb2[_0x549e11(0x1005)][_0x549e11(0x1806)](_0x54666d),(0x2d===_0x222796||0x3d===_0x222796)&&(_0x54666d=_0x410bb2[_0x549e11(0x156)](_0x54666d,_0x222796),_0x54666d=_0x410bb2[_0x549e11(0x28e7)](_0x54666d),_0x54666d>=_0x4388f3))){_0x1af94a=0x3d===_0x222796?0x1:0x2;break;}}if(_0x410bb2[_0x549e11(0x11e9)][_0x215464]<0x0)continue;let _0x4f192a=!0x1;for(let _0x460005=0x0,_0x1e3903=_0x25a6b7[_0x549e11(0x205b)];_0x460005<_0x1e3903;_0x460005++)if(_0x25a6b7[_0x460005](_0x410bb2,_0x215464,_0x17552c,!0x0)){_0x4f192a=!0x0;break;}if(_0x4f192a)break;}if(!_0x1af94a)return!0x1;const _0x58c313=_0x410bb2[_0x549e11(0x3131)](_0x5ab602,_0x215464,_0x410bb2[_0x549e11(0x2c69)],!0x1)[_0x549e11(0x1d0e)]();_0x410bb2[_0x549e11(0x3dde)]=_0x215464+0x1;const _0x4fe2e1=_0x410bb2[_0x549e11(0x4131)]('heading_open','h'+String(_0x1af94a),0x1);_0x4fe2e1[_0x549e11(0x2767)]=String[_0x549e11(0xc78)](_0x222796),_0x4fe2e1[_0x549e11(0x2c94)]=[_0x5ab602,_0x410bb2[_0x549e11(0x3dde)]];const _0x18664b=_0x410bb2[_0x549e11(0x4131)](_0x549e11(0x50d2),'',0x0);return _0x18664b['content']=_0x58c313,_0x18664b[_0x549e11(0x2c94)]=[_0x5ab602,_0x410bb2[_0x549e11(0x3dde)]-0x1],_0x18664b[_0x549e11(0x4538)]=[],_0x410bb2['push'](_0x549e11(0xbaa),'h'+String(_0x1af94a),-0x1)[_0x549e11(0x2767)]=String[_0x549e11(0xc78)](_0x222796),_0x410bb2[_0x549e11(0x1581)]=_0x2b940a,!0x0;}],['paragraph',function(_0x46efc8,_0x51be34,_0x475689){const _0x4e7333=_0x235866,_0x29b38b=_0x46efc8['md'][_0x4e7333(0x3eab)][_0x4e7333(0x536)][_0x4e7333(0x68d)](_0x4e7333(0x2d53)),_0x51d4e8=_0x46efc8['parentType'];let _0x45979e=_0x51be34+0x1;for(_0x46efc8[_0x4e7333(0x1581)]='paragraph';_0x45979e<_0x475689&&!_0x46efc8[_0x4e7333(0x272a)](_0x45979e);_0x45979e++){if(_0x46efc8[_0x4e7333(0x11e9)][_0x45979e]-_0x46efc8[_0x4e7333(0x2c69)]>0x3)continue;if(_0x46efc8['sCount'][_0x45979e]<0x0)continue;let _0x569a74=!0x1;for(let _0x27d701=0x0,_0x56fea3=_0x29b38b[_0x4e7333(0x205b)];_0x27d701<_0x56fea3;_0x27d701++)if(_0x29b38b[_0x27d701](_0x46efc8,_0x45979e,_0x475689,!0x0)){_0x569a74=!0x0;break;}if(_0x569a74)break;}const _0x4c413c=_0x46efc8[_0x4e7333(0x3131)](_0x51be34,_0x45979e,_0x46efc8['blkIndent'],!0x1)[_0x4e7333(0x1d0e)]();_0x46efc8['line']=_0x45979e,_0x46efc8[_0x4e7333(0x4131)](_0x4e7333(0x114e),'p',0x1)[_0x4e7333(0x2c94)]=[_0x51be34,_0x46efc8[_0x4e7333(0x3dde)]];const _0x177ea7=_0x46efc8[_0x4e7333(0x4131)](_0x4e7333(0x50d2),'',0x0);return _0x177ea7['content']=_0x4c413c,_0x177ea7[_0x4e7333(0x2c94)]=[_0x51be34,_0x46efc8[_0x4e7333(0x3dde)]],_0x177ea7[_0x4e7333(0x4538)]=[],_0x46efc8[_0x4e7333(0x4131)]('paragraph_close','p',-0x1),_0x46efc8['parentType']=_0x51d4e8,!0x0;}]];function _0x58041d(){const _0x151602=_0x235866;this[_0x151602(0x536)]=new _0x22236d();for(let _0x32156b=0x0;_0x32156b<_0x48c7d7[_0x151602(0x205b)];_0x32156b++)this[_0x151602(0x536)][_0x151602(0x4131)](_0x48c7d7[_0x32156b][0x0],_0x48c7d7[_0x32156b][0x1],{'alt':(_0x48c7d7[_0x32156b][0x2]||[])[_0x151602(0x428e)]()});}_0x58041d[_0x235866(0x2b5c)]['tokenize']=function(_0x402ceb,_0x47843e,_0x243be5){const _0x47333c=_0x235866,_0x394d16=this[_0x47333c(0x536)][_0x47333c(0x68d)](''),_0x2a0f0d=_0x394d16[_0x47333c(0x205b)],_0x58f109=_0x402ceb['md'][_0x47333c(0x199c)][_0x47333c(0x2056)];let _0x48cc4c=_0x47843e,_0x1eb5c6=!0x1;for(;_0x48cc4c<_0x243be5&&(_0x402ceb[_0x47333c(0x3dde)]=_0x48cc4c=_0x402ceb[_0x47333c(0xa5e)](_0x48cc4c),!(_0x48cc4c>=_0x243be5))&&!(_0x402ceb[_0x47333c(0x11e9)][_0x48cc4c]<_0x402ceb['blkIndent']);){if(_0x402ceb[_0x47333c(0x1279)]>=_0x58f109){_0x402ceb['line']=_0x243be5;break;}const _0x2ca768=_0x402ceb[_0x47333c(0x3dde)];let _0x1afeb6=!0x1;for(let _0x137dbc=0x0;_0x137dbc<_0x2a0f0d;_0x137dbc++)if(_0x1afeb6=_0x394d16[_0x137dbc](_0x402ceb,_0x48cc4c,_0x243be5,!0x1),_0x1afeb6){if(_0x2ca768>=_0x402ceb[_0x47333c(0x3dde)])throw new Error('block\x20rule\x20didn\x27t\x20increment\x20state.line');break;}if(!_0x1afeb6)throw new Error(_0x47333c(0xad0));_0x402ceb[_0x47333c(0x4ea7)]=!_0x1eb5c6,_0x402ceb['isEmpty'](_0x402ceb[_0x47333c(0x3dde)]-0x1)&&(_0x1eb5c6=!0x0),_0x48cc4c=_0x402ceb[_0x47333c(0x3dde)],_0x48cc4c<_0x243be5&&_0x402ceb['isEmpty'](_0x48cc4c)&&(_0x1eb5c6=!0x0,_0x48cc4c++,_0x402ceb[_0x47333c(0x3dde)]=_0x48cc4c);}},_0x58041d[_0x235866(0x2b5c)][_0x235866(0x1b07)]=function(_0x105cc8,_0x50492d,_0x97357e,_0x4417c9){const _0x381ccb=_0x235866;if(!_0x105cc8)return;const _0x269ef7=new this[(_0x381ccb(0x2153))](_0x105cc8,_0x50492d,_0x97357e,_0x4417c9);this[_0x381ccb(0x44f9)](_0x269ef7,_0x269ef7[_0x381ccb(0x3dde)],_0x269ef7[_0x381ccb(0x44dd)]);},_0x58041d[_0x235866(0x2b5c)][_0x235866(0x2153)]=_0xe2ba27;const _0x11a7a6=_0x58041d;function _0xcc19d3(_0x3ab803,_0x4b15fb,_0x133d51,_0x5a4f9c){const _0x2b8aa6=_0x235866;this[_0x2b8aa6(0x1005)]=_0x3ab803,this[_0x2b8aa6(0x2f9e)]=_0x133d51,this['md']=_0x4b15fb,this[_0x2b8aa6(0x30f4)]=_0x5a4f9c,this[_0x2b8aa6(0x407f)]=Array(_0x5a4f9c['length']),this[_0x2b8aa6(0x44ff)]=0x0,this[_0x2b8aa6(0x40d)]=this[_0x2b8aa6(0x1005)][_0x2b8aa6(0x205b)],this[_0x2b8aa6(0x1279)]=0x0,this[_0x2b8aa6(0x7cb)]='',this[_0x2b8aa6(0x1b3a)]=0x0,this[_0x2b8aa6(0x14f4)]={},this[_0x2b8aa6(0x14fc)]=[],this[_0x2b8aa6(0x4879)]=[],this[_0x2b8aa6(0x1c3c)]={},this[_0x2b8aa6(0x40f5)]=!0x1,this[_0x2b8aa6(0x249f)]=0x0;}_0xcc19d3['prototype']['pushPending']=function(){const _0x3518f1=_0x235866,_0x5a2c21=new _0x5a43f1(_0x3518f1(0x2f35),'',0x0);return _0x5a2c21[_0x3518f1(0x498f)]=this['pending'],_0x5a2c21[_0x3518f1(0x1279)]=this['pendingLevel'],this[_0x3518f1(0x30f4)]['push'](_0x5a2c21),this[_0x3518f1(0x7cb)]='',_0x5a2c21;},_0xcc19d3[_0x235866(0x2b5c)]['push']=function(_0x1c46a8,_0x94fd1a,_0x510f7b){const _0x28c389=_0x235866;this['pending']&&this[_0x28c389(0x3013)]();const _0x3c0b75=new _0x5a43f1(_0x1c46a8,_0x94fd1a,_0x510f7b);let _0x44c39f=null;return _0x510f7b<0x0&&(this[_0x28c389(0x1279)]--,this['delimiters']=this['_prev_delimiters'][_0x28c389(0x2895)]()),_0x3c0b75[_0x28c389(0x1279)]=this[_0x28c389(0x1279)],_0x510f7b>0x0&&(this[_0x28c389(0x1279)]++,this[_0x28c389(0x4879)][_0x28c389(0x4131)](this[_0x28c389(0x14fc)]),this[_0x28c389(0x14fc)]=[],_0x44c39f={'delimiters':this[_0x28c389(0x14fc)]}),this[_0x28c389(0x1b3a)]=this['level'],this[_0x28c389(0x30f4)][_0x28c389(0x4131)](_0x3c0b75),this[_0x28c389(0x407f)]['push'](_0x44c39f),_0x3c0b75;},_0xcc19d3[_0x235866(0x2b5c)][_0x235866(0x1052)]=function(_0xacb91,_0x44855f){const _0x172cff=_0x235866;let _0x5639ef,_0x44277c,_0x34c343=!0x0,_0x89d80e=!0x0;const _0x4d8978=this[_0x172cff(0x40d)],_0x177bb6=this[_0x172cff(0x1005)][_0x172cff(0x1806)](_0xacb91),_0x1e9434=_0xacb91>0x0?this[_0x172cff(0x1005)][_0x172cff(0x1806)](_0xacb91-0x1):0x20;let 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0x24:case 0x25:case 0x26:case 0x2a:case 0x2b:case 0x2d:case 0x3a:case 0x3c:case 0x3d:case 0x3e:case 0x40:case 0x5b:case 0x5c:case 0x5d:case 0x5e:case 0x5f:case 0x60:case 0x7b:case 0x7d:case 0x7e:return!0x0;default:return!0x1;}}const _0xecb062=/(?:^|[^a-z0-9.+-])([a-z][a-z0-9.+-]*)$/i,_0x238ca6=[];for(let _0x234bd5=0x0;_0x234bd5<0x100;_0x234bd5++)_0x238ca6[_0x235866(0x4131)](0x0);function _0x3d7fc4(_0x32acd9,_0x3706dd){const _0x159217=_0x235866;let _0x379c88;const _0x13ef20=[],_0x463892=_0x3706dd[_0x159217(0x205b)];for(let _0x1d85b3=0x0;_0x1d85b3<_0x463892;_0x1d85b3++){const _0x14cbb8=_0x3706dd[_0x1d85b3];if(0x7e!==_0x14cbb8[_0x159217(0x3e25)])continue;if(-0x1===_0x14cbb8[_0x159217(0x721)])continue;const _0x41a066=_0x3706dd[_0x14cbb8[_0x159217(0x721)]];_0x379c88=_0x32acd9[_0x159217(0x30f4)][_0x14cbb8[_0x159217(0x4154)]],_0x379c88[_0x159217(0x70a)]=_0x159217(0x5117),_0x379c88[_0x159217(0x17a2)]='s',_0x379c88[_0x159217(0x47ee)]=0x1,_0x379c88[_0x159217(0x2767)]='~~',_0x379c88[_0x159217(0x498f)]='',_0x379c88=_0x32acd9[_0x159217(0x30f4)][_0x41a066[_0x159217(0x4154)]],_0x379c88['type']=_0x159217(0x2696),_0x379c88[_0x159217(0x17a2)]='s',_0x379c88[_0x159217(0x47ee)]=-0x1,_0x379c88[_0x159217(0x2767)]='~~',_0x379c88['content']='',_0x159217(0x2f35)===_0x32acd9[_0x159217(0x30f4)][_0x41a066['token']-0x1][_0x159217(0x70a)]&&'~'===_0x32acd9[_0x159217(0x30f4)][_0x41a066[_0x159217(0x4154)]-0x1][_0x159217(0x498f)]&&_0x13ef20[_0x159217(0x4131)](_0x41a066[_0x159217(0x4154)]-0x1);}for(;_0x13ef20[_0x159217(0x205b)];){const _0x3f0240=_0x13ef20['pop']();let 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_0x44e890=0x0;_0x44e890<_0xc55bab;_0x44e890++)_0x3b3f98[_0x44e890]&&_0x3b3f98[_0x44e890][_0x323ee9(0x14fc)]&&_0x3d7fc4(_0x16478f,_0x3b3f98[_0x44e890][_0x323ee9(0x14fc)]);}};function _0x544c94(_0xb9cd8c,_0x8d8eae){const _0x3edafb=_0x235866;for(let _0x42d4e3=_0x8d8eae[_0x3edafb(0x205b)]-0x1;_0x42d4e3>=0x0;_0x42d4e3--){const _0x46225d=_0x8d8eae[_0x42d4e3];if(0x5f!==_0x46225d[_0x3edafb(0x3e25)]&&0x2a!==_0x46225d['marker'])continue;if(-0x1===_0x46225d['end'])continue;const 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_0x5d2e53=!0x1;if((_0x5b6e60[_0x50684e(0x1c79)]||_0x1255d8[_0x50684e(0x36fa)])&&(_0x5b6e60['length']+_0x1255d8[_0x50684e(0x205b)])%0x3==0x0&&(_0x5b6e60['length']%0x3==0x0&&_0x1255d8[_0x50684e(0x205b)]%0x3==0x0||(_0x5d2e53=!0x0)),!_0x5d2e53){const _0x19f61b=_0x526d2d>0x0&&!_0x82460f[_0x526d2d-0x1][_0x50684e(0x36fa)]?_0x3ae980[_0x526d2d-0x1]+0x1:0x0;_0x3ae980[_0x10eac4]=_0x10eac4-_0x526d2d+_0x19f61b,_0x3ae980[_0x526d2d]=_0x19f61b,_0x1255d8[_0x50684e(0x36fa)]=!0x1,_0x5b6e60[_0x50684e(0x721)]=_0x10eac4,_0x5b6e60[_0x50684e(0x1c79)]=!0x1,_0x6438e5=-0x1,_0x488607=-0x2;break;}}}-0x1!==_0x6438e5&&(_0x339a7e[_0x1255d8['marker']][(_0x1255d8['open']?0x3:0x0)+(_0x1255d8[_0x50684e(0x205b)]||0x0)%0x3]=_0x6438e5);}}const _0x42fce9=[[_0x235866(0x2f35),function(_0x4c716e,_0x1bb8a2){const _0x4f9bcf=_0x235866;let _0x3ee51e=_0x4c716e[_0x4f9bcf(0x44ff)];for(;_0x3ee51e<_0x4c716e['posMax']&&!_0x28c973(_0x4c716e[_0x4f9bcf(0x1005)][_0x4f9bcf(0x1806)](_0x3ee51e));)_0x3ee51e++;return 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0x0===_0x478f60[_0x1dbba3(0x2f9e)][_0x1dbba3(0x1b08)])return!0x1;if(_0x1bc62b<_0x5d4ab7&&0x5b===_0x478f60[_0x1dbba3(0x1005)][_0x1dbba3(0x1806)](_0x1bc62b)?(_0x5e58ab=_0x1bc62b+0x1,_0x1bc62b=_0x478f60['md'][_0x1dbba3(0x2f9b)][_0x1dbba3(0x4f0b)](_0x478f60,_0x1bc62b),_0x1bc62b>=0x0?_0x185dc8=_0x478f60[_0x1dbba3(0x1005)][_0x1dbba3(0x428e)](_0x5e58ab,_0x1bc62b++):_0x1bc62b=_0x183f95+0x1):_0x1bc62b=_0x183f95+0x1,_0x185dc8||(_0x185dc8=_0x478f60[_0x1dbba3(0x1005)][_0x1dbba3(0x428e)](_0x4e031d,_0x183f95)),_0x3a2fa5=_0x478f60[_0x1dbba3(0x2f9e)][_0x1dbba3(0x1b08)][_0x479708(_0x185dc8)],!_0x3a2fa5)return _0x478f60[_0x1dbba3(0x44ff)]=_0xc55510,!0x1;_0x260eab=_0x3a2fa5[_0x1dbba3(0x354f)],_0x5bc688=_0x3a2fa5['title'];}if(!_0x9780b0){_0x478f60[_0x1dbba3(0x44ff)]=_0x4e031d,_0x478f60[_0x1dbba3(0x40d)]=_0x183f95;const _0x35e29c=[['href',_0x260eab]];_0x478f60[_0x1dbba3(0x4131)]('link_open','a',0x1)['attrs']=_0x35e29c,_0x5bc688&&_0x35e29c[_0x1dbba3(0x4131)](['title',_0x5bc688]),_0x478f60[_0x1dbba3(0x249f)]++,_0x478f60['md'][_0x1dbba3(0x50d2)][_0x1dbba3(0x44f9)](_0x478f60),_0x478f60[_0x1dbba3(0x249f)]--,_0x478f60[_0x1dbba3(0x4131)]('link_close','a',-0x1);}return _0x478f60[_0x1dbba3(0x44ff)]=_0x1bc62b,_0x478f60['posMax']=_0x5d4ab7,!0x0;}],['image',function(_0x178f2a,_0xc0fe4e){const _0x566768=_0x235866;let _0x514f56,_0xb48707,_0x219e6b,_0x2247df,_0xc097bb,_0x941514,_0x485ff9,_0x334bcf,_0x31cd80='';const _0xc55500=_0x178f2a[_0x566768(0x44ff)],_0x24d991=_0x178f2a[_0x566768(0x40d)];if(0x21!==_0x178f2a[_0x566768(0x1005)]['charCodeAt'](_0x178f2a[_0x566768(0x44ff)]))return!0x1;if(0x5b!==_0x178f2a['src']['charCodeAt'](_0x178f2a[_0x566768(0x44ff)]+0x1))return!0x1;const _0x3f59cb=_0x178f2a[_0x566768(0x44ff)]+0x2,_0x438a1a=_0x178f2a['md'][_0x566768(0x2f9b)]['parseLinkLabel'](_0x178f2a,_0x178f2a[_0x566768(0x44ff)]+0x1,!0x1);if(_0x438a1a<0x0)return!0x1;if(_0x2247df=_0x438a1a+0x1,_0x2247df<_0x24d991&&0x28===_0x178f2a[_0x566768(0x1005)]['charCodeAt'](_0x2247df)){for(_0x2247df++;_0x2247df<_0x24d991&&(_0x514f56=_0x178f2a[_0x566768(0x1005)][_0x566768(0x1806)](_0x2247df),_0x2578e9(_0x514f56)||0xa===_0x514f56);_0x2247df++);if(_0x2247df>=_0x24d991)return!0x1;for(_0x334bcf=_0x2247df,_0x941514=_0x178f2a['md'][_0x566768(0x2f9b)][_0x566768(0x21e6)](_0x178f2a[_0x566768(0x1005)],_0x2247df,_0x178f2a[_0x566768(0x40d)]),_0x941514['ok']&&(_0x31cd80=_0x178f2a['md'][_0x566768(0x2da6)](_0x941514[_0x566768(0x31ea)]),_0x178f2a['md'][_0x566768(0x3a4a)](_0x31cd80)?_0x2247df=_0x941514[_0x566768(0x44ff)]:_0x31cd80=''),_0x334bcf=_0x2247df;_0x2247df<_0x24d991&&(_0x514f56=_0x178f2a[_0x566768(0x1005)][_0x566768(0x1806)](_0x2247df),_0x2578e9(_0x514f56)||0xa===_0x514f56);_0x2247df++);if(_0x941514=_0x178f2a['md']['helpers'][_0x566768(0x1608)](_0x178f2a[_0x566768(0x1005)],_0x2247df,_0x178f2a[_0x566768(0x40d)]),_0x2247df<_0x24d991&&_0x334bcf!==_0x2247df&&_0x941514['ok']){for(_0x485ff9=_0x941514[_0x566768(0x31ea)],_0x2247df=_0x941514[_0x566768(0x44ff)];_0x2247df<_0x24d991&&(_0x514f56=_0x178f2a['src'][_0x566768(0x1806)](_0x2247df),_0x2578e9(_0x514f56)||0xa===_0x514f56);_0x2247df++);}else _0x485ff9='';if(_0x2247df>=_0x24d991||0x29!==_0x178f2a[_0x566768(0x1005)]['charCodeAt'](_0x2247df))return _0x178f2a['pos']=_0xc55500,!0x1;_0x2247df++;}else{if(void 0x0===_0x178f2a[_0x566768(0x2f9e)][_0x566768(0x1b08)])return!0x1;if(_0x2247df<_0x24d991&&0x5b===_0x178f2a['src']['charCodeAt'](_0x2247df)?(_0x334bcf=_0x2247df+0x1,_0x2247df=_0x178f2a['md'][_0x566768(0x2f9b)]['parseLinkLabel'](_0x178f2a,_0x2247df),_0x2247df>=0x0?_0x219e6b=_0x178f2a[_0x566768(0x1005)][_0x566768(0x428e)](_0x334bcf,_0x2247df++):_0x2247df=_0x438a1a+0x1):_0x2247df=_0x438a1a+0x1,_0x219e6b||(_0x219e6b=_0x178f2a[_0x566768(0x1005)][_0x566768(0x428e)](_0x3f59cb,_0x438a1a)),_0xc097bb=_0x178f2a[_0x566768(0x2f9e)][_0x566768(0x1b08)][_0x479708(_0x219e6b)],!_0xc097bb)return _0x178f2a[_0x566768(0x44ff)]=_0xc55500,!0x1;_0x31cd80=_0xc097bb[_0x566768(0x354f)],_0x485ff9=_0xc097bb[_0x566768(0x5f1)];}if(!_0xc0fe4e){_0xb48707=_0x178f2a['src'][_0x566768(0x428e)](_0x3f59cb,_0x438a1a);const 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_0x534732=_0x2a7a78[_0x5e7937(0x1005)][_0x5e7937(0x428e)](_0x327eb0+0x1,_0x5491ae);if(_0x3f6765['test'](_0x534732)){const _0xbdf936=_0x2a7a78['md'][_0x5e7937(0x2da6)](_0x534732);if(!_0x2a7a78['md']['validateLink'](_0xbdf936))return!0x1;if(!_0x27900f){const _0x3264d7=_0x2a7a78['push'](_0x5e7937(0x4e14),'a',0x1);_0x3264d7[_0x5e7937(0x3d7f)]=[[_0x5e7937(0x354f),_0xbdf936]],_0x3264d7[_0x5e7937(0x2767)]=_0x5e7937(0x2527),_0x3264d7[_0x5e7937(0x368d)]='auto',_0x2a7a78[_0x5e7937(0x4131)](_0x5e7937(0x2f35),'',0x0)[_0x5e7937(0x498f)]=_0x2a7a78['md'][_0x5e7937(0x17c1)](_0x534732);const _0x3928d9=_0x2a7a78['push']('link_close','a',-0x1);_0x3928d9[_0x5e7937(0x2767)]='autolink',_0x3928d9['info']=_0x5e7937(0x4a75);}return _0x2a7a78['pos']+=_0x534732['length']+0x2,!0x0;}if(_0xc8fd78[_0x5e7937(0x34c)](_0x534732)){const _0x3d5f16=_0x2a7a78['md']['normalizeLink'](_0x5e7937(0x50c)+_0x534732);if(!_0x2a7a78['md'][_0x5e7937(0x3a4a)](_0x3d5f16))return!0x1;if(!_0x27900f){const 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null;},_0x5aea05={'lexicon':function(_0x5d5301){const _0x2d62bf=_0x235866,_0x43e49=_0x5d5301['world'];_0x5d5301['docs'][_0x2d62bf(0x4854)](_0x5ee396=>{const _0x1a9f4e=_0x2d62bf;for(let _0xf17876=0x0;_0xf17876<_0x5ee396[_0x1a9f4e(0x205b)];_0xf17876+=0x1)if(0x0===_0x5ee396[_0xf17876][_0x1a9f4e(0x23d1)]['size']){let _0x1d7f1c=null;_0x1d7f1c=_0x1d7f1c||_0x38e6ee(_0x5ee396,_0xf17876,_0x43e49),_0x1d7f1c=_0x1d7f1c||_0xcf7cb9(_0x5ee396,_0xf17876,_0x43e49);}});}},_0x4e52d2=function(_0x1c7dbd){const _0x4fb12e=_0x235866;let _0x58b5d6={},_0x2fd281={};return Object[_0x4fb12e(0x2f75)](_0x1c7dbd)[_0x4fb12e(0x4854)](_0x3d22ac=>{const _0x4d93dc=_0x4fb12e;let _0x16d4d5=_0x1c7dbd[_0x3d22ac],_0x35ce4a=(_0x3d22ac=(_0x3d22ac=_0x3d22ac[_0x4d93dc(0x1a57)]()[_0x4d93dc(0x1d0e)]())['replace'](/'s\b/,''))[_0x4d93dc(0x29d0)](/ /);_0x35ce4a[_0x4d93dc(0x205b)]>0x1&&(void 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_0x72aecf=_0x343e47[_0x240ddf(0x2607)]['length']-0x1;_0x343e47['choices'][_0x72aecf]=_0x543e07(_0x343e47[_0x240ddf(0x2607)][_0x72aecf]),_0x343e47['choices']=_0x343e47[_0x240ddf(0x2607)][_0x240ddf(0x2c94)](_0x1f44db=>_0x1f44db[_0x240ddf(0x1d0e)]()),_0x343e47['choices']=_0x343e47[_0x240ddf(0x2607)][_0x240ddf(0x295e)](_0x26e622=>_0x26e622),_0x343e47[_0x240ddf(0x2607)]=_0x343e47[_0x240ddf(0x2607)][_0x240ddf(0x2c94)](_0x509492=>_0x509492['split'](/ /g)[_0x240ddf(0x2c94)](_0x4ee4d4=>_0x7e6f8c(_0x4ee4d4,_0x4edbe2))),_0x38ec4d='';}if('{'===_0x1f4a92(_0x38ec4d)&&'}'===_0x622ced(_0x38ec4d)){if(_0x38ec4d=_0x2946c4(_0x38ec4d),_0x343e47[_0x240ddf(0xdf8)]=_0x38ec4d,/\//[_0x240ddf(0x34c)](_0x38ec4d)){let _0x48eac8=_0x343e47[_0x240ddf(0xdf8)][_0x240ddf(0x29d0)](/\//);_0x343e47[_0x240ddf(0xdf8)]=_0x48eac8[0x0],_0x343e47[_0x240ddf(0x44ff)]=_0x48eac8[0x1],_0x240ddf(0xa49)===_0x343e47[_0x240ddf(0x44ff)]&&(_0x343e47[_0x240ddf(0x44ff)]=_0x240ddf(0x19b5)),_0x343e47[_0x240ddf(0x44ff)]=_0x343e47[_0x240ddf(0x44ff)][_0x240ddf(0x32fe)](0x0)[_0x240ddf(0x4447)]()+_0x343e47[_0x240ddf(0x44ff)][_0x240ddf(0x32a4)](0x1)[_0x240ddf(0x1a57)](),void 0x0!==_0x48eac8[0x2]&&(_0x343e47[_0x240ddf(0x14f6)]=_0x48eac8[0x2]);}return _0x343e47;}if('<'===_0x1f4a92(_0x38ec4d)&&'>'===_0x622ced(_0x38ec4d))return _0x38ec4d=_0x2946c4(_0x38ec4d),_0x343e47[_0x240ddf(0x1637)]=_0x81f271(_0x38ec4d),_0x343e47[_0x240ddf(0x188a)]=!0x0,_0x343e47;if('%'===_0x1f4a92(_0x38ec4d)&&'%'===_0x622ced(_0x38ec4d))return _0x38ec4d=_0x2946c4(_0x38ec4d),_0x343e47[_0x240ddf(0x68)]=_0x38ec4d,_0x343e47;}return'#'===_0x1f4a92(_0x38ec4d)?(_0x343e47[_0x240ddf(0x17a2)]=_0x22a720(_0x38ec4d),_0x343e47[_0x240ddf(0x17a2)]=_0x81f271(_0x343e47[_0x240ddf(0x17a2)]),_0x343e47):'@'===_0x1f4a92(_0x38ec4d)?(_0x343e47[_0x240ddf(0xd7c)]=_0x22a720(_0x38ec4d),_0x343e47):'.'===_0x38ec4d?(_0x343e47[_0x240ddf(0x42e3)]=!0x0,_0x343e47):'*'===_0x38ec4d?(_0x343e47[_0x240ddf(0x42e3)]=!0x0,_0x343e47[_0x240ddf(0x188a)]=!0x0,_0x343e47[_0x240ddf(0x180e)]=!0x0,_0x343e47):(_0x38ec4d&&(_0x38ec4d=(_0x38ec4d=_0x38ec4d['replace']('\x5c*','*'))[_0x240ddf(0x1db7)]('\x5c.','.'),_0x4edbe2[_0x240ddf(0x2b73)]?_0x343e47['use']='text':_0x38ec4d=_0x38ec4d[_0x240ddf(0x1a57)](),_0x343e47[_0x240ddf(0x2013)]=_0x38ec4d),_0x343e47);},_0x1c7e56=_0x7e6f8c,_0x5e46c9=/[a-z0-9][-–—][a-z]/i,_0x214e70=function(_0x255400,_0x249e6f){const _0x3bdc3b=_0x235866;let _0x40adce=_0x249e6f['model']['one']['prefixes'];for(let _0x232cd6=_0x255400[_0x3bdc3b(0x205b)]-0x1;_0x232cd6>=0x0;_0x232cd6-=0x1){let _0x3cab9a=_0x255400[_0x232cd6];if(_0x3cab9a[_0x3bdc3b(0x2013)]&&_0x5e46c9[_0x3bdc3b(0x34c)](_0x3cab9a['word'])){let _0x4e7cb4=_0x3cab9a[_0x3bdc3b(0x2013)][_0x3bdc3b(0x29d0)](/[-–—]/g);if(_0x40adce['hasOwnProperty'](_0x4e7cb4[0x0]))continue;_0x4e7cb4=_0x4e7cb4[_0x3bdc3b(0x295e)](_0x3d7bd2=>_0x3d7bd2)[_0x3bdc3b(0x2a4c)](),_0x255400[_0x3bdc3b(0x506f)](_0x232cd6,0x1),_0x4e7cb4['forEach'](_0x457591=>{const _0x50e08c=_0x3bdc3b;let _0x599895=Object[_0x50e08c(0x11e8)]({},_0x3cab9a);_0x599895['word']=_0x457591,_0x255400[_0x50e08c(0x506f)](_0x232cd6,0x0,_0x599895);});}}return _0x255400;},_0x7e246=function(_0x476d69,_0x4eb1e1){const _0x576461=_0x235866;let {all:_0x48d445}=_0x4eb1e1[_0x576461(0x8f)][_0x576461(0x141f)][_0x576461(0x560)][_0x576461(0x91)]||{},_0x3a927f=_0x476d69[_0x576461(0xdf8)];return _0x48d445?_0x48d445(_0x3a927f,_0x4eb1e1[_0x576461(0x2c3e)]):[];},_0x2d5a00=function(_0x1084c5,_0x2b770d){const _0x594828=_0x235866;let {all:_0x5d25c3}=_0x2b770d[_0x594828(0x8f)][_0x594828(0x141f)][_0x594828(0x560)][_0x594828(0x3c45)]||{};return _0x5d25c3?_0x5d25c3(_0x1084c5[_0x594828(0xdf8)],_0x2b770d[_0x594828(0x2c3e)]):[_0x1084c5[_0x594828(0xdf8)]];},_0xdcf779=function(_0x587188,_0x5790a7){const _0x430688=_0x235866;let {all:_0x159648}=_0x5790a7[_0x430688(0x8f)][_0x430688(0x141f)][_0x430688(0x560)][_0x430688(0x2c03)]||{};return _0x159648?_0x159648(_0x587188[_0x430688(0xdf8)],_0x5790a7[_0x430688(0x2c3e)]):[_0x587188[_0x430688(0xdf8)]];},_0x9ecca8=function(_0x5e2ae9,_0x3490c6){const _0x397b6e=_0x235866;return _0x5e2ae9=_0x5e2ae9[_0x397b6e(0x2c94)](_0x4ae61c=>{const _0x4fe8ce=_0x397b6e;if(_0x4ae61c[_0x4fe8ce(0xdf8)]){if(_0x3490c6[_0x4fe8ce(0x8f)][_0x4fe8ce(0x141f)]&&_0x3490c6[_0x4fe8ce(0x8f)][_0x4fe8ce(0x141f)]['transform']){let _0x521e2a=[];_0x4ae61c['pos']?_0x4fe8ce(0x41f2)===_0x4ae61c[_0x4fe8ce(0x44ff)]?_0x521e2a=_0x521e2a[_0x4fe8ce(0x41bf)](_0x7e246(_0x4ae61c,_0x3490c6)):_0x4fe8ce(0x1ce5)===_0x4ae61c['pos']?_0x521e2a=_0x521e2a[_0x4fe8ce(0x41bf)](_0x2d5a00(_0x4ae61c,_0x3490c6)):'Adjective'===_0x4ae61c[_0x4fe8ce(0x44ff)]&&(_0x521e2a=_0x521e2a[_0x4fe8ce(0x41bf)](_0xdcf779(_0x4ae61c,_0x3490c6))):(_0x521e2a=_0x521e2a[_0x4fe8ce(0x41bf)](_0x7e246(_0x4ae61c,_0x3490c6)),_0x521e2a=_0x521e2a[_0x4fe8ce(0x41bf)](_0x2d5a00(_0x4ae61c,_0x3490c6)),_0x521e2a=_0x521e2a[_0x4fe8ce(0x41bf)](_0xdcf779(_0x4ae61c,_0x3490c6))),_0x521e2a=_0x521e2a['filter'](_0x413dd7=>_0x413dd7),_0x521e2a[_0x4fe8ce(0x205b)]>0x0&&(_0x4ae61c['operator']='or',_0x4ae61c['fastOr']=new Set(_0x521e2a));}else _0x4ae61c[_0x4fe8ce(0x194)]=_0x4ae61c['root'],delete _0x4ae61c['id'],delete _0x4ae61c['root'];}return _0x4ae61c;}),_0x5e2ae9;},_0x3165cb=function(_0x4e87c1){const _0x37c813=_0x235866;return _0x4e87c1=function(_0x28e6e1){const _0x1ef7e3=a0_0x329b;let _0x4972e5=0x0,_0x31807c=null;for(let _0x5a417d=0x0;_0x5a417d<_0x28e6e1[_0x1ef7e3(0x205b)];_0x5a417d++){const _0x1bd644=_0x28e6e1[_0x5a417d];!0x0===_0x1bd644[_0x1ef7e3(0x4dc3)]&&(_0x31807c=_0x1bd644[_0x1ef7e3(0x4941)],null===_0x31807c&&(_0x31807c=String(_0x4972e5),_0x4972e5+=0x1)),null!==_0x31807c&&(_0x1bd644['group']=_0x31807c),!0x0===_0x1bd644['groupEnd']&&(_0x31807c=null);}return _0x28e6e1;}(_0x4e87c1),_0x4e87c1=_0x4e87c1[_0x37c813(0x2c94)](_0x16d5a9=>{const _0x10bb99=_0x37c813;if(void 0x0!==_0x16d5a9[_0x10bb99(0x2607)]){if('or'!==_0x16d5a9['operator'])return _0x16d5a9;if(!0x0===_0x16d5a9[_0x10bb99(0x4bcb)])return _0x16d5a9;let _0x1c3fc0=_0x16d5a9['choices']['every'](_0x416e9f=>{const _0x48bc22=_0x10bb99;if(0x1!==_0x416e9f['length'])return!0x1;let _0x2d8142=_0x416e9f[0x0];return!0x0!==_0x2d8142[_0x48bc22(0x4bcb)]&&!_0x2d8142['start']&&!_0x2d8142['end']&&void 0x0!==_0x2d8142[_0x48bc22(0x2013)]&&!0x0!==_0x2d8142['negative']&&!0x0!==_0x2d8142[_0x48bc22(0x180e)]&&!0x0!==_0x2d8142[_0x48bc22(0xd7c)];});!0x0===_0x1c3fc0&&(_0x16d5a9[_0x10bb99(0x4eec)]=new Set(),_0x16d5a9[_0x10bb99(0x2607)][_0x10bb99(0x4854)](_0x4718e9=>{const _0x38706d=_0x10bb99;_0x16d5a9[_0x38706d(0x4eec)][_0x38706d(0x2fd8)](_0x4718e9[0x0][_0x38706d(0x2013)]);}),delete _0x16d5a9[_0x10bb99(0x2607)]);}return _0x16d5a9;}),_0x4e87c1=function(_0x3f7aa6){const _0x30a748=_0x37c813;return _0x3f7aa6[_0x30a748(0x2c94)](_0xb57b6d=>(_0xb57b6d[_0x30a748(0x4bcb)]&&_0xb57b6d[_0x30a748(0x2607)]&&_0xb57b6d[_0x30a748(0x2607)][_0x30a748(0x4854)](_0x20c16a=>{const _0x497de9=_0x30a748;0x1===_0x20c16a[_0x497de9(0x205b)]&&_0x20c16a[0x0][_0x497de9(0x2013)]&&(_0x20c16a[0x0][_0x497de9(0x4bcb)]=!0x0,_0x20c16a[0x0][_0x497de9(0x12f9)]=_0xb57b6d[_0x497de9(0x12f9)]);}),_0xb57b6d));}(_0x4e87c1),_0x4e87c1;},_0x996130=function(_0x5d9313,_0xfa3c7,_0x29f533){const _0x11729a=_0x235866;if(null==_0x5d9313||''===_0x5d9313)return[];_0xfa3c7=_0xfa3c7||{},_0x11729a(0x1ed7)==typeof _0x5d9313&&(_0x5d9313=String(_0x5d9313));let _0x58c33b=_0x5edb39(_0x5d9313);return _0x58c33b=_0x58c33b['map'](_0x2f9621=>_0x1c7e56(_0x2f9621,_0xfa3c7)),_0x58c33b=_0x214e70(_0x58c33b,_0x29f533),_0x58c33b=_0x9ecca8(_0x58c33b,_0x29f533),_0x58c33b=_0x3165cb(_0x58c33b,_0xfa3c7),_0x58c33b;},_0x21e24c=function(_0x162b1a,_0x365d2d){for(let _0x483d6e of _0x365d2d)if(_0x162b1a['has'](_0x483d6e))return!0x0;return!0x1;},_0x8c4700=function(_0x58e914,_0x375365){const _0x4ef50b=_0x235866;for(let _0x242032=0x0;_0x242032<_0x58e914[_0x4ef50b(0x205b)];_0x242032+=0x1){let _0x647d6c=_0x58e914[_0x242032];if(!0x0!==_0x647d6c[_0x4ef50b(0x180e)]&&!0x0!==_0x647d6c[_0x4ef50b(0x3fbe)]&&!0x0!==_0x647d6c['fuzzy']){if(void 0x0!==_0x647d6c[_0x4ef50b(0x2013)]&&!0x1===_0x375365[_0x4ef50b(0x5ec)](_0x647d6c['word']))return!0x0;if(void 0x0!==_0x647d6c['tag']&&!0x1===_0x375365[_0x4ef50b(0x5ec)]('#'+_0x647d6c['tag']))return!0x0;if(_0x647d6c[_0x4ef50b(0x4eec)]&&!0x1===_0x21e24c(_0x647d6c[_0x4ef50b(0x4eec)],_0x375365))return!0x1;}}return!0x1;},_0x526d59=function(_0x59938e,_0x2a9c8b,_0x365c33=0x3){const _0x4908a6=_0x235866;if(_0x59938e===_0x2a9c8b)return 0x1;if(_0x59938e['length']<_0x365c33||_0x2a9c8b[_0x4908a6(0x205b)]<_0x365c33)return 0x0;const _0x6d86bc=function(_0x2c5e36,_0x1b005a){const _0x2e57a8=_0x4908a6;let _0x56523a=_0x2c5e36[_0x2e57a8(0x205b)],_0x191b43=_0x1b005a['length'];if(0x0===_0x56523a)return _0x191b43;if(0x0===_0x191b43)return _0x56523a;let _0x23b288=(_0x191b43>_0x56523a?_0x191b43:_0x56523a)+0x1;if(Math[_0x2e57a8(0x444c)](_0x56523a-_0x191b43)>(_0x23b288||0x64))return _0x23b288||0x64;let _0x197aa3,_0x36ddc0,_0x565247,_0x3f2c1c,_0x176da6,_0x1a04e7,_0x2c6aad=[];for(let _0x4fb37b=0x0;_0x4fb37b<_0x23b288;_0x4fb37b++)_0x2c6aad[_0x4fb37b]=[_0x4fb37b],_0x2c6aad[_0x4fb37b][_0x2e57a8(0x205b)]=_0x23b288;for(let _0x674263=0x0;_0x674263<_0x23b288;_0x674263++)_0x2c6aad[0x0][_0x674263]=_0x674263;for(let _0x3e9ad1=0x1;_0x3e9ad1<=_0x56523a;++_0x3e9ad1)for(_0x36ddc0=_0x2c5e36[_0x3e9ad1-0x1],_0x197aa3=0x1;_0x197aa3<=_0x191b43;++_0x197aa3){if(_0x3e9ad1===_0x197aa3&&_0x2c6aad[_0x3e9ad1][_0x197aa3]>0x4)return _0x56523a;_0x565247=_0x1b005a[_0x197aa3-0x1],_0x3f2c1c=_0x36ddc0===_0x565247?0x0:0x1,_0x176da6=_0x2c6aad[_0x3e9ad1-0x1][_0x197aa3]+0x1,(_0x1a04e7=_0x2c6aad[_0x3e9ad1][_0x197aa3-0x1]+0x1)<_0x176da6&&(_0x176da6=_0x1a04e7),(_0x1a04e7=_0x2c6aad[_0x3e9ad1-0x1][_0x197aa3-0x1]+_0x3f2c1c)<_0x176da6&&(_0x176da6=_0x1a04e7);let _0x47887f=_0x3e9ad1>0x1&&_0x197aa3>0x1&&_0x36ddc0===_0x1b005a[_0x197aa3-0x2]&&_0x2c5e36[_0x3e9ad1-0x2]===_0x565247&&(_0x1a04e7=_0x2c6aad[_0x3e9ad1-0x2][_0x197aa3-0x2]+_0x3f2c1c)<_0x176da6;_0x2c6aad[_0x3e9ad1][_0x197aa3]=_0x47887f?_0x1a04e7:_0x176da6;}return _0x2c6aad[_0x56523a][_0x191b43];}(_0x59938e,_0x2a9c8b);let _0x24867c=Math[_0x4908a6(0x28c)](_0x59938e[_0x4908a6(0x205b)],_0x2a9c8b[_0x4908a6(0x205b)]);return 0x1-(0x0===_0x24867c?0x0:_0x6d86bc/_0x24867c);},_0xd885b8=/([\u0022\uFF02\u0027\u201C\u2018\u201F\u201B\u201E\u2E42\u201A\u00AB\u2039\u2035\u2036\u2037\u301D\u0060\u301F])/,_0x189141=/([\u0022\uFF02\u0027\u201D\u2019\u00BB\u203A\u2032\u2033\u2034\u301E\u00B4])/,_0xaaf537=/^[-–—]$/,_0x1ef0b9=/ [-–—]{1,3} /,_0x3db438=(_0x35eac7,_0x2fcffc)=>-0x1!==_0x35eac7['post'][_0x235866(0x3458)](_0x2fcffc),_0x346944={'hasQuote':_0x132974=>_0xd885b8[_0x235866(0x34c)](_0x132974[_0x235866(0x1553)])||_0x189141[_0x235866(0x34c)](_0x132974[_0x235866(0x9ce)]),'hasComma':_0x7036c7=>_0x3db438(_0x7036c7,','),'hasPeriod':_0x570311=>!0x0===_0x3db438(_0x570311,'.')&&!0x1===_0x3db438(_0x570311,_0x235866(0x23a)),'hasExclamation':_0x2a1dc6=>_0x3db438(_0x2a1dc6,'!'),'hasQuestionMark':_0x512720=>_0x3db438(_0x512720,'?')||_0x3db438(_0x512720,'¿'),'hasEllipses':_0x17a866=>_0x3db438(_0x17a866,'..')||_0x3db438(_0x17a866,'…'),'hasSemicolon':_0x1cfea7=>_0x3db438(_0x1cfea7,';'),'hasColon':_0x33cc36=>_0x3db438(_0x33cc36,':'),'hasSlash':_0x36118f=>/\//['test'](_0x36118f['text']),'hasHyphen':_0x4bd4d8=>_0xaaf537[_0x235866(0x34c)](_0x4bd4d8['post'])||_0xaaf537[_0x235866(0x34c)](_0x4bd4d8[_0x235866(0x1553)]),'hasDash':_0x4b59ad=>_0x1ef0b9[_0x235866(0x34c)](_0x4b59ad['post'])||_0x1ef0b9[_0x235866(0x34c)](_0x4b59ad[_0x235866(0x1553)]),'hasContraction':_0x3bd054=>Boolean(_0x3bd054[_0x235866(0x15c0)]),'isAcronym':_0x5a9d0b=>_0x5a9d0b[_0x235866(0x23d1)][_0x235866(0x5ec)](_0x235866(0x265b)),'isKnown':_0x170608=>_0x170608[_0x235866(0x23d1)][_0x235866(0x3b53)]>0x0,'isTitleCase':_0x57a08d=>/^\p{Lu}[a-z'\u00C0-\u00FF]/u[_0x235866(0x34c)](_0x57a08d[_0x235866(0x2f35)]),'isUpperCase':_0x206cd2=>/^\p{Lu}+$/u['test'](_0x206cd2[_0x235866(0x2f35)])};_0x346944['hasQuotation']=_0x346944[_0x235866(0x85e)];const _0x16bdae=_0x346944;let _0x20ab4c=function(){};_0x20ab4c=function(_0x10ce74,_0x4344aa,_0x586f23,_0xc39d0f){const _0x507e12=_0x235866;let _0x30f2b8=function(_0x39b4d2,_0x388b26,_0x8f2edd,_0x1bb78d){const _0x288688=a0_0x329b;if(!0x0===_0x388b26['anything'])return!0x0;if(!0x0===_0x388b26[_0x288688(0x1698)]&&0x0!==_0x8f2edd)return!0x1;if(!0x0===_0x388b26[_0x288688(0x721)]&&_0x8f2edd!==_0x1bb78d-0x1)return!0x1;if(void 0x0!==_0x388b26['id']&&_0x388b26['id']===_0x39b4d2['id'])return!0x0;if(void 0x0!==_0x388b26[_0x288688(0x2013)]){if(_0x388b26[_0x288688(0x3903)])return _0x388b26['word']===_0x39b4d2[_0x388b26[_0x288688(0x3903)]];if(null!==_0x39b4d2[_0x288688(0x194)]&&_0x39b4d2[_0x288688(0x194)]===_0x388b26[_0x288688(0x2013)])return!0x0;if(void 0x0!==_0x39b4d2[_0x288688(0x1f89)]&&_0x39b4d2[_0x288688(0x1f89)][_0x288688(0x32b5)](_0x388b26[_0x288688(0x2013)]))return!0x0;if(!0x0===_0x388b26[_0x288688(0x4bcb)]){if(_0x388b26[_0x288688(0x2013)]===_0x39b4d2[_0x288688(0xdf8)])return!0x0;if(_0x526d59(_0x388b26[_0x288688(0x2013)],_0x39b4d2[_0x288688(0x4cb)])>=_0x388b26[_0x288688(0x12f9)])return!0x0;}return!(!_0x39b4d2[_0x288688(0x1f89)]||!_0x39b4d2[_0x288688(0x1f89)]['some'](_0x18f25a=>_0x18f25a===_0x388b26[_0x288688(0x2013)]))||_0x388b26[_0x288688(0x2013)]===_0x39b4d2[_0x288688(0x2f35)]||_0x388b26[_0x288688(0x2013)]===_0x39b4d2['normal'];}if(void 0x0!==_0x388b26[_0x288688(0x17a2)])return!0x0===_0x39b4d2[_0x288688(0x23d1)][_0x288688(0x5ec)](_0x388b26[_0x288688(0x17a2)]);if(void 0x0!==_0x388b26[_0x288688(0xd7c)])return _0x288688(0x3dfb)==typeof _0x16bdae[_0x388b26[_0x288688(0xd7c)]]&&!0x0===_0x16bdae[_0x388b26[_0x288688(0xd7c)]](_0x39b4d2);if(void 0x0!==_0x388b26[_0x288688(0x1553)])return _0x39b4d2[_0x288688(0x1553)]&&_0x39b4d2[_0x288688(0x1553)]['includes'](_0x388b26[_0x288688(0x1553)]);if(void 0x0!==_0x388b26[_0x288688(0x9ce)])return _0x39b4d2[_0x288688(0x9ce)]&&_0x39b4d2[_0x288688(0x9ce)][_0x288688(0x1fa2)](_0x388b26[_0x288688(0x9ce)]);if(void 0x0!==_0x388b26['regex']){let _0x556023=_0x39b4d2[_0x288688(0x4cb)];return _0x388b26[_0x288688(0x3903)]&&(_0x556023=_0x39b4d2[_0x388b26[_0x288688(0x3903)]]),_0x388b26[_0x288688(0x12ab)]['test'](_0x556023);}if(void 0x0!==_0x388b26[_0x288688(0x1637)])return _0x39b4d2['chunk']===_0x388b26['chunk'];if(void 0x0!==_0x388b26['switch'])return _0x39b4d2['switch']===_0x388b26[_0x288688(0x68)];if(void 0x0!==_0x388b26['machine'])return _0x39b4d2['normal']===_0x388b26['machine']||_0x39b4d2[_0x288688(0x194)]===_0x388b26[_0x288688(0x194)]||_0x39b4d2[_0x288688(0xdf8)]===_0x388b26[_0x288688(0x194)];if(void 0x0!==_0x388b26['sense'])return _0x39b4d2[_0x288688(0x14f6)]===_0x388b26[_0x288688(0x14f6)];if(void 0x0!==_0x388b26['fastOr']){if(_0x388b26[_0x288688(0x44ff)]&&!_0x39b4d2[_0x288688(0x23d1)][_0x288688(0x5ec)](_0x388b26[_0x288688(0x44ff)]))return null;let _0x4e337d=_0x39b4d2[_0x288688(0xdf8)]||_0x39b4d2[_0x288688(0x15c0)]||_0x39b4d2[_0x288688(0x194)]||_0x39b4d2['normal'];return _0x388b26[_0x288688(0x4eec)][_0x288688(0x5ec)](_0x4e337d)||_0x388b26[_0x288688(0x4eec)][_0x288688(0x5ec)](_0x39b4d2[_0x288688(0x2f35)]);}return void 0x0!==_0x388b26[_0x288688(0x2607)]&&('and'===_0x388b26[_0x288688(0x5d3)]?_0x388b26[_0x288688(0x2607)][_0x288688(0x464d)](_0x4353fa=>_0x20ab4c(_0x39b4d2,_0x4353fa,_0x8f2edd,_0x1bb78d)):_0x388b26['choices'][_0x288688(0x3f5f)](_0x520158=>_0x20ab4c(_0x39b4d2,_0x520158,_0x8f2edd,_0x1bb78d)));}(_0x10ce74,_0x4344aa,_0x586f23,_0xc39d0f);return!0x0===_0x4344aa[_0x507e12(0x3fbe)]?!_0x30f2b8:_0x30f2b8;};const _0x4dea73=_0x20ab4c,_0x1836a6=function(_0xbbbd3e,_0x1b4ed8){const _0x26aa40=_0x235866;if(!0x0===_0xbbbd3e[_0x26aa40(0x721)]&&!0x0===_0xbbbd3e[_0x26aa40(0x188a)]&&_0x1b4ed8[_0x26aa40(0x3ef4)]+_0x1b4ed8['t']<_0x1b4ed8['phrase_length']-0x1){let _0x539687=Object['assign']({},_0xbbbd3e,{'end':!0x1});if(!0x0===_0x4dea73(_0x1b4ed8['terms'][_0x1b4ed8['t']],_0x539687,_0x1b4ed8['start_i']+_0x1b4ed8['t'],_0x1b4ed8[_0x26aa40(0xe13)]))return!0x0;}return!0x1;},_0x42da34=function(_0x3a6c77,_0x5770fb){const _0x3a2969=_0x235866;return _0x3a6c77[_0x3a2969(0xbbd)][_0x3a6c77[_0x3a2969(0xa7a)]]||(_0x3a6c77['groups'][_0x3a6c77[_0x3a2969(0xa7a)]]={'start':_0x5770fb,'length':0x0}),_0x3a6c77[_0x3a2969(0xbbd)][_0x3a6c77[_0x3a2969(0xa7a)]];},_0x11edc3=function(_0x2f4c9a){const _0x380003=_0x235866;let {regs:_0x3e4add}=_0x2f4c9a,_0x4e4880=_0x3e4add[_0x2f4c9a['r']],_0x1c9bcd=function(_0x49abf0,_0x4e765d){const _0xa6d118=a0_0x329b;let _0x476823=_0x49abf0['t'];if(!_0x4e765d)return _0x49abf0[_0xa6d118(0x18a5)][_0xa6d118(0x205b)];for(;_0x476823<_0x49abf0['terms']['length'];_0x476823+=0x1)if(!0x0===_0x4dea73(_0x49abf0[_0xa6d118(0x18a5)][_0x476823],_0x4e765d,_0x49abf0[_0xa6d118(0x3ef4)]+_0x476823,_0x49abf0[_0xa6d118(0xe13)]))return _0x476823;return null;}(_0x2f4c9a,_0x3e4add[_0x2f4c9a['r']+0x1]);if(null===_0x1c9bcd||0x0===_0x1c9bcd)return null;if(void 0x0!==_0x4e4880[_0x380003(0x12f9)]&&_0x1c9bcd-_0x2f4c9a['t']<_0x4e4880[_0x380003(0x12f9)])return null;if(void 0x0!==_0x4e4880['max']&&_0x1c9bcd-_0x2f4c9a['t']>_0x4e4880[_0x380003(0x28c)])return _0x2f4c9a['t']=_0x2f4c9a['t']+_0x4e4880[_0x380003(0x28c)],!0x0;return!0x0===_0x2f4c9a['hasGroup']&&(_0x42da34(_0x2f4c9a,_0x2f4c9a['t'])[_0x380003(0x205b)]=_0x1c9bcd-_0x2f4c9a['t']),(_0x2f4c9a['t']=_0x1c9bcd,!0x0);},_0x26a92d=function(_0x563328,_0x1c9697=0x0){const _0x590931=_0x235866;let _0x5ba639=_0x563328[_0x590931(0x4176)][_0x563328['r']],_0xe533df=!0x1;for(let _0x5ccc40=0x0;_0x5ccc40<_0x5ba639[_0x590931(0x2607)][_0x590931(0x205b)];_0x5ccc40+=0x1){let _0x20aa45=_0x5ba639[_0x590931(0x2607)][_0x5ccc40];if(_0x5284cf=_0x20aa45,_0x590931(0x4fba)!==Object['prototype']['toString'][_0x590931(0x4c9d)](_0x5284cf))return!0x1;if(_0xe533df=_0x20aa45[_0x590931(0x464d)]((_0x50e3c6,_0x136729)=>{const _0x179c2c=_0x590931;let _0x2033a4=0x0,_0x24dfa5=_0x563328['t']+_0x136729+_0x1c9697+_0x2033a4;if(void 0x0===_0x563328[_0x179c2c(0x18a5)][_0x24dfa5])return!0x1;let _0x338c73=_0x4dea73(_0x563328[_0x179c2c(0x18a5)][_0x24dfa5],_0x50e3c6,_0x24dfa5+_0x563328['start_i'],_0x563328[_0x179c2c(0xe13)]);if(!0x0===_0x338c73&&!0x0===_0x50e3c6[_0x179c2c(0x188a)])for(let _0x3c91d8=0x1;_0x3c91d8<_0x563328[_0x179c2c(0x18a5)][_0x179c2c(0x205b)];_0x3c91d8+=0x1){let _0xecffa5=_0x563328[_0x179c2c(0x18a5)][_0x24dfa5+_0x3c91d8];if(_0xecffa5){if(!0x0!==_0x4dea73(_0xecffa5,_0x50e3c6,_0x563328[_0x179c2c(0x3ef4)]+_0x3c91d8,_0x563328[_0x179c2c(0xe13)]))break;_0x2033a4+=0x1;}}return _0x1c9697+=_0x2033a4,_0x338c73;}),_0xe533df){_0x1c9697+=_0x20aa45[_0x590931(0x205b)];break;}}var _0x5284cf;return _0xe533df&&!0x0===_0x5ba639[_0x590931(0x188a)]?_0x26a92d(_0x563328,_0x1c9697):_0x1c9697;},_0x1f2512=function(_0x4af8f7){const _0x303760=_0x235866,{regs:_0x1a3019}=_0x4af8f7;let _0x292864=_0x1a3019[_0x4af8f7['r']],_0x3be85d=_0x26a92d(_0x4af8f7);if(_0x3be85d){if(!0x0===_0x292864['negative'])return null;!0x0===_0x4af8f7[_0x303760(0x1d68)]&&(_0x42da34(_0x4af8f7,_0x4af8f7['t'])[_0x303760(0x205b)]+=_0x3be85d);if(!0x0===_0x292864['end']){let _0x11f5c0=_0x4af8f7[_0x303760(0xe13)];if(_0x4af8f7['t']+_0x4af8f7[_0x303760(0x3ef4)]+_0x3be85d!==_0x11f5c0)return null;}return _0x4af8f7['t']+=_0x3be85d,!0x0;}return!!_0x292864[_0x303760(0x180e)]||null;},_0x200da2=function(_0x5b69ca){const _0x429ada=_0x235866,{regs:_0x53fb19}=_0x5b69ca;let _0x7ba0a0=_0x53fb19[_0x5b69ca['r']],_0x2169c6=function(_0x435c8f){const _0x48b0b1=a0_0x329b;let _0x37dee5=0x0,_0x152d94=_0x435c8f[_0x48b0b1(0x4176)][_0x435c8f['r']]['choices'][_0x48b0b1(0x464d)](_0x1d96f9=>{const _0x16489b=_0x48b0b1;let _0x35eabd=_0x1d96f9[_0x16489b(0x464d)]((_0x2ff9a0,_0x39e727)=>{const _0x3c726c=_0x16489b;let _0x46fedf=_0x435c8f['t']+_0x39e727;return void 0x0!==_0x435c8f[_0x3c726c(0x18a5)][_0x46fedf]&&_0x4dea73(_0x435c8f[_0x3c726c(0x18a5)][_0x46fedf],_0x2ff9a0,_0x46fedf,_0x435c8f[_0x3c726c(0xe13)]);});return!0x0===_0x35eabd&&_0x1d96f9[_0x16489b(0x205b)]>_0x37dee5&&(_0x37dee5=_0x1d96f9[_0x16489b(0x205b)]),_0x35eabd;});return!0x0===_0x152d94&&_0x37dee5;}(_0x5b69ca);if(_0x2169c6){if(!0x0===_0x7ba0a0[_0x429ada(0x3fbe)])return null;!0x0===_0x5b69ca[_0x429ada(0x1d68)]&&(_0x42da34(_0x5b69ca,_0x5b69ca['t'])[_0x429ada(0x205b)]+=_0x2169c6);if(!0x0===_0x7ba0a0['end']){let _0x58d06d=_0x5b69ca[_0x429ada(0xe13)]-0x1;if(_0x5b69ca['t']+_0x5b69ca['start_i']!==_0x58d06d)return null;}return _0x5b69ca['t']+=_0x2169c6,!0x0;}return!!_0x7ba0a0[_0x429ada(0x180e)]||null;},_0x30270a=function(_0x7f1dab,_0x3d61c4,_0x20305f){const _0x58f455=_0x235866;let _0x46edb9=0x0;for(let _0x40f678=_0x7f1dab['t'];_0x40f678<_0x7f1dab[_0x58f455(0x18a5)][_0x58f455(0x205b)];_0x40f678+=0x1){let _0x5eac96=_0x4dea73(_0x7f1dab['terms'][_0x40f678],_0x3d61c4,_0x7f1dab[_0x58f455(0x3ef4)]+_0x7f1dab['t'],_0x7f1dab[_0x58f455(0xe13)]);if(_0x5eac96)break;if(_0x20305f&&(_0x5eac96=_0x4dea73(_0x7f1dab['terms'][_0x40f678],_0x20305f,_0x7f1dab['start_i']+_0x7f1dab['t'],_0x7f1dab[_0x58f455(0xe13)]),_0x5eac96))break;if(_0x46edb9+=0x1,void 0x0!==_0x3d61c4['max']&&_0x46edb9===_0x3d61c4[_0x58f455(0x28c)])break;}return 0x0!==_0x46edb9&&(!(_0x3d61c4['min']&&_0x3d61c4[_0x58f455(0x12f9)]>_0x46edb9)&&(_0x7f1dab['t']+=_0x46edb9,!0x0));},_0x4190c2=function(_0x1645d5){const _0x26a81c=_0x235866,{regs:_0x332123}=_0x1645d5;let _0x15a855=_0x332123[_0x1645d5['r']],_0x41aa65=Object[_0x26a81c(0x11e8)]({},_0x15a855);if(_0x41aa65[_0x26a81c(0x3fbe)]=!0x1,_0x4dea73(_0x1645d5[_0x26a81c(0x18a5)][_0x1645d5['t']],_0x41aa65,_0x1645d5['start_i']+_0x1645d5['t'],_0x1645d5[_0x26a81c(0xe13)]))return!0x1;if(_0x15a855[_0x26a81c(0x180e)]){let _0x477fcb=_0x332123[_0x1645d5['r']+0x1];if(_0x477fcb){if(_0x4dea73(_0x1645d5[_0x26a81c(0x18a5)][_0x1645d5['t']],_0x477fcb,_0x1645d5[_0x26a81c(0x3ef4)]+_0x1645d5['t'],_0x1645d5[_0x26a81c(0xe13)]))_0x1645d5['r']+=0x1;else _0x477fcb[_0x26a81c(0x180e)]&&_0x332123[_0x1645d5['r']+0x2]&&(_0x4dea73(_0x1645d5['terms'][_0x1645d5['t']],_0x332123[_0x1645d5['r']+0x2],_0x1645d5[_0x26a81c(0x3ef4)]+_0x1645d5['t'],_0x1645d5[_0x26a81c(0xe13)])&&(_0x1645d5['r']+=0x2));}}return _0x15a855[_0x26a81c(0x188a)]?_0x30270a(_0x1645d5,_0x41aa65,_0x332123[_0x1645d5['r']+0x1]):(_0x1645d5['t']+=0x1,!0x0);},_0x534e50=function(_0x5144ca){const _0x1cedbc=_0x235866,{regs:_0x134cc7}=_0x5144ca;let _0x37579d=_0x134cc7[_0x5144ca['r']],_0x5ac269=_0x5144ca[_0x1cedbc(0x18a5)][_0x5144ca['t']],_0x502ca6=_0x4dea73(_0x5ac269,_0x134cc7[_0x5144ca['r']+0x1],_0x5144ca[_0x1cedbc(0x3ef4)]+_0x5144ca['t'],_0x5144ca[_0x1cedbc(0xe13)]);if(_0x37579d['negative']||_0x502ca6){let _0x196e71=_0x5144ca[_0x1cedbc(0x18a5)][_0x5144ca['t']+0x1];_0x196e71&&_0x4dea73(_0x196e71,_0x134cc7[_0x5144ca['r']+0x1],_0x5144ca[_0x1cedbc(0x3ef4)]+_0x5144ca['t'],_0x5144ca[_0x1cedbc(0xe13)])||(_0x5144ca['r']+=0x1);}},_0x3ec437=function(_0x49adc5){const _0x1af436=_0x235866,{regs:_0x5a5be7,phrase_length:_0x43db79}=_0x49adc5;let _0x25ba1e=_0x5a5be7[_0x49adc5['r']];return _0x49adc5['t']=function(_0x4434ea,_0x149317){const _0x8bbc26=a0_0x329b;let _0x25dbfc=Object['assign']({},_0x4434ea[_0x8bbc26(0x4176)][_0x4434ea['r']],{'start':!0x1,'end':!0x1}),_0x8c82b2=_0x4434ea['t'];for(;_0x4434ea['t']<_0x4434ea['terms'][_0x8bbc26(0x205b)];_0x4434ea['t']+=0x1){if(_0x149317&&_0x4dea73(_0x4434ea['terms'][_0x4434ea['t']],_0x149317,_0x4434ea[_0x8bbc26(0x3ef4)]+_0x4434ea['t'],_0x4434ea[_0x8bbc26(0xe13)]))return _0x4434ea['t'];let _0x5d0cc5=_0x4434ea['t']-_0x8c82b2+0x1;if(void 0x0!==_0x25dbfc[_0x8bbc26(0x28c)]&&_0x5d0cc5===_0x25dbfc['max'])return _0x4434ea['t'];if(!0x1===_0x4dea73(_0x4434ea[_0x8bbc26(0x18a5)][_0x4434ea['t']],_0x25dbfc,_0x4434ea['start_i']+_0x4434ea['t'],_0x4434ea['phrase_length']))return void 0x0!