diff --git a/ruben-solution/.gitignore b/ruben-solution/.gitignore new file mode 100644 index 0000000..338cc9e --- /dev/null +++ b/ruben-solution/.gitignore @@ -0,0 +1,170 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +data/ + +.DS_Store +/.Trash-0/ +/package.json +/package-lock.json +/node_modules/ diff --git a/ruben-solution/Dockerfile b/ruben-solution/Dockerfile new file mode 100644 index 0000000..29851ae --- /dev/null +++ b/ruben-solution/Dockerfile @@ -0,0 +1,12 @@ +FROM python:3.9-slim +WORKDIR /app +COPY . /app + +RUN pip install --upgrade pip +RUN pip install --no-cache-dir -r requirements.txt + +EXPOSE 8888 + +ENV PYTHONUNBUFFERED=1 + +CMD ["jupyter", "lab", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root"] diff --git a/ruben-solution/README.md b/ruben-solution/README.md new file mode 100644 index 0000000..ccedacb --- /dev/null +++ b/ruben-solution/README.md @@ -0,0 +1,74 @@ +# Idoven ECG analysis Project + +--- + +This repository contains the code and data for an ECG analysis project, focusing on distinguishing between Normal and Abnormal ECGs using machine learning models. The project is organized into various directories and notebooks that guide you through the entire process, from data exploration to model training and evaluation. + +## Project Structure + +### 1. `data/` +- **raw/**: Contains the raw ECG data files. +- **processed/**: Stores the preprocessed data that is ready for model training. + +### 2. `models/` +- `cnn_model.keras`: The best trained Convolutional Neural Network (CNN) model. +- `resnet_model.keras`: The best trained ResNet model. + +### 3. `notebooks/` +- **01-data-exploration.ipynb**: Notebook for initial data exploration and visualization. +- **02-data-preprocessing.ipynb**: Notebook detailing the preprocessing steps applied to the ECG data. +- **03-model-training.ipynb**: Notebook used to train the CNN and ResNet models. +- **04-results-analysis.ipynb**: Notebook for analyzing the results, including model evaluation metrics and visualizations. + +### 4. `reports/` +- `cnn_history.pkl`: Training history of the CNN model. +- `resnet_history.pkl`: Training history of the ResNet model. +- `coutour_plot.png`: Contour plot of the hyperparameter optimization. +- `parameter_importances.png`: Visualization of feature importances or parameter impact. +- `tscnn_hyperparameter_study.pkl`: Study results on hyperparameter tuning. + +### 5. `results/` +- `cnn_preds.npy`: Numpy array containing the predictions from the CNN model. +- `resnet_preds.npy`: Numpy array containing the predictions from the ResNet model. + +### 6. `src/` +- **models/**: Contains model definitions and scripts for training and evaluation. +- **utils/**: Utility scripts used across the project. + - `data_preprocessing.py`: Functions for preprocessing ECG data. + - `ecg_visualization.py`: Functions for visualizing ECG data. + - `example_physionet.py`: Example script for working with PhysioNet data. + - `hyperparameter_tuning.py`: Script for hyperparameter tuning of models. + +### 7. **Setup and Configuration Files** +- **Dockerfile**: Docker configuration file to set up the environment. +- **requirements.txt**: List of Python dependencies required for the project. +- **setup.py**: Script to install the `ecg_analysis` package. +- **.gitignore**: Specifies files and directories to be ignored by Git. +- **README.md**: This README file. + +## Getting Started + +### Prerequisites +Ensure you have Docker installed on your system. Alternatively, you can manually install the required Python packages listed in `requirements.txt`. + +### Installation + + ```bash + docker build -t ecg-analysis . + docker run -p 8888:8888 ecg-analysis + ``` + This will set up a Jupyter Lab server with the project environment. + +### Usage + +1. **Data Exploration**: + Open the `01-data-exploration.ipynb` notebook to explore the dataset and visualize ECG signals. + +2. **Data Preprocessing**: + Use `02-data-preprocessing.ipynb` to preprocess the raw ECG data and prepare it for model training. + +3. **Model Training**: + The `03-model-training.ipynb` notebook guides you through training both CNN and ResNet models on the processed data. + +4. **Results Analysis**: + Evaluate model performance and analyze results in the `04-results-analysis.ipynb` notebook. diff --git a/ruben-solution/__init__.py b/ruben-solution/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ruben-solution/models/cnn_model.keras b/ruben-solution/models/cnn_model.keras new file mode 100644 index 0000000..a21134c Binary files /dev/null and b/ruben-solution/models/cnn_model.keras differ diff --git a/ruben-solution/models/resnet_model.keras b/ruben-solution/models/resnet_model.keras new file mode 100644 index 0000000..ef7f67d Binary files /dev/null and b/ruben-solution/models/resnet_model.keras differ diff --git a/ruben-solution/notebooks/01-data-exploration.ipynb b/ruben-solution/notebooks/01-data-exploration.ipynb new file mode 100644 index 0000000..744105a --- /dev/null +++ b/ruben-solution/notebooks/01-data-exploration.ipynb @@ -0,0 +1,1410 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c850ff61-6d5c-4710-8407-d73ce24f9cea", + "metadata": {}, + "source": [ + "# Data Exploration" + ] + }, + { + "cell_type": "markdown", + "id": "3b372ce6-f7e9-4660-8de8-7ca8cb1bcaca", + "metadata": {}, + "source": [ + "In this notebook, I dive into the ECG dataset to get a better understanding of its structure and characteristics. My main goal here is to uncover patterns and insights that will be useful when building models later on." + ] + }, + { + "cell_type": "markdown", + "id": "98dc9358-d49a-4cd2-8742-b750fcf348c4", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "#### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "055993f8-1bad-4752-9887-f24a0a1cdc81", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "80862027-2214-4069-a11a-b537b97e00d5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-27 15:50:12.626619: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "import ast\n", + "from os.path import join\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from scipy.stats import chi2_contingency, mannwhitneyu, ttest_ind\n", + "\n", + "from utils.data_preprocessing import *\n", + "from utils.ecg_visualization import *\n", + "\n", + "pd.set_option(\"display.max_columns\", None)" + ] + }, + { + "cell_type": "markdown", + "id": "3d15254c-b55c-4770-8314-6404a5fb4838", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "#### PTB-XL Data Loading" + ] + }, + { + "cell_type": "markdown", + "id": "17fe7dd3-03ad-45e1-87a9-cb4ba632eb79", + "metadata": {}, + "source": [ + "Throughout the project, I focus on ECGs with sampling rate 100 for efficiency in computing." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "07281595-65d8-4013-805c-b69446209490", + "metadata": {}, + "outputs": [], + "source": [ + "data_path = \"../data/\"\n", + "sampling_rate = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9eb362cf-2c34-4ea6-860e-e8c467f592df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 21801 entries, 1 to 21837\n", + "Data columns (total 42 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 patient_id 21801 non-null float64\n", + " 1 age 21801 non-null float64\n", + " 2 sex 21801 non-null int64 \n", + " 3 height 6975 non-null float64\n", + " 4 weight 9422 non-null float64\n", + " 5 nurse 20326 non-null float64\n", + " 6 site 21783 non-null float64\n", + " 7 device 21801 non-null object \n", + " 8 recording_date 21801 non-null object \n", + " 9 report 21801 non-null object \n", + " 10 scp_codes 21801 non-null object \n", + " 11 heart_axis 13331 non-null object \n", + " 12 infarction_stadium1 5613 non-null object \n", + " 13 infarction_stadium2 103 non-null object \n", + " 14 validated_by 12421 non-null float64\n", + " 15 second_opinion 21801 non-null bool \n", + " 16 initial_autogenerated_report 21801 non-null bool \n", + " 17 validated_by_human 21801 non-null bool \n", + " 18 baseline_drift 1599 non-null object \n", + " 19 static_noise 3260 non-null object \n", + " 20 burst_noise 613 non-null object \n", + " 21 electrodes_problems 30 non-null object \n", + " 22 extra_beats 1949 non-null object \n", + " 23 pacemaker 291 non-null object \n", + " 24 strat_fold 21801 non-null int64 \n", + " 25 filename_lr 21801 non-null object \n", + " 26 r_peaks 21801 non-null object \n", + " 27 RS-LVH 21801 non-null bool \n", + " 28 S12-LVH 21801 non-null bool \n", + " 29 R56-LVH 21801 non-null bool \n", + " 30 QRS-LVH 21801 non-null bool \n", + " 31 LI-LVH 21801 non-null bool \n", + " 32 SLI-LVH 21801 non-null bool \n", + " 33 QRS-CLBB 21801 non-null bool \n", + " 34 ST-ELEV-MI 21801 non-null bool \n", + " 35 ST-DEPR-MI 21801 non-null bool \n", + " 36 Q-ISC 21801 non-null bool \n", + " 37 Q-ISC-QPeak 21801 non-null bool \n", + " 38 Q-ISC-V2V3 21801 non-null bool \n", + " 39 Q-ISC-RPeak 21801 non-null bool \n", + " 40 STRAIN 21801 non-null bool \n", + " 41 MI-ALL 21801 non-null bool \n", + "dtypes: bool(18), float64(7), int64(2), object(15)\n", + "memory usage: 4.5+ MB\n" + ] + } + ], + "source": [ + "Y = pd.read_csv(join(data_path, \"raw/ptbxl_database.csv\"), index_col=\"ecg_id\").drop(columns=\"filename_hr\")\n", + "Y.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "965fe145-d5d2-4391-9f0a-0f472ed8705f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 21,801 ECGs for 18,869 patients\n" + ] + } + ], + "source": [ + "print(f\"There are {Y.shape[0]:,} ECGs for {Y.patient_id.nunique():,} patients\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a3d7a27f-43d1-442b-89c7-e85ca6922141", + "metadata": {}, + "outputs": [], + "source": [ + "Y[Y.select_dtypes(include=\"bool\").columns] = Y.select_dtypes(include=\"bool\").astype(int)\n", + "Y = Y.astype({\"patient_id\": int})" + ] + }, + { + "cell_type": "markdown", + "id": "5537b924-579c-4035-81e3-80b1fc3f7226", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "#### Design choices" + ] + }, + { + "cell_type": "markdown", + "id": "c3ee041f-a5b2-4cf3-b7b8-34624eb6c29c", + "metadata": {}, + "source": [ + "I noticed several boolean indicators at the end of the dataset that flag specific ECG features. However, these indicators are not documented in the PhysioNet description, which only covers the first 28 columns. To maintain focus and ensure the use of well-documented data, I will exclude these indicators from my analysis and modeling. Instead, I'll concentrate on the SCP data, which is thoroughly documented and better understood." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "abc2a56f-d466-445b-b6c6-8a86c5fcba29", + "metadata": {}, + "outputs": [], + "source": [ + "Y = Y.drop(\n", + " columns=[\n", + " \"RS-LVH\",\n", + " \"S12-LVH\",\n", + " \"R56-LVH\",\n", + " \"QRS-LVH\",\n", + " \"LI-LVH\",\n", + " \"SLI-LVH\",\n", + " \"QRS-CLBB\",\n", + " \"ST-ELEV-MI\",\n", + " \"ST-DEPR-MI\",\n", + " \"Q-ISC\",\n", + " \"Q-ISC-QPeak\",\n", + " \"Q-ISC-V2V3\",\n", + " \"Q-ISC-RPeak\",\n", + " \"STRAIN\",\n", + " \"MI-ALL\",\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d3849cca-e0c7-4bd2-b84c-4b6d7e2bfdf8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " patient_id age sex height weight \\\n", + "count 21801.000000 21801.000000 21801.000000 6975.000000 9422.000000 \n", + "mean 11250.554287 62.769781 0.479106 166.703226 70.996391 \n", + "std 6235.025560 32.307421 0.499575 10.866804 15.878365 \n", + "min 302.000000 2.000000 0.000000 6.000000 5.000000 \n", + "25% 5975.000000 50.000000 0.000000 160.000000 60.000000 \n", + "50% 11419.000000 62.000000 0.000000 166.000000 70.000000 \n", + "75% 16608.000000 72.000000 1.000000 174.000000 80.000000 \n", + "max 21797.000000 300.000000 1.000000 209.000000 250.000000 \n", + "\n", + " nurse site validated_by second_opinion \\\n", + "count 20326.000000 21783.000000 12421.000000 21801.000000 \n", + "mean 2.291745 1.545012 0.746075 0.025458 \n", + "std 3.254033 4.172799 1.178003 0.157514 \n", + "min 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 \n", + "50% 1.000000 1.000000 1.000000 0.000000 \n", + "75% 3.000000 2.000000 1.000000 0.000000 \n", + "max 11.000000 50.000000 11.000000 1.000000 \n", + "\n", + " initial_autogenerated_report validated_by_human strat_fold \n", + "count 21801.000000 21801.000000 21801.000000 \n", + "mean 0.312509 0.736572 5.503142 \n", + "std 0.463527 0.440503 2.874868 \n", + "min 0.000000 0.000000 1.000000 \n", + "25% 0.000000 0.000000 3.000000 \n", + "50% 0.000000 1.000000 6.000000 \n", + "75% 1.000000 1.000000 8.000000 \n", + "max 1.000000 1.000000 10.000000 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e2e56542-2f19-48ac-a177-a0d55bc637b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "patient_id 0.000000\n", + "age 0.000000\n", + "sex 0.000000\n", + "height 0.680061\n", + "weight 0.567818\n", + "nurse 0.067657\n", + "site 0.000826\n", + "device 0.000000\n", + "recording_date 0.000000\n", + "report 0.000000\n", + "scp_codes 0.000000\n", + "heart_axis 0.388514\n", + "infarction_stadium1 0.742535\n", + "infarction_stadium2 0.995275\n", + "validated_by 0.430255\n", + "second_opinion 0.000000\n", + "initial_autogenerated_report 0.000000\n", + "validated_by_human 0.000000\n", + "baseline_drift 0.926655\n", + "static_noise 0.850466\n", + "burst_noise 0.971882\n", + "electrodes_problems 0.998624\n", + "extra_beats 0.910600\n", + "pacemaker 0.986652\n", + "strat_fold 0.000000\n", + "filename_lr 0.000000\n", + "r_peaks 0.000000\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.isnull().mean()" + ] + }, + { + "cell_type": "markdown", + "id": "62f90785-2a8f-4561-91be-4d81ceb9955a", + "metadata": {}, + "source": [ + "- Height and weight columns in the dataset contain a significant number of missing values. Given that these variables are not central to the primary objective of analyzing ECG data and predicting heart conditions, and the effort to impute or handle these missing values may not yield substantial benefits for the model's performance, I decided it's reasonable to exclude these columns from the analysis. This allows me to focus on more relevant and complete data for this analysis.\n", + "\n", + "- For simplicity, I decided to remove the metadata columns `nurse`, `site`, `device`, `recording_date`, and `validated_by`, `initial_autogenerated_report` as they are not directly relevant to the clinical outcomes we are focusing on. Additionally, these variables could introduce bias. I also remove `report` since it was converted into the `scp-codes` column.\n", + "\n", + "- I remove `second_opinion` column because it is present in only 2.5% of the data, making it too sparse to be useful for our analysis.\n", + "\n", + "- I also remove `validated_by_human` since I will be using the cross-validation folds recommended by physionet where folds 9 and 10, which will be used for validation and testing, contain records with high-quality labels validated by humans." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "27157d86-86de-4fff-a276-5f1b5e5317e0", + "metadata": {}, + "outputs": [], + "source": [ + "Y = Y.drop(\n", + " columns=[\n", + " \"height\",\n", + " \"weight\",\n", + " \"nurse\",\n", + " \"site\",\n", + " \"validated_by\",\n", + " \"device\",\n", + " \"recording_date\",\n", + " \"r_peaks\",\n", + " \"second_opinion\",\n", + " \"validated_by_human\",\n", + " \"initial_autogenerated_report\",\n", + " \"report\",\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ae20f6fd-9d68-4556-8dda-d88b652f0db3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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patient_idagesexscp_codesheart_axisinfarction_stadium1infarction_stadium2baseline_driftstatic_noiseburst_noiseelectrodes_problemsextra_beatspacemakerstrat_foldfilename_lr
ecg_id
11570956.01{'NORM': 100.0, 'LVOLT': 0.0, 'SR': 0.0}NaNNaNNaNNaN, I-V1,NaNNaNNaNNaN3records100/00000/00001_lr
21324319.00{'NORM': 80.0, 'SBRAD': 0.0}NaNNaNNaNNaNNaNNaNNaNNaNNaN2records100/00000/00002_lr
32037237.01{'NORM': 100.0, 'SR': 0.0}NaNNaNNaNNaNNaNNaNNaNNaNNaN5records100/00000/00003_lr
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" + ], + "text/plain": [ + " patient_id age sex scp_codes \\\n", + "ecg_id \n", + "1 15709 56.0 1 {'NORM': 100.0, 'LVOLT': 0.0, 'SR': 0.0} \n", + "2 13243 19.0 0 {'NORM': 80.0, 'SBRAD': 0.0} \n", + "3 20372 37.0 1 {'NORM': 100.0, 'SR': 0.0} \n", + "\n", + " heart_axis infarction_stadium1 infarction_stadium2 baseline_drift \\\n", + "ecg_id \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "\n", + " static_noise burst_noise electrodes_problems extra_beats pacemaker \\\n", + "ecg_id \n", + "1 , I-V1, NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "\n", + " strat_fold filename_lr \n", + "ecg_id \n", + "1 3 records100/00000/00001_lr \n", + "2 2 records100/00000/00002_lr \n", + "3 5 records100/00000/00003_lr " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "d0f72762-e112-4216-818f-e78d3d20aede", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "#### Data Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6f879990-b448-41ac-8afd-cb7c08ed5bf7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Y = Y.loc[Y.age < 100]\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.hist(Y.age)\n", + "plt.title(\"Distribution of Patient Ages\")\n", + "plt.xlabel(\"Age (years)\")\n", + "plt.ylabel(\"Number of Patients\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4f7b28eb-bf07-4e47-b3f3-25552bfd4289", + "metadata": {}, + "source": [ + "After removing outliers, the age distribution is slightly right skewed. This is expected given that there is a higher prevalence of cardiovascular disease in older people and therefore an increased amount of monitoring." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f3c8fa13-c05d-4f9c-8573-b557d6358780", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sex\n", + "0 0.514829\n", + "1 0.485171\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.drop_duplicates(\"patient_id\").sex.value_counts(normalize=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b4c79edc-f977-4e4e-81df-93f32bd5d97b", + "metadata": {}, + "source": [ + "Although it is not stated directly which sex value represents male or female, physionet states that there are 52% males and 48% females, which means 0 represents males and 1 females. The data is fairly balanced between the genders." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3706de1e-1efc-415e-92db-bfd5625dbc08", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['patient_id', 'age', 'sex', 'scp_codes', 'heart_axis',\n", + " 'infarction_stadium1', 'infarction_stadium2', 'baseline_drift',\n", + " 'static_noise', 'burst_noise', 'electrodes_problems', 'extra_beats',\n", + " 'pacemaker', 'strat_fold', 'filename_lr'],\n", + " dtype='object')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f4c981a3-590b-40f8-b8ed-326c16ac6b25", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "heart_axis\n", + "NaN 8341\n", + "MID 7649\n", + "LAD 3678\n", + "ALAD 1354\n", + "RAD 219\n", + "ARAD 118\n", + "AXL 95\n", + "AXR 51\n", + "SAG 3\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.heart_axis.value_counts(dropna=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9c90b823-d65b-4cdb-b3d0-fff1688f9e66", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(infarction_stadium1\n", + " NaN 16018\n", + " unknown 3372\n", + " Stadium II-III 935\n", + " Stadium III 933\n", + " Stadium I 161\n", + " Stadium II 84\n", + " Stadium I-II 5\n", + " Name: count, dtype: int64,\n", + " infarction_stadium2\n", + " NaN 21408\n", + " Stadium III 62\n", + " Stadium I 19\n", + " Stadium II 19\n", + " Name: count, dtype: int64)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.infarction_stadium1.value_counts(dropna=False), Y.infarction_stadium2.value_counts(dropna=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5b89997e-2955-4580-88bb-85917419da82", + "metadata": {}, + "outputs": [], + "source": [ + "Y[\"infarction_stadium1\"] = Y[\"infarction_stadium1\"].replace(\"unknown\", np.nan)" + ] + }, + { + "cell_type": "markdown", + "id": "6c0908c2-3ad3-44d7-9f94-709ecfca4732", + "metadata": {}, + "source": [ + "Heart axis and infarction stadium will help us ensure that normal ECGs do not have contradictions in their data (e.g. normal ECG with a known infarction stadium).\n", + "\n", + "We will use the columns `baseline_drift`, `static_noise`, `burst_noise`, `electrodes_problems`, `extra_beats`, `pacemaker` during preprocessing to ensure data quality." + ] + }, + { + "cell_type": "markdown", + "id": "b5df6579-f178-4fe6-a553-53a52dab4620", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "#### Data Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e2ed3d6e-0f31-42cf-aebf-a75f6142d085", + "metadata": {}, + "outputs": [ + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ecg_per_patient = Y.groupby(\"patient_id\").size()\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.hist(ecg_per_patient)\n", + "plt.yscale(\"log\")\n", + "plt.title(\"Distribution of ECG Recordings per Patient\")\n", + "plt.xlabel(\"Number of ECGs\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "aca1ac6e-c1ac-4727-81e9-a17167453c4c", + "metadata": {}, + "source": [ + "There are more than 1 ECG per patient. It is important during model training to ensure that train/val/test sets do not have patient overlaps. The physionet splits guarantee this." + ] + }, + { + "cell_type": "markdown", + "id": "f3df732c-ec34-46ff-a3b8-57dfb872a942", + "metadata": {}, + "source": [ + "Let's visualize ECGs of different SCP labels in a similar fashion as in a clinical setting." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "da99d150-1527-44d6-9c20-b59db3b2a790", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Annotations (SCP Codes) for Patient ID 15709, ECG ID 1: {'NORM': 100.0, 'LVOLT': 0.0, 'SR': 0.0}\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Annotations (SCP Codes) for Patient ID 11275, ECG ID 8: {'IMI': 35.0, 'ABQRS': 0.0, 'SR': 0.0}\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Annotations (SCP Codes) for Patient ID 13619, ECG ID 17: {'AFLT': 100.0, 'ABQRS': 0.0, 'AFIB': 0.0}\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "load_and_plot_ecg(0, Y, sampling_rate=100, path=join(data_path, \"raw\"))\n", + "load_and_plot_ecg(7, Y, sampling_rate=100, path=join(data_path, \"raw\"))\n", + "load_and_plot_ecg(16, Y, sampling_rate=100, path=join(data_path, \"raw\"))" + ] + }, + { + "cell_type": "markdown", + "id": "f2b76d83-a38d-4198-8e10-df0124093f18", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "#### Feature analysis" + ] + }, + { + "cell_type": "markdown", + "id": "7b1a0f7b-b517-46d1-96d2-f4268df76e33", + "metadata": {}, + "source": [ + "I aggregated diagnostic codes following the functions suggested by physionet. I added a requirement that the diagnosis was highly likely (>= 80% chance), to ensure data quality. This parameter could be tuned during model training, but I will not focus on it." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cc82c588-eba3-47c5-91aa-2cfb5c469a75", + "metadata": {}, + "outputs": [], + "source": [ + "Y.scp_codes = Y.scp_codes.apply(lambda x: ast.literal_eval(x))\n", + "agg_df = pd.read_csv(join(data_path, \"raw/scp_statements.csv\"), index_col=0)\n", + "agg_df = agg_df[agg_df.diagnostic == 1]\n", + "Y[\"diagnostic_class\"] = Y.scp_codes.apply(aggregate_diagnostic, args=(agg_df, \"diagnostic_class\", 80.0))\n", + "Y = Y.loc[Y[\"diagnostic_class\"] != \"\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "d831ca47-26e5-44aa-9733-ee0384dfb0e5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "diagnostic_class\n", + "NORM 8585\n", + "STTC 2624\n", + "CD 2478\n", + "MI 1554\n", + "MI|CD 766\n", + "STTC|HYP 555\n", + "STTC|CD 503\n", + "NORM|CD 320\n", + "MI|STTC 319\n", + "HYP 248\n", + "HYP|CD 204\n", + "STTC|HYP|CD 177\n", + "MI|HYP|STTC 146\n", + "MI|CD|STTC 105\n", + "MI|HYP|CD|STTC 72\n", + "MI|HYP|CD 69\n", + "MI|HYP 58\n", + "STTC|MI 4\n", + "STTC|HYP|MI 3\n", + "NORM|STTC|CD 1\n", + "STTC|HYP|MI|CD 1\n", + "NORM|HYP 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y[\"diagnostic_class\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1d3853a3-201b-4fb2-8dbb-54d01baca301", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CD 0.249880\n", + "HYP 0.081626\n", + "MI 0.164795\n", + "NORM 0.473953\n", + "STTC 0.239983\n", + "dtype: float64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diagnostic_superclass_df = Y[\"diagnostic_class\"].str.get_dummies()\n", + "diagnostic_superclass_df.mean()" + ] + }, + { + "cell_type": "markdown", + "id": "be5e216d-5a1a-4cf8-bb1e-18a2686073c4", + "metadata": {}, + "source": [ + "Patients in this dataset seem to commonly have comorbidities. I see that patients diagnosed as normal can also have high likelihood for other diseases. I will treat this issue during preprocessing." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "29bb88c9-32a2-4776-bb75-ead79201fff9", + "metadata": {}, + "outputs": [], + "source": [ + "Y = pd.concat([Y, diagnostic_superclass_df], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "65dced94-5b02-427d-8956-7528ed3b89ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['patient_id', 'age', 'sex', 'scp_codes', 'heart_axis',\n", + " 'infarction_stadium1', 'infarction_stadium2', 'baseline_drift',\n", + " 'static_noise', 'burst_noise', 'electrodes_problems', 'extra_beats',\n", + " 'pacemaker', 'strat_fold', 'filename_lr', 'diagnostic_class', 'CD',\n", + " 'HYP', 'MI', 'NORM', 'STTC'],\n", + " dtype='object')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8b0085ed-d234-40fc-815b-a25ea50a76f4", + "metadata": {}, + "outputs": [], + "source": [ + "Y[\"normal_heart_axis\"] = np.where(Y[\"heart_axis\"].isin([\"MID\", np.nan]), 1, 0)\n", + "Y[\"infarction\"] = np.where(Y[[\"infarction_stadium1\", \"infarction_stadium2\"]].isna().sum(1) != 2, 1, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3a323f80-1c6a-469d-9c91-986d2daf90cd", + "metadata": {}, + "outputs": [], + "source": [ + "Y[\"static_noise_bool\"] = np.where(Y[\"static_noise\"].isna(), 0, 1)\n", + "Y[\"burst_noise_bool\"] = np.where(Y[\"burst_noise\"].isna(), 0, 1)\n", + "Y[\"electrodes_problems_bool\"] = np.where(Y[\"electrodes_problems\"].isna(), 0, 1)\n", + "Y[\"extra_beats_bool\"] = np.where(Y[\"extra_beats\"].isna(), 0, 1)\n", + "Y[\"pacemaker_bool\"] = np.where(Y[\"pacemaker\"].isna(), 0, 1)\n", + "Y[\"baseline_drift_bool\"] = np.where(Y[\"baseline_drift\"].isna(), 0, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "af0f7b85-4612-4396-8ae8-74e85adc1cc0", + "metadata": {}, + "outputs": [], + "source": [ + "correlation_matrix = Y[\n", + " [\n", + " \"age\",\n", + " \"sex\",\n", + " \"normal_heart_axis\",\n", + " \"infarction\",\n", + " \"baseline_drift_bool\",\n", + " \"static_noise_bool\",\n", + " \"burst_noise_bool\",\n", + " \"electrodes_problems_bool\",\n", + " \"extra_beats_bool\",\n", + " \"pacemaker_bool\",\n", + " \"CD\",\n", + " \"HYP\",\n", + " \"MI\",\n", + " \"NORM\",\n", + " \"STTC\",\n", + " ]\n", + "].corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "23ffb875-d13c-4d00-a658-fed263313d78", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n", + "plt.title(\"Correlation Matrix of Features\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "02eb512d-9f95-469e-aacf-ad051443dfcb", + "metadata": {}, + "source": [ + "- Age correlates positively with conditions like CD (0.21), MI (0.19), and HYP (0.12), indicating these are more prevalent in older patients.\n", + "- MI and Infarction show a strong positive correlation (0.58), as expected.\n", + "- NORM has a strong negative correlation with CD (-0.47) and MI (-0.42), suggesting these conditions are inversely related to normal ECGs.\n", + "- STTC is negatively correlated with NORM (-0.53), reflecting that ST/T changes often indicate abnormalities.\n", + "- Normal heart axis correlates positively with NORM (0.32, which is expected) and negatively with CD (-0.35) and MI (-0.25), suggesting that deviations in heart axis are associated with these conditions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4a592441-464c-47b3-b96c-ec042cb3927c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "sns.countplot(x=\"sex\", hue=\"NORM\", data=Y)\n", + "plt.title(\"Distribution of Normal ECG by Gender\")\n", + "plt.xlabel(\"Gender\")\n", + "plt.ylabel(\"Count\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "8baa016c-262b-42b1-8b67-f0eb8813181a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "sns.histplot(data=Y, x=\"age\", hue=\"NORM\", kde=True, bins=20)\n", + "plt.title(\"Distribution of Age by ECG Normality\")\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.legend(title=\"ECG diagnosis\", labels=[\"Abnormal\", \"Normal\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "41e2cb31-24a8-42b6-ab68-98d5a6ac15a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chi-Square Statistic: 261.8628196596253\n", + "P-value: 6.73707538278752e-59\n" + ] + } + ], + "source": [ + "contingency_table = pd.crosstab(Y[\"sex\"], Y[\"NORM\"])\n", + "chi2, p, dof, expected = chi2_contingency(contingency_table)\n", + "print(f\"Chi-Square Statistic: {chi2}\")\n", + "print(f\"P-value: {p}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "324e4015-e4fc-4053-989a-0e50662251d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-Test Statistic: -60.92817871547189\n", + "P-value: 0.0\n" + ] + } + ], + "source": [ + "normal_age = Y[Y[\"NORM\"] == 1][\"age\"]\n", + "abnormal_age = Y[Y[\"NORM\"] == 0][\"age\"]\n", + "\n", + "t_stat, p_value = ttest_ind(normal_age, abnormal_age, equal_var=False) # T-Test\n", + "print(f\"T-Test Statistic: {t_stat}\")\n", + "print(f\"P-value: {p_value}\")" + ] + }, + { + "cell_type": "markdown", + "id": "2e9787e3-b4f6-48fa-a369-6b86770a3c8e", + "metadata": {}, + "source": [ + "In this dataset, normal ECGs are more common in individuals aged 50-70, while abnormal ECGs are more frequent in those aged 20-50. Additionally, males have a higher prevalence of abnormal ECGs, whereas females more often show normal ECGs. A Chi-Square test confirms a significant association between gender and ECG normality (p < 0.05), and a T-test indicates a significant difference in age distributions between normal and abnormal ECGs (p < 0.05)." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "3736b545-7630-4c14-a26c-8df3ec601385", + "metadata": {}, + "outputs": [], + "source": [ + "Y = Y.drop(\n", + " columns=[\n", + " \"scp_codes\",\n", + " \"CD\",\n", + " \"HYP\",\n", + " \"MI\",\n", + " \"NORM\",\n", + " \"STTC\",\n", + " \"normal_heart_axis\",\n", + " \"infarction\",\n", + " \"static_noise_bool\",\n", + " \"burst_noise_bool\",\n", + " \"electrodes_problems_bool\",\n", + " \"extra_beats_bool\",\n", + " \"pacemaker_bool\",\n", + " \"baseline_drift_bool\",\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "e9261d94-72d5-4ba2-867e-ff558431f97c", + "metadata": {}, + "outputs": [], + "source": [ + "Y.to_csv(join(data_path, \"processed/ecg_metadata.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f240f12-cd81-4d55-9e2b-0944d655050d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ruben-solution/notebooks/02-data-preprocessing.ipynb b/ruben-solution/notebooks/02-data-preprocessing.ipynb new file mode 100644 index 0000000..d14b42d --- /dev/null +++ b/ruben-solution/notebooks/02-data-preprocessing.ipynb @@ -0,0 +1,436 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ef2970cc-e686-486e-8a46-4589d4ba1804", + "metadata": {}, + "source": [ + "# Data Preprocessing" + ] + }, + { + "cell_type": "markdown", + "id": "803cd8a1-b504-4113-8a90-46e503ebea08", + "metadata": {}, + "source": [ + "This notebook outlines my approach to feature engineering and ensuring a high-quality dataset for model training. I have chosen to approach this problem as a binary classification task, aiming to build a model that differentiates Normal ECGs from Abnormal ECGs based on labels derived from highly likely and unambiguous SCP codes. The features used are the unprocessed ECG signals, preserving all the information inherent in the signal." + ] + }, + { + "cell_type": "markdown", + "id": "3101cb06-07f9-461f-a846-26636e3d198f", + "metadata": {}, + "source": [ + "#### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "096ace8d-dca4-4ee7-bb2c-51a2f2a4a005", + "metadata": {}, + "outputs": [], + "source": [ + "from os.path import join\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "pd.set_option(\"display.max_columns\", None)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9e76c820-d3be-4f8d-9d65-e0dbb6d8822c", + "metadata": {}, + "outputs": [], + "source": [ + "data_path = \"../data/\"\n", + "sampling_rate = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "beb927c7-a72f-4023-a409-d806c721778d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of ECGs: 18,793; Number of patients: 16,563\n" + ] + } + ], + "source": [ + "Y = pd.read_csv(join(data_path, \"processed/ecg_metadata.csv\"), index_col=\"ecg_id\")\n", + "print(f\"Number of ECGs: {Y.shape[0]:,}; Number of patients: {Y.patient_id.nunique():,}\")" + ] + }, + { + "cell_type": "markdown", + "id": "dc761901-49f3-4719-bc23-a935cfeb8610", + "metadata": {}, + "source": [ + "#### Problematic ECGs" + ] + }, + { + "cell_type": "markdown", + "id": "ab52f0ed-0d36-4452-8f78-b044d36193ac", + "metadata": {}, + "source": [ + "While the presence of extra beats is noteworthy, it does not necessarily indicate an unhealthy heart, particularly in asymptomatic individuals with normal heart structure. For the purpose of this analysis, I have chosen to exclude such ECGs to focus on more indicative cardiac events. Additionally, I have removed ECGs that exhibit recording issues to ensure the quality and reliability of the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8d025573-2050-444e-9234-c03af84e658e", + "metadata": {}, + "outputs": [], + "source": [ + "# Remove ECGs with problems\n", + "Y = Y.loc[Y.electrodes_problems.isna()].drop(columns=\"electrodes_problems\")\n", + "Y = Y.loc[Y.baseline_drift.isna()].drop(columns=\"baseline_drift\")\n", + "Y = Y.loc[Y.static_noise.isna()].drop(columns=\"static_noise\")\n", + "Y = Y.loc[Y.burst_noise.isna()].drop(columns=\"burst_noise\")\n", + "Y = Y.loc[Y.extra_beats.isna()].drop(columns=\"extra_beats\")" + ] + }, + { + "cell_type": "markdown", + "id": "74d80115-cadf-42ee-98c5-48ad4f8b8eac", + "metadata": {}, + "source": [ + "Since the presence of a pacemaker can significantly alter a patient’s ECG, including such patients could introduce biases into the analysis. Therefore, I have excluded patients with pacemakers from the dataset. Additionally, I will focus exclusively on patients over 18 years old, as ECG characteristics can differ considerably between children and adults." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0e761d71-2704-4b21-8b7f-935c36f702fc", + "metadata": {}, + "outputs": [], + "source": [ + "Y = Y.loc[Y.pacemaker.isna()].drop(columns=\"pacemaker\")\n", + "Y = Y.loc[Y.age >= 18.0]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "19f37566-fef9-4f7b-9134-c113021e4a2f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of ECGs: 13,245; Number of patients: 12,201\n" + ] + } + ], + "source": [ + "print(f\"Number of ECGs: {Y.shape[0]:,}; Number of patients: {Y.patient_id.nunique():,}\")" + ] + }, + { + "cell_type": "markdown", + "id": "91d2f848-a0c1-44ed-af1f-19409f1dcea6", + "metadata": {}, + "source": [ + "#### Multilabel ECGs" + ] + }, + { + "cell_type": "markdown", + "id": "a83d324b-600d-4237-88b5-86d9cecd7e34", + "metadata": {}, + "source": [ + "Many ECGs have a high likelihood for Normal and Abnormal labels. I exclude such ECGs to eliminate ambiguity." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a534b284-2f84-416b-b7ef-f903fbc9eca3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Ambiguous ECGs: 248\n" + ] + } + ], + "source": [ + "multilabel_ecgs = Y[\"diagnostic_class\"].str.contains(\"(?=.*\\|)(?=.*NORM)\")\n", + "Y = Y.loc[~multilabel_ecgs]\n", + "print(f\"# Ambiguous ECGs: {multilabel_ecgs.sum():,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "33cdf770-5716-4b15-b4d1-7ae51b49af1f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of ECGs: 12,997; Number of patients: 11,966\n" + ] + } + ], + "source": [ + "print(f\"Number of ECGs: {Y.shape[0]:,}; Number of patients: {Y.patient_id.nunique():,}\")" + ] + }, + { + "cell_type": "markdown", + "id": "79f99294-b897-40f0-9eaa-5943156e8520", + "metadata": {}, + "source": [ + "#### Normal ECGs with contradicting features" + ] + }, + { + "cell_type": "markdown", + "id": "487c8c1e-b414-42a7-9a9d-410587960556", + "metadata": {}, + "source": [ + "I removed normal ECGs that displayed contradicting features, such as infarction signs and abnormal heart axis, as these inconsistencies could compromise the accuracy of the labels." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "70003832-b03e-4219-a920-e9092638b59b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Contradicting ECGs: 248\n" + ] + } + ], + "source": [ + "problematic_patients = (Y.diagnostic_class == \"NORM\") & (\n", + " ~Y[\"heart_axis\"].isin([\"MID\", np.nan]) | ~Y[\"infarction_stadium1\"].isna() | ~Y[\"infarction_stadium2\"].isna()\n", + ")\n", + "Y = Y.loc[~problematic_patients]\n", + "print(f\"# Contradicting ECGs: {multilabel_ecgs.sum():,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0bb13ca2-38c0-4ae3-8213-ce00b74b6423", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of ECGs: 12,323; Number of patients: 11,354\n" + ] + } + ], + "source": [ + "print(f\"Number of ECGs: {Y.shape[0]:,}; Number of patients: {Y.patient_id.nunique():,}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a982f36c-cfcd-4657-b621-9fe541af1559", + "metadata": {}, + "source": [ + "#### Data Labeling" + ] + }, + { + "cell_type": "markdown", + "id": "11b697db-1f24-4f9f-8378-2139c4870e97", + "metadata": {}, + "source": [ + "Given the time constraints and my focus on a purely ECG-based approach for the model features, I’ve decided not to include age and gender in the initial feature set. However, I will assess the model’s performance across different age and gender subsets to evaluate consistency of the results." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9d4c8792-d311-4628-8dfc-9c465c9e8e19", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LABEL\n", + "1 0.533555\n", + "0 0.466445\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y[\"LABEL\"] = np.where(Y[\"diagnostic_class\"] == \"NORM\", 0, 1)\n", + "Y[\"LABEL\"].value_counts(normalize=True)" + ] + }, + { + "cell_type": "markdown", + "id": "3274d035-1bba-4b6d-a857-02cbb464c807", + "metadata": {}, + "source": [ + "The labels are fairly balanced, with slightly more ECGs in the abnormal class. Given the minor imbalance, I do not consider this to be a significant issue." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9a60ba32-b61f-4571-a812-687908130e7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " patient_id age sex strat_fold filename_lr \\\n", + "ecg_id \n", + "2 13243 19.0 0 2 records100/00000/00002_lr \n", + "3 20372 37.0 1 5 records100/00000/00003_lr \n", + "10 9456 22.0 1 9 records100/00000/00010_lr \n", + "\n", + " diagnostic_class LABEL \n", + "ecg_id \n", + "2 NORM 0 \n", + "3 NORM 0 \n", + "10 NORM 0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y = Y.drop(columns=[\"heart_axis\", \"infarction_stadium1\", \"infarction_stadium2\"])\n", + "Y.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d9279540-cb8e-479f-8db2-bf10bf79b02a", + "metadata": {}, + "outputs": [], + "source": [ + "Y.to_csv(join(data_path, \"processed/labels.csv\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ruben-solution/notebooks/03-model-training.ipynb b/ruben-solution/notebooks/03-model-training.ipynb new file mode 100644 index 0000000..2447679 --- /dev/null +++ b/ruben-solution/notebooks/03-model-training.ipynb @@ -0,0 +1,976 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "714b3e51-c340-45c1-b917-975ec7cd4532", + "metadata": {}, + "source": [ + "# Model Training" + ] + }, + { + "cell_type": "markdown", + "id": "6027e57d-858e-4f38-be50-b19a94709436", + "metadata": {}, + "source": [ + "In this project, I compared two neural network architectures: a times series CNN and a ResNet for analyzing ECG time series data, with the goal of predicting normal versus abnormal ECGs. The choices I made in selecting and designing these models were informed by my research and experience working in the field of ECG analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1b0ff0ed-5643-4119-80b5-de33abfbbda6", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1b1ee444-9dec-41d7-aa8a-9f6e5bbd0053", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", + "\n", + "import ast\n", + "import pickle\n", + "\n", + "import optuna\n", + "from tensorflow.keras.callbacks import EarlyStopping\n", + "\n", + "from models.resnet import *\n", + "from models.tscnn import *\n", + "from utils.data_preprocessing import *\n", + "from utils.hyperparameter_tuning import *\n", + "\n", + "pd.set_option(\"display.max_columns\", None)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "333c0e52-8d9d-48ce-ada3-9767ea76994d", + "metadata": {}, + "outputs": [], + "source": [ + "data_path = \"../data/\"\n", + "sampling_rate = 100\n", + "input_shape = (1000, 12)\n", + "num_classes = 1\n", + "batch_size = 32\n", + "epochs = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e378638d-ab5a-4dfa-84ca-6ea7392978ee", + "metadata": {}, + "outputs": [], + "source": [ + "Y = pd.read_csv(join(data_path, \"processed/labels.csv\"))" + ] + }, + { + "cell_type": "markdown", + "id": "b2bd7d06-6f24-40d0-9f88-81a5223b9e72", + "metadata": {}, + "source": [ + "For initial experiments, I use 50% of the data to accelerate model training given my limited access to computing resources. Models are trained on a CPU." + ] + }, + { + "cell_type": "markdown", + "id": "4f587a9f-d84b-4949-90f7-38b7b0a43835", + "metadata": {}, + "source": [ + "I follow the train/validation/test splitting guidelines from the PTB-XL documentation. This is roughly equivalent to a 80-10-10 split. These folds ensure that records from the same patient remain within the same fold. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bcd08d94-5f73-4bc8-ba45-f3ccf1e813b7", + "metadata": {}, + "outputs": [], + "source": [ + "window_size = 100\n", + "step_size = 100\n", + "sample_size = 0.5\n", + "\n", + "train_generator, val_generator, test_generator = create_generators_or_datasets(\n", + " y=Y,\n", + " window_size=window_size,\n", + " step_size=step_size,\n", + " batch_size=batch_size,\n", + " sample_size=sample_size,\n", + " sampling_rate=sampling_rate,\n", + " data_path=data_path,\n", + " return_generators=True,\n", + ")\n", + "\n", + "X_train, y_train, X_val, y_val, X_test, y_test = create_generators_or_datasets(\n", + " y=Y,\n", + " window_size=window_size,\n", + " step_size=step_size,\n", + " batch_size=batch_size,\n", + " sample_size=sample_size,\n", + " sampling_rate=sampling_rate,\n", + " data_path=data_path,\n", + " return_generators=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5b933f96-5e94-4cb8-8571-cbc264b2e783", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((4901, 1000, 12), (4901,), (604, 1000, 12), (604,), (656, 1000, 12), (656,))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape" + ] + }, + { + "cell_type": "markdown", + "id": "09bbce20-457c-4a11-bb6a-4c54a845642a", + "metadata": {}, + "source": [ + "## Times Series Convolutional Neural Network" + ] + }, + { + "cell_type": "markdown", + "id": "4c54002a-b94e-464f-bd3e-7c891767493d", + "metadata": {}, + "source": [ + "I chose this CNN model for its efficiency in real-time ECG analysis. I designed the architecture with convolutional and pooling layers to quickly and effectively extract key features from the data. By using a sliding window approach, I can process segments of the ECG signal independently, allowing the model to capture local patterns over time without needing the entire signal at once. I used `Conv1D` layers because they are well-suited for sequential data like ECG signals. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "19ce9c89-aa62-4e02-81d0-44dcbe76b55b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 35ms/step - accuracy: 0.7507 - auc: 0.8300 - loss: 0.4920 - val_accuracy: 0.8334 - val_auc: 0.9429 - val_loss: 0.3551\n", + "Epoch 2/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 38ms/step - accuracy: 0.8704 - auc: 0.9414 - loss: 0.3090 - val_accuracy: 0.8626 - val_auc: 0.9473 - val_loss: 0.3061\n", + "Epoch 3/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 44ms/step - accuracy: 0.8758 - auc: 0.9467 - loss: 0.2942 - val_accuracy: 0.8515 - val_auc: 0.9428 - val_loss: 0.4016\n", + "Epoch 4/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 40ms/step - accuracy: 0.8900 - auc: 0.9549 - loss: 0.2735 - val_accuracy: 0.8796 - val_auc: 0.9536 - val_loss: 0.2908\n", + "Epoch 5/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 34ms/step - accuracy: 0.8952 - auc: 0.9614 - loss: 0.2503 - val_accuracy: 0.8803 - val_auc: 0.9539 - val_loss: 0.2949\n", + "Epoch 6/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 34ms/step - accuracy: 0.9018 - auc: 0.9642 - loss: 0.2398 - val_accuracy: 0.8810 - val_auc: 0.9552 - val_loss: 0.2853\n", + "Epoch 7/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 31ms/step - accuracy: 0.9101 - auc: 0.9690 - loss: 0.2222 - val_accuracy: 0.8763 - val_auc: 0.9565 - val_loss: 0.3064\n", + "Epoch 8/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 42ms/step - accuracy: 0.9092 - auc: 0.9711 - loss: 0.2150 - val_accuracy: 0.8854 - val_auc: 0.9564 - val_loss: 0.2718\n", + "Epoch 9/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 71ms/step - accuracy: 0.9199 - auc: 0.9761 - loss: 0.1952 - val_accuracy: 0.8889 - val_auc: 0.9539 - val_loss: 0.2962\n", + "Epoch 10/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m127s\u001b[0m 83ms/step - accuracy: 0.9270 - auc: 0.9789 - loss: 0.1834 - val_accuracy: 0.8785 - val_auc: 0.9528 - val_loss: 0.2846\n", + "Epoch 11/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 90ms/step - accuracy: 0.9315 - auc: 0.9818 - loss: 0.1713 - val_accuracy: 0.8796 - val_auc: 0.9492 - val_loss: 0.3149\n", + "Epoch 12/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 75ms/step - accuracy: 0.9365 - auc: 0.9842 - loss: 0.1565 - val_accuracy: 0.8816 - val_auc: 0.9506 - val_loss: 0.3002\n", + "Epoch 13/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 76ms/step - accuracy: 0.9428 - auc: 0.9865 - loss: 0.1462 - val_accuracy: 0.8790 - val_auc: 0.9498 - val_loss: 0.3486\n", + "\n", + "Validation set Loss: 0.272, Accuracy: 0.904, AUC: 0.964\n" + ] + } + ], + "source": [ + "early_stopping = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n", + "baseline_cnn = TimeSeriesCNN(\n", + " input_shape=input_shape, window_size=window_size, dropout_rate=0.0, l2_reg=0.0, filters=[64, 128, 256]\n", + ")\n", + "history = baseline_cnn.train(train_generator, val_generator, epochs=epochs, batch_size=32, callbacks=[early_stopping])\n", + "\n", + "loss, accuracy, auc, predictions = evaluate_cnn_performance(baseline_cnn, val_generator, y_val)\n", + "print(f\"\\nValidation set Loss: {loss:.3f}, Accuracy: {accuracy:.3f}, AUC: {auc:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e22a56b2-3146-4e30-a832-a2216342e8af", + "metadata": {}, + "source": [ + "### Approaching Overfitting" + ] + }, + { + "cell_type": "markdown", + "id": "f77306b1-775c-4acc-b5be-382a7fc7c303", + "metadata": {}, + "source": [ + "While the model is performing well, with a validation AUC of 0.964, I have observed significant overfitting, evidenced by a much lower training set loss compared to the validation set loss. The following experiments explore the impact of dropout, L2 regularization, and convolution layer sizes to address this overfitting." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ccb7cbab-b870-4ad8-a9f3-ee2930b9d86f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 86ms/step - accuracy: 0.6986 - auc: 0.7716 - loss: 0.5566 - val_accuracy: 0.8629 - val_auc: 0.9364 - val_loss: 0.3285\n", + "Epoch 2/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 52ms/step - accuracy: 0.8527 - auc: 0.9245 - loss: 0.3448 - val_accuracy: 0.8558 - val_auc: 0.9418 - val_loss: 0.3332\n", + "Epoch 3/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m118s\u001b[0m 77ms/step - accuracy: 0.8620 - auc: 0.9358 - loss: 0.3250 - val_accuracy: 0.8536 - val_auc: 0.9473 - val_loss: 0.3298\n", + "Epoch 4/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m117s\u001b[0m 76ms/step - accuracy: 0.8756 - auc: 0.9427 - loss: 0.3056 - val_accuracy: 0.8508 - val_auc: 0.9484 - val_loss: 0.3270\n", + "Epoch 5/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m239s\u001b[0m 156ms/step - accuracy: 0.8690 - auc: 0.9398 - loss: 0.3118 - val_accuracy: 0.8742 - val_auc: 0.9482 - val_loss: 0.2862\n", + "Epoch 6/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 63ms/step - accuracy: 0.8804 - auc: 0.9451 - loss: 0.2997 - val_accuracy: 0.8793 - val_auc: 0.9514 - val_loss: 0.2793\n", + "Epoch 7/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 65ms/step - accuracy: 0.8798 - auc: 0.9469 - loss: 0.2937 - val_accuracy: 0.8694 - val_auc: 0.9520 - val_loss: 0.3135\n", + "Epoch 8/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 42ms/step - accuracy: 0.8786 - auc: 0.9488 - loss: 0.2908 - val_accuracy: 0.8710 - val_auc: 0.9503 - val_loss: 0.3112\n", + "Epoch 9/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 37ms/step - accuracy: 0.8901 - auc: 0.9555 - loss: 0.2694 - val_accuracy: 0.8507 - val_auc: 0.9524 - val_loss: 0.3292\n", + "Epoch 10/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 40ms/step - accuracy: 0.8892 - auc: 0.9530 - loss: 0.2737 - val_accuracy: 0.8685 - val_auc: 0.9537 - val_loss: 0.3152\n", + "Epoch 11/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 38ms/step - accuracy: 0.8911 - auc: 0.9563 - loss: 0.2658 - val_accuracy: 0.8447 - val_auc: 0.9516 - val_loss: 0.3799\n", + "\n", + "Validation set Loss: 0.279, Accuracy: 0.889, AUC: 0.959\n" + ] + } + ], + "source": [ + "early_stopping = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n", + "cnn = TimeSeriesCNN(\n", + " input_shape=input_shape, window_size=window_size, dropout_rate=0.4, l2_reg=0.0, filters=[64, 128, 256]\n", + ")\n", + "history = cnn.train(train_generator, val_generator, epochs=epochs, batch_size=32, callbacks=[early_stopping])\n", + "\n", + "loss, accuracy, auc, predictions = evaluate_cnn_performance(cnn, val_generator, y_val)\n", + "print(f\"\\nValidation set Loss: {loss:.3f}, Accuracy: {accuracy:.3f}, AUC: {auc:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "27a209dd-0312-41ff-a957-c7501d1b4d21", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 31ms/step - accuracy: 0.7342 - auc: 0.8129 - loss: 0.6618 - val_accuracy: 0.8472 - val_auc: 0.9325 - val_loss: 0.4155\n", + "Epoch 2/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 30ms/step - accuracy: 0.8540 - auc: 0.9255 - loss: 0.4151 - val_accuracy: 0.8609 - val_auc: 0.9397 - val_loss: 0.3941\n", + "Epoch 3/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 31ms/step - accuracy: 0.8539 - auc: 0.9269 - loss: 0.4017 - val_accuracy: 0.8247 - val_auc: 0.9407 - val_loss: 0.4368\n", + "Epoch 4/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 35ms/step - accuracy: 0.8613 - auc: 0.9320 - loss: 0.3846 - val_accuracy: 0.8654 - val_auc: 0.9432 - val_loss: 0.3568\n", + "Epoch 5/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 37ms/step - accuracy: 0.8747 - auc: 0.9397 - loss: 0.3650 - val_accuracy: 0.8694 - val_auc: 0.9451 - val_loss: 0.3492\n", + "Epoch 6/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 35ms/step - accuracy: 0.8770 - auc: 0.9436 - loss: 0.3499 - val_accuracy: 0.8644 - val_auc: 0.9441 - val_loss: 0.3692\n", + "Epoch 7/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 31ms/step - accuracy: 0.8771 - auc: 0.9438 - loss: 0.3485 - val_accuracy: 0.8705 - val_auc: 0.9489 - val_loss: 0.3448\n", + "Epoch 8/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 30ms/step - accuracy: 0.8755 - auc: 0.9450 - loss: 0.3467 - val_accuracy: 0.8732 - val_auc: 0.9475 - val_loss: 0.3378\n", + "Epoch 9/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 31ms/step - accuracy: 0.8755 - auc: 0.9450 - loss: 0.3445 - val_accuracy: 0.8685 - val_auc: 0.9462 - val_loss: 0.3426\n", + "Epoch 