==_0x25dbfc[_0x8bbc26(0x12f9)]&&_0x5d0cc5<_0x25dbfc['min']?null:_0x4434ea['t'];}return _0x4434ea['t'];}(_0x49adc5,_0x5a5be7[_0x49adc5['r']+0x1]),null===_0x49adc5['t']||_0x25ba1e['min']&&_0x25ba1e['min']>_0x49adc5['t']?null:!0x0!==_0x25ba1e['end']||_0x49adc5[_0x1af436(0x3ef4)]+_0x49adc5['t']===_0x43db79||null;},_0x3b1d6a=function(_0xfe2a14){const _0x12be92=_0x235866;let _0x44643f=_0xfe2a14[_0x12be92(0x18a5)][_0xfe2a14['t']],_0x156b18=_0xfe2a14[_0x12be92(0x4176)][_0xfe2a14['r']];if(_0x44643f['implicit']&&_0xfe2a14['terms'][_0xfe2a14['t']+0x1]){if(!_0xfe2a14[_0x12be92(0x18a5)][_0xfe2a14['t']+0x1]['implicit'])return;_0x156b18[_0x12be92(0x2013)]===_0x44643f[_0x12be92(0x4cb)]&&(_0xfe2a14['t']+=0x1),_0x12be92(0x2043)===_0x156b18[_0x12be92(0xd7c)]&&(_0xfe2a14['t']+=0x1);}},_0x22354c=function(_0x4f7729){const _0x591b38=_0x235866,{regs:_0x506a2d}=_0x4f7729;let _0xa7a396=_0x506a2d[_0x4f7729['r']],_0x4180d1=_0x4f7729[_0x591b38(0x18a5)][_0x4f7729['t']],_0x57a2b5=_0x4f7729['t'];if(_0xa7a396[_0x591b38(0x180e)]&&_0x506a2d[_0x4f7729['r']+0x1]&&_0xa7a396[_0x591b38(0x3fbe)])return!0x0;if(_0xa7a396[_0x591b38(0x180e)]&&_0x506a2d[_0x4f7729['r']+0x1]&&_0x534e50(_0x4f7729),_0x4180d1[_0x591b38(0x15c0)]&&_0x4f7729[_0x591b38(0x18a5)][_0x4f7729['t']+0x1]&&_0x3b1d6a(_0x4f7729),_0x4f7729['t']+=0x1,!0x0===_0xa7a396[_0x591b38(0x721)]&&_0x4f7729['t']!==_0x4f7729[_0x591b38(0x18a5)][_0x591b38(0x205b)]&&!0x0!==_0xa7a396[_0x591b38(0x188a)])return null;if(!0x0===_0xa7a396[_0x591b38(0x188a)]){if(!_0x3ec437(_0x4f7729))return null;}return!0x0===_0x4f7729[_0x591b38(0x1d68)]&&function(_0x5398ac,_0x746461){const _0x56ba7f=_0x591b38;let _0x4d5f2d=_0x5398ac[_0x56ba7f(0x4176)][_0x5398ac['r']];const _0xde3103=_0x42da34(_0x5398ac,_0x746461);_0x5398ac['t']>0x1&&_0x4d5f2d[_0x56ba7f(0x188a)]?_0xde3103[_0x56ba7f(0x205b)]+=_0x5398ac['t']-_0x746461:_0xde3103[_0x56ba7f(0x205b)]++;}(_0x4f7729,_0x57a2b5),!0x0;},_0x109d1d=function(_0x17d862,_0x11b634,_0x5a2671,_0x2f0aea){const _0x38bd24=_0x235866;if(0x0===_0x17d862[_0x38bd24(0x205b)]||0x0===_0x11b634[_0x38bd24(0x205b)])return null;let _0x46e3da={'t':0x0,'terms':_0x17d862,'r':0x0,'regs':_0x11b634,'groups':{},'start_i':_0x5a2671,'phrase_length':_0x2f0aea,'inGroup':null};for(;_0x46e3da['r']<_0x11b634[_0x38bd24(0x205b)];_0x46e3da['r']+=0x1){let _0x3a2485=_0x11b634[_0x46e3da['r']];if(_0x46e3da[_0x38bd24(0x1d68)]=Boolean(_0x3a2485[_0x38bd24(0x4941)]),!0x0===_0x46e3da[_0x38bd24(0x1d68)]?_0x46e3da[_0x38bd24(0xa7a)]=_0x3a2485[_0x38bd24(0x4941)]:_0x46e3da[_0x38bd24(0xa7a)]=null,!_0x46e3da[_0x38bd24(0x18a5)][_0x46e3da['t']]){if(!0x1===_0x11b634[_0x38bd24(0x428e)](_0x46e3da['r'])['some'](_0x23fdc8=>!_0x23fdc8[_0x38bd24(0x180e)]))break;return null;}if(!0x0!==_0x3a2485[_0x38bd24(0x42e3)]||!0x0!==_0x3a2485[_0x38bd24(0x188a)]){if(void 0x0===_0x3a2485[_0x38bd24(0x2607)]||'or'!==_0x3a2485[_0x38bd24(0x5d3)]){if(void 0x0===_0x3a2485[_0x38bd24(0x2607)]||_0x38bd24(0x43ea)!==_0x3a2485[_0x38bd24(0x5d3)]){if(!0x0!==_0x3a2485[_0x38bd24(0x42e3)]){if(!0x0!==_0x1836a6(_0x3a2485,_0x46e3da)){if(_0x3a2485[_0x38bd24(0x3fbe)]){if(!_0x4190c2(_0x46e3da))return null;}else{if(!0x0!==_0x4dea73(_0x46e3da['terms'][_0x46e3da['t']],_0x3a2485,_0x46e3da[_0x38bd24(0x3ef4)]+_0x46e3da['t'],_0x46e3da[_0x38bd24(0xe13)])){if(!0x0!==_0x3a2485['optional'])return null;}else{if(!_0x22354c(_0x46e3da))return null;}}}else{if(!_0x22354c(_0x46e3da))return null;}}else{if(_0x3a2485[_0x38bd24(0x3fbe)]&&_0x3a2485[_0x38bd24(0x42e3)])return null;if(!_0x22354c(_0x46e3da))return null;}}else{if(!_0x200da2(_0x46e3da))return null;}}else{if(!_0x1f2512(_0x46e3da))return null;}}else{if(!_0x11edc3(_0x46e3da))return null;}}let _0x14f2e5=[null,_0x5a2671,_0x46e3da['t']+_0x5a2671];if(_0x14f2e5[0x1]===_0x14f2e5[0x2])return null;let _0x459cb4={};return Object[_0x38bd24(0x2f75)](_0x46e3da['groups'])[_0x38bd24(0x4854)](_0x4e3aa9=>{const _0x561441=_0x38bd24;let _0x1f44cf=_0x46e3da[_0x561441(0xbbd)][_0x4e3aa9],_0x18c173=_0x5a2671+_0x1f44cf[_0x561441(0x1698)];_0x459cb4[_0x4e3aa9]=[null,_0x18c173,_0x18c173+_0x1f44cf[_0x561441(0x205b)]];}),{'pointer':_0x14f2e5,'groups':_0x459cb4};},_0x3d99ef=function(_0xfbe6ae,_0x50b651){const _0x278d4b=_0x235866;let _0x5a404c=[],_0x1f09ab={};return 0x0===_0xfbe6ae[_0x278d4b(0x205b)]||(_0x278d4b(0x1ed7)==typeof _0x50b651&&(_0x50b651=String(_0x50b651)),_0x50b651?_0xfbe6ae[_0x278d4b(0x4854)](_0x13a03a=>{const _0x189725=_0x278d4b;_0x13a03a[_0x189725(0xbbd)][_0x50b651]&&_0x5a404c[_0x189725(0x4131)](_0x13a03a[_0x189725(0xbbd)][_0x50b651]);}):_0xfbe6ae[_0x278d4b(0x4854)](_0x565453=>{const _0x450c20=_0x278d4b;_0x5a404c[_0x450c20(0x4131)](_0x565453[_0x450c20(0x1d95)]),Object['keys'](_0x565453[_0x450c20(0xbbd)])[_0x450c20(0x4854)](_0x586def=>{const _0x98ed82=_0x450c20;_0x1f09ab[_0x586def]=_0x1f09ab[_0x586def]||[],_0x1f09ab[_0x586def][_0x98ed82(0x4131)](_0x565453['groups'][_0x586def]);});})),{'ptrs':_0x5a404c,'byGroup':_0x1f09ab};},_0x4f4ee5=function(_0x1e69e3,_0x2cfe86,_0x1b05ee){const _0x13b4cc=_0x235866;return _0x1e69e3=_0x1e69e3[_0x13b4cc(0x295e)](_0x30fae0=>{const _0x3c1801=_0x13b4cc;let [_0x40f64e,_0x5613b,_0x3d2680]=_0x30fae0[_0x3c1801(0x1d95)],_0xb3fd53=_0x1b05ee[_0x40f64e][_0x3c1801(0x428e)](_0x5613b,_0x3d2680);for(let _0x3bcc31=0x0;_0x3bcc31<_0xb3fd53[_0x3c1801(0x205b)];_0x3bcc31+=0x1){let _0x93d748=_0xb3fd53[_0x3c1801(0x428e)](_0x3bcc31);if(null!==_0x109d1d(_0x93d748,_0x2cfe86,_0x3bcc31,_0xb3fd53[_0x3c1801(0x205b)]))return!0x1;}return!0x0;}),_0x1e69e3;},_0x168a0d=function(_0xa0ecfe,_0x1f7a5a){const _0x498018=_0x235866;return _0xa0ecfe['pointer'][0x0]=_0x1f7a5a,Object['keys'](_0xa0ecfe['groups'])[_0x498018(0x4854)](_0x48f923=>{const _0x215b29=_0x498018;_0xa0ecfe[_0x215b29(0xbbd)][_0x48f923][0x0]=_0x1f7a5a;}),_0xa0ecfe;},_0x7dad22=function(_0x115044,_0x4d0769,_0x3c533f){const _0x45fd09=_0x235866;let _0x505d15=_0x109d1d(_0x115044,_0x4d0769,0x0,_0x115044[_0x45fd09(0x205b)]);return _0x505d15?(_0x505d15=_0x168a0d(_0x505d15,_0x3c533f),_0x505d15):null;},_0x381967=function(_0xff7c27,_0x247dd6,_0x287ac8){const _0x2697e0=_0x235866;_0x287ac8=_0x287ac8||[];let {regs:_0x4fdb4d,group:_0x38ca5e,justOne:_0x1d2ea1}=_0x247dd6,_0xf29cfb=[];if(!_0x4fdb4d||0x0===_0x4fdb4d[_0x2697e0(0x205b)])return{'ptrs':[],'byGroup':{}};const _0x323fb2=_0x4fdb4d['filter'](_0x509ce2=>!0x0!==_0x509ce2[_0x2697e0(0x180e)]&&!0x0!==_0x509ce2[_0x2697e0(0x3fbe)])['length'];_0x45b515:for(let _0x292459=0x0;_0x292459<_0xff7c27[_0x2697e0(0x205b)];_0x292459+=0x1){let 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_0x37ae4a[_0x2bd5de(0x4116)][_0x2bd5de(0x1d56)][_0x3c8bac]:_0x37ae4a[_0x2bd5de(0x4116)][_0x2bd5de(0x1d56)][_0x3c8bac]=_0x25766a[_0x3c8bac];}),this;}},_0x646b0f={'model':{'one':{'typeahead':{}}},'api':_0x317e3f,'lib':_0x2a531f,'compute':_0x5a6ce2,'hooks':[_0x235866(0x1d56)]};_0x5d20a7['extend'](_0x40ef9a),_0x5d20a7[_0x235866(0x389d)](_0x1e9079),_0x5d20a7['extend'](_0x5c7b74),_0x5d20a7[_0x235866(0x389d)](_0x282ee5),_0x5d20a7[_0x235866(0x389d)](_0x557f42),_0x5d20a7[_0x235866(0x4c30)](_0x1876cc),_0x5d20a7[_0x235866(0x389d)](_0x4fccbd),_0x5d20a7[_0x235866(0x389d)](_0x4cb09b),_0x5d20a7[_0x235866(0x4c30)](_0x30866c),_0x5d20a7[_0x235866(0x389d)](_0x2fa8bb),_0x5d20a7[_0x235866(0x389d)](_0x646b0f),_0x5d20a7[_0x235866(0x389d)](_0x1ec109),_0x5d20a7[_0x235866(0x389d)](_0x4539be);const _0x3a5c82=_0x5d20a7,_0x49642c={'addendum':_0x235866(0x4030),'corpus':'corpora','criterion':_0x235866(0x1651),'curriculum':'curricula','genus':_0x235866(0x40ae),'memorandum':'memoranda','opus':'opera','ovum':_0x235866(0x48ea),'phenomenon':_0x235866(0x978),'referendum':'referenda','alga':_0x235866(0xc16),'alumna':'alumnae','antenna':_0x235866(0x4e03),'formula':_0x235866(0x235f),'larva':'larvae','nebula':'nebulae','vertebra':_0x235866(0x4d35),'analysis':_0x235866(0x490c),'axis':_0x235866(0x187d),'diagnosis':_0x235866(0x286e),'parenthesis':'parentheses','prognosis':_0x235866(0x2f28),'synopsis':'synopses','thesis':_0x235866(0x613),'neurosis':_0x235866(0xf8b),'appendix':_0x235866(0x3cf0),'index':'indices','matrix':_0x235866(0x17ef),'ox':_0x235866(0x1f78),'sex':_0x235866(0x2e7),'alumnus':_0x235866(0x2ee8),'bacillus':_0x235866(0x467e),'cactus':_0x235866(0x4c48),'fungus':_0x235866(0x38dc),'hippopotamus':_0x235866(0x2796),'libretto':'libretti','modulus':_0x235866(0x2b19),'nucleus':_0x235866(0x2009),'octopus':'octopi','radius':'radii','stimulus':'stimuli','syllabus':_0x235866(0x2158),'cookie':_0x235866(0x699),'calorie':_0x235866(0x5119),'auntie':_0x235866(0x28e0),'movie':_0x235866(0x1b90),'pie':_0x235866(0x129e),'rookie':_0x235866(0x4e67),'tie':'ties','zombie':_0x235866(0x9c8),'leaf':_0x235866(0x2d40),'loaf':_0x235866(0x3dd2),'thief':_0x235866(0x375f),'foot':_0x235866(0x6b7),'goose':'geese','tooth':_0x235866(0x3545),'beau':_0x235866(0x38e2),'chateau':'chateaux','tableau':_0x235866(0x39a7),'bus':_0x235866(0x2d62),'gas':_0x235866(0xc3c),'circus':_0x235866(0x2813),'crisis':_0x235866(0x3fcc),'virus':_0x235866(0x34cb),'database':_0x235866(0x46a3),'excuse':'excuses','abuse':_0x235866(0x2469),'avocado':'avocados','barracks':_0x235866(0x1a83),'child':_0x235866(0x4538),'clothes':'clothes','echo':'echoes','embargo':_0x235866(0x2341),'epoch':_0x235866(0x2150),'deer':_0x235866(0x3a03),'halo':_0x235866(0x329),'man':_0x235866(0x356c),'woman':_0x235866(0x177e),'mosquito':_0x235866(0x5006),'mouse':_0x235866(0x22f9),'person':'people','quiz':'quizzes','rodeo':_0x235866(0x3875),'shoe':_0x235866(0x24f),'sombrero':_0x235866(0x3515),'stomach':'stomachs','tornado':_0x235866(0x2587),'tuxedo':_0x235866(0x19c4),'volcano':_0x235866(0x1d62)},_0x21316d={'Comparative':_0x235866(0x3b3b),'Superlative':_0x235866(0xb5f),'PresentTense':'true¦bests,sounds','Condition':_0x235866(0xae4),'PastTense':_0x235866(0x43fe),'Participle':_0x235866(0x1f41),'Gerund':'true¦accord0be0doin,go0result0stain0;ing','Expression':_0x235866(0x4f4f),'Negative':'true¦n0;ever,o0;n,t','QuestionWord':_0x235866(0x2a8a),'Reflexive':_0x235866(0x4a83),'Plural':_0x235866(0x4ddd),'Unit|Noun':'true¦cEfDgChBinchAk9lb,m6newt5oz,p4qt,t1y0;ardEd;able1b0ea1sp;!l,sp;spo1;a,t,x;on9;!b,g,i1l,m,p0;h,s;!les;!b,elvin,g,m;!es;g,z;al,b;eet,oot,t;m,up0;!s','Value':'true¦a\x20few','Imperative':_0x235866(0x118),'Plural|Verb':_0x235866(0x5c1),'Demonym':'true¦0:15;1:12;a0Vb0Oc0Dd0Ce08f07g04h02iYjVkTlPmLnIomHpEqatari,rCs7t5u4v3welAz2;am0Gimbabwe0;enezuel0ietnam0I;gAkrai1;aiwTex0hai,rinida0Ju2;ni0Prkmen;a5cotti4e3ingapoOlovak,oma0Spaniard,udRw2y0W;ede,iss;negal0Cr09;sh;mo0uT;o5us0Jw2;and0;a2eru0Fhilippi0Nortugu07uerto\x20r0S;kist3lesti1na2raguay0;ma1;ani;ami00i2orweP;caragu0geri2;an,en;a3ex0Lo2;ngo0Drocc0;cedo1la2;gasy,y07;a4eb9i2;b2thua1;e0Cy0;o,t01;azakh,eny0o2uwaiI;re0;a2orda1;ma0Ap2;anO;celandic,nd4r2sraeli,ta01vo05;a2iB;ni0qi;i0oneU;aiAin2ondur0unO;di;amEe2hanai0reek,uatemal0;or2rm0;gi0;ilipino,ren8;cuadoVgyp4mira3ngli2sto1thiopi0urope0;shm0;ti;ti0;aPominUut3;a9h6o4roat3ub0ze2;ch;!i0;lom2ngol5;bi0;a6i2;le0n2;ese;lifor1m2na3;bo2eroo1;di0;angladeshi,el6o4r3ul2;gaE;azi9it;li2s1;vi0;aru2gi0;si0;fAl7merBngol0r5si0us2;sie,tr2;a2i0;li0;genti2me1;ne;ba1ge2;ri0;ni0;gh0r2;ic0;an','Organization':_0x235866(0x4d44),'Possessive':'true¦its,my,our0thy;!s','Noun|Verb':'true¦0:9W;1:AA;2:96;3:A3;4:9R;5:A2;6:9K;7:8N;8:7L;9:A8;A:93;B:8D;C:8X;a9Ob8Qc7Id6Re6Gf5Sg5Hh55i4Xj4Uk4Rl4Em40n3Vo3Sp2Squ2Rr21s0Jt02u00vVwGyFzD;ip,oD;ne,om;awn,e6Fie68;aOeMhJiHoErD;ap,e9Oink2;nd0rDuC;kDry,sh5Hth;!shop;ck,nDpe,re,sh;!d,g;e86iD;p,sD;k,p0t2;aDed,lco8W;r,th0;it,lk,rEsDt4ve,x;h,te;!ehou1ra9;aGen5FiFoD;iDmAte,w;ce,d;be,ew,sA;cuum,l4B;pDr7;da5gra6Elo6A;aReQhrPiOoMrGuEwiDy5Z;n,st;nDrn;e,n7O;aGeFiEoDu6;t,ub2;bu5ck4Jgg0m,p;at,k,nd;ck,de,in,nsDp,v7J;f0i8R;ll,ne,p,r4Yss,t94uD;ch,r;ck,de,e,le,me,p,re;e5Wow,u6;ar,e,ll,mp0st,xt;g,lDng2rg7Ps5x;k,ly;a0Sc0Ne0Kh0Fi0Dk0Cl0Am08n06o05pXquaBtKuFwD;ea88iD;ng,pe,t4;bGit,m,ppErD;fa3ge,pri1v2U;lDo6S;e6Py;!je8;aMeLiKoHrEuDy2;dy,ff,mb2;a85eEiDo5Pugg2;ke,ng;am,ss,t4;ckEop,p,rD;e,m;ing,pi2;ck,nk,t4;er,m,p;ck,ff,ge,in,ke,lEmp,nd,p2rDte,y;!e,t;k,l;aJeIiHlGoFrDur,y;ay,e56inDu3;g,k2;ns8Bt;a5Qit;ll,n,r87te;ed,ll;m,n,rk;b,uC;aDee1Tow;ke,p;a5Je4FiDo53;le,rk;eep,iDou4;ce,p,t;ateboa7Ii;de,gnDl2Vnk,p,ze;!al;aGeFiEoDuff2;ck,p,re,w;ft,p,v0;d,i3Ylt0;ck,de,pe,re,ve;aEed,nDrv1It;se,t2N;l,r4t;aGhedu2oBrD;aEeDibb2o3Z;en,w;pe,t4;le,n,r2M;cDfegua72il,mp2;k,rifi3;aZeHhy6LiGoEuD;b,in,le,n,s5X;a6ck,ll,oDpe,u5;f,t;de,ng,ot,p,s1W;aTcSdo,el,fQ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0x0,'as':'Adjective'})},_0x438ab5={'Copula':_0x235866(0x1e19),'PastTense':'Gerund','PresentTense':_0x235866(0x1e19),'Infinitive':'Gerund'},_0x370d31={'Value':_0x235866(0x1e19)},_0x639b83={'are':'Gerund','were':'Gerund','be':'Gerund','no':'Gerund','without':_0x235866(0x1e19),'you':_0x235866(0x1e19),'we':_0x235866(0x1e19),'they':'Gerund','he':_0x235866(0x1e19),'she':_0x235866(0x1e19),'us':'Gerund','them':'Gerund'},_0x2554f3={'the':'Gerund','this':_0x235866(0x1e19),'that':_0x235866(0x1e19),'me':'Gerund','us':_0x235866(0x1e19),'them':_0x235866(0x1e19)},_0x372b7e={'beforeTags':Object[_0x235866(0x11e8)]({},_0x97a0c4['beforeTags'],_0x86b88f[_0x235866(0x4142)],_0x438ab5),'afterTags':Object[_0x235866(0x11e8)]({},_0x97a0c4[_0x235866(0x3916)],_0x86b88f[_0x235866(0x3916)],_0x370d31),'beforeWords':Object['assign']({},_0x97a0c4[_0x235866(0x4af9)],_0x86b88f['beforeWords'],_0x639b83),'afterWords':Object[_0x235866(0x11e8)]({},_0x97a0c4[_0x235866(0x298f)],_0x86b88f['afterWords'],_0x2554f3)},_0x2ea2b6=_0x235866(0x3faa),_0x195c45=_0x235866(0x24ba),_0x4c7ecd={'beforeTags':Object[_0x235866(0x11e8)]({},_0x12f830[_0x235866(0x4142)],_0x86b88f[_0x235866(0x4142)],{'Adjective':_0x2ea2b6,'Particle':_0x2ea2b6}),'afterTags':Object[_0x235866(0x11e8)]({},_0x12f830['afterTags'],_0x86b88f[_0x235866(0x3916)],{'ProperNoun':_0x195c45,'Gerund':_0x195c45,'Adjective':_0x195c45,'Copula':_0x2ea2b6}),'beforeWords':Object[_0x235866(0x11e8)]({},_0x12f830[_0x235866(0x4af9)],_0x86b88f['beforeWords'],{'is':_0x2ea2b6,'was':_0x2ea2b6,'of':_0x2ea2b6,'have':null}),'afterWords':Object[_0x235866(0x11e8)]({},_0x12f830[_0x235866(0x298f)],_0x86b88f[_0x235866(0x298f)],{'instead':_0x195c45,'about':_0x195c45,'his':_0x195c45,'her':_0x195c45,'to':null,'by':null,'in':null})},_0x1512dc=_0x235866(0x29bf),_0x39fec2={'beforeTags':{'Honorific':_0x1512dc,'Person':_0x1512dc},'afterTags':{'Person':_0x1512dc,'ProperNoun':_0x1512dc,'Verb':_0x1512dc},'ownTags':{'ProperNoun':_0x1512dc},'beforeWords':{'hi':_0x1512dc,'hey':_0x1512dc,'yo':_0x1512dc,'dear':_0x1512dc,'hello':_0x1512dc},'afterWords':{'said':_0x1512dc,'says':_0x1512dc,'told':_0x1512dc,'tells':_0x1512dc,'feels':_0x1512dc,'felt':_0x1512dc,'seems':_0x1512dc,'thinks':_0x1512dc,'thought':_0x1512dc,'spends':_0x1512dc,'spendt':_0x1512dc,'plays':_0x1512dc,'played':_0x1512dc,'sing':_0x1512dc,'sang':_0x1512dc,'learn':_0x1512dc,'learned':_0x1512dc,'wants':_0x1512dc,'wanted':_0x1512dc}},_0x586b91='Month',_0x533d43={'beforeTags':{'Date':_0x586b91,'Value':_0x586b91},'afterTags':{'Date':_0x586b91,'Value':_0x586b91},'beforeWords':{'by':_0x586b91,'in':_0x586b91,'on':_0x586b91,'during':_0x586b91,'after':_0x586b91,'before':_0x586b91,'between':_0x586b91,'until':_0x586b91,'til':_0x586b91,'sometime':_0x586b91,'of':_0x586b91,'this':_0x586b91,'next':_0x586b91,'last':_0x586b91,'previous':_0x586b91,'following':_0x586b91,'with':_0x235866(0x29bf)},'afterWords':{'sometime':_0x586b91,'in':_0x586b91,'of':_0x586b91,'until':_0x586b91,'the':_0x586b91}},_0x40f3ee={'beforeTags':Object[_0x235866(0x11e8)]({},_0x39fec2[_0x235866(0x4142)],_0x533d43[_0x235866(0x4142)]),'afterTags':Object[_0x235866(0x11e8)]({},_0x39fec2['afterTags'],_0x533d43['afterTags']),'beforeWords':Object[_0x235866(0x11e8)]({},_0x39fec2[_0x235866(0x4af9)],_0x533d43[_0x235866(0x4af9)]),'afterWords':Object[_0x235866(0x11e8)]({},_0x39fec2['afterWords'],_0x533d43[_0x235866(0x298f)])},_0x18db53=_0x235866(0x3fae),_0x3ac8b4={'beforeTags':{'Place':_0x18db53},'afterTags':{'Place':_0x18db53,'Abbreviation':_0x18db53},'beforeWords':{'in':_0x18db53,'by':_0x18db53,'near':_0x18db53,'from':_0x18db53,'to':_0x18db53},'afterWords':{'in':_0x18db53,'by':_0x18db53,'near':_0x18db53,'from':_0x18db53,'to':_0x18db53,'government':_0x18db53,'council':_0x18db53,'region':_0x18db53,'city':_0x18db53}};let _0x2ffc35=_0x235866(0x283a);const _0x1011f9={'Actor|Verb':_0x18d6da,'Adj|Gerund':_0x16f949,'Adj|Noun':_0x1f6d91,'Adj|Past':_0x15973d,'Adj|Present':_0x3662a6,'Noun|Verb':_0x4c7ecd,'Noun|Gerund':_0x372b7e,'Person|Noun':{'beforeTags':Object['assign']({},_0x86b88f[_0x235866(0x4142)],_0x39fec2['beforeTags']),'afterTags':Object[_0x235866(0x11e8)]({},_0x86b88f[_0x235866(0x3916)],_0x39fec2[_0x235866(0x3916)]),'beforeWords':Object[_0x235866(0x11e8)]({},_0x86b88f[_0x235866(0x4af9)],_0x39fec2[_0x235866(0x4af9)],{'i':'Infinitive','we':_0x235866(0x24ba)}),'afterWords':Object[_0x235866(0x11e8)]({},_0x86b88f[_0x235866(0x298f)],_0x39fec2[_0x235866(0x298f)])},'Person|Date':_0x40f3ee,'Person|Verb':{'beforeTags':Object['assign']({},_0x86b88f[_0x235866(0x4142)],_0x39fec2[_0x235866(0x4142)],_0x12f830[_0x235866(0x4142)]),'afterTags':Object['assign']({},_0x86b88f[_0x235866(0x3916)],_0x39fec2[_0x235866(0x3916)],_0x12f830[_0x235866(0x3916)]),'beforeWords':Object[_0x235866(0x11e8)]({},_0x86b88f[_0x235866(0x4af9)],_0x39fec2[_0x235866(0x4af9)],_0x12f830[_0x235866(0x4af9)]),'afterWords':Object[_0x235866(0x11e8)]({},_0x86b88f[_0x235866(0x298f)],_0x39fec2[_0x235866(0x298f)],_0x12f830['afterWords'])},'Person|Place':{'beforeTags':Object[_0x235866(0x11e8)]({},_0x3ac8b4['beforeTags'],_0x39fec2[_0x235866(0x4142)]),'afterTags':Object[_0x235866(0x11e8)]({},_0x3ac8b4['afterTags'],_0x39fec2['afterTags']),'beforeWords':Object[_0x235866(0x11e8)]({},_0x3ac8b4['beforeWords'],_0x39fec2[_0x235866(0x4af9)]),'afterWords':Object['assign']({},_0x3ac8b4[_0x235866(0x298f)],_0x39fec2[_0x235866(0x298f)])},'Person|Adj':{'beforeTags':Object[_0x235866(0x11e8)]({},_0x39fec2[_0x235866(0x4142)],_0x13cfe4['beforeTags']),'afterTags':Object[_0x235866(0x11e8)]({},_0x39fec2[_0x235866(0x3916)],_0x13cfe4[_0x235866(0x3916)]),'beforeWords':Object[_0x235866(0x11e8)]({},_0x39fec2[_0x235866(0x4af9)],_0x13cfe4[_0x235866(0x4af9)]),'afterWords':Object[_0x235866(0x11e8)]({},_0x39fec2[_0x235866(0x298f)],_0x13cfe4['afterWords'])},'Unit|Noun':{'beforeTags':{'Value':_0x2ffc35},'afterTags':{},'beforeWords':{'per':_0x2ffc35,'every':_0x2ffc35,'each':_0x2ffc35,'square':_0x2ffc35,'cubic':_0x2ffc35,'sq':_0x2ffc35,'metric':_0x2ffc35},'afterWords':{'per':_0x2ffc35,'squared':_0x2ffc35,'cubed':_0x2ffc35,'long':_0x2ffc35}}},_0x10041f=(_0x1296dc,_0x5ac83b)=>{const 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_0x5d2d7c=_0x191012,_0x4f573b=function(_0x10b599){const _0x106192=_0x235866;let _0x1c05c1=_0x10b599[_0x106192(0x3934)](_0x10b599[_0x106192(0x205b)]-0x3);if(!0x0===_0x5d2d7c[_0x106192(0x32b5)](_0x1c05c1))return _0x5d2d7c[_0x1c05c1];let _0x22f1bc=_0x10b599['substring'](_0x10b599['length']-0x2);return!0x0===_0x5d2d7c[_0x106192(0x32b5)](_0x22f1bc)?_0x5d2d7c[_0x22f1bc]:'s'===_0x10b599[_0x106192(0x3934)](_0x10b599[_0x106192(0x205b)]-0x1)?_0x106192(0x17a6):null;},_0x3d1df3={'are':'be','were':'be','been':'be','is':'be','am':'be','was':'be','be':'be','being':'be'},_0x3286d2=function(_0x14aa26,_0x20b9ca,_0x5f140b){const _0x3b956a=_0x235866,{fromPast:_0x7c22ad,fromPresent:_0x28d279,fromGerund:_0x467e60,fromParticiple:_0x148250}=_0x20b9ca[_0x3b956a(0x141f)][_0x3b956a(0x1526)];let {prefix:_0x1663ef,verb:_0x45cff0,particle:_0x1cf2b9}=function(_0x4cd474,_0x2aa22c){const _0x5cd0bf=_0x3b956a;let _0x421171='',_0x4ffa48={};_0x2aa22c['one']&&_0x2aa22c[_0x5cd0bf(0x4116)][_0x5cd0bf(0x447a)]&&(_0x4ffa48=_0x2aa22c[_0x5cd0bf(0x4116)][_0x5cd0bf(0x447a)]);let [_0x576823,_0x5a2677]=_0x4cd474[_0x5cd0bf(0x29d0)](/ /);return _0x5a2677&&!0x0===_0x4ffa48[_0x576823]&&(_0x421171=_0x576823,_0x576823=_0x5a2677,_0x5a2677=''),{'prefix':_0x421171,'verb':_0x576823,'particle':_0x5a2677};}(_0x14aa26,_0x20b9ca),_0x33e59c='';if(_0x5f140b||(_0x5f140b=_0x4f573b(_0x14aa26)),_0x3d1df3['hasOwnProperty'](_0x14aa26))_0x33e59c=_0x3d1df3[_0x14aa26];else{if(_0x3b956a(0x184e)===_0x5f140b)_0x33e59c=_0x3ab3c0(_0x45cff0,_0x148250);else{if('PastTense'===_0x5f140b)_0x33e59c=_0x3ab3c0(_0x45cff0,_0x7c22ad);else{if(_0x3b956a(0x17a6)===_0x5f140b)_0x33e59c=_0x3ab3c0(_0x45cff0,_0x28d279);else{if('Gerund'!==_0x5f140b)return _0x14aa26;_0x33e59c=_0x3ab3c0(_0x45cff0,_0x467e60);}}}}return 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_0xb36875=_0x235866;if(_0x2624b1[_0xb36875(0x32b5)](_0x52ff63))return _0x2624b1[_0x52ff63];let _0x5da2b0=_0x4ead73(_0x52ff63,_0x38076c);return _0x5da2b0||(_0x5da2b0=_0x52ff63+'ly'),_0x5da2b0;},_0x2fb634={'toSuperlative':_0x5f405f,'toComparative':_0x23f0ee,'toAdverb':_0x5ea3ed,'toNoun':function(_0x3d0d4f,_0x198089){const _0x2bc3c5=_0x235866,_0x2aaef3=_0x198089['two'][_0x2bc3c5(0x1526)]['adjToNoun'];return _0x3ab3c0(_0x3d0d4f,_0x2aaef3);},'fromAdverb':function(_0x3868db){const _0x7489a2=_0x235866;return _0x3868db[_0x7489a2(0xe37)]('ly')?_0x94cd99[_0x7489a2(0x5ec)](_0x3868db)?_0x3868db[_0x7489a2(0x1db7)](/ically/,_0x7489a2(0xa96)):_0x5eadc4[_0x7489a2(0x5ec)](_0x3868db)?null:_0x36c786[_0x7489a2(0x32b5)](_0x3868db)?_0x36c786[_0x3868db]:_0x4ead73(_0x3868db,_0x17e73b)||_0x3868db:null;},'fromSuperlative':function(_0x89989a,_0x14bd11){const _0xa9fe55=_0x235866,_0x1b9157=_0x14bd11[_0xa9fe55(0x141f)][_0xa9fe55(0x1526)]['fromSuperlative'];return _0x3ab3c0(_0x89989a,_0x1b9157);},'fromComparative':function(_0x332c1b,_0x34b890){const _0x2e636b=_0x235866,_0x4be40d=_0x34b890[_0x2e636b(0x141f)][_0x2e636b(0x1526)]['fromComparative'];return _0x3ab3c0(_0x332c1b,_0x4be40d);},'all':function(_0x49d072,_0x51cad2){const _0x59997e=_0x235866;let _0x535fd9=[_0x49d072];return _0x535fd9['push'](_0x5f405f(_0x49d072,_0x51cad2)),_0x535fd9[_0x59997e(0x4131)](_0x23f0ee(_0x49d072,_0x51cad2)),_0x535fd9['push'](_0x5ea3ed(_0x49d072)),_0x535fd9=_0x535fd9[_0x59997e(0x295e)](_0x1c4f88=>_0x1c4f88),_0x535fd9=new Set(_0x535fd9),Array[_0x59997e(0xc81)](_0x535fd9);}},_0x2b5576={'noun':_0x260fab,'verb':_0xaf65af,'adjective':_0x2fb634},_0x524c3a={'Singular':(_0x39b3d2,_0x4a95c2,_0x4b9602,_0x5e08ee)=>{const _0x2bc8ca=_0x235866;let _0x3bb817=_0x5e08ee['one'][_0x2bc8ca(0x4ab0)],_0x3e95cd=_0x4b9602[_0x2bc8ca(0x141f)]['transform'][_0x2bc8ca(0x3c45)]['toPlural'](_0x39b3d2,_0x5e08ee);_0x3bb817[_0x3e95cd]||(_0x4a95c2[_0x3e95cd]=_0x4a95c2[_0x3e95cd]||'Plural');},'Actor':(_0x32f2d8,_0x47f0ee,_0x4ef521,_0x289398)=>{const _0x31ddbf=_0x235866;let _0x20d99c=_0x289398[_0x31ddbf(0x4116)]['lexicon'],_0x53d8ae=_0x4ef521[_0x31ddbf(0x141f)][_0x31ddbf(0x560)][_0x31ddbf(0x3c45)]['toPlural'](_0x32f2d8,_0x289398);_0x20d99c[_0x53d8ae]||(_0x47f0ee[_0x53d8ae]=_0x47f0ee[_0x53d8ae]||[_0x31ddbf(0x27b3),'Actor']);},'Comparable':(_0x32f30a,_0x4ef281,_0x338bb0,_0x391fec)=>{const _0x1dd894=_0x235866;let _0x26a968=_0x391fec[_0x1dd894(0x4116)][_0x1dd894(0x4ab0)],{toSuperlative:_0x19fda3,toComparative:_0x1c8ca9}=_0x338bb0[_0x1dd894(0x141f)][_0x1dd894(0x560)][_0x1dd894(0x2c03)],_0x4eee94=_0x19fda3(_0x32f30a,_0x391fec);_0x26a968[_0x4eee94]||(_0x4ef281[_0x4eee94]=_0x4ef281[_0x4eee94]||'Superlative');let _0x3dbb94=_0x1c8ca9(_0x32f30a,_0x391fec);_0x26a968[_0x3dbb94]||(_0x4ef281[_0x3dbb94]=_0x4ef281[_0x3dbb94]||_0x1dd894(0x1ab0)),_0x4ef281[_0x32f30a]=_0x1dd894(0x19b5);},'Demonym':(_0xb6f413,_0x153c50,_0x27dcf7,_0x190aaa)=>{const _0x1ac720=_0x235866;let _0x4de29a=_0x27dcf7[_0x1ac720(0x141f)][_0x1ac720(0x560)][_0x1ac720(0x3c45)]['toPlural'](_0xb6f413,_0x190aaa);_0x153c50[_0x4de29a]=_0x153c50[_0x4de29a]||['Demonym',_0x1ac720(0x27b3)];},'Infinitive':(_0x80acdf,_0x2369f0,_0x16cee1,_0xe13084)=>{const _0x4d3eee=_0x235866;let _0x530f42=_0xe13084[_0x4d3eee(0x4116)]['lexicon'],_0x3a8b9a=_0x16cee1[_0x4d3eee(0x141f)][_0x4d3eee(0x560)][_0x4d3eee(0x91)]['conjugate'](_0x80acdf,_0xe13084);Object[_0x4d3eee(0x5088)](_0x3a8b9a)[_0x4d3eee(0x4854)](_0x5522a3=>{_0x530f42[_0x5522a3[0x1]]||_0x2369f0[_0x5522a3[0x1]]||'FutureTense'===_0x5522a3[0x0]||(_0x2369f0[_0x5522a3[0x1]]=_0x5522a3[0x0]);});},'PhrasalVerb':(_0xa1dc3d,_0x1d1dfe,_0x726e80,_0x1e16be)=>{const _0x1b3df6=_0x235866;let _0x117400=_0x1e16be['one']['lexicon'];_0x1d1dfe[_0xa1dc3d]=['PhrasalVerb',_0x1b3df6(0x24ba)];let _0x15f4a9=_0x1e16be[_0x1b3df6(0x4116)]['_multiCache'],[_0x559c10,_0x16f0ba]=_0xa1dc3d[_0x1b3df6(0x29d0)]('\x20');_0x117400[_0x559c10]||(_0x1d1dfe[_0x559c10]=_0x1d1dfe[_0x559c10]||_0x1b3df6(0x24ba));let _0x3ec043=_0x726e80[_0x1b3df6(0x141f)][_0x1b3df6(0x560)]['verb'][_0x1b3df6(0x431b)](_0x559c10,_0x1e16be);delete _0x3ec043['FutureTense'],Object[_0x1b3df6(0x5088)](_0x3ec043)[_0x1b3df6(0x4854)](_0x5eeb77=>{const _0x327175=_0x1b3df6;if(_0x327175(0x432f)===_0x5eeb77[0x0]||''===_0x5eeb77[0x1])return;_0x1d1dfe[_0x5eeb77[0x1]]||_0x117400[_0x5eeb77[0x1]]||(_0x1d1dfe[_0x5eeb77[0x1]]=_0x5eeb77[0x0]),_0x15f4a9[_0x5eeb77[0x1]]=0x2;let _0x121f78=_0x5eeb77[0x1]+'\x20'+_0x16f0ba;_0x1d1dfe[_0x121f78]=_0x1d1dfe[_0x121f78]||[_0x5eeb77[0x0],_0x327175(0x4634)];});},'Multiple':(_0x392f31,_0x188b2c)=>{const _0x3a59b5=_0x235866;_0x188b2c[_0x392f31]=[_0x3a59b5(0x4408),_0x3a59b5(0x2346)],_0x188b2c[_0x392f31+'th']=[_0x3a59b5(0x4408),_0x3a59b5(0x529)],_0x188b2c[_0x392f31+_0x3a59b5(0x376f)]=[_0x3a59b5(0x4408),_0x3a59b5(0x3793)];},'Cardinal':(_0xdf6afe,_0x13495d)=>{const _0x1a7cf1=_0x235866;_0x13495d[_0xdf6afe]=[_0x1a7cf1(0x20ed),_0x1a7cf1(0x2346)];},'Ordinal':(_0x173173,_0x2431da)=>{const _0x1c26f6=_0x235866;_0x2431da[_0x173173]=[_0x1c26f6(0x20ed),_0x1c26f6(0x529)],_0x2431da[_0x173173+'s']=['TextValue',_0x1c26f6(0x3793)];},'Place':(_0x5c85c4,_0x45831d)=>{const _0x356399=_0x235866;_0x45831d[_0x5c85c4]=[_0x356399(0x3fae),'ProperNoun'];},'Region':(_0x6bf997,_0x348802)=>{const _0x592577=_0x235866;_0x348802[_0x6bf997]=[_0x592577(0x4bf7),_0x592577(0x374d)];}},_0x4db76e=function(_0x3d83c5,_0x2fe61e){const _0x364c60=_0x235866,{methods:_0x254c3f,model:_0x227ad3}=_0x2fe61e;let _0x267ab8={},_0x333e3d={};return Object[_0x364c60(0x2f75)](_0x3d83c5)[_0x364c60(0x4854)](_0x5bb8d4=>{const _0x87daf6=_0x364c60;let _0x478b96=_0x3d83c5[_0x5bb8d4],_0x38227b=(_0x5bb8d4=(_0x5bb8d4=_0x5bb8d4['toLowerCase']()['trim']())[_0x87daf6(0x1db7)](/'s\b/,''))['split'](/ /);_0x38227b[_0x87daf6(0x205b)]>0x1&&(void 0x0===_0x333e3d[_0x38227b[0x0]]||_0x38227b[_0x87daf6(0x205b)]>_0x333e3d[_0x38227b[0x0]])&&(_0x333e3d[_0x38227b[0x0]]=_0x38227b[_0x87daf6(0x205b)]),!0x0===_0x524c3a[_0x87daf6(0x32b5)](_0x478b96)&&_0x524c3a[_0x478b96](_0x5bb8d4,_0x267ab8,_0x254c3f,_0x227ad3),_0x267ab8[_0x5bb8d4]=_0x267ab8[_0x5bb8d4]||_0x478b96;}),delete _0x267ab8[''],delete _0x267ab8['null'],delete _0x267ab8['\x20'],{'lex':_0x267ab8,'_multi':_0x333e3d};},_0x2454b6=function(_0x4dffb8){const _0x4305b0=_0x235866,_0x414b11=/[,:;]/;let _0x5f46c4=[];return _0x4dffb8[_0x4305b0(0x4854)](_0x5a83de=>{const _0x2a642e=_0x4305b0;let _0xbf71b=0x0;_0x5a83de[_0x2a642e(0x4854)]((_0x2a1b96,_0x3c787d)=>{const _0x4ab31e=_0x2a642e;_0x414b11[_0x4ab31e(0x34c)](_0x2a1b96[_0x4ab31e(0x9ce)])&&function(_0x99b8ba,_0xfd6fed){const _0x713297=_0x4ab31e,_0x5dd87b=/^[0-9]+$/;let _0x58fd66=_0x99b8ba[_0xfd6fed];if(!_0x58fd66)return!0x1;const _0x11790a=new Set(['may',_0x713297(0x3562),_0x713297(0x3943),_0x713297(0x40a4)]);if(_0x713297(0x1fb0)===_0x58fd66[_0x713297(0x4cb)]||_0x11790a[_0x713297(0x5ec)](_0x58fd66[_0x713297(0x4cb)]))return!0x1;if(_0x58fd66[_0x713297(0x23d1)]['has'](_0x713297(0x3fae))||_0x58fd66['tags'][_0x713297(0x5ec)]('Date'))return!0x1;if(_0x99b8ba[_0xfd6fed-0x1]){let _0x3d79bd=_0x99b8ba[_0xfd6fed-0x1];if(_0x3d79bd[_0x713297(0x23d1)][_0x713297(0x5ec)]('Date')||_0x11790a[_0x713297(0x5ec)](_0x3d79bd[_0x713297(0x4cb)]))return!0x1;if(_0x3d79bd['tags']['has']('Adjective')||_0x58fd66[_0x713297(0x23d1)][_0x713297(0x5ec)]('Adjective'))return!0x1;}let _0x2b5b02=_0x58fd66['normal'];return 0x1!==_0x2b5b02['length']&&0x2!==_0x2b5b02[_0x713297(0x205b)]&&0x4!==_0x2b5b02[_0x713297(0x205b)]||!_0x5dd87b[_0x713297(0x34c)](_0x2b5b02);}(_0x5a83de,_0x3c787d+0x1)&&(_0x5f46c4[_0x4ab31e(0x4131)](_0x5a83de[_0x4ab31e(0x428e)](_0xbf71b,_0x3c787d+0x1)),_0xbf71b=_0x3c787d+0x1);}),_0xbf71b<_0x5a83de[_0x2a642e(0x205b)]&&_0x5f46c4[_0x2a642e(0x4131)](_0x5a83de[_0x2a642e(0x428e)](_0xbf71b,_0x5a83de['length']));}),_0x5f46c4;},_0x22c2e5={'e':[_0x235866(0x22f9),_0x235866(0x2e75),'antennae',_0x235866(0x235f),'nebulae',_0x235866(0x4d35),_0x235866(0x3972)],'i':['tia','octopi',_0x235866(0x3b2e),_0x235866(0x1436),'nuclei',_0x235866(0x38dc),'cacti','stimuli'],'n':['men'],'t':[_0x235866(0x6b7)]},_0x26b4ca=new Set([_0x235866(0x1054),'menus',_0x235866(0x368)]),_0x4c29ed=[_0x235866(0x35e8),_0x235866(0x3156),'was',_0x235866(0x1bfc),_0x235866(0x4fe1),_0x235866(0x167d),_0x235866(0x4740),_0x235866(0x2135),_0x235866(0xb0a),'ois',_0x235866(0x1d3c),_0x235866(0x3d10),'tis',_0x235866(0x159),_0x235866(0x36b8),_0x235866(0x1d24),_0x235866(0x1127),_0x235866(0x3fc9),_0x235866(0x241e),_0x235866(0x25ae),'lus','nus',_0x235866(0x2726),_0x235866(0x41c5),_0x235866(0x4333),_0x235866(0x494d),_0x235866(0x2dbb),_0x235866(0x4e44),'xus','aos',_0x235866(0x354a),_0x235866(0x18e5),'ogos','\x27s','ss'],_0xdc5701=function(_0x3881ee){const _0x4903c9=_0x235866;if(!_0x3881ee||_0x3881ee[_0x4903c9(0x205b)]<=0x3)return!0x1;if(_0x26b4ca['has'](_0x3881ee))return!0x0;let _0x5e5264=_0x3881ee[_0x3881ee[_0x4903c9(0x205b)]-0x1];return _0x22c2e5[_0x4903c9(0x32b5)](_0x5e5264)?_0x22c2e5[_0x5e5264]['find'](_0x3dbb8c=>_0x3881ee[_0x4903c9(0xe37)](_0x3dbb8c)):'s'===_0x5e5264&&!_0x4c29ed[_0x4903c9(0x12b9)](_0x4ded79=>_0x3881ee['endsWith'](_0x4ded79));},_0x474f59={'two':{'quickSplit':_0x2454b6,'expandLexicon':_0x4db76e,'transform':_0x2b5576,'looksPlural':_0xdc5701}},_0x4eb4c2=function(_0x3e42c2){const _0x251d17=_0x235866,{irregularPlurals:_0xf14be2}=_0x3e42c2['two'],{lexicon:_0x5c9f02}=_0x3e42c2[_0x251d17(0x4116)];return Object[_0x251d17(0x5088)](_0xf14be2)['forEach'](_0x406245=>{const _0x4d0cc3=_0x251d17;_0x5c9f02[_0x406245[0x0]]=_0x5c9f02[_0x406245[0x0]]||_0x4d0cc3(0x3faa),_0x5c9f02[_0x406245[0x1]]=_0x5c9f02[_0x406245[0x1]]||'Plural';}),_0x3e42c2;};let _0x18c1f0={'one':{'lexicon':{}},'two':{'models':_0x4be2c6}};const _0x5813d8={'Actor|Verb':_0x235866(0x432f),'Adj|Gerund':_0x235866(0x19b5),'Adj|Noun':_0x235866(0x19b5),'Adj|Past':_0x235866(0x19b5),'Adj|Present':_0x235866(0x19b5),'Noun|Verb':_0x235866(0x3faa),'Noun|Gerund':'Gerund','Person|Noun':_0x235866(0x1ce5),'Person|Date':'Month','Person|Verb':_0x235866(0xcbd),'Person|Place':_0x235866(0x29bf),'Person|Adj':_0x235866(0x1ab0),'Plural|Verb':'Plural','Unit|Noun':_0x235866(0x1ce5)},_0x35ffb3=function(_0x55f625,_0x11d4b1){const _0x5c0615=_0x235866,_0x28faf0={'model':_0x11d4b1,'methods':_0x474f59};let {lex:_0x41bbdc,_multi:_0x44d740}=_0x474f59[_0x5c0615(0x141f)]['expandLexicon'](_0x55f625,_0x28faf0);return Object['assign'](_0x11d4b1[_0x5c0615(0x4116)][_0x5c0615(0x4ab0)],_0x41bbdc),Object[_0x5c0615(0x11e8)](_0x11d4b1[_0x5c0615(0x4116)][_0x5c0615(0x1398)],_0x44d740),_0x11d4b1;},_0x8bca8b=function(_0x1533bc,_0x5d7c94,_0x536b78){const _0x56e0be=_0x235866;let _0x1d20c6=_0x78c4dc(_0x1533bc,_0x18c1f0);_0x5d7c94[_0x1d20c6[_0x56e0be(0x2192)]]=_0x5d7c94[_0x1d20c6[_0x56e0be(0x2192)]]||_0x56e0be(0x2192),_0x5d7c94[_0x1d20c6[_0x56e0be(0x1e19)]]=_0x5d7c94[_0x1d20c6['Gerund']]||_0x56e0be(0x1e19),!0x0===_0x536b78&&(_0x5d7c94[_0x1d20c6['PresentTense']]=_0x5d7c94[_0x1d20c6[_0x56e0be(0x17a6)]]||_0x56e0be(0x17a6));},_0x51ce06=function(_0xe7b714,_0x46eb27,_0x38eb22){const _0x410d93=_0x235866;let _0x57e2f4=_0x5f405f(_0xe7b714,_0x38eb22);_0x46eb27[_0x57e2f4]=_0x46eb27[_0x57e2f4]||_0x410d93(0x2d21);let _0x220e1a=_0x23f0ee(_0xe7b714,_0x38eb22);_0x46eb27[_0x220e1a]=_0x46eb27[_0x220e1a]||_0x410d93(0x1ab0);},_0x2d35d9=function(_0x3763f1,_0x518c3b){const _0x3bf033=_0x235866;let _0x4395b7={};const _0x46f1e9=_0x518c3b['one'][_0x3bf033(0x4ab0)];return Object['keys'](_0x3763f1)[_0x3bf033(0x4854)](_0x133eec=>{const 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/,'');},_0x563b4c=function(){const _0x9a2bad=_0x235866;let _0x883ba1=this[_0x9a2bad(0x4163)](_0x9a2bad(0x40b1)),_0x5843ac=_0x883ba1['match'](_0x9a2bad(0x20e));return 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Set([_0x235866(0x2427),'how',_0x235866(0x489c),'if','too']);let _0x59a2d1=new Set([_0x235866(0x1074),_0x235866(0x1ebc),_0x235866(0x4a49)]);const _0x592ffc=function(_0xa610e8,_0x574f47){const _0xf036b4=_0x235866;let _0xe903f0=_0xa610e8[_0x574f47][_0xf036b4(0x4cb)][_0xf036b4(0x29d0)](_0x2bfc4b)[0x0];if(_0xf036b4(0x4647)===_0xe903f0)return[_0xe903f0,'us'];if(_0xf036b4(0x2105)===_0xe903f0){let _0x45b9ab=_0xa610e8[_0x574f47+0x1];if(_0x45b9ab&&_0x45b9ab['tags'][_0xf036b4(0x5ec)](_0xf036b4(0x27b3)))return[_0xe903f0,'are'];}return _0xf036b4(0x5ec)===((_0x1538a4,_0x131d3b)=>{const _0x135527=_0xf036b4;for(let _0x98fde1=_0x131d3b+0x1;_0x98fde1<_0x1538a4[_0x135527(0x205b)];_0x98fde1+=0x1){let 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Set([_0x235866(0x44fa),_0x235866(0x48dc),_0x235866(0x19f2),'it','had']),_0x574f6a=new Set(['have','be']),_0x1212e2=function(_0x949ef1,_0x284013){const _0x5c965e=_0x235866;let _0x20d405=_0x949ef1[_0x284013]['normal'][_0x5c965e(0x29d0)](_0x1a9d99)[0x0];return _0x5c965e(0x3626)===_0x20d405||_0x5c965e(0x2427)===_0x20d405?[_0x20d405,'did']:_0x5c965e(0x455a)===((_0x3705c2,_0x52396d)=>{const _0x4b0482=_0x5c965e;for(let _0x3e18f1=_0x52396d+0x1;_0x3e18f1<_0x3705c2[_0x4b0482(0x205b)];_0x3e18f1+=0x1){let _0x45b1a9=_0x3705c2[_0x3e18f1];if(_0x563a96[_0x4b0482(0x5ec)](_0x45b1a9[_0x4b0482(0x4cb)]))return'had';if(_0x574f6a['has'](_0x45b1a9['normal']))return'would';if(_0x45b1a9['tags'][_0x4b0482(0x5ec)](_0x4b0482(0x2192))||'Adj|Past'===_0x45b1a9[_0x4b0482(0x68)])return _0x4b0482(0x455a);if(_0x45b1a9[_0x4b0482(0x23d1)][_0x4b0482(0x5ec)](_0x4b0482(0x17a6))||_0x45b1a9[_0x4b0482(0x23d1)][_0x4b0482(0x5ec)](_0x4b0482(0x24ba)))return _0x4b0482(0x416a);if(_0x45b1a9['tags'][_0x4b0482(0x5ec)]('#Determiner'))return _0x4b0482(0x455a);if(_0x45b1a9[_0x4b0482(0x23d1)]['has'](_0x4b0482(0x19b5)))return _0x4b0482(0x416a);}return!0x1;})(_0x949ef1,_0x284013)?[_0x20d405,_0x5c965e(0x455a)]:[_0x20d405,'would'];},_0x1038d3=function(_0x1d8415,_0xb4baff){const _0x8739fb=_0x235866;if(_0x8739fb(0x2e85)===_0x1d8415[_0xb4baff][_0x8739fb(0x4cb)]||_0x8739fb(0x284f)===_0x1d8415[_0xb4baff][_0x8739fb(0x4cb)]){if(_0x1d8415[_0xb4baff+0x1]&&_0x8739fb(0x4b8c)===_0x1d8415[_0xb4baff+0x1][_0x8739fb(0x4cb)])return[_0x8739fb(0xb66)];let _0x3f96f7=function(_0x363ffc,_0x5695b3){const _0x14b4d7=_0x8739fb;for(let _0x231f76=_0x5695b3-0x1;_0x231f76>=0x0;_0x231f76-=0x1)if(_0x363ffc[_0x231f76]['tags']['has'](_0x14b4d7(0x1ce5))||_0x363ffc[_0x231f76][_0x14b4d7(0x23d1)]['has']('Pronoun')||_0x363ffc[_0x231f76][_0x14b4d7(0x23d1)][_0x14b4d7(0x5ec)](_0x14b4d7(0x27b3))||_0x363ffc[_0x231f76][_0x14b4d7(0x23d1)]['has'](_0x14b4d7(0x3faa)))return 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_0x68c62a=_0x53f933[_0x1bbe03];if(_0x182b54[_0x1c445f(0x32b5)](_0x68c62a[_0x1c445f(0x194)]||_0x68c62a[_0x1c445f(0x4cb)]))return!0x1;if(_0x68c62a['tags'][_0x1c445f(0x5ec)](_0x1c445f(0x1e24)))return!0x0;if(_0x68c62a[_0x1c445f(0x23d1)][_0x1c445f(0x5ec)](_0x1c445f(0x12d4)))return!0x1;if('he\x27s'===_0x68c62a[_0x1c445f(0x4cb)]||_0x1c445f(0x3c75)===_0x68c62a[_0x1c445f(0x4cb)])return!0x1;let _0x2f001f=_0x53f933[_0x1bbe03+0x1];if(!_0x2f001f)return!0x0;if(_0x1c445f(0x49b1)===_0x68c62a['normal'])return!!_0x2f001f[_0x1c445f(0x23d1)][_0x1c445f(0x5ec)]('#Noun');if(_0x1c445f(0x3a55)==_0x2f001f[_0x1c445f(0x68)]){let _0x19dabd=_0x53f933[_0x1bbe03+0x2];return 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_0x2d4762=_0x11111e[_0x399ce0(0x3e8f)](_0x45433a[_0x399ce0(0x379c)]('\x20'));return _0x2d4762[_0x399ce0(0x210d)]('id'),_0x2d4762[_0x399ce0(0x3f72)][0x0];},_0xb83c5f={'contractionTwo':_0x8d615c=>{const _0xcf0f5e=_0x235866;let {world:_0x5bfdeb,document:_0x2e3cf8}=_0x8d615c;_0x2e3cf8[_0xcf0f5e(0x4854)]((_0x697862,_0x592b75)=>{const _0x347b61=_0xcf0f5e;for(let _0x6ccdc4=_0x697862['length']-0x1;_0x6ccdc4>=0x0;_0x6ccdc4-=0x1){if(_0x697862[_0x6ccdc4][_0x347b61(0x15c0)])return;let _0x51e37c=null;!0x0===_0x1a86ab[_0x347b61(0x34c)](_0x697862[_0x6ccdc4][_0x347b61(0x4cb)])&&(_0x51e37c=_0x697862[_0x6ccdc4][_0x347b61(0x4cb)][_0x347b61(0x29d0)](_0x1a86ab)[0x1]);let 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_0x461ea4=_0x77b020+_0x4a4cee(0xe15)+_0x47745c+'\x20\x0a\x0a\x20QUESTION:\x20'+_0x4575c8+_0x4a4cee(0xd8e)+_0x379feb,_0x2f8de1={'prompt':_0x461ea4,'messages':_0x3de7d2};_0x2f8de1[_0x4a4cee(0x2c3e)]=_0x58daab,_0x4dd22a+=0x1,_0x501c91(_0x403bd6,_0x4dd22a);let _0xc2225c=0x0;_0x4dd22a+=0x1,_0x501c91(_0x403bd6,_0x4dd22a);try{for await(let _0x3acd9e of _0x6b503a(_0x2f8de1,_0x4586ac,!0x0,!0x0))0x0===_0xc2225c&&(_0x4dd22a+=0x1,_0x501c91(_0x403bd6,_0x4dd22a)),_0xc2225c+=0x1,_0x324c51+=_0x3acd9e,_0x60383d(_0x324c51,_0x487553);}catch(_0x3a7f6a){_0x26f7f9(_0x2c6959,_0x3a7f6a+_0x4a4cee(0x593),_0x4a4cee(0x4b47));}_0x37bbfd(_0x36a8f0||0x1,_0x4a4cee(0x6cd),_0x324c51,_0x4586ac),_0x4c99f2=_0x324c51+_0x4a4cee(0x3b9a);const _0xbf5f40=_0x574c15(_0x2c6959,0x0,_0x19cc1a);_0x101050(_0x2c6959,_0x487553,_0xbf5f40),await _0x4f50a1(_0x487553,'progress'),_0x184d80(_0x487553),_0x4c0aad(_0x2c6959),_0x6eaaba(_0x4a4cee(0x203b),_0x2c6959),await 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fetch(_0x72c272,{'method':'POST','headers':{'Content-Type':_0x2cb6f1(0x198d),'Authorization':_0x2cb6f1(0x2485)+_0x576f5c},'body':JSON[_0x2cb6f1(0xf0b)](_0x13ac50)});if(!_0x28af3a['ok'])throw new Error(_0x2cb6f1(0x1a58)+_0x28af3a[_0x2cb6f1(0x33de)]);return await _0x28af3a[_0x2cb6f1(0x25b9)]();}catch(_0x13fb96){}}(_0x4a581a,_0x4586ac),_0x291eba(_0x5bd6f8),_0x133944+=0x1,_0x501c91(_0x4885fb,_0x133944),Array[_0x15a455(0x477f)](_0x38da71))_0x457895(_0x5bd6f8,_0x38da71);else{if(null!