10/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 33ms/step - accuracy: 0.8835 - auc: 0.9462 - loss: 0.3379 - val_accuracy: 0.8697 - val_auc: 0.9481 - val_loss: 0.3404\n", + "Epoch 11/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 33ms/step - accuracy: 0.8850 - auc: 0.9485 - loss: 0.3312 - val_accuracy: 0.8758 - val_auc: 0.9480 - val_loss: 0.3375\n", + "Epoch 12/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 41ms/step - accuracy: 0.8823 - auc: 0.9480 - loss: 0.3332 - val_accuracy: 0.8735 - val_auc: 0.9497 - val_loss: 0.3367\n", + "Epoch 13/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 35ms/step - accuracy: 0.8800 - auc: 0.9483 - loss: 0.3318 - val_accuracy: 0.8724 - val_auc: 0.9449 - val_loss: 0.3446\n", + "Epoch 14/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 33ms/step - accuracy: 0.8819 - auc: 0.9473 - loss: 0.3349 - val_accuracy: 0.8700 - val_auc: 0.9477 - val_loss: 0.3435\n", + "Epoch 15/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 40ms/step - accuracy: 0.8823 - auc: 0.9503 - loss: 0.3274 - val_accuracy: 0.8727 - val_auc: 0.9489 - val_loss: 0.3339\n", + "Epoch 16/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 36ms/step - accuracy: 0.8892 - auc: 0.9538 - loss: 0.3158 - val_accuracy: 0.8728 - val_auc: 0.9483 - val_loss: 0.3400\n", + "Epoch 17/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 33ms/step - accuracy: 0.8840 - auc: 0.9502 - loss: 0.3257 - val_accuracy: 0.8767 - val_auc: 0.9506 - val_loss: 0.3277\n", + "Epoch 18/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 39ms/step - accuracy: 0.8810 - auc: 0.9490 - loss: 0.3284 - val_accuracy: 0.8707 - val_auc: 0.9476 - val_loss: 0.3412\n", + "Epoch 19/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 36ms/step - accuracy: 0.8886 - auc: 0.9537 - loss: 0.3162 - val_accuracy: 0.8717 - val_auc: 0.9472 - val_loss: 0.3464\n", + "Epoch 20/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 31ms/step - accuracy: 0.8908 - auc: 0.9541 - loss: 0.3129 - val_accuracy: 0.8780 - val_auc: 0.9507 - val_loss: 0.3274\n", + "Epoch 21/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 30ms/step - accuracy: 0.8826 - auc: 0.9471 - loss: 0.3331 - val_accuracy: 0.8624 - val_auc: 0.9503 - val_loss: 0.3654\n", + "Epoch 22/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 31ms/step - accuracy: 0.8886 - auc: 0.9522 - loss: 0.3171 - val_accuracy: 0.8758 - val_auc: 0.9506 - val_loss: 0.3302\n", + "Epoch 23/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 34ms/step - accuracy: 0.8896 - auc: 0.9537 - loss: 0.3138 - val_accuracy: 0.8798 - val_auc: 0.9499 - val_loss: 0.3323\n", + "Epoch 24/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 37ms/step - accuracy: 0.8851 - auc: 0.9515 - loss: 0.3219 - val_accuracy: 0.8765 - val_auc: 0.9512 - val_loss: 0.3419\n", + "Epoch 25/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 31ms/step - accuracy: 0.8891 - auc: 0.9553 - loss: 0.3118 - val_accuracy: 0.8727 - val_auc: 0.9500 - val_loss: 0.3376\n", + "\n", + "Validation set Loss: 0.327, Accuracy: 0.891, AUC: 0.961\n" + ] + } + ], + "source": [ + "early_stopping = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n", + "cnn = TimeSeriesCNN(\n", + " input_shape=input_shape, window_size=window_size, dropout_rate=0.0, l2_reg=0.001, filters=[64, 128, 256]\n", + ")\n", + "history = cnn.train(train_generator, val_generator, epochs=epochs, batch_size=32, callbacks=[early_stopping])\n", + "\n", + "loss, accuracy, auc, predictions = evaluate_cnn_performance(cnn, val_generator, y_val)\n", + "print(f\"\\nValidation set Loss: {loss:.3f}, Accuracy: {accuracy:.3f}, AUC: {auc:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "931e8102-fcaf-470e-b25d-756250353991", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 14ms/step - accuracy: 0.7509 - auc: 0.8326 - loss: 0.4891 - val_accuracy: 0.8563 - val_auc: 0.9422 - val_loss: 0.3155\n", + "Epoch 2/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 15ms/step - accuracy: 0.8661 - auc: 0.9388 - loss: 0.3165 - val_accuracy: 0.8551 - val_auc: 0.9433 - val_loss: 0.3169\n", + "Epoch 3/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 20ms/step - accuracy: 0.8782 - auc: 0.9437 - loss: 0.3023 - val_accuracy: 0.8722 - val_auc: 0.9505 - val_loss: 0.2873\n", + "Epoch 4/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 19ms/step - accuracy: 0.8876 - auc: 0.9514 - loss: 0.2768 - val_accuracy: 0.8816 - val_auc: 0.9534 - val_loss: 0.2745\n", + "Epoch 5/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 21ms/step - accuracy: 0.8917 - auc: 0.9577 - loss: 0.2587 - val_accuracy: 0.8823 - val_auc: 0.9549 - val_loss: 0.2737\n", + "Epoch 6/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 19ms/step - accuracy: 0.9002 - auc: 0.9632 - loss: 0.2423 - val_accuracy: 0.8747 - val_auc: 0.9543 - val_loss: 0.3059\n", + "Epoch 7/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 19ms/step - accuracy: 0.9050 - auc: 0.9643 - loss: 0.2378 - val_accuracy: 0.8851 - val_auc: 0.9555 - val_loss: 0.2758\n", + "Epoch 8/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 15ms/step - accuracy: 0.9078 - auc: 0.9679 - loss: 0.2261 - val_accuracy: 0.8806 - val_auc: 0.9540 - val_loss: 0.2770\n", + "Epoch 9/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 17ms/step - accuracy: 0.9189 - auc: 0.9730 - loss: 0.2045 - val_accuracy: 0.8805 - val_auc: 0.9531 - val_loss: 0.2969\n", + "Epoch 10/50\n", + "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 16ms/step - accuracy: 0.9157 - auc: 0.9738 - loss: 0.2027 - val_accuracy: 0.8790 - val_auc: 0.9523 - val_loss: 0.2955\n", + "\n", + "Validation set Loss: 0.274, Accuracy: 0.896, AUC: 0.963\n" + ] + } + ], + "source": [ + "early_stopping = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n", + "cnn = TimeSeriesCNN(\n", + " input_shape=input_shape, window_size=window_size, dropout_rate=0.0, l2_reg=0.0, filters=[32, 64, 128]\n", + ")\n", + "history = cnn.train(train_generator, val_generator, epochs=epochs, batch_size=32, callbacks=[early_stopping])\n", + "\n", + "loss, accuracy, auc, predictions = evaluate_cnn_performance(cnn, val_generator, y_val)\n", + "print(f\"\\nValidation set Loss: {loss:.3f}, Accuracy: {accuracy:.3f}, AUC: {auc:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "55f0d7d3-ec93-4bac-8dfc-a551a57cca35", + "metadata": {}, + "source": [ + "### Hyperparameter tuning" + ] + }, + { + "cell_type": "markdown", + "id": "36d5e298-1bde-42d6-b537-33d74166ae48", + "metadata": {}, + "source": [ + "Reducing model complexity through filter sizes does not seem to improve overfitting or model performance. However, while dropout and regularization have a positive impact on overfitting, the model performs worse on the validation set loss than our baseline. I will tune these parameters along with window size and step size using Optuna. Optuna's adaptive search capabilities allow for effective exploration of the hyperparameter space. For efficiency, I use 25% of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d9950167-2264-4248-9861-2bba22bffa84", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-08-27 10:10:42,173] A new study created in memory with name: no-name-f1999ddd-e8d1-4f17-871f-cf7e9c9304bb\n", + "[I 2024-08-27 10:15:44,505] Trial 0 finished with value: 0.3307325839996338 and parameters: {'window_size': 191, 'step_size': 165, 'l2_reg': 9.159969462271967e-09, 'dropout_rate': 0.3496380249038563}. Best is trial 0 with value: 0.3307325839996338.\n", + "[I 2024-08-27 10:25:46,619] Trial 1 finished with value: 0.253520667552948 and parameters: {'window_size': 96, 'step_size': 56, 'l2_reg': 5.609971398816462e-07, 'dropout_rate': 0.3284531233399967}. Best is trial 1 with value: 0.253520667552948.\n", + "[I 2024-08-27 10:29:38,111] Trial 2 finished with value: 0.27166062593460083 and parameters: {'window_size': 96, 'step_size': 77, 'l2_reg': 4.515053448565958e-09, 'dropout_rate': 0.2956701707108168}. Best is trial 1 with value: 0.253520667552948.\n", + "[I 2024-08-27 10:40:15,140] Trial 3 finished with value: 0.27635514736175537 and parameters: {'window_size': 178, 'step_size': 74, 'l2_reg': 1.6883155511961273e-06, 'dropout_rate': 0.34532822161819926}. Best is trial 1 with value: 0.253520667552948.\n", + "[I 2024-08-27 10:56:09,947] Trial 4 finished with value: 0.2881554663181305 and parameters: {'window_size': 147, 'step_size': 38, 'l2_reg': 4.7778580526888115e-08, 'dropout_rate': 0.10295132890433883}. Best is trial 1 with value: 0.253520667552948.\n", + "[I 2024-08-27 11:04:16,259] Trial 5 finished with value: 0.27512556314468384 and parameters: {'window_size': 134, 'step_size': 47, 'l2_reg': 2.226835185552366e-08, 'dropout_rate': 0.034671188076624115}. Best is trial 1 with value: 0.253520667552948.\n", + "[I 2024-08-27 11:08:14,460] Trial 6 finished with value: 0.2901320159435272 and parameters: {'window_size': 190, 'step_size': 176, 'l2_reg': 2.8164209418739096e-09, 'dropout_rate': 0.32608683874165956}. Best is trial 1 with value: 0.253520667552948.\n", + "[I 2024-08-27 11:11:49,678] Trial 7 finished with value: 0.25873324275016785 and parameters: {'window_size': 151, 'step_size': 120, 'l2_reg': 1.3483049591195485e-09, 'dropout_rate': 0.3360082738208757}. Best is trial 1 with value: 0.253520667552948.\n", + "[I 2024-08-27 11:20:04,682] Trial 8 finished with value: 0.24977916479110718 and parameters: {'window_size': 196, 'step_size': 75, 'l2_reg': 3.6206124274583214e-11, 'dropout_rate': 0.43035840459539587}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 11:30:57,217] Trial 9 finished with value: 0.2634241282939911 and parameters: {'window_size': 188, 'step_size': 84, 'l2_reg': 3.436137384796619e-08, 'dropout_rate': 0.30122415492202137}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 12:04:43,176] Trial 10 finished with value: 0.3670230209827423 and parameters: {'window_size': 60, 'step_size': 10, 'l2_reg': 1.372551401467255e-12, 'dropout_rate': 0.4915348783436427}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 12:10:29,657] Trial 11 finished with value: 0.32630836963653564 and parameters: {'window_size': 99, 'step_size': 60, 'l2_reg': 8.982328874621064e-05, 'dropout_rate': 0.47146670056036366}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 12:19:25,251] Trial 12 finished with value: 0.3011062443256378 and parameters: {'window_size': 100, 'step_size': 62, 'l2_reg': 1.9730735044550064e-11, 'dropout_rate': 0.20553946261589795}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 12:27:45,903] Trial 13 finished with value: 0.3827795386314392 and parameters: {'window_size': 53, 'step_size': 18, 'l2_reg': 3.2543221471791575e-06, 'dropout_rate': 0.42963137425248743}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 12:34:54,171] Trial 14 finished with value: 0.29134759306907654 and parameters: {'window_size': 76, 'step_size': 51, 'l2_reg': 8.855612312063551e-11, 'dropout_rate': 0.4138120358612248}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 12:42:04,319] Trial 15 finished with value: 0.28909891843795776 and parameters: {'window_size': 164, 'step_size': 107, 'l2_reg': 1.470943744091721e-06, 'dropout_rate': 0.18868974281950024}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 12:50:15,293] Trial 16 finished with value: 0.32794585824012756 and parameters: {'window_size': 120, 'step_size': 95, 'l2_reg': 0.0007308424915242517, 'dropout_rate': 0.41236768746440344}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 13:06:26,014] Trial 17 finished with value: 0.25718769431114197 and parameters: {'window_size': 115, 'step_size': 30, 'l2_reg': 1.1254071261066058e-12, 'dropout_rate': 0.2406023121072139}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 13:11:57,982] Trial 18 finished with value: 0.34317925572395325 and parameters: {'window_size': 70, 'step_size': 54, 'l2_reg': 2.4904150745511236e-07, 'dropout_rate': 0.38923078924290977}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 13:16:00,941] Trial 19 finished with value: 0.30860385298728943 and parameters: {'window_size': 79, 'step_size': 62, 'l2_reg': 2.594042987431691e-10, 'dropout_rate': 0.2604250743962534}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 13:20:33,329] Trial 20 finished with value: 0.30430883169174194 and parameters: {'window_size': 130, 'step_size': 90, 'l2_reg': 2.5386937228572227e-05, 'dropout_rate': 0.16577839422933516}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 13:36:50,758] Trial 21 finished with value: 0.26282960176467896 and parameters: {'window_size': 113, 'step_size': 33, 'l2_reg': 1.3153399775782122e-12, 'dropout_rate': 0.24639679983822116}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 13:53:29,011] Trial 22 finished with value: 0.2855621874332428 and parameters: {'window_size': 110, 'step_size': 30, 'l2_reg': 2.0913379928721383e-11, 'dropout_rate': 0.25450168106122417}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 14:07:05,706] Trial 23 finished with value: 0.29714035987854004 and parameters: {'window_size': 84, 'step_size': 39, 'l2_reg': 5.146361921007855e-12, 'dropout_rate': 0.1253709490360865}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 14:25:00,936] Trial 24 finished with value: 0.2606784999370575 and parameters: {'window_size': 144, 'step_size': 21, 'l2_reg': 5.178443375735198e-10, 'dropout_rate': 0.45613630707502784}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 14:29:27,477] Trial 25 finished with value: 0.3182644248008728 and parameters: {'window_size': 163, 'step_size': 130, 'l2_reg': 4.384182192194628e-11, 'dropout_rate': 0.37452403967839615}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 14:34:47,523] Trial 26 finished with value: 0.2883239984512329 and parameters: {'window_size': 91, 'step_size': 68, 'l2_reg': 5.684193645458154e-12, 'dropout_rate': 0.2832969085271087}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 14:37:53,027] Trial 27 finished with value: 0.2687927782535553 and parameters: {'window_size': 200, 'step_size': 152, 'l2_reg': 1.9122972862614355e-07, 'dropout_rate': 0.2300209276012381}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 14:45:26,241] Trial 28 finished with value: 0.2583532929420471 and parameters: {'window_size': 113, 'step_size': 45, 'l2_reg': 7.421740212283889e-12, 'dropout_rate': 0.3788239003041082}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 14:50:03,004] Trial 29 finished with value: 0.28074881434440613 and parameters: {'window_size': 124, 'step_size': 72, 'l2_reg': 4.1545192594206467e-10, 'dropout_rate': 0.15537891391862502}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 14:54:44,296] Trial 30 finished with value: 0.2920912206172943 and parameters: {'window_size': 138, 'step_size': 100, 'l2_reg': 9.899001788393014e-11, 'dropout_rate': 0.4483770858684955}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 15:05:28,737] Trial 31 finished with value: 0.2715526819229126 and parameters: {'window_size': 108, 'step_size': 45, 'l2_reg': 4.727410090771915e-12, 'dropout_rate': 0.37437522113887967}. Best is trial 8 with value: 0.24977916479110718.\n", + "[I 2024-08-27 15:13:48,597] Trial 32 finished with value: 0.2999795377254486 and parameters: {'window_size': 89, 'step_size': 56, 'l2_reg': 7.245382395900272e-12, 'dropout_rate': 0.3125579324885738}. Best is trial 8 with value: 0.24977916479110718.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best trial: 0.24977916479110718\n", + "Best window_size: 196\n", + "Best step_size: 75\n", + "Best dropout_rate: 0.43035840459539587\n", + "Best l2_reg: 3.6206124274583214e-11\n" + ] + } + ], + "source": [ + "# Lambda function to pass additional parameters\n", + "\n", + "study = optuna.create_study(direction=\"minimize\")\n", + "study.optimize(\n", + " lambda trial: objective(\n", + " trial,\n", + " objective_metric=\"val_loss\",\n", + " input_shape=input_shape,\n", + " Y=Y,\n", + " sampling_rate=sampling_rate,\n", + " sample_size=0.25,\n", + " data_path=data_path,\n", + " batch_size=batch_size,\n", + " num_classes=num_classes,\n", + " model_class=TimeSeriesCNN,\n", + " filters=[64, 128, 256],\n", + " epochs=epochs,\n", + " ),\n", + " n_trials=200,\n", + " timeout=60 * 60 * 5,\n", + ")\n", + "\n", + "\n", + "print(f\"Best trial: {study.best_trial.value}\")\n", + "print(f\"Best window_size: {study.best_trial.params['window_size']}\")\n", + "print(f\"Best step_size: {study.best_trial.params['step_size']}\")\n", + "print(f\"Best dropout_rate: {study.best_trial.params['dropout_rate']}\")\n", + "print(f\"Best l2_reg: {study.best_trial.params['l2_reg']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c0b7aa4b-2ad9-4a8d-84ce-49e892d65b07", + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"../reports/tscnn_hyperparameter_study.pkl\", \"wb\") as f:\n", + " pickle.dump(study, f)" + ] + }, + { + "cell_type": "markdown", + "id": "9dfbb5ab-6535-41d6-add9-138a06c74a6f", + "metadata": {}, + "source": [ + "### Final Model \n", + "Let's train the optimal model on all of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "62a92511-c3d2-4fc5-b62d-39ec3a2247f3", + "metadata": {}, + "outputs": [], + "source": [ + "window_size = study.best_trial.params[\"window_size\"]\n", + "step_size = study.best_trial.params[\"step_size\"]\n", + "dropout_rate = study.best_trial.params[\"dropout_rate\"]\n", + "l2_reg = study.best_trial.params[\"l2_reg\"]\n", + "\n", + "train_generator, val_generator, test_generator = create_generators_or_datasets(\n", + " y=Y,\n", + " window_size=window_size,\n", + " step_size=step_size,\n", + " batch_size=batch_size,\n", + " sample_size=1.0,\n", + " sampling_rate=sampling_rate,\n", + " data_path=data_path,\n", + " return_generators=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "873f0257-5c41-488a-bc29-889233ded758", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m3410/3410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 49ms/step - accuracy: 0.7686 - auc: 0.8528 - loss: 0.4585 - val_accuracy: 0.8203 - val_auc: 0.9414 - val_loss: 0.4530\n", + "Epoch 2/50\n", + "\u001b[1m3410/3410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m192s\u001b[0m 56ms/step - accuracy: 0.8664 - auc: 0.9357 - loss: 0.3232 - val_accuracy: 0.8098 - val_auc: 0.9489 - val_loss: 0.4207\n", + "Epoch 3/50\n", + "\u001b[1m3410/3410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m274s\u001b[0m 80ms/step - accuracy: 0.8766 - auc: 0.9440 - loss: 0.3011 - val_accuracy: 0.8831 - val_auc: 0.9533 - val_loss: 0.2758\n", + "Epoch 4/50\n", + "\u001b[1m3410/3410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m283s\u001b[0m 83ms/step - accuracy: 0.8853 - auc: 0.9493 - loss: 0.2843 - val_accuracy: 0.8694 - val_auc: 0.9515 - val_loss: 0.3081\n", + "Epoch 5/50\n", + "\u001b[1m3410/3410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m260s\u001b[0m 76ms/step - accuracy: 0.8882 - auc: 0.9548 - loss: 0.2713 - val_accuracy: 0.8803 - val_auc: 0.9558 - val_loss: 0.2831\n", + "Epoch 6/50\n", + "\u001b[1m3410/3410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m260s\u001b[0m 76ms/step - accuracy: 0.8931 - auc: 0.9557 - loss: 0.2686 - val_accuracy: 0.8861 - val_auc: 0.9572 - val_loss: 0.2703\n", + "Epoch 7/50\n", + "\u001b[1m3410/3410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m250s\u001b[0m 73ms/step - accuracy: 0.8936 - auc: 0.9568 - loss: 0.2675 - val_accuracy: 0.8698 - val_auc: 0.9567 - val_loss: 0.3295\n", + "Epoch 8/50\n", + "\u001b[1m3410/3410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 77ms/step - accuracy: 0.8907 - auc: 0.9538 - loss: 0.2775 - val_accuracy: 0.8746 - val_auc: 0.9555 - val_loss: 0.2874\n", + "Epoch 9/50\n", + "\u001b[1m3410/3410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m230s\u001b[0m 67ms/step - accuracy: 0.9012 - auc: 0.9608 - loss: 0.2534 - val_accuracy: 0.8580 - val_auc: 0.9583 - val_loss: 0.3342\n", + "Epoch 10/50\n", + "\u001b[1m3410/3410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m240s\u001b[0m 70ms/step - accuracy: 0.8964 - auc: 0.9595 - loss: 0.2585 - val_accuracy: 0.8583 - val_auc: 0.9599 - val_loss: 0.3476\n", + "Epoch 11/50\n", + "\u001b[1m3410/3410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m278s\u001b[0m 82ms/step - accuracy: 0.8996 - auc: 0.9613 - loss: 0.2511 - val_accuracy: 0.8753 - val_auc: 0.9546 - val_loss: 0.2903\n", + "\n", + "Validation set Loss: 0.270, Accuracy: 0.891, AUC: 0.962\n" + ] + } + ], + "source": [ + "early_stopping = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n", + "ecg_cnn = TimeSeriesCNN(\n", + " input_shape=input_shape, window_size=window_size, dropout_rate=dropout_rate, l2_reg=l2_reg, filters=[64, 128, 256]\n", + ")\n", + "history = ecg_cnn.train(\n", + " train_generator, val_generator, epochs=epochs, batch_size=batch_size, callbacks=[early_stopping]\n", + ")\n", + "\n", + "loss, accuracy, auc, predictions = evaluate_cnn_performance(ecg_cnn, val_generator, y_val)\n", + "print(f\"\\nValidation set Loss: {loss:.3f}, Accuracy: {accuracy:.3f}, AUC: {auc:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "df340c2d-47b5-4f04-aa7b-792a57d0ffb2", + "metadata": {}, + "outputs": [], + "source": [ + "ecg_cnn.model.save(\"../models/cnn_model.keras\", include_optimizer=False)\n", + "\n", + "with open(\"../reports/cnn_history.pkl\", \"wb\") as f:\n", + " pickle.dump(history.history, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c064a6a6-0dc6-4dde-a0ef-cd4db40c8572", + "metadata": {}, + "outputs": [], + "source": [ + "loss, accuracy, auc, predictions = evaluate_cnn_performance(ecg_cnn, test_generator, y_test)\n", + "np.save(\"../results/cnn_preds.npy\", predictions)" + ] + }, + { + "cell_type": "markdown", + "id": "f38c333d-8a3d-4b1a-ae73-2b091faf59fb", + "metadata": {}, + "source": [ + "## ResNet" + ] + }, + { + "cell_type": "markdown", + "id": "8892bcda-10d4-4a08-be26-bb87ba9dcd9e", + "metadata": {}, + "source": [ + "I chose to compare the CNN with a ResNet model because ResNet's residual connections allow it to capture complex patterns and long-range dependencies in ECG data, which a standard CNN might miss. Unlike the CNN, ResNet mitigates the vanishing gradient problem, making it more effective at learning deep features. Additionally, the global feature aggregation layer in ResNet enhances generalization, improving its ability to consistently detect important patterns across various ECG signals." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6c9b42c8-761b-4d53-96ab-aca89f9ae072", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 860ms/step - accuracy: 0.8095 - auc: 0.8861 - loss: 0.4180 - val_accuracy: 0.5430 - val_auc: 0.8560 - val_loss: 0.8135\n", + "Epoch 2/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m125s\u001b[0m 813ms/step - accuracy: 0.8747 - auc: 0.9481 - loss: 0.2885 - val_accuracy: 0.8659 - val_auc: 0.9482 - val_loss: 0.3181\n", + "Epoch 3/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 893ms/step - accuracy: 0.8842 - auc: 0.9524 - loss: 0.2775 - val_accuracy: 0.8510 - val_auc: 0.9581 - val_loss: 0.3401\n", + "Epoch 4/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m162s\u001b[0m 1s/step - accuracy: 0.8990 - auc: 0.9599 - loss: 0.2544 - val_accuracy: 0.8642 - val_auc: 0.9549 - val_loss: 0.2991\n", + "Epoch 5/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 804ms/step - accuracy: 0.9038 - auc: 0.9647 - loss: 0.2372 - val_accuracy: 0.8858 - val_auc: 0.9584 - val_loss: 0.2819\n", + "Epoch 6/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m151s\u001b[0m 979ms/step - accuracy: 0.8942 - auc: 0.9594 - loss: 0.2580 - val_accuracy: 0.9007 - val_auc: 0.9569 - val_loss: 0.2668\n", + "Epoch 7/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m130s\u001b[0m 847ms/step - accuracy: 0.9144 - auc: 0.9722 - loss: 0.2112 - val_accuracy: 0.8841 - val_auc: 0.9552 - val_loss: 0.2955\n", + "Epoch 8/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 944ms/step - accuracy: 0.9084 - auc: 0.9698 - loss: 0.2186 - val_accuracy: 0.8493 - val_auc: 0.9490 - val_loss: 0.4070\n", + "Epoch 9/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m156s\u001b[0m 1s/step - accuracy: 0.9193 - auc: 0.9731 - loss: 0.2059 - val_accuracy: 0.8725 - val_auc: 0.9570 - val_loss: 0.2927\n", + "Epoch 10/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 935ms/step - accuracy: 0.9170 - auc: 0.9716 - loss: 0.2126 - val_accuracy: 0.8825 - val_auc: 0.9533 - val_loss: 0.2770\n", + "Epoch 11/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 898ms/step - accuracy: 0.9238 - auc: 0.9783 - loss: 0.1847 - val_accuracy: 0.8825 - val_auc: 0.9606 - val_loss: 0.3132\n", + "\n", + "Validation set Loss: 0.267, Accuracy: 0.901, AUC: 0.957\n" + ] + } + ], + "source": [ + "early_stopping = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n", + "resnet = ECGResNet(input_shape=input_shape, num_classes=num_classes, l2_reg=0.0, dropout_rate=0.0)\n", + "resnet_history = resnet.train(\n", + " X_train, y_train, X_val, y_val, epochs=epochs, batch_size=batch_size, callbacks=[early_stopping]\n", + ")\n", + "\n", + "loss, accuracy, auc, predictions = evaluate_resnet_performance(resnet, X_val, y_val)\n", + "print(f\"\\nValidation set Loss: {loss:.3f}, Accuracy: {accuracy:.3f}, AUC: {auc:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "6f8089b1-a0f6-4577-ae6a-dc0f420bb0b0", + "metadata": {}, + "source": [ + "### Approaching overfitting \n", + "\n", + "Overfitting can be observed here as well. Given that it takes a long time to perform a full parameter search and that current performance nears CNN performance on validation set loss, I will focus on a few tries on the dropout rate and l2 regularization parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cca7de82-6b5a-40d1-acce-4d8b7b7a523e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m173s\u001b[0m 1s/step - accuracy: 0.7991 - auc: 0.8778 - loss: 0.4266 - val_accuracy: 0.8096 - val_auc: 0.8983 - val_loss: 0.4757\n", + "Epoch 2/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m154s\u001b[0m 999ms/step - accuracy: 0.8679 - auc: 0.9443 - loss: 0.3002 - val_accuracy: 0.8725 - val_auc: 0.9473 - val_loss: 0.3824\n", + "Epoch 3/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 729ms/step - accuracy: 0.8800 - auc: 0.9472 - loss: 0.2898 - val_accuracy: 0.8411 - val_auc: 0.9432 - val_loss: 0.3502\n", + "Epoch 4/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m153s\u001b[0m 998ms/step - accuracy: 0.8994 - auc: 0.9588 - loss: 0.2555 - val_accuracy: 0.8841 - val_auc: 0.9592 - val_loss: 0.2714\n", + "Epoch 5/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 913ms/step - accuracy: 0.8902 - auc: 0.9595 - loss: 0.2557 - val_accuracy: 0.8725 - val_auc: 0.9615 - val_loss: 0.2729\n", + "Epoch 6/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m153s\u001b[0m 991ms/step - accuracy: 0.9109 - auc: 0.9689 - loss: 0.2273 - val_accuracy: 0.8924 - val_auc: 0.9603 - val_loss: 0.2637\n", + "Epoch 7/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 903ms/step - accuracy: 0.9025 - auc: 0.9661 - loss: 0.2335 - val_accuracy: 0.9007 - val_auc: 0.9612 - val_loss: 0.2655\n", + "Epoch 8/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 930ms/step - accuracy: 0.9175 - auc: 0.9737 - loss: 0.2068 - val_accuracy: 0.8510 - val_auc: 0.9521 - val_loss: 0.3768\n", + "Epoch 9/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m133s\u001b[0m 861ms/step - accuracy: 0.9122 - auc: 0.9708 - loss: 0.2183 - val_accuracy: 0.8725 - val_auc: 0.9578 - val_loss: 0.3020\n", + "Epoch 10/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 874ms/step - accuracy: 0.9170 - auc: 0.9751 - loss: 0.1992 - val_accuracy: 0.8907 - val_auc: 0.9615 - val_loss: 0.2485\n", + "Epoch 11/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m123s\u001b[0m 800ms/step - accuracy: 0.9150 - auc: 0.9736 - loss: 0.2047 - val_accuracy: 0.8825 - val_auc: 0.9603 - val_loss: 0.2743\n", + "Epoch 12/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m115s\u001b[0m 744ms/step - accuracy: 0.9242 - auc: 0.9765 - loss: 0.1938 - val_accuracy: 0.8642 - val_auc: 0.9570 - val_loss: 0.3483\n", + "Epoch 13/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m132s\u001b[0m 858ms/step - accuracy: 0.9191 - auc: 0.9777 - loss: 0.1890 - val_accuracy: 0.8891 - val_auc: 0.9625 - val_loss: 0.3102\n", + "Epoch 14/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 668ms/step - accuracy: 0.9332 - auc: 0.9796 - loss: 0.1759 - val_accuracy: 0.8940 - val_auc: 0.9604 - val_loss: 0.2870\n", + "Epoch 15/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m132s\u001b[0m 857ms/step - accuracy: 0.9326 - auc: 0.9825 - loss: 0.1665 - val_accuracy: 0.8907 - val_auc: 0.9605 - val_loss: 0.2879\n", + "\n", + "Validation set Loss: 0.249, Accuracy: 0.891, AUC: 0.962\n" + ] + } + ], + "source": [ + "early_stopping = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n", + "resnet = ECGResNet(input_shape=input_shape, num_classes=num_classes, l2_reg=0.0, dropout_rate=0.3)\n", + "resnet_history = resnet.train(\n", + " X_train, y_train, X_val, y_val, epochs=epochs, batch_size=batch_size, callbacks=[early_stopping]\n", + ")\n", + "\n", + "loss, accuracy, auc, predictions = evaluate_resnet_performance(resnet, X_val, y_val)\n", + "print(f\"\\nValidation set Loss: {loss:.3f}, Accuracy: {accuracy:.3f}, AUC: {auc:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "db88bf99-a79d-4fac-ad8f-24b206fdddce", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m129s\u001b[0m 775ms/step - accuracy: 0.8050 - auc: 0.8911 - loss: 1.2017 - val_accuracy: 0.5430 - val_auc: 0.8058 - val_loss: 1.4148\n", + "Epoch 2/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m131s\u001b[0m 851ms/step - accuracy: 0.8704 - auc: 0.9408 - loss: 0.7280 - val_accuracy: 0.6258 - val_auc: 0.8819 - val_loss: 1.0400\n", + "Epoch 3/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 944ms/step - accuracy: 0.8703 - auc: 0.9411 - loss: 0.5771 - val_accuracy: 0.8560 - val_auc: 0.9533 - val_loss: 0.5784\n", + "Epoch 4/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 819ms/step - accuracy: 0.8753 - auc: 0.9535 - loss: 0.4741 - val_accuracy: 0.8377 - val_auc: 0.9569 - val_loss: 0.4962\n", + "Epoch 5/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m162s\u001b[0m 1s/step - accuracy: 0.8953 - auc: 0.9588 - loss: 0.4139 - val_accuracy: 0.9007 - val_auc: 0.9618 - val_loss: 0.3793\n", + "Epoch 6/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m152s\u001b[0m 986ms/step - accuracy: 0.9006 - auc: 0.9633 - loss: 0.3697 - val_accuracy: 0.8957 - val_auc: 0.9612 - val_loss: 0.3701\n", + "Epoch 7/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m133s\u001b[0m 865ms/step - accuracy: 0.9010 - auc: 0.9619 - loss: 0.3555 - val_accuracy: 0.9007 - val_auc: 0.9587 - val_loss: 0.3551\n", + "Epoch 8/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m119s\u001b[0m 770ms/step - accuracy: 0.8915 - auc: 0.9627 - loss: 0.3480 - val_accuracy: 0.8924 - val_auc: 0.9575 - val_loss: 0.3532\n", + "Epoch 9/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m125s\u001b[0m 811ms/step - accuracy: 0.9025 - auc: 0.9654 - loss: 0.3196 - val_accuracy: 0.8775 - val_auc: 0.9534 - val_loss: 0.3576\n", + "Epoch 10/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 911ms/step - accuracy: 0.8946 - auc: 0.9571 - loss: 0.3357 - val_accuracy: 0.8825 - val_auc: 0.9586 - val_loss: 0.3301\n", + "Epoch 11/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m123s\u001b[0m 793ms/step - accuracy: 0.9114 - auc: 0.9701 - loss: 0.2861 - val_accuracy: 0.8858 - val_auc: 0.9578 - val_loss: 0.3307\n", + "Epoch 12/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m117s\u001b[0m 759ms/step - accuracy: 0.9057 - auc: 0.9692 - loss: 0.2848 - val_accuracy: 0.8907 - val_auc: 0.9583 - val_loss: 0.3229\n", + "Epoch 13/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m121s\u001b[0m 786ms/step - accuracy: 0.9057 - auc: 0.9709 - loss: 0.2782 - val_accuracy: 0.8907 - val_auc: 0.9544 - val_loss: 0.3284\n", + "Epoch 14/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 701ms/step - accuracy: 0.9038 - auc: 0.9672 - loss: 0.2889 - val_accuracy: 0.8891 - val_auc: 0.9577 - val_loss: 0.3177\n", + "Epoch 15/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 693ms/step - accuracy: 0.9140 - auc: 0.9726 - loss: 0.2627 - val_accuracy: 0.8427 - val_auc: 0.9522 - val_loss: 0.4091\n", + "Epoch 16/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 691ms/step - accuracy: 0.9119 - auc: 0.9703 - loss: 0.2735 - val_accuracy: 0.8957 - val_auc: 0.9565 - val_loss: 0.3246\n", + "Epoch 17/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 718ms/step - accuracy: 0.9273 - auc: 0.9781 - loss: 0.2350 - val_accuracy: 0.9023 - val_auc: 0.9617 - val_loss: 0.2964\n", + "Epoch 18/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 704ms/step - accuracy: 0.9227 - auc: 0.9751 - loss: 0.2482 - val_accuracy: 0.8791 - val_auc: 0.9634 - val_loss: 0.3063\n", + "Epoch 19/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 732ms/step - accuracy: 0.9154 - auc: 0.9735 - loss: 0.2581 - val_accuracy: 0.8758 - val_auc: 0.9568 - val_loss: 0.3159\n", + "Epoch 20/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 685ms/step - accuracy: 0.8991 - auc: 0.9649 - loss: 0.2993 - val_accuracy: 0.8775 - val_auc: 0.9520 - val_loss: 0.3467\n", + "Epoch 21/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 697ms/step - accuracy: 0.9210 - auc: 0.9786 - loss: 0.2351 - val_accuracy: 0.8940 - val_auc: 0.9577 - val_loss: 0.3244\n", + "Epoch 22/50\n", + "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m180s\u001b[0m 1s/step - accuracy: 0.9126 - auc: 0.9734 - loss: 0.2570 - val_accuracy: 0.8858 - val_auc: 0.9558 - val_loss: 0.3223\n", + "\n", + "Validation set Loss: 0.296, Accuracy: 0.902, AUC: 0.962\n" + ] + } + ], + "source": [ + "early_stopping = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n", + "resnet = ECGResNet(input_shape=input_shape, num_classes=num_classes, l2_reg=0.001, dropout_rate=0.0)\n", + "resnet_history = resnet.train(\n", + " X_train, y_train, X_val, y_val, epochs=epochs, batch_size=batch_size, callbacks=[early_stopping]\n", + ")\n", + "\n", + "loss, accuracy, auc, predictions = evaluate_resnet_performance(resnet, X_val, y_val)\n", + "print(f\"\\nValidation set Loss: {loss:.3f}, Accuracy: {accuracy:.3f}, AUC: {auc:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ea8fd21c-8340-4de4-9dbf-ff059d7a8b0f", + "metadata": {}, + "source": [ + "### Final Model \n", + "Let's train the optimal model on all of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c405db4e-064e-4d13-b7db-7a7ac6a20b82", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, y_train, X_val, y_val, X_test, y_test = create_generators_or_datasets(\n", + " y=Y,\n", + " window_size=None,\n", + " step_size=None,\n", + " batch_size=batch_size,\n", + " sample_size=1.0,\n", + " sampling_rate=sampling_rate,\n", + " data_path=data_path,\n", + " return_generators=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "bf9a8d74-e2bd-4c9c-879d-daaac42ec57b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m217s\u001b[0m 680ms/step - accuracy: 0.8228 - auc: 0.9001 - loss: 0.3905 - val_accuracy: 0.8100 - val_auc: 0.9391 - val_loss: 