==_0x38da71&&_0x15a455(0x2b58)==typeof _0x38da71){const _0x4adbab=Object[_0x15a455(0x2f75)](_0x38da71);if(_0x4adbab[_0x15a455(0x205b)]>0x0&&_0x38da71[_0x4adbab[0x0]][_0x15a455(0x205b)]>0x0){const _0x33d57c=_0x4adbab[0x0];_0x457895(_0x5bd6f8,_0x38da71[_0x33d57c]);}else _0x38da71='0',_0x457895(_0x5bd6f8,_0x38da71),_0x26f7f9(_0x2c6959,_0x15a455(0xd62),'Error');}else 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STORY_CREATION_USER_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE_ACTION\x20SYSRES_CONST_ALL_ACCEPT_CONDITION_RUS\x20SYSRES_CONST_ALL_USERS_GROUP\x20SYSRES_CONST_ALL_USERS_GROUP_NAME\x20SYSRES_CONST_ALL_USERS_SERVER_GROUP_NAME\x20SYSRES_CONST_ALLOWED_ACCESS_TYPE_CODE\x20SYSRES_CONST_ALLOWED_ACCESS_TYPE_NAME\x20SYSRES_CONST_APP_VIEWER_TYPE_REQUISITE_CODE\x20SYSRES_CONST_APPROVING_SIGNATURE_NAME\x20SYSRES_CONST_APPROVING_SIGNATURE_REQUISITE_CODE\x20SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE\x20SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE_CODE\x20SYSRES_CONST_ATTACH_TYPE_COMPONENT_TOKEN\x20SYSRES_CONST_ATTACH_TYPE_DOC\x20SYSRES_CONST_ATTACH_TYPE_EDOC\x20SYSRES_CONST_ATTACH_TYPE_FOLDER\x20SYSRES_CONST_ATTACH_TYPE_JOB\x20SYSRES_CONST_ATTACH_TYPE_REFERENCE\x20SYSRES_CONST_ATTACH_TYPE_TASK\x20SYSRES_CONST_AUTH_ENCODED_PASSWORD\x20SYSRES_CONST_AUTH_ENCODED_PASSWORD_CODE\x20SYSRES_CONST_AUTH_NOVELL\x20SYSRES_CONST_AUTH_PASSWORD\x20SYSRES_CONST_AUTH_PASSWORD_CODE\x20SYSRES_CONST_AUTH_WINDOWS\x20SYSRES_CONST_AUTHENTICATING_SIGNATURE_NAME\x20SYSRES_CONST_AUTHENTICATING_SIGNATURE_REQUISITE_CODE\x20SYSRES_CONST_AUTO_ENUM_METHOD_FLAG\x20SYSRES_CONST_AUTO_NUMERATION_CODE\x20SYSRES_CONST_AUTO_STRONG_ENUM_METHOD_FLAG\x20SYSRES_CONST_AUTOTEXT_NAME_REQUISITE_CODE\x20SYSRES_CONST_AUTOTEXT_TEXT_REQUISITE_CODE\x20SYSRES_CONST_AUTOTEXT_USAGE_ALL\x20SYSRES_CONST_AUTOTEXT_USAGE_ALL_CODE\x20SYSRES_CONST_AUTOTEXT_USAGE_SIGN\x20SYSRES_CONST_AUTOTEXT_USAGE_SIGN_CODE\x20SYSRES_CONST_AUTOTEXT_USAGE_WORK\x20SYSRES_CONST_AUTOTEXT_USAGE_WORK_CODE\x20SYSRES_CONST_AUTOTEXT_USE_ANYWHERE_CODE\x20SYSRES_CONST_AUTOTEXT_USE_ON_SIGNING_CODE\x20SYSRES_CONST_AUTOTEXT_USE_ON_WORK_CODE\x20SYSRES_CONST_BEGIN_DATE_REQUISITE_CODE\x20SYSRES_CONST_BLACK_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_BLUE_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_BTN_PART\x20SYSRES_CONST_CALCULATED_ROLE_TYPE_CODE\x20SYSRES_CONST_CALL_TYPE_VARIABLE_BUTTON_VALUE\x20SYSRES_CONST_CALL_TYPE_VARIABLE_PROGRAM_VALUE\x20SYSRES_CONST_CANCEL_MESSAGE_FUNCTION_RESULT\x20SYSRES_CONST_CARD_PART\x20SYSRES_CONST_CARD_REFERENCE_MODE_NAME\x20SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_ENCRYPT_VALUE\x20SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_AND_ENCRYPT_VALUE\x20SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_VALUE\x20SYSRES_CONST_CHECK_PARAM_VALUE_DATE_PARAM_TYPE\x20SYSRES_CONST_CHECK_PARAM_VALUE_FLOAT_PARAM_TYPE\x20SYSRES_CONST_CHECK_PARAM_VALUE_INTEGER_PARAM_TYPE\x20SYSRES_CONST_CHECK_PARAM_VALUE_PICK_PARAM_TYPE\x20SYSRES_CONST_CHECK_PARAM_VALUE_REEFRENCE_PARAM_TYPE\x20SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_FEMININE\x20SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_MASCULINE\x20SYSRES_CONST_CODE_COMPONENT_TYPE_ADMIN\x20SYSRES_CONST_CODE_COMPONENT_TYPE_DEVELOPER\x20SYSRES_CONST_CODE_COMPONENT_TYPE_DOCS\x20SYSRES_CONST_CODE_COMPONENT_TYPE_EDOC_CARDS\x20SYSRES_CONST_CODE_COMPONENT_TYPE_EXTERNAL_EXECUTABLE\x20SYSRES_CONST_CODE_COMPONENT_TYPE_OTHER\x20SYSRES_CONST_CODE_COMPONENT_TYPE_REFERENCE\x20SYSRES_CONST_CODE_COMPONENT_TYPE_REPORT\x20SYSRES_CONST_CODE_COMPONENT_TYPE_SCRIPT\x20SYSRES_CONST_CODE_COMPONENT_TYPE_URL\x20SYSRES_CONST_CODE_REQUISITE_ACCESS\x20SYSRES_CONST_CODE_REQUISITE_CODE\x20SYSRES_CONST_CODE_REQUISITE_COMPONENT\x20SYSRES_CONST_CODE_REQUISITE_DESCRIPTION\x20SYSRES_CONST_CODE_REQUISITE_EXCLUDE_COMPONENT\x20SYSRES_CONST_CODE_REQUISITE_RECORD\x20SYSRES_CONST_COMMENT_REQ_CODE\x20SYSRES_CONST_COMMON_SETTINGS_REQUISITE_CODE\x20SYSRES_CONST_COMP_CODE_GRD\x20SYSRES_CONST_COMPONENT_GROUP_TYPE_REQUISITE_CODE\x20SYSRES_CONST_COMPONENT_TYPE_ADMIN_COMPONENTS\x20SYSRES_CONST_COMPONENT_TYPE_DEVELOPER_COMPONENTS\x20SYSRES_CONST_COMPONENT_TYPE_DOCS\x20SYSRES_CONST_COMPONENT_TYPE_EDOC_CARDS\x20SYSRES_CONST_COMPONENT_TYPE_EDOCS\x20SYSRES_CONST_COMPONENT_TYPE_EXTERNAL_EXECUTABLE\x20SYSRES_CONST_COMPONENT_TYPE_OTHER\x20SYSRES_CONST_COMPONENT_TYPE_REFERENCE_TYPES\x20SYSRES_CONST_COMPONENT_TYPE_REFERENCES\x20SYSRES_CONST_COMPONENT_TYPE_REPORTS\x20SYSRES_CONST_COMPONENT_TYPE_SCRIPTS\x20SYSRES_CONST_COMPONENT_TYPE_URL\x20SYSRES_CONST_COMPONENTS_REMOTE_SERVERS_VIEW_CODE\x20SYSRES_CONST_CONDITION_BLOCK_DESCRIPTION\x20SYSRES_CONST_CONST_FIRM_STATUS_COMMON\x20SYSRES_CONST_CONST_FIRM_STATUS_INDIVIDUAL\x20SYSRES_CONST_CONST_NEGATIVE_VALUE\x20SYSRES_CONST_CONST_POSITIVE_VALUE\x20SYSRES_CONST_CONST_SERVER_STATUS_DONT_REPLICATE\x20SYSRES_CONST_CONST_SERVER_STATUS_REPLICATE\x20SYSRES_CONST_CONTENTS_REQUISITE_CODE\x20SYSRES_CONST_DATA_TYPE_BOOLEAN\x20SYSRES_CONST_DATA_TYPE_DATE\x20SYSRES_CONST_DATA_TYPE_FLOAT\x20SYSRES_CONST_DATA_TYPE_INTEGER\x20SYSRES_CONST_DATA_TYPE_PICK\x20SYSRES_CONST_DATA_TYPE_REFERENCE\x20SYSRES_CONST_DATA_TYPE_STRING\x20SYSRES_CONST_DATA_TYPE_TEXT\x20SYSRES_CONST_DATA_TYPE_VARIANT\x20SYSRES_CONST_DATE_CLOSE_REQ_CODE\x20SYSRES_CONST_DATE_FORMAT_DATE_ONLY_CHAR\x20SYSRES_CONST_DATE_OPEN_REQ_CODE\x20SYSRES_CONST_DATE_REQUISITE\x20SYSRES_CONST_DATE_REQUISITE_CODE\x20SYSRES_CONST_DATE_REQUISITE_NAME\x20SYSRES_CONST_DATE_REQUISITE_TYPE\x20SYSRES_CONST_DATE_TYPE_CHAR\x20SYSRES_CONST_DATETIME_FORMAT_VALUE\x20SYSRES_CONST_DEA_ACCESS_RIGHTS_ACTION_CODE\x20SYSRES_CONST_DESCRIPTION_LOCALIZE_ID_REQUISITE_CODE\x20SYSRES_CONST_DESCRIPTION_REQUISITE_CODE\x20SYSRES_CONST_DET1_PART\x20SYSRES_CONST_DET2_PART\x20SYSRES_CONST_DET3_PART\x20SYSRES_CONST_DET4_PART\x20SYSRES_CONST_DET5_PART\x20SYSRES_CONST_DET6_PART\x20SYSRES_CONST_DETAIL_DATASET_KEY_REQUISITE_CODE\x20SYSRES_CONST_DETAIL_PICK_REQUISITE_CODE\x20SYSRES_CONST_DETAIL_REQ_CODE\x20SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_CODE\x20SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_NAME\x20SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_CODE\x20SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_NAME\x20SYSRES_CONST_DOCUMENT_STORAGES_CODE\x20SYSRES_CONST_DOCUMENT_TEMPLATES_TYPE_NAME\x20SYSRES_CONST_DOUBLE_REQUISITE_CODE\x20SYSRES_CONST_EDITOR_CLOSE_FILE_OBSERV_TYPE_CODE\x20SYSRES_CONST_EDITOR_CLOSE_PROCESS_OBSERV_TYPE_CODE\x20SYSRES_CONST_EDITOR_TYPE_REQUISITE_CODE\x20SYSRES_CONST_EDITORS_APPLICATION_NAME_REQUISITE_CODE\x20SYSRES_CONST_EDITORS_CREATE_SEVERAL_PROCESSES_REQUISITE_CODE\x20SYSRES_CONST_EDITORS_EXTENSION_REQUISITE_CODE\x20SYSRES_CONST_EDITORS_OBSERVER_BY_PROCESS_TYPE\x20SYSRES_CONST_EDITORS_REFERENCE_CODE\x20SYSRES_CONST_EDITORS_REPLACE_SPEC_CHARS_REQUISITE_CODE\x20SYSRES_CONST_EDITORS_USE_PLUGINS_REQUISITE_CODE\x20SYSRES_CONST_EDITORS_VIEW_DOCUMENT_OPENED_TO_EDIT_CODE\x20SYSRES_CONST_EDOC_CARD_TYPE_REQUISITE_CODE\x20SYSRES_CONST_EDOC_CARD_TYPES_LINK_REQUISITE_CODE\x20SYSRES_CONST_EDOC_CERTIFICATE_AND_PASSWORD_ENCODE_CODE\x20SYSRES_CONST_EDOC_CERTIFICATE_ENCODE_CODE\x20SYSRES_CONST_EDOC_DATE_REQUISITE_CODE\x20SYSRES_CONST_EDOC_KIND_REFERENCE_CODE\x20SYSRES_CONST_EDOC_KINDS_BY_TEMPLATE_ACTION_CODE\x20SYSRES_CONST_EDOC_MANAGE_ACCESS_CODE\x20SYSRES_CONST_EDOC_NONE_ENCODE_CODE\x20SYSRES_CONST_EDOC_NUMBER_REQUISITE_CODE\x20SYSRES_CONST_EDOC_PASSWORD_ENCODE_CODE\x20SYSRES_CONST_EDOC_READONLY_ACCESS_CODE\x20SYSRES_CONST_EDOC_SHELL_LIFE_TYPE_VIEW_VALUE\x20SYSRES_CONST_EDOC_SIZE_RESTRICTION_PRIORITY_REQUISITE_CODE\x20SYSRES_CONST_EDOC_STORAGE_CHECK_ACCESS_RIGHTS_REQUISITE_CODE\x20SYSRES_CONST_EDOC_STORAGE_COMPUTER_NAME_REQUISITE_CODE\x20SYSRES_CONST_EDOC_STORAGE_DATABASE_NAME_REQUISITE_CODE\x20SYSRES_CONST_EDOC_STORAGE_EDIT_IN_STORAGE_REQUISITE_CODE\x20SYSRES_CONST_EDOC_STORAGE_LOCAL_PATH_REQUISITE_CODE\x20SYSRES_CONST_EDOC_STORAGE_SHARED_SOURCE_NAME_REQUISITE_CODE\x20SYSRES_CONST_EDOC_TEMPLATE_REQUISITE_CODE\x20SYSRES_CONST_EDOC_TYPES_REFERENCE_CODE\x20SYSRES_CONST_EDOC_VERSION_ACTIVE_STAGE_CODE\x20SYSRES_CONST_EDOC_VERSION_DESIGN_STAGE_CODE\x20SYSRES_CONST_EDOC_VERSION_OBSOLETE_STAGE_CODE\x20SYSRES_CONST_EDOC_WRITE_ACCES_CODE\x20SYSRES_CONST_EDOCUMENT_CARD_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE\x20SYSRES_CONST_ENCODE_CERTIFICATE_TYPE_CODE\x20SYSRES_CONST_END_DATE_REQUISITE_CODE\x20SYSRES_CONST_ENUMERATION_TYPE_REQUISITE_CODE\x20SYSRES_CONST_EXECUTE_ACCESS_RIGHTS_TYPE_CODE\x20SYSRES_CONST_EXECUTIVE_FILE_STORAGE_TYPE\x20SYSRES_CONST_EXIST_CONST\x20SYSRES_CONST_EXIST_VALUE\x20SYSRES_CONST_EXPORT_LOCK_TYPE_ASK\x20SYSRES_CONST_EXPORT_LOCK_TYPE_WITH_LOCK\x20SYSRES_CONST_EXPORT_LOCK_TYPE_WITHOUT_LOCK\x20SYSRES_CONST_EXPORT_VERSION_TYPE_ASK\x20SYSRES_CONST_EXPORT_VERSION_TYPE_LAST\x20SYSRES_CONST_EXPORT_VERSION_TYPE_LAST_ACTIVE\x20SYSRES_CONST_EXTENSION_REQUISITE_CODE\x20SYSRES_CONST_FILTER_NAME_REQUISITE_CODE\x20SYSRES_CONST_FILTER_REQUISITE_CODE\x20SYSRES_CONST_FILTER_TYPE_COMMON_CODE\x20SYSRES_CONST_FILTER_TYPE_COMMON_NAME\x20SYSRES_CONST_FILTER_TYPE_USER_CODE\x20SYSRES_CONST_FILTER_TYPE_USER_NAME\x20SYSRES_CONST_FILTER_VALUE_REQUISITE_NAME\x20SYSRES_CONST_FLOAT_NUMBER_FORMAT_CHAR\x20SYSRES_CONST_FLOAT_REQUISITE_TYPE\x20SYSRES_CONST_FOLDER_AUTHOR_VALUE\x20SYSRES_CONST_FOLDER_KIND_ANY_OBJECTS\x20SYSRES_CONST_FOLDER_KIND_COMPONENTS\x20SYSRES_CONST_FOLDER_KIND_EDOCS\x20SYSRES_CONST_FOLDER_KIND_JOBS\x20SYSRES_CONST_FOLDER_KIND_TASKS\x20SYSRES_CONST_FOLDER_TYPE_COMMON\x20SYSRES_CONST_FOLDER_TYPE_COMPONENT\x20SYSRES_CONST_FOLDER_TYPE_FAVORITES\x20SYSRES_CONST_FOLDER_TYPE_INBOX\x20SYSRES_CONST_FOLDER_TYPE_OUTBOX\x20SYSRES_CONST_FOLDER_TYPE_QUICK_LAUNCH\x20SYSRES_CONST_FOLDER_TYPE_SEARCH\x20SYSRES_CONST_FOLDER_TYPE_SHORTCUTS\x20SYSRES_CONST_FOLDER_TYPE_USER\x20SYSRES_CONST_FROM_DICTIONARY_ENUM_METHOD_FLAG\x20SYSRES_CONST_FULL_SUBSTITUTE_TYPE\x20SYSRES_CONST_FULL_SUBSTITUTE_TYPE_CODE\x20SYSRES_CONST_FUNCTION_CANCEL_RESULT\x20SYSRES_CONST_FUNCTION_CATEGORY_SYSTEM\x20SYSRES_CONST_FUNCTION_CATEGORY_USER\x20SYSRES_CONST_FUNCTION_FAILURE_RESULT\x20SYSRES_CONST_FUNCTION_SAVE_RESULT\x20SYSRES_CONST_GENERATED_REQUISITE\x20SYSRES_CONST_GREEN_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_GROUP_ACCOUNT_TYPE_VALUE_CODE\x20SYSRES_CONST_GROUP_CATEGORY_NORMAL_CODE\x20SYSRES_CONST_GROUP_CATEGORY_NORMAL_NAME\x20SYSRES_CONST_GROUP_CATEGORY_SERVICE_CODE\x20SYSRES_CONST_GROUP_CATEGORY_SERVICE_NAME\x20SYSRES_CONST_GROUP_COMMON_CATEGORY_FIELD_VALUE\x20SYSRES_CONST_GROUP_FULL_NAME_REQUISITE_CODE\x20SYSRES_CONST_GROUP_NAME_REQUISITE_CODE\x20SYSRES_CONST_GROUP_RIGHTS_T_REQUISITE_CODE\x20SYSRES_CONST_GROUP_SERVER_CODES_REQUISITE_CODE\x20SYSRES_CONST_GROUP_SERVER_NAME_REQUISITE_CODE\x20SYSRES_CONST_GROUP_SERVICE_CATEGORY_FIELD_VALUE\x20SYSRES_CONST_GROUP_USER_REQUISITE_CODE\x20SYSRES_CONST_GROUPS_REFERENCE_CODE\x20SYSRES_CONST_GROUPS_REQUISITE_CODE\x20SYSRES_CONST_HIDDEN_MODE_NAME\x20SYSRES_CONST_HIGH_LVL_REQUISITE_CODE\x20SYSRES_CONST_HISTORY_ACTION_CREATE_CODE\x20SYSRES_CONST_HISTORY_ACTION_DELETE_CODE\x20SYSRES_CONST_HISTORY_ACTION_EDIT_CODE\x20SYSRES_CONST_HOUR_CHAR\x20SYSRES_CONST_ID_REQUISITE_CODE\x20SYSRES_CONST_IDSPS_REQUISITE_CODE\x20SYSRES_CONST_IMAGE_MODE_COLOR\x20SYSRES_CONST_IMAGE_MODE_GREYSCALE\x20SYSRES_CONST_IMAGE_MODE_MONOCHROME\x20SYSRES_CONST_IMPORTANCE_HIGH\x20SYSRES_CONST_IMPORTANCE_LOW\x20SYSRES_CONST_IMPORTANCE_NORMAL\x20SYSRES_CONST_IN_DESIGN_VERSION_STATE_PICK_VALUE\x20SYSRES_CONST_INCOMING_WORK_RULE_TYPE_CODE\x20SYSRES_CONST_INT_REQUISITE\x20SYSRES_CONST_INT_REQUISITE_TYPE\x20SYSRES_CONST_INTEGER_NUMBER_FORMAT_CHAR\x20SYSRES_CONST_INTEGER_TYPE_CHAR\x20SYSRES_CONST_IS_GENERATED_REQUISITE_NEGATIVE_VALUE\x20SYSRES_CONST_IS_PUBLIC_ROLE_REQUISITE_CODE\x20SYSRES_CONST_IS_REMOTE_USER_NEGATIVE_VALUE\x20SYSRES_CONST_IS_REMOTE_USER_POSITIVE_VALUE\x20SYSRES_CONST_IS_STORED_REQUISITE_NEGATIVE_VALUE\x20SYSRES_CONST_IS_STORED_REQUISITE_STORED_VALUE\x20SYSRES_CONST_ITALIC_LIFE_CYCLE_STAGE_DRAW_STYLE\x20SYSRES_CONST_JOB_BLOCK_DESCRIPTION\x20SYSRES_CONST_JOB_KIND_CONTROL_JOB\x20SYSRES_CONST_JOB_KIND_JOB\x20SYSRES_CONST_JOB_KIND_NOTICE\x20SYSRES_CONST_JOB_STATE_ABORTED\x20SYSRES_CONST_JOB_STATE_COMPLETE\x20SYSRES_CONST_JOB_STATE_WORKING\x20SYSRES_CONST_KIND_REQUISITE_CODE\x20SYSRES_CONST_KIND_REQUISITE_NAME\x20SYSRES_CONST_KINDS_CREATE_SHADOW_COPIES_REQUISITE_CODE\x20SYSRES_CONST_KINDS_DEFAULT_EDOC_LIFE_STAGE_REQUISITE_CODE\x20SYSRES_CONST_KINDS_EDOC_ALL_TEPLATES_ALLOWED_REQUISITE_CODE\x20SYSRES_CONST_KINDS_EDOC_ALLOW_LIFE_CYCLE_STAGE_CHANGING_REQUISITE_CODE\x20SYSRES_CONST_KINDS_EDOC_ALLOW_MULTIPLE_ACTIVE_VERSIONS_REQUISITE_CODE\x20SYSRES_CONST_KINDS_EDOC_SHARE_ACCES_RIGHTS_BY_DEFAULT_CODE\x20SYSRES_CONST_KINDS_EDOC_TEMPLATE_REQUISITE_CODE\x20SYSRES_CONST_KINDS_EDOC_TYPE_REQUISITE_CODE\x20SYSRES_CONST_KINDS_SIGNERS_REQUISITES_CODE\x20SYSRES_CONST_KOD_INPUT_TYPE\x20SYSRES_CONST_LAST_UPDATE_DATE_REQUISITE_CODE\x20SYSRES_CONST_LIFE_CYCLE_START_STAGE_REQUISITE_CODE\x20SYSRES_CONST_LILAC_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_LINK_OBJECT_KIND_COMPONENT\x20SYSRES_CONST_LINK_OBJECT_KIND_DOCUMENT\x20SYSRES_CONST_LINK_OBJECT_KIND_EDOC\x20SYSRES_CONST_LINK_OBJECT_KIND_FOLDER\x20SYSRES_CONST_LINK_OBJECT_KIND_JOB\x20SYSRES_CONST_LINK_OBJECT_KIND_REFERENCE\x20SYSRES_CONST_LINK_OBJECT_KIND_TASK\x20SYSRES_CONST_LINK_REF_TYPE_REQUISITE_CODE\x20SYSRES_CONST_LIST_REFERENCE_MODE_NAME\x20SYSRES_CONST_LOCALIZATION_DICTIONARY_MAIN_VIEW_CODE\x20SYSRES_CONST_MAIN_VIEW_CODE\x20SYSRES_CONST_MANUAL_ENUM_METHOD_FLAG\x20SYSRES_CONST_MASTER_COMP_TYPE_REQUISITE_CODE\x20SYSRES_CONST_MASTER_TABLE_REC_ID_REQUISITE_CODE\x20SYSRES_CONST_MAXIMIZED_MODE_NAME\x20SYSRES_CONST_ME_VALUE\x20SYSRES_CONST_MESSAGE_ATTENTION_CAPTION\x20SYSRES_CONST_MESSAGE_CONFIRMATION_CAPTION\x20SYSRES_CONST_MESSAGE_ERROR_CAPTION\x20SYSRES_CONST_MESSAGE_INFORMATION_CAPTION\x20SYSRES_CONST_MINIMIZED_MODE_NAME\x20SYSRES_CONST_MINUTE_CHAR\x20SYSRES_CONST_MODULE_REQUISITE_CODE\x20SYSRES_CONST_MONITORING_BLOCK_DESCRIPTION\x20SYSRES_CONST_MONTH_FORMAT_VALUE\x20SYSRES_CONST_NAME_LOCALIZE_ID_REQUISITE_CODE\x20SYSRES_CONST_NAME_REQUISITE_CODE\x20SYSRES_CONST_NAME_SINGULAR_REQUISITE_CODE\x20SYSRES_CONST_NAMEAN_INPUT_TYPE\x20SYSRES_CONST_NEGATIVE_PICK_VALUE\x20SYSRES_CONST_NEGATIVE_VALUE\x20SYSRES_CONST_NO\x20SYSRES_CONST_NO_PICK_VALUE\x20SYSRES_CONST_NO_SIGNATURE_REQUISITE_CODE\x20SYSRES_CONST_NO_VALUE\x20SYSRES_CONST_NONE_ACCESS_RIGHTS_TYPE_CODE\x20SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE\x20SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE_MASCULINE\x20SYSRES_CONST_NORMAL_ACCESS_RIGHTS_TYPE_CODE\x20SYSRES_CONST_NORMAL_LIFE_CYCLE_STAGE_DRAW_STYLE\x20SYSRES_CONST_NORMAL_MODE_NAME\x20SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_CODE\x20SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_NAME\x20SYSRES_CONST_NOTE_REQUISITE_CODE\x20SYSRES_CONST_NOTICE_BLOCK_DESCRIPTION\x20SYSRES_CONST_NUM_REQUISITE\x20SYSRES_CONST_NUM_STR_REQUISITE_CODE\x20SYSRES_CONST_NUMERATION_AUTO_NOT_STRONG\x20SYSRES_CONST_NUMERATION_AUTO_STRONG\x20SYSRES_CONST_NUMERATION_FROM_DICTONARY\x20SYSRES_CONST_NUMERATION_MANUAL\x20SYSRES_CONST_NUMERIC_TYPE_CHAR\x20SYSRES_CONST_NUMREQ_REQUISITE_CODE\x20SYSRES_CONST_OBSOLETE_VERSION_STATE_PICK_VALUE\x20SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE\x20SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_CODE\x20SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_FEMININE\x20SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_MASCULINE\x20SYSRES_CONST_OPTIONAL_FORM_COMP_REQCODE_PREFIX\x20SYSRES_CONST_ORANGE_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_ORIGINALREF_REQUISITE_CODE\x20SYSRES_CONST_OURFIRM_REF_CODE\x20SYSRES_CONST_OURFIRM_REQUISITE_CODE\x20SYSRES_CONST_OURFIRM_VAR\x20SYSRES_CONST_OUTGOING_WORK_RULE_TYPE_CODE\x20SYSRES_CONST_PICK_NEGATIVE_RESULT\x20SYSRES_CONST_PICK_POSITIVE_RESULT\x20SYSRES_CONST_PICK_REQUISITE\x20SYSRES_CONST_PICK_REQUISITE_TYPE\x20SYSRES_CONST_PICK_TYPE_CHAR\x20SYSRES_CONST_PLAN_STATUS_REQUISITE_CODE\x20SYSRES_CONST_PLATFORM_VERSION_COMMENT\x20SYSRES_CONST_PLUGINS_SETTINGS_DESCRIPTION_REQUISITE_CODE\x20SYSRES_CONST_POSITIVE_PICK_VALUE\x20SYSRES_CONST_POWER_TO_CREATE_ACTION_CODE\x20SYSRES_CONST_POWER_TO_SIGN_ACTION_CODE\x20SYSRES_CONST_PRIORITY_REQUISITE_CODE\x20SYSRES_CONST_QUALIFIED_TASK_TYPE\x20SYSRES_CONST_QUALIFIED_TASK_TYPE_CODE\x20SYSRES_CONST_RECSTAT_REQUISITE_CODE\x20SYSRES_CONST_RED_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_REF_ID_T_REF_TYPE_REQUISITE_CODE\x20SYSRES_CONST_REF_REQUISITE\x20SYSRES_CONST_REF_REQUISITE_TYPE\x20SYSRES_CONST_REF_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE\x20SYSRES_CONST_REFERENCE_RECORD_HISTORY_CREATE_ACTION_CODE\x20SYSRES_CONST_REFERENCE_RECORD_HISTORY_DELETE_ACTION_CODE\x20SYSRES_CONST_REFERENCE_RECORD_HISTORY_MODIFY_ACTION_CODE\x20SYSRES_CONST_REFERENCE_TYPE_CHAR\x20SYSRES_CONST_REFERENCE_TYPE_REQUISITE_NAME\x20SYSRES_CONST_REFERENCES_ADD_PARAMS_REQUISITE_CODE\x20SYSRES_CONST_REFERENCES_DISPLAY_REQUISITE_REQUISITE_CODE\x20SYSRES_CONST_REMOTE_SERVER_STATUS_WORKING\x20SYSRES_CONST_REMOTE_SERVER_TYPE_MAIN\x20SYSRES_CONST_REMOTE_SERVER_TYPE_SECONDARY\x20SYSRES_CONST_REMOTE_USER_FLAG_VALUE_CODE\x20SYSRES_CONST_REPORT_APP_EDITOR_INTERNAL\x20SYSRES_CONST_REPORT_BASE_REPORT_ID_REQUISITE_CODE\x20SYSRES_CONST_REPORT_BASE_REPORT_REQUISITE_CODE\x20SYSRES_CONST_REPORT_SCRIPT_REQUISITE_CODE\x20SYSRES_CONST_REPORT_TEMPLATE_REQUISITE_CODE\x20SYSRES_CONST_REPORT_VIEWER_CODE_REQUISITE_CODE\x20SYSRES_CONST_REQ_ALLOW_COMPONENT_DEFAULT_VALUE\x20SYSRES_CONST_REQ_ALLOW_RECORD_DEFAULT_VALUE\x20SYSRES_CONST_REQ_ALLOW_SERVER_COMPONENT_DEFAULT_VALUE\x20SYSRES_CONST_REQ_MODE_AVAILABLE_CODE\x20SYSRES_CONST_REQ_MODE_EDIT_CODE\x20SYSRES_CONST_REQ_MODE_HIDDEN_CODE\x20SYSRES_CONST_REQ_MODE_NOT_AVAILABLE_CODE\x20SYSRES_CONST_REQ_MODE_VIEW_CODE\x20SYSRES_CONST_REQ_NUMBER_REQUISITE_CODE\x20SYSRES_CONST_REQ_SECTION_VALUE\x20SYSRES_CONST_REQ_TYPE_VALUE\x20SYSRES_CONST_REQUISITE_FORMAT_BY_UNIT\x20SYSRES_CONST_REQUISITE_FORMAT_DATE_FULL\x20SYSRES_CONST_REQUISITE_FORMAT_DATE_TIME\x20SYSRES_CONST_REQUISITE_FORMAT_LEFT\x20SYSRES_CONST_REQUISITE_FORMAT_RIGHT\x20SYSRES_CONST_REQUISITE_FORMAT_WITHOUT_UNIT\x20SYSRES_CONST_REQUISITE_NUMBER_REQUISITE_CODE\x20SYSRES_CONST_REQUISITE_SECTION_ACTIONS\x20SYSRES_CONST_REQUISITE_SECTION_BUTTON\x20SYSRES_CONST_REQUISITE_SECTION_BUTTONS\x20SYSRES_CONST_REQUISITE_SECTION_CARD\x20SYSRES_CONST_REQUISITE_SECTION_TABLE\x20SYSRES_CONST_REQUISITE_SECTION_TABLE10\x20SYSRES_CONST_REQUISITE_SECTION_TABLE11\x20SYSRES_CONST_REQUISITE_SECTION_TABLE12\x20SYSRES_CONST_REQUISITE_SECTION_TABLE13\x20SYSRES_CONST_REQUISITE_SECTION_TABLE14\x20SYSRES_CONST_REQUISITE_SECTION_TABLE15\x20SYSRES_CONST_REQUISITE_SECTION_TABLE16\x20SYSRES_CONST_REQUISITE_SECTION_TABLE17\x20SYSRES_CONST_REQUISITE_SECTION_TABLE18\x20SYSRES_CONST_REQUISITE_SECTION_TABLE19\x20SYSRES_CONST_REQUISITE_SECTION_TABLE2\x20SYSRES_CONST_REQUISITE_SECTION_TABLE20\x20SYSRES_CONST_REQUISITE_SECTION_TABLE21\x20SYSRES_CONST_REQUISITE_SECTION_TABLE22\x20SYSRES_CONST_REQUISITE_SECTION_TABLE23\x20SYSRES_CONST_REQUISITE_SECTION_TABLE24\x20SYSRES_CONST_REQUISITE_SECTION_TABLE3\x20SYSRES_CONST_REQUISITE_SECTION_TABLE4\x20SYSRES_CONST_REQUISITE_SECTION_TABLE5\x20SYSRES_CONST_REQUISITE_SECTION_TABLE6\x20SYSRES_CONST_REQUISITE_SECTION_TABLE7\x20SYSRES_CONST_REQUISITE_SECTION_TABLE8\x20SYSRES_CONST_REQUISITE_SECTION_TABLE9\x20SYSRES_CONST_REQUISITES_PSEUDOREFERENCE_REQUISITE_NUMBER_REQUISITE_CODE\x20SYSRES_CONST_RIGHT_ALIGNMENT_CODE\x20SYSRES_CONST_ROLES_REFERENCE_CODE\x20SYSRES_CONST_ROUTE_STEP_AFTER_RUS\x20SYSRES_CONST_ROUTE_STEP_AND_CONDITION_RUS\x20SYSRES_CONST_ROUTE_STEP_OR_CONDITION_RUS\x20SYSRES_CONST_ROUTE_TYPE_COMPLEX\x20SYSRES_CONST_ROUTE_TYPE_PARALLEL\x20SYSRES_CONST_ROUTE_TYPE_SERIAL\x20SYSRES_CONST_SBDATASETDESC_NEGATIVE_VALUE\x20SYSRES_CONST_SBDATASETDESC_POSITIVE_VALUE\x20SYSRES_CONST_SBVIEWSDESC_POSITIVE_VALUE\x20SYSRES_CONST_SCRIPT_BLOCK_DESCRIPTION\x20SYSRES_CONST_SEARCH_BY_TEXT_REQUISITE_CODE\x20SYSRES_CONST_SEARCHES_COMPONENT_CONTENT\x20SYSRES_CONST_SEARCHES_CRITERIA_ACTION_NAME\x20SYSRES_CONST_SEARCHES_EDOC_CONTENT\x20SYSRES_CONST_SEARCHES_FOLDER_CONTENT\x20SYSRES_CONST_SEARCHES_JOB_CONTENT\x20SYSRES_CONST_SEARCHES_REFERENCE_CODE\x20SYSRES_CONST_SEARCHES_TASK_CONTENT\x20SYSRES_CONST_SECOND_CHAR\x20SYSRES_CONST_SECTION_REQUISITE_ACTIONS_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_CARD_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_CODE\x20SYSRES_CONST_SECTION_REQUISITE_DETAIL_1_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_DETAIL_2_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_DETAIL_3_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_DETAIL_4_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_DETAIL_5_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_DETAIL_6_VALUE\x20SYSRES_CONST_SELECT_REFERENCE_MODE_NAME\x20SYSRES_CONST_SELECT_TYPE_SELECTABLE\x20SYSRES_CONST_SELECT_TYPE_SELECTABLE_ONLY_CHILD\x20SYSRES_CONST_SELECT_TYPE_SELECTABLE_WITH_CHILD\x20SYSRES_CONST_SELECT_TYPE_UNSLECTABLE\x20SYSRES_CONST_SERVER_TYPE_MAIN\x20SYSRES_CONST_SERVICE_USER_CATEGORY_FIELD_VALUE\x20SYSRES_CONST_SETTINGS_USER_REQUISITE_CODE\x20SYSRES_CONST_SIGNATURE_AND_ENCODE_CERTIFICATE_TYPE_CODE\x20SYSRES_CONST_SIGNATURE_CERTIFICATE_TYPE_CODE\x20SYSRES_CONST_SINGULAR_TITLE_REQUISITE_CODE\x20SYSRES_CONST_SQL_SERVER_AUTHENTIFICATION_FLAG_VALUE_CODE\x20SYSRES_CONST_SQL_SERVER_ENCODE_AUTHENTIFICATION_FLAG_VALUE_CODE\x20SYSRES_CONST_STANDART_ROUTE_REFERENCE_CODE\x20SYSRES_CONST_STANDART_ROUTE_REFERENCE_COMMENT_REQUISITE_CODE\x20SYSRES_CONST_STANDART_ROUTES_GROUPS_REFERENCE_CODE\x20SYSRES_CONST_STATE_REQ_NAME\x20SYSRES_CONST_STATE_REQUISITE_ACTIVE_VALUE\x20SYSRES_CONST_STATE_REQUISITE_CLOSED_VALUE\x20SYSRES_CONST_STATE_REQUISITE_CODE\x20SYSRES_CONST_STATIC_ROLE_TYPE_CODE\x20SYSRES_CONST_STATUS_PLAN_DEFAULT_VALUE\x20SYSRES_CONST_STATUS_VALUE_AUTOCLEANING\x20SYSRES_CONST_STATUS_VALUE_BLUE_SQUARE\x20SYSRES_CONST_STATUS_VALUE_COMPLETE\x20SYSRES_CONST_STATUS_VALUE_GREEN_SQUARE\x20SYSRES_CONST_STATUS_VALUE_ORANGE_SQUARE\x20SYSRES_CONST_STATUS_VALUE_PURPLE_SQUARE\x20SYSRES_CONST_STATUS_VALUE_RED_SQUARE\x20SYSRES_CONST_STATUS_VALUE_SUSPEND\x20SYSRES_CONST_STATUS_VALUE_YELLOW_SQUARE\x20SYSRES_CONST_STDROUTE_SHOW_TO_USERS_REQUISITE_CODE\x20SYSRES_CONST_STORAGE_TYPE_FILE\x20SYSRES_CONST_STORAGE_TYPE_SQL_SERVER\x20SYSRES_CONST_STR_REQUISITE\x20SYSRES_CONST_STRIKEOUT_LIFE_CYCLE_STAGE_DRAW_STYLE\x20SYSRES_CONST_STRING_FORMAT_LEFT_ALIGN_CHAR\x20SYSRES_CONST_STRING_FORMAT_RIGHT_ALIGN_CHAR\x20SYSRES_CONST_STRING_REQUISITE_CODE\x20SYSRES_CONST_STRING_REQUISITE_TYPE\x20SYSRES_CONST_STRING_TYPE_CHAR\x20SYSRES_CONST_SUBSTITUTES_PSEUDOREFERENCE_CODE\x20SYSRES_CONST_SUBTASK_BLOCK_DESCRIPTION\x20SYSRES_CONST_SYSTEM_SETTING_CURRENT_USER_PARAM_VALUE\x20SYSRES_CONST_SYSTEM_SETTING_EMPTY_VALUE_PARAM_VALUE\x20SYSRES_CONST_SYSTEM_VERSION_COMMENT\x20SYSRES_CONST_TASK_ACCESS_TYPE_ALL\x20SYSRES_CONST_TASK_ACCESS_TYPE_ALL_MEMBERS\x20SYSRES_CONST_TASK_ACCESS_TYPE_MANUAL\x20SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION\x20SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION_AND_PASSWORD\x20SYSRES_CONST_TASK_ENCODE_TYPE_NONE\x20SYSRES_CONST_TASK_ENCODE_TYPE_PASSWORD\x20SYSRES_CONST_TASK_ROUTE_ALL_CONDITION\x20SYSRES_CONST_TASK_ROUTE_AND_CONDITION\x20SYSRES_CONST_TASK_ROUTE_OR_CONDITION\x20SYSRES_CONST_TASK_STATE_ABORTED\x20SYSRES_CONST_TASK_STATE_COMPLETE\x20SYSRES_CONST_TASK_STATE_CONTINUED\x20SYSRES_CONST_TASK_STATE_CONTROL\x20SYSRES_CONST_TASK_STATE_INIT\x20SYSRES_CONST_TASK_STATE_WORKING\x20SYSRES_CONST_TASK_TITLE\x20SYSRES_CONST_TASK_TYPES_GROUPS_REFERENCE_CODE\x20SYSRES_CONST_TASK_TYPES_REFERENCE_CODE\x20SYSRES_CONST_TEMPLATES_REFERENCE_CODE\x20SYSRES_CONST_TEST_DATE_REQUISITE_NAME\x20SYSRES_CONST_TEST_DEV_DATABASE_NAME\x20SYSRES_CONST_TEST_DEV_SYSTEM_CODE\x20SYSRES_CONST_TEST_EDMS_DATABASE_NAME\x20SYSRES_CONST_TEST_EDMS_MAIN_CODE\x20SYSRES_CONST_TEST_EDMS_MAIN_DB_NAME\x20SYSRES_CONST_TEST_EDMS_SECOND_CODE\x20SYSRES_CONST_TEST_EDMS_SECOND_DB_NAME\x20SYSRES_CONST_TEST_EDMS_SYSTEM_CODE\x20SYSRES_CONST_TEST_NUMERIC_REQUISITE_NAME\x20SYSRES_CONST_TEXT_REQUISITE\x20SYSRES_CONST_TEXT_REQUISITE_CODE\x20SYSRES_CONST_TEXT_REQUISITE_TYPE\x20SYSRES_CONST_TEXT_TYPE_CHAR\x20SYSRES_CONST_TYPE_CODE_REQUISITE_CODE\x20SYSRES_CONST_TYPE_REQUISITE_CODE\x20SYSRES_CONST_UNDEFINED_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_UNITS_SECTION_ID_REQUISITE_CODE\x20SYSRES_CONST_UNITS_SECTION_REQUISITE_CODE\x20SYSRES_CONST_UNOPERATING_RECORD_FLAG_VALUE_CODE\x20SYSRES_CONST_UNSTORED_DATA_REQUISITE_CODE\x20SYSRES_CONST_UNSTORED_DATA_REQUISITE_NAME\x20SYSRES_CONST_USE_ACCESS_TYPE_CODE\x20SYSRES_CONST_USE_ACCESS_TYPE_NAME\x20SYSRES_CONST_USER_ACCOUNT_TYPE_VALUE_CODE\x20SYSRES_CONST_USER_ADDITIONAL_INFORMATION_REQUISITE_CODE\x20SYSRES_CONST_USER_AND_GROUP_ID_FROM_PSEUDOREFERENCE_REQUISITE_CODE\x20SYSRES_CONST_USER_CATEGORY_NORMAL\x20SYSRES_CONST_USER_CERTIFICATE_REQUISITE_CODE\x20SYSRES_CONST_USER_CERTIFICATE_STATE_REQUISITE_CODE\x20SYSRES_CONST_USER_CERTIFICATE_SUBJECT_NAME_REQUISITE_CODE\x20SYSRES_CONST_USER_CERTIFICATE_THUMBPRINT_REQUISITE_CODE\x20SYSRES_CONST_USER_COMMON_CATEGORY\x20SYSRES_CONST_USER_COMMON_CATEGORY_CODE\x20SYSRES_CONST_USER_FULL_NAME_REQUISITE_CODE\x20SYSRES_CONST_USER_GROUP_TYPE_REQUISITE_CODE\x20SYSRES_CONST_USER_LOGIN_REQUISITE_CODE\x20SYSRES_CONST_USER_REMOTE_CONTROLLER_REQUISITE_CODE\x20SYSRES_CONST_USER_REMOTE_SYSTEM_REQUISITE_CODE\x20SYSRES_CONST_USER_RIGHTS_T_REQUISITE_CODE\x20SYSRES_CONST_USER_SERVER_NAME_REQUISITE_CODE\x20SYSRES_CONST_USER_SERVICE_CATEGORY\x20SYSRES_CONST_USER_SERVICE_CATEGORY_CODE\x20SYSRES_CONST_USER_STATUS_ADMINISTRATOR_CODE\x20SYSRES_CONST_USER_STATUS_ADMINISTRATOR_NAME\x20SYSRES_CONST_USER_STATUS_DEVELOPER_CODE\x20SYSRES_CONST_USER_STATUS_DEVELOPER_NAME\x20SYSRES_CONST_USER_STATUS_DISABLED_CODE\x20SYSRES_CONST_USER_STATUS_DISABLED_NAME\x20SYSRES_CONST_USER_STATUS_SYSTEM_DEVELOPER_CODE\x20SYSRES_CONST_USER_STATUS_USER_CODE\x20SYSRES_CONST_USER_STATUS_USER_NAME\x20SYSRES_CONST_USER_STATUS_USER_NAME_DEPRECATED\x20SYSRES_CONST_USER_TYPE_FIELD_VALUE_USER\x20SYSRES_CONST_USER_TYPE_REQUISITE_CODE\x20SYSRES_CONST_USERS_CONTROLLER_REQUISITE_CODE\x20SYSRES_CONST_USERS_IS_MAIN_SERVER_REQUISITE_CODE\x20SYSRES_CONST_USERS_REFERENCE_CODE\x20SYSRES_CONST_USERS_REGISTRATION_CERTIFICATES_ACTION_NAME\x20SYSRES_CONST_USERS_REQUISITE_CODE\x20SYSRES_CONST_USERS_SYSTEM_REQUISITE_CODE\x20SYSRES_CONST_USERS_USER_ACCESS_RIGHTS_TYPR_REQUISITE_CODE\x20SYSRES_CONST_USERS_USER_AUTHENTICATION_REQUISITE_CODE\x20SYSRES_CONST_USERS_USER_COMPONENT_REQUISITE_CODE\x20SYSRES_CONST_USERS_USER_GROUP_REQUISITE_CODE\x20SYSRES_CONST_USERS_VIEW_CERTIFICATES_ACTION_NAME\x20SYSRES_CONST_VIEW_DEFAULT_CODE\x20SYSRES_CONST_VIEW_DEFAULT_NAME\x20SYSRES_CONST_VIEWER_REQUISITE_CODE\x20SYSRES_CONST_WAITING_BLOCK_DESCRIPTION\x20SYSRES_CONST_WIZARD_FORM_LABEL_TEST_STRING\x20\x20SYSRES_CONST_WIZARD_QUERY_PARAM_HEIGHT_ETALON_STRING\x20SYSRES_CONST_WIZARD_REFERENCE_COMMENT_REQUISITE_CODE\x20SYSRES_CONST_WORK_RULES_DESCRIPTION_REQUISITE_CODE\x20SYSRES_CONST_WORK_TIME_CALENDAR_REFERENCE_CODE\x20SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE\x20SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE\x20SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE_RUS\x20SYSRES_CONST_WORK_WORKFLOW_SOFT_ROUTE_TYPE_VALUE_CODE_RUS\x20SYSRES_CONST_WORKFLOW_ROUTE_TYPR_HARD\x20SYSRES_CONST_WORKFLOW_ROUTE_TYPR_SOFT\x20SYSRES_CONST_XML_ENCODING\x20SYSRES_CONST_XREC_STAT_REQUISITE_CODE\x20SYSRES_CONST_XRECID_FIELD_NAME\x20SYSRES_CONST_YES\x20SYSRES_CONST_YES_NO_2_REQUISITE_CODE\x20SYSRES_CONST_YES_NO_REQUISITE_CODE\x20SYSRES_CONST_YES_NO_T_REF_TYPE_REQUISITE_CODE\x20SYSRES_CONST_YES_PICK_VALUE\x20SYSRES_CONST_YES_VALUE\x20CR\x20FALSE\x20nil\x20NO_VALUE\x20NULL\x20TAB\x20TRUE\x20YES_VALUE\x20ADMINISTRATORS_GROUP_NAME\x20CUSTOMIZERS_GROUP_NAME\x20DEVELOPERS_GROUP_NAME\x20SERVICE_USERS_GROUP_NAME\x20DECISION_BLOCK_FIRST_OPERAND_PROPERTY\x20DECISION_BLOCK_NAME_PROPERTY\x20DECISION_BLOCK_OPERATION_PROPERTY\x20DECISION_BLOCK_RESULT_TYPE_PROPERTY\x20DECISION_BLOCK_SECOND_OPERAND_PROPERTY\x20ANY_FILE_EXTENTION\x20COMPRESSED_DOCUMENT_EXTENSION\x20EXTENDED_DOCUMENT_EXTENSION\x20SHORT_COMPRESSED_DOCUMENT_EXTENSION\x20SHORT_EXTENDED_DOCUMENT_EXTENSION\x20JOB_BLOCK_ABORT_DEADLINE_PROPERTY\x20JOB_BLOCK_AFTER_FINISH_EVENT\x20JOB_BLOCK_AFTER_QUERY_PARAMETERS_EVENT\x20JOB_BLOCK_ATTACHMENT_PROPERTY\x20JOB_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY\x20JOB_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY\x20JOB_BLOCK_BEFORE_QUERY_PARAMETERS_EVENT\x20JOB_BLOCK_BEFORE_START_EVENT\x20JOB_BLOCK_CREATED_JOBS_PROPERTY\x20JOB_BLOCK_DEADLINE_PROPERTY\x20JOB_BLOCK_EXECUTION_RESULTS_PROPERTY\x20JOB_BLOCK_IS_PARALLEL_PROPERTY\x20JOB_BLOCK_IS_RELATIVE_ABORT_DEADLINE_PROPERTY\x20JOB_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY\x20JOB_BLOCK_JOB_TEXT_PROPERTY\x20JOB_BLOCK_NAME_PROPERTY\x20JOB_BLOCK_NEED_SIGN_ON_PERFORM_PROPERTY\x20JOB_BLOCK_PERFORMER_PROPERTY\x20JOB_BLOCK_RELATIVE_ABORT_DEADLINE_TYPE_PROPERTY\x20JOB_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY\x20JOB_BLOCK_SUBJECT_PROPERTY\x20ENGLISH_LANGUAGE_CODE\x20RUSSIAN_LANGUAGE_CODE\x20smHidden\x20smMaximized\x20smMinimized\x20smNormal\x20wmNo\x20wmYes\x20COMPONENT_TOKEN_LINK_KIND\x20DOCUMENT_LINK_KIND\x20EDOCUMENT_LINK_KIND\x20FOLDER_LINK_KIND\x20JOB_LINK_KIND\x20REFERENCE_LINK_KIND\x20TASK_LINK_KIND\x20COMPONENT_TOKEN_LOCK_TYPE\x20EDOCUMENT_VERSION_LOCK_TYPE\x20MONITOR_BLOCK_AFTER_FINISH_EVENT\x20MONITOR_BLOCK_BEFORE_START_EVENT\x20MONITOR_BLOCK_DEADLINE_PROPERTY\x20MONITOR_BLOCK_INTERVAL_PROPERTY\x20MONITOR_BLOCK_INTERVAL_TYPE_PROPERTY\x20MONITOR_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY\x20MONITOR_BLOCK_NAME_PROPERTY\x20MONITOR_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY\x20MONITOR_BLOCK_SEARCH_SCRIPT_PROPERTY\x20NOTICE_BLOCK_AFTER_FINISH_EVENT\x20NOTICE_BLOCK_ATTACHMENT_PROPERTY\x20NOTICE_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY\x20NOTICE_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY\x20NOTICE_BLOCK_BEFORE_START_EVENT\x20NOTICE_BLOCK_CREATED_NOTICES_PROPERTY\x20NOTICE_BLOCK_DEADLINE_PROPERTY\x20NOTICE_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY\x20NOTICE_BLOCK_NAME_PROPERTY\x20NOTICE_BLOCK_NOTICE_TEXT_PROPERTY\x20NOTICE_BLOCK_PERFORMER_PROPERTY\x20NOTICE_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY\x20NOTICE_BLOCK_SUBJECT_PROPERTY\x20dseAfterCancel\x20dseAfterClose\x20dseAfterDelete\x20dseAfterDeleteOutOfTransaction\x20dseAfterInsert\x20dseAfterOpen\x20dseAfterScroll\x20dseAfterUpdate\x20dseAfterUpdateOutOfTransaction\x20dseBeforeCancel\x20dseBeforeClose\x20dseBeforeDelete\x20dseBeforeDetailUpdate\x20dseBeforeInsert\x20dseBeforeOpen\x20dseBeforeUpdate\x20dseOnAnyRequisiteChange\x20dseOnCloseRecord\x20dseOnDeleteError\x20dseOnOpenRecord\x20dseOnPrepareUpdate\x20dseOnUpdateError\x20dseOnUpdateRatifiedRecord\x20dseOnValidDelete\x20dseOnValidUpdate\x20reOnChange\x20reOnChangeValues\x20SELECTION_BEGIN_ROUTE_EVENT\x20SELECTION_END_ROUTE_EVENT\x20CURRENT_PERIOD_IS_REQUIRED\x20PREVIOUS_CARD_TYPE_NAME\x20SHOW_RECORD_PROPERTIES_FORM\x20ACCESS_RIGHTS_SETTING_DIALOG_CODE\x20ADMINISTRATOR_USER_CODE\x20ANALYTIC_REPORT_TYPE\x20asrtHideLocal\x20asrtHideRemote\x20CALCULATED_ROLE_TYPE_CODE\x20COMPONENTS_REFERENCE_DEVELOPER_VIEW_CODE\x20DCTS_TEST_PROTOCOLS_FOLDER_PATH\x20E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED\x20E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED_BY_USER\x20E_EDOC_VERSION_ALREDY_SIGNED\x20E_EDOC_VERSION_ALREDY_SIGNED_BY_USER\x20EDOC_TYPES_CODE_REQUISITE_FIELD_NAME\x20EDOCUMENTS_ALIAS_NAME\x20FILES_FOLDER_PATH\x20FILTER_OPERANDS_DELIMITER\x20FILTER_OPERATIONS_DELIMITER\x20FORMCARD_NAME\x20FORMLIST_NAME\x20GET_EXTENDED_DOCUMENT_EXTENSION_CREATION_MODE\x20GET_EXTENDED_DOCUMENT_EXTENSION_IMPORT_MODE\x20INTEGRATED_REPORT_TYPE\x20IS_BUILDER_APPLICATION_ROLE\x20IS_BUILDER_APPLICATION_ROLE2\x20IS_BUILDER_USERS\x20ISBSYSDEV\x20LOG_FOLDER_PATH\x20mbCancel\x20mbNo\x20mbNoToAll\x20mbOK\x20mbYes\x20mbYesToAll\x20MEMORY_DATASET_DESRIPTIONS_FILENAME\x20mrNo\x20mrNoToAll\x20mrYes\x20mrYesToAll\x20MULTIPLE_SELECT_DIALOG_CODE\x20NONOPERATING_RECORD_FLAG_FEMININE\x20NONOPERATING_RECORD_FLAG_MASCULINE\x20OPERATING_RECORD_FLAG_FEMININE\x20OPERATING_RECORD_FLAG_MASCULINE\x20PROFILING_SETTINGS_COMMON_SETTINGS_CODE_VALUE\x20PROGRAM_INITIATED_LOOKUP_ACTION\x20ratDelete\x20ratEdit\x20ratInsert\x20REPORT_TYPE\x20REQUIRED_PICK_VALUES_VARIABLE\x20rmCard\x20rmList\x20SBRTE_PROGID_DEV\x20SBRTE_PROGID_RELEASE\x20STATIC_ROLE_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1.5em;\n  font-size: 1.1rem;\n}\n\nh5 {\n  margin-top: 1.5em;\n  font-size: 1rem;\n}\n\n/* Code ------------------------------------------------ */\n\ncode a:any-link {\n  text-decoration: underline;\n}\n\n/* Mixin for callout styling */\n.callout {\n  margin-top: 1em;\n  margin-bottom: 1em;\n  border-radius: .25rem;\n  border-left: solid #acacac .3rem;\n  border-right: solid 0.5px silver;\n  border-top: solid 0.5px silver;\n  border-bottom: solid 0.5px silver;\n}\n\n/* Styling for answer callout body */\n.callout-body-container.callout-body {\n  padding: 0px 0px 0px 23.04px; /* Top Right Bottom Left */\n}\n\n/* Exercise callout styling */\ndiv.callout-slide.callout {\n  margin-top: 1em;\n  margin-bottom: 1em;\n  border-radius: .25rem;\n  border-left: solid #8f00ff .3rem !important;\n  border-right: solid 0.5px silver;\n  border-top: solid 0.5px silver;\n  border-bottom: solid 0.5px silver;\n}\n\ndiv.callout-slide.callout > .callout-header::before {\n  font-family: "Font Awesome 5 Free";\n  content: "🎫";\n  margin-right: 10px;\n}\n\n/* Exercise callout styling */\ndiv.callout-video.callout {\n  margin-top: 1em;\n  margin-bottom: 1em;\n  border-radius: .25rem;\n  border-left: solid #8ccd00 .3rem !important;\n  border-right: solid 0.5px silver;\n  border-top: solid 0.5px silver;\n  border-bottom: solid 0.5px silver;\n}\n\ndiv.callout-video.callout > .callout-header::before {\n  font-family: "Font Awesome 5 Free";\n  content: "📺";\n  margin-right: 10px;\n}\n\n/* Exercise callout styling */\ndiv.callout-lab.callout {\n  margin-top: 1em;\n  margin-bottom: 1em;\n  border-radius: .25rem;\n  border-left: solid #ff006d .3rem !important;\n  border-right: solid 0.5px silver;\n  border-top: solid 0.5px silver;\n  border-bottom: solid 0.5px silver;\n}\n\ndiv.callout-lab.callout > .callout-header::before {\n  font-family: "Font Awesome 5 Free";\n  content: "👩‍💻";\n  margin-right: 10px;\n}\n\n/* Question callout styling */\ndiv.callout-question.callout {\n  margin-top: 1em;\n  margin-bottom: 1em;\n  border-radius: .25rem;\n  border-left: solid #ff7d00 .3rem !important;\n  border-right: solid 0.5px silver;\n  border-top: solid 0.5px silver;\n  border-bottom: solid 0.5px silver;\n}\n\ndiv.callout-question.callout > .callout-header::before {\n  font-family: "Font Awesome 5 Free";\n  content: "❓";\n  margin-right: 10px;\n}\n\n/* Exercise callout styling */\ndiv.callout-exercise.callout {\n  margin-top: 1em;\n  margin-bottom: 1em;\n  border-radius: .25rem;\n  border-left: solid #ff7d00 .3rem !important;\n  border-right: solid 0.5px silver;\n  border-top: solid 0.5px silver;\n  border-bottom: solid 0.5px silver;\n}\n\ndiv.callout-exercise.callout > .callout-header::before {\n  font-family: "Font Awesome 5 Free";\n  content: "📚";\n  margin-right: 10px;\n}\n\n/* Answer callout styling */\ndiv.callout-answer.callout {\n  margin-top: 1em;\n  margin-bottom: 1em;\n  border-radius: .25rem;\n  border-left: solid #ffdd00 .3rem !important;\n  border-right: solid 0.5px silver;\n  border-top: solid 0.5px silver;\n  border-bottom: solid 0.5px silver;\n}\n\ndiv.callout-answer.callout > .callout-header::before {\n  font-family: "Font Awesome 5 Free";\n  content: "✅";\n  margin-right: 10px;\n}\n\n/* Hint callout styling */\ndiv.callout-hint.callout {\n  margin-top: 1em;\n  margin-bottom: 1em;\n  border-radius: .25rem;\n  border-left: solid #01befe .3rem !important;\n  border-right: solid 0.5px silver;\n  border-top: solid 0.5px silver;\n  border-bottom: solid 0.5px silver;\n}\n\ndiv.callout-hint.callout > .callout-header::before {\n  font-family: "Font Awesome 5 Free";\n  content: "🤔";\n  margin-right: 10px;\n}\n\n.callout-toggle::before {\n  background-image: url(${u});\n}\n\n/* Examples of how to use the above styles\n\n::: {.callout-slide collapse="false"}\n## Slides\n\nNow would be a great time for you to try out a small [Image Classification](../image_classification/image_classification.qmd) computer vision model out of the box.\n:::\n\n::: {.callout-exercise collapse="false"}\n# Exercises\n\nNow would be a great time for you to try out a small computer vision model out of the box.\n:::\n\n::: {.callout-lab collapse="false"}\n## Labs\n\nNow would be a great time for you to try out a small [Image Classification](../image_classification/image_classification.qmd) computer vision model out of the box.\n:::\n\n::: {.callout-question collapse="false"}\n## Questions\n\nNow would be a great time for you to try out a small [Image Classification](../image_classification/image_classification.qmd) computer vision model out of the box.\n:::\n\n::: {.callout-answer collapse="false"}\n## Answer\n\nNow would be a great time for you to try out a small [Image Classification](../image_classification/image_classification.qmd) computer vision model out of the box.\n:::\n\n::: {.callout-hint collapse="false"}\n## Hint\n\nNow would be a great time for you to try out a small [Image Classification](../image_classification/image_classification.qmd) computer vision model out of the box.\n:::\n\n*/\n\n\n\n\n/* MODAL */\n       /* Modal styles */\n       .modal {\n        display: block; /* Automatically opens when the page loads */\n        position: fixed;\n        z-index: 1000;\n        right: 20px; /* Position on the right side */\n        top: 30%;\n        transform: translateY(-50%);\n        width: 200px;\n        background-color: #f0f0f0;\n        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);\n        border-radius: 10px;\n        padding: 5px;\n    }\n\n    .modal-header {\n        display: flex;\n        justify-content: space-between;\n        align-items: center;\n        padding-bottom: 1px;\n        border-bottom: 1px solid #ccc;\n        cursor: move; /* Indicate draggable area */\n    }\n\n    .modal-content {\n        padding-top: 10px;\n    }\n\n    .close-icon {\n        cursor: pointer;\n        font-size: 1.2rem;\n    }\n\n    @keyframes fadeIn {\n      to {\n        opacity: 1;\n      }\n    }\n\n\n    .prompts-for-ai{\n      font-size: 1.2rem;\n      font-family: Arial, sans-serif;\n    }\n\n\n\n/* CONTROL PANEL V2*//* CONTROL PANEL V2 */\n.body_genAI_SocratiQ {\n  font-family: 'Courier New', Courier, monospace;\n  /* background-color: #f9f9f9; */\n  display: flex;\n  justify-content: center;\n  align-items: center;\n  margin: 0;\n}\n.card_genAI_SocratiQ {\n  background-color: #f5f5f5;\n  color: #333333;\n  box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);\n  border-radius: 8px;\n  padding: 20px;\n  max-width: 90%;\n  width: 500px;\n  box-sizing: border-box;\n  margin-bottom: 50px;\n}\n.card_genAI_SocratiQ h2 {\n  font-size: 24px;\n  margin-bottom: 16px;\n  color: #b22222; /* Reddish Maroon Color */\n}\n.card_genAI_SocratiQ .description_genAI_SocratiQ {\n  font-size: 14px;\n  color: #666666;\n  margin-bottom: 24px;\n}\n.card_genAI_SocratiQ .input-group_genAI_SocratiQ {\n  display: flex;\n  flex-direction: column;\n  gap: 12px;\n}\n.card_genAI_SocratiQ input[type="text"] {\n  padding: 10px;\n  border: 1px solid #cccccc;\n  border-radius: 4px;\n  font-size: 14px;\n  color: #333333;\n  background-color: #ffffff;\n  width: 100%;\n  box-sizing: border-box;\n}\n.card_genAI_SocratiQ button {\n  padding: 10px;\n  background-color: #b22222; /* Reddish Maroon Color */\n  color: #ffffff;\n  border: none;\n  border-radius: 4px;\n  font-size: 16px;\n  cursor: pointer;\n  transition: background-color 0.3s ease;\n  display: flex;\n  justify-content: center;\n  align-items: center;\n}\n.card_genAI_SocratiQ button:hover {\n  background-color: #8b0000; /* Darker Maroon Color */\n}\n.card_genAI_SocratiQ button:disabled {\n  background-color: #cccccc;\n  cursor: not-allowed;\n}\n.spinner_genAI_SocratiQ {\n  border: 4px solid #f3f3f3;\n  border-top: 4px solid #b22222;\n  border-radius: 50%;\n  width: 16px;\n  height: 16px;\n  animation: spin_genAI_SocratiQ 1s linear infinite;\n}\n\n@keyframes spin_genAI_SocratiQ {\n  0% { transform: rotate(0deg); }\n  100% { transform: rotate(360deg); }\n}\n\n\n/* BUTTONS */\n.text-gray-800 {\n  color: #1f2937; /* Tailwind's gray-800 color */\n}\n\n.focus\\:outline-none:focus {\n  outline: none; /* Removes outline on focus */\n}\n\n.transition-transform {\n  transition-property: transform; /* Transitions only the transform property */\n}\n\n.transform {\n  transform: scale(1); /* Initial transform state */\n}\n\n.hover\\:scale-105:hover {\n  transform: scale(1.05); /* Scales element by 1.05 on hover */\n}\n\n.size-6 {\n  width: 1.5rem; /* Assuming Tailwind's size-6 is 1.5rem */\n  height: 1.5rem; /* Assuming Tailwind's size-6 is 1.5rem */\n}\n\n.icon-button-SocratiQ {\n  border-radius: 50%; /* Ensures the button is circular */\n  border: none;\n  display: flex; /* Flex to center the icon */\n  align-items: center; /* Center vertically */\n  justify-content: center; /* Center horizontally */\n  width: 3rem; /* Ensures the button is large enough to be circular */\n  height: 3rem; /* Ensures the button is large enough to be circular */\n  background-color: #fff; /* Optional: background color */\n  box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); /* Optional: adds a subtle shadow */\n  padding: 0; /* Remove padding */\n  margin: 0; /* Remove margin */\n}\n\n`,""]);const p=d},3765:(e,t,n)=>{"use strict";n.r(t),n.d(t,{default:()=>p});var r=n(1601),i=n.n(r),a=n(6314),o=n.n(a),s=n(4417),l=n.n(s),c=new URL(n(6734),n.b),d=o()(i());d.push([e.id,"@import url(https://fonts.googleapis.com/css2?family=Nunito:wght@400;800&display=swap);"]);var u=l()(c);d.push([e.id,`/* Override font color in Sandstone theme */\n.theme-sandstone {\n    color: #A51C30; /* Set font color to #A51C30 */\n  }\n  \n  /* Define the CSS variable for link color */\n  :root {\n    --link-color: #A51C30;\n  }\n  \n  /* Use the CSS variable for link color */\n  body {\n    -webkit-font-smoothing: auto;\n  }\n  \n  a {\n    color: var(--link-color); /* Use the CSS variable for link color */\n    text-decoration: underline;\n    -webkit-text-decoration: underline;\n    -moz-text-decoration: underline;\n    -ms-text-decoration: underline;\n    -o-text-decoration: underline;\n  }\n  \n  a:hover {\n    color: var(--link-color); /* Ensure link color remains the same on hover */\n  }\n  \n  a:not([href]):not([class]),\n  a:not([href]):not([class]):hover {\n    color: inherit;\n    text-decoration: none;\n  }\n  \n  /* Set color and link styles for active items in the sidebar */\n  div.sidebar-item-container .active,\n  div.sidebar-item-container .show > .nav-link,\n  div.sidebar-item-container .sidebar-link > code {\n    color: var(--link-color);\n  }\n  \n  /* Set color for TOC links on hover */\n  .sidebar nav[role=doc-toc] ul > li > a:hover,\n  .sidebar nav[role=doc-toc] ul > li > ul > li > a:hover {\n    color: var(--link-color) !important;\n  }\n  \n  /* Set border color and text color for active TOC links */\n  .sidebar nav[role=doc-toc] ul > li > a.active,\n  .sidebar nav[role=doc-toc] ul > li > ul > li > a.active {\n    border-left: 1px solid var(--link-color);\n    color: var(--link-color) !important;\n  }\n  \n  /* Set text color for active TOC links on hover */\n  .sidebar nav[role=doc-toc] ul > li > a.active:hover,\n  .sidebar nav[role=doc-toc] ul > li > ul > li > a.active:hover {\n    color: var(--link-color);\n    /* Add any additional hover styles here */\n  }\n  \n  .navbar-dark,\n  .navbar[data-bs-theme=dark] {\n    --bs-navbar-active-color: var(--link-color);\n    --bs-navbar-brand-hover-color: var(--link-color);\n    --bs-navbar-hover-color: var(--link-color);\n    --bs-navbar-hover-color: var(--link-color);\n    --bs-navbar-active-color: var(--link-color);\n    --bs-navbar-brand-hover-color: var(--link-color);\n  }\n  \n  .navbar .quarto-navbar-tools .quarto-navigation-tool:hover {\n    color: var(--link-color);\n  }\n  \n  /* Define the font size root */\n  :root {\n    font-size: 16px;\n  }\n  \n  /* Set font weight for active items */\n  div.sidebar-item-container .active {\n    font-weight: bold;\n  }\n  \n  /* Set font weight for active TOC links */\n  .sidebar nav[role=doc-toc] ul > li > a.active, .sidebar nav[role=doc-toc] ul > li > ul > li > a.active {\n    font-weight: bold;\n  }\n  \n  /* Set color and background for active TOC links */\n  .sidebar nav[role="doc-toc"] ul > li > a.active {\n    color: #A51C30 !important;\n    background-color: snow;\n    font-weight: bold;\n  }\n  \n  /* Headings ------------------------------------------------------ */\n  \n  #title-block-header.quarto-title-block.default .quarto-title h1.title {\n    margin-bottom: 0.5rem;\n  }\n  \n  h2 {\n    margin-top: 2rem;\n    margin-bottom: 1rem;\n    font-size: 1.4rem;\n    font-weight: 600;\n  }\n  \n  h3 {\n    margin-top: 1.5em;\n    font-size: 1.2rem;\n    font-weight: 500;\n  }\n  \n  h4 {\n    margin-top: 1.5em;\n    font-size: 1.1rem;\n  }\n  \n  h5 {\n    margin-top: 1.5em;\n    font-size: 1rem;\n  }\n  \n  /* Code ------------------------------------------------ */\n  \n  code a:any-link {\n    text-decoration: underline;\n  }\n  \n  /* Mixin for callout styling */\n  .callout {\n    margin-top: 1em;\n    margin-bottom: 1em;\n    border-radius: .25rem;\n    border-left: solid #acacac .3rem;\n    border-right: solid 0.5px silver;\n    border-top: solid 0.5px silver;\n    border-bottom: solid 0.5px silver;\n  }\n  \n  /* Styling for answer callout body */\n  .callout-body-container.callout-body {\n    padding: 0px 0px 0px 23.04px; /* Top Right Bottom Left */\n  }\n  \n  /* Exercise callout styling */\n  div.callout-slide.callout {\n    margin-top: 1em;\n    margin-bottom: 1em;\n    border-radius: .25rem;\n    border-left: solid #8f00ff .3rem !important;\n    border-right: solid 0.5px silver;\n    border-top: solid 0.5px silver;\n    border-bottom: solid 0.5px silver;\n  }\n  \n  div.callout-slide.callout > .callout-header::before {\n    font-family: "Font Awesome 5 Free";\n    content: "🎫";\n    margin-right: 10px;\n  }\n  \n  /* Exercise callout styling */\n  div.callout-video.callout {\n    margin-top: 1em;\n    margin-bottom: 1em;\n    border-radius: .25rem;\n    border-left: solid #8ccd00 .3rem !important;\n    border-right: solid 0.5px silver;\n    border-top: solid 0.5px silver;\n    border-bottom: solid 0.5px silver;\n  }\n  \n  div.callout-video.callout > .callout-header::before {\n    font-family: "Font Awesome 5 Free";\n    content: "📺";\n    margin-right: 10px;\n  }\n  \n  /* Exercise callout styling */\n  div.callout-lab.callout {\n    margin-top: 1em;\n    margin-bottom: 1em;\n    border-radius: .25rem;\n    border-left: solid #ff006d .3rem !important;\n    border-right: solid 0.5px silver;\n    border-top: solid 0.5px silver;\n    border-bottom: solid 0.5px silver;\n  }\n  \n  div.callout-lab.callout > .callout-header::before {\n    font-family: "Font Awesome 5 Free";\n    content: "👩‍💻";\n    margin-right: 10px;\n  }\n  \n  /* Question callout styling */\n  div.callout-question.callout {\n    margin-top: 1em;\n    margin-bottom: 1em;\n    border-radius: .25rem;\n    border-left: solid #ff7d00 .3rem !important;\n    border-right: solid 0.5px silver;\n    border-top: solid 0.5px silver;\n    border-bottom: solid 0.5px silver;\n  }\n  \n  div.callout-question.callout > .callout-header::before {\n    font-family: "Font Awesome 5 Free";\n    content: "❓";\n    margin-right: 10px;\n  }\n  \n  /* Exercise callout styling */\n  div.callout-exercise.callout {\n    margin-top: 1em;\n    margin-bottom: 1em;\n    border-radius: .25rem;\n    border-left: solid #ff7d00 .3rem !important;\n    border-right: solid 0.5px silver;\n    border-top: solid 0.5px silver;\n    border-bottom: solid 0.5px silver;\n  }\n  \n  div.callout-exercise.callout > .callout-header::before {\n    font-family: "Font Awesome 5 Free";\n    content: "📚";\n    margin-right: 10px;\n  }\n  \n  /* Answer callout styling */\n  div.callout-answer.callout {\n    margin-top: 1em;\n    margin-bottom: 1em;\n    border-radius: .25rem;\n    border-left: solid #ffdd00 .3rem !important;\n    border-right: solid 0.5px silver;\n    border-top: solid 0.5px silver;\n    border-bottom: solid 0.5px silver;\n  }\n  \n  div.callout-answer.callout > .callout-header::before {\n    font-family: "Font Awesome 5 Free";\n    content: "✅";\n    margin-right: 10px;\n  }\n  \n  /* Hint callout styling */\n  div.callout-hint.callout {\n    margin-top: 1em;\n    margin-bottom: 1em;\n    border-radius: .25rem;\n    border-left: solid #01befe .3rem !important;\n    border-right: solid 0.5px silver;\n    border-top: solid 0.5px silver;\n    border-bottom: solid 0.5px silver;\n  }\n  \n  div.callout-hint.callout > .callout-header::before {\n    font-family: "Font Awesome 5 Free";\n    content: "🤔";\n    margin-right: 10px;\n  }\n  \n  .callout-toggle::before {\n    background-image: url(${u});\n  }\n  \n  /* Examples of how to use the above styles\n  \n  ::: {.callout-slide collapse="false"}\n  ## Slides\n  \n  Now would be a great time for you to try out a small [Image Classification](../image_classification/image_classification.qmd) computer vision model out of the box.\n  :::\n  \n  ::: {.callout-exercise collapse="false"}\n  # Exercises\n  \n  Now would be a great time for you to try out a small computer vision model out of the box.\n  :::\n  \n  ::: {.callout-lab collapse="false"}\n  ## Labs\n  \n  Now would be a great time for you to try out a small [Image Classification](../image_classification/image_classification.qmd) computer vision model out of the box.\n  :::\n  \n  ::: {.callout-question collapse="false"}\n  ## Questions\n  \n  Now would be a great time for you to try out a small [Image Classification](../image_classification/image_classification.qmd) computer vision model out of the box.\n  :::\n  \n  ::: {.callout-answer collapse="false"}\n  ## Answer\n  \n  Now would be a great time for you to try out a small [Image Classification](../image_classification/image_classification.qmd) computer vision model out of the box.\n  :::\n  \n  ::: {.callout-hint collapse="false"}\n  ## Hint\n  \n  Now would be a great time for you to try out a small [Image Classification](../image_classification/image_classification.qmd) computer vision model out of the box.\n  :::\n  \n  */\n  `,""]);const p=d},2681:(e,t,n)=>{"use strict";n.r(t),n.d(t,{default:()=>s});var r=n(1601),i=n.n(r),a=n(6314),o=n.n(a)()(i());o.push([e.id,"\n#text-selection-menu{\n  z-index: 9999;\n  position: fixed;\n}\n\n\n/* If the icons class is for SVGs within the buttons, adjust their size here */\n.icons {\n    width: 24px; /* Adjust SVG size as needed */\n    height: 24px; /* Adjust SVG size as needed */\n    margin: auto;\n}\n.icon-tiny\n{\n    width: 12px; /* Adjust SVG size as needed */\n    height: 12px; /* Adjust SVG size as needed */\n}\n\n.icons-left {\n  width: 24px; /* Adjust SVG size as needed */\n  height: 24px; /* Adjust SVG size as needed */\n}\n/*  */\n\n/* Basic table styles */\ntable {\n  width: 100%; /* Full-width */\n  border-collapse: collapse; /* Collapse borders */\n  margin: 20px 0; /* Add some margin around the table */\n  font-size: 0.9em; /* Adjust font size */\n  font-family: Arial, sans-serif; /* Use a nice font-family */\n  box-shadow: 0 0 20px rgba(0, 0, 0, 0.15); /* Slight shadow around table */\n}\n\n/* Table headers */\nth {\n  background-color: #009879; /* A nice green background */\n  color: #ffffff; /* White text color */\n  text-align: left; /* Align text to the left */\n  padding: 12px 15px; /* Add some padding */\n}\n\n/* Table cells */\ntd {\n  padding: 12px 15px; /* Add some padding */\n  border-bottom: 1px solid #dddddd; /* A light border for each cell */\n}\n\n/* Table row hover effect */\ntr:nth-of-type(even) {\n  background-color: #f3f3f3; /* Light grey background for even rows */\n}\n\ntr:hover {\n  background-color: #f1f1f1; /* Slightly different grey for hover effect */\n}\n\n/* Responsive tables */\n@media screen and (max-width: 600px) {\n  table {\n    width: 100%;\n    display: block;\n    overflow-x: auto; /* Enable horizontal scrolling on small devices */\n  }\n}\n\n\n/* iframe */\n\n/* body {\n    margin: 0;\n    padding: 0;\n} */\n\n/* .iframe-container {\n    position: relative;\n    background-color: transparent ;\n    max-width: 200px;\n    border: none;\n    max-height: fit-content;\n    overflow: hidden;\n    border: 2px solid black;\n    overflow: visible;\n    max-height: 300px; \n    overflow-y: auto; \n    box-sizing: border-box;\n    background-color: #fff;\n\n} */\n\n\n\n#markdown-preview {\n    /* min-width: 50px; */\n    width: 100%;\n    background-color: #fff;\n    overflow: visible; /* Let it expand */ /* hide any overflow initially */\n    transition: height 0.3s ease-out, opacity 0.3s ease-out; /* Add opacity to the transition *//* animate height changes */\n    padding: 4px;\n}\n  \n  .iframe-container .markdown-preview {\n    border: none;\n    max-height: 300px; /* set maximum height */\n    overflow-y: auto; /* show scrollbars when needed */\n  }\n\n  \n\n\n\n\n\n.button-row{\n    display: flex;\n    justify-content: last baseline;\n    gap: 10px;\n    flex-direction: row;\n    background-color: transparent;\n}\n\n#quiz-form {\n    margin-bottom: 1rem; /* Tailwind class mb-4 */\n  }\n  \n  #submit-quiz-btn {\n    margin-top: 1rem; /* Tailwind class mt-4 */\n    width: 100%; /* Tailwind class w-full */\n    background-color: black; /* Tailwind class bg-blue-500 */\n    color: white; /* Tailwind class text-white */\n    padding: 0.5rem 1rem; /* Tailwind classes px-4 py-2 */\n    border-radius: 0.25rem; /* Tailwind class rounded */\n    transition: background-color 0.2s; /* Tailwind hover state */\n    margin-bottom: 10px;\n  }\n  \n  #submit-quiz-btn:hover {\n    background-color: #2779bd; /* Tailwind class hover:bg-blue-600 */\n  }\n  \n  .submit-quiz-btn {\n    margin-top: 1rem; /* Tailwind class mt-4 */\n    width: 100%; /* Tailwind class w-full */\n    background-color: black; /* Tailwind class bg-blue-500 */\n    color: white; /* Tailwind class text-white */\n    padding: 0.5rem 1rem; /* Tailwind classes px-4 py-2 */\n    border-radius: 0.25rem; /* Tailwind class rounded */\n    transition: background-color 0.2s; /* Tailwind hover state */\n    margin-bottom: 10px;\n  }\n  \n  .submit-quiz-btn:hover {\n    background-color: #2779bd; /* Tailwind class hover:bg-blue-600 */\n  }\n  \n\n  #quiz form{\n    display: block;\n    /* flex-direction: column; */\n    box-shadow: 0 0 0px 0 rgba(0, 0, 0, 0.1);\n  }\n\n  #quiz h4 {\n    font-size: 16px; /* Change this value to adjust the font size */\n  }\n\n #quiz ul {\n    list-style-type: none;\n  }\n\n  #result {\n    margin-top: 1rem; /* Tailwind class mt-4 */\n    text-align: center; /* Tailwind class text-center */\n  }\n  \n  details > .spoiler-content,\n  details > p,\n  details > em {\n    color: black;\n    font-style: normal !important; /* This will override other styles unless they also use !important */\n  }\n/*   \n  .correct-answer::after {\n    content: '✅';\n    color: green;\n    margin-right: 8px;\n} */\n.wrong-answer::after {\n    content: '❌';\n    color: #f87171;\n    margin-left: 8px;\n}\n\n\n/* Style adjustments */\n .icon-button {\n    cursor: pointer;\n    padding: 10px 20px;\n    background-color: #000; /* Make buttons black */\n    color: #fff; /* Text color white for contrast */\n    border: none;\n    border-radius: 5px;\n    margin: 20px;\n  }\n/* ///////////////////////////////////////////////////////////////////// */\n    /* General styles for the slider */\n  input[type=range] {\n    -webkit-appearance: none; /* Override default CSS styles */\n    appearance: none;\n    width: 100%; /* Full-width */\n    height: 5px; /* Specified height */\n    background: black; /* Black track */\n    outline: none; /* Remove outline */\n    opacity: 0.7; /* Partial transparency */\n    transition: opacity .2s; /* Transition for the slider */\n  }\n  \n/* Initially hide the popup div */\n\n/* Show the popup div when hovering over the button or the popup div itself */\n.hover-btn:hover + .popup-content, .popup-content:hover {\n  visibility: visible;\n  opacity: 1;\n  transition-delay: 0s; /* Make popup appear immediately */\n}\n  \n  /* Style for Webkit browsers like Chrome, Safari */\n  input[type=range]::-webkit-slider-thumb {\n    -webkit-appearance: none; /* Override default CSS styles */\n    appearance: none;\n    width: 25px; /* Width of the thumb */\n    height: 25px; /* Height of the thumb */\n    background: black; /* Black thumb */\n    cursor: pointer; /* Cursor on hover */\n  }\n\n  /* Style for Mozilla Firefox */\n  input[type=range]::-moz-range-thumb {\n    width: 25px; /* Width of the thumb */\n    height: 25px; /* Height of the thumb */\n    background: black; /* Black thumb */\n    cursor: pointer; /* Cursor on hover */\n  }\n\n  /* Style for the focus state */\n 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SYSRES_CONST_ACCESS_RIGHTS_CHANGE_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_CHANGE_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_DELETE_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_DELETE_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_EXECUTE_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_EXECUTE_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_NO_ACCESS_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_NO_ACCESS_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_RATIFY_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_RATIFY_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_VIEW SYSRES_CONST_ACCESS_RIGHTS_VIEW_CODE SYSRES_CONST_ACCESS_RIGHTS_VIEW_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_VIEW_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_TYPE_CHANGE SYSRES_CONST_ACCESS_TYPE_CHANGE_CODE SYSRES_CONST_ACCESS_TYPE_EXISTS SYSRES_CONST_ACCESS_TYPE_EXISTS_CODE SYSRES_CONST_ACCESS_TYPE_FULL SYSRES_CONST_ACCESS_TYPE_FULL_CODE SYSRES_CONST_ACCESS_TYPE_VIEW SYSRES_CONST_ACCESS_TYPE_VIEW_CODE SYSRES_CONST_ACTION_TYPE_ABORT SYSRES_CONST_ACTION_TYPE_ACCEPT SYSRES_CONST_ACTION_TYPE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ADD_ATTACHMENT SYSRES_CONST_ACTION_TYPE_CHANGE_CARD SYSRES_CONST_ACTION_TYPE_CHANGE_KIND SYSRES_CONST_ACTION_TYPE_CHANGE_STORAGE SYSRES_CONST_ACTION_TYPE_CONTINUE SYSRES_CONST_ACTION_TYPE_COPY SYSRES_CONST_ACTION_TYPE_CREATE SYSRES_CONST_ACTION_TYPE_CREATE_VERSION SYSRES_CONST_ACTION_TYPE_DELETE SYSRES_CONST_ACTION_TYPE_DELETE_ATTACHMENT SYSRES_CONST_ACTION_TYPE_DELETE_VERSION SYSRES_CONST_ACTION_TYPE_DISABLE_DELEGATE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ENABLE_DELEGATE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_CERTIFICATE SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_CERTIFICATE_AND_PASSWORD SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_PASSWORD SYSRES_CONST_ACTION_TYPE_EXPORT_WITH_LOCK SYSRES_CONST_ACTION_TYPE_EXPORT_WITHOUT_LOCK SYSRES_CONST_ACTION_TYPE_IMPORT_WITH_UNLOCK SYSRES_CONST_ACTION_TYPE_IMPORT_WITHOUT_UNLOCK SYSRES_CONST_ACTION_TYPE_LIFE_CYCLE_STAGE SYSRES_CONST_ACTION_TYPE_LOCK SYSRES_CONST_ACTION_TYPE_LOCK_FOR_SERVER SYSRES_CONST_ACTION_TYPE_LOCK_MODIFY SYSRES_CONST_ACTION_TYPE_MARK_AS_READED SYSRES_CONST_ACTION_TYPE_MARK_AS_UNREADED SYSRES_CONST_ACTION_TYPE_MODIFY SYSRES_CONST_ACTION_TYPE_MODIFY_CARD SYSRES_CONST_ACTION_TYPE_MOVE_TO_ARCHIVE SYSRES_CONST_ACTION_TYPE_OFF_ENCRYPTION SYSRES_CONST_ACTION_TYPE_PASSWORD_CHANGE SYSRES_CONST_ACTION_TYPE_PERFORM SYSRES_CONST_ACTION_TYPE_RECOVER_FROM_LOCAL_COPY SYSRES_CONST_ACTION_TYPE_RESTART SYSRES_CONST_ACTION_TYPE_RESTORE_FROM_ARCHIVE SYSRES_CONST_ACTION_TYPE_REVISION SYSRES_CONST_ACTION_TYPE_SEND_BY_MAIL SYSRES_CONST_ACTION_TYPE_SIGN SYSRES_CONST_ACTION_TYPE_START SYSRES_CONST_ACTION_TYPE_UNLOCK SYSRES_CONST_ACTION_TYPE_UNLOCK_FROM_SERVER SYSRES_CONST_ACTION_TYPE_VERSION_STATE SYSRES_CONST_ACTION_TYPE_VERSION_VISIBILITY SYSRES_CONST_ACTION_TYPE_VIEW SYSRES_CONST_ACTION_TYPE_VIEW_SHADOW_COPY SYSRES_CONST_ACTION_TYPE_WORKFLOW_DESCRIPTION_MODIFY SYSRES_CONST_ACTION_TYPE_WRITE_HISTORY SYSRES_CONST_ACTIVE_VERSION_STATE_PICK_VALUE SYSRES_CONST_ADD_REFERENCE_MODE_NAME SYSRES_CONST_ADDITION_REQUISITE_CODE SYSRES_CONST_ADDITIONAL_PARAMS_REQUISITE_CODE SYSRES_CONST_ADITIONAL_JOB_END_DATE_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_READ_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_START_DATE_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_STATE_REQUISITE_NAME SYSRES_CONST_ADMINISTRATION_HISTORY_ADDING_USER_TO_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_ADDING_USER_TO_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_COMP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_COMP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE_ACTION SYSRES_CONST_ALL_ACCEPT_CONDITION_RUS SYSRES_CONST_ALL_USERS_GROUP SYSRES_CONST_ALL_USERS_GROUP_NAME SYSRES_CONST_ALL_USERS_SERVER_GROUP_NAME SYSRES_CONST_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_APP_VIEWER_TYPE_REQUISITE_CODE SYSRES_CONST_APPROVING_SIGNATURE_NAME SYSRES_CONST_APPROVING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE_CODE SYSRES_CONST_ATTACH_TYPE_COMPONENT_TOKEN SYSRES_CONST_ATTACH_TYPE_DOC SYSRES_CONST_ATTACH_TYPE_EDOC SYSRES_CONST_ATTACH_TYPE_FOLDER SYSRES_CONST_ATTACH_TYPE_JOB SYSRES_CONST_ATTACH_TYPE_REFERENCE SYSRES_CONST_ATTACH_TYPE_TASK SYSRES_CONST_AUTH_ENCODED_PASSWORD SYSRES_CONST_AUTH_ENCODED_PASSWORD_CODE SYSRES_CONST_AUTH_NOVELL SYSRES_CONST_AUTH_PASSWORD SYSRES_CONST_AUTH_PASSWORD_CODE SYSRES_CONST_AUTH_WINDOWS SYSRES_CONST_AUTHENTICATING_SIGNATURE_NAME SYSRES_CONST_AUTHENTICATING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_AUTO_ENUM_METHOD_FLAG SYSRES_CONST_AUTO_NUMERATION_CODE SYSRES_CONST_AUTO_STRONG_ENUM_METHOD_FLAG SYSRES_CONST_AUTOTEXT_NAME_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_TEXT_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_USAGE_ALL SYSRES_CONST_AUTOTEXT_USAGE_ALL_CODE SYSRES_CONST_AUTOTEXT_USAGE_SIGN SYSRES_CONST_AUTOTEXT_USAGE_SIGN_CODE SYSRES_CONST_AUTOTEXT_USAGE_WORK SYSRES_CONST_AUTOTEXT_USAGE_WORK_CODE SYSRES_CONST_AUTOTEXT_USE_ANYWHERE_CODE SYSRES_CONST_AUTOTEXT_USE_ON_SIGNING_CODE SYSRES_CONST_AUTOTEXT_USE_ON_WORK_CODE SYSRES_CONST_BEGIN_DATE_REQUISITE_CODE SYSRES_CONST_BLACK_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BLUE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BTN_PART SYSRES_CONST_CALCULATED_ROLE_TYPE_CODE SYSRES_CONST_CALL_TYPE_VARIABLE_BUTTON_VALUE SYSRES_CONST_CALL_TYPE_VARIABLE_PROGRAM_VALUE SYSRES_CONST_CANCEL_MESSAGE_FUNCTION_RESULT SYSRES_CONST_CARD_PART SYSRES_CONST_CARD_REFERENCE_MODE_NAME SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_AND_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_VALUE SYSRES_CONST_CHECK_PARAM_VALUE_DATE_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_FLOAT_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_INTEGER_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_PICK_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_REEFRENCE_PARAM_TYPE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_CODE_COMPONENT_TYPE_ADMIN SYSRES_CONST_CODE_COMPONENT_TYPE_DEVELOPER SYSRES_CONST_CODE_COMPONENT_TYPE_DOCS SYSRES_CONST_CODE_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_CODE_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_CODE_COMPONENT_TYPE_OTHER SYSRES_CONST_CODE_COMPONENT_TYPE_REFERENCE SYSRES_CONST_CODE_COMPONENT_TYPE_REPORT SYSRES_CONST_CODE_COMPONENT_TYPE_SCRIPT SYSRES_CONST_CODE_COMPONENT_TYPE_URL SYSRES_CONST_CODE_REQUISITE_ACCESS SYSRES_CONST_CODE_REQUISITE_CODE SYSRES_CONST_CODE_REQUISITE_COMPONENT SYSRES_CONST_CODE_REQUISITE_DESCRIPTION SYSRES_CONST_CODE_REQUISITE_EXCLUDE_COMPONENT SYSRES_CONST_CODE_REQUISITE_RECORD SYSRES_CONST_COMMENT_REQ_CODE SYSRES_CONST_COMMON_SETTINGS_REQUISITE_CODE SYSRES_CONST_COMP_CODE_GRD SYSRES_CONST_COMPONENT_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_COMPONENT_TYPE_ADMIN_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DEVELOPER_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DOCS SYSRES_CONST_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_COMPONENT_TYPE_EDOCS SYSRES_CONST_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_COMPONENT_TYPE_OTHER SYSRES_CONST_COMPONENT_TYPE_REFERENCE_TYPES SYSRES_CONST_COMPONENT_TYPE_REFERENCES SYSRES_CONST_COMPONENT_TYPE_REPORTS SYSRES_CONST_COMPONENT_TYPE_SCRIPTS SYSRES_CONST_COMPONENT_TYPE_URL SYSRES_CONST_COMPONENTS_REMOTE_SERVERS_VIEW_CODE SYSRES_CONST_CONDITION_BLOCK_DESCRIPTION SYSRES_CONST_CONST_FIRM_STATUS_COMMON SYSRES_CONST_CONST_FIRM_STATUS_INDIVIDUAL SYSRES_CONST_CONST_NEGATIVE_VALUE SYSRES_CONST_CONST_POSITIVE_VALUE SYSRES_CONST_CONST_SERVER_STATUS_DONT_REPLICATE SYSRES_CONST_CONST_SERVER_STATUS_REPLICATE SYSRES_CONST_CONTENTS_REQUISITE_CODE SYSRES_CONST_DATA_TYPE_BOOLEAN SYSRES_CONST_DATA_TYPE_DATE SYSRES_CONST_DATA_TYPE_FLOAT SYSRES_CONST_DATA_TYPE_INTEGER SYSRES_CONST_DATA_TYPE_PICK SYSRES_CONST_DATA_TYPE_REFERENCE SYSRES_CONST_DATA_TYPE_STRING SYSRES_CONST_DATA_TYPE_TEXT SYSRES_CONST_DATA_TYPE_VARIANT SYSRES_CONST_DATE_CLOSE_REQ_CODE SYSRES_CONST_DATE_FORMAT_DATE_ONLY_CHAR SYSRES_CONST_DATE_OPEN_REQ_CODE SYSRES_CONST_DATE_REQUISITE SYSRES_CONST_DATE_REQUISITE_CODE SYSRES_CONST_DATE_REQUISITE_NAME SYSRES_CONST_DATE_REQUISITE_TYPE SYSRES_CONST_DATE_TYPE_CHAR SYSRES_CONST_DATETIME_FORMAT_VALUE SYSRES_CONST_DEA_ACCESS_RIGHTS_ACTION_CODE SYSRES_CONST_DESCRIPTION_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_DET1_PART SYSRES_CONST_DET2_PART SYSRES_CONST_DET3_PART SYSRES_CONST_DET4_PART SYSRES_CONST_DET5_PART SYSRES_CONST_DET6_PART SYSRES_CONST_DETAIL_DATASET_KEY_REQUISITE_CODE SYSRES_CONST_DETAIL_PICK_REQUISITE_CODE SYSRES_CONST_DETAIL_REQ_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_NAME SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_NAME SYSRES_CONST_DOCUMENT_STORAGES_CODE SYSRES_CONST_DOCUMENT_TEMPLATES_TYPE_NAME SYSRES_CONST_DOUBLE_REQUISITE_CODE SYSRES_CONST_EDITOR_CLOSE_FILE_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_CLOSE_PROCESS_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_TYPE_REQUISITE_CODE SYSRES_CONST_EDITORS_APPLICATION_NAME_REQUISITE_CODE SYSRES_CONST_EDITORS_CREATE_SEVERAL_PROCESSES_REQUISITE_CODE SYSRES_CONST_EDITORS_EXTENSION_REQUISITE_CODE SYSRES_CONST_EDITORS_OBSERVER_BY_PROCESS_TYPE SYSRES_CONST_EDITORS_REFERENCE_CODE SYSRES_CONST_EDITORS_REPLACE_SPEC_CHARS_REQUISITE_CODE SYSRES_CONST_EDITORS_USE_PLUGINS_REQUISITE_CODE SYSRES_CONST_EDITORS_VIEW_DOCUMENT_OPENED_TO_EDIT_CODE SYSRES_CONST_EDOC_CARD_TYPE_REQUISITE_CODE SYSRES_CONST_EDOC_CARD_TYPES_LINK_REQUISITE_CODE SYSRES_CONST_EDOC_CERTIFICATE_AND_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_CERTIFICATE_ENCODE_CODE SYSRES_CONST_EDOC_DATE_REQUISITE_CODE SYSRES_CONST_EDOC_KIND_REFERENCE_CODE SYSRES_CONST_EDOC_KINDS_BY_TEMPLATE_ACTION_CODE SYSRES_CONST_EDOC_MANAGE_ACCESS_CODE SYSRES_CONST_EDOC_NONE_ENCODE_CODE SYSRES_CONST_EDOC_NUMBER_REQUISITE_CODE SYSRES_CONST_EDOC_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_READONLY_ACCESS_CODE SYSRES_CONST_EDOC_SHELL_LIFE_TYPE_VIEW_VALUE SYSRES_CONST_EDOC_SIZE_RESTRICTION_PRIORITY_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_CHECK_ACCESS_RIGHTS_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_COMPUTER_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_DATABASE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_EDIT_IN_STORAGE_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_LOCAL_PATH_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_SHARED_SOURCE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_EDOC_TYPES_REFERENCE_CODE SYSRES_CONST_EDOC_VERSION_ACTIVE_STAGE_CODE SYSRES_CONST_EDOC_VERSION_DESIGN_STAGE_CODE SYSRES_CONST_EDOC_VERSION_OBSOLETE_STAGE_CODE SYSRES_CONST_EDOC_WRITE_ACCES_CODE SYSRES_CONST_EDOCUMENT_CARD_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_END_DATE_REQUISITE_CODE SYSRES_CONST_ENUMERATION_TYPE_REQUISITE_CODE SYSRES_CONST_EXECUTE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_EXECUTIVE_FILE_STORAGE_TYPE SYSRES_CONST_EXIST_CONST SYSRES_CONST_EXIST_VALUE SYSRES_CONST_EXPORT_LOCK_TYPE_ASK SYSRES_CONST_EXPORT_LOCK_TYPE_WITH_LOCK SYSRES_CONST_EXPORT_LOCK_TYPE_WITHOUT_LOCK SYSRES_CONST_EXPORT_VERSION_TYPE_ASK SYSRES_CONST_EXPORT_VERSION_TYPE_LAST SYSRES_CONST_EXPORT_VERSION_TYPE_LAST_ACTIVE SYSRES_CONST_EXTENSION_REQUISITE_CODE SYSRES_CONST_FILTER_NAME_REQUISITE_CODE SYSRES_CONST_FILTER_REQUISITE_CODE SYSRES_CONST_FILTER_TYPE_COMMON_CODE SYSRES_CONST_FILTER_TYPE_COMMON_NAME SYSRES_CONST_FILTER_TYPE_USER_CODE SYSRES_CONST_FILTER_TYPE_USER_NAME SYSRES_CONST_FILTER_VALUE_REQUISITE_NAME SYSRES_CONST_FLOAT_NUMBER_FORMAT_CHAR SYSRES_CONST_FLOAT_REQUISITE_TYPE SYSRES_CONST_FOLDER_AUTHOR_VALUE SYSRES_CONST_FOLDER_KIND_ANY_OBJECTS SYSRES_CONST_FOLDER_KIND_COMPONENTS SYSRES_CONST_FOLDER_KIND_EDOCS SYSRES_CONST_FOLDER_KIND_JOBS SYSRES_CONST_FOLDER_KIND_TASKS SYSRES_CONST_FOLDER_TYPE_COMMON SYSRES_CONST_FOLDER_TYPE_COMPONENT SYSRES_CONST_FOLDER_TYPE_FAVORITES SYSRES_CONST_FOLDER_TYPE_INBOX SYSRES_CONST_FOLDER_TYPE_OUTBOX SYSRES_CONST_FOLDER_TYPE_QUICK_LAUNCH SYSRES_CONST_FOLDER_TYPE_SEARCH SYSRES_CONST_FOLDER_TYPE_SHORTCUTS SYSRES_CONST_FOLDER_TYPE_USER SYSRES_CONST_FROM_DICTIONARY_ENUM_METHOD_FLAG SYSRES_CONST_FULL_SUBSTITUTE_TYPE SYSRES_CONST_FULL_SUBSTITUTE_TYPE_CODE SYSRES_CONST_FUNCTION_CANCEL_RESULT SYSRES_CONST_FUNCTION_CATEGORY_SYSTEM SYSRES_CONST_FUNCTION_CATEGORY_USER SYSRES_CONST_FUNCTION_FAILURE_RESULT SYSRES_CONST_FUNCTION_SAVE_RESULT SYSRES_CONST_GENERATED_REQUISITE SYSRES_CONST_GREEN_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_GROUP_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_NAME SYSRES_CONST_GROUP_CATEGORY_SERVICE_CODE SYSRES_CONST_GROUP_CATEGORY_SERVICE_NAME SYSRES_CONST_GROUP_COMMON_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_FULL_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_CODES_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_SERVICE_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_USER_REQUISITE_CODE SYSRES_CONST_GROUPS_REFERENCE_CODE SYSRES_CONST_GROUPS_REQUISITE_CODE SYSRES_CONST_HIDDEN_MODE_NAME SYSRES_CONST_HIGH_LVL_REQUISITE_CODE SYSRES_CONST_HISTORY_ACTION_CREATE_CODE SYSRES_CONST_HISTORY_ACTION_DELETE_CODE SYSRES_CONST_HISTORY_ACTION_EDIT_CODE SYSRES_CONST_HOUR_CHAR SYSRES_CONST_ID_REQUISITE_CODE SYSRES_CONST_IDSPS_REQUISITE_CODE SYSRES_CONST_IMAGE_MODE_COLOR SYSRES_CONST_IMAGE_MODE_GREYSCALE SYSRES_CONST_IMAGE_MODE_MONOCHROME SYSRES_CONST_IMPORTANCE_HIGH SYSRES_CONST_IMPORTANCE_LOW SYSRES_CONST_IMPORTANCE_NORMAL SYSRES_CONST_IN_DESIGN_VERSION_STATE_PICK_VALUE SYSRES_CONST_INCOMING_WORK_RULE_TYPE_CODE SYSRES_CONST_INT_REQUISITE SYSRES_CONST_INT_REQUISITE_TYPE SYSRES_CONST_INTEGER_NUMBER_FORMAT_CHAR SYSRES_CONST_INTEGER_TYPE_CHAR SYSRES_CONST_IS_GENERATED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_PUBLIC_ROLE_REQUISITE_CODE SYSRES_CONST_IS_REMOTE_USER_NEGATIVE_VALUE SYSRES_CONST_IS_REMOTE_USER_POSITIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_STORED_VALUE SYSRES_CONST_ITALIC_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_JOB_BLOCK_DESCRIPTION SYSRES_CONST_JOB_KIND_CONTROL_JOB SYSRES_CONST_JOB_KIND_JOB SYSRES_CONST_JOB_KIND_NOTICE SYSRES_CONST_JOB_STATE_ABORTED SYSRES_CONST_JOB_STATE_COMPLETE SYSRES_CONST_JOB_STATE_WORKING SYSRES_CONST_KIND_REQUISITE_CODE SYSRES_CONST_KIND_REQUISITE_NAME SYSRES_CONST_KINDS_CREATE_SHADOW_COPIES_REQUISITE_CODE SYSRES_CONST_KINDS_DEFAULT_EDOC_LIFE_STAGE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALL_TEPLATES_ALLOWED_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_LIFE_CYCLE_STAGE_CHANGING_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_MULTIPLE_ACTIVE_VERSIONS_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_SHARE_ACCES_RIGHTS_BY_DEFAULT_CODE SYSRES_CONST_KINDS_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_TYPE_REQUISITE_CODE SYSRES_CONST_KINDS_SIGNERS_REQUISITES_CODE SYSRES_CONST_KOD_INPUT_TYPE SYSRES_CONST_LAST_UPDATE_DATE_REQUISITE_CODE SYSRES_CONST_LIFE_CYCLE_START_STAGE_REQUISITE_CODE SYSRES_CONST_LILAC_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_LINK_OBJECT_KIND_COMPONENT SYSRES_CONST_LINK_OBJECT_KIND_DOCUMENT SYSRES_CONST_LINK_OBJECT_KIND_EDOC SYSRES_CONST_LINK_OBJECT_KIND_FOLDER SYSRES_CONST_LINK_OBJECT_KIND_JOB SYSRES_CONST_LINK_OBJECT_KIND_REFERENCE SYSRES_CONST_LINK_OBJECT_KIND_TASK SYSRES_CONST_LINK_REF_TYPE_REQUISITE_CODE SYSRES_CONST_LIST_REFERENCE_MODE_NAME SYSRES_CONST_LOCALIZATION_DICTIONARY_MAIN_VIEW_CODE SYSRES_CONST_MAIN_VIEW_CODE SYSRES_CONST_MANUAL_ENUM_METHOD_FLAG SYSRES_CONST_MASTER_COMP_TYPE_REQUISITE_CODE SYSRES_CONST_MASTER_TABLE_REC_ID_REQUISITE_CODE SYSRES_CONST_MAXIMIZED_MODE_NAME SYSRES_CONST_ME_VALUE SYSRES_CONST_MESSAGE_ATTENTION_CAPTION SYSRES_CONST_MESSAGE_CONFIRMATION_CAPTION SYSRES_CONST_MESSAGE_ERROR_CAPTION SYSRES_CONST_MESSAGE_INFORMATION_CAPTION SYSRES_CONST_MINIMIZED_MODE_NAME SYSRES_CONST_MINUTE_CHAR SYSRES_CONST_MODULE_REQUISITE_CODE SYSRES_CONST_MONITORING_BLOCK_DESCRIPTION SYSRES_CONST_MONTH_FORMAT_VALUE SYSRES_CONST_NAME_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_NAME_REQUISITE_CODE SYSRES_CONST_NAME_SINGULAR_REQUISITE_CODE SYSRES_CONST_NAMEAN_INPUT_TYPE SYSRES_CONST_NEGATIVE_PICK_VALUE SYSRES_CONST_NEGATIVE_VALUE SYSRES_CONST_NO SYSRES_CONST_NO_PICK_VALUE SYSRES_CONST_NO_SIGNATURE_REQUISITE_CODE SYSRES_CONST_NO_VALUE SYSRES_CONST_NONE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_NORMAL_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NORMAL_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_NORMAL_MODE_NAME SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_NOTE_REQUISITE_CODE SYSRES_CONST_NOTICE_BLOCK_DESCRIPTION SYSRES_CONST_NUM_REQUISITE SYSRES_CONST_NUM_STR_REQUISITE_CODE SYSRES_CONST_NUMERATION_AUTO_NOT_STRONG SYSRES_CONST_NUMERATION_AUTO_STRONG SYSRES_CONST_NUMERATION_FROM_DICTONARY SYSRES_CONST_NUMERATION_MANUAL SYSRES_CONST_NUMERIC_TYPE_CHAR SYSRES_CONST_NUMREQ_REQUISITE_CODE SYSRES_CONST_OBSOLETE_VERSION_STATE_PICK_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_OPTIONAL_FORM_COMP_REQCODE_PREFIX SYSRES_CONST_ORANGE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_ORIGINALREF_REQUISITE_CODE SYSRES_CONST_OURFIRM_REF_CODE SYSRES_CONST_OURFIRM_REQUISITE_CODE SYSRES_CONST_OURFIRM_VAR SYSRES_CONST_OUTGOING_WORK_RULE_TYPE_CODE SYSRES_CONST_PICK_NEGATIVE_RESULT SYSRES_CONST_PICK_POSITIVE_RESULT SYSRES_CONST_PICK_REQUISITE SYSRES_CONST_PICK_REQUISITE_TYPE SYSRES_CONST_PICK_TYPE_CHAR SYSRES_CONST_PLAN_STATUS_REQUISITE_CODE SYSRES_CONST_PLATFORM_VERSION_COMMENT SYSRES_CONST_PLUGINS_SETTINGS_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_POSITIVE_PICK_VALUE SYSRES_CONST_POWER_TO_CREATE_ACTION_CODE SYSRES_CONST_POWER_TO_SIGN_ACTION_CODE SYSRES_CONST_PRIORITY_REQUISITE_CODE SYSRES_CONST_QUALIFIED_TASK_TYPE SYSRES_CONST_QUALIFIED_TASK_TYPE_CODE SYSRES_CONST_RECSTAT_REQUISITE_CODE SYSRES_CONST_RED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_REF_ID_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_REF_REQUISITE SYSRES_CONST_REF_REQUISITE_TYPE SYSRES_CONST_REF_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_REFERENCE_RECORD_HISTORY_CREATE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_DELETE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_MODIFY_ACTION_CODE SYSRES_CONST_REFERENCE_TYPE_CHAR SYSRES_CONST_REFERENCE_TYPE_REQUISITE_NAME SYSRES_CONST_REFERENCES_ADD_PARAMS_REQUISITE_CODE SYSRES_CONST_REFERENCES_DISPLAY_REQUISITE_REQUISITE_CODE SYSRES_CONST_REMOTE_SERVER_STATUS_WORKING SYSRES_CONST_REMOTE_SERVER_TYPE_MAIN SYSRES_CONST_REMOTE_SERVER_TYPE_SECONDARY SYSRES_CONST_REMOTE_USER_FLAG_VALUE_CODE SYSRES_CONST_REPORT_APP_EDITOR_INTERNAL SYSRES_CONST_REPORT_BASE_REPORT_ID_REQUISITE_CODE SYSRES_CONST_REPORT_BASE_REPORT_REQUISITE_CODE SYSRES_CONST_REPORT_SCRIPT_REQUISITE_CODE SYSRES_CONST_REPORT_TEMPLATE_REQUISITE_CODE SYSRES_CONST_REPORT_VIEWER_CODE_REQUISITE_CODE SYSRES_CONST_REQ_ALLOW_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_RECORD_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_SERVER_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ_MODE_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_EDIT_CODE SYSRES_CONST_REQ_MODE_HIDDEN_CODE SYSRES_CONST_REQ_MODE_NOT_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_VIEW_CODE SYSRES_CONST_REQ_NUMBER_REQUISITE_CODE SYSRES_CONST_REQ_SECTION_VALUE SYSRES_CONST_REQ_TYPE_VALUE SYSRES_CONST_REQUISITE_FORMAT_BY_UNIT SYSRES_CONST_REQUISITE_FORMAT_DATE_FULL SYSRES_CONST_REQUISITE_FORMAT_DATE_TIME SYSRES_CONST_REQUISITE_FORMAT_LEFT SYSRES_CONST_REQUISITE_FORMAT_RIGHT SYSRES_CONST_REQUISITE_FORMAT_WITHOUT_UNIT SYSRES_CONST_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_REQUISITE_SECTION_ACTIONS SYSRES_CONST_REQUISITE_SECTION_BUTTON SYSRES_CONST_REQUISITE_SECTION_BUTTONS SYSRES_CONST_REQUISITE_SECTION_CARD SYSRES_CONST_REQUISITE_SECTION_TABLE SYSRES_CONST_REQUISITE_SECTION_TABLE10 SYSRES_CONST_REQUISITE_SECTION_TABLE11 SYSRES_CONST_REQUISITE_SECTION_TABLE12 SYSRES_CONST_REQUISITE_SECTION_TABLE13 SYSRES_CONST_REQUISITE_SECTION_TABLE14 SYSRES_CONST_REQUISITE_SECTION_TABLE15 SYSRES_CONST_REQUISITE_SECTION_TABLE16 SYSRES_CONST_REQUISITE_SECTION_TABLE17 SYSRES_CONST_REQUISITE_SECTION_TABLE18 SYSRES_CONST_REQUISITE_SECTION_TABLE19 SYSRES_CONST_REQUISITE_SECTION_TABLE2 SYSRES_CONST_REQUISITE_SECTION_TABLE20 SYSRES_CONST_REQUISITE_SECTION_TABLE21 SYSRES_CONST_REQUISITE_SECTION_TABLE22 SYSRES_CONST_REQUISITE_SECTION_TABLE23 SYSRES_CONST_REQUISITE_SECTION_TABLE24 SYSRES_CONST_REQUISITE_SECTION_TABLE3 SYSRES_CONST_REQUISITE_SECTION_TABLE4 SYSRES_CONST_REQUISITE_SECTION_TABLE5 SYSRES_CONST_REQUISITE_SECTION_TABLE6 SYSRES_CONST_REQUISITE_SECTION_TABLE7 SYSRES_CONST_REQUISITE_SECTION_TABLE8 SYSRES_CONST_REQUISITE_SECTION_TABLE9 SYSRES_CONST_REQUISITES_PSEUDOREFERENCE_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_RIGHT_ALIGNMENT_CODE SYSRES_CONST_ROLES_REFERENCE_CODE SYSRES_CONST_ROUTE_STEP_AFTER_RUS SYSRES_CONST_ROUTE_STEP_AND_CONDITION_RUS SYSRES_CONST_ROUTE_STEP_OR_CONDITION_RUS SYSRES_CONST_ROUTE_TYPE_COMPLEX SYSRES_CONST_ROUTE_TYPE_PARALLEL SYSRES_CONST_ROUTE_TYPE_SERIAL SYSRES_CONST_SBDATASETDESC_NEGATIVE_VALUE SYSRES_CONST_SBDATASETDESC_POSITIVE_VALUE SYSRES_CONST_SBVIEWSDESC_POSITIVE_VALUE SYSRES_CONST_SCRIPT_BLOCK_DESCRIPTION SYSRES_CONST_SEARCH_BY_TEXT_REQUISITE_CODE SYSRES_CONST_SEARCHES_COMPONENT_CONTENT SYSRES_CONST_SEARCHES_CRITERIA_ACTION_NAME SYSRES_CONST_SEARCHES_EDOC_CONTENT SYSRES_CONST_SEARCHES_FOLDER_CONTENT SYSRES_CONST_SEARCHES_JOB_CONTENT SYSRES_CONST_SEARCHES_REFERENCE_CODE SYSRES_CONST_SEARCHES_TASK_CONTENT SYSRES_CONST_SECOND_CHAR SYSRES_CONST_SECTION_REQUISITE_ACTIONS_VALUE SYSRES_CONST_SECTION_REQUISITE_CARD_VALUE SYSRES_CONST_SECTION_REQUISITE_CODE SYSRES_CONST_SECTION_REQUISITE_DETAIL_1_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_2_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_3_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_4_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_5_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_6_VALUE SYSRES_CONST_SELECT_REFERENCE_MODE_NAME SYSRES_CONST_SELECT_TYPE_SELECTABLE SYSRES_CONST_SELECT_TYPE_SELECTABLE_ONLY_CHILD