0.4179\n", + "Epoch 2/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m211s\u001b[0m 680ms/step - accuracy: 0.8801 - auc: 0.9497 - loss: 0.2841 - val_accuracy: 0.8594 - val_auc: 0.9552 - val_loss: 0.3119\n", + "Epoch 3/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m214s\u001b[0m 689ms/step - accuracy: 0.8993 - auc: 0.9608 - loss: 0.2506 - val_accuracy: 0.8745 - val_auc: 0.9572 - val_loss: 0.2934\n", + "Epoch 4/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 692ms/step - accuracy: 0.8976 - auc: 0.9614 - loss: 0.2482 - val_accuracy: 0.8987 - val_auc: 0.9635 - val_loss: 0.2461\n", + "Epoch 5/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m217s\u001b[0m 699ms/step - accuracy: 0.9077 - auc: 0.9669 - loss: 0.2303 - val_accuracy: 0.8820 - val_auc: 0.9635 - val_loss: 0.2754\n", + "Epoch 6/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m219s\u001b[0m 706ms/step - accuracy: 0.9055 - auc: 0.9662 - loss: 0.2312 - val_accuracy: 0.8962 - val_auc: 0.9659 - val_loss: 0.2518\n", + "Epoch 7/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 694ms/step - accuracy: 0.9092 - auc: 0.9712 - loss: 0.2137 - val_accuracy: 0.8971 - val_auc: 0.9636 - val_loss: 0.2428\n", + "Epoch 8/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m219s\u001b[0m 707ms/step - accuracy: 0.9209 - auc: 0.9745 - loss: 0.2004 - val_accuracy: 0.8854 - val_auc: 0.9666 - val_loss: 0.2877\n", + "Epoch 9/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m225s\u001b[0m 725ms/step - accuracy: 0.9138 - auc: 0.9736 - loss: 0.2048 - val_accuracy: 0.9063 - val_auc: 0.9665 - val_loss: 0.2356\n", + "Epoch 10/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m218s\u001b[0m 704ms/step - accuracy: 0.9168 - auc: 0.9747 - loss: 0.2014 - val_accuracy: 0.9046 - val_auc: 0.9658 - val_loss: 0.2370\n", + "Epoch 11/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m232s\u001b[0m 748ms/step - accuracy: 0.9205 - auc: 0.9773 - loss: 0.1904 - val_accuracy: 0.9004 - val_auc: 0.9636 - val_loss: 0.2728\n", + "Epoch 12/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m225s\u001b[0m 726ms/step - accuracy: 0.9207 - auc: 0.9770 - loss: 0.1919 - val_accuracy: 0.8837 - val_auc: 0.9629 - val_loss: 0.2861\n", + "Epoch 13/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m221s\u001b[0m 714ms/step - accuracy: 0.9264 - auc: 0.9794 - loss: 0.1816 - val_accuracy: 0.8987 - val_auc: 0.9634 - val_loss: 0.2510\n", + "Epoch 14/50\n", + "\u001b[1m310/310\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m223s\u001b[0m 721ms/step - accuracy: 0.9289 - auc: 0.9796 - loss: 0.1794 - val_accuracy: 0.8837 - val_auc: 0.9595 - val_loss: 0.3208\n", + "\n", + "Validation set Loss: 0.236, Accuracy: 0.906, AUC: 0.967\n" + ] + } + ], + "source": [ + "early_stopping = EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n", + "ecg_resnet = ECGResNet(input_shape=input_shape, num_classes=num_classes, l2_reg=0.0, dropout_rate=0.3)\n", + "resnet_history = ecg_resnet.train(\n", + " X_train, y_train, X_val, y_val, epochs=epochs, batch_size=batch_size, callbacks=[early_stopping]\n", + ")\n", + "\n", + "loss, accuracy, auc, predictions = evaluate_resnet_performance(ecg_resnet, X_val, y_val)\n", + "print(f\"\\nValidation set Loss: {loss:.3f}, Accuracy: {accuracy:.3f}, AUC: {auc:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b8e929b9-6a4d-4788-9286-14f26f04c40e", + "metadata": {}, + "outputs": [], + "source": [ + "ecg_resnet.model.save(\"../models/resnet_model.keras\", include_optimizer=False)\n", + "\n", + "with open(\"../reports/resnet_history.pkl\", \"wb\") as f:\n", + " pickle.dump(resnet_history.history, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a704f83b-2c0c-4489-b00e-25d6f309f712", + "metadata": {}, + "outputs": [], + "source": [ + "loss, accuracy, auc, predictions = evaluate_resnet_performance(ecg_resnet, X_test, y_test)\n", + "np.save(\"../results/resnet_preds.npy\", predictions)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ruben-solution/notebooks/04-results-analysis.ipynb b/ruben-solution/notebooks/04-results-analysis.ipynb new file mode 100644 index 0000000..10cc0cf --- /dev/null +++ b/ruben-solution/notebooks/04-results-analysis.ipynb @@ -0,0 +1,1067 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "59d36211-21c0-442d-a1bd-00352ae79573", + "metadata": {}, + "source": [ + "# Results analysis" + ] + }, + { + "cell_type": "markdown", + "id": "eafbd8ee-3db4-42a8-908e-545d4898d0a7", + "metadata": {}, + "source": [ + "In this notebook, I evaluate the performance of two models. Both models were optimized based on validation loss, and I will visualize the training history and hyperparameter search process. After identifying the best-performing models, I evaluated them on the test set, and the results of this evaluation will be discussed here." + ] + }, + { + "cell_type": "markdown", + "id": "ad5acf57-2ba3-4adc-9887-d8d5b428584a", + "metadata": {}, + "source": [ + "### Load imports, data and model predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6f1a9a96-e351-467e-9f7b-768b77b5e7c3", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "03921168-4f39-4120-a921-c02cb810338b", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", + "\n", + "import pickle\n", + "from os.path import join\n", + "\n", + "import matplotlib.image as mpimg\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import optuna\n", + "import optuna.visualization as vis\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn.metrics import auc, classification_report, confusion_matrix, roc_auc_score, roc_curve\n", + "from tensorflow.keras.models import load_model\n", + "\n", + "from utils.data_preprocessing import *\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "99ab1870-c042-48cf-a39e-79044fb0cbdd", + "metadata": {}, + "outputs": [], + "source": [ + "def calc_sens_spec(y_true, y_pred):\n", + " tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n", + " sensitivity = tp / (tp + fn) if (tp + fn) != 0 else 0\n", + " specificity = tn / (tn + fp) if (tn + fp) != 0 else 0\n", + " return sensitivity, specificity" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1ceb5c3b-baf7-4e71-99ac-7184b52adbdc", + "metadata": {}, + "outputs": [], + "source": [ + "cnn_preds = np.load(\"../results/cnn_preds.npy\")\n", + "resnet_preds = np.load(\"../results/resnet_preds.npy\")\n", + "\n", + "with open(\"../reports/cnn_history.pkl\", \"rb\") as f:\n", + " cnn_history = pickle.load(f)\n", + "with open(\"../reports/resnet_history.pkl\", \"rb\") as f:\n", + " resnet_history = pickle.load(f)\n", + "\n", + "with open(\"../reports/tscnn_hyperparameter_study.pkl\", \"rb\") as f:\n", + " tscnn_study = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "361d772d-166b-4f21-89bd-c49b83399f09", + "metadata": {}, + "outputs": [], + "source": [ + "data_path = \"../data/\"\n", + "sampling_rate = 100\n", + "window_size = 100\n", + "step_size = 100\n", + "batch_size = 32\n", + "sample_size = 1.0\n", + "test_fold = 10\n", + "Y = pd.read_csv(join(data_path, \"processed/labels.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "78e7bf6d-4447-49a4-819a-841ba5406217", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, y_train, X_val, y_val, X_test, y_test = create_generators_or_datasets(\n", + " y=Y,\n", + " window_size=None,\n", + " step_size=None,\n", + " batch_size=batch_size,\n", + " sample_size=sample_size,\n", + " sampling_rate=sampling_rate,\n", + " data_path=data_path,\n", + " return_generators=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ec2037e3-5f04-43b2-bcd1-33e23757a931", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((9920, 1000, 12),\n", + " (9920,),\n", + " (1195, 1000, 12),\n", + " (1195,),\n", + " (1208, 1000, 12),\n", + " (1208,))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape" + ] + }, + { + "cell_type": "markdown", + "id": "5710d425-b0c7-4d0e-ac0a-5959cb94919e", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "### Model training analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5e2411b8-8242-4b3b-bcf0-aa0597c122b9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OTo69WrVq9mrVqtmzs7Ptdrvdvnv3bvuQIUPs4eHhdg8PD3vFihXtt9xyi33hwoWO855//nl78+bN7cHBwXYfHx977dq17S+88II9MzPTccz48ePtgP3o0aMXjNuuXTs7cNG573vvvWdv0qSJ3cfHxx4QEGBv0KCB/fHHH7cfOnTIcUxSUpK9W7du9oCAADtgb9eu3WW/B5ea6wL25557zm632+2nTp2yP/zww/YKFSrYPTw87DVq1LC/+uqrdpvN5rjOsmXL7D179rRXqFDB7unpaa9QoYJ94MCB9h07djiO+d///mdv27atvVy5cnYvLy97tWrV7I899pg9NTX1sjGKiNjtdrvFbv/Xs6kiIiIiIiIiIiIiYhrVtBURERERERERERFxIkraioiIiIiIiIiIiDgRJW1FREREREREREREnIiStiIiIiIiIiIiIiJORElbERERERERERERESeipK2IiIiIiIiIiIiIE3E3OwBXZbPZOHToEAEBAVgsFrPDERERESmV7HY7p06dokKFClitWo9wOZq/ioiIiJgvv/NXJW0L6NChQ0RGRpodhoiIiIgA+/fvp1KlSmaH4dQ0fxURERFxHleavyppW0ABAQGA8Q0ODAw0ORoRERGR0iktLY3IyEjH3EwuTfNXEREREfPld/6qpG0B5T5SFhgYqEmviIiIiMn0uP+Vaf4qIiIi4jyuNH9V4S8RERERERERERERJ6KkrYiIiIiIiIiIiIgTUdJWRERERERERERExImopq2IiIgUipycHLKysswOQ0oYDw8P3NzczA5DRERESiCbzUZmZqbZYUgJU1jzVyVtRURE5JrY7XaSkpJISUkxOxQpoYKDgwkPD1ezMRERESk0mZmZ7NmzB5vNZnYoUgIVxvxVSVsRERG5JrkJ29DQUHx9fZVYk0Jjt9s5c+YMR44cASAiIsLkiERERKQksNvtHD58GDc3NyIjI7FaVT1UCkdhzl+VtBUREZECy8nJcSRsy5UrZ3Y4UgL5+PgAcOTIEUJDQ1UqQURERK5ZdnY2Z86coUKFCvj6+podjpQwhTV/1Z8SREREpMBya9hqsitFKffnSzWTRUREpDDk5OQA4OnpaXIkUlIVxvxVSVsRERG5ZiqJIEVJP18iIiJSFDTHkKJSGD9bStqKiIiIiIiIiIiIOBElbUVEREQKSVRUFJMnT8738StWrMBisZCSklJkMYmIiIiIXIrmr85LSVsREREpdSwWy2Vfzz77bIGuu379eu666658H3/DDTdw+PBhgoKCCjRefmlyLSIiIuLaNH8tfdzNDkBERESkuB0+fNixPW/ePMaNG8f27dsd+/z9/R3bdrudnJwc3N2vPG0qX778VcXh6elJeHj4VZ0jIiIiIqWP5q+lj1baioiISKkTHh7ueAUFBWGxWBxf//nnnwQEBPD999/TpEkTvLy8WL16Nbt376Znz56EhYXh7+9Ps2bN+PHHH/Nc99+Pl1ksFt5//3169+6Nr68vNWrUYNGiRY7P/72CYObMmQQHB7NkyRLq1KmDv78/nTt3zjNJz87O5oEHHiA4OJhy5crxxBNPcOedd9KrV68Cfz9OnjzJkCFDKFOmDL6+vnTp0oWdO3c6Pt+3bx/du3enTJky+Pn5Ua9ePb777jvHuYMGDaJ8+fL4+PhQo0YNZsyYUeBYRERERORCmr/mVRrmr0rauork382OQEREJF/sdjtnMrNNednt9kK7jyeffJKXXnqJhIQEGjZsyOnTp+natSvLli1j8+bNdO7cme7du5OYmHjZ60yYMIF+/fqxZcsWunbtyqBBgzhx4sQljz9z5gyvvfYas2bNYuXKlSQmJvLoo486Pn/55ZeZPXs2M2bM4JdffiEtLY0vv/zymu516NChbNiwgUWLFhEXF4fdbqdr165kZWUBcN9995GRkcHKlSvZunUrL7/8smM1xzPPPMMff/zB999/T0JCAu+++y4hISHXFI+UEDnZkH7M7ChERESuSPPXvDR/dQ4qj+AKfn0XFo+BLq9Ai/zXGRERETHD2awc6o5bYsrYf0zshK9n4UxvJk6cyE033eT4umzZsjRq1Mjx9XPPPccXX3zBokWLGDVq1CWvM3ToUAYOHAjAiy++yFtvvcW6devo3LnzRY/Pyspi2rRpVKtWDYBRo0YxceJEx+dTpkxhzJgx9O7dG4CpU6c6Vg0UxM6dO1m0aBG//PILN9xwAwCzZ88mMjKSL7/8kttuu43ExET69OlDgwYNAKhatarj/MTERBo3bkzTpk0BY7WGCAlfw5f3QuWWMGi+2dGIiIhcluaveWn+6hy00tYVZJ4G7PD948YEWERERIpc7iQu1+nTp3n00UepU6cOwcHB+Pv7k5CQcMWVCg0bNnRs+/n5ERgYyJEjRy55vK+vr2PCCxAREeE4PjU1leTkZJo3b+743M3NjSZNmlzVvf1TQkIC7u7utGjRwrGvXLly1KpVi4SEBAAeeOABnn/+eVq1asX48ePZsmWL49h77rmHuXPnEhMTw+OPP86aNWsKHIuUIEGRkJEG+9YYK25FRESkyGn+WrLmr6avtH377bd59dVXSUpKolGjRkyZMiXP/5CXMnfuXAYOHEjPnj3zLKkeOnQoH330UZ5jO3XqxOLFix1fnzhxgvvvv5+vv/4aq9VKnz59ePPNN/MUbXYqbR6F1AOwcSZ8NgLu/Boir/w9EhERMYOPhxt/TOxk2tiFxc/PL8/Xjz76KEuXLuW1116jevXq+Pj40LdvXzIzMy97HQ8PjzxfWywWbDbbVR1fmI/NFcSIESPo1KkT3377LT/88AOTJk3i9ddf5/7776dLly7s27eP7777jqVLl9KxY0fuu+8+XnvtNVNjFpOFNwDvIDiXCofjoVLTK54iIiJiFs1f89L81TmYutJ23rx5jB49mvHjx7Np0yYaNWpEp06dLpu9B9i7dy+PPvoobdq0uejnuQWPc19z5szJ8/mgQYP4/fffWbp0Kd988w0rV67krrucuOyAxQJdX4canSD7HHzaH47tMjsqERGRi7JYLPh6upvyslgsRXZfv/zyC0OHDqV37940aNCA8PBw9u7dW2TjXUxQUBBhYWGsX7/esS8nJ4dNmzYV+Jp16tQhOzubtWvXOvYdP36c7du3U7duXce+yMhI/u///o/PP/+cRx55hOnTpzs+K1++PHfeeSeffPIJkydP5r333itwPFJCWN2gSmtje89Kc2MRERG5As1fi47mrwVn6krbN954g5EjRzJs2DAApk2bxrfffsuHH37Ik08+edFzcnJyGDRoEBMmTGDVqlWObnX/5OXlRXh4+EXPT0hIYPHixaxfv96xbHzKlCl07dqV1157jQoVKhTOzRU2N3e4bQbM7AaHNsMnt8KIH8E/1OzIRERESoUaNWrw+eef0717dywWC88888xlVxwUlfvvv59JkyZRvXp1ateuzZQpUzh58mS+Jvxbt24lICDA8bXFYqFRo0b07NmTkSNH8r///Y+AgACefPJJKlasSM+ePQF46KGH6NKlCzVr1uTkyZMsX76cOnXqADBu3DiaNGlCvXr1yMjI4JtvvnF8JqVcdBvY/i3sXQVtRpsdjYiISKmj+atrz19NW2mbmZnJxo0biY2N/TsYq5XY2Fji4uIued7EiRMJDQ1l+PDhlzxmxYoVhIaGUqtWLe655x6OHz/u+CwuLo7g4OA8dT5iY2OxWq15MvROydMPbp8PZaIgZR982g8y082OSkREpFR44403KFOmDDfccAPdu3enU6dOXHfddcUexxNPPMHAgQMZMmQILVu2xN/fn06dOuHt7X3Fc9u2bUvjxo0dr9xaYjNmzKBJkybccssttGzZErvdznfffed41C0nJ4f77ruPOnXq0LlzZ2rWrMk777wDgKenJ2PGjKFhw4a0bdsWNzc35s6dW3TfAHEd0W2N98RfIfvyj2GKiIhI4dP81bXnrxa7SUUmDh06RMWKFVmzZg0tW7Z07H/88cf5+eefL5pAXb16NQMGDCA+Pp6QkBCGDh1KSkpKnpq2c+fOxdfXl+joaHbv3s1TTz2Fv78/cXFxuLm58eKLL/LRRx+xffv2PNcODQ1lwoQJ3HPPPReNNyMjg4yMDMfXaWlpREZGkpqaSmBg4DV+N67SsV3wwU1w9oRRMmHAp8ZKXBERkWJ27tw59uzZQ3R0dL4mXVL4bDYbderUoV+/fjz33HNmh1MkLvdzlpaWRlBQkDlzMhdT7N8rmw1eqw5njsN/lkDl64t+TBERkXzQHNZcmr/mb05mak3bq3Hq1CnuuOMOpk+fTkhIyCWPGzBgAD169KBBgwb06tWLb775hvXr17NixYprGn/SpEkEBQU5XpGRkdd0vWsSUh1unwfu3rBzCXw7Gkwu8CwiIiLFY9++fUyfPp0dO3awdetW7rnnHvbs2cPtt99udmgieVmtEJVb13aVubGIiIiIaTR/LRjTkrYhISG4ubmRnJycZ39ycvJF69Hu3r2bvXv30r17d9zd3XF3d+fjjz9m0aJFuLu7s3v37ouOU7VqVUJCQti1y2jcFR4efkGjs+zsbE6cOHHJOrgAY8aMITU11fHav3//1d5y4YpsDn0+ACyw6SNY5Vwd7kRERKRoWK1WZs6cSbNmzWjVqhVbt27lxx9/dMo6XCJEnW8cvOdnc+MQERER02j+WjCmPVPv6elJkyZNWLZsGb169QKM5dHLli1j1KhRFxxfu3Zttm7dmmff008/zalTp3jzzTcvufL1wIEDHD9+nIiICABatmxJSkoKGzdudNTB+Omnn7DZbLRo0eKS8Xp5eeHl5VWQWy06dW6Brq/Cd4/CT89DYEWI0V8pRERESrLIyEh++eUXs8MQyZ/curb710HWOfDQI6giIiKljeavBWNqIdTRo0dz55130rRpU5o3b87kyZNJT09n2LBhAAwZMoSKFSsyadIkvL29qV+/fp7zg4ODARz7T58+zYQJE+jTpw/h4eHs3r2bxx9/nOrVq9OpUycARwHikSNHMm3aNLKyshg1ahQDBgygQoUKxXfzhaX5SEjdD7+8CYvuh4BwqHaj2VGJiIiIiEBITfAPg9PJcGA9RLcxOyIRERERl2BqTdv+/fvz2muvMW7cOGJiYoiPj2fx4sWEhYUBkJiYyOHDh/N9PTc3N7Zs2UKPHj2oWbMmw4cPp0mTJqxatSrPKtnZs2dTu3ZtOnbsSNeuXWndujXvvfdeod9fsen4LNTvC7ZsmDcEkrZe8RQRERERkSJnsfxdImGv6tqKiIiI5JepK20BRo0addFyCMAVm4fNnDkzz9c+Pj4sWbLkimOWLVuWTz/9NL8hOj+rFXq9Y6xg2LsKZt8Gw5dCsInN0kREREREwFhdu22h0Yysg9nBiIiIiLgGU1faSiFy94L+n0D5OnDqMMzuC2dPmh2ViIiIiJR2uSttD6yHzDPmxiIiIiLiIpS0LUl8gmHwQgiIgKN/wtzBkJ1hdlQiIiIiUpqVrWo0zLVlwf5fzY5GRERExCUoaVvSBFWCQQvBMwD2rYYv7wGbzeyoRERERKS0+mdd2z2qaysiIiKSH0ralkTh9WHAJ2B1h22fwY/jzY5IRESkRGrfvj0PPfSQ4+uoqCgmT5582XMsFgtffvnlNY9dWNcRKRbRbY13NSMTERExleavrkNJ25Kqanvo+baxveYtWPs/U8MRERFxJt27d6dz584X/WzVqlVYLBa2bNly1dddv349d91117WGl8ezzz5LTEzMBfsPHz5Mly5dCnWsf5s5cybBwcFFOoaUEtHnV9oe3AQZp8yNRURExAVp/po/JWn+qqRtSdZoAHQcZ2x//wQkfG1uPCIiIk5i+PDhLF26lAMHDlzw2YwZM2jatCkNGza86uuWL18eX1/fwgjxisLDw/Hy8iqWsUSuWXBlCK4C9hxIVF1bERGRq6X5a+mjpG1J13o0NP0PYIfPRkDiWrMjEhERMd0tt9xC+fLlmTlzZp79p0+fZsGCBQwfPpzjx48zcOBAKlasiK+vLw0aNGDOnDmXve6/Hy/buXMnbdu2xdvbm7p167J06dILznniiSeoWbMmvr6+VK1alWeeeYasrCzAWCkwYcIEfvvtNywWCxaLxRHzvx8v27p1KzfeeCM+Pj6UK1eOu+66i9OnTzs+Hzp0KL169eK1114jIiKCcuXKcd999znGKojExER69uyJv78/gYGB9OvXj+TkZMfnv/32Gx06dCAgIIDAwECaNGnChg0bANi3bx/du3enTJky+Pn5Ua9ePb777rsCxyIuIHe17Z6V5sYhIiLigjR/LX3zV/ciu7I4B4sFurwKaYdhx/cwpz8MXwohNcyOTERESiq7HbLOmDO2h6/x374rcHd3Z8iQIcycOZOxY8diOX/OggULyMnJYeDAgZw+fZomTZrwxBNPEBgYyLfffssdd9xBtWrVaN68+RXHsNls3HrrrYSFhbF27VpSU1Pz1A/LFRAQwMyZM6lQoQJbt25l5MiRBAQE8Pjjj9O/f3+2bdvG4sWL+fHHHwEICgq64Brp6el06tSJli1bsn79eo4cOcKIESMYNWpUnon98uXLiYiIYPny5ezatYv+/fsTExPDyJEjr3g/F7u/3Anvzz//THZ2Nvfddx/9+/dnxYoVAAwaNIjGjRvz7rvv4ubmRnx8PB4eHgDcd999ZGZmsnLlSvz8/Pjjjz/w9/e/6jjEhUS1hc2fKGkrIiLOR/NXQPNXcK75q5K2pYGbO/T9AD7qDgc3wid9YMSP4B9qdmQiIlISZZ2BFyuYM/ZTh8DTL1+H/uc//+HVV1/l559/pn379oDxaFmfPn0ICgoiKCiIRx991HH8/fffz5IlS5g/f36+Jr0//vgjf/75J0uWLKFCBeP78eKLL15Qx+vpp592bEdFRfHoo48yd+5cHn/8cXx8fPD398fd3Z3w8PBLjvXpp59y7tw5Pv74Y/z8jPufOnUq3bt35+WXXyYsLAyAMmXKMHXqVNzc3KhduzbdunVj2bJlBZr0Llu2jK1bt7Jnzx4iIyMB+Pjjj6lXrx7r16+nWbNmJCYm8thjj1G7dm0AatT4+4/GiYmJ9OnThwYNGgBQtWrVq45BXEzuStukLXA2BXyCzYxGRETkb5q/Apq/Otv8VeURSgtPPxg4D8pEQ8o+mH0bZJy+8nkiIiIlVO3atbnhhhv48MMPAdi1axerVq1i+PDhAOTk5PDcc8/RoEEDypYti7+/P0uWLCExMTFf109ISCAyMtIx4QVo2bLlBcfNmzePVq1aER4ejr+/P08//XS+x/jnWI0aNXJMeAFatWqFzWZj+/btjn316tXDzc3N8XVERARHjhy5qrH+OWZkZKRjwgtQt25dgoODSUhIAGD06NGMGDGC2NhYXnrpJXbv3u049oEHHuD555+nVatWjB8/vkCNM8TFBFaActXBboN9a8yORkRExOVo/lq65q9aaVua+JeHwZ/BBzfB4XhYOAwGzDFW4oqIiBQWD19jxYBZY1+F4cOHc//99/P2228zY8YMqlWrRrt27QB49dVXefPNN5k8eTINGjTAz8+Phx56iMzMzEILNy4ujkGDBjFhwgQ6depEUFAQc+fO5fXXXy+0Mf4p99GuXBaLBZvNViRjgdE5+Pbbb+fbb7/l+++/Z/z48cydO5fevXszYsQIOnXqxLfffssPP/zApEmTeP3117n//vuLLB5xAlFt4Pgu2LsKanc1OxoRERGD5q/5pvlr8c1ftdK2tClXzVhx6+4DO3+Ab0cbtVtEREQKi8ViPOFhxisf9cD+qV+/flitVj799FM+/vhj/vOf/zjqg/3yyy/07NmTwYMH06hRI6pWrcqOHTvyfe06deqwf/9+Dh8+7Nj366+/5jlmzZo1VKlShbFjx9K0aVNq1KjBvn378hzj6elJTk7OFcf67bffSE9Pd+z75ZdfsFqt1KpVK98xX43c+9u/f79j3x9//EFKSgp169Z17KtZsyYPP/wwP/zwA7feeiszZsxwfBYZGcn//d//8fnnn/PII48wffr0IolVnIijGdkqc+MQERH5J81fAc1fcznL/FVJ29IosplR49ZihU0fwcrXzI5IRETEFP7+/vTv358xY8Zw+PBhhg4d6visRo0aLF26lDVr1pCQkMDdd9+dp7PslcTGxlKzZk3uvPNOfvvtN1atWsXYsWPzHFOjRg0SExOZO3cuu3fv5q233uKLL77Ic0xUVBR79uwhPj6eY8eOkZGRccFYgwYNwtvbmzvvvJNt27axfPly7r//fu644w5HPbCCysnJIT4+Ps8rISGB2NhYGjRowKBBg9i0aRPr1q1jyJAhtGvXjqZNm3L27FlGjRrFihUr2LdvH7/88gvr16+nTp06ADz00EMsWbKEPXv2sGnTJpYvX+74rCR4++23iYqKwtvbmxYtWrBu3bp8nTd37lwsFgu9evXKs99utzNu3DgiIiLw8fEhNjaWnTt3FkHkRSzqfNI2eSucOWFuLCIiIi5I89crKynzVyVtS6va3aDLK8b28udh82xz4xERETHJ8OHDOXnyJJ06dcpTv+vpp5/muuuuo1OnTrRv357w8PALEmmXY7Va+eKLLzh79izNmzdnxIgRvPDCC3mO6dGjBw8//DCjRo0iJiaGNWvW8Mwzz+Q5pk+fPnTu3JkOHTpQvnx55syZc8FYvr6+LFmyhBMnTtCsWTP69u1Lx44dmTp16tV9My7i9OnTNG7cOM+re/fuWCwWvvrqK8qUKUPbtm2JjY2latWqzJs3DwA3NzeOHz/OkCFDqFmzJv369aNLly5MmDABMCbT9913H3Xq1KFz587UrFmTd95555rjdQbz5s1j9OjRjB8/nk2bNtGoUSM6dep0xfpre/fu5dFHH6VNmzYXfPbKK6/w1ltvMW3aNNauXYufnx+dOnXi3LlzRXUbRcM/FMobjT3Yq9W2IiIiBaH56+WVlPmrxW7Xs/EFkZaWRlBQEKmpqQQGBpodTsEtHQ+/TAarO9w+H6p3NDsiERFxIefOnWPPnj1ER0fj7e1tdjhSQl3u58wZ52QtWrSgWbNmjl86bDYbkZGR3H///Tz55JMXPScnJ4e2bdvyn//8h1WrVpGSksKXX34JGKtsK1SowCOPPOLoCJ2amkpYWBgzZ85kwIAB+YrLab5X3z4K66dDs5HQTU98iYhI8dMcVopaYcxftdK2tOs4HhrcBrZsmD8EDqtzs4iIiEhBZWZmsnHjRmJjYx37rFYrsbGxxMXFXfK8iRMnEhoa6uj+/E979uwhKSkpzzWDgoJo0aLFZa+ZkZFBWlpanpdTiG5rvGulrYiIiMglKWlb2lmt0PNto75Y5mmYfRukJJodlYiIiIhLOnbsGDk5ORfUYgsLCyMpKemi56xevZoPPvjgko0scs+7mmsCTJo0iaCgIMcrMjLyam6l6ES1Bixw9E84ffmSESIiIiKllZK2Au5e0P8TCK0Lp5Pgk75w9qTZUYmIiIiUeKdOneKOO+5g+vTphISEFOq1x4wZQ2pqquP1z07JpvItC2H1jW2tthURERG5KHezAxAn4RMMgxbA+zfBse0wdxAM/hw8VNtFREREJL9CQkJwc3O7oFNzcnIy4eHhFxy/e/du9u7dS/fu3R37bDYbAO7u7mzfvt1xXnJyMhEREXmuGRMTc8lYvLy88PLyupbbKTrRbSB5K+xZBfX7mB2NiIiIiNPRSlv5W1AlGLwQvAJh3y/w5f/B+V8aREREROTKPD09adKkCcuWLXPss9lsLFu2jJYtW15wfO3atdm6dSvx8fGOV48ePejQoQPx8fFERkYSHR1NeHh4nmumpaWxdu3ai17TJUS1Md73rDQ3DhEREREnpZW2kldYPaNUwid94PcvjETuzc+bHZWIiDg5m/7IJ0XI1X6+Ro8ezZ133knTpk1p3rw5kydPJj09nWHDhgEwZMgQKlasyKRJk/D29qZ+/fp5zg8ODgbIs/+hhx7i+eefp0aNGkRHR/PMM89QoUIFevXqVVy3Vbiq3AAWK5zYDWmHILCC2RGJiEgpZLfbzQ5BSqjCmL8qaSsXqtoOer0Dn4+ENVMgsBJc/39mRyUiIk7I09MTq9XKoUOHKF++PJ6enlgsFrPDkhLCbreTmZnJ0aNHsVqteHp6mh1SvvTv35+jR48ybtw4kpKSiImJYfHixY5GYomJiVitV/fA2+OPP056ejp33XUXKSkptG7dmsWLF+Pt7aKlrHyCIaIRHNpslEho1N/siEREpBTx8PDAYrFw9OhRypcvr/mrFJrCnL9a7PqzQoGkpaURFBREamoqgYGBZodTNFa9AcsmABbo9zHU7WF2RCIi4oQyMzM5fPgwZ86cMTsUKaF8fX2JiIi46KS3VMzJConTfa9+eAbWvAWNB0PPt82ORkRESpnTp09z4MABrbaVIlEY81ettJVLa/0wpB6ADR8Yq279Q6Hy9WZHJSIiTsbT05PKlSuTnZ1NTk6O2eFICePm5oa7u7tWwJRE0W2NpO2eVWZHIiIipZC/vz81atQgKyvL7FCkhCms+auStnJpFgt0fRVOHYbt38GcATB8KYTUMDsyERFxMhaLBQ8PDzw8PMwORURcReXrweIGKfvg5D4oU8XsiEREpJRxc3PDzc3N7DBELurqimlJ6WN1gz4fQMWmcPYkfHIrnEo2OyoRERERcXVeAVDxOmN7r1bbioiIiPyTkrZyZZ6+cPs8KFsVUhLh036QcdrsqERERETE1UW1Md5VIkFEREQkDyVtJX/8QmDQQvAtB4fjYcFQyMk2OyoRERERcWXRbY33vatAjWBEREREHJS0lfwrVw1unw/uPrBrKXz7sCbXIiIiIlJwkS3A6gFpB+HEX2ZHIyIiIuI0lLSVq1OpKfT9ECxW2PQxrHzV7IhERERExFV5+kKlZsa26tqKiIiIOChpK1evdlfo+pqxvfwF2Dzb3HhERERExHVFq66tiIiIyL8paSsF02w4tB5tbH/9AOz60dx4RERERMQ1OZqRrVTpLREREZHzlLSVgus4Dhr2B1s2zL8TDv9mdkQiIiIi4moqNQM3L0g/Asd2mB2NiIiIiFNQ0lYKzmKBHlMhuh1knobZt0FKotlRiYiIiIgr8fCGyi2M7T0rzY1FRERExEkoaSvXxt0T+s+C0HpwOhk+6QtnTpgdlYiIiIi4kqi2xruakYmIiIgAStpKYfAOgkELILAiHNsOcwdB1jmzoxIRERERV5HbjGzvarDZzI1FRERExAkoaSuFI6iikbj1CoTENfDF3Zpwi4iIiEj+VLgOPHzhzHE4mmB2NCIiIiKmU9JWCk9YPej/CVg94I8vYekzZkckIiIiIq7A3RMqX29sq66tiIiIiPlJ27fffpuoqCi8vb1p0aIF69aty9d5c+fOxWKx0KtXL8e+rKwsnnjiCRo0aICfnx8VKlRgyJAhHDp0KM+5UVFRWCyWPK+XXnqpMG+r9KraDnq9a2zHTYW4d8yNR0RERERcQ9T5Egl7VNdWRERExNSk7bx58xg9ejTjx49n06ZNNGrUiE6dOnHkyJHLnrd3714effRR2rRpk2f/mTNn2LRpE8888wybNm3i888/Z/v27fTo0eOCa0ycOJHDhw87Xvfff3+h3lup1vA2iH3W2F7yFPzxlanhiIiIiIgLiG5nvO9bDbYcc2MRERERMZmpSds33niDkSNHMmzYMOrWrcu0adPw9fXlww8/vOQ5OTk5DBo0iAkTJlC1atU8nwUFBbF06VL69etHrVq1uP7665k6dSobN24kMTExz7EBAQGEh4c7Xn5+fkVyj6VWq4eg2QjADp+NhMRfzY5IRERERJxZRCPwDIBzqZC01exoRERERExlWtI2MzOTjRs3Ehsb+3cwViuxsbHExcVd8ryJEycSGhrK8OHD8zVOamoqFouF4ODgPPtfeuklypUrR+PGjXn11VfJzs4u0H3IJVgs0OUVqNUNcjJgzgA4usPsqERERETEWbm5Q5UbjO29KpEgIiIipZtpSdtjx46Rk5NDWFhYnv1hYWEkJSVd9JzVq1fzwQcfMH369HyNce7cOZ544gkGDhxIYGCgY/8DDzzA3LlzWb58OXfffTcvvvgijz/++GWvlZGRQVpaWp6XXIHVDfq8DxWbwtmTMLsPnEo2OyoRERERcVbRuXVt1YxMRERESjd3swPIr1OnTnHHHXcwffp0QkJCrnh8VlYW/fr1w2638+677+b5bPTo0Y7thg0b4unpyd13382kSZPw8vK66PUmTZrEhAkTru0mSiNPX7h9HnxwE5z4Cz69DYZ+B17+ZkcmIiIiIs4mtxnZvjjIyTZW34qIiIiUQqattA0JCcHNzY3k5LwrL5OTkwkPD7/g+N27d7N37166d++Ou7s77u7ufPzxxyxatAh3d3d2797tODY3Ybtv3z6WLl2aZ5XtxbRo0YLs7Gz27t17yWPGjBlDamqq47V///6ru+HSzC8EBn8GviFw+DdYcCfkZJkdlYiIiIg4m/AG4B0MmafgcLzZ0YiIiIiYxrSkraenJ02aNGHZsmWOfTabjWXLltGyZcsLjq9duzZbt24lPj7e8erRowcdOnQgPj6eyMhI4O+E7c6dO/nxxx8pV67cFWOJj4/HarUSGhp6yWO8vLwIDAzM85KrULYq3D4f3H1g14/wzcNgt5sdlYiIiIg4E6sbRLU2tlUiQUREREoxU583Gj16NHfeeSdNmzalefPmTJ48mfT0dIYNGwbAkCFDqFixIpMmTcLb25v69evnOT+3uVju/qysLPr27cumTZv45ptvyMnJcdTHLVu2LJ6ensTFxbF27Vo6dOhAQEAAcXFxPPzwwwwePJgyZcoU382XRpWawG0zYO7tsHkWBEVC+yfMjkpEREREnElUG/jzG6MZWZvRVz5eREREpAQyNWnbv39/jh49yrhx40hKSiImJobFixc7mpMlJiZiteZ/MfDBgwdZtGgRADExMXk+W758Oe3bt8fLy4u5c+fy7LPPkpGRQXR0NA8//HCeOrdShGp1gW6vGyttV7wIQRWh8WCzoxIRERERZ5HbjCzxV8jOBHdPc+MRERERMYHFbtcz6gWRlpZGUFAQqampKpVQEMsmwqrXweIGg+ZD9VizIxIREREXpDlZ/rnM98pmg9eqw5njMGwxVLmwdJqIiIiIq8rvnMy0mrZSyt34DDQcAPYcmH8nHIo3OyIRERERcQZW6991bfeuMjcWEREREZMoaSvmsFigxxSIbgeZp+HTfpB+3OyoRERERMQZRLc13tWMTEREREopJW3FPO6e0H8WlK0Gp5Nhx2KzIxIRERERZxB1Pmm7fx1knTM3FhERERETKGkr5vIOMpqTARz+zdxYRERERMQ5hNQA/zDIyYAD682ORkRERKTYKWkr5otoZLwfjjc1DBERERFxEhYLRLUxtlXXVkREREohJW3FfLlJ26StYMsxNxYRERERcQ7R55O2qmsrIiIipZCStmK+ctXBww+yzsDxXWZHIyIiIiLOIHel7YENkHnG3FhEREREipmStmI+qxuENzC2D8WbGoqIiIiIOImyVSGwEtiyYP+vZkcjIiIiUqyUtBXnUCHGeFczMhEREREBo66to0SC6tqKiIhI6aKkrTgHNSMTERERkX9TMzIREREppZS0FecQEWO8H94CNpupoYiIiIiIk8hdaXtwE2ScMjcWERERkWKkpK04h5Ca4O4Nmafg5B6zoxERERERZxBcGYKrgD0H9sWZHY2IiIhIsVHSVpyDmzuE1Te2D202NxYRERERcR65q233rjQ3DhEREZFipKStOA81IxMRERGRf4tuZ7yrGZmIiIiUIkraivNQMzIRERER+bfcZmRJW+BsiqmhiIiIiBQXJW3FeTiakf0GdrupoYiIiIiIkwiMgHLVwW6DfWvMjkZERESkWChpK86jfG1w84RzqZCyz+xoRERERMRZ5K623asSCSIiIlI6KGkrzsPdE0LrGtuH4k0NRUREREScSG4zsj1qRiYiIiKlg5K24lzUjExERERE/i13pW3yNkg/bm4sIiIiIsVASVtxLmpGJiIiIiL/5h8K5esY2/tWmxuLiIiISDFQ0laci5qRiYiIiMjFOEokqK6tiIiIlHxK2opzCa0LVnc4cxxSD5gdjYiIiIg4CzUjExERkVJESVtxLh7efz/6prq2IiIiIpIrqjVggaN/wukjZkcjIiIiUqSUtBXnUyG3rq2StiIiIiJynm9ZCKtvbO9ZaW4sIiIiIkVMSVtxPo66tvFmRiEiIiIiziZaJRJERESkdFDSVpzPP5uRiYiIiIjkim5rvKsZmYiIiJRwStqK8wmrBxYrnE6GtMNmRyMiIiIizqLKDcY88cRuSDtkdjQiIiIiRUZJW3E+nr4QUsvY1mpbEREREcnlHQQR5/sfaLWtiIiIlGBK2opzqhBjvCtpKyIiIiL/FJVb11bNyERERKTkUtJWnFPuCgo1IxMRERGRf3LUtVXSVkREREouJW3FOakZmYiIiIhcTOXrweIGKYlwcp/Z0YiIiIgUCSVtxTmF1wcskHYQTh81OxoRERERcRZeAVCxibG9V3VtRUREpGRS0lack1cAlKtubGu1rYiIiIj8U/T5urZqRiYiIiIllJK24rwczcjizYxCRERERJyNoxnZKrDbzY1FREREpAgoaSvOS83IRERERORiIluA1cMopXXiL7OjERERESl0StqK81IzMhERERG5GE9fqNTM2N6z0txYRERERIqAkrbivCIaGu8piXDmhLmxiIiIiIhzif5HiQQRERGREkZJW3Fe3kFQtqqxrdW2IiIiIvJP0W2N9z2qaysiIiIlj+lJ27fffpuoqCi8vb1p0aIF69aty9d5c+fOxWKx0KtXrzz77XY748aNIyIiAh8fH2JjY9m5c2eeY06cOMGgQYMIDAwkODiY4cOHc/r06cK6JSlMjrq2StqKiIiIyD9Uagbu3pB+BI7tMDsaERERkUJlatJ23rx5jB49mvHjx7Np0yYaNWpEp06dOHLkyGXP27t3L48++iht2rS54LNXXnmFt956i2nTprF27Vr8/Pzo1KkT586dcxwzaNAgfv/9d5YuXco333zDypUrueuuuwr9/qQQqBmZiIiIiFyMuxdENje2VddWREREShhTk7ZvvPEGI0eOZNiwYdStW5dp06bh6+vLhx9+eMlzcnJyGDRoEBMmTKBq1ap5PrPb7UyePJmnn36anj170rBhQz7++GMOHTrEl19+CUBCQgKLFy/m/fffp0WLFrRu3ZopU6Ywd+5cDh06VJS3KwWhZmQiIiIicilR50skqK6tiIiIlDCmJW0zMzPZuHEjsbGxfwdjtRIbG0tcXNwlz5s4cSKhoaEMHz78gs/27NlDUlJSnmsGBQXRokULxzXj4uIIDg6madOmjmNiY2OxWq2sXbu2MG5NClPuStsTf8G5VHNjERERERHnktuMbM8qsNnMjUVERESkEJmWtD127Bg5OTmEhYXl2R8WFkZSUtJFz1m9ejUffPAB06dPv+jnuedd7ppJSUmEhobm+dzd3Z2yZcteclyAjIwM0tLS8rykGPiWheDKxvbhLebGIiIiIpJPV9O34fPPP6dp06YEBwfj5+dHTEwMs2bNynPM0KFDsVgseV6dO3cu6ttwfhWuAw8/OHsCjvxhdjQiIiIihcb0RmT5derUKe644w6mT59OSEhIsY8/adIkgoKCHK/IyMhij6HUUl1bERERcSFX27ehbNmyjB07lri4OLZs2cKwYcMYNmwYS5YsyXNc586dOXz4sOM1Z86c4rgd5+buCZWvN7ZVIkFERERKENOStiEhIbi5uZGcnJxnf3JyMuHh4Rccv3v3bvbu3Uv37t1xd3fH3d2djz/+mEWLFuHu7s7u3bsd513umuHh4RdMmLOzszlx4sRFx801ZswYUlNTHa/9+/cX6L6lABxJW9W1FREREed3tX0b2rdvT+/evalTpw7VqlXjwQcfpGHDhqxevTrPcV5eXoSHhzteZcqUKY7bcX7/LJEgIiIiUkKYlrT19PSkSZMmLFu2zLHPZrOxbNkyWrZsecHxtWvXZuvWrcTHxztePXr0oEOHDsTHxxMZGUl0dDTh4eF5rpmWlsbatWsd12zZsiUpKSls3LjRccxPP/2EzWajRYsWl4zXy8uLwMDAPC8pJhGNjXclbUVERMTJFbRvQy673c6yZcvYvn07bdu2zfPZihUrCA0NpVatWtxzzz0cP3680ON3SbnNyPatBluOubGIiIiIFBJ3MwcfPXo0d955J02bNqV58+ZMnjyZ9PR0hg0bBsCQIUOoWLEikyZNwtvbm/r16+c5Pzg4GCDP/oceeojnn3+eGjVqEB0dzTPPPEOFChXo1asXAHXq1KFz586MHDmSadOmkZWVxahRoxgwYAAVKlQolvuWq5S70vbYTsg4BV4B5sYjIiIicgmX69vw559/XvK81NRUKlasSEZGBm5ubrzzzjvcdNNNjs87d+7MrbfeSnR0NLt37+app56iS5cuxMXF4ebmdtFrZmRkkJGR4fi6xPZkiGgEngFG09qkrVAhxuyIRERERK6ZqUnb/v37c/ToUcaNG0dSUhIxMTEsXrzYMclNTEzEar26xcCPP/446enp3HXXXaSkpNC6dWsWL16Mt7e345jZs2czatQoOnbsiNVqpU+fPrz11luFem9SiPzLQ2BFSDsISdugyoUrsUVERERcWUBAAPHx8Zw+fZply5YxevRoqlatSvv27QEYMGCA49gGDRrQsGFDqlWrxooVK+jYseNFrzlp0iQmTJhQHOGby80dqtwAO5fAnpVK2oqIiEiJYLHb7Xazg3BFaWlpBAUFkZqaqlIJxWHOQNj+HXR+Ca6/x+xoRERExEk425wsMzMTX19fFi5c6HjSC+DOO+8kJSWFr776Kl/XGTFiBPv377+gGdk/lS9fnueff5677777op9fbKVtZGSk03yvCtWaqfDDWKhxMwxaYHY0IiIiIpeU3/mraTVtRa6KmpGJiIiIC7javg2XYrPZ8iRc/+3AgQMcP36ciIiISx5Tqnoy5DYj2xcHOdnmxiIiIiJSCEwtjyCSbxExxruStiIiIuLkrqZvAxhlDJo2bUq1atXIyMjgu+++Y9asWbz77rsAnD59mgkTJtCnTx/Cw8PZvXs3jz/+ONWrV6dTp06m3adTCWsA3sFwLgUOx0OlpiYHJCIiInJtlLQV15C70vbon5B5Bjx9zY1HRERE5BKutm9Deno69957LwcOHMDHx4fatWvzySef0L9/fwDc3NzYsmULH330ESkpKVSoUIGbb76Z5557Di8vL1Pu0elYrRDVGv78xqhrq6StiIiIuDjVtC0gZ6ufViq8VhNOJ8PwHyGymdnRiIiIiBPQnCz/Svz36tdpsPgJqHYj3PGF2dGIiIiIXJRq2krJ46hrG29qGCIiIiLihHLr2ib+CtmZ5sYiIiIico2UtBXXoaStiIiIiFxK+TrgGwJZZ+DgRrOjEREREbkmStqK61AzMhERERG5lNy6tgB7V5kbi4iIiMg1UtJWXEfuStsjCZB1ztxYRERERMT55JZI2LPS3DhERERErpGStuI6giqBbzmwZcORP8yORkREREScTVRb433/Ov2RX0RERFyakrbiOiwW1bUVERERkUsLqQH+YZCTAQfWmx2NiIiISIEpaSuuRXVtRURERJze/hNneOn7P/lg9Z7iHdhigSiVSBARERHXp6StuJbclbaH4k0NQ0REREQubVPiSab9vJv3V/1Fdo6teAePPl8iQc3IRERExIUpaSuuxdGM7A/IzjQ3FhERERG5qE71winj68Hh1HP8vONo8Q6e24zswAbIPFO8Y4uIiIgUEiVtxbWUiQLvIMjJhKN/mh2NiIiIiFyEt4cbfa6rBMCcdYnFO3iZaAisBLYs2P9r8Y4tIiIiUkiUtBXXomZkIiIiIi5hQPPKAPz05xEOp54tvoEtlr9X2+5RiQQRERFxTUraiutRMzIRERERp1c91J/m0WWx2WH++gPFO3huMzLVtRUREREXpaStuB41IxMRERFxCQObRwIwf8N+cmz24hs4d6XtwU2Qcar4xhUREREpJErauhC7vRgnus4sd6Vt8jbIyTY1FBERERG5tC71Iwjy8eBgyllW7izGhmTBlY1eCPYc2BdXfOOKiIiIFBIlbV3AV/EH6T5lNYu3JZkdinMoWxU8AyD7HBzbYXY0IiIiInIJ3h5u3HpdRQDmrC3mhmSOEgkri3dcERERkUKgpK0LSDh8iq0HU5m3Yb/ZoTgHqxUiGhrbakYmIiIi4tQGnm9ItuzPIySnnSu+gaPbGu9qRiYiIiIuSElbF9C/mVELbOWOo8XbedeZqRmZiIiIiEuoGRZA0yplyLHZWVCcixByV9ombYGzKcU3roiIiEghUNLWBUSH+Dk67y7cUMydd52VmpGJiIiIuIzc1bZz1+/HVlwNyQIjoFx1sNtg35riGVNERESkkChp6yL6NzVW287bUIwTXWeWm7RN2gq2HHNjEREREZHL6togggBvdw6cPMvqXceKb+Dc1bZ7VNdWREREXIuSti6ia4MIAryMiW7cX8fNDsd8ITXAwxey0uH4brOjEREREZHL8PF049bG5xuSrSvGhmS5dW33qq6tiIiIuBYlbV2Ej6cbPWIqADBvvRqSYXWD8AbGtpqRiYiIiDi9gS2MEglL/0jmyKliakiWu9I2eRuka+GDiIiIuA4lbV1IbkOyxb8nkXIm0+RonICakYmIiIi4jNrhgTSuHEy2zc7CjcXUp8G/PJSvY2zvW108Y4qIiIgUAiVtXUiDikHUDg8gM9vGl5sPmh2O+dSMTERERMSlOBqSrSvGPg3RuXVtVSJBREREXIeSti7EYrEwoFluQ7ID2O2lvCGZoxnZFrDZzI1FRERERK7oloZGn4bEE2eKr09DbokE1bUVERERF6KkrYvp1bginu5WEg6nse1gmtnhmKt8bXD3how0OLnH7GhERERE5Ap8Pd3pdb4h2afF1ZAsqjVggaN/wqnk4hlTRERE5Bopaetign096VQvHIC564ux864zcnOHsHrGtpqRiYiIiLiEAc2NJ8d++D2JY6czin5A37IQXt/Y1mpbERERcRFK2rqg/k2Nie6i+EOczcwxORqTqRmZiIiIiEupVyGIRpWCyMqx81lxNSSLamu8K2krIiIiLkJJWxd0Q7VyVCrjw6mMbL7fdtjscMylZmQiIiIiLie3IdmcdYnF06dBzchERETExShp64KsVgv9zq+2nbd+v8nRmKxCjPF++Dco7Y3ZRERERFxE90YV8PN0Y+/xYmpIVuUGsFjhxG5IO1T044mIiIhcIyVtXVTfJpWwWGDtnhPsOZZudjjmKV8HrB5wLgVS9pkdjYiIiIjkg5+XOz3PNySbu64YFiF4B/39hJZW24qIiIgLUNLWRVUI9qFtjfIAzN9QilfbuntCWF1jW3VtRURERFzG7edLJCzelsSJ9MyiHzAqt0TCyqIfS0REROQaKWnrwgY0M0okLNx4gOwcm8nRmEjNyERERERcTv2KQdSvGEhmjo3PNxVDQ7Lodsb7XiVtRURExPkpaevCOtYJo5yfJ0dPZbB8+1GzwzGPmpGJiIiIuKTchmSfFkdDssrXg9UdUhLhpMpqiYiIiHNT0taFebpb6X2+FlipbkimZmQiIiIiLqlHowr4errx19F01u05UbSDeflDheuM7b2qaysiIiLOzfSk7dtvv01UVBTe3t60aNGCdevWXfLYzz//nKZNmxIcHIyfnx8xMTHMmjUrzzEWi+Wir1dffdVxTFRU1AWfv/TSS0V2j0Wp//kSCcu3H+FI2jmTozFJaD2wuMGZY5B20OxoRERERCSfArw96NGoAgBz1iUW/YDRuXVtlbQVERER52Zq0nbevHmMHj2a8ePHs2nTJho1akSnTp04cuTIRY8vW7YsY8eOJS4uji1btjBs2DCGDRvGkiVLHMccPnw4z+vDDz/EYrHQp0+fPNeaOHFinuPuv//+Ir3XolIjLIDrKgeTY7Pz2aZSmrD08IbQOsa26tqKiIiIuJTcEgnfbUsi5UwRNyTLbUa2d5We0BIRERGnZmrS9o033mDkyJEMGzaMunXrMm3aNHx9ffnwww8venz79u3p3bs3derUoVq1ajz44IM0bNiQ1atXO44JDw/P8/rqq6/o0KEDVatWzXOtgICAPMf5+fkV6b0WpdzVtvM37C/6WmDOSs3IRERERFxSw0pB1I0IJDPbxudFvQghsgVYPYyns078VbRjiYiIiFwD05K2mZmZbNy4kdjY2L+DsVqJjY0lLi7uiufb7XaWLVvG9u3badu27UWPSU5O5ttvv2X48OEXfPbSSy9Rrlw5GjduzKuvvkp2dvZlx8vIyCAtLS3Py1l0a2jUAttzrBhqgTkrNSMTERERcUkWi4WBzY1FCHOKuiGZpy9ENje296wsunFERERErpFpSdtjx46Rk5NDWFhYnv1hYWEkJSVd8rzU1FT8/f3x9PSkW7duTJkyhZtuuumix3700UcEBARw66235tn/wAMPMHfuXJYvX87dd9/Niy++yOOPP37ZeCdNmkRQUJDjFRkZmc87LXr+Xu50b2jUApu3oZQ2JPtnMzIRERERcSk9G1fE28PKziOn2bjvZNEO9s8SCSIiIiJOyvRGZFcrICCA+Ph41q9fzwsvvMDo0aNZsWLFRY/98MMPGTRoEN7e3nn2jx49mvbt29OwYUP+7//+j9dff50pU6aQkZFxyXHHjBlDamqq47V/v3MlR/udL5Hw3dbDpJ3LMjkaE4TVA4sVTifBqUsn/UVERETE+QR6ezgWIXxa1A3J/tmMrLSWFhMREZG/fXUffHkvHNtldiR5mJa0DQkJwc3NjeTk5Dz7k5OTCQ8Pv+R5VquV6tWrExMTwyOPPELfvn2ZNGnSBcetWrWK7du3M2LEiCvG0qJFC7Kzs9m7d+8lj/Hy8iIwMDDPy5lcVzmY6qH+nMuysSj+kNnhFD9PPwipaWxrta2IiIiIyxnYwmhI9u2Ww6SeKcJFCJWagbs3pB+BYzuKbhwRERFxfmdOwJb5ED8bMk+bHU0epiVtPT09adKkCcuWLXPss9lsLFu2jJYtW+b7Ojab7aIrZD/44AOaNGlCo0aNrniN+Ph4rFYroaGh+R7X2VgsFgb8oyFZqaRmZCIiIiIuq3FkMLXDA8jItvFlfBE2JHP3Ul1bERERMWyZBzmZEN7g79KbTsLU8gijR49m+vTpfPTRRyQkJHDPPfeQnp7OsGHDABgyZAhjxoxxHD9p0iSWLl3KX3/9RUJCAq+//jqzZs1i8ODBea6blpbGggULLrrKNi4ujsmTJ/Pbb7/x119/MXv2bB5++GEGDx5MmTJlivaGi1jvxhXxcLOw5UAqfxxynkZpxUbNyERERERcltGQzFhtW+QNyaLPNzJW0lZERKT0stth0yxju/EQc2O5CHczB+/fvz9Hjx5l3LhxJCUlERMTw+LFix3NyRITE7Fa/84rp6enc++993LgwAF8fHyoXbs2n3zyCf37989z3blz52K32xk4cOAFY3p5eTF37lyeffZZMjIyiI6O5uGHH2b06NFFe7PFoJy/F7F1wvh+WxLzN+zn2R71zA6peKkZmYiIiIhL6xVTkRe/S+DPpFNs3p/CdZWLaFFF1Pmk7d7VYLOB1eVafYiIiMi1OrQJjvwObl7Q8Dazo7mAxV6kf8IuudLS0ggKCiI1NdWp6tsu336EYTPWE+TjwdqnOuLt4WZ2SMUn4xRMqmRsP7Yb/ELMjUdERESKnLPOyZyRq3yvRs+P5/NNB7mtSSVeve3Kpc4KJCcLXqoCWenwf79AeP2iGUdERESc19cPwcYZUL8v9P2g2IbN75xMf1IuYdrWKE9EkDepZ7P44Y/kK59QkngFQLnqxvbheFNDEREREZGCuf18iYSvtxwi7VwRNSRz84DK1xvbe1cVzRgiIiLivDLPwLbPjO3r7jA3lktQ0raEcbNauK2Jsdp0/vpS2JAstxmZ6tqKiIiIuKQmVcpQI9Sfc1k2vtpchA3JotsY73uUtBURESl1EhZBRhoEV/m7bJKTUdK2BLqtaSQAq3cdY/+JMyZHU8xym5Gprq2IiIiIS/pnQ7JP1+0vuoZkeera5hTNGCIiIuKcHA3IBjttbXvnjEquSWRZX1pVLwfAgg2lbLWtmpGJiIiIuLxbr6uIp7uVhMNpbDmQWjSDRDQCr0DISIWkLUUzhoiIiDif47th32rAAjG3mx3NJSlpW0L1b2asTliw8QA5tlLUay68ofGesg/OnDA3FhEREREpkGBfT7rWDwdgzrrEohnEzR2q3GBsq0SCiIhI6bH5E+O9ekcIqmRuLJehpG0JdXPdMIJ8PDiceo5VO4+aHU7x8QmGMtHGtlZMiIiIiLis3BIJi347xKmiakgWdb6urZqRiYiIlA452RD/qbHd2DkbkOVS0raE8vZwo3fjigDMK20NyXLr2qoZmYiIiIjLah5dlqrl/TiTmcOi3w4VzSC5zcj2xRm/xImIiEjJtutHOJ0EvuWgVlezo7ksJW1LsP7NjIZkPyYkc/x0hsnRFCM1IxMRERFxeRaLhdvPr7YtshIJYQ3AOxgyT8Hh+KIZQ0RERJzH5vMNyBoOAHdPc2O5AiVtS7A6EYE0rBREVo6dLzYfNDuc4qNmZCIiIiIlwq3XVcLTzcq2g2lsLYqGZFYrRLU2tnf/VPjXFxEREedx+gjsWGxsX+fcpRFASdsSr19TY7Xt3PX7sdtLSUOyiBjj/cRuOFdE3YZFREREpMiV9fOkc25DsvVFtNq2anvjfcUk+Ol5yM4smnFERETEXL/NAVs2VGwKoXXMjuaKlLQt4XrEVMDbw8quI6fZlJhidjjFw7csBBmP0pG01dxYREREROSaDGhuLEL4avNB0jOKoO7sdUOgwW1gt8HKV+H9jnAkofDHEREREfPY7bDpfGkEF1hlC0ralniB3h50bRABwPzS1JAsoqHxrmZkIiIiIi6tZdVyRJXzJT0zh6+LoiGZuxf0eR9umwk+ZSBpC/yvHayZAracwh9PREREit/+tXB8J3j4Qr1bzY4mX5S0LQX6ny+R8PWWQ5wuitUJzii3RILq2oqIiIi4NIvFwsCibkgGUK833Psr1LgZcjLgh6dh5i1wcm/RjSkiIiLFI3eVbb3e4B1obiz5pKRtKdA8uizRIX6cyczh2y1FsDrBGakZmYiIiEiJ0adJJTzcLPx2IJXfDxVhz4KAcLh9PnR/Ezz9IXENvNsKNn5kPFYpIiIirifjFPz+hbHd2DVKI4CStqWCxWJxNCSbV1pKJEQ0Mt6P7YCM0+bGIiIiIiLXJMTfi5vrGQ3J5q4r4vmsxQJNhsL/rYbKN0Dmafj6Afi0P5xKKtqxRUREpPBt+xyy0qFcDah8vdnR5JuStqVEnyYVcbNa2JSYwq4jp8wOp+j5h0JABcAOydvMjkZERERErtHt50skfLn5IGcyi6HkV9loGPoN3PQcuHnCziXwzvV/r9QRERER17D5fGmExoONP866iAIlbffv38+BAwccX69bt46HHnqI9957r9ACk8IVGuBNh1qhQClcbatmZCIiIpIPmuM6t5ZVy1G5rC+nMrL5Zsvh4hnU6gatHoC7fobwhnD2JCwYCp+NMLZFRETEuR35Ew6sB4sbNBpodjRXpUBJ29tvv53ly5cDkJSUxE033cS6desYO3YsEydOLNQApfD0b2aUSPhs00Eys20mR1MMcpO2qmsrIiIi+aA5rnOzWi0MaG7MZ4u0IdnFhNWFEcug7WPGL31bF8A7LWHXsuKNQ0RERK5O7irbmp0hIMzcWK5SgZK227Zto3nz5gDMnz+f+vXrs2bNGmbPns3MmTMLMz4pRB1qlSc0wIsT6ZksS0g2O5yi52hGFm9mFCIiIuIiNMd1fn2bVMLdamFzYgoJh9OKd3B3T7jxaRj+A5StBqcOwye3wjejITO9eGMRERGRK8vOhN/mGNuNB5sbSwEUKGmblZWFl5cXAD/++CM9evQAoHbt2hw+XEyPKslVc3ez0qdJJQDmbSgFJRJyV9oe/RMyz5gbi4iIiDg9zXGdX2iANzfVNVbJzC3u1ba5KjU1mpQ1v8v4esMHMK01JK41Jx4RERG5uB3fw5nj4B8GNW42O5qrVqCkbb169Zg2bRqrVq1i6dKldO7cGYBDhw5Rrly5Qg1QCle/psYjZT/vOMqhlLMmR1PEAiLALxTsNjjyh9nRiIiIiJPTHNc1DDzfkOyLzQc5m5ljThCevtD1VbjjSwisCCf+ghmd4ccJkJ1hTkwiIiKS16bzpREaDQQ3d3NjKYACJW1ffvll/ve//9G+fXsGDhxIo0bGisZFixY5HikT5xQd4kfz6LLY7bBw44Ern+DKLJZ/NCPbbG4sIiIi4vQ0x3UNrauHUKmMD2nnsvluq8kroKt1gHvWQMMBxkKB1W/A9BshaZu5cYmIiJR2qQdh9/na843vMDeWAipQ0rZ9+/YcO3aMY8eO8eGHHzr233XXXUybNq3QgpOiMeB8Q7L5G/Zjs9lNjqaIqRmZiIiI5JPmuK7BarU4VtsWe0Oyi/EJhlv/B/1mgW85SN4G77WH1f8Fm0krgUVEREq7+E+NP6hWvgFCqpsdTYEUKGl79uxZMjIyKFOmDAD79u1j8uTJbN++ndDQ0EINUApfl/oRBHi5c+DkWeL+Om52OEVLzchEREQknzTHdR23NamEm9XChn0n2ZF8yuxwDHV7wL2/Qq2uYMuCH5+FGV3g+G6zIxMRESldbDbYfL40wnWuucoWCpi07dmzJx9//DEAKSkptGjRgtdff51evXrx7rvvFmqAUvh8PN3oEVMBgLnrS3hDstyVtkcSVF9MRERELqsw57hvv/02UVFReHt706JFC9atW3fJYz///HOaNm1KcHAwfn5+xMTEMGvWrDzH2O12xo0bR0REBD4+PsTGxrJz586rv8kSIjTQm461jUS6U6y2zeUfCgM+hZ7vgGcA7F9rNClb/wHYS/gTbiIiIs5i7ypI2Wf8t7huT7OjKbACJW03bdpEmzZtAFi4cCFhYWHs27ePjz/+mLfeeqtQA5SiMaCZ8UjZkt+TSDmTaXI0RSgoEnzKgi1bzchERETksgprjjtv3jxGjx7N+PHj2bRpE40aNaJTp04cOXLkoseXLVuWsWPHEhcXx5YtWxg2bBjDhg1jyZIljmNeeeUV3nrrLaZNm8batWvx8/OjU6dOnDt37tpu2oUNbGHMZz/fdJBzWU5UhsBigcaD4N41ENUGss7At6Nhdl9IO2R2dCIiIiVf7irbBn3A08/cWK5BgZK2Z86cISAgAIAffviBW2+9FavVyvXXX8++ffsKNUApGvUrBlInIpDMbBtfbj5odjhFJ08zsnhTQxERERHnVlhz3DfeeIORI0cybNgw6taty7Rp0/D19c1TJ/ef2rdvT+/evalTpw7VqlXjwQcfpGHDhqxevRowVtlOnjyZp59+mp49e9KwYUM+/vhjDh06xJdffnnN9+2q2tYoT8VgH1LPZrF4W5LZ4VwouDIMWQSdJoGbF+z6Ed5pCVsXmh2ZiIhIyXX2JPyxyNhuPMTcWK5RgZK21atX58svv2T//v0sWbKEm2++GYAjR44QGBhYqAFK0bBYLPRvWgkwSiTYS/LjWmpGJiIiIvlQGHPczMxMNm7cSGxsrGOf1WolNjaWuLi4K55vt9tZtmwZ27dvp23btgDs2bOHpKSkPNcMCgqiRYsWl71mRkYGaWlpeV4liZvVQv/zDXY/daYSCf9ktULLe+HulRARA+dS4LPhsGAonDlhcnAiIiIl0NaFkJMBoXWh4nVmR3NNCpS0HTduHI8++ihRUVE0b96cli1bAsaKhMaNGxdqgFJ0ejWuiKe7lT+TTrH1YKrZ4RQdNSMTERGRfCiMOe6xY8fIyckhLCwsz/6wsDCSki69GjQ1NRV/f388PT3p1q0bU6ZM4aabbgJwnHe115w0aRJBQUGOV2RkZL7uwZX0axqJ1QLr9pxg15HTZodzaaG1YcSP0H4MWNzg9y/gnethxw9mRyYiIlKybDL6E9D4DuPpaxdWoKRt3759SUxMZMOGDXlqbXXs2JH//ve/hRacFK1gX0861wsHYF5JbkiWu9I2+XfIyTI3FhEREXFaZs5xAwICiI+PZ/369bzwwguMHj2aFStWXNM1x4wZQ2pqquO1f3/Jm++FB3lz4/mGZHOddbVtLjcPaP+kkbwNqQmnk+HT22DRA5BxyuzoREREXN/h3yBpC1g9oGF/s6O5ZgVK2gKEh4fTuHFjDh06xIEDBwBo3rw5tWvXLrTgpOjlPlK2KP4QZzOdqIFDYSoTDV5BkJMJR/80OxoRERFxYtc6xw0JCcHNzY3k5OQ8+5OTkwkPD7/keVarlerVqxMTE8MjjzxC3759mTRpkiOm3GtczTW9vLwIDAzM8yqJBjY3GpJ9tukAGdkuMJ+teJ1RLuH6+4yvN30E77aCfWvMjUtERMTVbTrfgKx2N/ArZ24shaBASVubzcbEiRMJCgqiSpUqVKlSheDgYJ577jlsNlthxyhFqGXVckSW9eFURjbfbT1sdjhFw2KBiIbGtpqRiYiIyCUUxhzX09OTJk2asGzZsjzXXbZsmaPcQn5jycjIACA6Oprw8PA810xLS2Pt2rVXdc2Sql3N8kQEeXPyTBZLfk++8gnOwMMHOr8Id34NQZGQsg9mdIUfnoasc2ZHJyIi4nqyzsLW+cb2dXeYG0shKVDSduzYsUydOpWXXnqJzZs3s3nzZl588UWmTJnCM888U9gxShGyWi30a2Kstp23oeQ9MufgqGurZmQiIiJycYU1xx09ejTTp0/no48+IiEhgXvuuYf