SYSRES_CONST_SELECT_TYPE_SELECTABLE_WITH_CHILD SYSRES_CONST_SELECT_TYPE_UNSLECTABLE SYSRES_CONST_SERVER_TYPE_MAIN SYSRES_CONST_SERVICE_USER_CATEGORY_FIELD_VALUE SYSRES_CONST_SETTINGS_USER_REQUISITE_CODE SYSRES_CONST_SIGNATURE_AND_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SIGNATURE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SINGULAR_TITLE_REQUISITE_CODE SYSRES_CONST_SQL_SERVER_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_SQL_SERVER_ENCODE_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_STANDART_ROUTES_GROUPS_REFERENCE_CODE SYSRES_CONST_STATE_REQ_NAME SYSRES_CONST_STATE_REQUISITE_ACTIVE_VALUE SYSRES_CONST_STATE_REQUISITE_CLOSED_VALUE SYSRES_CONST_STATE_REQUISITE_CODE SYSRES_CONST_STATIC_ROLE_TYPE_CODE SYSRES_CONST_STATUS_PLAN_DEFAULT_VALUE SYSRES_CONST_STATUS_VALUE_AUTOCLEANING SYSRES_CONST_STATUS_VALUE_BLUE_SQUARE SYSRES_CONST_STATUS_VALUE_COMPLETE SYSRES_CONST_STATUS_VALUE_GREEN_SQUARE SYSRES_CONST_STATUS_VALUE_ORANGE_SQUARE SYSRES_CONST_STATUS_VALUE_PURPLE_SQUARE SYSRES_CONST_STATUS_VALUE_RED_SQUARE SYSRES_CONST_STATUS_VALUE_SUSPEND SYSRES_CONST_STATUS_VALUE_YELLOW_SQUARE SYSRES_CONST_STDROUTE_SHOW_TO_USERS_REQUISITE_CODE SYSRES_CONST_STORAGE_TYPE_FILE SYSRES_CONST_STORAGE_TYPE_SQL_SERVER SYSRES_CONST_STR_REQUISITE SYSRES_CONST_STRIKEOUT_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_STRING_FORMAT_LEFT_ALIGN_CHAR SYSRES_CONST_STRING_FORMAT_RIGHT_ALIGN_CHAR SYSRES_CONST_STRING_REQUISITE_CODE SYSRES_CONST_STRING_REQUISITE_TYPE SYSRES_CONST_STRING_TYPE_CHAR SYSRES_CONST_SUBSTITUTES_PSEUDOREFERENCE_CODE SYSRES_CONST_SUBTASK_BLOCK_DESCRIPTION SYSRES_CONST_SYSTEM_SETTING_CURRENT_USER_PARAM_VALUE SYSRES_CONST_SYSTEM_SETTING_EMPTY_VALUE_PARAM_VALUE SYSRES_CONST_SYSTEM_VERSION_COMMENT SYSRES_CONST_TASK_ACCESS_TYPE_ALL SYSRES_CONST_TASK_ACCESS_TYPE_ALL_MEMBERS SYSRES_CONST_TASK_ACCESS_TYPE_MANUAL SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION_AND_PASSWORD SYSRES_CONST_TASK_ENCODE_TYPE_NONE SYSRES_CONST_TASK_ENCODE_TYPE_PASSWORD SYSRES_CONST_TASK_ROUTE_ALL_CONDITION SYSRES_CONST_TASK_ROUTE_AND_CONDITION SYSRES_CONST_TASK_ROUTE_OR_CONDITION SYSRES_CONST_TASK_STATE_ABORTED SYSRES_CONST_TASK_STATE_COMPLETE SYSRES_CONST_TASK_STATE_CONTINUED SYSRES_CONST_TASK_STATE_CONTROL SYSRES_CONST_TASK_STATE_INIT SYSRES_CONST_TASK_STATE_WORKING SYSRES_CONST_TASK_TITLE SYSRES_CONST_TASK_TYPES_GROUPS_REFERENCE_CODE SYSRES_CONST_TASK_TYPES_REFERENCE_CODE SYSRES_CONST_TEMPLATES_REFERENCE_CODE SYSRES_CONST_TEST_DATE_REQUISITE_NAME SYSRES_CONST_TEST_DEV_DATABASE_NAME SYSRES_CONST_TEST_DEV_SYSTEM_CODE SYSRES_CONST_TEST_EDMS_DATABASE_NAME SYSRES_CONST_TEST_EDMS_MAIN_CODE SYSRES_CONST_TEST_EDMS_MAIN_DB_NAME SYSRES_CONST_TEST_EDMS_SECOND_CODE SYSRES_CONST_TEST_EDMS_SECOND_DB_NAME SYSRES_CONST_TEST_EDMS_SYSTEM_CODE SYSRES_CONST_TEST_NUMERIC_REQUISITE_NAME SYSRES_CONST_TEXT_REQUISITE SYSRES_CONST_TEXT_REQUISITE_CODE SYSRES_CONST_TEXT_REQUISITE_TYPE SYSRES_CONST_TEXT_TYPE_CHAR SYSRES_CONST_TYPE_CODE_REQUISITE_CODE SYSRES_CONST_TYPE_REQUISITE_CODE SYSRES_CONST_UNDEFINED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_UNITS_SECTION_ID_REQUISITE_CODE SYSRES_CONST_UNITS_SECTION_REQUISITE_CODE SYSRES_CONST_UNOPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_NAME SYSRES_CONST_USE_ACCESS_TYPE_CODE SYSRES_CONST_USE_ACCESS_TYPE_NAME SYSRES_CONST_USER_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_USER_ADDITIONAL_INFORMATION_REQUISITE_CODE SYSRES_CONST_USER_AND_GROUP_ID_FROM_PSEUDOREFERENCE_REQUISITE_CODE SYSRES_CONST_USER_CATEGORY_NORMAL SYSRES_CONST_USER_CERTIFICATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_STATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_SUBJECT_NAME_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_THUMBPRINT_REQUISITE_CODE SYSRES_CONST_USER_COMMON_CATEGORY SYSRES_CONST_USER_COMMON_CATEGORY_CODE SYSRES_CONST_USER_FULL_NAME_REQUISITE_CODE SYSRES_CONST_USER_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_USER_LOGIN_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_SYSTEM_REQUISITE_CODE SYSRES_CONST_USER_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_USER_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_USER_SERVICE_CATEGORY SYSRES_CONST_USER_SERVICE_CATEGORY_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_NAME SYSRES_CONST_USER_STATUS_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_DEVELOPER_NAME SYSRES_CONST_USER_STATUS_DISABLED_CODE SYSRES_CONST_USER_STATUS_DISABLED_NAME SYSRES_CONST_USER_STATUS_SYSTEM_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_USER_CODE SYSRES_CONST_USER_STATUS_USER_NAME SYSRES_CONST_USER_STATUS_USER_NAME_DEPRECATED SYSRES_CONST_USER_TYPE_FIELD_VALUE_USER SYSRES_CONST_USER_TYPE_REQUISITE_CODE SYSRES_CONST_USERS_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USERS_IS_MAIN_SERVER_REQUISITE_CODE SYSRES_CONST_USERS_REFERENCE_CODE SYSRES_CONST_USERS_REGISTRATION_CERTIFICATES_ACTION_NAME SYSRES_CONST_USERS_REQUISITE_CODE SYSRES_CONST_USERS_SYSTEM_REQUISITE_CODE SYSRES_CONST_USERS_USER_ACCESS_RIGHTS_TYPR_REQUISITE_CODE SYSRES_CONST_USERS_USER_AUTHENTICATION_REQUISITE_CODE SYSRES_CONST_USERS_USER_COMPONENT_REQUISITE_CODE SYSRES_CONST_USERS_USER_GROUP_REQUISITE_CODE SYSRES_CONST_USERS_VIEW_CERTIFICATES_ACTION_NAME SYSRES_CONST_VIEW_DEFAULT_CODE SYSRES_CONST_VIEW_DEFAULT_NAME SYSRES_CONST_VIEWER_REQUISITE_CODE SYSRES_CONST_WAITING_BLOCK_DESCRIPTION SYSRES_CONST_WIZARD_FORM_LABEL_TEST_STRING  SYSRES_CONST_WIZARD_QUERY_PARAM_HEIGHT_ETALON_STRING SYSRES_CONST_WIZARD_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_WORK_RULES_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_WORK_TIME_CALENDAR_REFERENCE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORK_WORKFLOW_SOFT_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORKFLOW_ROUTE_TYPR_HARD SYSRES_CONST_WORKFLOW_ROUTE_TYPR_SOFT SYSRES_CONST_XML_ENCODING SYSRES_CONST_XREC_STAT_REQUISITE_CODE SYSRES_CONST_XRECID_FIELD_NAME SYSRES_CONST_YES SYSRES_CONST_YES_NO_2_REQUISITE_CODE SYSRES_CONST_YES_NO_REQUISITE_CODE SYSRES_CONST_YES_NO_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_YES_PICK_VALUE SYSRES_CONST_YES_VALUE CR FALSE nil NO_VALUE NULL TAB TRUE YES_VALUE ADMINISTRATORS_GROUP_NAME CUSTOMIZERS_GROUP_NAME DEVELOPERS_GROUP_NAME SERVICE_USERS_GROUP_NAME DECISION_BLOCK_FIRST_OPERAND_PROPERTY DECISION_BLOCK_NAME_PROPERTY DECISION_BLOCK_OPERATION_PROPERTY DECISION_BLOCK_RESULT_TYPE_PROPERTY DECISION_BLOCK_SECOND_OPERAND_PROPERTY ANY_FILE_EXTENTION COMPRESSED_DOCUMENT_EXTENSION EXTENDED_DOCUMENT_EXTENSION SHORT_COMPRESSED_DOCUMENT_EXTENSION SHORT_EXTENDED_DOCUMENT_EXTENSION JOB_BLOCK_ABORT_DEADLINE_PROPERTY JOB_BLOCK_AFTER_FINISH_EVENT JOB_BLOCK_AFTER_QUERY_PARAMETERS_EVENT JOB_BLOCK_ATTACHMENT_PROPERTY JOB_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY JOB_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY JOB_BLOCK_BEFORE_QUERY_PARAMETERS_EVENT JOB_BLOCK_BEFORE_START_EVENT JOB_BLOCK_CREATED_JOBS_PROPERTY JOB_BLOCK_DEADLINE_PROPERTY JOB_BLOCK_EXECUTION_RESULTS_PROPERTY JOB_BLOCK_IS_PARALLEL_PROPERTY JOB_BLOCK_IS_RELATIVE_ABORT_DEADLINE_PROPERTY JOB_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY JOB_BLOCK_JOB_TEXT_PROPERTY JOB_BLOCK_NAME_PROPERTY JOB_BLOCK_NEED_SIGN_ON_PERFORM_PROPERTY JOB_BLOCK_PERFORMER_PROPERTY JOB_BLOCK_RELATIVE_ABORT_DEADLINE_TYPE_PROPERTY JOB_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY JOB_BLOCK_SUBJECT_PROPERTY ENGLISH_LANGUAGE_CODE RUSSIAN_LANGUAGE_CODE smHidden smMaximized smMinimized smNormal wmNo wmYes COMPONENT_TOKEN_LINK_KIND DOCUMENT_LINK_KIND EDOCUMENT_LINK_KIND FOLDER_LINK_KIND JOB_LINK_KIND REFERENCE_LINK_KIND TASK_LINK_KIND COMPONENT_TOKEN_LOCK_TYPE EDOCUMENT_VERSION_LOCK_TYPE MONITOR_BLOCK_AFTER_FINISH_EVENT MONITOR_BLOCK_BEFORE_START_EVENT MONITOR_BLOCK_DEADLINE_PROPERTY MONITOR_BLOCK_INTERVAL_PROPERTY MONITOR_BLOCK_INTERVAL_TYPE_PROPERTY MONITOR_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY MONITOR_BLOCK_NAME_PROPERTY MONITOR_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY MONITOR_BLOCK_SEARCH_SCRIPT_PROPERTY NOTICE_BLOCK_AFTER_FINISH_EVENT NOTICE_BLOCK_ATTACHMENT_PROPERTY NOTICE_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY NOTICE_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY NOTICE_BLOCK_BEFORE_START_EVENT NOTICE_BLOCK_CREATED_NOTICES_PROPERTY NOTICE_BLOCK_DEADLINE_PROPERTY NOTICE_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY NOTICE_BLOCK_NAME_PROPERTY NOTICE_BLOCK_NOTICE_TEXT_PROPERTY NOTICE_BLOCK_PERFORMER_PROPERTY NOTICE_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY NOTICE_BLOCK_SUBJECT_PROPERTY dseAfterCancel dseAfterClose dseAfterDelete dseAfterDeleteOutOfTransaction dseAfterInsert dseAfterOpen dseAfterScroll dseAfterUpdate dseAfterUpdateOutOfTransaction dseBeforeCancel dseBeforeClose dseBeforeDelete dseBeforeDetailUpdate dseBeforeInsert dseBeforeOpen dseBeforeUpdate dseOnAnyRequisiteChange dseOnCloseRecord dseOnDeleteError dseOnOpenRecord dseOnPrepareUpdate dseOnUpdateError dseOnUpdateRatifiedRecord dseOnValidDelete dseOnValidUpdate reOnChange reOnChangeValues SELECTION_BEGIN_ROUTE_EVENT SELECTION_END_ROUTE_EVENT CURRENT_PERIOD_IS_REQUIRED PREVIOUS_CARD_TYPE_NAME SHOW_RECORD_PROPERTIES_FORM ACCESS_RIGHTS_SETTING_DIALOG_CODE ADMINISTRATOR_USER_CODE ANALYTIC_REPORT_TYPE asrtHideLocal asrtHideRemote CALCULATED_ROLE_TYPE_CODE COMPONENTS_REFERENCE_DEVELOPER_VIEW_CODE DCTS_TEST_PROTOCOLS_FOLDER_PATH E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED_BY_USER 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declare_constvalue declare_dimensions declare_fundamental_dimensions declare_fundamental_units declare_qty declare_translated declare_unit_conversion declare_units declare_weights decsym defcon define define_alt_display define_variable defint defmatch defrule defstruct deftaylor degree_sequence del delete deleten delta demo demoivre denom depends derivdegree derivlist describe desolve determinant dfloat dgauss_a dgauss_b dgeev dgemm dgeqrf dgesv dgesvd diag diagmatrix diag_matrix diagmatrixp diameter diff digitcharp dimacs_export dimacs_import dimension dimensionless dimensions dimensions_as_list direct directory discrete_freq disjoin disjointp disolate disp dispcon dispform dispfun dispJordan display disprule dispterms distrib divide divisors divsum dkummer_m dkummer_u dlange dodecahedron_graph dotproduct dotsimp dpart draw draw2d draw3d drawdf draw_file draw_graph dscalar echelon edge_coloring edge_connectivity edges eigens_by_jacobi eigenvalues eigenvectors eighth einstein eivals eivects elapsed_real_time elapsed_run_time ele2comp ele2polynome ele2pui elem elementp elevation_grid elim elim_allbut eliminate eliminate_using ellipse elliptic_e elliptic_ec elliptic_eu elliptic_f elliptic_kc elliptic_pi ematrix empty_graph emptyp endcons entermatrix entertensor entier equal equalp equiv_classes erf erfc erf_generalized erfi errcatch error errormsg errors euler ev eval_string evenp every evolution evolution2d evundiff example exp expand expandwrt expandwrt_factored expint expintegral_chi expintegral_ci expintegral_e expintegral_e1 expintegral_ei expintegral_e_simplify expintegral_li expintegral_shi expintegral_si explicit explose exponentialize express expt exsec extdiff extract_linear_equations extremal_subset ezgcd %f f90 facsum factcomb factor factorfacsum factorial factorout factorsum facts fast_central_elements fast_linsolve fasttimes featurep fernfale fft fib fibtophi fifth filename_merge file_search file_type fillarray findde find_root find_root_abs find_root_error find_root_rel first fix flatten flength float floatnump floor flower_snark flush flush1deriv flushd flushnd flush_output fmin_cobyla forget fortran fourcos fourexpand fourier fourier_elim fourint fourintcos fourintsin foursimp foursin fourth fposition frame_bracket freeof freshline fresnel_c fresnel_s from_adjacency_matrix frucht_graph full_listify fullmap fullmapl fullratsimp fullratsubst fullsetify funcsolve fundamental_dimensions fundamental_units fundef funmake funp fv g0 g1 gamma gamma_greek gamma_incomplete gamma_incomplete_generalized gamma_incomplete_regularized gauss gauss_a gauss_b gaussprob gcd gcdex gcdivide gcfac gcfactor gd generalized_lambert_w genfact gen_laguerre genmatrix gensym geo_amortization geo_annuity_fv geo_annuity_pv geomap geometric geometric_mean geosum get getcurrentdirectory get_edge_weight getenv get_lu_factors get_output_stream_string get_pixel get_plot_option get_tex_environment get_tex_environment_default get_vertex_label gfactor gfactorsum ggf girth global_variances gn gnuplot_close gnuplot_replot gnuplot_reset gnuplot_restart gnuplot_start go Gosper GosperSum gr2d gr3d gradef gramschmidt graph6_decode graph6_encode graph6_export graph6_import graph_center graph_charpoly graph_eigenvalues graph_flow graph_order graph_periphery graph_product graph_size graph_union great_rhombicosidodecahedron_graph great_rhombicuboctahedron_graph grid_graph grind grobner_basis grotzch_graph hamilton_cycle hamilton_path hankel hankel_1 hankel_2 harmonic harmonic_mean hav heawood_graph hermite hessian hgfred hilbertmap hilbert_matrix hipow histogram histogram_description hodge horner hypergeometric i0 i1 %ibes ic1 ic2 ic_convert ichr1 ichr2 icosahedron_graph icosidodecahedron_graph icurvature ident identfor identity idiff idim idummy ieqn %if ifactors iframes ifs igcdex igeodesic_coords ilt image imagpart imetric implicit implicit_derivative implicit_plot indexed_tensor indices induced_subgraph inferencep inference_result infix info_display init_atensor init_ctensor in_neighbors innerproduct inpart inprod inrt integerp integer_partitions integrate intersect intersection intervalp intopois intosum invariant1 invariant2 inverse_fft inverse_jacobi_cd inverse_jacobi_cn inverse_jacobi_cs inverse_jacobi_dc inverse_jacobi_dn inverse_jacobi_ds inverse_jacobi_nc inverse_jacobi_nd inverse_jacobi_ns inverse_jacobi_sc inverse_jacobi_sd inverse_jacobi_sn invert invert_by_adjoint invert_by_lu inv_mod irr is is_biconnected is_bipartite is_connected is_digraph is_edge_in_graph is_graph is_graph_or_digraph ishow is_isomorphic isolate isomorphism is_planar isqrt isreal_p is_sconnected is_tree is_vertex_in_graph items_inference %j j0 j1 jacobi jacobian jacobi_cd jacobi_cn jacobi_cs jacobi_dc jacobi_dn jacobi_ds jacobi_nc jacobi_nd jacobi_ns jacobi_p jacobi_sc jacobi_sd jacobi_sn JF jn join jordan julia julia_set julia_sin %k kdels kdelta kill killcontext kostka kron_delta kronecker_product kummer_m kummer_u kurtosis kurtosis_bernoulli kurtosis_beta kurtosis_binomial kurtosis_chi2 kurtosis_continuous_uniform kurtosis_discrete_uniform kurtosis_exp kurtosis_f kurtosis_gamma kurtosis_general_finite_discrete kurtosis_geometric kurtosis_gumbel kurtosis_hypergeometric kurtosis_laplace kurtosis_logistic kurtosis_lognormal kurtosis_negative_binomial kurtosis_noncentral_chi2 kurtosis_noncentral_student_t kurtosis_normal kurtosis_pareto kurtosis_poisson kurtosis_rayleigh kurtosis_student_t kurtosis_weibull label labels lagrange laguerre lambda lambert_w laplace laplacian_matrix last lbfgs lc2kdt lcharp lc_l lcm lc_u ldefint ldisp ldisplay legendre_p legendre_q leinstein length let letrules letsimp levi_civita lfreeof lgtreillis lhs li liediff limit Lindstedt linear linearinterpol linear_program linear_regression line_graph linsolve listarray list_correlations listify list_matrix_entries list_nc_monomials listoftens listofvars listp lmax lmin load loadfile local locate_matrix_entry log logcontract log_gamma lopow lorentz_gauge lowercasep lpart lratsubst lreduce lriemann lsquares_estimates lsquares_estimates_approximate lsquares_estimates_exact lsquares_mse lsquares_residual_mse lsquares_residuals lsum ltreillis lu_backsub lucas lu_factor %m macroexpand macroexpand1 make_array makebox makefact makegamma make_graph make_level_picture makelist makeOrders make_poly_continent make_poly_country make_polygon make_random_state make_rgb_picture makeset make_string_input_stream make_string_output_stream make_transform mandelbrot mandelbrot_set map mapatom maplist matchdeclare matchfix mat_cond mat_fullunblocker mat_function mathml_display mat_norm matrix matrixmap matrixp matrix_size mattrace mat_trace mat_unblocker max max_clique max_degree max_flow maximize_lp max_independent_set max_matching maybe md5sum mean mean_bernoulli mean_beta mean_binomial mean_chi2 mean_continuous_uniform mean_deviation mean_discrete_uniform mean_exp mean_f mean_gamma mean_general_finite_discrete mean_geometric mean_gumbel mean_hypergeometric mean_laplace mean_logistic mean_lognormal mean_negative_binomial mean_noncentral_chi2 mean_noncentral_student_t mean_normal mean_pareto mean_poisson mean_rayleigh mean_student_t mean_weibull median median_deviation member mesh metricexpandall mgf1_sha1 min min_degree min_edge_cut minfactorial minimalPoly minimize_lp minimum_spanning_tree minor minpack_lsquares minpack_solve min_vertex_cover min_vertex_cut mkdir mnewton mod mode_declare mode_identity ModeMatrix moebius mon2schur mono monomial_dimensions multibernstein_poly multi_display_for_texinfo multi_elem multinomial multinomial_coeff multi_orbit multiplot_mode multi_pui multsym multthru mycielski_graph nary natural_unit nc_degree ncexpt ncharpoly negative_picture neighbors new newcontext newdet new_graph newline newton new_variable next_prime nicedummies niceindices ninth nofix nonarray noncentral_moment nonmetricity nonnegintegerp nonscalarp nonzeroandfreeof notequal nounify nptetrad npv nroots nterms ntermst nthroot nullity nullspace num numbered_boundaries numberp number_to_octets num_distinct_partitions numerval numfactor num_partitions nusum nzeta nzetai nzetar octets_to_number octets_to_oid odd_girth oddp ode2 ode_check odelin oid_to_octets op opena opena_binary openr openr_binary openw openw_binary operatorp opsubst optimize %or orbit orbits ordergreat ordergreatp orderless orderlessp orthogonal_complement orthopoly_recur orthopoly_weight outermap out_neighbors outofpois pade parabolic_cylinder_d parametric parametric_surface parg parGosper parse_string parse_timedate part part2cont partfrac partition partition_set partpol path_digraph path_graph pathname_directory pathname_name pathname_type pdf_bernoulli pdf_beta pdf_binomial pdf_cauchy pdf_chi2 pdf_continuous_uniform pdf_discrete_uniform pdf_exp pdf_f pdf_gamma pdf_general_finite_discrete pdf_geometric pdf_gumbel pdf_hypergeometric pdf_laplace pdf_logistic pdf_lognormal pdf_negative_binomial pdf_noncentral_chi2 pdf_noncentral_student_t pdf_normal pdf_pareto pdf_poisson pdf_rank_sum pdf_rayleigh pdf_signed_rank pdf_student_t pdf_weibull pearson_skewness permanent permut permutation permutations petersen_graph petrov pickapart picture_equalp picturep piechart piechart_description planar_embedding playback plog plot2d plot3d plotdf ploteq plsquares pochhammer points poisdiff poisexpt poisint poismap poisplus poissimp poissubst poistimes poistrim polar polarform polartorect polar_to_xy poly_add poly_buchberger poly_buchberger_criterion poly_colon_ideal poly_content polydecomp poly_depends_p poly_elimination_ideal poly_exact_divide poly_expand poly_expt poly_gcd polygon poly_grobner poly_grobner_equal poly_grobner_member poly_grobner_subsetp poly_ideal_intersection poly_ideal_polysaturation poly_ideal_polysaturation1 poly_ideal_saturation poly_ideal_saturation1 poly_lcm poly_minimization polymod poly_multiply polynome2ele polynomialp poly_normal_form poly_normalize poly_normalize_list poly_polysaturation_extension poly_primitive_part poly_pseudo_divide poly_reduced_grobner poly_reduction poly_saturation_extension poly_s_polynomial poly_subtract polytocompanion pop postfix potential power_mod powerseries powerset prefix prev_prime primep primes principal_components print printf printfile print_graph printpois printprops prodrac product properties propvars psi psubst ptriangularize pui pui2comp pui2ele pui2polynome pui_direct puireduc push put pv qput qrange qty quad_control quad_qag quad_qagi quad_qagp quad_qags quad_qawc quad_qawf quad_qawo quad_qaws quadrilateral quantile quantile_bernoulli quantile_beta quantile_binomial quantile_cauchy quantile_chi2 quantile_continuous_uniform quantile_discrete_uniform quantile_exp quantile_f quantile_gamma quantile_general_finite_discrete quantile_geometric quantile_gumbel quantile_hypergeometric quantile_laplace quantile_logistic quantile_lognormal quantile_negative_binomial quantile_noncentral_chi2 quantile_noncentral_student_t quantile_normal quantile_pareto quantile_poisson quantile_rayleigh quantile_student_t quantile_weibull quartile_skewness quit qunit quotient racah_v racah_w radcan radius random random_bernoulli random_beta random_binomial random_bipartite_graph random_cauchy random_chi2 random_continuous_uniform random_digraph random_discrete_uniform random_exp random_f random_gamma random_general_finite_discrete random_geometric random_graph random_graph1 random_gumbel random_hypergeometric random_laplace random_logistic random_lognormal random_negative_binomial random_network random_noncentral_chi2 random_noncentral_student_t random_normal random_pareto random_permutation random_poisson random_rayleigh random_regular_graph random_student_t random_tournament random_tree random_weibull range rank rat ratcoef ratdenom ratdiff ratdisrep ratexpand ratinterpol rational rationalize ratnumer ratnump ratp ratsimp ratsubst ratvars ratweight read read_array read_binary_array read_binary_list read_binary_matrix readbyte readchar read_hashed_array readline read_list read_matrix read_nested_list readonly read_xpm real_imagpart_to_conjugate realpart realroots rearray rectangle rectform rectform_log_if_constant recttopolar rediff reduce_consts reduce_order region region_boundaries region_boundaries_plus rem remainder remarray rembox remcomps remcon remcoord remfun remfunction remlet remove remove_constvalue remove_dimensions remove_edge remove_fundamental_dimensions remove_fundamental_units remove_plot_option remove_vertex rempart remrule remsym remvalue rename rename_file reset reset_displays residue resolvante resolvante_alternee1 resolvante_bipartite resolvante_diedrale resolvante_klein resolvante_klein3 resolvante_produit_sym resolvante_unitaire resolvante_vierer rest resultant return reveal reverse revert revert2 rgb2level rhs ricci riemann rinvariant risch rk rmdir rncombine romberg room rootscontract round row rowop rowswap rreduce run_testsuite %s save 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fcmovb fcmovbe fcmove fcmovnb fcmovnbe fcmovne fcmovnu fcmovu fcom fcomi fcomip fcomp fcompp fcos fdecstp fdisi fdiv fdivp fdivr fdivrp femms feni ffree ffreep fiadd ficom ficomp fidiv fidivr fild fimul fincstp finit fist fistp fisttp fisub fisubr fld fld1 fldcw fldenv fldl2e fldl2t fldlg2 fldln2 fldpi fldz fmul fmulp fnclex fndisi fneni fninit fnop fnsave fnstcw fnstenv fnstsw fpatan fprem fprem1 fptan frndint frstor fsave fscale fsetpm fsin fsincos fsqrt fst fstcw fstenv fstp fstsw fsub fsubp fsubr fsubrp ftst fucom fucomi fucomip fucomp fucompp fxam fxch fxtract fyl2x fyl2xp1 hlt ibts icebp idiv imul in inc incbin insb insd insw int int01 int1 int03 int3 into invd invpcid invlpg invlpga iret iretd iretq iretw jcxz jecxz jrcxz jmp jmpe lahf lar lds lea leave les lfence lfs lgdt lgs lidt lldt lmsw loadall loadall286 lodsb lodsd lodsq lodsw loop loope loopne loopnz loopz lsl lss ltr mfence monitor mov movd movq movsb movsd movsq movsw movsx movsxd movzx mul mwait neg nop not or out 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sqrtss stmxcsr subps subss ucomiss unpckhps unpcklps xorps fxrstor fxrstor64 fxsave fxsave64 xgetbv xsetbv xsave xsave64 xsaveopt xsaveopt64 xrstor xrstor64 prefetchnta prefetcht0 prefetcht1 prefetcht2 maskmovq movntq pavgb pavgw pextrw pinsrw pmaxsw pmaxub pminsw pminub pmovmskb pmulhuw psadbw pshufw pf2iw pfnacc pfpnacc pi2fw pswapd maskmovdqu clflush movntdq movnti movntpd movdqa movdqu movdq2q movq2dq paddq pmuludq pshufd pshufhw pshuflw pslldq psrldq psubq punpckhqdq punpcklqdq addpd addsd andnpd andpd cmpeqpd cmpeqsd cmplepd cmplesd cmpltpd cmpltsd cmpneqpd cmpneqsd cmpnlepd cmpnlesd cmpnltpd cmpnltsd cmpordpd cmpordsd cmpunordpd cmpunordsd cmppd comisd cvtdq2pd cvtdq2ps cvtpd2dq cvtpd2pi cvtpd2ps cvtpi2pd cvtps2dq cvtps2pd cvtsd2si cvtsd2ss cvtsi2sd cvtss2sd cvttpd2pi cvttpd2dq cvttps2dq cvttsd2si divpd divsd maxpd maxsd minpd minsd movapd movhpd movlpd movmskpd movupd mulpd mulsd orpd shufpd sqrtpd sqrtsd subpd subsd ucomisd unpckhpd unpcklpd xorpd addsubpd addsubps haddpd 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vcmplt_oqps vcmple_oqps vcmpunord_sps vcmpneq_usps vcmpnlt_uqps vcmpnle_uqps vcmpord_sps vcmpeq_usps vcmpnge_uqps vcmpngt_uqps vcmpfalse_osps vcmpneq_osps vcmpge_oqps vcmpgt_oqps vcmptrue_usps vcmpps vcmpeq_ossd vcmpeqsd vcmplt_ossd vcmpltsd vcmple_ossd vcmplesd vcmpunord_qsd vcmpunordsd vcmpneq_uqsd vcmpneqsd vcmpnlt_ussd vcmpnltsd vcmpnle_ussd vcmpnlesd vcmpord_qsd vcmpordsd vcmpeq_uqsd vcmpnge_ussd vcmpngesd vcmpngt_ussd vcmpngtsd vcmpfalse_oqsd vcmpfalsesd vcmpneq_oqsd vcmpge_ossd vcmpgesd vcmpgt_ossd vcmpgtsd vcmptrue_uqsd vcmptruesd vcmplt_oqsd vcmple_oqsd vcmpunord_ssd vcmpneq_ussd vcmpnlt_uqsd vcmpnle_uqsd vcmpord_ssd vcmpeq_ussd vcmpnge_uqsd vcmpngt_uqsd vcmpfalse_ossd vcmpneq_ossd vcmpge_oqsd vcmpgt_oqsd vcmptrue_ussd vcmpsd vcmpeq_osss vcmpeqss vcmplt_osss vcmpltss vcmple_osss vcmpless vcmpunord_qss vcmpunordss vcmpneq_uqss vcmpneqss vcmpnlt_usss vcmpnltss vcmpnle_usss vcmpnless vcmpord_qss vcmpordss vcmpeq_uqss vcmpnge_usss vcmpngess vcmpngt_usss vcmpngtss vcmpfalse_oqss vcmpfalsess vcmpneq_oqss vcmpge_osss vcmpgess vcmpgt_osss vcmpgtss vcmptrue_uqss vcmptruess vcmplt_oqss vcmple_oqss vcmpunord_sss vcmpneq_usss vcmpnlt_uqss vcmpnle_uqss vcmpord_sss vcmpeq_usss vcmpnge_uqss vcmpngt_uqss vcmpfalse_osss vcmpneq_osss vcmpge_oqss vcmpgt_oqss vcmptrue_usss vcmpss vcomisd vcomiss vcvtdq2pd vcvtdq2ps vcvtpd2dq vcvtpd2ps vcvtps2dq vcvtps2pd vcvtsd2si vcvtsd2ss vcvtsi2sd vcvtsi2ss vcvtss2sd vcvtss2si vcvttpd2dq vcvttps2dq vcvttsd2si vcvttss2si vdivpd vdivps vdivsd vdivss vdppd vdpps vextractf128 vextractps vhaddpd vhaddps vhsubpd vhsubps vinsertf128 vinsertps vlddqu vldqqu vldmxcsr vmaskmovdqu vmaskmovps vmaskmovpd vmaxpd vmaxps vmaxsd vmaxss vminpd vminps vminsd vminss vmovapd vmovaps vmovd vmovq vmovddup vmovdqa vmovqqa vmovdqu vmovqqu vmovhlps vmovhpd vmovhps vmovlhps vmovlpd vmovlps vmovmskpd vmovmskps vmovntdq vmovntqq vmovntdqa vmovntpd vmovntps vmovsd vmovshdup vmovsldup vmovss vmovupd vmovups vmpsadbw vmulpd vmulps vmulsd vmulss vorpd vorps vpabsb vpabsw vpabsd vpacksswb vpackssdw vpackuswb vpackusdw vpaddb vpaddw vpaddd vpaddq vpaddsb vpaddsw vpaddusb vpaddusw vpalignr vpand vpandn vpavgb vpavgw vpblendvb vpblendw vpcmpestri vpcmpestrm vpcmpistri vpcmpistrm vpcmpeqb vpcmpeqw vpcmpeqd vpcmpeqq vpcmpgtb vpcmpgtw vpcmpgtd vpcmpgtq vpermilpd vpermilps vperm2f128 vpextrb vpextrw vpextrd vpextrq vphaddw vphaddd vphaddsw vphminposuw vphsubw vphsubd vphsubsw vpinsrb vpinsrw vpinsrd vpinsrq vpmaddwd vpmaddubsw vpmaxsb vpmaxsw vpmaxsd vpmaxub vpmaxuw vpmaxud vpminsb vpminsw vpminsd vpminub vpminuw vpminud vpmovmskb vpmovsxbw vpmovsxbd vpmovsxbq vpmovsxwd vpmovsxwq vpmovsxdq vpmovzxbw vpmovzxbd vpmovzxbq vpmovzxwd vpmovzxwq vpmovzxdq vpmulhuw vpmulhrsw vpmulhw vpmullw vpmulld vpmuludq vpmuldq vpor vpsadbw vpshufb vpshufd vpshufhw vpshuflw vpsignb vpsignw vpsignd vpslldq vpsrldq vpsllw vpslld vpsllq vpsraw vpsrad vpsrlw vpsrld vpsrlq vptest vpsubb vpsubw vpsubd vpsubq vpsubsb vpsubsw vpsubusb vpsubusw vpunpckhbw vpunpckhwd vpunpckhdq vpunpckhqdq vpunpcklbw vpunpcklwd vpunpckldq vpunpcklqdq vpxor vrcpps vrcpss vrsqrtps vrsqrtss vroundpd vroundps vroundsd vroundss vshufpd vshufps vsqrtpd vsqrtps vsqrtsd vsqrtss vstmxcsr vsubpd vsubps vsubsd vsubss vtestps vtestpd vucomisd vucomiss vunpckhpd vunpckhps vunpcklpd vunpcklps vxorpd vxorps vzeroall vzeroupper pclmullqlqdq pclmulhqlqdq pclmullqhqdq pclmulhqhqdq pclmulqdq vpclmullqlqdq vpclmulhqlqdq vpclmullqhqdq vpclmulhqhqdq vpclmulqdq vfmadd132ps vfmadd132pd vfmadd312ps vfmadd312pd vfmadd213ps vfmadd213pd vfmadd123ps vfmadd123pd vfmadd231ps vfmadd231pd vfmadd321ps vfmadd321pd vfmaddsub132ps vfmaddsub132pd vfmaddsub312ps vfmaddsub312pd vfmaddsub213ps vfmaddsub213pd vfmaddsub123ps vfmaddsub123pd vfmaddsub231ps vfmaddsub231pd vfmaddsub321ps vfmaddsub321pd vfmsub132ps vfmsub132pd vfmsub312ps vfmsub312pd vfmsub213ps vfmsub213pd vfmsub123ps vfmsub123pd vfmsub231ps vfmsub231pd vfmsub321ps vfmsub321pd vfmsubadd132ps vfmsubadd132pd vfmsubadd312ps vfmsubadd312pd vfmsubadd213ps vfmsubadd213pd vfmsubadd123ps vfmsubadd123pd vfmsubadd231ps vfmsubadd231pd vfmsubadd321ps vfmsubadd321pd vfnmadd132ps vfnmadd132pd vfnmadd312ps vfnmadd312pd vfnmadd213ps vfnmadd213pd vfnmadd123ps vfnmadd123pd vfnmadd231ps vfnmadd231pd vfnmadd321ps vfnmadd321pd vfnmsub132ps vfnmsub132pd vfnmsub312ps vfnmsub312pd vfnmsub213ps vfnmsub213pd vfnmsub123ps vfnmsub123pd vfnmsub231ps vfnmsub231pd vfnmsub321ps vfnmsub321pd vfmadd132ss vfmadd132sd vfmadd312ss vfmadd312sd vfmadd213ss vfmadd213sd vfmadd123ss vfmadd123sd vfmadd231ss vfmadd231sd vfmadd321ss vfmadd321sd vfmsub132ss vfmsub132sd vfmsub312ss vfmsub312sd vfmsub213ss vfmsub213sd vfmsub123ss vfmsub123sd vfmsub231ss vfmsub231sd vfmsub321ss vfmsub321sd vfnmadd132ss vfnmadd132sd vfnmadd312ss vfnmadd312sd vfnmadd213ss vfnmadd213sd vfnmadd123ss vfnmadd123sd vfnmadd231ss vfnmadd231sd vfnmadd321ss vfnmadd321sd vfnmsub132ss vfnmsub132sd vfnmsub312ss vfnmsub312sd vfnmsub213ss vfnmsub213sd vfnmsub123ss vfnmsub123sd vfnmsub231ss vfnmsub231sd vfnmsub321ss vfnmsub321sd rdfsbase rdgsbase rdrand wrfsbase wrgsbase vcvtph2ps vcvtps2ph adcx adox rdseed clac stac xstore xcryptecb xcryptcbc xcryptctr xcryptcfb xcryptofb montmul xsha1 xsha256 llwpcb slwpcb lwpval lwpins vfmaddpd vfmaddps vfmaddsd vfmaddss vfmaddsubpd vfmaddsubps vfmsubaddpd vfmsubaddps vfmsubpd vfmsubps vfmsubsd vfmsubss vfnmaddpd vfnmaddps vfnmaddsd vfnmaddss vfnmsubpd vfnmsubps vfnmsubsd vfnmsubss vfrczpd vfrczps vfrczsd vfrczss vpcmov vpcomb vpcomd vpcomq vpcomub vpcomud vpcomuq vpcomuw vpcomw vphaddbd vphaddbq vphaddbw vphadddq vphaddubd vphaddubq vphaddubw vphaddudq vphadduwd vphadduwq vphaddwd vphaddwq vphsubbw vphsubdq vphsubwd vpmacsdd vpmacsdqh vpmacsdql vpmacssdd vpmacssdqh vpmacssdql vpmacsswd vpmacssww vpmacswd vpmacsww vpmadcsswd vpmadcswd vpperm vprotb vprotd vprotq vprotw vpshab vpshad vpshaq vpshaw vpshlb vpshld vpshlq vpshlw vbroadcasti128 vpblendd vpbroadcastb vpbroadcastw vpbroadcastd vpbroadcastq vpermd vpermpd vpermps vpermq vperm2i128 vextracti128 vinserti128 vpmaskmovd vpmaskmovq vpsllvd vpsllvq vpsravd vpsrlvd vpsrlvq vgatherdpd vgatherqpd vgatherdps vgatherqps vpgatherdd vpgatherqd vpgatherdq vpgatherqq xabort xbegin xend xtest andn bextr blci blcic blsi blsic blcfill blsfill blcmsk blsmsk blsr blcs bzhi mulx pdep pext rorx sarx shlx shrx tzcnt tzmsk t1mskc valignd valignq vblendmpd vblendmps vbroadcastf32x4 vbroadcastf64x4 vbroadcasti32x4 vbroadcasti64x4 vcompresspd vcompressps vcvtpd2udq vcvtps2udq vcvtsd2usi vcvtss2usi vcvttpd2udq vcvttps2udq vcvttsd2usi vcvttss2usi vcvtudq2pd vcvtudq2ps vcvtusi2sd vcvtusi2ss vexpandpd vexpandps vextractf32x4 vextractf64x4 vextracti32x4 vextracti64x4 vfixupimmpd vfixupimmps vfixupimmsd vfixupimmss vgetexppd vgetexpps vgetexpsd vgetexpss vgetmantpd vgetmantps vgetmantsd vgetmantss vinsertf32x4 vinsertf64x4 vinserti32x4 vinserti64x4 vmovdqa32 vmovdqa64 vmovdqu32 vmovdqu64 vpabsq vpandd vpandnd vpandnq vpandq vpblendmd vpblendmq vpcmpltd vpcmpled vpcmpneqd vpcmpnltd vpcmpnled vpcmpd vpcmpltq 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\xA0\u1680\u2000-\u200A\u2028\u2029\u202F\u205F\u3000]/,k=new Uint16Array('ᵁ<Õıʊҝջאٵ۞ޢߖࠏ੊ઑඡ๭༉༦჊ረዡᐕᒝᓃᓟᔥ\0\0\0\0\0\0ᕫᛍᦍᰒᷝ὾⁠↰⊍⏀⏻⑂⠤⤒ⴈ⹈⿎〖㊺㘹㞬㣾㨨㩱㫠㬮ࠀEMabcfglmnoprstu\\bfms„‹•˜¦³¹ÈÏlig耻Æ䃆P耻&䀦cute耻Á䃁reve;䄂Āiyx}rc耻Â䃂;䐐r;쀀𝔄rave耻À䃀pha;䎑acr;䄀d;橓Āgp¡on;䄄f;쀀𝔸plyFunction;恡ing耻Å䃅Ācs¾Ãr;쀀𝒜ign;扔ilde耻Ã䃃ml耻Ä䃄ЀaceforsuåûþėĜĢħĪĀcrêòkslash;或Ŷöø;櫧ed;挆y;䐑ƀcrtąċĔause;戵noullis;愬a;䎒r;쀀𝔅pf;쀀𝔹eve;䋘còēmpeq;扎܀HOacdefhilorsuōőŖƀƞƢƵƷƺǜȕɳɸɾcy;䐧PY耻©䂩ƀcpyŝŢźute;䄆Ā;iŧŨ拒talDifferentialD;慅leys;愭ȀaeioƉƎƔƘron;䄌dil耻Ç䃇rc;䄈nint;戰ot;䄊ĀdnƧƭilla;䂸terDot;䂷òſi;䎧rcleȀDMPTLJNjǑǖot;抙inus;抖lus;投imes;抗oĀcsǢǸkwiseContourIntegral;戲eCurlyĀDQȃȏoubleQuote;思uote;怙ȀlnpuȞȨɇɕonĀ;eȥȦ户;橴ƀgitȯȶȺruent;扡nt;戯ourIntegral;戮ĀfrɌɎ;愂oduct;成nterClockwiseContourIntegral;戳oss;樯cr;쀀𝒞pĀ;Cʄʅ拓ap;才րDJSZacefiosʠʬʰʴʸˋ˗ˡ˦̳ҍĀ;oŹʥtrahd;椑cy;䐂cy;䐅cy;䐏ƀgrsʿ˄ˇger;怡r;憡hv;櫤Āayː˕ron;䄎;䐔lĀ;t˝˞戇a;䎔r;쀀𝔇Āaf˫̧Ācm˰̢riticalȀADGT̖̜̀̆cute;䂴oŴ̋̍;䋙bleAcute;䋝rave;䁠ilde;䋜ond;拄ferentialD;慆Ѱ̽\0\0\0͔͂\0Ѕf;쀀𝔻ƀ;DE͈͉͍䂨ot;惜qual;扐blèCDLRUVͣͲ΂ϏϢϸontourIntegraìȹoɴ͹\0\0ͻ»͉nArrow;懓Āeo·ΤftƀARTΐΖΡrrow;懐ightArrow;懔eåˊngĀLRΫτeftĀARγιrrow;柸ightArrow;柺ightArrow;柹ightĀATϘϞrrow;懒ee;抨pɁϩ\0\0ϯrrow;懑ownArrow;懕erticalBar;戥ǹABLRTaВЪаўѿͼrrowƀ;BUНОТ憓ar;椓pArrow;懵reve;䌑eft˒к\0ц\0ѐightVector;楐eeVector;楞ectorĀ;Bљњ憽ar;楖ightǔѧ\0ѱeeVector;楟ectorĀ;BѺѻ懁ar;楗eeĀ;A҆҇护rrow;憧ĀctҒҗr;쀀𝒟rok;䄐ࠀNTacdfglmopqstuxҽӀӄӋӞӢӧӮӵԡԯԶՒ՝ՠեG;䅊H耻Ð䃐cute耻É䃉ƀaiyӒӗӜron;䄚rc耻Ê䃊;䐭ot;䄖r;쀀𝔈rave耻È䃈ement;戈ĀapӺӾcr;䄒tyɓԆ\0\0ԒmallSquare;旻erySmallSquare;斫ĀgpԦԪon;䄘f;쀀𝔼silon;䎕uĀaiԼՉlĀ;TՂՃ橵ilde;扂librium;懌Āci՗՚r;愰m;橳a;䎗ml耻Ë䃋Āipժկsts;戃onentialE;慇ʀcfiosօֈ֍ֲ׌y;䐤r;쀀𝔉lledɓ֗\0\0֣mallSquare;旼erySmallSquare;斪Ͱֺ\0ֿ\0\0ׄf;쀀𝔽All;戀riertrf;愱cò׋؀JTabcdfgorstר׬ׯ׺؀ؒؖ؛؝أ٬ٲcy;䐃耻>䀾mmaĀ;d׷׸䎓;䏜reve;䄞ƀeiy؇،ؐdil;䄢rc;䄜;䐓ot;䄠r;쀀𝔊;拙pf;쀀𝔾eater̀EFGLSTصلَٖٛ٦qualĀ;Lؾؿ扥ess;招ullEqual;执reater;檢ess;扷lantEqual;橾ilde;扳cr;쀀𝒢;扫ЀAacfiosuڅڋږڛڞڪھۊRDcy;䐪Āctڐڔek;䋇;䁞irc;䄤r;愌lbertSpace;愋ǰگ\0ڲf;愍izontalLine;攀Āctۃۅòکrok;䄦mpńېۘownHumðįqual;扏܀EJOacdfgmnostuۺ۾܃܇܎ܚܞܡܨ݄ݸދޏޕcy;䐕lig;䄲cy;䐁cute耻Í䃍Āiyܓܘrc耻Î䃎;䐘ot;䄰r;愑rave耻Ì䃌ƀ;apܠܯܿĀcgܴܷr;䄪inaryI;慈lieóϝǴ݉\0ݢĀ;eݍݎ戬Āgrݓݘral;戫section;拂isibleĀCTݬݲomma;恣imes;恢ƀgptݿރވon;䄮f;쀀𝕀a;䎙cr;愐ilde;䄨ǫޚ\0ޞcy;䐆l耻Ï䃏ʀcfosuެ޷޼߂ߐĀiyޱ޵rc;䄴;䐙r;쀀𝔍pf;쀀𝕁ǣ߇\0ߌr;쀀𝒥rcy;䐈kcy;䐄΀HJacfosߤߨ߽߬߱ࠂࠈcy;䐥cy;䐌ppa;䎚Āey߶߻dil;䄶;䐚r;쀀𝔎pf;쀀𝕂cr;쀀𝒦րJTaceflmostࠥࠩࠬࡐࡣ঳সে্਷ੇcy;䐉耻<䀼ʀcmnpr࠷࠼ࡁࡄࡍute;䄹bda;䎛g;柪lacetrf;愒r;憞ƀaeyࡗ࡜ࡡron;䄽dil;䄻;䐛Āfsࡨ॰tԀACDFRTUVarࡾࢩࢱࣦ࣠ࣼयज़ΐ४Ānrࢃ࢏gleBracket;柨rowƀ;BR࢙࢚࢞憐ar;懤ightArrow;懆eiling;挈oǵࢷ\0ࣃbleBracket;柦nǔࣈ\0࣒eeVector;楡ectorĀ;Bࣛࣜ懃ar;楙loor;挊ightĀAV࣯ࣵrrow;憔ector;楎Āerँगeƀ;AVउऊऐ抣rrow;憤ector;楚iangleƀ;BEतथऩ抲ar;槏qual;抴pƀDTVषूौownVector;楑eeVector;楠ectorĀ;Bॖॗ憿ar;楘ectorĀ;B॥०憼ar;楒ightáΜs̀EFGLSTॾঋকঝঢভqualGreater;拚ullEqual;扦reater;扶ess;檡lantEqual;橽ilde;扲r;쀀𝔏Ā;eঽা拘ftarrow;懚idot;䄿ƀnpw৔ਖਛgȀLRlr৞৷ਂਐeftĀAR০৬rrow;柵ightArrow;柷ightArrow;柶eftĀarγਊightáοightáϊf;쀀𝕃erĀLRਢਬeftArrow;憙ightArrow;憘ƀchtਾੀੂòࡌ;憰rok;䅁;扪Ѐacefiosuਗ਼੝੠੷੼અઋ઎p;椅y;䐜Ādl੥੯iumSpace;恟lintrf;愳r;쀀𝔐nusPlus;戓pf;쀀𝕄cò੶;䎜ҀJacefostuણધભીଔଙඑ඗ඞcy;䐊cute;䅃ƀaey઴હાron;䅇dil;䅅;䐝ƀgswે૰଎ativeƀMTV૓૟૨ediumSpace;怋hiĀcn૦૘ë૙eryThiî૙tedĀGL૸ଆreaterGreateòٳessLesóੈLine;䀊r;쀀𝔑ȀBnptଢନଷ଺reak;恠BreakingSpace;䂠f;愕ڀ;CDEGHLNPRSTV୕ୖ୪୼஡௫ఄ౞಄ದ೘ൡඅ櫬Āou୛୤ngruent;扢pCap;扭oubleVerticalBar;戦ƀlqxஃஊ஛ement;戉ualĀ;Tஒஓ扠ilde;쀀≂̸ists;戄reater΀;EFGLSTஶஷ஽௉௓௘௥扯qual;扱ullEqual;쀀≧̸reater;쀀≫̸ess;批lantEqual;쀀⩾̸ilde;扵umpń௲௽ownHump;쀀≎̸qual;쀀≏̸eĀfsఊధtTriangleƀ;BEచఛడ拪ar;쀀⧏̸qual;括s̀;EGLSTవశ఼ౄోౘ扮qual;扰reater;扸ess;쀀≪̸lantEqual;쀀⩽̸ilde;扴estedĀGL౨౹reaterGreater;쀀⪢̸essLess;쀀⪡̸recedesƀ;ESಒಓಛ技qual;쀀⪯̸lantEqual;拠ĀeiಫಹverseElement;戌ghtTriangleƀ;BEೋೌ೒拫ar;쀀⧐̸qual;拭ĀquೝഌuareSuĀbp೨೹setĀ;E೰ೳ쀀⊏̸qual;拢ersetĀ;Eഃആ쀀⊐̸qual;拣ƀbcpഓതൎsetĀ;Eഛഞ쀀⊂⃒qual;抈ceedsȀ;ESTലള഻െ抁qual;쀀⪰̸lantEqual;拡ilde;쀀≿̸ersetĀ;E൘൛쀀⊃⃒qual;抉ildeȀ;EFT൮൯൵ൿ扁qual;扄ullEqual;扇ilde;扉erticalBar;戤cr;쀀𝒩ilde耻Ñ䃑;䎝܀Eacdfgmoprstuvලෂ෉෕ෛ෠෧෼ขภยา฿ไlig;䅒cute耻Ó䃓Āiy෎ීrc耻Ô䃔;䐞blac;䅐r;쀀𝔒rave耻Ò䃒ƀaei෮ෲ෶cr;䅌ga;䎩cron;䎟pf;쀀𝕆enCurlyĀDQฎบoubleQuote;怜uote;怘;橔Āclวฬr;쀀𝒪ash耻Ø䃘iŬื฼de耻Õ䃕es;樷ml耻Ö䃖erĀBP๋๠Āar๐๓r;怾acĀek๚๜;揞et;掴arenthesis;揜Ҁacfhilors๿ງຊຏຒດຝະ໼rtialD;戂y;䐟r;쀀𝔓i;䎦;䎠usMinus;䂱Āipຢອncareplanåڝf;愙Ȁ;eio຺ູ໠໤檻cedesȀ;EST່້໏໚扺qual;檯lantEqual;扼ilde;找me;怳Ādp໩໮uct;戏ortionĀ;aȥ໹l;戝Āci༁༆r;쀀𝒫;䎨ȀUfos༑༖༛༟OT耻"䀢r;쀀𝔔pf;愚cr;쀀𝒬؀BEacefhiorsu༾གྷཇའཱིྦྷྪྭ႖ႩႴႾarr;椐G耻®䂮ƀcnrཎནབute;䅔g;柫rĀ;tཛྷཝ憠l;椖ƀaeyཧཬཱron;䅘dil;䅖;䐠Ā;vླྀཹ愜erseĀEUྂྙĀlq྇ྎement;戋uilibrium;懋pEquilibrium;楯r»ཹo;䎡ghtЀACDFTUVa࿁࿫࿳ဢဨၛႇϘĀnr࿆࿒gleBracket;柩rowƀ;BL࿜࿝࿡憒ar;懥eftArrow;懄eiling;按oǵ࿹\0စbleBracket;柧nǔည\0နeeVector;楝ectorĀ;Bဝသ懂ar;楕loor;挋Āerိ၃eƀ;AVဵံြ抢rrow;憦ector;楛iangleƀ;BEၐၑၕ抳ar;槐qual;抵pƀDTVၣၮၸownVector;楏eeVector;楜ectorĀ;Bႂႃ憾ar;楔ectorĀ;B႑႒懀ar;楓Āpuႛ႞f;愝ndImplies;楰ightarrow;懛ĀchႹႼr;愛;憱leDelayed;槴ڀHOacfhimoqstuფჱჷჽᄙᄞᅑᅖᅡᅧᆵᆻᆿĀCcჩხHcy;䐩y;䐨FTcy;䐬cute;䅚ʀ;aeiyᄈᄉᄎᄓᄗ檼ron;䅠dil;䅞rc;䅜;䐡r;쀀𝔖ortȀDLRUᄪᄴᄾᅉownArrow»ОeftArrow»࢚ightArrow»࿝pArrow;憑gma;䎣allCircle;战pf;쀀𝕊ɲᅭ\0\0ᅰt;戚areȀ;ISUᅻᅼᆉᆯ斡ntersection;抓uĀbpᆏᆞsetĀ;Eᆗᆘ抏qual;抑ersetĀ;Eᆨᆩ抐qual;抒nion;抔cr;쀀𝒮ar;拆ȀbcmpᇈᇛሉላĀ;sᇍᇎ拐etĀ;Eᇍᇕqual;抆ĀchᇠህeedsȀ;ESTᇭᇮᇴᇿ扻qual;檰lantEqual;扽ilde;承Tháྌ;我ƀ;esሒሓሣ拑rsetĀ;Eሜም抃qual;抇et»ሓրHRSacfhiorsሾቄ቉ቕ቞ቱቶኟዂወዑORN耻Þ䃞ADE;愢ĀHc቎ቒcy;䐋y;䐦Ābuቚቜ;䀉;䎤ƀaeyብቪቯron;䅤dil;䅢;䐢r;쀀𝔗Āeiቻ኉Dzኀ\0ኇefore;戴a;䎘Ācn኎ኘkSpace;쀀  Space;怉ldeȀ;EFTካኬኲኼ戼qual;扃ullEqual;扅ilde;扈pf;쀀𝕋ipleDot;惛Āctዖዛr;쀀𝒯rok;䅦ૡዷጎጚጦ\0ጬጱ\0\0\0\0\0ጸጽ፷ᎅ\0᏿ᐄᐊᐐĀcrዻጁute耻Ú䃚rĀ;oጇገ憟cir;楉rǣጓ\0጖y;䐎ve;䅬Āiyጞጣrc耻Û䃛;䐣blac;䅰r;쀀𝔘rave耻Ù䃙acr;䅪Ādiፁ፩erĀBPፈ፝Āarፍፐr;䁟acĀekፗፙ;揟et;掵arenthesis;揝onĀ;P፰፱拃lus;抎Āgp፻፿on;䅲f;쀀𝕌ЀADETadps᎕ᎮᎸᏄϨᏒᏗᏳrrowƀ;BDᅐᎠᎤar;椒ownArrow;懅ownArrow;憕quilibrium;楮eeĀ;AᏋᏌ报rrow;憥ownáϳerĀLRᏞᏨeftArrow;憖ightArrow;憗iĀ;lᏹᏺ䏒on;䎥ing;䅮cr;쀀𝒰ilde;䅨ml耻Ü䃜ҀDbcdefosvᐧᐬᐰᐳᐾᒅᒊᒐᒖash;披ar;櫫y;䐒ashĀ;lᐻᐼ抩;櫦Āerᑃᑅ;拁ƀbtyᑌᑐᑺar;怖Ā;iᑏᑕcalȀBLSTᑡᑥᑪᑴar;戣ine;䁼eparator;杘ilde;所ThinSpace;怊r;쀀𝔙pf;쀀𝕍cr;쀀𝒱dash;抪ʀcefosᒧᒬᒱᒶᒼirc;䅴dge;拀r;쀀𝔚pf;쀀𝕎cr;쀀𝒲Ȁfiosᓋᓐᓒᓘr;쀀𝔛;䎞pf;쀀𝕏cr;쀀𝒳ҀAIUacfosuᓱᓵᓹᓽᔄᔏᔔᔚᔠcy;䐯cy;䐇cy;䐮cute耻Ý䃝Āiyᔉᔍrc;䅶;䐫r;쀀𝔜pf;쀀𝕐cr;쀀𝒴ml;䅸ЀHacdefosᔵᔹᔿᕋᕏᕝᕠᕤcy;䐖cute;䅹Āayᕄᕉron;䅽;䐗ot;䅻Dzᕔ\0ᕛoWidtè૙a;䎖r;愨pf;愤cr;쀀𝒵௡ᖃᖊᖐ\0ᖰᖶᖿ\0\0\0\0ᗆᗛᗫᙟ᙭\0ᚕ᚛ᚲᚹ\0ᚾcute耻á䃡reve;䄃̀;Ediuyᖜᖝᖡᖣᖨᖭ戾;쀀∾̳;房rc耻â䃢te肻´̆;䐰lig耻æ䃦Ā;r²ᖺ;쀀𝔞rave耻à䃠ĀepᗊᗖĀfpᗏᗔsym;愵èᗓha;䎱ĀapᗟcĀclᗤᗧr;䄁g;樿ɤᗰ\0\0ᘊʀ;adsvᗺᗻᗿᘁᘇ戧nd;橕;橜lope;橘;橚΀;elmrszᘘᘙᘛᘞᘿᙏᙙ戠;榤e»ᘙsdĀ;aᘥᘦ戡ѡᘰᘲᘴᘶᘸᘺᘼᘾ;榨;榩;榪;榫;榬;榭;榮;榯tĀ;vᙅᙆ戟bĀ;dᙌᙍ抾;榝Āptᙔᙗh;戢»¹arr;捼Āgpᙣᙧon;䄅f;쀀𝕒΀;Eaeiop዁ᙻᙽᚂᚄᚇᚊ;橰cir;橯;扊d;手s;䀧roxĀ;e዁ᚒñᚃing耻å䃥ƀctyᚡᚦᚨr;쀀𝒶;䀪mpĀ;e዁ᚯñʈilde耻ã䃣ml耻ä䃤Āciᛂᛈoninôɲnt;樑ࠀNabcdefiklnoprsu᛭ᛱᜰ᜼ᝃᝈ᝸᝽០៦ᠹᡐᜍ᤽᥈ᥰot;櫭Ācrᛶ᜞kȀcepsᜀᜅᜍᜓong;扌psilon;䏶rime;怵imĀ;e᜚᜛戽q;拍Ŷᜢᜦee;抽edĀ;gᜬᜭ挅e»ᜭrkĀ;t፜᜷brk;掶Āoyᜁᝁ;䐱quo;怞ʀcmprtᝓ᝛ᝡᝤᝨausĀ;eĊĉptyv;榰séᜌnoõēƀahwᝯ᝱ᝳ;䎲;愶een;扬r;쀀𝔟g΀costuvwឍឝឳេ៕៛៞ƀaiuបពរðݠrc;旯p»፱ƀdptឤឨឭot;樀lus;樁imes;樂ɱឹ\0\0ើcup;樆ar;昅riangleĀdu៍្own;施p;斳plus;樄eåᑄåᒭarow;植ƀako៭ᠦᠵĀcn៲ᠣkƀlst៺֫᠂ozenge;槫riangleȀ;dlr᠒᠓᠘᠝斴own;斾eft;旂ight;斸k;搣Ʊᠫ\0ᠳƲᠯ\0ᠱ;斒;斑4;斓ck;斈ĀeoᠾᡍĀ;qᡃᡆ쀀=⃥uiv;쀀≡⃥t;挐Ȁptwxᡙᡞᡧᡬf;쀀𝕓Ā;tᏋᡣom»Ꮜtie;拈؀DHUVbdhmptuvᢅᢖᢪᢻᣗᣛᣬ᣿ᤅᤊᤐᤡȀLRlrᢎᢐᢒᢔ;敗;敔;敖;敓ʀ;DUduᢡᢢᢤᢦᢨ敐;敦;敩;敤;敧ȀLRlrᢳᢵᢷᢹ;敝;敚;敜;教΀;HLRhlrᣊᣋᣍᣏᣑᣓᣕ救;敬;散;敠;敫;敢;敟ox;槉ȀLRlrᣤᣦᣨᣪ;敕;敒;攐;攌ʀ;DUduڽ᣷᣹᣻᣽;敥;敨;攬;攴inus;抟lus;択imes;抠ȀLRlrᤙᤛᤝ᤟;敛;敘;攘;攔΀;HLRhlrᤰᤱᤳᤵᤷ᤻᤹攂;敪;敡;敞;攼;攤;攜Āevģ᥂bar耻¦䂦Ȁceioᥑᥖᥚᥠr;쀀𝒷mi;恏mĀ;e᜚᜜lƀ;bhᥨᥩᥫ䁜;槅sub;柈Ŭᥴ᥾lĀ;e᥹᥺怢t»᥺pƀ;Eeįᦅᦇ;檮Ā;qۜۛೡᦧ\0᧨ᨑᨕᨲ\0ᨷᩐ\0\0᪴\0\0᫁\0\0ᬡᬮ᭍᭒\0᯽\0ᰌƀcpr᦭ᦲ᧝ute;䄇̀;abcdsᦿᧀᧄ᧊᧕᧙戩nd;橄rcup;橉Āau᧏᧒p;橋p;橇ot;橀;쀀∩︀Āeo᧢᧥t;恁îړȀaeiu᧰᧻ᨁᨅǰ᧵\0᧸s;橍on;䄍dil耻ç䃧rc;䄉psĀ;sᨌᨍ橌m;橐ot;䄋ƀdmnᨛᨠᨦil肻¸ƭptyv;榲t脀¢;eᨭᨮ䂢räƲr;쀀𝔠ƀceiᨽᩀᩍy;䑇ckĀ;mᩇᩈ朓ark»ᩈ;䏇r΀;Ecefms᩟᩠ᩢᩫ᪤᪪᪮旋;槃ƀ;elᩩᩪᩭ䋆q;扗eɡᩴ\0\0᪈rrowĀlr᩼᪁eft;憺ight;憻ʀRSacd᪒᪔᪖᪚᪟»ཇ;擈st;抛irc;抚ash;抝nint;樐id;櫯cir;槂ubsĀ;u᪻᪼晣it»᪼ˬ᫇᫔᫺\0ᬊonĀ;eᫍᫎ䀺Ā;qÇÆɭ᫙\0\0᫢aĀ;t᫞᫟䀬;䁀ƀ;fl᫨᫩᫫戁îᅠeĀmx᫱᫶ent»᫩eóɍǧ᫾\0ᬇĀ;dኻᬂot;橭nôɆƀfryᬐᬔᬗ;쀀𝕔oäɔ脀©;sŕᬝr;愗Āaoᬥᬩrr;憵ss;朗Ācuᬲᬷr;쀀𝒸Ābpᬼ᭄Ā;eᭁᭂ櫏;櫑Ā;eᭉᭊ櫐;櫒dot;拯΀delprvw᭠᭬᭷ᮂᮬᯔ᯹arrĀlr᭨᭪;椸;椵ɰ᭲\0\0᭵r;拞c;拟arrĀ;p᭿ᮀ憶;椽̀;bcdosᮏᮐᮖᮡᮥᮨ截rcap;橈Āauᮛᮞp;橆p;橊ot;抍r;橅;쀀∪︀Ȁalrv᮵ᮿᯞᯣrrĀ;mᮼᮽ憷;椼yƀevwᯇᯔᯘqɰᯎ\0\0ᯒreã᭳uã᭵ee;拎edge;拏en耻¤䂤earrowĀlrᯮ᯳eft»ᮀight»ᮽeäᯝĀciᰁᰇoninôǷnt;戱lcty;挭ঀAHabcdefhijlorstuwz᰸᰻᰿ᱝᱩᱵᲊᲞᲬᲷ᳻᳿ᴍᵻᶑᶫᶻ᷆᷍rò΁ar;楥Ȁglrs᱈ᱍ᱒᱔ger;怠eth;愸òᄳhĀ;vᱚᱛ怐»ऊūᱡᱧarow;椏aã̕Āayᱮᱳron;䄏;䐴ƀ;ao̲ᱼᲄĀgrʿᲁr;懊tseq;橷ƀglmᲑᲔᲘ耻°䂰ta;䎴ptyv;榱ĀirᲣᲨsht;楿;쀀𝔡arĀlrᲳᲵ»ࣜ»သʀaegsv᳂͸᳖᳜᳠mƀ;oș᳊᳔ndĀ;ș᳑uit;晦amma;䏝in;拲ƀ;io᳧᳨᳸䃷de脀÷;o᳧ᳰntimes;拇nø᳷cy;䑒cɯᴆ\0\0ᴊrn;挞op;挍ʀlptuwᴘᴝᴢᵉᵕlar;䀤f;쀀𝕕ʀ;emps̋ᴭᴷᴽᵂqĀ;d͒ᴳot;扑inus;戸lus;戔quare;抡blebarwedgåúnƀadhᄮᵝᵧownarrowóᲃarpoonĀlrᵲᵶefôᲴighôᲶŢᵿᶅkaro÷གɯᶊ\0\0ᶎrn;挟op;挌ƀcotᶘᶣᶦĀryᶝᶡ;쀀𝒹;䑕l;槶rok;䄑Ādrᶰᶴot;拱iĀ;fᶺ᠖斿Āah᷀᷃ròЩaòྦangle;榦Āci᷒ᷕy;䑟grarr;柿ऀDacdefglmnopqrstuxḁḉḙḸոḼṉṡṾấắẽỡἪἷὄ὎὚ĀDoḆᴴoôᲉĀcsḎḔute耻é䃩ter;橮ȀaioyḢḧḱḶron;䄛rĀ;cḭḮ扖耻ê䃪lon;払;䑍ot;䄗ĀDrṁṅot;扒;쀀𝔢ƀ;rsṐṑṗ檚ave耻è䃨Ā;dṜṝ檖ot;檘Ȁ;ilsṪṫṲṴ檙nters;揧;愓Ā;dṹṺ檕ot;檗ƀapsẅẉẗcr;䄓tyƀ;svẒẓẕ戅et»ẓpĀ1;ẝẤijạả;怄;怅怃ĀgsẪẬ;䅋p;怂ĀgpẴẸon;䄙f;쀀𝕖ƀalsỄỎỒrĀ;sỊị拕l;槣us;橱iƀ;lvỚớở䎵on»ớ;䏵ȀcsuvỪỳἋἣĀioữḱrc»Ḯɩỹ\0\0ỻíՈantĀglἂἆtr»ṝess»Ṻƀaeiἒ἖Ἒls;䀽st;扟vĀ;DȵἠD;橸parsl;槥ĀDaἯἳot;打rr;楱ƀcdiἾὁỸr;愯oô͒ĀahὉὋ;䎷耻ð䃰Āmrὓὗl耻ë䃫o;悬ƀcipὡὤὧl;䀡sôծĀeoὬὴctatioîՙnentialåչৡᾒ\0ᾞ\0ᾡᾧ\0\0ῆῌ\0ΐ\0ῦῪ \0 ⁚llingdotseñṄy;䑄male;晀ƀilrᾭᾳ῁lig;耀ffiɩᾹ\0\0᾽g;耀ffig;耀ffl;쀀𝔣lig;耀filig;쀀fjƀaltῙ῜ῡt;晭ig;耀flns;斱of;䆒ǰ΅\0ῳf;쀀𝕗ĀakֿῷĀ;vῼ´拔;櫙artint;樍Āao‌⁕Ācs‑⁒ႉ‸⁅⁈\0⁐β•‥‧‪‬\0‮耻½䂽;慓耻¼䂼;慕;慙;慛Ƴ‴\0‶;慔;慖ʴ‾⁁\0\0⁃耻¾䂾;慗;慜5;慘ƶ⁌\0⁎;慚;慝8;慞l;恄wn;挢cr;쀀𝒻ࢀEabcdefgijlnorstv₂₉₟₥₰₴⃰⃵⃺⃿℃ℒℸ̗ℾ⅒↞Ā;lٍ₇;檌ƀcmpₐₕ₝ute;䇵maĀ;dₜ᳚䎳;檆reve;䄟Āiy₪₮rc;䄝;䐳ot;䄡Ȁ;lqsؾق₽⃉ƀ;qsؾٌ⃄lanô٥Ȁ;cdl٥⃒⃥⃕c;檩otĀ;o⃜⃝檀Ā;l⃢⃣檂;檄Ā;e⃪⃭쀀⋛︀s;檔r;쀀𝔤Ā;gٳ؛mel;愷cy;䑓Ȁ;Eajٚℌℎℐ;檒;檥;檤ȀEaesℛℝ℩ℴ;扩pĀ;p℣ℤ檊rox»ℤĀ;q℮ℯ檈Ā;q℮ℛim;拧pf;쀀𝕘Āci⅃ⅆr;愊mƀ;el٫ⅎ⅐;檎;檐茀>;cdlqr׮ⅠⅪⅮⅳⅹĀciⅥⅧ;檧r;橺ot;拗Par;榕uest;橼ʀadelsↄⅪ←ٖ↛ǰ↉\0↎proø₞r;楸qĀlqؿ↖lesó₈ií٫Āen↣↭rtneqq;쀀≩︀Å↪ԀAabcefkosy⇄⇇⇱⇵⇺∘∝∯≨≽ròΠȀilmr⇐⇔⇗⇛rsðᒄf»․ilôکĀdr⇠⇤cy;䑊ƀ;cwࣴ⇫⇯ir;楈;憭ar;意irc;䄥ƀalr∁∎∓rtsĀ;u∉∊晥it»∊lip;怦con;抹r;쀀𝔥sĀew∣∩arow;椥arow;椦ʀamopr∺∾≃≞≣rr;懿tht;戻kĀlr≉≓eftarrow;憩ightarrow;憪f;쀀𝕙bar;怕ƀclt≯≴≸r;쀀𝒽asè⇴rok;䄧Ābp⊂⊇ull;恃hen»ᱛૡ⊣\0⊪\0⊸⋅⋎\0⋕⋳\0\0⋸⌢⍧⍢⍿\0⎆⎪⎴cute耻í䃭ƀ;iyݱ⊰⊵rc耻î䃮;䐸Ācx⊼⊿y;䐵cl耻¡䂡ĀfrΟ⋉;쀀𝔦rave耻ì䃬Ȁ;inoܾ⋝⋩⋮Āin⋢⋦nt;樌t;戭fin;槜ta;愩lig;䄳ƀaop⋾⌚⌝ƀcgt⌅⌈⌗r;䄫ƀelpܟ⌏⌓inåގarôܠh;䄱f;抷ed;䆵ʀ;cfotӴ⌬⌱⌽⍁are;愅inĀ;t⌸⌹戞ie;槝doô⌙ʀ;celpݗ⍌⍐⍛⍡al;抺Āgr⍕⍙eróᕣã⍍arhk;樗rod;樼Ȁcgpt⍯⍲⍶⍻y;䑑on;䄯f;쀀𝕚a;䎹uest耻¿䂿Āci⎊⎏r;쀀𝒾nʀ;EdsvӴ⎛⎝⎡ӳ;拹ot;拵Ā;v⎦⎧拴;拳Ā;iݷ⎮lde;䄩ǫ⎸\0⎼cy;䑖l耻ï䃯̀cfmosu⏌⏗⏜⏡⏧⏵Āiy⏑⏕rc;䄵;䐹r;쀀𝔧ath;䈷pf;쀀𝕛ǣ⏬\0⏱r;쀀𝒿rcy;䑘kcy;䑔Ѐacfghjos␋␖␢␧␭␱␵␻ppaĀ;v␓␔䎺;䏰Āey␛␠dil;䄷;䐺r;쀀𝔨reen;䄸cy;䑅cy;䑜pf;쀀𝕜cr;쀀𝓀஀ABEHabcdefghjlmnoprstuv⑰⒁⒆⒍⒑┎┽╚▀♎♞♥♹♽⚚⚲⛘❝❨➋⟀⠁⠒ƀart⑷⑺⑼rò৆òΕail;椛arr;椎Ā;gঔ⒋;檋ar;楢ॣ⒥\0⒪\0⒱\0\0\0\0\0⒵Ⓔ\0ⓆⓈⓍ\0⓹ute;䄺mptyv;榴raîࡌbda;䎻gƀ;dlࢎⓁⓃ;榑åࢎ;檅uo耻«䂫rЀ;bfhlpst࢙ⓞⓦⓩ⓫⓮⓱⓵Ā;f࢝ⓣs;椟s;椝ë≒p;憫l;椹im;楳l;憢ƀ;ae⓿─┄檫il;椙Ā;s┉┊檭;쀀⪭︀ƀabr┕┙┝rr;椌rk;杲Āak┢┬cĀek┨┪;䁻;䁛Āes┱┳;榋lĀdu┹┻;榏;榍Ȁaeuy╆╋╖╘ron;䄾Ādi═╔il;䄼ìࢰâ┩;䐻Ȁcqrs╣╦╭╽a;椶uoĀ;rนᝆĀdu╲╷har;楧shar;楋h;憲ʀ;fgqs▋▌উ◳◿扤tʀahlrt▘▤▷◂◨rrowĀ;t࢙□aé⓶arpoonĀdu▯▴own»њp»०eftarrows;懇ightƀahs◍◖◞rrowĀ;sࣴࢧarpoonó྘quigarro÷⇰hreetimes;拋ƀ;qs▋ও◺lanôবʀ;cdgsব☊☍☝☨c;檨otĀ;o☔☕橿Ā;r☚☛檁;檃Ā;e☢☥쀀⋚︀s;檓ʀadegs☳☹☽♉♋pproøⓆot;拖qĀgq♃♅ôউgtò⒌ôছiíলƀilr♕࣡♚sht;楼;쀀𝔩Ā;Eজ♣;檑š♩♶rĀdu▲♮Ā;l॥♳;楪lk;斄cy;䑙ʀ;achtੈ⚈⚋⚑⚖rò◁orneòᴈard;楫ri;旺Āio⚟⚤dot;䅀ustĀ;a⚬⚭掰che»⚭ȀEaes⚻⚽⛉⛔;扨pĀ;p⛃⛄檉rox»⛄Ā;q⛎⛏檇Ā;q⛎⚻im;拦Ѐa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di㩀㩑Ābg㩅㩉ar;機eĀ;qᗺ㩏;扙erp;愘r;쀀𝔴pf;쀀𝕨Ā;eᑹ㩦atèᑹcr;쀀𝓌ૣណ㪇\0㪋\0㪐㪛\0\0㪝㪨㪫㪯\0\0㫃㫎\0㫘ៜ៟tré៑r;쀀𝔵ĀAa㪔㪗ròσrò৶;䎾ĀAa㪡㪤ròθrò৫að✓is;拻ƀdptឤ㪵㪾Āfl㪺ឩ;쀀𝕩imåឲĀAa㫇㫊ròώròਁĀcq㫒ីr;쀀𝓍Āpt៖㫜ré។Ѐacefiosu㫰㫽㬈㬌㬑㬕㬛㬡cĀuy㫶㫻te耻ý䃽;䑏Āiy㬂㬆rc;䅷;䑋n耻¥䂥r;쀀𝔶cy;䑗pf;쀀𝕪cr;쀀𝓎Ācm㬦㬩y;䑎l耻ÿ䃿Ԁacdefhiosw㭂㭈㭔㭘㭤㭩㭭㭴㭺㮀cute;䅺Āay㭍㭒ron;䅾;䐷ot;䅼Āet㭝㭡træᕟa;䎶r;쀀𝔷cy;䐶grarr;懝pf;쀀𝕫cr;쀀𝓏Ājn㮅㮇;怍j;怌'.split("").map((e=>e.charCodeAt(0)))),M=new 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o=r;for(s.push(e.bMarks[m]),e.bMarks[m]=i;i=a,l.push(e.bsCount[m]),e.bsCount[m]=e.sCount[m]+1+(t?1:0),c.push(e.sCount[m]),e.sCount[m]=o-r,d.push(e.tShift[m]),e.tShift[m]=i-e.bMarks[m];continue}if(g)break;let r=!1;for(let t=0,i=u.length;t";const f=[t,0];h.map=f,e.md.block.tokenize(e,t,m),e.push("blockquote_close","blockquote",-1).markup=">",e.lineMax=o,e.parentType=p,f[1]=e.line;for(let n=0;n=4)return!1;let a=e.bMarks[t]+e.tShift[t];const o=e.src.charCodeAt(a++);if(42!==o&&45!==o&&95!==o)return!1;let s=1;for(;a=4)return!1;if(e.listIndent>=0&&e.sCount[l]-e.listIndent>=4&&e.sCount[l]=e.blkIndent&&(m=!0),(p=at(e,l))>=0){if(d=!0,o=e.bMarks[l]+e.tShift[l],u=Number(e.src.slice(o,p-1)),m&&1!==u)return!1}else{if(!((p=it(e,l))>=0))return!1;d=!1}if(m&&e.skipSpaces(p)>=e.eMarks[l])return!1;if(r)return!0;const g=e.src.charCodeAt(p-1),_=e.tokens.length;d?(s=e.push("ordered_list_open","ol",1),1!==u&&(s.attrs=[["start",u]])):s=e.push("bullet_list_open","ul",1);const h=[l,0];s.map=h,s.markup=String.fromCharCode(g);let f=!1;const b=e.md.block.ruler.getRules("list"),E=e.parentType;for(e.parentType="list";l=i?1:r-t,m>4&&(m=1);const _=t+m;s=e.push("list_item_open","li",1),s.markup=String.fromCharCode(g);const h=[l,0];s.map=h,d&&(s.info=e.src.slice(o,p-1));const E=e.tight,S=e.tShift[l],y=e.sCount[l],v=e.listIndent;if(e.listIndent=e.blkIndent,e.blkIndent=_,e.tight=!0,e.tShift[l]=u-e.bMarks[l],e.sCount[l]=r,u>=i&&e.isEmpty(l+1)?e.line=Math.min(e.line+2,n):e.md.block.tokenize(e,l,n,!0),e.tight&&!f||(c=!1),f=e.line-l>1&&e.isEmpty(e.line-1),e.blkIndent=e.listIndent,e.listIndent=v,e.tShift[l]=S,e.sCount[l]=y,e.tight=E,s=e.push("list_item_close","li",-1),s.markup=String.fromCharCode(g),l=e.line,h[1]=l,l>=n)break;if(e.sCount[l]=4)break;let T=!1;for(let t=0,r=b.length;t=4)return!1;if(91!==e.src.charCodeAt(a))return!1;for(;++a3)continue;if(e.sCount[s]<0)continue;let t=!1;for(let n=0,r=c.length;n=4)return!1;if(!e.md.options.html)return!1;if(60!==e.src.charCodeAt(i))return!1;let o=e.src.slice(i,a),s=0;for(;s=4)return!1;let o=e.src.charCodeAt(i);if(35!==o||i>=a)return!1;let s=1;for(o=e.src.charCodeAt(++i);35===o&&i6||ii&&Ee(e.src.charCodeAt(l-1))&&(a=l),e.line=t+1;const c=e.push("heading_open","h"+String(s),1);c.markup="########".slice(0,s),c.map=[t,e.line];const d=e.push("inline","",0);return d.content=e.src.slice(i,a).trim(),d.map=[t,e.line],d.children=[],e.push("heading_close","h"+String(s),-1).markup="########".slice(0,s),!0},["paragraph","reference","blockquote"]],["lheading",function(e,t,n){const r=e.md.block.ruler.getRules("paragraph");if(e.sCount[t]-e.blkIndent>=4)return!1;const i=e.parentType;e.parentType="paragraph";let a,o=0,s=t+1;for(;s3)continue;if(e.sCount[s]>=e.blkIndent){let t=e.bMarks[s]+e.tShift[s];const n=e.eMarks[s];if(t=n))){o=61===a?1:2;break}}if(e.sCount[s]<0)continue;let t=!1;for(let i=0,a=r.length;i3)continue;if(e.sCount[a]<0)continue;let t=!1;for(let i=0,o=r.length;i=n))&&!(e.sCount[o]=a){e.line=n;break}const t=e.line;let l=!1;for(let a=0;a=e.line)throw new Error("block rule didn't increment state.line");break}if(!l)throw new Error("none of the block rules matched");e.tight=!s,e.isEmpty(e.line-1)&&(s=!0),o=e.line,o0&&(this.level++,this._prev_delimiters.push(this.delimiters),this.delimiters=[],i={delimiters:this.delimiters}),this.pendingLevel=this.level,this.tokens.push(r),this.tokens_meta.push(i),r},gt.prototype.scanDelims=function(e,t){let n,r,i=!0,a=!0;const o=this.posMax,s=this.src.charCodeAt(e),l=e>0?this.src.charCodeAt(e-1):32;let c=e;for(;c?@[]^_`{|}~-".split("").forEach((function(e){bt[e.charCodeAt(0)]=1}));const St={tokenize:function(e,t){const n=e.pos,r=e.src.charCodeAt(n);if(t)return!1;if(126!==r)return!1;const i=e.scanDelims(e.pos,!0);let a=i.length;const o=String.fromCharCode(r);if(a<2)return!1;let s;a%2&&(s=e.push("text","",0),s.content=o,a--);for(let t=0;t=0;n--){const r=t[n];if(95!==r.marker&&42!==r.marker)continue;if(-1===r.end)continue;const i=t[r.end],a=n>0&&t[n-1].end===r.end+1&&t[n-1].marker===r.marker&&t[n-1].token===r.token-1&&t[r.end+1].token===i.token+1,o=String.fromCharCode(r.marker),s=e.tokens[r.token];s.type=a?"strong_open":"em_open",s.tag=a?"strong":"em",s.nesting=1,s.markup=a?o+o:o,s.content="";const l=e.tokens[i.token];l.type=a?"strong_close":"em_close",l.tag=a?"strong":"em",l.nesting=-1,l.markup=a?o+o:o,l.content="",a&&(e.tokens[t[n-1].token].content="",e.tokens[t[r.end+1].token].content="",n--)}}const vt={tokenize:function(e,t){const n=e.pos,r=e.src.charCodeAt(n);if(t)return!1;if(95!==r&&42!==r)return!1;const i=e.scanDelims(e.pos,42===r);for(let t=0;t\x00-\x20]*)$/,Nt=/^&#((?:x[a-f0-9]{1,6}|[0-9]{1,7}));/i,At=/^&([a-z][a-z0-9]{1,31});/i;function xt(e){const t={},n=e.length;if(!n)return;let r=0,i=-2;const a=[];for(let o=0;os;l-=a[l]+1){const t=e[l];if(t.marker===n.marker&&t.open&&t.end<0){let r=!1;if((t.close||n.open)&&(t.length+n.length)%3==0&&(t.length%3==0&&n.length%3==0||(r=!0)),!r){const r=l>0&&!e[l-1].open?a[l-1]+1:0;a[o]=o-l+r,a[l]=r,n.open=!1,t.end=o,t.close=!1,c=-1,i=-2;break}}}-1!==c&&(t[n.marker][(n.open?3:0)+(n.length||0)%3]=c)}}const Ot=[["text",function(e,t){let n=e.pos;for(;n0)return!1;const n=e.pos;if(n+3>e.posMax)return!1;if(58!==e.src.charCodeAt(n))return!1;if(47!==e.src.charCodeAt(n+1))return!1;if(47!==e.src.charCodeAt(n+2))return!1;const r=e.pending.match(ft);if(!r)return!1;const i=r[1],a=e.md.linkify.matchAtStart(e.src.slice(n-i.length));if(!a)return!1;let o=a.url;if(o.length<=i.length)return!1;o=o.replace(/\*+$/,"");const s=e.md.normalizeLink(o);if(!e.md.validateLink(s))return!1;if(!t){e.pending=e.pending.slice(0,-i.length);const t=e.push("link_open","a",1);t.attrs=[["href",s]],t.markup="linkify",t.info="auto",e.push("text","",0).content=e.md.normalizeLinkText(o);const n=e.push("link_close","a",-1);n.markup="linkify",n.info="auto"}return e.pos+=o.length-i.length,!0}],["newline",function(e,t){let n=e.pos;if(10!==e.src.charCodeAt(n))return!1;const r=e.pending.length-1,i=e.posMax;if(!t)if(r>=0&&32===e.pending.charCodeAt(r))if(r>=1&&32===e.pending.charCodeAt(r-1)){let t=r-1;for(;t>=1&&32===e.pending.charCodeAt(t-1);)t--;e.pending=e.pending.slice(0,t),e.push("hardbreak","br",0)}else e.pending=e.pending.slice(0,-1),e.push("softbreak","br",0);else e.push("softbreak","br",0);for(n++;n=r)return!1;let i=e.src.charCodeAt(n);if(10===i){for(t||e.push("hardbreak","br",0),n++;n=55296&&i<=56319&&n+1=56320&&t<=57343&&(a+=e.src[n+1],n++)}const o="\\"+a;if(!t){const t=e.push("text_special","",0);i<256&&0!==bt[i]?t.content=a:t.content=o,t.markup=o,t.info="escape"}return e.pos=n+1,!0}],["backticks",function(e,t){let n=e.pos;if(96!==e.src.charCodeAt(n))return!1;const r=n;n++;const i=e.posMax;for(;n=u)return!1;if(l=g,i=e.md.helpers.parseLinkDestination(e.src,g,e.posMax),i.ok){for(o=e.md.normalizeLink(i.str),e.md.validateLink(o)?g=i.pos:o="",l=g;g=u||41!==e.src.charCodeAt(g))&&(c=!0),g++}if(c){if(void 0===e.env.references)return!1;if(g=0?r=e.src.slice(l,g++):g=m+1):g=m+1,r||(r=e.src.slice(p,m)),a=e.env.references[Te(r)],!a)return e.pos=d,!1;o=a.href,s=a.title}if(!t){e.pos=p,e.posMax=m;const t=[["href",o]];e.push("link_open","a",1).attrs=t,s&&t.push(["title",s]),e.linkLevel++,e.md.inline.tokenize(e),e.linkLevel--,e.push("link_close","a",-1)}return e.pos=g,e.posMax=u,!0}],["image",function(e,t){let n,r,i,a,o,s,l,c,d="";const u=e.pos,p=e.posMax;if(33!==e.src.charCodeAt(e.pos))return!1;if(91!==e.src.charCodeAt(e.pos+1))return!1;const m=e.pos+2,g=e.md.helpers.parseLinkLabel(e,e.pos+1,!1);if(g<0)return!1;if(a=g+1,a=p)return!1;for(c=a,s=e.md.helpers.parseLinkDestination(e.src,a,e.posMax),s.ok&&(d=e.md.normalizeLink(s.str),e.md.validateLink(d)?a=s.pos:d=""),c=a;a=p||41!==e.src.charCodeAt(a))return e.pos=u,!1;a++}else{if(void 0===e.env.references)return!1;if(a=0?i=e.src.slice(c,a++):a=g+1):a=g+1,i||(i=e.src.slice(m,g)),o=e.env.references[Te(i)],!o)return e.pos=u,!1;d=o.href,l=o.title}if(!t){r=e.src.slice(m,g);const t=[];e.md.inline.parse(r,e.md,e.env,t);const n=e.push("image","img",0),i=[["src",d],["alt",""]];n.attrs=i,n.children=t,n.content=r,l&&i.push(["title",l])}return e.pos=a,e.posMax=p,!0}],["autolink",function(e,t){let n=e.pos;if(60!==e.src.charCodeAt(n))return!1;const r=e.pos,i=e.posMax;for(;;){if(++n>=i)return!1;const t=e.src.charCodeAt(n);if(60===t)return!1;if(62===t)break}const a=e.src.slice(r+1,n);if(Ct.test(a)){const n=e.md.normalizeLink(a);if(!e.md.validateLink(n))return!1;if(!t){const t=e.push("link_open","a",1);t.attrs=[["href",n]],t.markup="autolink",t.info="auto",e.push("text","",0).content=e.md.normalizeLinkText(a);const r=e.push("link_close","a",-1);r.markup="autolink",r.info="auto"}return e.pos+=a.length+2,!0}if(Tt.test(a)){const n=e.md.normalizeLink("mailto:"+a);if(!e.md.validateLink(n))return!1;if(!t){const t=e.push("link_open","a",1);t.attrs=[["href",n]],t.markup="autolink",t.info="auto",e.push("text","",0).content=e.md.normalizeLinkText(a);const r=e.push("link_close","a",-1);r.markup="autolink",r.info="auto"}return e.pos+=a.length+2,!0}return!1}],["html_inline",function(e,t){if(!e.md.options.html)return!1;const n=e.posMax,r=e.pos;if(60!==e.src.charCodeAt(r)||r+2>=n)return!1;const i=e.src.charCodeAt(r+1);if(33!==i&&63!==i&&47!==i&&!function(e){const t=32|e;return t>=97&&t<=122}(i))return!1;const a=e.src.slice(r).match(lt);if(!a)return!1;if(!t){const t=e.push("html_inline","",0);t.content=a[0],o=t.content,/^\s]/i.test(o)&&e.linkLevel++,function(e){return/^<\/a\s*>/i.test(e)}(t.content)&&e.linkLevel--}var o;return e.pos+=a[0].length,!0}],["entity",function(e,t){const n=e.pos,r=e.posMax;if(38!==e.src.charCodeAt(n))return!1;if(n+1>=r)return!1;if(35===e.src.charCodeAt(n+1)){const r=e.src.slice(n).match(Nt);if(r){if(!t){const t="x"===r[1][0].toLowerCase()?parseInt(r[1].slice(1),16):parseInt(r[1],10),n=e.push("text_special","",0);n.content=ae(t)?oe(t):oe(65533),n.markup=r[0],n.info="entity"}return e.pos+=r[0].length,!0}}else{const r=e.src.slice(n).match(At);if(r){const n=W(r[0]);if(n!==r[0]){if(!t){const t=e.push("text_special","",0);t.content=n,t.markup=r[0],t.info="entity"}return e.pos+=r[0].length,!0}}}return!1}]],wt=[["balance_pairs",function(e){const t=e.tokens_meta,n=e.tokens_meta.length;xt(e.delimiters);for(let e=0;e0&&r++,"text"===i[t].type&&t+1=e.pos)throw new Error("inline rule didn't increment state.pos");break}}else 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e._cache.$bar}(t,e.type);let r,i,a,o,s=t._length;const l=()=>{32767!==a&&-32768!==a&&(ki(o)&&(s=Math.min(s,Math.abs(a-o)||s)),o=a)};for(r=0,i=n.length;rMath.abs(s)&&(l=s,c=o),t[n.axis]=c,t._custom={barStart:l,barEnd:c,start:i,end:a,min:o,max:s}}(e,t,n,r):t[n.axis]=n.parse(e,r),t}function ps(e,t,n,r){const i=e.iScale,a=e.vScale,o=i.getLabels(),s=i===a,l=[];let c,d,u,p;for(c=n,d=n+r;ce.x,n="left",r="right"):(t=e.base{e[o]&&e[o](t[n],i)&&(a.push({element:e,datasetIndex:r,index:l}),s=s||e.inRange(t.x,t.y,i))})),r&&!s?[]:a}var Ts={evaluateInteractionItems:Es,modes:{index(e,t,n,r){const i=Ro(t,e),a=n.axis||"x",o=n.includeInvisible||!1,s=n.intersect?Ss(e,i,a,r,o):ys(e,i,a,!1,r,o),l=[];return s.length?(e.getSortedVisibleDatasetMetas().forEach((e=>{const t=s[0].index,n=e.data[t];n&&!n.skip&&l.push({element:n,datasetIndex:e.index,index:t})})),l):[]},dataset(e,t,n,r){const i=Ro(t,e),a=n.axis||"xy",o=n.includeInvisible||!1;let s=n.intersect?Ss(e,i,a,r,o):ys(e,i,a,!1,r,o);if(s.length>0){const t=s[0].datasetIndex,n=e.getDatasetMeta(t).data;s=[];for(let e=0;eSs(e,Ro(t,e),n.axis||"xy",r,n.includeInvisible||!1),nearest(e,t,n,r){const i=Ro(t,e),a=n.axis||"xy",o=n.includeInvisible||!1;return ys(e,i,a,n.intersect,r,o)},x:(e,t,n,r)=>vs(e,Ro(t,e),"x",n.intersect,r),y:(e,t,n,r)=>vs(e,Ro(t,e),"y",n.intersect,r)}};const Cs=["left","top","right","bottom"];function Ns(e,t){return e.filter((e=>e.pos===t))}function As(e,t){return e.filter((e=>-1===Cs.indexOf(e.pos)&&e.box.axis===t))}function xs(e,t){return e.sort(((e,n)=>{const r=t?n:e,i=t?e:n;return r.weight===i.weight?r.index-i.index:r.weight-i.weight}))}function Os(e,t,n,r){return Math.max(e[n],t[n])+Math.max(e[r],t[r])}function ws(e,t){e.top=Math.max(e.top,t.top),e.left=Math.max(e.left,t.left),e.bottom=Math.max(e.bottom,t.bottom),e.right=Math.max(e.right,t.right)}function Rs(e,t,n,r){const{pos:i,box:a}=n,o=e.maxPadding;if(!hi(i)){n.size&&(e[i]-=n.size);const 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t=Math.max(0,t||e.width),n=n||e.height,{width:t,height:Math.max(0,r?Math.floor(t/r):n)}}isAttached(e){return!0}updateConfig(e){}}class Fs extends Ls{acquireContext(e){return e&&e.getContext&&e.getContext("2d")||null}updateConfig(e){e.options.animation=!1}}const Bs="$chartjs",Us={touchstart:"mousedown",touchmove:"mousemove",touchend:"mouseup",pointerenter:"mouseenter",pointerdown:"mousedown",pointermove:"mousemove",pointerup:"mouseup",pointerleave:"mouseout",pointerout:"mouseout"},Gs=e=>null===e||""===e,zs=!!ko&&{passive:!0};function Hs(e,t,n){e&&e.canvas&&e.canvas.removeEventListener(t,n,zs)}function Vs(e,t){for(const n of e)if(n===t||n.contains(t))return!0}function qs(e,t,n){const r=e.canvas,i=new MutationObserver((e=>{let t=!1;for(const n of e)t=t||Vs(n.addedNodes,r),t=t&&!Vs(n.removedNodes,r);t&&n()}));return i.observe(document,{childList:!0,subtree:!0}),i}function Ys(e,t,n){const r=e.canvas,i=new MutationObserver((e=>{let t=!1;for(const n of 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n=e.$proxies||(e.$proxies={}),r=n[t];r&&(({attach:Qs,detach:Qs,resize:Qs}[t]||Hs)(e,t,r),n[t]=void 0)}getDevicePixelRatio(){return window.devicePixelRatio}getMaximumSize(e,t,n,r){return function(e,t,n,r){const i=Ao(e),a=Oo(i,"margin"),o=No(i.maxWidth,e,"clientWidth")||Ui,s=No(i.maxHeight,e,"clientHeight")||Ui,l=function(e,t,n){let r,i;if(void 0===t||void 0===n){const a=e&&Co(e);if(a){const e=a.getBoundingClientRect(),o=Ao(a),s=Oo(o,"border","width"),l=Oo(o,"padding");t=e.width-l.width-s.width,n=e.height-l.height-s.height,r=No(o.maxWidth,a,"clientWidth"),i=No(o.maxHeight,a,"clientHeight")}else t=e.clientWidth,n=e.clientHeight}return{width:t,height:n,maxWidth:r||Ui,maxHeight:i||Ui}}(e,t,n);let{width:c,height:d}=l;if("content-box"===i.boxSizing){const e=Oo(i,"border","width"),t=Oo(i,"padding");c-=t.width+e.width,d-=t.height+e.height}return c=Math.max(0,c-a.width),d=Math.max(0,r?c/r:d-a.height),c=Io(Math.min(c,o,l.maxWidth)),d=Io(Math.min(d,s,l.maxHeight)),c&&!d&&(d=Io(c/2)),(void 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this._labelItems||(this._labelItems=this._computeLabelItems(e))}beforeLayout(){this._cache={},this._dataLimitsCached=!1}beforeUpdate(){yi(this.options.beforeUpdate,[this])}update(e,t,n){const{beginAtZero:r,grace:i,ticks:a}=this.options,o=a.sampleSize;this.beforeUpdate(),this.maxWidth=e,this.maxHeight=t,this._margins=n=Object.assign({left:0,right:0,top:0,bottom:0},n),this.ticks=null,this._labelSizes=null,this._gridLineItems=null,this._labelItems=null,this.beforeSetDimensions(),this.setDimensions(),this.afterSetDimensions(),this._maxLength=this.isHorizontal()?this.width+n.left+n.right:this.height+n.top+n.bottom,this._dataLimitsCached||(this.beforeDataLimits(),this.determineDataLimits(),this.afterDataLimits(),this._range=function(e,t,n){const{min:r,max:i}=e,a=Si(t,(i-r)/2),o=(e,t)=>n&&0===e?0:e+t;return{min:o(r,-Math.abs(a)),max:o(i,a)}}(this,i,r),this._dataLimitsCached=!0),this.beforeBuildTicks(),this.ticks=this.buildTicks()||[],this.afterBuildTicks();const s=oi)return function(e,t,n,r){let i,a=0,o=n[0];for(r=Math.ceil(r),i=0;ie-t)).pop(),t}(r);for(let e=0,t=a.length-1;ei)return t}return Math.max(i,1)}(a,t,i);if(o>0){let e,n;const r=o>1?Math.round((l-s)/(o-1)):null;for(el(t,c,d,gi(r)?0:s-r,s),e=0,n=o-1;e=i||n<=1||!this.isHorizontal())return void(this.labelRotation=r);const c=this._getLabelSizes(),d=c.widest.width,u=c.highest.height,p=na(this.chart.width-d,0,this.maxWidth);a=e.offset?this.maxWidth/n:p/(n-1),d+6>a&&(a=p/(n-(e.offset?.5:1)),o=this.maxHeight-al(e.grid)-t.padding-ol(e.title,this.chart.options.font),s=Math.sqrt(d*d+u*u),l=Math.min(Math.asin(na((c.highest.height+6)/a,-1,1)),Math.asin(na(o/s,-1,1))-Math.asin(na(u/s,-1,1)))*(180/Li),l=Math.max(r,Math.min(i,l))),this.labelRotation=l}afterCalculateLabelRotation(){yi(this.options.afterCalculateLabelRotation,[this])}afterAutoSkip(){}beforeFit(){yi(this.options.beforeFit,[this])}fit(){const e={width:0,height:0},{chart:t,options:{ticks:n,title:r,grid:i}}=this,a=this._isVisible(),o=this.isHorizontal();if(a){const a=ol(r,t.options.font);if(o?(e.width=this.maxWidth,e.height=al(i)+a):(e.height=this.maxHeight,e.width=al(i)+a),n.display&&this.ticks.length){const{first:t,last:r,widest:i,highest:a}=this._getLabelSizes(),s=2*n.padding,l=Ki(this.labelRotation),c=Math.cos(l),d=Math.sin(l);if(o){const t=n.mirror?0:d*i.width+c*a.height;e.height=Math.min(this.maxHeight,e.height+t+s)}else{const t=n.mirror?0:c*i.width+d*a.height;e.width=Math.min(this.maxWidth,e.width+t+s)}this._calculatePadding(t,r,d,c)}}this._handleMargins(),o?(this.width=this._length=t.width-this._margins.left-this._margins.right,this.height=e.height):(this.width=e.width,this.height=this._length=t.height-this._margins.top-this._margins.bottom)}_calculatePadding(e,t,n,r){const{ticks:{align:i,padding:a},position:o}=this.options,s=0!==this.labelRotation,l="top"!==o&&"x"===this.axis;if(this.isHorizontal()){const o=this.getPixelForTick(0)-this.left,c=this.right-this.getPixelForTick(this.ticks.length-1);let d=0,u=0;s?l?(d=r*e.width,u=n*t.height):(d=n*e.height,u=r*t.width):"start"===i?u=t.width:"end"===i?d=e.width:"inner"!==i&&(d=e.width/2,u=t.width/2),this.paddingLeft=Math.max((d-o+a)*this.width/(this.width-o),0),this.paddingRight=Math.max((u-c+a)*this.width/(this.width-c),0)}else{let n=t.height/2,r=e.height/2;"start"===i?(n=0,r=e.height):"end"===i&&(n=t.height,r=0),this.paddingTop=n+a,this.paddingBottom=r+a}}_handleMargins(){this._margins&&(this._margins.left=Math.max(this.paddingLeft,this._margins.left),this._margins.top=Math.max(this.paddingTop,this._margins.top),this._margins.right=Math.max(this.paddingRight,this._margins.right),this._margins.bottom=Math.max(this.paddingBottom,this._margins.bottom))}afterFit(){yi(this.options.afterFit,[this])}isHorizontal(){const{axis:e,position:t}=this.options;return"top"===t||"bottom"===t||"x"===e}isFullSize(){return this.options.fullSize}_convertTicksToLabels(e){let t,n;for(this.beforeTickToLabelConversion(),this.generateTickLabels(e),t=0,n=e.length;t{const n=e.gc,r=n.length/2;let i;if(r>t){for(i=0;i({width:a[e]||0,height:o[e]||0});return{first:T(0),last:T(t-1),widest:T(y),highest:T(v),widths:a,heights:o}}getLabelForValue(e){return e}getPixelForValue(e,t){return NaN}getValueForPixel(e){}getPixelForTick(e){const t=this.ticks;return e<0||e>t.length-1?null:this.getPixelForValue(t[e].value)}getPixelForDecimal(e){this._reversePixels&&(e=1-e);const t=this._startPixel+e*this._length;return na(this._alignToPixels?ka(this.chart,t,0):t,-32768,32767)}getDecimalForPixel(e){const t=(e-this._startPixel)/this._length;return this._reversePixels?1-t:t}getBasePixel(){return this.getPixelForValue(this.getBaseValue())}getBaseValue(){const{min:e,max:t}=this;return e<0&&t<0?t:e>0&&t>0?e:0}getContext(e){const t=this.ticks||[];if(e>=0&&eo*r?o/n:s/r:s*r0}_computeGridLineItems(e){const t=this.axis,n=this.chart,r=this.options,{grid:i,position:a,border:o}=r,s=i.offset,l=this.isHorizontal(),c=this.ticks.length+(s?1:0),d=al(i),u=[],p=o.setContext(this.getContext()),m=p.display?p.width:0,g=m/2,_=function(e){return ka(n,e,m)};let h,f,b,E,S,y,v,T,C,N,A,x;if("top"===a)h=_(this.bottom),y=this.bottom-d,T=h-g,N=_(e.top)+g,x=e.bottom;else if("bottom"===a)h=_(this.top),N=e.top,x=_(e.bottom)-g,y=h+g,T=this.top+d;else if("left"===a)h=_(this.right),S=this.right-d,v=h-g,C=_(e.left)+g,A=e.right;else if("right"===a)h=_(this.left),C=e.left,A=_(e.right)-g,S=h+g,v=this.left+d;else if("x"===t){if("center"===a)h=_((e.top+e.bottom)/2+.5);else if(hi(a)){const e=Object.keys(a)[0],t=a[e];h=_(this.chart.scales[e].getPixelForValue(t))}N=e.top,x=e.bottom,y=h+g,T=y+d}else if("y"===t){if("center"===a)h=_((e.left+e.right)/2);else if(hi(a)){const e=Object.keys(a)[0],t=a[e];h=_(this.chart.scales[e].getPixelForValue(t))}S=h-g,v=S-d,C=e.left,A=e.right}const O=Ei(r.ticks.maxTicksLimit,c),w=Math.max(1,Math.ceil(c/O));for(f=0;f0&&(a-=r/2)}u={left:a,top:i,width:r+t.width,height:n+t.height,color:e.backdropColor}}_.push({label:E,font:C,textOffset:x,options:{rotation:g,color:n,strokeColor:s,strokeWidth:c,textAlign:p,textBaseline:O,translation:[S,y],backdrop:u}})}return _}_getXAxisLabelAlignment(){const{position:e,ticks:t}=this.options;if(-Ki(this.labelRotation))return"top"===e?"left":"right";let n="center";return"start"===t.align?n="left":"end"===t.align?n="right":"inner"===t.align&&(n="inner"),n}_getYAxisLabelAlignment(e){const{position:t,ticks:{crossAlign:n,mirror:r,padding:i}}=this.options,a=e+i,o=this._getLabelSizes().widest.width;let s,l;return"left"===t?r?(l=this.right+i,"near"===n?s="left":"center"===n?(s="center",l+=o/2):(s="right",l+=o)):(l=this.right-a,"near"===n?s="right":"center"===n?(s="center",l-=o/2):(s="left",l=this.left)):"right"===t?r?(l=this.left+i,"near"===n?s="right":"center"===n?(s="center",l-=o/2):(s="left",l-=o)):(l=this.left+a,"near"===n?s="left":"center"===n?(s="center",l+=o/2):(s="right",l=this.right)):s="right",{textAlign:s,x:l}}_computeLabelArea(){if(this.options.ticks.mirror)return;const e=this.chart,t=this.options.position;return"left"===t||"right"===t?{top:0,left:this.left,bottom:e.height,right:this.right}:"top"===t||"bottom"===t?{top:this.top,left:0,bottom:this.bottom,right:e.width}:void 0}drawBackground(){const{ctx:e,options:{backgroundColor:t},left:n,top:r,width:i,height:a}=this;t&&(e.save(),e.fillStyle=t,e.fillRect(n,r,i,a),e.restore())}getLineWidthForValue(e){const t=this.options.grid;if(!this._isVisible()||!t.display)return 0;const n=this.ticks.findIndex((t=>t.value===e));return n>=0?t.setContext(this.getContext(n)).lineWidth:0}drawGrid(e){const t=this.options.grid,n=this.ctx,r=this._gridLineItems||(this._gridLineItems=this._computeGridLineItems(e));let i,a;const o=(e,t,r)=>{r.width&&r.color&&(n.save(),n.lineWidth=r.width,n.strokeStyle=r.color,n.setLineDash(r.borderDash||[]),n.lineDashOffset=r.borderDashOffset,n.beginPath(),n.moveTo(e.x,e.y),n.lineTo(t.x,t.y),n.stroke(),n.restore())};if(t.display)for(i=0,a=r.length;i{this.drawBackground(),this.drawGrid(e),this.drawTitle()}},{z:r,draw:()=>{this.drawBorder()}},{z:t,draw:e=>{this.drawLabels(e)}}]:[{z:t,draw:e=>{this.draw(e)}}]}getMatchingVisibleMetas(e){const t=this.chart.getSortedVisibleDatasetMetas(),n=this.axis+"AxisID",r=[];let i,a;for(i=0,a=t.length;i{const r=n.split("."),i=r.pop(),a=[e].concat(r).join("."),o=t[n].split("."),s=o.pop(),l=o.join(".");Ia.route(a,i,l,s)}))}(t,e.defaultRoutes),e.descriptors&&Ia.describe(t,e.descriptors)}(e,a,n),this.override&&Ia.override(e.id,e.overrides)),a}get(e){return this.items[e]}unregister(e){const t=this.items,n=e.id,r=this.scope;n in t&&delete t[n],r&&n in Ia[r]&&(delete Ia[r][n],this.override&&delete Aa[n])}}class dl{constructor(){this.controllers=new cl(cs,"datasets",!0),this.elements=new cl(Js,"elements"),this.plugins=new cl(Object,"plugins"),this.scales=new cl(ll,"scales"),this._typedRegistries=[this.controllers,this.scales,this.elements]}add(...e){this._each("register",e)}remove(...e){this._each("unregister",e)}addControllers(...e){this._each("register",e,this.controllers)}addElements(...e){this._each("register",e,this.elements)}addPlugins(...e){this._each("register",e,this.plugins)}addScales(...e){this._each("register",e,this.scales)}getController(e){return this._get(e,this.controllers,"controller")}getElement(e){return this._get(e,this.elements,"element")}getPlugin(e){return this._get(e,this.plugins,"plugin")}getScale(e){return this._get(e,this.scales,"scale")}removeControllers(...e){this._each("unregister",e,this.controllers)}removeElements(...e){this._each("unregister",e,this.elements)}removePlugins(...e){this._each("unregister",e,this.plugins)}removeScales(...e){this._each("unregister",e,this.scales)}_each(e,t,n){[...t].forEach((t=>{const r=n||this._getRegistryForType(t);n||r.isForType(t)||r===this.plugins&&t.id?this._exec(e,r,t):vi(t,(t=>{const r=n||this._getRegistryForType(t);this._exec(e,r,t)}))}))}_exec(e,t,n){const r=Di(e);yi(n["before"+r],[],n),t[e](n),yi(n["after"+r],[],n)}_getRegistryForType(e){for(let t=0;te.filter((e=>!t.some((t=>e.plugin.id===t.plugin.id))));this._notify(r(t,n),e,"stop"),this._notify(r(n,t),e,"start")}}function ml(e,t){return t||!1!==e?!0===e?{}:e:null}function gl(e,{plugin:t,local:n},r,i){const a=e.pluginScopeKeys(t),o=e.getOptionScopes(r,a);return n&&t.defaults&&o.push(t.defaults),e.createResolver(o,i,[""],{scriptable:!1,indexable:!1,allKeys:!0})}function _l(e,t){const n=Ia.datasets[e]||{};return((t.datasets||{})[e]||{}).indexAxis||t.indexAxis||n.indexAxis||"x"}function hl(e){if("x"===e||"y"===e||"r"===e)return e}function fl(e,...t){if(hl(e))return e;for(const r of t){const t=r.axis||("top"===(n=r.position)||"bottom"===n?"x":"left"===n||"right"===n?"y":void 0)||e.length>1&&hl(e[0].toLowerCase());if(t)return t}var n;throw new Error(`Cannot determine type of '${e}' axis. Please provide 'axis' or 'position' option.`)}function bl(e,t,n){if(n[t+"AxisID"]===e)return{axis:t}}function El(e){const t=e.options||(e.options={});t.plugins=Ei(t.plugins,{}),t.scales=function(e,t){const n=Aa[e.type]||{scales:{}},r=t.scales||{},i=_l(e.type,t),a=Object.create(null);return Object.keys(r).forEach((t=>{const o=r[t];if(!hi(o))return console.error(`Invalid scale configuration for scale: ${t}`);if(o._proxy)return console.warn(`Ignoring resolver passed as options for scale: ${t}`);const s=fl(t,o,function(e,t){if(t.data&&t.data.datasets){const n=t.data.datasets.filter((t=>t.xAxisID===e||t.yAxisID===e));if(n.length)return bl(e,"x",n[0])||bl(e,"y",n[0])}return{}}(t,e),Ia.scales[o.type]),l=function(e,t){return e===t?"_index_":"_value_"}(s,i),c=n.scales||{};a[t]=Oi(Object.create(null),[{axis:s},o,c[s],c[l]])})),e.data.datasets.forEach((n=>{const i=n.type||e.type,o=n.indexAxis||_l(i,t),s=(Aa[i]||{}).scales||{};Object.keys(s).forEach((e=>{const t=function(e,t){let n=e;return"_index_"===e?n=t:"_value_"===e&&(n="x"===t?"y":"x"),n}(e,o),i=n[t+"AxisID"]||t;a[i]=a[i]||Object.create(null),Oi(a[i],[{axis:t},r[i],s[e]])}))})),Object.keys(a).forEach((e=>{const t=a[e];Oi(t,[Ia.scales[t.type],Ia.scale])})),a}(e,t)}function Sl(e){return(e=e||{}).datasets=e.datasets||[],e.labels=e.labels||[],e}const yl=new Map,vl=new Set;function Tl(e,t){let n=yl.get(e);return n||(n=t(),yl.set(e,n),vl.add(n)),n}const Cl=(e,t,n)=>{const r=Ii(t,n);void 0!==r&&e.add(r)};class Nl{constructor(e){this._config=function(e){return(e=e||{}).data=Sl(e.data),El(e),e}(e),this._scopeCache=new Map,this._resolverCache=new Map}get platform(){return this._config.platform}get type(){return this._config.type}set type(e){this._config.type=e}get data(){return this._config.data}set data(e){this._config.data=Sl(e)}get options(){return this._config.options}set options(e){this._config.options=e}get plugins(){return this._config.plugins}update(){const e=this._config;this.clearCache(),El(e)}clearCache(){this._scopeCache.clear(),this._resolverCache.clear()}datasetScopeKeys(e){return Tl(e,(()=>[[`datasets.${e}`,""]]))}datasetAnimationScopeKeys(e,t){return Tl(`${e}.transition.${t}`,(()=>[[`datasets.${e}.transitions.${t}`,`transitions.${t}`],[`datasets.${e}`,""]]))}datasetElementScopeKeys(e,t){return Tl(`${e}-${t}`,(()=>[[`datasets.${e}.elements.${t}`,`datasets.${e}`,`elements.${t}`,""]]))}pluginScopeKeys(e){const t=e.id;return Tl(`${this.type}-plugin-${t}`,(()=>[[`plugins.${t}`,...e.additionalOptionScopes||[]]]))}_cachedScopes(e,t){const n=this._scopeCache;let r=n.get(e);return r&&!t||(r=new Map,n.set(e,r)),r}getOptionScopes(e,t,n){const{options:r,type:i}=this,a=this._cachedScopes(e,n),o=a.get(t);if(o)return o;const s=new Set;t.forEach((t=>{e&&(s.add(e),t.forEach((t=>Cl(s,e,t)))),t.forEach((e=>Cl(s,r,e))),t.forEach((e=>Cl(s,Aa[i]||{},e))),t.forEach((e=>Cl(s,Ia,e))),t.forEach((e=>Cl(s,xa,e)))}));const l=Array.from(s);return 0===l.length&&l.push(Object.create(null)),vl.has(t)&&a.set(t,l),l}chartOptionScopes(){const{options:e,type:t}=this;return[e,Aa[t]||{},Ia.datasets[t]||{},{type:t},Ia,xa]}resolveNamedOptions(e,t,n,r=[""]){const i={$shared:!0},{resolver:a,subPrefixes:o}=Al(this._resolverCache,e,r);let s=a;(function(e,t){const{isScriptable:n,isIndexable:r}=ao(e);for(const i of t){const t=n(i),a=r(i),o=(a||t)&&e[i];if(t&&(Mi(o)||xl(o))||a&&_i(o))return!0}return!1})(a,t)&&(i.$shared=!1,s=io(a,n=Mi(n)?n():n,this.createResolver(e,n,o)));for(const e of t)i[e]=s[e];return i}createResolver(e,t,n=[""],r){const{resolver:i}=Al(this._resolverCache,e,n);return hi(t)?io(i,t,void 0,r):i}}function Al(e,t,n){let r=e.get(t);r||(r=new Map,e.set(t,r));const i=n.join();let a=r.get(i);return a||(a={resolver:ro(t,n),subPrefixes:n.filter((e=>!e.toLowerCase().includes("hover")))},r.set(i,a)),a}const xl=e=>hi(e)&&Object.getOwnPropertyNames(e).some((t=>Mi(e[t]))),Ol=["top","bottom","left","right","chartArea"];function wl(e,t){return"top"===e||"bottom"===e||-1===Ol.indexOf(e)&&"x"===t}function Rl(e,t){return function(n,r){return n[e]===r[e]?n[t]-r[t]:n[e]-r[e]}}function Il(e){const t=e.chart,n=t.options.animation;t.notifyPlugins("afterRender"),yi(n&&n.onComplete,[e],t)}function Dl(e){const t=e.chart,n=t.options.animation;yi(n&&n.onProgress,[e],t)}function kl(e){return To()&&"string"==typeof e?e=document.getElementById(e):e&&e.length&&(e=e[0]),e&&e.canvas&&(e=e.canvas),e}const Ml={},Pl=e=>{const t=kl(e);return Object.values(Ml).filter((e=>e.canvas===t)).pop()};function Ll(e,t,n){const r=Object.keys(e);for(const i of r){const r=+i;if(r>=t){const a=e[i];delete e[i],(n>0||r>t)&&(e[r+n]=a)}}}function Fl(e,t,n){return e.options.clip?e[n]:t[n]}class Bl{static defaults=Ia;static instances=Ml;static overrides=Aa;static registry=ul;static version="4.4.5";static getChart=Pl;static register(...e){ul.add(...e),Ul()}static unregister(...e){ul.remove(...e),Ul()}constructor(e,t){const n=this.config=new Nl(t),r=kl(e),i=Pl(r);if(i)throw new Error("Canvas is already in use. Chart with ID '"+i.id+"' must be destroyed before the canvas with ID '"+i.canvas.id+"' can be reused.");const a=n.createResolver(n.chartOptionScopes(),this.getContext());this.platform=new(n.platform||function(e){return!To()||"undefined"!=typeof OffscreenCanvas&&e instanceof OffscreenCanvas?Fs:Xs}(r)),this.platform.updateConfig(n);const o=this.platform.acquireContext(r,a.aspectRatio),s=o&&o.canvas,l=s&&s.height,c=s&&s.width;this.id=mi(),this.ctx=o,this.canvas=s,this.width=c,this.height=l,this._options=a,this._aspectRatio=this.aspectRatio,this._layers=[],this._metasets=[],this._stacks=void 0,this.boxes=[],this.currentDevicePixelRatio=void 0,this.chartArea=void 0,this._active=[],this._lastEvent=void 0,this._listeners={},this._responsiveListeners=void 0,this._sortedMetasets=[],this.scales={},this._plugins=new pl,this.$proxies={},this._hiddenIndices={},this.attached=!1,this._animationsDisabled=void 0,this.$context=void 0,this._doResize=function(e,t){let n;return function(...r){return t?(clearTimeout(n),n=setTimeout(e,t,r)):e.apply(this,r),t}}((e=>this.update(e)),a.resizeDelay||0),this._dataChanges=[],Ml[this.id]=this,o&&s?($o.listen(this,"complete",Il),$o.listen(this,"progress",Dl),this._initialize(),this.attached&&this.update()):console.error("Failed to create chart: can't acquire context from the given item")}get aspectRatio(){const{options:{aspectRatio:e,maintainAspectRatio:t},width:n,height:r,_aspectRatio:i}=this;return gi(e)?t&&i?i:r?n/r:null:e}get data(){return this.config.data}set data(e){this.config.data=e}get options(){return this._options}set options(e){this.config.options=e}get registry(){return ul}_initialize(){return this.notifyPlugins("beforeInit"),this.options.responsive?this.resize():Do(this,this.options.devicePixelRatio),this.bindEvents(),this.notifyPlugins("afterInit"),this}clear(){return Ma(this.canvas,this.ctx),this}stop(){return $o.stop(this),this}resize(e,t){$o.running(this)?this._resizeBeforeDraw={width:e,height:t}:this._resize(e,t)}_resize(e,t){const n=this.options,r=this.canvas,i=n.maintainAspectRatio&&this.aspectRatio,a=this.platform.getMaximumSize(r,e,t,i),o=n.devicePixelRatio||this.platform.getDevicePixelRatio(),s=this.width?"resize":"attach";this.width=a.width,this.height=a.height,this._aspectRatio=this.aspectRatio,Do(this,o,!0)&&(this.notifyPlugins("resize",{size:a}),yi(n.onResize,[this,a],this),this.attached&&this._doResize(s)&&this.render())}ensureScalesHaveIDs(){vi(this.options.scales||{},((e,t)=>{e.id=t}))}buildOrUpdateScales(){const e=this.options,t=e.scales,n=this.scales,r=Object.keys(n).reduce(((e,t)=>(e[t]=!1,e)),{});let i=[];t&&(i=i.concat(Object.keys(t).map((e=>{const n=t[e],r=fl(e,n),i="r"===r,a="x"===r;return{options:n,dposition:i?"chartArea":a?"bottom":"left",dtype:i?"radialLinear":a?"category":"linear"}})))),vi(i,(t=>{const i=t.options,a=i.id,o=fl(a,i),s=Ei(i.type,t.dtype);void 0!==i.position&&wl(i.position,o)===wl(t.dposition)||(i.position=t.dposition),r[a]=!0;let l=null;a in n&&n[a].type===s?l=n[a]:(l=new(ul.getScale(s))({id:a,type:s,ctx:this.ctx,chart:this}),n[l.id]=l),l.init(i,e)})),vi(r,((e,t)=>{e||delete n[t]})),vi(n,(e=>{Ps.configure(this,e,e.options),Ps.addBox(this,e)}))}_updateMetasets(){const e=this._metasets,t=this.data.datasets.length,n=e.length;if(e.sort(((e,t)=>e.index-t.index)),n>t){for(let e=t;et.length&&delete this._stacks,e.forEach(((e,n)=>{0===t.filter((t=>t===e._dataset)).length&&this._destroyDatasetMeta(n)}))}buildOrUpdateControllers(){const e=[],t=this.data.datasets;let n,r;for(this._removeUnreferencedMetasets(),n=0,r=t.length;n{this.getDatasetMeta(t).controller.reset()}),this)}reset(){this._resetElements(),this.notifyPlugins("reset")}update(e){const t=this.config;t.update();const n=this._options=t.createResolver(t.chartOptionScopes(),this.getContext()),r=this._animationsDisabled=!n.animation;if(this._updateScales(),this._checkEventBindings(),this._updateHiddenIndices(),this._plugins.invalidate(),!1===this.notifyPlugins("beforeUpdate",{mode:e,cancelable:!0}))return;const i=this.buildOrUpdateControllers();this.notifyPlugins("beforeElementsUpdate");let a=0;for(let e=0,t=this.data.datasets.length;e{e.reset()})),this._updateDatasets(e),this.notifyPlugins("afterUpdate",{mode:e}),this._layers.sort(Rl("z","_idx"));const{_active:o,_lastEvent:s}=this;s?this._eventHandler(s,!0):o.length&&this._updateHoverStyles(o,o,!0),this.render()}_updateScales(){vi(this.scales,(e=>{Ps.removeBox(this,e)})),this.ensureScalesHaveIDs(),this.buildOrUpdateScales()}_checkEventBindings(){const e=this.options,t=new Set(Object.keys(this._listeners)),n=new Set(e.events);Pi(t,n)&&!!this._responsiveListeners===e.responsive||(this.unbindEvents(),this.bindEvents())}_updateHiddenIndices(){const{_hiddenIndices:e}=this,t=this._getUniformDataChanges()||[];for(const{method:n,start:r,count:i}of t)Ll(e,r,"_removeElements"===n?-i:i)}_getUniformDataChanges(){const e=this._dataChanges;if(!e||!e.length)return;this._dataChanges=[];const t=this.data.datasets.length,n=t=>new Set(e.filter((e=>e[0]===t)).map(((e,t)=>t+","+e.splice(1).join(",")))),r=n(0);for(let e=1;ee.split(","))).map((e=>({method:e[1],start:+e[2],count:+e[3]})))}_updateLayout(e){if(!1===this.notifyPlugins("beforeLayout",{cancelable:!0}))return;Ps.update(this,this.width,this.height,e);const t=this.chartArea,n=t.width<=0||t.height<=0;this._layers=[],vi(this.boxes,(e=>{n&&"chartArea"===e.position||(e.configure&&e.configure(),this._layers.push(...e._layers()))}),this),this._layers.forEach(((e,t)=>{e._idx=t})),this.notifyPlugins("afterLayout")}_updateDatasets(e){if(!1!==this.notifyPlugins("beforeDatasetsUpdate",{mode:e,cancelable:!0})){for(let e=0,t=this.data.datasets.length;e=0;--t)this._drawDataset(e[t]);this.notifyPlugins("afterDatasetsDraw")}_drawDataset(e){const t=this.ctx,n=e._clip,r=!n.disabled,i=function(e,t){const{xScale:n,yScale:r}=e;return n&&r?{left:Fl(n,t,"left"),right:Fl(n,t,"right"),top:Fl(r,t,"top"),bottom:Fl(r,t,"bottom")}:t}(e,this.chartArea),a={meta:e,index:e.index,cancelable:!0};!1!==this.notifyPlugins("beforeDatasetDraw",a)&&(r&&Ba(t,{left:!1===n.left?0:i.left-n.left,right:!1===n.right?this.width:i.right+n.right,top:!1===n.top?0:i.top-n.top,bottom:!1===n.bottom?this.height:i.bottom+n.bottom}),e.controller.draw(),r&&Ua(t),a.cancelable=!1,this.notifyPlugins("afterDatasetDraw",a))}isPointInArea(e){return Fa(e,this.chartArea,this._minPadding)}getElementsAtEventForMode(e,t,n,r){const i=Ts.modes[t];return"function"==typeof i?i(this,e,n,r):[]}getDatasetMeta(e){const t=this.data.datasets[e],n=this._metasets;let r=n.filter((e=>e&&e._dataset===t)).pop();return r||(r={type:null,data:[],dataset:null,controller:null,hidden:null,xAxisID:null,yAxisID:null,order:t&&t.order||0,index:e,_dataset:t,_parsed:[],_sorted:!1},n.push(r)),r}getContext(){return this.$context||(this.$context=no(null,{chart:this,type:"chart"}))}getVisibleDatasetCount(){return this.getSortedVisibleDatasetMetas().length}isDatasetVisible(e){const t=this.data.datasets[e];if(!t)return!1;const n=this.getDatasetMeta(e);return"boolean"==typeof n.hidden?!n.hidden:!t.hidden}setDatasetVisibility(e,t){this.getDatasetMeta(e).hidden=!t}toggleDataVisibility(e){this._hiddenIndices[e]=!this._hiddenIndices[e]}getDataVisibility(e){return!this._hiddenIndices[e]}_updateVisibility(e,t,n){const r=n?"show":"hide",i=this.getDatasetMeta(e),a=i.controller._resolveAnimations(void 0,r);ki(t)?(i.data[t].hidden=!n,this.update()):(this.setDatasetVisibility(e,n),a.update(i,{visible:n}),this.update((t=>t.datasetIndex===e?r:void 0)))}hide(e,t){this._updateVisibility(e,t,!1)}show(e,t){this._updateVisibility(e,t,!0)}_destroyDatasetMeta(e){const t=this._metasets[e];t&&t.controller&&t.controller._destroy(),delete this._metasets[e]}_stop(){let e,t;for(this.stop(),$o.remove(this),e=0,t=this.data.datasets.length;e{t.addEventListener(this,n,r),e[n]=r},r=(e,t,n)=>{e.offsetX=t,e.offsetY=n,this._eventHandler(e)};vi(this.options.events,(e=>n(e,r)))}bindResponsiveEvents(){this._responsiveListeners||(this._responsiveListeners={});const e=this._responsiveListeners,t=this.platform,n=(n,r)=>{t.addEventListener(this,n,r),e[n]=r},r=(n,r)=>{e[n]&&(t.removeEventListener(this,n,r),delete e[n])},i=(e,t)=>{this.canvas&&this.resize(e,t)};let a;const o=()=>{r("attach",o),this.attached=!0,this.resize(),n("resize",i),n("detach",a)};a=()=>{this.attached=!1,r("resize",i),this._stop(),this._resize(0,0),n("attach",o)},t.isAttached(this.canvas)?o():a()}unbindEvents(){vi(this._listeners,((e,t)=>{this.platform.removeEventListener(this,t,e)})),this._listeners={},vi(this._responsiveListeners,((e,t)=>{this.platform.removeEventListener(this,t,e)})),this._responsiveListeners=void 0}updateHoverStyle(e,t,n){const r=n?"set":"remove";let i,a,o,s;for("dataset"===t&&(i=this.getDatasetMeta(e[0].datasetIndex),i.controller["_"+r+"DatasetHoverStyle"]()),o=0,s=e.length;o{const n=this.getDatasetMeta(e);if(!n)throw new Error("No dataset found at index "+e);return{datasetIndex:e,element:n.data[t],index:t}}));!Ti(n,t)&&(this._active=n,this._lastEvent=null,this._updateHoverStyles(n,t))}notifyPlugins(e,t,n){return this._plugins.notify(this,e,t,n)}isPluginEnabled(e){return 1===this._plugins._cache.filter((t=>t.plugin.id===e)).length}_updateHoverStyles(e,t,n){const r=this.options.hover,i=(e,t)=>e.filter((e=>!t.some((t=>e.datasetIndex===t.datasetIndex&&e.index===t.index)))),a=i(t,e),o=n?e:i(e,t);a.length&&this.updateHoverStyle(a,r.mode,!1),o.length&&r.mode&&this.updateHoverStyle(o,r.mode,!0)}_eventHandler(e,t){const n={event:e,replay:t,cancelable:!0,inChartArea:this.isPointInArea(e)},r=t=>(t.options.events||this.options.events).includes(e.native.type);if(!1===this.notifyPlugins("beforeEvent",n,r))return;const i=this._handleEvent(e,t,n.inChartArea);return n.cancelable=!1,this.notifyPlugins("afterEvent",n,r),(i||n.changed)&&this.render(),this}_handleEvent(e,t,n){const{_active:r=[],options:i}=this,a=t,o=this._getActiveElements(e,r,n,a),s=function(e){return"mouseup"===e.type||"click"===e.type||"contextmenu"===e.type}(e),l=function(e,t,n,r){return n&&"mouseout"!==e.type?r?t:e:null}(e,this._lastEvent,n,s);n&&(this._lastEvent=null,yi(i.onHover,[e,o,this],this),s&&yi(i.onClick,[e,o,this],this));const c=!Ti(o,r);return(c||t)&&(this._active=o,this._updateHoverStyles(o,r,t)),this._lastEvent=l,c}_getActiveElements(e,t,n,r){if("mouseout"===e.type)return[];if(!n)return t;const i=this.options.hover;return this.getElementsAtEventForMode(e,i.mode,i,r)}}function Ul(){return vi(Bl.instances,(e=>e._plugins.invalidate()))}function Gl(e,t,n,r){return{x:n+e*Math.cos(t),y:r+e*Math.sin(t)}}function zl(e,t,n,r,i,a){const{x:o,y:s,startAngle:l,pixelMargin:c,innerRadius:d}=t,u=Math.max(t.outerRadius+r+n-c,0),p=d>0?d+r+n+c:0;let m=0;const g=i-l;if(r){const e=((d>0?d-r:0)+(u>0?u-r:0))/2;m=(g-(0!==e?g*e/(e+r):g))/2}const _=(g-Math.max(.001,g*u-n/Li)/u)/2,h=l+_+m,f=i-_-m,{outerStart:b,outerEnd:E,innerStart:S,innerEnd:y}=function(e,t,n,r){const i=Qa(e.options.borderRadius,["outerStart","outerEnd","innerStart","innerEnd"]),a=(n-t)/2,o=Math.min(a,r*t/2),s=e=>{const t=(n-Math.min(a,e))*r/2;return na(e,0,Math.min(a,t))};return{outerStart:s(i.outerStart),outerEnd:s(i.outerEnd),innerStart:na(i.innerStart,0,o),innerEnd:na(i.innerEnd,0,o)}}(t,p,u,f-h),v=u-b,T=u-E,C=h+b/v,N=f-E/T,A=p+S,x=p+y,O=h+S/A,w=f-y/x;if(e.beginPath(),a){const t=(C+N)/2;if(e.arc(o,s,u,C,t),e.arc(o,s,u,t,N),E>0){const t=Gl(T,N,o,s);e.arc(t.x,t.y,E,N,f+zi)}const n=Gl(x,f,o,s);if(e.lineTo(n.x,n.y),y>0){const t=Gl(x,w,o,s);e.arc(t.x,t.y,y,f+zi,w+Math.PI)}const r=(f-y/p+(h+S/p))/2;if(e.arc(o,s,p,f-y/p,r,!0),e.arc(o,s,p,r,h+S/p,!0),S>0){const t=Gl(A,O,o,s);e.arc(t.x,t.y,S,O+Math.PI,h-zi)}const i=Gl(v,h,o,s);if(e.lineTo(i.x,i.y),b>0){const t=Gl(v,C,o,s);e.arc(t.x,t.y,b,h-zi,C)}}else{e.moveTo(o,s);const t=Math.cos(C)*u+o,n=Math.sin(C)*u+s;e.lineTo(t,n);const r=Math.cos(N)*u+o,i=Math.sin(N)*u+s;e.lineTo(r,i)}e.closePath()}function Hl(e,t,n=t){e.lineCap=Ei(n.borderCapStyle,t.borderCapStyle),e.setLineDash(Ei(n.borderDash,t.borderDash)),e.lineDashOffset=Ei(n.borderDashOffset,t.borderDashOffset),e.lineJoin=Ei(n.borderJoinStyle,t.borderJoinStyle),e.lineWidth=Ei(n.borderWidth,t.borderWidth),e.strokeStyle=Ei(n.borderColor,t.borderColor)}function Vl(e,t,n){e.lineTo(n.x,n.y)}function ql(e,t,n={}){const r=e.length,{start:i=0,end:a=r-1}=n,{start:o,end:s}=t,l=Math.max(i,o),c=Math.min(a,s),d=is&&a>s;return{count:r,start:l,loop:t.loop,ilen:c(o+(c?s-e:e))%a,E=()=>{m!==g&&(e.lineTo(h,g),e.lineTo(h,m),e.lineTo(h,_))};for(l&&(u=i[b(0)],e.moveTo(u.x,u.y)),d=0;d<=s;++d){if(u=i[b(d)],u.skip)continue;const t=u.x,n=u.y,r=0|t;r===p?(ng&&(g=n),h=(f*h+t)/++f):(E(),e.lineTo(t,n),p=r,f=0,m=g=n),_=n}E()}function $l(e){const t=e.options,n=t.borderDash&&t.borderDash.length;return e._decimated||e._loop||t.tension||"monotone"===t.cubicInterpolationMode||t.stepped||n?Yl:jl}const Wl="function"==typeof Path2D;function Kl(e,t,n,r){const i=e.options,{[n]:a}=e.getProps([n],r);return Math.abs(t-a){let{boxHeight:n=t,boxWidth:r=t}=e;return e.usePointStyle&&(n=Math.min(n,t),r=e.pointStyleWidth||Math.min(r,t)),{boxWidth:r,boxHeight:n,itemHeight:Math.max(t,n)}};class nc extends Js{constructor(e){super(),this._added=!1,this.legendHitBoxes=[],this._hoveredItem=null,this.doughnutMode=!1,this.chart=e.chart,this.options=e.options,this.ctx=e.ctx,this.legendItems=void 0,this.columnSizes=void 0,this.lineWidths=void 0,this.maxHeight=void 0,this.maxWidth=void 0,this.top=void 0,this.bottom=void 0,this.left=void 0,this.right=void 0,this.height=void 0,this.width=void 0,this._margins=void 0,this.position=void 0,this.weight=void 0,this.fullSize=void 0}update(e,t,n){this.maxWidth=e,this.maxHeight=t,this._margins=n,this.setDimensions(),this.buildLabels(),this.fit()}setDimensions(){this.isHorizontal()?(this.width=this.maxWidth,this.left=this._margins.left,this.right=this.width):(this.height=this.maxHeight,this.top=this._margins.top,this.bottom=this.height)}buildLabels(){const e=this.options.labels||{};let t=yi(e.generateLabels,[this.chart],this)||[];e.filter&&(t=t.filter((t=>e.filter(t,this.chart.data)))),e.sort&&(t=t.sort(((t,n)=>e.sort(t,n,this.chart.data)))),this.options.reverse&&t.reverse(),this.legendItems=t}fit(){const{options:e,ctx:t}=this;if(!e.display)return void(this.width=this.height=0);const n=e.labels,r=eo(n.font),i=r.size,a=this._computeTitleHeight(),{boxWidth:o,itemHeight:s}=tc(n,i);let l,c;t.font=r.string,this.isHorizontal()?(l=this.maxWidth,c=this._fitRows(a,i,o,s)+10):(c=this.maxHeight,l=this._fitCols(a,r,o,s)+10),this.width=Math.min(l,e.maxWidth||this.maxWidth),this.height=Math.min(c,e.maxHeight||this.maxHeight)}_fitRows(e,t,n,r){const{ctx:i,maxWidth:a,options:{labels:{padding:o}}}=this,s=this.legendHitBoxes=[],l=this.lineWidths=[0],c=r+o;let d=e;i.textAlign="left",i.textBaseline="middle";let u=-1,p=-c;return this.legendItems.forEach(((e,m)=>{const g=n+t/2+i.measureText(e.text).width;(0===m||l[l.length-1]+g+2*o>a)&&(d+=c,l[l.length-(m>0?0:1)]=0,p+=c,u++),s[m]={left:0,top:p,row:u,width:g,height:r},l[l.length-1]+=g+o})),d}_fitCols(e,t,n,r){const{ctx:i,maxHeight:a,options:{labels:{padding:o}}}=this,s=this.legendHitBoxes=[],l=this.columnSizes=[],c=a-e;let d=o,u=0,p=0,m=0,g=0;return this.legendItems.forEach(((e,a)=>{const{itemWidth:_,itemHeight:h}=function(e,t,n,r,i){const a=function(e,t,n,r){let i=e.text;return i&&"string"!=typeof i&&(i=i.reduce(((e,t)=>e.length>t.length?e:t))),t+n.size/2+r.measureText(i).width}(r,e,t,n),o=function(e,t,n){let r=e;return"string"!=typeof t.text&&(r=rc(t,n)),r}(i,r,t.lineHeight);return{itemWidth:a,itemHeight:o}}(n,t,i,e,r);a>0&&p+h+2*o>c&&(d+=u+o,l.push({width:u,height:p}),m+=u+o,g++,u=p=0),s[a]={left:m,top:p,col:g,width:_,height:h},u=Math.max(u,_),p+=h+o})),d+=u,l.push({width:u,height:p}),d}adjustHitBoxes(){if(!this.options.display)return;const e=this._computeTitleHeight(),{legendHitBoxes:t,options:{align:n,labels:{padding:r},rtl:i}}=this,a=Bo(i,this.left,this.width);if(this.isHorizontal()){let i=0,o=pa(n,this.left+r,this.right-this.lineWidths[i]);for(const s of t)i!==s.row&&(i=s.row,o=pa(n,this.left+r,this.right-this.lineWidths[i])),s.top+=this.top+e+r,s.left=a.leftForLtr(a.x(o),s.width),o+=s.width+r}else{let i=0,o=pa(n,this.top+e+r,this.bottom-this.columnSizes[i].height);for(const s of t)s.col!==i&&(i=s.col,o=pa(n,this.top+e+r,this.bottom-this.columnSizes[i].height)),s.top=o,s.left+=this.left+r,s.left=a.leftForLtr(a.x(s.left),s.width),o+=s.height+r}}isHorizontal(){return"top"===this.options.position||"bottom"===this.options.position}draw(){if(this.options.display){const e=this.ctx;Ba(e,this),this._draw(),Ua(e)}}_draw(){const{options:e,columnSizes:t,lineWidths:n,ctx:r}=this,{align:i,labels:a}=e,o=Ia.color,s=Bo(e.rtl,this.left,this.width),l=eo(a.font),{padding:c}=a,d=l.size,u=d/2;let p;this.drawTitle(),r.textAlign=s.textAlign("left"),r.textBaseline="middle",r.lineWidth=.5,r.font=l.string;const{boxWidth:m,boxHeight:g,itemHeight:_}=tc(a,d),h=this.isHorizontal(),f=this._computeTitleHeight();p=h?{x:pa(i,this.left+c,this.right-n[0]),y:this.top+c+f,line:0}:{x:this.left+c,y:pa(i,this.top+f+c,this.bottom-t[0].height),line:0},Uo(this.ctx,e.textDirection);const b=_+c;this.legendItems.forEach(((E,S)=>{r.strokeStyle=E.fontColor,r.fillStyle=E.fontColor;const y=r.measureText(E.text).width,v=s.textAlign(E.textAlign||(E.textAlign=a.textAlign)),T=m+u+y;let C=p.x,N=p.y;if(s.setWidth(this.width),h?S>0&&C+T+c>this.right&&(N=p.y+=b,p.line++,C=p.x=pa(i,this.left+c,this.right-n[p.line])):S>0&&N+b>this.bottom&&(C=p.x=C+t[p.line].width+c,p.line++,N=p.y=pa(i,this.top+f+c,this.bottom-t[p.line].height)),function(e,t,n){if(isNaN(m)||m<=0||isNaN(g)||g<0)return;r.save();const i=Ei(n.lineWidth,1);if(r.fillStyle=Ei(n.fillStyle,o),r.lineCap=Ei(n.lineCap,"butt"),r.lineDashOffset=Ei(n.lineDashOffset,0),r.lineJoin=Ei(n.lineJoin,"miter"),r.lineWidth=i,r.strokeStyle=Ei(n.strokeStyle,o),r.setLineDash(Ei(n.lineDash,[])),a.usePointStyle){const o={radius:g*Math.SQRT2/2,pointStyle:n.pointStyle,rotation:n.rotation,borderWidth:i},l=s.xPlus(e,m/2);La(r,o,l,t+u,a.pointStyleWidth&&m)}else{const a=t+Math.max((d-g)/2,0),o=s.leftForLtr(e,m),l=Xa(n.borderRadius);r.beginPath(),Object.values(l).some((e=>0!==e))?Ya(r,{x:o,y:a,w:m,h:g,radius:l}):r.rect(o,a,m,g),r.fill(),0!==i&&r.stroke()}r.restore()}(s.x(C),N,E),C=((e,t,n,r)=>e===(r?"left":"right")?n:"center"===e?(t+n)/2:t)(v,C+m+u,h?C+T:this.right,e.rtl),function(e,t,n){qa(r,n.text,e,t+_/2,l,{strikethrough:n.hidden,textAlign:s.textAlign(n.textAlign)})}(s.x(C),N,E),h)p.x+=T+c;else if("string"!=typeof E.text){const e=l.lineHeight;p.y+=rc(E,e)+c}else p.y+=b})),Go(this.ctx,e.textDirection)}drawTitle(){const e=this.options,t=e.title,n=eo(t.font),r=Ja(t.padding);if(!t.display)return;const i=Bo(e.rtl,this.left,this.width),a=this.ctx,o=t.position,s=n.size/2,l=r.top+s;let c,d=this.left,u=this.width;if(this.isHorizontal())u=Math.max(...this.lineWidths),c=this.top+l,d=pa(e.align,d,this.right-u);else{const t=this.columnSizes.reduce(((e,t)=>Math.max(e,t.height)),0);c=l+pa(e.align,this.top,this.bottom-t-e.labels.padding-this._computeTitleHeight())}const p=pa(o,d,d+u);a.textAlign=i.textAlign(ua(o)),a.textBaseline="middle",a.strokeStyle=t.color,a.fillStyle=t.color,a.font=n.string,qa(a,t.text,p,c,n)}_computeTitleHeight(){const e=this.options.title,t=eo(e.font),n=Ja(e.padding);return e.display?t.lineHeight+n.height:0}_getLegendItemAt(e,t){let n,r,i;if(ra(e,this.left,this.right)&&ra(t,this.top,this.bottom))for(i=this.legendHitBoxes,n=0;nnull!==e&&null!==t&&e.datasetIndex===t.datasetIndex&&e.index===t.index)(r,n);r&&!i&&yi(t.onLeave,[e,r,this],this),this._hoveredItem=n,n&&!i&&yi(t.onHover,[e,n,this],this)}else n&&yi(t.onClick,[e,n,this],this)}}function rc(e,t){return t*(e.text?e.text.length:0)}var ic={id:"legend",_element:nc,start(e,t,n){const r=e.legend=new nc({ctx:e.ctx,options:n,chart:e});Ps.configure(e,r,n),Ps.addBox(e,r)},stop(e){Ps.removeBox(e,e.legend),delete e.legend},beforeUpdate(e,t,n){const r=e.legend;Ps.configure(e,r,n),r.options=n},afterUpdate(e){const t=e.legend;t.buildLabels(),t.adjustHitBoxes()},afterEvent(e,t){t.replay||e.legend.handleEvent(t.event)},defaults:{display:!0,position:"top",align:"center",fullSize:!0,reverse:!1,weight:1e3,onClick(e,t,n){const r=t.datasetIndex,i=n.chart;i.isDatasetVisible(r)?(i.hide(r),t.hidden=!0):(i.show(r),t.hidden=!1)},onHover:null,onLeave:null,labels:{color:e=>e.chart.options.color,boxWidth:40,padding:10,generateLabels(e){const t=e.data.datasets,{labels:{usePointStyle:n,pointStyle:r,textAlign:i,color:a,useBorderRadius:o,borderRadius:s}}=e.legend.options;return e._getSortedDatasetMetas().map((e=>{const l=e.controller.getStyle(n?0:void 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s=document.createElement("input");s.type="radio";const l=o();s.id=l,s.name=`question${t}`,s.value=n,s.classList.add("mr-2"),s.dataset.correct=e.correct;const c=document.createElement("label");c.htmlFor=l,c.textContent=e.text,c.classList.add("flex-1");const d=document.createElement("div");if(d.classList.add("explains_wrapper","hidden"),e.correct){d.classList.add("relative","mt-2","rounded-lg","border-2","border-green-500","p-3");const e=document.createElement("div");e.classList.add("absolute","-top-3","left-2","rounded-lg","border-2","border-green-500","bg-white","px-2","text-xs","text-green-500"),e.textContent="Correct",d.appendChild(e)}const u=document.createElement("p");u.textContent=e.explanation,u.classList.add("explanation","text-sm","text-purple-500"),d.appendChild(u),i.appendChild(s),i.appendChild(c),r.append(i),r.appendChild(d),a.appendChild(r)})),n.appendChild(a),i.appendChild(n)}))}(t)}class 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e=this.pointer;if(!e)return!0;if(0===e.length||0!==e[0][0])return!1;let t=0,n=0;return this.document.forEach((e=>t+=e.length)),this.docs.forEach((e=>n+=e.length)),t===n},getNth:function(e){return"number"==typeof e?this.eq(e):"string"==typeof e?this.if(e):this}};Bc.group=Bc.groups,Bc.fullSentence=Bc.fullSentences,Bc.sentence=Bc.fullSentences,Bc.lastTerm=Bc.lastTerms,Bc.firstTerm=Bc.firstTerms;const Uc=Bc,Gc=Object.assign({},Uc,Lc,Fc);Gc.get=Gc.eq;const zc=Gc;class Hc{constructor(e,t,n={}){[["document",e],["world",Mc],["_groups",n],["_cache",null],["viewType","View"]].forEach((e=>{Object.defineProperty(this,e[0],{value:e[1],writable:!0})})),this.ptrs=t}get docs(){let e=this.document;return this.ptrs&&(e=Mc.methods.one.getDoc(this.ptrs,this.document)),e}get pointer(){return this.ptrs}get methods(){return this.world.methods}get model(){return this.world.model}get hooks(){return this.world.hooks}get isView(){return!0}get found(){return this.docs.length>0}get length(){return this.docs.length}get fullPointer(){let{docs:e,ptrs:t,document:n}=this,r=t||e.map(((e,t)=>[t]));return r.map((e=>{let[t,r,i,a,o]=e;return r=r||0,i=i||(n[t]||[]).length,n[t]&&n[t][r]&&(a=a||n[t][r].id,n[t][i-1]&&(o=o||n[t][i-1].id)),[t,r,i,a,o]}))}update(e){let t=new Hc(this.document,e);if(this._cache&&e&&e.length>0){let n=[];e.forEach(((e,t)=>{let[r,i,a]=e;(1===e.length||0===i&&this.document[r].length===a)&&(n[t]=this._cache[r])})),n.length>0&&(t._cache=n)}return t.world=this.world,t}toView(e){return new Hc(this.document,e||this.pointer)}fromText(e){const{methods:t}=this;let n=t.one.tokenize.fromString(e,this.world),r=new Hc(n);return r.world=this.world,r.compute(["normal","freeze","lexicon"]),this.world.compute.preTagger&&r.compute("preTagger"),r.compute("unfreeze"),r}clone(){let e=this.document.slice(0);e=e.map((e=>e.map((e=>((e=Object.assign({},e)).tags=new Set(e.tags),e)))));let t=this.update(this.pointer);return 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n=e.two.models||{};Object.keys(t).forEach((e=>{t[e].pastTense&&(n.toPast&&(n.toPast.ex[e]=t[e].pastTense),n.fromPast&&(n.fromPast.ex[t[e].pastTense]=e)),t[e].presentTense&&(n.toPresent&&(n.toPresent.ex[e]=t[e].presentTense),n.fromPresent&&(n.fromPresent.ex[t[e].presentTense]=e)),t[e].gerund&&(n.toGerund&&(n.toGerund.ex[e]=t[e].gerund),n.fromGerund&&(n.fromGerund.ex[t[e].gerund]=e)),t[e].comparative&&(n.toComparative&&(n.toComparative.ex[e]=t[e].comparative),n.fromComparative&&(n.fromComparative.ex[t[e].comparative]=e)),t[e].superlative&&(n.toSuperlative&&(n.toSuperlative.ex[e]=t[e].superlative),n.fromSuperlative&&(n.fromSuperlative.ex[t[e].superlative]=e))}))}(a,e.irregulars),e.compute&&Object.assign(o,e.compute),s&&(t.hooks=s.concat(e.hooks||[])),e.api&&e.api(n),e.lib&&Object.keys(e.lib).forEach((t=>r[t]=e.lib[t])),e.tags&&r.addTags(e.tags),e.words&&r.addWords(e.words),e.frozen&&r.addWords(e.frozen,!0),e.mutate&&e.mutate(t)},$c=function(e){return"[object 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jc(e,this._world,Vc,this),this},Qc.extend=Qc.plugin,Qc.world=function(){return this._world},Qc.model=function(){return this._world.model},Qc.methods=function(){return this._world.methods},Qc.hooks=function(){return this._world.hooks},Qc.verbose=function(e){const t="undefined"!=typeof process&&process.env?process.env:self.env||{};return t.DEBUG_TAGS="tagger"===e||!0===e||"",t.DEBUG_MATCH="match"===e||!0===e||"",t.DEBUG_CHUNKS="chunker"===e||!0===e||"",this},Qc.version="14.13.0";const Zc=Qc,Xc={cache:function(){return this._cache=this.methods.one.cacheDoc(this.document),this},uncache:function(){return this._cache=null,this}},Jc={api:function(e){Object.assign(e.prototype,Xc)},compute:{cache:function(e){e._cache=e.methods.one.cacheDoc(e.document)}},methods:{one:{cacheDoc:function(e){let t=e.map((e=>{let t=new Set;return 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r=t.model.one.aliases;if(r.hasOwnProperty(n)&&(e.alias=e.alias||[],e.alias.push(r[n])),Ag.test(n)&&!xg.test(n)&&!Og.test(n)){let t=n.split(Ag);t.length<=3&&t.forEach((t=>{""!==(t=t.trim())&&(e.alias=e.alias||[],e.alias.push(t))}))}return e},Rg=/^\p{Letter}+-\p{Letter}+$/u,Ig=function(e){let t=e.implicit||e.normal||e.text;t=t.replace(/['’]s$/,""),t=t.replace(/s['’]$/,"s"),t=t.replace(/([aeiou][ktrp])in'$/,"$1ing"),Rg.test(t)&&(t=t.replace(/-/g,"")),t=t.replace(/^[#@]/,""),t!==e.normal&&(e.machine=t)},Dg=function(e,t){let n=e.docs;for(let r=0;rDg(e,wg),machine:e=>Dg(e,Ig),normal:e=>Dg(e,gg),freq:function(e){let t=e.docs,n={};for(let e=0;e{let o=(e=e.toLowerCase().trim()).length;t.max&&o>t.max&&(o=t.max);for(let s=t.min;s{delete r[e]})),r}(e,t,this.world());return Object.keys(i).forEach((e=>{n.one.typeahead.hasOwnProperty(e)?delete n.one.typeahead[e]:n.one.typeahead[e]=i[e]})),this}},Bg={model:{one:{typeahead:{}}},api:function(e){e.prototype.autoFill=Pg},lib:Fg,compute:Mg,hooks:["typeahead"]};Zc.extend(Od),Zc.extend(Xp),Zc.extend(Tp),Zc.extend(lm),Zc.extend(Um),Zc.plugin(Jd),Zc.extend(kg),Zc.extend(iu),Zc.plugin(Jc),Zc.extend(fu),Zc.extend(Bg),Zc.extend(uu),Zc.extend(dm);const Ug=Zc,Gg={addendum:"addenda",corpus:"corpora",criterion:"criteria",curriculum:"curricula",genus:"genera",memorandum:"memoranda",opus:"opera",ovum:"ova",phenomenon:"phenomena",referendum:"referenda",alga:"algae",alumna:"alumnae",antenna:"antennae",formula:"formulae",larva:"larvae",nebula:"nebulae",vertebra:"vertebrae",analysis:"analyses",axis:"axes",diagnosis:"diagnoses",parenthesis:"parentheses",prognosis:"prognoses",synopsis:"synopses",thesis:"theses",neurosis:"neuroses",appendix:"appendices",index:"indices",matrix:"matrices",ox:"oxen",sex:"sexes",alumnus:"alumni",bacillus:"bacilli",cactus:"cacti",fungus:"fungi",hippopotamus:"hippopotami",libretto:"libretti",modulus:"moduli",nucleus:"nuclei",octopus:"octopi",radius:"radii",stimulus:"stimuli",syllabus:"syllabi",cookie:"cookies",calorie:"calories",auntie:"aunties",movie:"movies",pie:"pies",rookie:"rookies",tie:"ties",zombie:"zombies",leaf:"leaves",loaf:"loaves",thief:"thieves",foot:"feet",goose:"geese",tooth:"teeth",beau:"beaux",chateau:"chateaux",tableau:"tableaux",bus:"buses",gas:"gases",circus:"circuses",crisis:"crises",virus:"viruses",database:"databases",excuse:"excuses",abuse:"abuses",avocado:"avocados",barracks:"barracks",child:"children",clothes:"clothes",echo:"echoes",embargo:"embargoes",epoch:"epochs",deer:"deer",halo:"halos",man:"men",woman:"women",mosquito:"mosquitoes",mouse:"mice",person:"people",quiz:"quizzes",rodeo:"rodeos",shoe:"shoes",sombrero:"sombreros",stomach:"stomachs",tornado:"tornados",tuxedo:"tuxedos",volcano:"volcanoes"},zg={Comparative:"true¦bett1f0;arth0ew0in0;er",Superlative:"true¦earlier",PresentTense:"true¦bests,sounds",Condition:"true¦lest,unless",PastTense:"true¦began,came,d4had,kneel3l2m0sa4we1;ea0sg2;nt;eap0i0;ed;id",Participle:"true¦0:09;a06b01cZdXeat0fSgQhPoJprov0rHs7t6u4w1;ak0ithdra02o2r1;i02uY;k0v0;nd1pr04;ergoJoJ;ak0hHo3;e9h7lain,o6p5t4un3w1;o1um;rn;g,k;ol0reS;iQok0;ught,wn;ak0o1runk;ne,wn;en,wn;ewriNi1uJ;dd0s0;ut3ver1;do4se0t1;ak0h2;do2g1;roG;ne;ast0i7;iv0o1;ne,tt0;all0loBor1;bi3g2s1;ak0e0;iv0o9;dd0;ove,r1;a5eamt,iv0;hos0lu1;ng;e4i3lo2ui1;lt;wn;tt0;at0en,gun;r2w1;ak0ok0;is0;en",Gerund:"true¦accord0be0doin,go0result0stain0;ing",Expression:"true¦a0Yb0Uc0Sd0Oe0Mfarew0Lg0FhZjeez,lWmVnToOpLsJtIuFvEw7y0;a5e3i1u0;ck,p;k04p0;ee,pee;a0p,s;!h;!a,h,y;a5h2o1t0;af,f;rd up,w;atsoever,e1o0;a,ops;e,w;hoo,t;ery w06oi0L;gh,h0;! 0h,m;huh,oh;here nPsk,ut tut;h0ic;eesh,hh,it,oo;ff,h1l0ow,sst;ease,s,z;ew,ooey;h1i,mg,o0uch,w,y;h,o,ps;! 0h;hTmy go0wT;d,sh;a7evertheless,o0;!pe;eh,mm;ah,eh,m1ol0;!s;ao,fao;aCeBi9o2u0;h,mph,rra0zzC;h,y;l1o0;r6y9;la,y0;! 0;c1moCsmok0;es;ow;!p hip hoor0;ay;ck,e,llo,y;ha1i,lleluj0;ah;!ha;ah,ee4o1r0;eat scott,r;l1od0sh; grief,bye;ly;! whiz;ell;e0h,t cetera,ureka,ww,xcuse me;k,p;'oh,a0rat,uh;m0ng;mit,n0;!it;mon,o0;ngratulations,wabunga;a2oo1r0tw,ye;avo,r;!ya;h,m; 1h0ka,las,men,rgh,ye;!a,em,h,oy;la",Negative:"true¦n0;ever,o0;n,t",QuestionWord:"true¦how3wh0;at,e1ich,o0y;!m,se;n,re; come,'s",Reflexive:"true¦h4it5my5o1the0your2;ir1m1;ne3ur0;sel0;f,ves;er0im0;self",Plural:"true¦dick0gre0ones,records;ens","Unit|Noun":"true¦cEfDgChBinchAk9lb,m6newt5oz,p4qt,t1y0;ardEd;able1b0ea1sp;!l,sp;spo1;a,t,x;on9;!b,g,i1l,m,p0;h,s;!les;!b,elvin,g,m;!es;g,z;al,b;eet,oot,t;m,up0;!s",Value:"true¦a few",Imperative:"true¦bewa0come he0;re","Plural|Verb":"true¦leaves",Demonym:"true¦0:15;1:12;a0Vb0Oc0Dd0Ce08f07g04h02iYjVkTlPmLnIomHpEqatari,rCs7t5u4v3welAz2;am0Gimbabwe0;enezuel0ietnam0I;gAkrai1;aiwTex0hai,rinida0Ju2;ni0Prkmen;a5cotti4e3ingapoOlovak,oma0Spaniard,udRw2y0W;ede,iss;negal0Cr09;sh;mo0uT;o5us0Jw2;and0;a2eru0Fhilippi0Nortugu07uerto r0S;kist3lesti1na2raguay0;ma1;ani;ami00i2orweP;caragu0geri2;an,en;a3ex0Lo2;ngo0Drocc0;cedo1la2;gasy,y07;a4eb9i2;b2thua1;e0Cy0;o,t01;azakh,eny0o2uwaiI;re0;a2orda1;ma0Ap2;anO;celandic,nd4r2sraeli,ta01vo05;a2iB;ni0qi;i0oneU;aiAin2ondur0unO;di;amEe2hanai0reek,uatemal0;or2rm0;gi0;ilipino,ren8;cuadoVgyp4mira3ngli2sto1thiopi0urope0;shm0;ti;ti0;aPominUut3;a9h6o4roat3ub0ze2;ch;!i0;lom2ngol5;bi0;a6i2;le0n2;ese;lifor1m2na3;bo2eroo1;di0;angladeshi,el6o4r3ul2;gaE;azi9it;li2s1;vi0;aru2gi0;si0;fAl7merBngol0r5si0us2;sie,tr2;a2i0;li0;genti2me1;ne;ba1ge2;ri0;ni0;gh0r2;ic0;an",Organization:"true¦0:4Q;a3Tb3Bc2Od2He2Df27g1Zh1Ti1Pj1Nk1Ll1Gm12n0Po0Mp0Cqu0Br02sTtHuCv9w3xiaomi,y1;amaha,m1Bou1w1B;gov,tu3C;a4e2iki1orld trade organizati33;leaRped0O;lls fargo,st1;fie2Hinghou2R;l1rner br3U;gree3Jl street journ2Im1E;an halOeriz2Xisa,o1;dafo2Yl1;kswagMvo;b4kip,n2ps,s1;a tod3Aps;es3Mi1;lev3Fted natio3C;er,s; mobi32aco beRd bOe9gi frida3Lh3im horto3Amz,o1witt3D;shi49y1;ota,s r 05;e 1in lizzy;b3carpen3Jdaily ma3Dguess w2holli0s1w2;mashing pumpki35uprem0;ho;ea1lack eyed pe3Xyr0Q;ch bo3Dtl0;l2n3Qs1xas instrumen1U;co,la m1F;efoni0Kus;a8cientology,e5ieme2Ymirnoff,np,o3pice gir6quare0Ata1ubaru;rbuc1to34;ks;ny,undgard1;en;a2x pisto1;ls;g1Wrs;few2Minsbur31lesfor03msu2E;adiohead,b8e4o1yana3C;man empi1Xyal 1;b1dutch she4;ank;a3d 1max,vl20;bu1c2Ahot chili peppe2Ylobst2N;ll;ders dige1Ll madrid;c,s;ant3Aizn2Q;a8bs,e5fiz2Ihilip4i3r1;emier 1udenti1D;leagTo2K;nk floyd,zza hut; morrBs;psi2tro1uge0E;br33chi0Tn33;!co;lant2Un1yp16; 2ason27da2P;ld navy,pec,range juli2xf1;am;us;aAb9e6fl,h5i4o1sa,vid3wa;k2tre dame,vart1;is;ia;ke,ntendo,ss0QvZ;l,s;c,st1Otflix,w1; 1sweek;kids on the block,york0D;a,c;nd22s2t1;ional aca2Po,we0U;a,c02d0S;aDcdonalCe9i6lb,o3tv,y1;spa1;ce;b1Tnsanto,ody blu0t1;ley cr1or0T;ue;c2t1;as,subisO;helin,rosoft;dica2rcedes benz,talli1;ca;id,re;ds;cs milk,tt19z24;a3e1g,ittle caesa1P; ore09novo,x1;is,mark,us; 1bour party;pres0Dz boy;atv,fc,kk,lm,m1od1O;art;iffy lu0Roy divisi0Jpmorgan1sa;! cha09;bm,hop,k3n1tv;g,te1;l,rpol;ea;a5ewlett pack1Vi3o1sbc,yundai;me dep1n1P;ot;tac1zbollah;hi;lliburt08sbro;eneral 6hq,ithub,l5mb,o2reen d0Ou1;cci,ns n ros0;ldman sachs,o1;dye1g0H;ar;axo smith kli04encoW;electr0Nm1;oto0Z;a5bi,c barcelo4da,edex,i2leetwood m03o1rito l0G;rd,xcY;at,fa,nancial1restoZ; tim0;na;cebook,nnie mae;b0Asa,u3xxon1; m1m1;ob0J;!rosceptics;aiml0De5isney,o4u1;nkin donu2po0Zran dur1;an;ts;j,w jon0;a,f lepp12ll,peche mode,r spieg02stiny's chi1;ld;aJbc,hFiDloudflaCnn,o3r1;aigsli5eedence clearwater reviv1ossra09;al;c7inba6l4m1o0Est09;ca2p1;aq;st;dplSg1;ate;se;a c1o chanQ;ola;re;a,sco1tigroup;! systems;ev2i1;ck fil a,na daily;r1y;on;d2pital o1rls jr;ne;bury,ill1;ac;aEbc,eBf9l5mw,ni,o1p,rexiteeU;ei3mbardiIston 1;glo1pizza;be;ng;o2ue c1;roV;ckbuster video,omingda1;le; g1g1;oodriL;cht2e ge0rkshire hathaw1;ay;el;cardi,idu,nana republ3s1xt5y5;f,kin robbi1;ns;ic;bYcTdidSerosmith,iRlKmEnheuser busDol,ppleAr6s4u3v2y1;er;is,on;di,todesk;hland o1sociated E;il;b3g2m1;co;os;ys; compu1be0;te1;rs;ch;c,d,erican3t1;!r1;ak; ex1;pre1;ss; 5catel2ta1;ir;! lu1;ce1;nt;jazeera,qae1;da;g,rbnb;as;/dc,a3er,tivision1;! blizz1;ard;demy of scienc0;es;ba",Possessive:"true¦its,my,our0thy;!s","Noun|Verb":"true¦0:9W;1:AA;2:96;3:A3;4:9R;5:A2;6:9K;7:8N;8:7L;9:A8;A:93;B:8D;C:8X;a9Ob8Qc7Id6Re6Gf5Sg5Hh55i4Xj4Uk4Rl4Em40n3Vo3Sp2Squ2Rr21s0Jt02u00vVwGyFzD;ip,oD;ne,om;awn,e6Fie68;aOeMhJiHoErD;ap,e9Oink2;nd0rDuC;kDry,sh5Hth;!shop;ck,nDpe,re,sh;!d,g;e86iD;p,sD;k,p0t2;aDed,lco8W;r,th0;it,lk,rEsDt4ve,x;h,te;!ehou1ra9;aGen5FiFoD;iDmAte,w;ce,d;be,ew,sA;cuum,l4B;pDr7;da5gra6Elo6A;aReQhrPiOoMrGuEwiDy5Z;n,st;nDrn;e,n7O;aGeFiEoDu6;t,ub2;bu5ck4Jgg0m,p;at,k,nd;ck,de,in,nsDp,v7J;f0i8R;ll,ne,p,r4Yss,t94uD;ch,r;ck,de,e,le,me,p,re;e5Wow,u6;ar,e,ll,mp0st,xt;g,lDng2rg7Ps5x;k,ly;a0Sc0Ne0Kh0Fi0Dk0Cl0Am08n06o05pXquaBtKuFwD;ea88iD;ng,pe,t4;bGit,m,ppErD;fa3ge,pri1v2U;lDo6S;e6Py;!je8;aMeLiKoHrEuDy2;dy,ff,mb2;a85eEiDo5Pugg2;ke,ng;am,ss,t4;ckEop,p,rD;e,m;ing,pi2;ck,nk,t4;er,m,p;ck,ff,ge,in,ke,lEmp,nd,p2rDte,y;!e,t;k,l;aJeIiHlGoFrDur,y;ay,e56inDu3;g,k2;ns8Bt;a5Qit;ll,n,r87te;ed,ll;m,n,rk;b,uC;aDee1Tow;ke,p;a5Je4FiDo53;le,rk;eep,iDou4;ce,p,t;ateboa7Ii;de,gnDl2Vnk,p,ze;!al;aGeFiEoDuff2;ck,p,re,w;ft,p,v0;d,i3Ylt0;ck,de,pe,re,ve;aEed,nDrv1It;se,t2N;l,r4t;aGhedu2oBrD;aEeDibb2o3Z;en,w;pe,t4;le,n,r2M;cDfegua72il,mp2;k,rifi3;aZeHhy6LiGoEuD;b,in,le,n,s5X;a6ck,ll,oDpe,u5;f,t;de,ng,ot,p,s1W;aTcSdo,el,fQgPje8lOmMnLo17pJque6sFturn,vDwa6V;eDi27;al,r1;er74oFpe8tEuD;lt,me;!a55;l71rt;air,eaDly,o53;l,t;dezvo2Zt;aDedy;ke,rk;ea1i4G;a6Iist0r5N;act6Yer1Vo71uD;nd,se;a38o6F;ch,s6G;c1Dge,iEke,lly,nDp1Wt1W;ge,k,t;n,se;es6Biv0;a04e00hYiXlToNrEsy4uD;mp,n4rcha1sh;aKeIiHoDu4O;be,ceFdu3fi2grDje8mi1p,te6;amDe6W;!me;ed,ss;ce,de,nt;sDy;er6Cs;cti3i1;iHlFoEp,re,sDuCw0;e,i5Yt;l,p;iDl;ce,sh;nt,s5V;aEce,e32uD;g,mp,n7;ce,nDy;!t;ck,le,n17pe,tNvot;a1oD;ne,tograph;ak,eFnErDt;fu55mA;!c32;!l,r;ckJiInHrFsEtDu1y;ch,e9;s,te;k,tD;!y;!ic;nt,r,se;!a7;bje8ff0il,oErDutli3Qver4B;bAd0ie9;ze;a4ReFoDur1;d,tD;e,i3;ed,gle8tD;!work;aMeKiIoEuD;rd0;ck,d3Rld,nEp,uDve;nt,th;it5EkD;ey;lk,n4Brr5CsDx;s,ta2B;asuBn4UrDss;ge,it;il,nFp,rk3WsEtD;ch,t0;h,k,t0;da5n0oeuvB;aLeJiHoEuD;mp,st;aEbby,ck,g,oDve;k,t;d,n;cDe,ft,mAnIst;en1k;aDc0Pe4vK;ch,d,k,p,se;bFcEnd,p,t4uD;gh,n4;e,k;el,o2U;eEiDno4E;ck,d,ll,ss;el,y;aEo1OuD;i3mp;m,zz;mpJnEr46ssD;ue;c1Rdex,fluGha2k,se2HteDvoi3;nt,rD;e6fa3viD;ew;en3;a8le2A;aJeHiGoEuD;g,nt;l3Ano2Dok,pDr1u1;!e;ghli1Fke,nt,re,t;aDd7lp;d,t;ck,mGndFrEsh,tDu9;ch,e;bo3Xm,ne4Eve6;!le;!m0;aMear,ift,lKossJrFuD;arDe4Alp,n;antee,d;aFiEoDumb2;uCwth;ll,nd,p;de,sp;ip;aBoDue;ss,w;g,in,me,ng,s,te,ze;aZeWiRlNoJrFuD;ck,el,nDss,zz;c38d;aEoDy;st,wn;cDgme,me,nchi1;tuB;cFg,il,ld,rD;ce,e29mDwa31;!at;us;aFe0Vip,oDy;at,ck,od,wD;!er;g,ke,me,re,sh,vo1E;eGgFlEnDre,sh,t,x;an3i0Q;e,m,t0;ht,uB;ld;aEeDn3;d,l;r,tuB;ce,il,ll,rm,vo2W;cho,d7ffe8nMsKxFyeD;!baD;ll;cGerci1hFpDtra8;eriDo0W;en3me9;au6ibA;el,han7u1;caDtima5;pe;count0d,vy;a01eSiMoJrEuDye;b,el,mp,pli2X;aGeFiEoD;ne,p;ft,ll,nk,p,ve;am,ss;ft,g,in;cEd7ubt,wnloD;ad;k,u0E;ge6p,sFt4vD;e,iDor3;de;char7gui1h,liEpD;at4lay,u5;ke;al,bKcJfeIlGmaCposAsEtaD;il;e07iD;gn,re;ay,ega5iD;ght;at,ct;li04rea1;a5ut;b,ma7n3rDte;e,t;a0Eent0Dh06irc2l03oKrFuD;be,e,rDt;b,e,l,ve;aGeFoEuDy;sh;p,ss,wd;dAep;ck,ft,sh;at,de,in,lTmMnFordina5py,re,st,uDv0;gh,nDp2rt;s01t;ceHdu8fli8glomeIsFtDveN;a8rD;a6ol;e9tru8;ct;ntDrn;ra5;bHfoGmFpD;leDouCromi1;me9;aCe9it,u5;rt;at,iD;ne;lap1oD;r,ur;aEiDoud,ub;ck,p;im,w;aEeDip;at,ck,er;iGllen7nErD;ge,m,t;ge,nD;el;n,r;er,re;ke,ll,mp,noe,pGrXsFtEuDve;se,ti0I;alog,ch;h,t;!tuB;re;a03eZiXlToPrHuEyD;pa11;bb2ck2dgEff0mp,rDst,zz;den,n;et;anJeHiFoadEuD;i1sh;ca6;be,d7;ge;aDed;ch,k;ch,d;aFg,mb,nEoDrd0tt2x,ycott;k,st,t;d,e;rd,st;aFeCiDoYur;nk,tz;nd;me;as,d,ke,nd,opsy,tD;!ch,e;aFef,lt,nDt;d,efA;it;r,t;ck,il,lan3nIrFsEtt2;le;e,h;!gDk;aDe;in;!d,g,k;bu1c05dZge,iYlVnTppQrLsIttGucEwaD;rd;tiD;on;aDempt;ck;k,sD;i6ocia5;st;chFmD;!oD;ur;!iD;ve;eEroa4;ch;al;chDg0sw0;or;aEt0;er;rm;d,m,r;dreHvD;an3oD;ca5;te;ce;ss;cDe,he,t;eFoD;rd,u9;nt;nt,ss;se",Actor:"true¦0:7B;1:7G;2:6A;3:7F;4:7O;5:7K;a6Nb62c4Ud4Be41f3Sg3Bh30i2Uj2Qkin2Pl2Km26n1Zo1Sp0Vqu0Tr0JsQtJuHvEw8yo6;gi,ut6;h,ub0;aAe9i8o7r6;estl0it0;m2rk0;fe,nn0t2Bza2H;atherm2ld0;ge earn0it0nder0rri1;eter7i6oyF;ll5Qp,s3Z;an,ina2U;n6s0;c6Uder03;aoisea23e9herapi5iktok0o8r6ut1yco6S;a6endseLo43;d0mp,nscri0Bvel0;ddl0u1G;a0Qchn7en6na4st0;ag0;i3Oo0D;aiXcUeRhPiMki0mu26oJpGquaFtBu7wee6;p0theart;lt2per7r6;f0ge6Iviv1;h6inten0Ist5Ivis1;ero,um2;a8ep7r6;ang0eam0;bro2Nc2Ofa2Nmo2Nsi20;ff0tesm2;tt0;ec7ir2Do6;kesp59u0M;ia5Jt3;l7me6An,rcere6ul;r,ss;di0oi5;n7s6;sy,t0;g0n0;am2ephe1Iow6;girl,m2r2Q;cretInior cit3Fr6;gea4v6;a4it1;hol4Xi7reen6ulpt1;wr2C;e01on;l1nt;aEe9o8u6;l0nn6;er up,ingE;g40le mod3Zof0;a4Zc8fug2Ppo32searQv6;ere4Uolution6;ary;e6luYru22;ptio3T;bbi,dic5Vpp0;arter6e2Z;back;aYeWhSiRlOoKr8sycho7u6;nk,p31;logi5;aGeDiBo6;d9fess1g7ph47s6;pe2Ktitu51;en6ramm0;it1y;igy,uc0;est4Nme mini0Unce6s3E;!ss;a7si6;de4;ch0;ctiti39nk0P;dca0Oet,li6pula50rnst42;c2Itic6;al scie6i2;nti5;a6umb0;nn0y6;er,ma4Lwright;lgrim,one0;a8iloso7otogra7ra6ysi1V;se;ph0;ntom,rmaci5;r6ssi1T;form0s4O;i3El,nel3Yr8st1tr6wn;i6on;arWot;ent4Wi42tn0;ccupa4ffBp8r7ut6;ca5l0B;ac4Iganiz0ig2Fph2;er3t6;i1Jomet6;ri5;ic0spring;aBe9ie4Xo7u6;n,rser3J;b6mad,vi4V;le2Vo4D;i6mesis,phew;ce,ghb1;nny,rr3t1X;aEeDiAo7u6yst1Y;m8si16;der3gul,m7n6th0;arDk;!my;ni7s6;f02s0Jt0;on,st0;chan1Qnt1rcha4;gi9k0n8rtyr,t6y1;e,riar6;ch;ag0iac;ci2stra3I;a7e2Aieutena4o6;rd,s0v0;bor0d7ndlo6ss,urea3Fwy0ym2;rd;!y;!s28;e8o7u6;ggl0;gg0urna2U;st0;c3Hdol,llu3Ummigra4n6; l9c1Qfa4habi42nov3s7ve6;nt1stig3;pe0Nt6;a1Fig3ru0M;aw;airFeBistoAo8u6ygie1K;man6sba2H;!ita8;bo,st6usekN;age,e3P;ri2;ir,r6;m7o6;!ine;it;dress0sty2C;aLeIhostGirl26ladi3oCrand7u6;e5ru;c9daug0Jfa8m7pa6s2Y;!re4;a,o6;th0;hi1B;al7d6lf0;!de3A;ie,k6te26;eep0;!wr6;it0;isha,n6;i6tl04;us;mbl0rden0;aDella,iAo7r6;eela2Nie1P;e,re6ster pare4;be1Hm2r6st0;unn0;an2ZgZlmm17nanci0r6tt0;e6st la2H; marsh2OfigXm2;rm0th0;conoEdDlectriCm8n7x6;amin0cellency,i2A;emy,trepreneur,vironmenta1J;c8p6;er1loye6;e,r;ee;ci2;it1;mi5;aKeBi8ork,ri7u6we02;de,tche2H;ft0v0;ct3eti7plom2Hre6va;ct1;ci2ti2;aDcor3fencCi0InAput9s7tectLvel6;op0;ce1Ge6ign0;rt0;ee,y;iz6;en;em2;c1Ml0;d8nc0redev7ug6;ht0;il;!dy;a06e04fo,hXitizenWlToBr9u6;r3stomer6;! representat6;ive;e3it6;ic;lJmGnAord9rpor1Nu7w6;boy,ork0;n6ri0;ciTte1Q;in3;fidantAgressSs9t6;e0Kr6;ibut1o6;ll0;tab13ul1O;!e;edi2m6pos0rade;a0EeQissi6;on0;leag8on7um6;ni5;el;ue;e6own;an0r6;ic,k;!s;a9e7i6um;ld;erle6f;ad0;ir7nce6plFract0;ll1;m2wI;lebri6o;ty;dBptAr6shi0;e7pe6;nt0;r,t6;ak0;ain;et;aMeLiJlogg0oErBu6;dd0Fild0rgl9siness6;m2p7w6;om2;ers05;ar;i7o6;!k0th0;cklay0de,gadi0;hemi2oge8y6;!frie6;nd;ym2;an;cyc6sR;li5;atbox0ings;by,nk0r6;b0on7te6;nd0;!e07;c04dWge4nQpLrHsFtAu7yatull6;ah;nt7t6;h1oG;!ie;h8t6;e6orney;nda4;ie5le6;te;sis00tron6;aut,om0;chbis8isto7tis6;an,t;crU;hop;ost9p6;ari6rentiS;ti6;on;le;a9cest1im3nou8y6;bo6;dy;nc0;ly5rc6;hi5;mi8v6;entur0is1;er;ni7r6;al;str3;at1;or;counBquaintanArob9t6;ivi5or,re6;ss;st;at;ce;ta4;nt","Adj|Noun":"true¦0:16;a1Db17c0Ud0Re0Mf0Dg0Ah08i06ju05l02mWnUoSpNrIsBt7u4v1watershed;a1ision0Z;gabo4nilla,ria1;b0Vnt;ndergr1pstairs;adua14ou1;nd;a3e1oken,ri0;en,r1;min0rori13;boo,n;age,e5ilv0Flack,o3quat,ta2u1well;bordina0Xper5;b0Lndard;ciali0Yl1vereign;e,ve16;cret,n1ri0;ior;a4e2ou1ubbiL;nd,tiY;ar,bBl0Wnt0p1side11;resent0Vublican;ci0Qsh;a4eriodic0last0Zotenti0r1;emi2incip0o1;!fession0;er,um;rall4st,tie0U;ff1pposi0Hv0;ens0Oi0C;agg01ov1uts;el;a5e3iniatJo1;bi01der07r1;al,t0;di1tr0N;an,um;le,riG;attOi2u1;sh;ber0ght,qC;stice,veniT;de0mpressioYn1;cumbe0Edividu0no0Dsta0Eterim;alf,o1umdrum;bby,melF;en2old,ra1;ph0Bve;er0ious;a7e5i4l3u1;git03t1;ure;uid;ne;llow,m1;aFiL;ir,t,vo1;riOuriO;l3p00x1;c1ecutUpeV;ess;d1iK;er;ar2e1;mographUrivO;k,l2;hiGlassSo2rude,unn1;ing;m5n1operK;creCstitueOte2vertab1;le;mpor1nt;ary;ic,m2p1;anion,lex;er2u1;ni8;ci0;al;e5lank,o4r1;i2u1;te;ef;ttom,urgeois;st;cadem9d6l2ntarct9r1;ab,ct8;e3tern1;at1;ive;rt;oles1ult;ce1;nt;ic","Adj|Past":"true¦0:4Q;1:4C;2:4H;3:4E;a44b3Tc36d2Je29f20g1Wh1Si1Jj1Gkno1Fl1Am15n12o0Xp0Mqu0Kr08sLtEuAv9w4yellow0;a7ea6o4rinkl0;r4u3Y;n,ri0;k31th3;rp0sh0tZ;ari0e1O;n5p4s0;d1li1Rset;cov3derstood,i4;fi0t0;a8e3Rhr7i6ouTr4urn0wi4C;a4imm0ou2G;ck0in0pp0;ed,r0;eat2Qi37;m0nn0r4;get0ni2T;aOcKeIhGimFm0Hoak0pDt7u4;bsid3Ogge44s4;pe4ta2Y;ct0nd0;a8e7i2Eok0r5u4;ff0mp0nn0;ength2Hip4;ed,p0;am0reotyp0;in0t0;eci4ik0oH;al3Efi0;pRul1;a4ock0ut;d0r0;a4c1Jle2t31;l0s3Ut0;a6or5r4;at4e25;ch0;r0tt3;t4ut0;is2Mur1;aEe5o4;tt0;cAdJf2Bg9je2l8m0Knew0p7qu6s4;eTpe2t4;or0ri2;e3Dir0;e1lac0;at0e2Q;i0Rul1;eiv0o4ycl0;mme2Lrd0v3;in0lli0ti2A;a4ot0;li28;aCer30iBlAo9r5u4;mp0zzl0;e6i2Oo4;ce2Fd4lo1Anou30pos0te2v0;uc0;fe1CocCp0Iss0;i2Kli1L;ann0e2CuS;ck0erc0ss0;ck0i2Hr4st0;allLk0;bse7c6pp13rgan2Dver4;lo4whelm0;ok0;cupi0;rv0;aJe5o4;t0uri1A;ed0gle2;a6e5ix0o4ut0ys1N;di1Nt15u26;as0Clt0;n4rk0;ag0ufact0A;e6i5o4;ad0ck0st,v0;cens0m04st0;ft,v4;el0;tt0wn;a5o15u4;dg0s1B;gg0;llumSmpAn4sol1;br0cre1Ldebt0f8jZspir0t5v4;it0olv0;e4ox0Y;gr1n4re23;d0si15;e2l1o1Wuri1;li0o01r4;ov0;a6e1o4um03;ok0r4;ri0Z;mm3rm0;i6r5u4;a1Bid0;a0Ui0Rown;ft0;aAe9i8l6oc0Ir4;a4i0oz0Y;ctHg19m0;avo0Ju4;st3;ni08tt0x0;ar0;d0il0sc4;in1;dCl1mBn9quipp0s8x4;agger1c6p4te0T;a0Se4os0;ct0rie1D;it0;cap0tabliZ;cha0XgFha1As4;ur0;a0Zbarra0N;i0Buc1;aMeDi5r4;a01i0;gni08miniSre2s4;a9c6grun0Ft4;o4re0Hu17;rt0;iplWou4;nt0r4;ag0;bl0;cBdRf9l8p7ra6t5v4;elop0ot0;ail0ermQ;ng0;re07;ay0ight0;e4in0o0M;rr0;ay0enTor1;m5t0z4;ed,zl0;ag0p4;en0;aPeLhIlHo9r6u4;lt4r0stom03;iv1;a5owd0u4;sh0;ck0mp0;d0loAm7n4ok0v3;centr1f5s4troC;id3olid1;us0;b5pl4;ic1;in0;r0ur0;assi9os0utt3;ar5i4;ll0;g0m0;lebr1n6r4;ti4;fi0;tralJ;g0lcul1;aDewild3iCl9o7r5urn4;ed,t;ok4uis0;en;il0r0t4und;tl0;e5i4;nd0;ss0;as0;ffl0k0laMs0tt3;bPcNdKfIg0lFmaz0nDppBrm0ss9u5wa4;rd0;g5thor4;iz0;me4;nt0;o6u4;m0r0;li0re4;ci1;im1ticip1;at0;a5leg0t3;er0;rm0;fe2;ct0;ju5o7va4;nc0;st0;ce4knowledg0;pt0;and5so4;rb0;on0;ed",Singular:"true¦0:5I;1:5G;2:4V;3:4R;4:51;5:56;6:5K;a4Zb4Ic3Ld33e2Vf2Mg2Hh26in22j21k20l1Sm1Jn1Fo19p0Pqu0Or0DsXtKuFvAw7x 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gu0E;a5his17i4oo3;d,l;ldlife,ne;rm8t1;apor,ernacul29i3;neg28ol1Otae;eDhBiAo8r4un3yranny;a,gst1B;aff2Oea1Ko4ue nor3;th;o08u3;bleshoot2Ose1Tt;night,othpas1Vwn3;foEsfoE;me off,n;er3und1;e,mod2S;a,nnis;aDcCeBhAi9ki8o7p6t4u3weepstak0;g1Unshi2Hshi;ati08e3;am,el;ace2Keci0;ap,cc1meth2C;n,ttl0;lk;eep,ingl0or1C;lf,na1Gri0;ene1Kisso1C;d0Wfe2l4nd,t3;i0Iurn;m1Ut;abi0e4ic3;e,ke15;c3i01laxa11search;ogni10rea10;a9e8hys7luto,o5re3ut2;amble,mis0s3ten20;en1Zs0L;l3rk;i28l0EyH; 16i28;a24tr0F;nt3ti0M;i0s;bstetri24vercrowd1Qxyg09;a5e4owada3utella;ys;ptu1Ows;il poliZtional securi2;aAe8o5u3;m3s1H;ps;n3o1K;ey,o3;gamy;a3cha0Elancholy,rchandi1Htallurgy;sl0t;chine3g1Aj1Hrs,thema1Q; learn1Cry;aught1e6i5ogi4u3;ck,g12;c,s1M;ce,ghtn18nguis1LteratWv1;ath1isVss;ara0EindergartPn3;icke0Aowled0Y;e3upit1;a3llyfiGwel0G;ns;ce,gnor6mp5n3;forma00ter3;net,sta07;atiSort3rov;an18;a7e6isto09o3ung1;ckey,mework,ne4o3rseradi8spitali2use arrest;ky;s2y;adquarteXre;ir,libut,ppiHs3;hi3te;sh;ene8l6o5r3um,ymnas11;a3eZ;niUss;lf,re;ut3yce0F;en; 3ti0W;edit0Hpo3;ol;aNicFlour,o4urnit3;ure;od,rgive3uri1wl;ness;arCcono0LducaBlectr9n7quip8thi0Pvery6x3;ist4per3;ti0B;en0J;body,o08th07;joy3tertain3;ment;ici2o3;ni0H;tiS;nings,th;emi02i6o4raugh3ynas2;ts;pe,wnstai3;rs;abet0ce,s3;honZrepu3;te;aDelciChAivi07l8o3urrency;al,ld w6mmenta5n3ral,ttIuscoB;fusiHt 3;ed;ry;ar;assi01oth0;es;aos,e3;eMwK;us;d,rO;a8i6lood,owlHread5u3;ntGtt1;er;!th;lliarJs3;on;g3ss;ga3;ge;cKdviJeroGirFmBn6ppeal court,r4spi3thleL;rin;ithmet3sen3;ic;i6y3;o4th3;ing;ne;se;en5n3;es2;ty;ds;craft;bi8d3nau7;yna3;mi6;ce;id,ous3;ti3;cs",Infinitive:"true¦0:9G;1:9T;2:AD;3:90;4:9Z;5:84;6:AH;7:A9;8:92;9:A0;A:AG;B:AI;C:9V;D:8R;E:8O;F:97;G:6H;H:7D;a94b8Hc7Jd68e4Zf4Mg4Gh4Ai3Qj3Nk3Kl3Bm34nou48o2Vp2Equ2Dr1Es0CtZuTvRwI;aOeNiLors5rI;eJiI;ng,te;ak,st3;d5e8TthI;draw,er;a2d,ep;i2ke,nIrn;d1t;aIie;liADniAry;nJpI;ho8Llift;cov1dJear8Hfound8DlIplug,rav82tie,ve94;eaAo3X;erIo;cut,go,staAFvalA3w2G;aSeQhNoMrIu73;aIe72;ffi3Smp3nsI;aBfo7CpI;i8oD;pp3ugh5;aJiJrIwaD;eat5i2;nk;aImA0;ch,se;ck3ilor,keImp1r8L;! paD;a0Ic0He0Fh0Bi0Al08mugg3n07o05p02qu01tUuLwI;aJeeIim;p,t5;ll7Wy;bNccMffLggeCmmKppJrI;mouFpa6Zvi2;o0re6Y;ari0on;er,i4;e7Numb;li9KmJsiIveD;de,st;er9it;aMe8MiKrI;ang3eIi2;ng27w;fIng;f5le;b,gg1rI;t3ve;a4AiA;a4UeJit,l7DoI;il,of;ak,nd;lIot7Kw;icEve;atGeak,i0O;aIi6;m,y;ft,ng,t;aKi6CoJriIun;nk,v6Q;ot,rt5;ke,rp5tt1;eIll,nd,que8Gv1w;!k,m;aven9ul8W;dd5tis1Iy;a0FeKiJoI;am,t,ut;d,p5;a0Ab08c06d05f01group,hea00iZjoi4lXmWnVpTq3MsOtMup,vI;amp,eJiIo3B;sEve;l,rI;e,t;i8rI;ie2ofE;eLiKpo8PtIurfa4;o24rI;aHiBuctu8;de,gn,st;mb3nt;el,hra0lIreseF;a4e71;d1ew,o07;aHe3Fo2;a7eFiIo6Jy;e2nq41ve;mbur0nf38;r0t;inKleBocus,rJuI;el,rbiA;aBeA;an4e;aBu4;ei2k8Bla43oIyc3;gni39nci3up,v1;oot,uI;ff;ct,d,liIp;se,ze;tt3viA;aAenGit,o7;aWerUinpoiFlumm1LoTrLuI;b47ke,niArIt;poDsuI;aFe;eMoI;cKd,fe4XhibEmo7noJpo0sp1tru6vI;e,i6o5L;un4;la3Nu8;aGclu6dJf1occupy,sup0JvI;a6BeF;etermi4TiB;aGllu7rtr5Ksse4Q;cei2fo4NiAmea7plex,sIva6;eve8iCua6;mp1rItrol,ve;a6It6E;bOccuNmEpMutLverIwe;l07sJtu6Yu0wI;helm;ee,h1F;gr5Cnu2Cpa4;era7i4Ipo0;py,r;ey,seItaH;r2ss;aMe0ViJoIultiply;leCu6Pw;micJnIspla4;ce,g3us;!k;iIke,na9;m,ntaH;aPeLiIo0u3N;ke,ng1quIv5;eIi6S;fy;aKnIss5;d,gI;th5;rn,ve;ng2Gu1N;eep,idnJnI;e4Cow;ap;oHuI;gg3xtaI;po0;gno8mVnIrk;cTdRfQgeChPitia7ju8q1CsNtKun6EvI;a6eIo11;nt,rt,st;erJimi6BoxiPrI;odu4u6;aBn,pr03ru6C;iCpi8tIu8;all,il,ruB;abEibE;eCo3Eu0;iIul9;ca7;i7lu6;b5Xmer0pI;aLer4Uin9ly,oJrI;e3Ais6Bo2;rt,se,veI;riA;le,rt;aLeKiIoiCuD;de,jaInd1;ck;ar,iT;mp1ng,pp5raIve;ng5Mss;ath1et,iMle27oLrI;aJeIow;et;b,pp3ze;!ve5A;gg3ve;aTer45i5RlSorMrJuI;lf4Cndrai0r48;eJiIolic;ght5;e0Qsh5;b3XeLfeEgJsI;a3Dee;eIi2;!t;clo0go,shIwa4Z;ad3F;att1ee,i36;lt1st5;a0OdEl0Mm0FnXquip,rWsVtGvTxI;aRcPeDhOiNpJtIu6;ing0Yol;eKi8lIo0un9;aHoI;it,re;ct,di7l;st,t;a3oDu3B;e30lI;a10u6;lt,mi28;alua7oI;ke,l2;chew,pou0tab19;a0u4U;aYcVdTfSgQhan4joy,lPqOrNsuMtKvI;e0YisI;a9i50;er,i4rI;aHenGuC;e,re;iGol0F;ui8;ar9iC;a9eIra2ulf;nd1;or4;ang1oIu8;r0w;irc3lo0ou0ErJuI;mb1;oaGy4D;b3ct;bKer9pI;hasiIow1;ze;aKody,rI;a4oiI;d1l;lm,rk;ap0eBuI;ci40de;rIt;ma0Rn;a0Re04iKo,rIwind3;aw,ed9oI;wn;agno0e,ff1g,mi2Kne,sLvI;eIul9;rIst;ge,t;aWbVcQlod9mant3pNru3TsMtI;iIoDu37;lJngI;uiA;!l;ol2ua6;eJlIo0ro2;a4ea0;n0r0;a2Xe36lKoIu0S;uIv1;ra9;aIo0;im;a3Kur0;b3rm;af5b01cVduBep5fUliTmQnOpMrLsiCtaGvI;eIol2;lop;ch;a20i2;aDiBloIoD;re,y;oIy;te,un4;eJoI;liA;an;mEv1;a4i0Ao06raud,y;ei2iMla8oKrI;ee,yI;!pt;de,mIup3;missi34po0;de,ma7ph1;aJrief,uI;g,nk;rk;mp5rk5uF;a0Dea0h0Ai09l08oKrIurta1G;a2ea7ipp3uI;mb3;ales4e04habEinci6ll03m00nIrro6;cXdUfQju8no7qu1sLtKvI;eIin4;ne,r9y;aHin2Bribu7;er2iLoli2Epi8tJuI;lt,me;itu7raH;in;d1st;eKiJoIroFu0;rm;de,gu8rm;ss;eJoI;ne;mn,n0;eIlu6ur;al,i2;buCe,men4pI;eIi3ly;l,te;eBi6u6;r4xiC;ean0iT;rcumveFte;eJirp,oI;o0p;riAw;ncIre5t1ulk;el;a02eSi6lQoPrKuI;iXrIy;st,y;aLeaKiJoad5;en;ng;stfeLtX;ke;il,l11mba0WrrMth1;eIow;ed;!coQfrie1LgPhMliLqueaKstJtrIwild1;ay;ow;th;e2tt3;a2eJoI;ld;ad;!in,ui3;me;bysEckfi8ff3tI;he;b15c0Rd0Iff0Ggree,l0Cm09n03ppZrXsQttOuMvJwaE;it;eDoI;id;rt;gIto0X;meF;aIeCraB;ch,in;pi8sJtoI;niA;aKeIi04u8;mb3rt,ss;le;il;re;g0Hi0ou0rI;an9i2;eaKly,oiFrI;ai0o2;nt;r,se;aMi0GnJtI;icipa7;eJoIul;un4y;al;ly0;aJu0;se;lga08ze;iKlI;e9oIu6;t,w;gn;ix,oI;rd;a03jNmiKoJsoI;rb;pt,rn;niIt;st1;er;ouJuC;st;rn;cLhie2knowled9quiItiva7;es4re;ce;ge;eQliOoKrJusI;e,tom;ue;mIst;moJpI;any,liA;da7;ma7;te;pt;andPduBet,i6oKsI;coKol2;ve;liArt,uI;nd;sh;de;ct;on",Person:"true¦0:1Q;a29b1Zc1Md1Ee18f15g13h0Ri0Qj0Nk0Jl0Gm09n06o05p00rPsItCusain bolt,v9w4xzibit,y1;anni,oko on2uji,v1;an,es;en,o;a3ednesday adams,i2o1;lfram,o0Q;ll ferrell,z khalifa;lt disn1Qr1;hol,r0G;a2i1oltai06;n dies0Zrginia wo17;lentino rossi,n goG;a4h3i2ripp,u1yra banks;lZpac shakur;ger woods,mba07;eresa may,or;kashi,t1ylor;um,ya1B;a5carlett johanss0h4i3lobodan milosevic,no2ocr1Lpider1uperm0Fwami; m0Em0E;op dogg,w whi1H;egfried,nbad;akespeaTerlock holm1Sia labeouf;ddam hussa16nt1;a cla11ig9;aAe6i5o3u1za;mi,n dmc,paul,sh limbau1;gh;bin hood,d stew16nald1thko;in0Mo;han0Yngo starr,valdo;ese witherspo0i1mbrandt;ll2nh1;old;ey,y;chmaninoff,ffi,iJshid,y roma1H;a4e3i2la16o1uff daddy;cahont0Ie;lar,p19;le,rZ;lm17ris hilt0;leg,prah winfr0Sra;a2e1iles cra1Bostradam0J; yo,l5tt06wmQ;pole0s;a5e4i2o1ubar03;by,lie5net,rriss0N;randa ju1tt romn0M;ly;rl0GssiaB;cklemo1rkov,s0ta hari,ya angelou;re;ady gaga,e1ibera0Pu;bron jam0Xch wale1e;sa;anye west,e3i1obe bryant;d cudi,efer suther1;la0P;ats,sha;a2effers0fk,k rowling,rr tolki1;en;ck the ripp0Mwaharlal nehru,y z;liTnez,ron m7;a7e5i3u1;lk hog5mphrey1sa01;! bog05;l1tl0H;de; m1dwig,nry 4;an;ile selassFlle ber4m3rrison1;! 1;ford;id,mo09;ry;ast0iannis,o1;odwPtye;ergus0lorence nightinga08r1;an1ederic chopN;s,z;ff5m2nya,ustaXzeki1;el;eril lagasse,i1;le zatop1nem;ek;ie;a6e4i2octor w1rake;ho;ck w1ego maradoC;olf;g1mi lovaOnzel washingt0;as;l1nHrth vadR;ai lNt0;a8h5lint0o1thulhu;n1olio;an,fuci1;us;on;aucKop2ristian baMy1;na;in;millo,ptain beefhe4r1;dinal wols2son1;! palmF;ey;art;a8e5hatt,i3oHro1;ck,n1;te;ll g1ng crosby;atB;ck,nazir bhut2rtil,yon1;ce;to;nksy,rack ob1;ama;l 6r3shton kutch2vril lavig8yn ra1;nd;er;chimed2istot1;le;es;capo2paci1;no;ne",Adjective:"true¦0:AI;1:BS;2:BI;3:BA;4:A8;5:84;6:AV;7:AN;8:AF;9:7H;A:BQ;B:AY;C:BC;D:BH;E:9Y;aA2b9Ec8Fd7We79f6Ng6Eh61i4Xj4Wk4Tl4Im41n3Po36p2Oquart7Pr2Ds1Dt14uSvOwFye29;aMeKhIiHoF;man5oFrth7G;dADzy;despreB1n w97s86;acked1UoleF;!sa6;ather1PeFll o70ste1D;!k5;nt1Ist6Ate4;aHeGiFola5T;bBUce versa,gi3Lle;ng67rsa5R;ca1gBSluAV;lt0PnLpHrGsFttermoBL;ef9Ku3;b96ge1; Hb32pGsFtiAH;ca6ide d4R;er,i85;f52to da2;a0Fbeco0Hc0Bd04e02f01gu1XheaBGiXkn4OmUnTopp06pRrNsJtHus0wF;aFiel3K;nt0rra0P;app0eXoF;ld,uS;eHi37o5ApGuF;perv06spec39;e1ok9O;en,ttl0;eFu5;cogn06gul2RlGqu84sF;erv0olv0;at0en33;aFrecede0E;id,rallel0;am0otic0;aFet;rri0tF;ch0;nFq26vers3;sur0terFv7U;eFrupt0;st0;air,inish0orese98;mploy0n7Ov97xpF;ect0lain0;eHisFocume01ue;clFput0;os0;cid0rF;!a8Scov9ha8Jlyi8nea8Gprivileg0sMwF;aFei9I;t9y;hGircumcFonvin2U;is0;aFeck0;lleng0rt0;b20ppea85ssuGttend0uthorF;iz0;mi8;i4Ara;aLeIhoHip 25oGrF;anspare1encha1i2;geth9leADp notch,rpB;rny,ugh6H;ena8DmpGrFs6U;r49tia4;eCo8P;leFst4M;nt0;a0Dc09e07h06i04ki03l01mug,nobbi4XoVpRqueami4XtKuFymb94;bHccinAi generis,pFr5;erFre7N;! dup9b,vi70;du0li7Lp6IsFurb7J;eq9Atanda9X;aKeJi16o2QrGubboFy4Q;rn;aightFin5GungS; fFfF;or7V;adfa9Pri6;lwa6Ftu82;arHeGir6NlendBot Fry;on;c3Qe1S;k5se; call0lImb9phistic16rHuFviV;ndFth1B;proof;dBry;dFub6; o2A;e60ipF;pe4shod;ll0n d7R;g2HnF;ceEg6ist9;am3Se9;co1Zem5lfFn6Are7; suf4Xi43;aGholFient3A;ar5;rlFt4A;et;cr0me,tisfac7F;aOeIheumatoBiGoF;bu8Ztt7Gy3;ghtFv3; 1Sf6X;cJdu8PlInown0pro69sGtF;ard0;is47oF;lu2na1;e1Suc45;alcit8Xe1ondi2;bBci3mpa1;aSePicayu7laOoNrGuF;bl7Tnjabi;eKiIoF;b7VfGmi49pFxi2M;er,ort81;a7uD;maFor,sti7va2;!ry;ciDexis0Ima2CpaB;in55puli8G;cBid;ac2Ynt 3IrFti2;ma40tFv7W;!i3Z;i2YrFss7R;anoBtF; 5XiF;al,s5V;bSffQkPld OnMrLth9utKverF;!aIbMdHhGni75seas,t,wF;ei74rou74;a63e7A;ue;ll;do1Ger,si6A;d3Qg2Aotu5Z; bFbFe on o7g3Uli7;oa80;fashion0school;!ay; gua7XbFha5Uli7;eat;eHligGsF;ce7er0So1C;at0;diFse;a1e1;aOeNiMoGuF;anc0de; moEnHrthFt6V;!eFwe7L;a7Krn;chaGdescri7Iprof30sF;top;la1;ght5;arby,cessa4ighbor5wlyw0xt;k0usiaFv3;ti8;aQeNiLoHuF;dIltiF;facet0p6;deHlGnFot,rbBst;ochro4Xth5;dy;rn,st;ddle ag0nF;dbloZi,or;ag9diocEga,naGrFtropolit4Q;e,ry;ci8;cIgenta,inHj0Fkeshift,mmGnFri4Oscu61ver18;da5Dy;ali4Lo4U;!stream;abEho;aOeLiIoFumberi8;ngFuti1R;stan3RtF;erm,i4H;ghtGteraF;l,ry,te;heart0wei5O;ft JgFss9th3;al,eFi0M;nda4;nguBps0te5;apGind5noF;wi8;ut;ad0itte4uniW;ce co0Hgno6Mll0Cm04nHpso 2UrF;a2releF;va1; ZaYcoWdReQfOgrNhibi4Ri05nMoLsHtFvalu5M;aAeF;nDrdepe2K;a7iGolFuboI;ub6ve1;de,gF;nifica1;rdi5N;a2er;own;eriIiLluenVrF;ar0eq5H;pt,rt;eHiGoFul1O;or;e,reA;fiFpe26termi5E;ni2;mpFnsideCrreA;le2;ccuCdeq5Ene,ppr4J;fFsitu,vitro;ro1;mJpF;arHeGl15oFrop9;li2r11;n2LrfeA;ti3;aGeFi18;d4BnD;tuE;egGiF;c0YteC;al,iF;tiF;ma2;ld;aOelNiLoFuma7;a4meInHrrGsFur5;ti6;if4E;e58o3U; ma3GsF;ick;ghfalut2HspF;an49;li00pf33;i4llow0ndGrdFtM; 05coEworki8;sy,y;aLener44iga3Blob3oKrGuF;il1Nng ho;aFea1Fizzl0;cGtF;ef2Vis;ef2U;ld3Aod;iFuc2D;nf2R;aVeSiQlOoJrF;aGeFil5ug3;q43tf2O;gFnt3S;i6ra1;lk13oHrF; keeps,eFge0Vm9tu41;g0Ei2Ds3R;liF;sh;ag4Mowe4uF;e1or45;e4nF;al,i2;d Gmini7rF;ti6ve1;up;bl0lDmIr Fst pac0ux;oGreacF;hi8;ff;ed,ili0R;aXfVlTmQnOqu3rMthere3veryday,xF;aApIquisi2traHuF;be48lF;ta1;!va2L;edRlF;icF;it;eAstF;whi6; Famor0ough,tiE;rou2sui2;erGiF;ne1;ge1;dFe2Aoq34;er5;ficF;ie1;g9sF;t,ygF;oi8;er;aWeMiHoGrFue;ea4owY;ci6mina1ne,r31ti8ubQ;dact2Jfficult,m,sGverF;ge1se;creGePjoi1paCtF;a1inA;et,te; Nadp0WceMfiLgeneCliJmuEpeIreliAsGvoF;id,ut;pFtitu2ul1L;eCoF;nde1;ca2ghF;tf13;a1ni2;as0;facto;i5ngero0I;ar0Ce09h07i06l05oOrIuF;rmudgeon5stoma4teF;sy;ly;aIeHu1EystalF; cleFli7;ar;epy;fFv17z0;ty;erUgTloSmPnGrpoCunterclVveFy;rt;cLdJgr21jIsHtrF;aFi2;dic0Yry;eq1Yta1;oi1ug3;escenFuN;di8;a1QeFiD;it0;atoDmensuCpF;ass1SulF;so4;ni3ss3;e1niza1;ci1J;ockwiD;rcumspeAvil;eFintzy;e4wy;leGrtaF;in;ba2;diac,ef00;a00ePiLliJoGrFuck nak0;and new,isk,on22;gGldface,naF; fi05fi05;us;nd,tF;he;gGpartisFzarE;an;tiF;me;autifOhiNlLnHsFyoN;iWtselF;li8;eGiFt;gn;aFfi03;th;at0oF;v0w;nd;ul;ckwards,rF;e,rT; priori,b13c0Zd0Tf0Ng0Ihe0Hl09mp6nt06pZrTsQttracti0MuLvIwF;aGkF;wa1B;ke,re;ant garGeraF;ge;de;diIsteEtF;heFoimmu7;nt07;re;to4;hGlFtu2;eep;en;bitIchiv3roHtF;ifiFsy;ci3;ga1;ra4;ry;pFt;aHetizi8rF;oprF;ia2;llFre1;ed,i8;ng;iquFsy;at0e;ed;cohKiJkaHl,oGriFterX;ght;ne,of;li7;ne;ke,ve;olF;ic;ad;ain07gressiIi6rF;eeF;ab6;le;ve;fGraB;id;ectGlF;ue1;ioF;na2; JaIeGvF;erD;pt,qF;ua2;ma1;hoc,infinitum;cuCquiGtu3u2;al;esce1;ra2;erSjeAlPoNrKsGuF;nda1;e1olu2trF;aAuD;se;te;eaGuF;pt;st;aFve;rd;aFe;ze;ct;ra1;nt",Pronoun:"true¦elle,h3i2me,she,th0us,we,you;e0ou;e,m,y;!l,t;e,im",Preposition:"true¦aPbMcLdKexcept,fIinGmid,notwithstandiWoDpXqua,sCt7u4v2w0;/o,hereSith0;! whHin,oW;ersus,i0;a,s a vis;n1p0;!on;like,til;h1ill,oward0;!s;an,ereby,r0;ough0u;!oM;ans,ince,o that,uch G;f1n0ut;!to;!f;! 0to;effect,part;or,r0;om;espite,own,u3;hez,irca;ar1e0oBy;sides,tween;ri7;bo8cross,ft7lo6m4propos,round,s1t0;!op;! 0;a whole,long 0;as;id0ong0;!st;ng;er;ut",SportsTeam:"true¦0:18;1:1E;2:1D;3:14;a1Db15c0Sd0Kfc dallas,g0Ihouston 0Hindiana0Gjacksonville jagua0k0El0Am01new UoRpKqueens parkJreal salt lake,sBt6utah jazz,vancouver whitecaps,w4yW;ashington 4h10;natio1Mredski2wizar0W;ampa bay 7e6o4;ronto 4ttenham hotspur;blue ja0Mrapto0;nnessee tita2xasD;buccanee0ra0K;a8eattle 6porting kansas0Wt4; louis 4oke0V;c1Drams;marine0s4;eah13ounH;cramento Rn 4;antonio spu0diego 4francisco gJjose earthquak1;char08paB; ran07;a9h6ittsburgh 5ortland t4;imbe0rail blaze0;pirat1steele0;il4oenix su2;adelphia 4li1;eagl1philNunE;dr1;akland 4klahoma city thunder,rlando magic;athle0Lrai4;de0;england 8orleans 7york 4;g5je3knYme3red bul0Xy4;anke1;ian3;pelica2sain3;patrio3revolut4;ion;anchEeAi4ontreal impact;ami 8lwaukee b7nnesota 4;t5vi4;kings;imberwolv1wi2;rewe0uc0J;dolphi2heat,marli2;mphis grizz4ts;li1;a6eic5os angeles 4;clippe0dodFlaB;esterV; galaxy,ke0;ansas city 4nF;chiefs,roya0D; pace0polis col3;astr05dynamo,rocke3texa2;olden state warrio0reen bay pac4;ke0;allas 8e4i04od6;nver 6troit 4;lio2pisto2ti4;ge0;broncYnugge3;cowbo5maver4;icZ;ys;arEelLhAincinnati 8leveland 6ol4;orado r4umbus crew sc;api7ocki1;brow2cavalie0guar4in4;dia2;bengaVre4;ds;arlotte horAicago 4;b5cubs,fire,wh4;iteB;ea0ulQ;diff4olina panthe0; city;altimore Alackburn rove0oston 6rooklyn 4uffalo bilN;ne3;ts;cel5red4; sox;tics;rs;oriol1rave2;rizona Ast8tlanta 4;brav1falco2h4;awA;ns;es;on villa,r4;os;c6di4;amondbac4;ks;ardi4;na4;ls",Unit:"true¦a07b04cXdWexVfTgRhePinYjoule0BkMlJmDnan08oCp9quart0Bsq ft,t7volts,w6y2ze3°1µ0;g,s;c,f,n;dVear1o0;ttR; 0s 0;old;att,b;erNon0;!ne02;ascals,e1i0;cXnt00;rcent,tJ;hms,unceY;/s,e4i0m²,²,³;/h,cro2l0;e0liK;!²;grLsR;gCtJ;it1u0;menQx;erPreP;b5elvins,ilo1m0notO;/h,ph,²;!byGgrEmCs;ct0rtzL;aJogrC;allonJb0ig3rB;ps;a0emtEl oz,t4;hrenheit,radG;aby9;eci3m1;aratDe1m0oulombD;²,³;lsius,nti0;gr2lit1m0;et0;er8;am7;b1y0;te5;l,ps;c2tt0;os0;econd1;re0;!s","Noun|Gerund":"true¦0:3O;1:3M;2:3N;3:3D;4:32;5:2V;6:3E;7:3K;8:36;9:3J;A:3B;a3Pb37c2Jd27e23f1Vg1Sh1Mi1Ij1Gk1Dl18m13n11o0Wp0Pques0Sr0EsTtNunderMvKwFyDzB;eroi0oB;ni0o3P;aw2eB;ar2l3;aEed4hispe5i5oCrB;ap8est3i1;n0ErB;ki0r31;i1r2s9tc9;isualizi0oB;lunt1Vti0;stan4ta6;aFeDhin6iCraBy8;c6di0i2vel1M;mi0p8;aBs1;c9si0;l6n2s1;aUcReQhOiMkatKl2Wmo6nowJpeItFuCwB;ea5im37;b35f0FrB;fi0vB;e2Mi2J;aAoryt1KrCuB;d2KfS;etc9ugg3;l3n4;bCi0;ebBi0;oar4;gnBnAt1;a3i0;ip8oB;p8rte2u1;a1r27t1;hCo5reBulp1;a2Qe2;edu3oo3;i3yi0;aKeEi4oCuB;li0n2;oBwi0;fi0;aFcEhear7laxi0nDpor1sB;pon4tructB;r2Iu5;de5;or4yc3;di0so2;p8ti0;aFeacek20laEoCrBublis9;a1Teten4in1oces7;iso2siB;tio2;n2yi0;ckaAin1rB;ki0t1O;fEpeDrganiCvB;erco24ula1;si0zi0;ni0ra1;fe5;avi0QeBur7;gotia1twor6;aDeCi2oB;de3nito5;a2dita1e1ssaA;int0XnBrke1;ifUufactu5;aEeaDiBodAyi0;cen7f1mi1stB;e2i0;r2si0;n4ug9;iCnB;ea4it1;c6l3;ogAuB;dAgg3stif12;ci0llust0VmDnBro2;nova1sp0NterBven1;ac1vie02;agi2plo4;aDea1iCoBun1;l4w3;ki0ri0;nd3rB;roWvB;es1;aCene0Lli4rBui4;ee1ie0N;rde2the5;aHeGiDlCorBros1un4;e0Pmat1;ir1oo4;gh1lCnBs9;anZdi0;i0li0;e3nX;r0Zscina1;a1du01nCxB;erci7plo5;chan1di0ginB;ee5;aLeHiGoub1rCum8wB;el3;aDeCiB;bb3n6vi0;a0Qs7;wi0;rTscoDvi0;ba1coZlBvelo8;eCiB;ve5;ga1;nGti0;aVelebUhSlPoDrBur3yc3;aBos7yi0;f1w3;aLdi0lJmFnBo6pi0ve5;dDsCvinB;ci0;trBul1;uc1;muniDpB;lBo7;ai2;ca1;lBo5;ec1;c9ti0;ap8eaCimToBubT;ni0t9;ni0ri0;aBee5;n1t1;ra1;m8rCs1te5;ri0;vi0;aPeNitMlLoGrDuB;dge1il4llBr8;yi0;an4eat9oadB;cas1;di0;a1mEokB;i0kB;ee8;pi0;bi0;es7oa1;c9i0;gin2lonAt1;gi0;bysit1c6ki0tt3;li0;ki0;bando2cGdverti7gi0pproac9rgDssuCtB;trac1;mi0;ui0;hi0;si0;coun1ti0;ti0;ni0;ng",PhrasalVerb:"true¦0:92;1:96;2:8H;3:8V;4:8A;5:83;6:85;7:98;8:90;9:8G;A:8X;B:8R;C:8U;D:8S;E:70;F:97;G:8Y;H:81;I:7H;J:79;a9Fb7Uc6Rd6Le6Jf5Ig50h4Biron0j47k40l3Em31n2Yo2Wp2Cquiet Hr1Xs0KtZuXvacuu6QwNyammerBzK;ero Dip LonK;e0k0;by,ov9up;aQeMhLiKor0Mrit19;mp0n3Fpe0r5s5;ackAeel Di0S;aLiKn33;gh 3Wrd0;n Dr K;do1in,oJ;it 79k5lk Lrm 69sh Kt83v60;aw3do1o7up;aw3in,oC;rgeBsK;e 2herE;a00eYhViRoQrMuKypP;ckErn K;do1in,oJup;aLiKot0y 30;ckl7Zp F;ck HdK;e 5Y;n7Wp 3Es5K;ck MdLe Kghten 6me0p o0Rre0;aw3ba4do1in,up;e Iy 2;by,oG;ink Lrow K;aw3ba4in,up;ba4ov9up;aKe 77ll62;m 2r 5M;ckBke Llk K;ov9shit,u47;aKba4do1in,leave,o4Dup;ba4ft9pa69w3;a0Vc0Te0Mh0Ii0Fl09m08n07o06p01quar5GtQuOwK;earMiK;ngLtch K;aw3ba4o8K; by;cKi6Bm 2ss0;k 64;aReQiPoNrKud35;aigh2Det75iK;ke 7Sng K;al6Yup;p Krm2F;by,in,oG;c3Ln3Lr 2tc4O;p F;c3Jmp0nd LrKveAy 2O;e Ht 2L;ba4do1up;ar3GeNiMlLrKurB;ead0ingBuc5;a49it 6H;c5ll o3Cn 2;ak Fe1Xll0;a3Bber 2rt0und like;ap 5Vow Duggl5;ash 6Noke0;eep NiKow 6;cLp K;o6Dup;e 68;in,oK;ff,v9;de19gn 4NnKt 6Gz5;gKkE; al6Ale0;aMoKu5W;ot Kut0w 7M;aw3ba4f48oC;c2WdeEk6EveA;e Pll1Nnd Orv5tK; Ktl5J;do1foLin,o7upK;!on;ot,r5Z;aw3ba4do1in,o33up;oCto;al66out0rK;ap65ew 6J;ilAv5;aXeUiSoOuK;b 5Yle0n Kstl5;aLba4do1inKo2Ith4Nu5P;!to;c2Xr8w3;ll Mot LpeAuK;g3Ind17;a2Wf3Po7;ar8in,o7up;ng 68p oKs5;ff,p18;aKelAinEnt0;c6Hd K;o4Dup;c27t0;aZeYiWlToQrOsyc35uK;ll Mn5Kt K;aKba4do1in,oJto47up;pa4Dw3;a3Jdo1in,o21to45up;attleBess KiNop 2;ah2Fon;iLp Kr4Zu1Gwer 6N;do1in,o6Nup;nt0;aLuK;gEmp 6;ce u20y 6D;ck Kg0le 4An 6p5B;oJup;el 5NncilE;c53ir 39n0ss MtLy K;ba4oG; Hc2R;aw3ba4in,oJ;pKw4Y;e4Xt D;aLerd0oK;dAt53;il Hrrow H;aTeQiPoLuK;ddl5ll I;c1FnkeyMp 6uthAve K;aKdo1in,o4Lup;l4Nw3; wi4K;ss0x 2;asur5e3SlLss K;a21up;t 6;ke Ln 6rKs2Ax0;k 6ryA;do,fun,oCsure,up;a02eViQoLuK;ck0st I;aNc4Fg MoKse0;k Kse4D;aft9ba4do1forw37in56o0Zu46;in,oJ;d 6;e NghtMnLsKve 00;ten F;e 2k 2; 2e46;ar8do1in;aMt LvelK; oC;do1go,in,o7up;nEve K;in,oK;pKut;en;c5p 2sh LtchBughAy K;do1o59;in4Po7;eMick Lnock K;do1oCup;oCup;eLy K;in,up;l Ip K;aw3ba4do1f04in,oJto,up;aMoLuK;ic5mpE;ke3St H;c43zz 2;a01eWiToPuK;nLrrKsh 6;y 2;keLt K;ar8do1;r H;lKneErse3K;d Ke 2;ba4dKfast,o0Cup;ear,o1;de Lt K;ba4on,up;aw3o7;aKlp0;d Ml Ir Kt 2;fKof;rom;f11in,o03uW;cPm 2nLsh0ve Kz2P;at,it,to;d Lg KkerP;do1in,o2Tup;do1in,oK;ut,v9;k 2;aZeTive Rloss IoMrLunK; f0S;ab hold,in43ow 2U; Kof 2I;aMb1Mit,oLr8th1IuK;nd9;ff,n,v9;bo7ft9hQw3;aw3bKdo1in,oJrise,up,w3;a4ir2H;ar 6ek0t K;aLb1Fdo1in,oKr8up;ff,n,ut,v9;cLhKl2Fr8t,w3;ead;ross;d aKng 2;bo7;a0Ee07iYlUoQrMuK;ck Ke2N;ar8up;eLighten KownBy 2;aw3oG;eKshe27; 2z5;g 2lMol Krk I;aKwi20;bo7r8;d 6low 2;aLeKip0;sh0;g 6ke0mKrKtten H;e F;gRlPnNrLsKzzle0;h F;e Km 2;aw3ba4up;d0isK;h 2;e Kl 1T;aw3fPin,o7;ht ba4ure0;ePnLsK;s 2;cMd K;fKoG;or;e D;d04l 2;cNll Krm0t1G;aLbKdo1in,o09sho0Eth08victim;a4ehi2O;pa0C;e K;do1oGup;at Kdge0nd 12y5;in,o7up;aOi1HoNrK;aLess 6op KuN;aw3b03in,oC;gBwB; Ile0ubl1B;m 2;a0Ah05l02oOrLut K;aw3ba4do1oCup;ackBeep LoKy0;ss Dwd0;by,do1in,o0Uup;me NoLuntK; o2A;k 6l K;do1oG;aRbQforOin,oNtKu0O;hLoKrue;geth9;rough;ff,ut,v9;th,wK;ard;a4y;paKr8w3;rt;eaLose K;in,oCup;n 6r F;aNeLiK;ll0pE;ck Der Kw F;on,up;t 2;lRncel0rOsMtch LveE; in;o1Nup;h Dt K;doubt,oG;ry LvK;e 08;aw3oJ;l Km H;aLba4do1oJup;ff,n,ut;r8w3;a0Ve0MiteAl0Fo04rQuK;bblNckl05il0Dlk 6ndl05rLsKtMy FzzA;t 00;n 0HsK;t D;e I;ov9;anWeaUiLush K;oGup;ghQng K;aNba4do1forMin,oLuK;nd9p;n,ut;th;bo7lKr8w3;ong;teK;n 2;k K;do1in,o7up;ch0;arTg 6iRn5oPrNssMttlLunce Kx D;aw3ba4;e 6; ar8;e H;do1;k Dt 2;e 2;l 6;do1up;d 2;aPeed0oKurt0;cMw K;aw3ba4do1o7up;ck;k K;in,oC;ck0nk0stA; oQaNef 2lt0nd K;do1ov9up;er;up;r Lt K;do1in,oCup;do1o7;ff,nK;to;ck Pil0nMrgLsK;h D;ainBe D;g DkB; on;in,o7;aw3do1in,oCup;ff,ut;ay;ct FdQir0sk MuctionA; oG;ff;ar8o7;ouK;nd; o7;d K;do1oKup;ff,n;wn;o7up;ut",ProperNoun:"true¦abid,barbie,c3e2f1iron maiden,kirby,m0nis,riel;cgill,ercedes,issy;lorence,ranco;lmo,uro;atalina,hristi","Person|Place":"true¦a8d6h4jordan,k3orlando,s1vi0;ctor9rgin9;a0ydney;lvador,mara,ntia4;ent,obe;amil0ous0;ton;arw2ie0;go;lexandr1ust0;in;ia",LastName:"true¦0:BR;1:BF;2:B5;3:BH;4:AX;5:9Y;6:B6;7:BK;8:B0;9:AV;A:AL;B:8Q;C:8G;D:7K;E:BM;F:AH;aBDb9Zc8Wd88e81f7Kg6Wh64i60j5Lk4Vl4Dm39n2Wo2Op25quispe,r1Ls0Pt0Ev03wTxSyKzG;aIhGimmerm6A;aGou,u;ng,o;khar5ytsE;aKeun9BiHoGun;koya32shiBU;!lG;diGmaz;rim,z;maGng;da,g52mo83sGzaC;aChiBV;iao,u;aLeJiHoGright,u;jcA5lff,ng;lGmm0nkl0sniewsC;kiB1liams33s3;bGiss,lt0;b,er,st0;a6Vgn0lHtG;anabe,s3;k0sh,tG;e2Non;aLeKiHoGukD;gt,lk5roby5;dHllalGnogr3Kr1Css0val3S;ba,ob1W;al,ov4;lasHsel8W;lJn dIrgBEsHzG;qu7;ilyEqu7siljE;en b6Aijk,yk;enzueAIverde;aPeix1VhKi2j8ka43oJrIsui,uG;om5UrG;c2n0un1;an,emblA7ynisC;dorAMlst3Km4rrAth;atch0i8UoG;mHrG;are84laci79;ps3sG;en,on;hirDkah9Mnaka,te,varA;a06ch01eYhUiRmOoMtIuHvGzabo;en9Jobod3N;ar7bot4lliv2zuC;aIeHoG;i7Bj4AyanAB;ele,in2FpheBvens25;l8rm0;kol5lovy5re7Tsa,to,uG;ng,sa;iGy72;rn5tG;!h;l71mHnGrbu;at9cla9Egh;moBo7M;aIeGimizu;hu,vchG;en8Luk;la,r1G;gu9infe5YmGoh,pulveA7rra5P;jGyG;on5;evi6iltz,miHneid0roed0uGwarz;be3Elz;dHtG;!t,z;!t;ar4Th8ito,ka4OlJnGr4saCto,unde19v4;ch7dHtGz;a5Le,os;b53e16;as,ihDm4Po0Y;aVeSiPoJuHyG;a6oo,u;bio,iz,sG;so,u;bKc8Fdrigue67ge10j9YmJosevelt,sItHux,wG;e,li6;a9Ch;enb4Usi;a54e4L;erts15i93;bei4JcHes,vGzzo;as,e9;ci,hards12;ag2es,iHut0yG;es,nol5N;s,t0;dImHnGsmu97v6C;tan1;ir7os;ic,u;aUeOhMiJoHrGut8;asad,if6Zochazk27;lishc2GpGrti72u10we76;e3Aov51;cHe45nG;as,to;as70hl0;aGillips;k,m,n6I;a3Hde3Wete0Bna,rJtG;ersHrovGters54;!a,ic;!en,on;eGic,kiBss3;i9ra,tz,z;h86k,padopoulIrk0tHvG;ic,l4N;el,te39;os;bMconn2Ag2TlJnei6PrHsbor6XweBzG;dem7Rturk;ella4DtGwe6N;ega,iz;iGof7Hs8I;vGyn1R;ei9;aSri1;aPeNiJoGune50ym2;rHvGwak;ak4Qik5otn66;odahl,r4S;cholsZeHkolGls4Jx3;ic,ov84;ls1miG;!n1;ils3mG;co4Xec;gy,kaGray2sh,var38;jiGmu9shiG;ma;a07c04eZiWoMuHyeG;rs;lJnIrGssoli6S;atGp03r7C;i,ov4;oz,te58;d0l0;h2lOnNo0RrHsGza1A;er,s;aKeJiIoz5risHtG;e56on;!on;!n7K;au,i9no,t5J;!lA;r1Btgome59;i3El0;cracFhhail5kkeHlG;l0os64;ls1;hmeJiIj30lHn3Krci0ssiGyer2N;!er;n0Po;er,j0;dDti;cartHlG;aughl8e2;hy;dQe7Egnu68i0jer3TkPmNnMrItHyG;er,r;ei,ic,su21thews;iHkDquAroqu8tinG;ez,s;a5Xc,nG;!o;ci5Vn;a5UmG;ad5;ar5e6Kin1;rig77s1;aVeOiLoJuHyG;!nch;k4nGo;d,gu;mbarGpe3Fvr4we;di;!nGu,yana2B;coln,dG;b21holm,strom;bedEfeKhIitn0kaHn8rGw35;oy;!j;m11tG;in1on1;bvGvG;re;iGmmy,ng,rs2Qu,voie,ws3;ne,t1F;aZeYh2iWlUnez50oNrJuHvar2woG;k,n;cerGmar68znets5;a,o34;aHem0isGyeziu;h23t3O;m0sni4Fus3KvG;ch4O;bay57ch,rh0Usk16vaIwalGzl5;czGsC;yk;cIlG;!cGen4K;huk;!ev4ic,s;e8uiveG;rt;eff0kGl4mu9nnun1;ucF;ll0nnedy;hn,llKminsCne,pIrHstra3Qto,ur,yGzl5;a,s0;j0Rls22;l2oG;or;oe;aPenOha6im14oHuG;ng,r4;e32hInHrge32u6vG;anD;es,ss3;anHnsG;en,on,t3;nesGs1R;en,s1;kiBnings,s1;cJkob4EnGrv0E;kDsG;en,sG;en0Ion;ks3obs2A;brahimDglesi5Nke5Fl0Qno07oneIshikHto,vanoG;u,v54;awa;scu;aVeOiNjaltal8oIrist50uG;!aGb0ghAynh;m2ng;a6dz4fIjgaa3Hk,lHpUrGwe,x3X;ak1Gvat;mAt;er,fm3WmG;ann;ggiBtchcock;iJmingw4BnHrGss;nand7re9;deGriks1;rs3;kkiHnG;on1;la,n1;dz4g1lvoQmOns0ZqNrMsJuIwHyG;asFes;kiB;g1ng;anHhiG;mo14;i,ov0J;di6p0r10t;ue;alaG;in1;rs1;aVeorgUheorghe,iSjonRoLrJuGw3;errGnnar3Co,staf3Ctierr7zm2;a,eG;ro;ayli6ee2Lg4iffithGub0;!s;lIme0UnHodGrbachE;e,m2;calvAzale0S;dGubE;bGs0E;erg;aj,i;bs3l,mGordaO;en7;iev3U;gnMlJmaIndFo,rGsFuthi0;cGdn0za;ia;ge;eaHlG;agh0i,o;no;e,on;aVerQiLjeldsted,lKoIrHuG;chs,entAji41ll0;eem2iedm2;ntaGrt8urni0wl0;na;emi6orA;lipIsHtzgeraG;ld;ch0h0;ovG;!ic;hatDnanIrG;arGei9;a,i;deY;ov4;b0rre1D;dKinsJriksIsGvaB;cob3GpGtra3D;inoza,osiQ;en,s3;te8;er,is3warG;ds;aXePiNjurhuMoKrisco15uHvorakG;!oT;arte,boHmitru,nn,rGt3C;and,ic;is;g2he0Omingu7nErd1ItG;to;us;aGcki2Hmitr2Ossanayake,x3;s,z; JbnaIlHmirGrvisFvi,w2;!ov4;gado,ic;th;bo0groot,jo6lHsilGvriA;va;a cruz,e3uG;ca;hl,mcevsCnIt2WviG;dGes,s;ov,s3;ielsGku22;!en;ki;a0Be06hRiobQlarkPoIrGunningh1H;awfo0RivGuz;elli;h1lKntJoIrGs2Nx;byn,reG;a,ia;ke,p0;i,rer2K;em2liB;ns;!e;anu;aOeMiu,oIristGu6we;eGiaG;ns1;i,ng,p9uHwGy;!dH;dGng;huJ;!n,onGu6;!g;kJnIpm2ttHudhGv7;ry;erjee,o14;!d,g;ma,raboG;rty;bJl0Cng4rG;eghetHnG;a,y;ti;an,ota1C;cerAlder3mpbeLrIstGvadi0B;iGro;llo;doHl0Er,t0uGvalho;so;so,zo;ll;a0Fe01hYiXlUoNrKuIyG;rLtyG;qi;chan2rG;ke,ns;ank5iem,oGyant;oks,wG;ne;gdan5nIruya,su,uchaHyKziG;c,n5;rd;darGik;enG;ko;ov;aGond15;nco,zG;ev4;ancFshw16;a08oGuiy2;umGwmG;ik;ckRethov1gu,ktPnNrG;gJisInG;ascoGds1;ni;ha;er,mG;anG;!n;gtGit7nP;ss3;asF;hi;er,hG;am;b4ch,ez,hRiley,kk0ldw8nMrIshHtAu0;es;ir;bInHtlGua;ett;es,i0;ieYosa;dGik;a9yoG;padhyG;ay;ra;k,ng;ic;bb0Acos09d07g04kht05lZnPrLsl2tJyG;aHd8;in;la;chis3kiG;ns3;aImstro6sl2;an;ng;ujo,ya;dJgelHsaG;ri;ovG;!a;ersJov,reG;aGjEws;ss1;en;en,on,s3;on;eksejEiyEmeiIvG;ar7es;ez;da;ev;arwHuilG;ar;al;ams,l0;er;ta;as",Ordinal:"true¦eBf7nin5s3t0zeroE;enDhir1we0;lfCn7;d,t3;e0ixt8;cond,vent7;et0th;e6ie7;i2o0;r0urt3;tie4;ft1rst;ight0lev1;e0h,ie1;en0;th",Cardinal:"true¦bEeBf5mEnine7one,s4t0zero;en,h2rDw0;e0o;lve,n5;irt6ousands,ree;even2ix2;i3o0;r1ur0;!t2;ty;ft0ve;e2y;ight0lev1;!e0y;en;illions",Multiple:"true¦b3hundred,m3qu2se1t0;housand,r2;pt1xt1;adr0int0;illion",City:"true¦0:74;1:61;2:6G;3:6J;4:5S;a68b53c4Id48e44f3Wg3Hh39i31j2Wk2Fl23m1Mn1Co19p0Wq0Ur0Os05tRuQvLwDxiBy9z5;a7h5i4Muri4O;a5e5ongsh0;ng3H;greb,nzib5G;ang2e5okoha3Sunfu;katerin3Hrev0;a5n0Q;m5Hn;arsBeAi6roclBu5;h0xi,zh5P;c7n5;d5nipeg,terth4;hoek,s1L;hi5Zkl3A;l63xford;aw;a8e6i5ladivost5Molgogr6L;en3lni6S;ni22r5;o3saill4N;lenc4Wncouv3Sr3ughn;lan 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wor6ale5;za;th;d5indhov0Pl paso;in5mont2;bur5;gh;aBe8ha0Xisp4o7resd0Lu5;b5esseldorf,nkirk,rb0shanbe;ai,l0I;ha,nggu0rtmu13;hradSl6nv5troit;er;hi;donghIe6k09l5masc1Zr es sala1KugavpiY;i0lU;gu,je2;aJebu,hAleve0Vo5raio02uriti1Q;lo7n6penhag0Ar5;do1Ok;akKst0V;gUm5;bo;aBen8i6ongqi1ristchur5;ch;ang m7ca5ttago1;go;g6n5;ai;du,zho1;ng5ttogr14;ch8sha,zh07;gliari,i9lga8mayenJn6pe town,r5tanO;acCdiff;ber1Ac5;un;ry;ro;aWeNhKirmingh0WoJr9u5;chareTdapeTenos air7r5s0tu0;g5sa;as;es;a9is6usse5;ls;ba6t5;ol;ne;sil8tisla7zzav5;il5;le;va;ia;goZst2;op6ubaneshw5;ar;al;iCl9ng8r5;g6l5n;in;en;aluru,hazi;fa6grade,o horizon5;te;st;ji1rut;ghd0BkFn9ot8r7s6yan n4;ur;el,r07;celo3i,ranquil09;ou;du1g6ja lu5;ka;alo6k5;ok;re;ng;ers5u;field;a05b02cc01ddis aba00gartaZhmedXizawl,lSmPnHqa00rEsBt7uck5;la5;nd;he7l5;an5;ta;ns;h5unci2;dod,gab5;at;li5;ngt2;on;a8c5kaOtwerp;hora6o3;na;ge;h7p5;ol5;is;eim;aravati,m0s5;terd5;am; 7buquerq6eppo,giers,ma5;ty;ue;basrah al qadim5mawsil al jadid5;ah;ab5;ad;la;ba;ra;idj0u 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el,ylG;p4toba;ur;anca3e4incoln3ouisI;e4iR;ds;a6e5h4omi;aka06ul1;ntucky,ra01;bardino,lmyk0ns0Qr4;achay,el0nata0X;alis6har4iangxi;kh4;and;co;daho,llino7n4owa;d5gush4;et0;ia1;is;a6ert5i4un2;dalFm0D;ford3;mp3rya1waii;ansu,eorg0lou7oa,u4;an4izhou,jarat;ajuato,gdo4;ng;cester3;lori4uji2;da;sex;ageUe7o5uran4;go;rs4;et;lawaMrby3;aFeaEh9o4rim08umbr0;ahui7l6nnectic5rsi4ventry;ca;ut;i03orado;la;e5hattisgarh,i4uvash0;apRhuahua;chn5rke4;ss0;ya;ra;lGm4;bridge3peche;a9ihar,r8u4;ck4ryat0;ingham3;shi4;re;emen,itish columb0;h0ja cal8lk7s4v7;hkorto4que;st2;an;ar0;iforn0;ia;dygHguascalientes,lBndhr9r5ss4;am;izo1kans5un4;achal 7;as;na;a 4;pradesh;a6ber5t4;ai;ta;ba5s4;ka;ma;ea",Country:"true¦0:38;1:2L;2:3B;a2Xb2Ec22d1Ye1Sf1Mg1Ch1Ai14j12k0Zl0Um0Gn05om2pZqat1KrXsKtCu7v5wal4yemTz3;a25imbabwe;es,lis and futu2Y;a3enezue32ietnam;nuatu,tican city;gTk6nited 4ruXs3zbeE; 2Ca,sr;arab emirat0Kkingdom,states3;! of am2Y;!raiV;a8haCimor les0Co7rinidad 5u3;nis0rk3valu;ey,me2Zs and caic1V;and t3t3;oba1L;go,kel10nga;iw2ji3nz2T;ki2V;aDcotl1eCi9lov8o6pa2Dri lanka,u5w3yr0;az3edAitzerl1;il1;d2riname;lomon1Xmal0uth 3;afr2KkMsud2;ak0en0;erra leoFn3;gapo1Yt maart3;en;negLrb0ychellZ;int 3moa,n marino,udi arab0;hele26luc0mart21;epublic of ir0Eom2Euss0w3;an27;a4eIhilippinUitcairn1Mo3uerto riN;l1rtugF;ki2Dl4nama,pua new0Vra3;gu7;au,esti3;ne;aBe9i7or3;folk1Ith4w3;ay; k3ern mariana1D;or0O;caragua,ger3ue;!ia;p3ther1Aw zeal1;al;mib0u3;ru;a7exi6icro0Bo3yanm06;ldova,n3roc5zambA;a4gol0t3;enegro,serrat;co;cAdagasc01l7r5urit4yot3;te;an0i16;shall0Xtin3;ique;a4div3i,ta;es;wi,ys0;ao,ed02;a6e5i3uxembourg;b3echtenste12thu1G;er0ya;ban0Isotho;os,tv0;azakh1Fe4iriba04o3uwait,yrgyz1F;rXsovo;eling0Knya;a3erG;ma16p2;c7nd6r4s3taly,vory coast;le of m2rael;a3el1;n,q;ia,oJ;el1;aiTon3ungary;dur0Ng kong;aBermany,ha0QibraltAre8u3;a6ern5inea3ya0P;! biss3;au;sey;deloupe,m,tema0Q;e3na0N;ce,nl1;ar;bUmb0;a7i6r3;ance,ench 3;guia0Epoly3;nes0;ji,nl1;lklandUroeU;ast tim7cu6gypt,l salv6ngl1quatorial4ritr5st3thiop0;on0; 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kremlJosrae,rasnoyar0ul;sk;ax,cn,nd0st;ianSochina;arlem,kg,nd,ov0;d,enweep;a2odavari,re0;at 0enwich;britaBlakI;ngHy village;co,ra;urope,vergladF;anube,en,fw,own4xb;arrizo pla6dg,edar 5gk,h1lt,olosse0;um;a2i0uuk;chen itza,mney rock,na0ricahua;town;morro,tham;breaks,fa5;in;cn,e2kk,ro0;oklyn,wns cany0;on;l air,verly hi0;lls;driadic,frica,lhambra,m7n3rc2sia,tl1zor0;es;!ant2; de triomphe,t1;adyr,tarct0;ic0; oce0;an;ericas,s",FirstName:"true¦aTblair,cQdOfrancoZgabMhinaLilya,jHkClBm6ni4quinn,re3s0;h0umit,yd;ay,e0iloh;a,lby;g9ne;co,ko0;!s;a1el0ina,org6;!okuhF;ds,naia,r1tt0xiB;i,y;ion,lo;ashawn,eif,uca;a3e1ir0rM;an;lsFn0rry;dall,yat5;i,sD;a0essIie,ude;i1m0;ie,mG;me;ta;rie0y;le;arcy,ev0;an,on;as1h0;arl8eyenne;ey,sidy;drien,kira,l4nd1ubr0vi;ey;i,r0;a,e0;a,y;ex2f1o0;is;ie;ei,is",WeekDay:"true¦fri2mon2s1t0wednesd3;hurs1ues1;aturd1und1;!d0;ay0;!s",Month:"true¦dec0february,july,nov0octo1sept0;em0;ber",Date:"true¦ago,on4som4t1week0yesterd5; 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marsh2lieutenant1rear0sergeant major,vice0; admir1; gener0;al","Adj|Gerund":"true¦0:3F;1:3H;2:31;3:2X;4:35;5:33;6:3C;7:2Z;8:36;9:29;a33b2Tc2Bd1Te1If19g12h0Zi0Rl0Nm0Gnu0Fo0Ap04rYsKtEuBvAw1Ayiel3;ar6e08;nBpA;l1Rs0B;fol3n1Zsett2;aEeDhrBi4ouc7rAwis0;e0Bif2oub2us0yi1;ea1SiA;l2vi1;l2mp0rr1J;nt1Vxi1;aMcreec7enten2NhLkyrocke0lo0Vmi2oJpHtDuBweA;e0Ul2;pp2ArA;gi1pri5roun3;aBea8iAri2Hun9;mula0r4;gge4rA;t2vi1;ark2eAraw2;e3llb2F;aAot7;ki1ri1;i9oc29;dYtisf6;aEeBive0oAus7;a4l2;assu4defi9fres7ig9juve07mai9s0vAwar3;ea2italiAol1G;si1zi1;gi1ll6mb2vi1;a6eDier23lun1VrAun2C;eBoA;mi5vo1Z;ce3s5vai2;n3rpleA;xi1;ffCpWutBverAwi1;arc7lap04p0Pri3whel8;goi1l6st1J;en3sA;et0;m2Jrtu4;aEeDiCoBuAyst0L;mb2;t1Jvi1;s5tiga0;an1Rl0n3smeri26;dAtu4;de9;aCeaBiAo0U;fesa0Tvi1;di1ni1;c1Fg19s0;llumiGmFnArri0R;cDfurHsCtBviA;go23ti1;e1Oimi21oxica0rig0V;pi4ul0;orpo20r0K;po5;na0;eaBorr02umilA;ia0;li1rtwar8;lFrA;atiDipCoBuelA;i1li1;undbrea10wi1;pi1;f6ng;a4ea8;a3etc7it0lEoCrBulfA;il2;ee1FighXust1L;rAun3;ebo3thco8;aCoA;a0wA;e4i1;mi1tte4;lectrJmHnExA;aCci0hBis0pA;an3lo3;aOila1B;c0spe1A;ab2coura0CdBergi13ga0Clive9ric7s02tA;hral2i0J;ea4u4;barras5er09pA;owe4;if6;aQeIiBrA;if0;sAzz6;aEgDhearCsen0tA;rAur11;ac0es5;te9;us0;ppoin0r8;biliGcDfi9gra3ligh0mBpres5sAvasG;erE;an3ea9orA;ali0L;a6eiBli9rA;ea5;vi1;ta0;maPri1s7un0zz2;aPhMlo5oAripp2ut0;mGnArrespon3;cer9fDspi4tA;inBrA;as0ibu0ol2;ui1;lic0u5;ni1;fDmCpA;eAromi5;l2ti1;an3;or0;aAil2;llenAnAr8;gi1;l8ptAri1;iva0;aff2eGin3lFoDrBuA;d3st2;eathtaAui5;ki1;gg2i2o8ri1unA;ci1;in3;co8wiA;lAtc7;de4;bsorVcOgonMlJmHnno6ppea2rFsA;pi4su4toA;nBun3;di1;is7;hi1;res0;li1;aFu5;si1;ar8lu4;ri1;mi1;iAzi1;zi1;cAhi1;eleDomA;moBpan6;yi1;da0;ra0;ti1;bi1;ng",Comparable:"true¦0:3C;1:3Q;2:3F;a3Tb3Cc33d2Te2Mf2Ag1Wh1Li1Fj1Ek1Bl13m0Xn0So0Rp0Iqu0Gr07sHtCug0vAw4y3za0Q;el10ouN;ary,e6hi5i3ry;ck0Cde,l3n1ry,se;d,y;ny,te;a3i3R;k,ry;a3erda2ulgar;gue,in,st;a6en2Xhi5i4ouZr3;anqu2Cen1ue;dy,g36me0ny;ck,rs28;ll,me,rt,wd3I;aRcaPeOhMiLkin0BlImGoEpDt6u4w3;eet,ift;b3dd0Wperfi21rre28;sta26t21;a8e7iff,r4u3;pUr1;a4ict,o3;ng;ig2Vn0N;a1ep,rn;le,rk,te0;e1Si2Vright0;ci1Yft,l3on,re;emn,id;a3el0;ll,rt;e4i3y;g2Mm0Z;ek,nd2T;ck24l0mp1L;a3iRrill,y;dy,l01rp;ve0Jxy;n1Jr3;ce,y;d,fe,int0l1Hv0V;a8e6i5o3ude;mantic,o19sy,u3;gh;pe,t1P;a3d,mo0A;dy,l;gg4iFndom,p3re,w;id;ed;ai2i3;ck,et;hoAi1Fl9o8r5u3;ny,r3;e,p11;egna2ic4o3;fouSud;ey,k0;liXor;ain,easa2;ny;dd,i0ld,ranL;aive,e5i4o3u14;b0Sisy,rm0Ysy;bb0ce,mb0R;a3r1w;r,t;ad,e5ild,o4u3;nda12te;ist,o1;a4ek,l3;low;s0ty;a8e7i6o3ucky;f0Jn4o15u3ve0w10y0N;d,sy;e0g;ke0l,mp,tt0Eve0;e1Qwd;me,r3te;ge;e4i3;nd;en;ol0ui19;cy,ll,n3;secu6t3;e3ima4;llege2rmedia3;te;re;aAe7i6o5u3;ge,m3ng1C;bYid;me0t;gh,l0;a3fXsita2;dy,rWv3;en0y;nd13ppy,r3;d3sh;!y;aFenEhCiBlAoofy,r3;a8e6i5o3ue0Z;o3ss;vy;m,s0;at,e3y;dy,n;nd,y;ad,ib,ooD;a2d1;a3o3;st0;tDuiS;u1y;aCeebBi9l8o6r5u3;ll,n3r0N;!ny;aCesh,iend0;a3nd,rmD;my;at,ir7;erce,nan3;ci9;le;r,ul3;ty;a6erie,sse4v3xtre0B;il;nti3;al;r4s3;tern,y;ly,th0;appZe9i5ru4u3;mb;nk;r5vi4z3;zy;ne;e,ty;a3ep,n9;d3f,r;!ly;agey,h8l7o5r4u3;dd0r0te;isp,uel;ar3ld,mmon,st0ward0zy;se;evKou1;e3il0;ap,e3;sy;aHiFlCoAr5u3;ff,r0sy;ly;a6i3oad;g4llia2;nt;ht;sh,ve;ld,un3;cy;a4o3ue;nd,o1;ck,nd;g,tt3;er;d,ld,w1;dy;bsu6ng5we3;so3;me;ry;rd",Adverb:"true¦a08b05d00eYfSheQinPjustOkinda,likewiZmMnJoEpCquite,r9s5t2u0very,well;ltima01p0; to,wards5;h1iny bit,o0wiO;o,t6;en,us;eldom,o0uch;!me1rt0; of;how,times,w0C;a1e0;alS;ndomRth05;ar excellenEer0oint blank; Lhaps;f3n0utright;ce0ly;! 0;ag05moX; courGten;ewJo0; longWt 0;onHwithstand9;aybe,eanwhiNore0;!ovT;! aboX;deed,steY;lla,n0;ce;or3u0;ck1l9rther0;!moK;ing; 0evK;exampCgood,suH;n mas0vI;se;e0irect2; 2fini0;te0;ly;juAtrop;ackward,y 0;far,no0; means,w; GbroFd nauseam,gEl7ny5part,s4t 2w0;ay,hi0;le;be7l0mo7wor7;arge,ea6; soon,i4;mo0way;re;l 3mo2ongsi1ready,so,togeth0ways;er;de;st;b1t0;hat;ut;ain;ad;lot,posteriori",Conjunction:"true¦aXbTcReNhowMiEjust00noBo9p8supposing,t5wh0yet;e1il0o3;e,st;n1re0thN; if,by,vM;evL;h0il,o;erefOo0;!uU;lus,rovided th9;r0therwiM;! not; mattEr,w0;! 0;since,th4w7;f4n0; 0asmuch;as mIcaForder t0;h0o;at;! 0;only,t0w0;hen;!ev3;ith2ven0;! 0;if,tB;er;o0uz;s,z;e0ut,y the time;cau1f0;ore;se;lt3nd,s 0;far1if,m0soon1t2;uch0; as;hou0;gh",Currency:"true¦$,aud,bQcOdJeurIfHgbp,hkd,iGjpy,kElDp8r7s3usd,x2y1z0¢,£,¥,ден,лв,руб,฿,₡,₨,€,₭,﷼;lotyQł;en,uanP;af,of;h0t5;e0il5;k0q0;elK;oubleJp,upeeJ;e2ound st0;er0;lingG;n0soF;ceEnies;empi7i7;n,r0wanzaCyatC;!onaBw;ls,nr;ori7ranc9;!os;en3i2kk,o0;b0ll2;ra5;me4n0rham4;ar3;e0ny;nt1;aht,itcoin0;!s",Determiner:"true¦aBboth,d9e6few,le5mu8neiDplenty,s4th2various,wh0;at0ich0;evC;a0e4is,ose;!t;everal,ome;!ast,s;a1l0very;!se;ch;e0u;!s;!n0;!o0y;th0;er","Adj|Present":"true¦a07b04cVdQeNfJhollIidRlEmCnarrIoBp9qua8r7s3t2uttFw0;aKet,ro0;ng,u08;endChin;e2hort,l1mooth,our,pa9tray,u0;re,speU;i2ow;cu6da02leSpaN;eplica01i02;ck;aHerfePr0;eseUime,omV;bscu1pen,wn;atu0e3odeH;re;a2e1ive,ow0;er;an;st,y;ow;a2i1oul,r0;ee,inge;rm;iIke,ncy,st;l1mpty,x0;emHpress;abo4ic7;amp,e2i1oub0ry,ull;le;ffu9re6;fu8libe0;raE;alm,l5o0;mpleCn3ol,rr1unterfe0;it;e0u7;ct;juga8sum7;ea1o0;se;n,r;ankru1lu0;nt;pt;li2pproxi0rticula1;ma0;te;ght","Person|Adj":"true¦b3du2earnest,frank,mi2r0san1woo1;an0ich,u1;dy;sty;ella,rown",Modal:"true¦c5lets,m4ought3sh1w0;ill,o5;a0o4;ll,nt;! to,a;ight,ust;an,o0;uld",Verb:"true¦born,cannot,gonna,has,keep tabs,msg","Person|Verb":"true¦b8ch7dr6foster,gra5ja9lan4ma2ni9ollie,p1rob,s0wade;kip,pike,t5ue;at,eg,ier2;ck,r0;k,shal;ce;ce,nt;ew;ase,u1;iff,l1ob,u0;ck;aze,ossom","Person|Date":"true¦a2j0sep;an0une;!uary;p0ugust,v0;ril"},Hg="0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ".split("").reduce((function(e,t,n){return e[t]=n,e}),{}),Vg=function(e){if(void 0!==Hg[e])return Hg[e];let t=0,n=1,r=36,i=1;for(;n=0;n--,i*=36){let r=e.charCodeAt(n)-48;r>10&&(r-=7),t+=r*i}return t},qg=function(e,t,n){const r=Vg(t);return r{let a=e.nodes[r];"!"===a[0]&&(t.push(i),a=a.slice(1));const o=a.split(/([A-Z0-9,]+)/g);for(let a=0;a{let t=function(e){if(!e)return{};const t=e.split("|").reduce(((e,t)=>{const n=t.split("¦");return e[n[0]]=n[1],e}),{}),n={};return Object.keys(t).forEach((function(e){const r=Yg(t[e]);"true"===e&&(e=!0);for(let t=0;t{if(Xg[t]=e,"Noun|Verb"===e){let e=Kg(t,Jg);Xg[e]="Plural|Verb"}})):Object.keys(t).forEach((t=>{Zg[t]=e}))})),[":(",":)",":P",":p",":O",";(",";)",";P",";p",";O",":3",":|",":/",":\\",":$",":*",":@",":-(",":-)",":-P",":-p",":-O",":-3",":-|",":-/",":-\\",":-$",":-*",":-@",":^(",":^)",":^P",":^p",":^O",":^3",":^|",":^/",":^\\",":^$",":^*",":^@","):","(:","$:","*:",")-:","(-:","$-:","*-:",")^:","(^:","$^:","*^:","<3","Zg[e]="Emoticon")),delete Zg[""],delete Zg.null,delete Zg[" "];const e_="Singular",t_={beforeTags:{Determiner:e_,Possessive:e_,Acronym:e_,Noun:e_,Adjective:e_,PresentTense:e_,Gerund:e_,PastTense:e_,Infinitive:e_,Date:e_,Ordinal:e_,Demonym:e_},afterTags:{Value:e_,Modal:e_,Copula:e_,PresentTense:e_,PastTense:e_,Demonym:e_,Actor:e_},beforeWords:{the:e_,with:e_,without:e_,of:e_,for:e_,any:e_,all:e_,on:e_,cut:e_,cuts:e_,increase:e_,decrease:e_,raise:e_,drop:e_,save:e_,saved:e_,saves:e_,make:e_,makes:e_,made:e_,minus:e_,plus:e_,than:e_,another:e_,versus:e_,neither:e_,about:e_,favorite:e_,best:e_,daily:e_,weekly:e_,linear:e_,binary:e_,mobile:e_,lexical:e_,technical:e_,computer:e_,scientific:e_,security:e_,government:e_,popular:e_,formal:e_,no:e_,more:e_,one:e_,let:e_,her:e_,his:e_,their:e_,our:e_,us:e_,sheer:e_,monthly:e_,yearly:e_,current:e_,previous:e_,upcoming:e_,last:e_,next:e_,main:e_,initial:e_,final:e_,beginning:e_,end:e_,top:e_,bottom:e_,future:e_,past:e_,major:e_,minor:e_,side:e_,central:e_,peripheral:e_,public:e_,private:e_},afterWords:{of:e_,system:e_,aid:e_,method:e_,utility:e_,tool:e_,reform:e_,therapy:e_,philosophy:e_,room:e_,authority:e_,says:e_,said:e_,wants:e_,wanted:e_,is:e_,did:e_,do:e_,can:e_,wise:e_}},n_="Infinitive",r_={beforeTags:{Modal:n_,Adverb:n_,Negative:n_,Plural:n_},afterTags:{Determiner:n_,Adverb:n_,Possessive:n_,Reflexive:n_,Preposition:n_,Cardinal:n_,Comparative:n_,Superlative:n_},beforeWords:{i:n_,we:n_,you:n_,they:n_,to:n_,please:n_,will:n_,have:n_,had:n_,would:n_,could:n_,should:n_,do:n_,did:n_,does:n_,can:n_,must:n_,us:n_,me:n_,let:n_,even:n_,when:n_,help:n_,he:n_,she:n_,it:n_,being:n_,bi:n_,co:n_,contra:n_,de:n_,inter:n_,intra:n_,mis:n_,pre:n_,out:n_,counter:n_,nobody:n_,somebody:n_,anybody:n_,everybody:n_},afterWords:{the:n_,me:n_,you:n_,him:n_,us:n_,her:n_,his:n_,them:n_,they:n_,it:n_,himself:n_,herself:n_,itself:n_,myself:n_,ourselves:n_,themselves:n_,something:n_,anything:n_,a:n_,an:n_,up:n_,down:n_,by:n_,out:n_,off:n_,under:n_,what:n_,all:n_,to:n_,because:n_,although:n_,how:n_,otherwise:n_,together:n_,though:n_,into:n_,yet:n_,more:n_,here:n_,there:n_,away:n_}},i_={beforeTags:Object.assign({},r_.beforeTags,t_.beforeTags,{}),afterTags:Object.assign({},r_.afterTags,t_.afterTags,{}),beforeWords:Object.assign({},r_.beforeWords,t_.beforeWords,{}),afterWords:Object.assign({},r_.afterWords,t_.afterWords,{})},a_="Adjective",o_={beforeTags:{Determiner:a_,Possessive:a_,Hyphenated:a_},afterTags:{Adjective:a_},beforeWords:{seem:a_,seemed:a_,seems:a_,feel:a_,feels:a_,felt:a_,stay:a_,appear:a_,appears:a_,appeared:a_,also:a_,over:a_,under:a_,too:a_,it:a_,but:a_,still:a_,really:a_,quite:a_,well:a_,very:a_,truly:a_,how:a_,deeply:a_,hella:a_,profoundly:a_,extremely:a_,so:a_,badly:a_,mostly:a_,totally:a_,awfully:a_,rather:a_,nothing:a_,something:a_,anything:a_,not:a_,me:a_,is:a_,face:a_,faces:a_,faced:a_,look:a_,looks:a_,looked:a_,reveal:a_,reveals:a_,revealed:a_,sound:a_,sounded:a_,sounds:a_,remains:a_,remained:a_,prove:a_,proves:a_,proved:a_,becomes:a_,stays:a_,tastes:a_,taste:a_,smells:a_,smell:a_,gets:a_,grows:a_,as:a_,rings:a_,radiates:a_,conveys:a_,convey:a_,conveyed:a_,of:a_},afterWords:{too:a_,also:a_,or:a_,enough:a_,as:a_}},s_="Gerund",l_={beforeTags:{Adverb:s_,Preposition:s_,Conjunction:s_},afterTags:{Adverb:s_,Possessive:s_,Person:s_,Pronoun:s_,Determiner:s_,Copula:s_,Preposition:s_,Conjunction:s_,Comparative:s_},beforeWords:{been:s_,keep:s_,continue:s_,stop:s_,am:s_,be:s_,me:s_,began:s_,start:s_,starts:s_,started:s_,stops:s_,stopped:s_,help:s_,helps:s_,avoid:s_,avoids:s_,love:s_,loves:s_,loved:s_,hate:s_,hates:s_,hated:s_},afterWords:{you:s_,me:s_,her:s_,him:s_,his:s_,them:s_,their:s_,it:s_,this:s_,there:s_,on:s_,about:s_,for:s_,up:s_,down:s_}},c_="Gerund",d_="Adjective",u_={beforeTags:Object.assign({},o_.beforeTags,l_.beforeTags,{Imperative:c_,Infinitive:d_,Plural:c_}),afterTags:Object.assign({},o_.afterTags,l_.afterTags,{Noun:d_}),beforeWords:Object.assign({},o_.beforeWords,l_.beforeWords,{is:d_,are:c_,was:d_,of:d_,suggest:c_,suggests:c_,suggested:c_,recommend:c_,recommends:c_,recommended:c_,imagine:c_,imagines:c_,imagined:c_,consider:c_,considered:c_,considering:c_,resist:c_,resists:c_,resisted:c_,avoid:c_,avoided:c_,avoiding:c_,except:d_,accept:d_,assess:c_,explore:c_,fear:c_,fears:c_,appreciate:c_,question:c_,help:c_,embrace:c_,with:d_}),afterWords:Object.assign({},o_.afterWords,l_.afterWords,{to:c_,not:c_,the:c_})},p_={Determiner:void 0,Cardinal:"Noun",PhrasalVerb:"Adjective"},m_={},g_={beforeTags:Object.assign({},o_.beforeTags,t_.beforeTags,p_),afterTags:Object.assign({},o_.afterTags,t_.afterTags,m_),beforeWords:Object.assign({},o_.beforeWords,t_.beforeWords,{are:"Adjective",is:"Adjective",was:"Adjective",be:"Adjective",off:"Adjective",out:"Adjective"}),afterWords:Object.assign({},o_.afterWords,t_.afterWords)};let __="PastTense",h_="Adjective";const f_={beforeTags:{Adverb:__,Pronoun:__,ProperNoun:__,Auxiliary:__,Noun:__},afterTags:{Possessive:__,Pronoun:__,Determiner:__,Adverb:__,Comparative:__,Date:__,Gerund:__},beforeWords:{be:__,who:__,get:h_,had:__,has:__,have:__,been:__,it:__,as:__,for:h_,more:h_,always:h_},afterWords:{by:__,back:__,out:__,in:__,up:__,down:__,before:__,after:__,for:__,the:__,with:__,as:__,on:__,at:__,between:__,to:__,into:__,us:__,them:__,his:__,her:__,their:__,our:__,me:__,about:h_}},b_={beforeTags:Object.assign({},o_.beforeTags,f_.beforeTags),afterTags:Object.assign({},o_.afterTags,f_.afterTags),beforeWords:Object.assign({},o_.beforeWords,f_.beforeWords),afterWords:Object.assign({},o_.afterWords,f_.afterWords)},E_={beforeTags:Object.assign({},o_.beforeTags,r_.beforeTags,{Adverb:void 0,Negative:void 0}),afterTags:Object.assign({},o_.afterTags,r_.afterTags,{Noun:"Adjective",Conjunction:void 0}),beforeWords:Object.assign({},o_.beforeWords,r_.beforeWords,{have:void 0,had:void 0,not:void 0,went:"Adjective",goes:"Adjective",got:"Adjective",be:"Adjective"}),afterWords:Object.assign({},o_.afterWords,r_.afterWords,{to:void 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