09HSGDRsGwJAhQxgzZozj+EmTJrF06VL++usvEhISeP3115k1axaDBw8GjAayDz30EM8//zyLFi1i69atDBkyhAoVKtCrV69C/R64Inc3K/2aGvPZOWudvETCv0W3hXvWQMxgwA5rpsB77TVnFRERuVoJ38C5VAisBFU7mB1NoXAvyEkfffQR77//Pj169HDsa9iwIRUrVuTee+/lhRdeKLQApej1bVqJ//64g3V7TrDnWDrRIX5mh1T4ImKMdzUjExERkUsorDlu//79OXr0KOPGjSMpKYmYmBgWL17saCSWmJiI1fr32on09HTuvfdeDhw4gI+PD7Vr1+aTTz6hf/+/a7E9/vjjpKenc9ddd5GSkkLr1q1ZvHgx3t7ehXT3rq1fs0im/LSTuL+O89fR01Qt7292SPnnHQi93obaXeHrB+FoAky/0ah/2+phcCvQr2wiIiKly+bcBmSDwOpmbiyFxGK32+1Xe5K3tzdbtmyhZs2aefZv376dmJgYzp49W2gBOqu0tDSCgoJITU0tEfXBhs5Yx4rtR7mnfTWe6FwC6xIf2wlTm4K7D4w5oMmviIhICVGYc7KSPsctafPXf/vPzPX89OcR7m5blTFd65gdTsGkHzMSt39+Y3xdsSn0/h+EVDc3LhEREWd2ci+82QiwwIO/QZkqZkd0WfmdkxWoPEKjRo2YOnXqBfunTp1Kw4YNC3JJMVn/84+ULdx4gOycEliXuGw18PSH7LNwbIfZ0YiIiIgT0hzXtQ0432B3wUYXaUh2MX4h0P8TI1HrFQgHN8C01rD2PVDvEBERkYvbPNt4r9rO6RO2V6NAyw1feeUVunXrxo8//uhofhAXF8f+/fv57rvvCjVAKR4d64RRzs+To6cyWL79KDfVDTM7pMJltUJ4Q0hcY9QIC6trdkQiIiLiZDTHdW031g4lNMCLI6cyWPpHMrc0rGB2SAVjsUCjARDVGr68F/b8DN8/Btu/hZ5vQ1AlsyMUERFxHrYciD+ftG1cMhqQ5SrQStt27dqxY8cOevfuTUpKCikpKdx66638/vvvzJo1q7BjlGLg6W7l1usqAjBvfQltSKZmZCIiInIZmuO6Nnc3K/3Pr7adu64EzGeDKsEdX0KXV40SX3+tgHdugN/mwtVXuBMRESmZdi+HtIPgHQy1bzE7mkJVoKQtQIUKFXjhhRf47LPP+Oyzz3j++ec5efIkH3zwwVVd5+233yYqKgpvb29atGjBunXrLnns559/TtOmTQkODsbPz4+YmJgLJtBDhw7FYrHkeXXu3DnPMSdOnGDQoEEEBgYSHBzM8OHDOX369FXFXRLlTnKXbz/CkbRzJkdTBCIaGe9qRiYiIiKXUFhzXDFHv6aRWCywetcx9h1PNzuca2e1Qou74P9WG/VtM1Lhi7thzRSzIxMREXEOuQ3IGvYHj5LVoLXASdvCMG/ePEaPHs348ePZtGkTjRo1olOnThw5cuSix5ctW5axY8cSFxfHli1bGDZsGMOGDWPJkiV5juvcuTOHDx92vObMmZPn80GDBvH777+zdOlSvvnmG1auXMldd91VZPfpKqqHBnBd5WBybHYWbjpgdjiFz5G03aKaYCIiIiIlUGRZX9rWKA/A3JL09FhIdfjPEmjziPH1ipfgVJK5MYmIiJgt/Rj8eb6E1XUlqzQCmJy0feONNxg5ciTDhg2jbt26TJs2DV9fXz788MOLHt++fXt69+5NnTp1qFatGg8++CANGzZk9erVeY7z8vIiPDzc8SpTpozjs4SEBBYvXsz7779PixYtaN26NVOmTGHu3LkcOnSoSO/XFQxoVhmABRsOYC9pj12F1DQeLctKh+O7zI5GRERERIrAwOa589n9ZGaXoD/Uu7nDjc9ApWbGfHbZc2ZHJCIiYq4t88CWBRExEN7A7GgKnWlJ28zMTDZu3EhsbOzfwVitxMbGEhcXd8Xz7XY7y5YtY/v27bRt2zbPZytWrCA0NJRatWpxzz33cPz4ccdncXFxBAcH07RpU8e+2NhYrFYra9euveR4GRkZpKWl5XmVRN0aRuDn6caeY+ms23PC7HAKl9Xt7/8Tq66tiIiISInUsU4oIf5eHDudybKEZLPDKVwWC3SaZGzHz4ZDm82NR0RExCx2O2w6XzK1BK6yBXC/moNvvfXWy36ekpKS72sdO3aMnJwcwsLC8uwPCwvjzz//vOR5qampVKxYkYyMDNzc3HjnnXe46aabHJ937tyZW2+9lejoaHbv3s1TTz1Fly5diIuLw83NjaSkJEJDQ/Nc093dnbJly5KUdOlHjCZNmsSECRPyfX+uys/LnVsaVmDehv3MW7+fFlXLmR1S4aoQAwfWGXVtG95mdjQiIiLiBApzjivm83Cz0q9pJd5ZsZtP1yXSpUGE2SEVrshm0KAfbJ0Pi5+CYd8ZyVwREZHS5OBGOJoA7t5Qv6/Z0RSJq0raBgUFXfHzIUOGXFNAVxIQEEB8fDynT59m2bJljB49mqpVq9K+fXsABgwY4Di2QYMGNGzYkGrVqrFixQo6duxY4HHHjBnD6NGjHV+npaURGRlZ4Os5s/7NI5m3YT/fbTvMsz3rEejtYXZIhcdR11YrbUVERMTgDHNcKVwDmlXmnRW7Wb3rGPtPnCGyrK/ZIRWu2PGQ8DUkroE/voJ6vcyOSEREpHhtOt+ArG5P8Ak2NZSiclVJ2xkzZhTawCEhIbi5uZGcnPeRpeTkZMLDwy95ntVqpXr16gDExMSQkJDApEmTHEnbf6tatSohISHs2rWLjh07Eh4efkGjs+zsbE6cOHHZcb28vPDy8srn3bm2xpHB1Aj1Z+eR0yyKP8Tg66uYHVLh+WfS1mYzOvKKiIhIqVaYc1xxDpXL+dKmRgirdh5j3vr9PNqpltkhFa6gStDqAfj5ZVj6DNTsXOI6ZouIiFxSZjps+9zYblwySyOAiTVtPT09adKkCcuWLXPss9lsLFu2jJYtW+b7OjabjYyMjEt+fuDAAY4fP05EhPFYVMuWLUlJSWHjxo2OY3766SdsNhstWrQowJ2UPBaLhf7NjFXE8zeUoK67AOVrg5sXZKTByT1mRyMiIqVRdgacK5m18UWcSW5Dsvkb9pOVU4IakuVq9SAEREBKIvz6jtnRiIiIFJ/fv4TMU1AmGqJamx1NkTF1meHo0aOZPn06H330EQkJCdxzzz2kp6czbNgwAIYMGcKYMWMcx0+aNImlS5fy119/kZCQwOuvv86sWbMYPHgwAKdPn+axxx7j119/Ze/evSxbtoyePXtSvXp1OnXqBECdOnXo3LkzI0eOZN26dfzyyy+MGjWKAQMGUKFCheL/Jjip3o0r4uFmYcuBVP44VIJ+sXTzgLB6xrZKJIiISHE6cwKWT4LXa8Hk+nDiL7MjEinRYuuEEeLvyZFTGfz055Ern+BqPP0g9llje9UbcKqENV0TKU452caTmCLiGjafb0DWeHCJrutuatK2f//+vPbaa4wbN46YmBji4+NZvHixozlZYmIihw8fdhyfnp7OvffeS7169WjVqhWfffYZn3zyCSNGjADAzc2NLVu20KNHD2rWrMnw4cNp0qQJq1atylPaYPbs2dSuXZuOHTvStWtXWrduzXvvvVe8N+/kyvl7cVNd43+HErfatkKM8a6krYiIFIfUg7B4DPy3Hvz8Epw9CedSYeXrZkcmUqJ5ulvp06QSAHPWJZocTRFp0A8qXGesNlr+vNnRiLim1IPGf6NndlPiVsQVHNsJiXFgsULM7WZHU6QsdrvdbnYQrigtLY2goCBSU1MJDAw0O5wisWL7EYbOWE+Qjwdrn+qIt4eb2SEVjo0z4esHoWp7GPKV2dGIiEhJdXQH/PImbJkHtixjX3hDo1nCT8+BxQ3u3wBlq5obp4srDXOywlIav1d7jqXT4bUVWCyw6vEOVCpTwhqSASSuhQ9vBixw90qIaGh2RCKuZcFQ+P0LY7vPB9CgZHahFykxlo4z5tg1OsGg+WZHUyD5nZOpC5NcUpsa5akQ5E3q2Sx++KMEPW71z2Zk+puFiIgUtoMbYd5geLs5xH9iJGyj2sDgz4yESttHoXos2HNglVbbihSl6BA/bqhWDrsd5m84YHY4RaNyC6jfB7Abq/o1vxXJv79W/J2wBVgxySiVIOKsju2CLfONHgmlUU4WxM8xtq8ruQ3IcilpK5fkZrXQt6nRkGze+hL0SFloXbB6GI+nppSg+xIREfPY7bB7OXzUA6bfCAlfA3ao1Q2G/whDvzEStbk1t9o9abzHz4ETaowpUpQcDcnW7ye7JDYkA6O2rbs37FsNf35jdjQiriE7E757zNiOGQQ+ZeH4Ltgy19y4RC7mSAIsHA5vN4PPRxpPD5fGP9Lt/AHSj4BfeajZ2exoipyStnJZtzWphMUCv+w6zv4TZ8wOp3C4e0FoHWNbdW1FRORa2GzwxyKY3gFm9YI9PxtlDxoOgHt/hYGfQmSzC8+LbAbVbtRqW5FicHO9MMr6eZKUdo4V24+aHU7RCK4MN9xvbP/wdOldgSVyNX59B47tAN8Q6PQitH7Y2L/iZSOhK+IMkrbC/CHwTkvYthDsNsACv80xSj+WNpvONyBrNMBoNF/CKWkrlxVZ1pdW1UIAWFCSGpKpGZmIiFyL7EzY/IlRAmH+HXBos7HKrfld8GA83Pq/v/9AeCm5q21/mwMn9xZ1xCKllpe7G31LekMygFYPgX+48e/J2mlmRyPi3FIPws+vGNs3Pwc+wdBsBPiHQWoibP7Y1PBEOLQZ5twO01rDH18BdqjT3Si1FTveOOb7x43jSotTScZKW4DGQ8yNpZgoaStX1K+ZUSJhwcYD5NhKyPJ7R13beFPDEBERF5NxGuLegbdi4Kv74PhO8A6CNo/CQ9ug66vGirf8qNwCqnYAW7ZW24oUsf7n57PLtx/hUMpZk6MpIl7+0HGcsf3zq3D6iLnxiDizH8ZCVjpEXm88HQPg6Qttz5dLWPkaZJXQfyvEue1fD7Nvg/faw/ZvAYtRt/yeOOj/iZHLaPUQ1OoKOZnGKtwzJ0wOupjEf2o8pRbZAsrXNDuaYqGkrVzRzXXDCPb14HDqOVbuLCGPlEU0Nt4PxZfOOjAiInJ1zpyAFS/B5PqwZAykHTRWtN30nJGs7fgM+Je/+uu2z61t+ymc3Fe4MYuIQ7Xy/rSILovNDvNL0tNj/9ZooPELfeYpWP6C2dGIOKfdy43mYxYrdHsNrP9Ii1w3BIIi4dRhWP+BeTFK6bMvDj7uBR/EGqtJLVZo2B/uWwd9P4Swun8fa7FAr3ehTJTRp+eLu42SXSWZ3W485QbQuOQ3IMulpK1ckbeHG71iKgJGA4cSIayuUXPwzDFIO2R2NCIi4qxSD8Lip+C/9Y2O0mdPQtmq0P1NePA3aPUAeAcW/PqVr4eq7bXaVqQY3N7i74ZkJebpsX+zWqHzS8b2po8haZu58Yg4m382H2s2EsIb5P3c3QvaPWFsr34DMk4Vb3xSutjtsGclzLwFZnSGv5aD1R1iBsOoDXDre5deUeoTDP1mGeW5dv4Aq0v4PHLfGjixGzz9oV5vs6MpNkraSr7kPlL2Y0Iyx06XgMYGHj5Qvraxrbq2IiLyb8d2GuUP3mwEv75tPEIZ3sBY6TBqAzQZCh7ehTNWbm3b+NnGagkRKRKd6oUT7OvBodRzrNxRQp4eu5gqN0DdXkazmiVP6akykX/69R2jtJFfeejw1MWPaTTQ+APtmeOqDy1Fw26HXcvgw87wUXfYuwqsHsb88v6N0OttKFftyteJaAhdXzO2l78If60oyqjNtfl8A7J6vY1yQKWEkraSL3UiAmlYKYisHDtfbDpodjiFw9GMLN7MKERExJkc3ATz7oCpzYxHsGxZUKU1DPoM7l5l1BSzuhXumFVaQnQ7rbYVKWLeHm70uc5oSPZpSW5IBnDTBHDzgj0/w/bvzY5GxDmkHvi7+dhNE42Vihfj5g7tzyd0f5liPGUjUhjsdtixBN7vCJ/cCvt/Nf6tbjbSaGTb/U2j5MHVuO4OaDzY+EPdwuEl80nic6nw+5fG9nWlowFZLiVtJd9yV9vO27Afe0n4i72jGZlW2oqIlGp2u7Ey4eOeML0DJCwC7EaDh+FLYdi3UCPWqB9WVHJr227WaluRojSwuTGf/enPIySlnjM5miJUJgpa3mds/zDWeCRcpLRbcpHmY5dSvw+E1oWMVFgztXjik5LLZoOEb+C9dvBpPzi4Edx94Pp7jXJb3V6DoEoFv37X14wnws4cg/l3lrx/87d9BtlnIaQmVGpmdjTFSklbybfujSrg7WFl15HTbEpMMTucaxcRY7wraSsiUjrZbPDHIph+o5Gw/WuFUe+84QCjQ+/AORDZvHhiqXIDRLc1VvaueqN4xhQphaqHBtAsqgw5NjsLSnJDMoA2o8EvFE78BeveMzsaEXPtXg5/fHnx5mMXY7X+XT7h13ch/ViRhyglkM1mNL2b1hrmDTJyDx5+cMMD8NAW6DwJAiOufRwPH+j3MXgFwYF1sHTctV/TmWw6Xxqh8R1Fu4jCCSlpK/kW6O1B1wbGPyjz1peAVUDh9QGL0Rn0VLLZ0YiISHHJzjRKH7zdHObfAYc2GU0cmt8FD2yGW/+Xt0Nvccmtbbv5E0gp4ckkERMNbG40JJu7fj+2ktqQDMArADqe/8X951eUdJLS65/Nx5rfdWHzsUupfYux0CcrHVb/t8jCkxLIlgNbFsA718OCoXDkd/AMgDaPwENb4ebnwD+0cMcsWxV6v2tsr30Xtn1euNc3S/Lvxlzd6m7Umy5llLSVqzKgmTHJ/WbLYU5nZJsczTXy9DOW14NW24qIlAaZ6RD3DrwVYzQZO77TWJHQ5lF4aBt0fRXKVDEvvqhWENXGWG2rXw5FikzXBhEEertzMOUsq3aV8ERmzO1Ggioj1WhSI1Ia/fr2383H2o/J/3kWC9z4jLG9/v2SWStUCldONsR/aiwM+HwEHNtuzDXbPWmsrO04DvzKFd34tbtBq4eM7UX3w9EdRTdWccldZVuzM/iXNzcWEyhpK1elWVQZqob4cSYzh2+3lID/aKkZmYhIyXfmBKx4Cf5bD5aMgbSD4B9mNCF5eBt0fMZ5JoG5tW03fWw0TBGRQuft4cat5xuSzVlbAp4euxyrG3SaZGxvnAHJf5gbj0hxy9N87LlLNx+7lOodjRq42efULFQuLTsTNn4EU5vAl/fA8V3gUwZufBoe3godxoBv2eKJ5cZnjEUAmadh/hBj0YKrys6ALXON7VLWgCyXkrZyVSwWC7c1Pd+QbH0JeHRTzchEREqu1ING05H/1ocVk4zuz2Wi4ZbJ8OAWaPUgeAeaHWVeUa212lakGOSWSPgxIZkjaSW4IRlAdBuo093oLL7kKaP5okhpsWQsZJ2Byi2h0RWaj12MxWL8cReMpNzJfYUbn7i27AxjFfaU6+DrB+DkXvANgdgJRhmEto+Bd1DxxuTmDn0+AP9wOJoAXz/kuv/u//mtMX8PiIBqHc2OxhRK2spV69OkIm5WC5sSU9iZfMrscK6NmpGJiJQ8x3bCV6PgzUYQN9WoRRfWAPp+CKM2QNNh4OFtdpSX1u4J433Tx0biWUQKXa3wAJpUKUO2zc6CjaVgVftNE8HNE/5aDjt/MDsakeKx+6fzzcfcoOtrBW9gFNUaqrY3/qCau2pXSress/DrNHgzBr59BFL3G09xdXrRKIPQ+iGjrrhZAsLgthnGz/7W+bDhA/NiuRabz5dGiLndSEaXQkraylULDfDmxtpG0WyXX22bW4Q+dT+kHzc3FhERuTaHNsO8O2BqM2OSZ8uCKq1g0Gfwf6ugfh/XmPBFt4EqrSEnU6ttRYrQ3w3JEkt2QzIwGtRcf4+xveQpyMkyNx6RopadCd89bmw3H3m+CfU1yK1t+9unxh+HpXTKTIc1U2ByQ1j8BJw6BAEVoMur8OBv0PI+o3eOM6hyA8Q+a2x//yQc2GhqOFctJRF2Lze2Gw82NxYTKWkrBdL/fImEzzcfJDPbZnI018A7EMpWM7ZV11ZExPXY7fDXz/BxT3ivPSQsAuxQswv85wcY9h3UiC346hqztM9dbfuRVtuKFJFuDSII8HZn/4mz/LK7hDckA6Ppol95o9bi+vfNjsa5nNgDWxcaTYSkZHA0Hwu9uuZjl1KpqTG3sNuMkktSumScMv6QPrkB/PA0pB+BoMpwy3/hwXhocRd4+Jgd5YVuuN8oj2PLggV3Gn0eXEX8p4DdKBtWtqrZ0ZhGSVspkPa1yhMa4MWJ9EyWJSSbHc61UTMyERHXY7NBwtfwfkf4uAf8tcJ4BKxhf7hnDdw+Fyq3MDvKgotqY6wSzsmEXyabHY1IieTj6UbvxhUBmLvOxZ8eyw/vQKMpDhhJJ1f65b0oHd0O02+Ez4bDnAFGckZc2z+bj91cgOZjl9LhKeN922eQtK1wrinO7Vwq/Pyqkaz98Vk4cxzKREGPqfDAJmj6H3D3MjvKS7NYoOfbRtIzdT98PtKYQzs7mw02zza2G99hbiwmU9JWCsTdzUrfJkbX3bmuXiJBzchERFxHdqYxiXunBcwbDAc3grs3NBtpTJ5vfQ/C6pkd5bWzWP6ubbtxJqQdMjUckZJqQDOjRMKS35M4eirD5GiKQeM7IKy+kYjQakFI2Q+zesPZ8wnsXUthRlc4lWRuXHJtljz1d/Oxhv0L77oRDaFeb2N7+YuFd11xPmdOwE8vwH8bwPLnjWZY5apD7//BqI1w3R3g5mF2lPnjHQT9ZoG7D+z6EVa+anZEV7bnZ0hNBK8gqNvD7GhMpaStFFi/8yUSVu48yqGUsyZHcw3UjExExPllpsOv78JbjeGre+HYDmMi1+YReGgbdHvNWPlQkkS3hco3nK9tO9nsaERKpLoVAomJDCbbZmf6qr+wu2qH7fyyukHn88na9R/AkT/NjcdM6ceMhG3aQQipBYMWGl3fk7bA+7FwJMHsCKUgdv8Ef3x17c3HLqX9U2CxwvZvjT8cS8mSfsxYUTu5Aax8BTJSoXxt6PMB3LcOGg1wjf4I/xZeH255w9heMQl2LTM3nivJbUDWoK9zlp0oRkraSoFFhfjRIrosdjssdOWuuxENjfeTe42/oImIiPM4cwJWvAz/rQ+Ln4S0A0Z9utgJ8PA26DgO/MubHWXRsFj+rm27cSakHTY1HJGS6s4bqgDw3sq/uHvWRk6mZ5ocURGLbgu1uoE9B34Ya3Y05sg4BbP7GjVPAyvBHZ9DjZtgxI/GarrU/fBBJ9iz0uxI5WpkZ8B3jxnbze+69uZjF1O+JjQcYGz/9HzhX1/MkXHKqFU7uYFRuzbztPFUwm0fwT1xRvLQ6mZ2lNcm5na47k7ADp+NMJ40cEZnTkDCN8b2daW7NAIoaSvXqH8zY7Xt/A37Xbfrrk8ZCDYm6xzeYm4sIiJiSDsES8YaydoVLxqPrpaJMho+PLQVWj9k1Gcs6aLbGY935mSotq1IEekVU5Gnu9XBw83CD38k0+XNVcTtPm52WEXr5ufA6mE8KrtzqdnRFK/sDJh7OxzaDL7l4I4vIMgo+0bZaBi+FCKvN1bYzboVtsw3N17Jv7i3jUZ7fqHQoRCaj11Ku8fB6m6s6t37S9GNI8XDZoOF/4E1U4yyGhExMOBTuHsV1OsF1hKUNuvyilEe8uwJWDDUKDvmbLYuMOa9YQ3+fiq6FCtBP31ihi71ja67B06eZY0rT27VjExExHn8NhcmN4S4qZCVbkza+nxg1BBr+h/w8DY7wuLz79q2qrMoUugsFgsj2lTli3tbUTXEj6S0c9z+/q+8tmQ7WTku0LClIMpVgxZ3G9tLxkJOlrnxFBdbjrHCbM9K8PQ3SiKUr5n3GN+yMOQrqNvL6Lj++UhY+RqU9NIZri5l/9+1Om9+zqjjWVTKRv/dHOmn5/Wz4erWvAU7fzB6JAyYA3etgNrdSlayNpeHN/T7GLyD4eAG53vawm6HTedLI1x3R+GXN3FBJfCnUIqTj6cbPWMqADBvg5Mur88PNSMTEXEOaYfh20eNX5QrtzR+of6/VcZjaa5YQ6wwVG0PkS0g+5xq24oUofoVg/j6/tb0a1oJux2mLt9Fv//Fsf/EGbNDKxptHzNWmh7bDhtmmB1N0bPb4ZuHIWERuHkaK+kqXnfxYz28oe8MuOF+4+ufnoOvH4Sc7OKLV67OD2OLpvnYpbR9DNy8IHGNseJWXNO+OFg20dju8jLU7lryE4VloozGvQDr3oOtC00NJ4/D8ZC81fj/VoPbzI7GKShpK9esf9PzXXe3JbluDbDcZfeH4s2MQkREljwFmaegYlMY+p1RY7CkT56vxGKB9k8a2xtnaLWtSBHy83Lnlb6NmHp7YwK83dmcmELXN1fxVfxBs0MrfD7B0OH8KqsVLxp1BEuyn56DTR8ZTaT6fABV213+eKsVbn7+fDMrq3HunP5G7UtxLruWFW3zsYsJqgjNhhvbWm3rmtKPGWUR7DnQoN/5eq+lRM1ORjNfgEUPOE9TytxVtnVuMZ56ECVt5drVrxhInYhAMnNsfOmqE9rcpO2J3XAuzdRQRERKrd0/we+fG78c3/JGyXwsraCqdoBKzY3Vtr+8aXY0IiXeLQ0r8N0DbbiucjCnMrJ5cG48jy74jfSMErbS8ro7IbSu0Yz351fMjqborJkKq143tm/5L9Ttkf9zm4+E/rPB3ceoATyjqxpDOpPsDPj+cWO7qJqPXUrr0eDhC4c2wfbvim9cuXY2G3x+F5w6BCE1jX8XStsigQ5jjcaUWekw/w7z/yCVdfbvVb+N1YAsl34bkmtmsVgYcL4h2bz1+7G74l8Z/cpBkHEPJG01NxYRkdIoO8MoiwDGL125ZWvE8M/Vths+hFPJ5sYjUgpElvVl/t0teeDG6lgtsHDjAW6ZspptB1PNDq3wuLlDpxeM7fXT4egOc+MpCvFz/q7b2HE8NBl69deo3RWGfgt+5SFpC7wfC0cSCjVMKaDiaj52Mf7locX/Gds/vWAkAsU1rH4Ddi8z/hhz20fg5W92RMXP6gZ9PoSACDi2w1hxa2Yu549FRgPI4MpGI14BlLSVQtIrpiKe7lb+TDrFVledyDrq2sabGoaISKn0y1vG0w7+YdDhKbOjcU7VboRKzbTaVqQYubtZGX1zLeaMvJ6IIG/2HEun9zu/MH3lX9hsLrhQ4WKq3Qg1u4AtG3542uxoCtf27+Gr+4ztlqOg9cMFv1alJjB8KZSrDmkH4INORkMzMU+e5mPPF23zsUu54X7wCoQjv8MfXxT/+HL19q6G5ef/WNXtNQira248ZvIvD7fNBKu78bTbuvfMi2Xz+dIIMYP1tN0/6DshhSLI14PO9cIBY7WtS1IzMhERc5zYA6teM7Y7vWjOL12uQKttRUzTomo5vn+wDZ3rhZOVY+eF7xK4c8Y6jpw6Z3ZohePm541f2ncuMUoAlAR7f4EFQ416lY1uN+7xWh9/LhttJG4rtzRWhM26FX6bVyjhSgEseep887EboGE/c2LwLft3w7rlL6pZnbM7fQQWDge7zfh3ofFgsyMyX+Xr4abnjO0lY2H/+uKP4cRfsHcVYIGY24t/fCempK0UmtwSCYviD3E2M8fkaApAzchERIqf3W7Uoss+Z9TVqt/H7IicW7WORpO27LOw5i2zoxEpVYJ9PXl38HW80Ls+3h5WVu08Rtc3V7F8+xGzQ7t2IdWN0jRg/NLu6omnw1tgzgDjvy21ukKPKYVXr9K3LNzxJdTrDbYs+OIuY7WnK5aIc2W7lkHCIqP5WLdiaj52KS3+D3zKGmUatiiJ77RsOfD5SDidBOVrGz83Yrj+Hqjby/g3bcGdRpO24rT5E+O92o0QHFm8Yzs5JW2l0FxftRyRZX04lZHNd1tdsDh/7krbYzsgM93cWERESos/v4WdP4DVA7q+XvqaQFwtiwXan6/Zt/4DY8WIiBQbi8XCoBZV+HpUa2qHB3DsdCbDZqxn4td/kJHtgosW/qnd4+BTBo7+CRtnmB1NwR3fDZ/0gYw0qNIK+n5o1O4tTB7eRi3I3BWWPz0PXz8AOVmFO45c3D+bj7W4G8LqmRuPd+DfpTd+fgmyM82NRy5u1evw1wqjedxtH4Gnn9kROQ+LxfjjVrnqkHYQPhthJLmLQ042xH9qbF+nBmT/pqStFBqr1UK/Jucbkm1wwRIJAWFGEW7skLTN7GhE5FrYbLDsOVgzxexI5HIy0+H7J4ztVg9A+ZrmxuMqqneEik2M1baqbStiihphAXx5XyuG3hAFwIe/7KH322vYdeS0uYFdC58yRjdxMB7zPptiajgFknYYZvWC9CMQ3gAGzgEPn6IZy2o1Si50fQ0sVtj0sbG61+wO7KVB3FRjVat/2N9lg8zWbIQRT0oibP7Y7Gjk3/ashBWTjO1ub0BobXPjcUbegdBvlpHU/ms5/Pxy8Yy7exmcOmysVq/VtXjGdCFK2kqh6tu0ElYLrNtzgr+OuuCkVc3IRP6fvfuOq6p+Azj+ufeyt4AsRVFR3OLEPUkcmaammTtXllba0na/hmZlalqW5ii1zNKGlrkyF+69t6ICCsje3Pv748uQBEQFLhee9+t1XxzuPfec5x4HX57z/T5P2XD2L1UjdcObqhOpKJ3+naGauThWgXYvGzsa03HXbNtbxo1HiHLKylzHu4/VY+GwZlSwMedkaCy9vtjByn1XMZjqUvmmI9Wy4aSonAZPpiLpNizrq5JmztVhyOqSqZHeYgwMXK660J/fBIu7q+SxKB7RIfCvkZuP5cXCJmcss+1TSEsybjwiR1x4Th3bxkPAf5CxIyq93OtCr8wJAf9+DOc2Fv85D2be5Gj0JJhZFv/5TIwkbUWR8nS0pkOtigD8tP+akaN5ANKMTAjTZzCowXKWtZMkqVUa3TytZsoA9JihftkRhecbCF5NpLatEKVAYF131r/Ynja+LiSlZfDaL8eY8MMhYpJMcKm8zgyCMruq75kPEeeNG09hpSbCioFw8yTYecDQNWDnVnLnr90DRqwD24oQdgwWBsLNUyV3/vLk79fVz76qbaDBE8aOJremw8HRW80a3PetsaMRoJb4/zJKzb53qwvdTexmlDE0HADNRqnt1WPUjbDiEn8Tzq5X242lNEJeJGkritzAzIZkvxy8RnqG3sjR3CdpRiaE6buwBW4cVDNeKtaGxAhYN0kahJQmBgOsewn06WoZlF93Y0dkejSanCWh+xbKjQkhjMzdwYrvnw7gtW61MdNqWHc0lB6zt7P/cpSxQ7t/voFQs6v6P3rjW8aO5t4y0uCnYRCyR826HLoaKviUfByVm8KojeBSU60i+TYILv5b8nGUZec35TQf6/FJ6auDb2apakMD7JgJKSa48rSs+fdjuLwdzG0z69jKJIFC6TZNTQ5Iuq3+f01PKZ7zHPlR/ayp1FTN8hV3kaStKHKda7vjYmvBrbgU/jljYr9EZs20vXValrQIYaq2f6a+NhsJfReA1gxO/QHHfjZuXCLH0ZVwZYdKrHcvoXpZZVHNruDVGNISIVjqNwthbFqthvEda/Dz+NZUcbbhenQSA74OZvamc2ToTezGYdcPVWLszJ9w4R9jR5M/vR5+fRbOb1Q/U55aZdymVM7VYNQGqNIKUmJUQ7QjK40XT1mSngJ/ZjUfe8b4zcfy0+gpVZ4jMRL2fGXsaMq3C1tUKS6AXrOkd8L9MLOEAUtVrfMbh2D91KI/h8EAh75X2zLLNl+StBVFzsJMS7+mlQFYua8Yp9IXBwcvtazJkAHhJ40djRDifl3ZBVd2gs5CdXT2bAgdMhtd/fmS1JgrDZJuq1rDoGajOFUxbjymTKOBDpmzbfcugIQI48YjhADA39uJdc+35fHGldAb4PNNZxn0zW6uR5vQhICKtVStVlDL0TPSjRtPXgwGWD8Fjv2kbtAO/B6qBBg7KrBxhqG/Qr3HQZ8Ga8aq+sCy4ufh7PoCoi6UruZjedGZQcfX1fbOL9S4R5S82FD4ZQxggCbD1ZJ/cX+cqqgJMGhg/7dw9KeiPX7IXog4qxqf1e9XtMcuQ4yetJ03bx4+Pj5YWVkREBDA3r1789139erVNGvWDCcnJ2xtbfH39+f777/Pfj0tLY3XXnuNBg0aYGtri5eXF8OGDePGjRu5juPj44NGo8n1mD59erF9xvJoQDNVIuGfM7e4GZts5Gjug0ZzR13bQ8aNRQhx/7Jq2foPVjdhANpOUrMRk2Pgj+fllyZj2/IBJNwC11rQaoKxozF9tYJUaZ+0RPULrRCiVLC3Mufzgf58PrARthY69l6Oovusbfx1zIRuHnZ4DaycVJ3YQ98ZO5q7bfsE9n6ttvvMh5qPGDeeO5lbQb9F0Pp59f2WD9QYJMME6xyXBtEhOWO8rh+oLvelWf2+ULGOmm29a66xoyl/MtLhl9GqRJp7A1nV9TBqPpJT8uOPF4p2YlvWz5W6fUr/v2kjMmrSduXKlUyePJl33nmHgwcP0qhRI4KCgrh582ae+zs7O/PGG28QHBzM0aNHGTlyJCNHjuTvv/8GIDExkYMHD/LWW29x8OBBVq9ezZkzZ3jsscfuOtb//vc/QkNDsx8TJ04s1s9a3vi62dG0agUy9AZ+PmhiDcmkGZkQpun6AbiwWS3nbPtizvM6c/XLnM4Szm3IWYYjSt71gzmNOXp+BmYWxo2nLLiztu3eBZAQadx4hBC5PN64Mn++0I5G3k7EJqczfvlBpq4+SlJqhrFDuzcbZ+iYuSR2y4fq5mdpsW8h/JPZMK37DGhYyhpSAWi10PV96PEpaLSqQ/qKgZASZ+zITM/fU0tv87G8aHXQ+Q21vfsrWQlT0rZOU2W4LOzgiSVgbm3siExbh9egRmc1QeCnoZAc+/DHTImD42vUdhMpjVAQoyZtZ86cyZgxYxg5ciR169Zl/vz52NjYsGjRojz379ixI48//jh16tShRo0avPDCCzRs2JAdO3YA4OjoyMaNGxkwYAB+fn60bNmSuXPncuDAAa5ezb1M397eHg8Pj+yHra1tsX/e8mZg5mzbn/aFYDClmW3SjEwI07R9pvracMDdDUjcakPnzCX5618v3i6oIm/6DFg3GTBAgwFQrb2xIyo7anVTNxzTEqS2rRClUFUXW35+phXjO9ZAo4Ef9obQa+4OTt4ogl98i1vzUaqxVmJEzkxHYzv+C6x7WW13eA0Cxhk3nntpMQYGLlc1dy9shsXdpVzT/Ti/SfUm0OgyE+ClrPlYfmo/mrkSJgF2fG7saAptxIgRuVYku7i40K1bN44ePVpk53j33Xfx9/cvsv1yOb8pp7/FY3PA1fe+4yuso0eP0q5dO6ysrPD29mbGjBkF7n/kyBEGDRqEt7c31tbW1KlTh9mzZ+faZ8eOHbRp0wYXFxesra2pXbs2n39+99+f69evM2TIkOz9GjRowP79+4v082XT6qDvQnCoBJHn4fcJD79y8cQa9W/DxVfVABf5MlrSNjU1lQMHDhAYGJgTjFZLYGAgwcHB93y/wWBg8+bNnDlzhvbt8//FLyYmBo1Gg5OTU67np0+fjouLC40bN+aTTz4hPb3gOk0pKSnExsbmeoiC9Wzoia2FjsuRiey5ZEKdc7Nm2t48VXxdEoUQRSv8BJxeC2ig7eS892n1HHi3hNQ4+O051bxElJwDi1UjA0sHtbRRFJ07a9vu+UZm2wpRCpnrtLzWrTbLRgXgZm/J+Zvx9PlyJ0t2Xirdkxt05hD0kdre/RVEXjBuPOc3w+pxgAGaj86ZCVza1e4BI9ep3hlhx2BhoPTPKIy7mo+ZUHd5jQY6v6W29y00qUR9t27dslckb968GTMzMx599FFjh3VvMddh9VjAAM1GFWud1NjYWLp27UrVqlU5cOAAn3zyCe+++y7ffPNNvu85cOAAbm5uLFu2jBMnTvDGG28wdepU5s7NKaFha2vLhAkT2LZtG6dOneLNN9/kzTffzHXc27dv06ZNG8zNzfnrr784efIkn332GRUqVCi2z4utCzyxFLTmcPI39fPgYRzMakA2xHRuxBiJ0ZK2ERERZGRk4O7unut5d3d3wsLC8n1fTEwMdnZ2WFhY0LNnT7744gseeSTv+kXJycm89tprDBo0CAeHnBoZzz//PD/++CP//PMP48aN46OPPuLVV18tMN5p06bh6OiY/fD29r6PT1s+2Vqa0auRqin5074QI0dzH5yqqC6J+jRVw0sIUfplzbKt2zv/zrBaHfT5UhW7v7RNDaBFyYi/CZv+p7Y7vwX27gXvL+6fX3fwaJg521bq5wlRWrXxdeWvF9rRpbYbqel63v3jJKOX7icyvhRPFKj5CNToosbGG982Xhwh+2DlEBVHvb7Q/RPT+mW/UlMYvUnNXI69BouC4OK/xo6qdDOV5mP58e2iJgykJ8P2UjJTvRAsLS2zVyT7+/szZcoUQkJCuHXrVvY+ISEhDBgwACcnJ5ydnenduzeXL1/Ofn3r1q20aNECW1tbnJycaNOmDVeuXGHJkiW89957HDlyJHs275IlSx4ozmPHjtG5c2esra1xcXFh7GMtiY+OUOOhoI/yjQHUrNdOnTphb2+Pg4MDTZs2va+ZqsuXLyc1NZVFixZRr149nnzySZ5//nlmzpyZ73uefvppZs+eTYcOHahevTpDhgxh5MiRrF69Onufxo0bM2jQIOrVq4ePjw9DhgwhKCiI7du3Z+/z8ccf4+3tzeLFi2nRogXVqlWja9eu1KhR4wGu4n3wbg5BmWVpNr4FV3c/2HFunYFre9Xs+UZPFV18ZZTRG5HdL3t7ew4fPsy+ffv48MMPmTx5Mlu3br1rv7S0NAYMGIDBYOCrr3LfBZg8eTIdO3akYcOGPPPMM3z22Wd88cUXpKTkP1iaOnUqMTEx2Y+QEBNKQhrRgOYquf3n8VBikkyk8H6uZmRS11aIUi/yApzIHOy0f7ngfV1qQOB7anvTO8afMVRebHxbNePwaKiW2oqil6u27TeQaEIrXIQoZ1zsLFk4vBnvPVYPCzMtm0/fpPvs7ew4V0rrXmo06hd1jU6tarm0reRjuHkKVjyhairW6AyPf61qxpqaCj4waoNaDpwSC8v6wZEfjR1V6RR99Y7mYx+aZqMijSanPNeBpXD7inHjeQDx8fEsW7YMX19fXFxcAJVrCQoKwt7enu3bt7Nz507s7Ozo1q0bqamppKen06dPHzp06MDRo0cJDg5m7NixaDQaBg4cyEsvvUS9evWyZ/MOHDjwvuNKSEggKCiIChUqsG/fPla91pNNR68zYX0GPLGEdI1ZvjEADB48mMqVK7Nv3z4OHDjAlClTMDc3zz7+vZLJwcHBtG/fHguLnP4MQUFBnDlzhtu3bxf6c8TExODs7Jzv64cOHWLXrl106NAh+7nff/+dZs2a8cQTT+Dm5kbjxo1ZsGBBoc/5UFqMVTOY9emwagTE37rnW+5yMLMBWa0gmchRCEb7Sefq6opOpyM8PDzX8+Hh4Xh4eOT7Pq1Wi6+vL/7+/rz00kv079+fadOm5donK2F75coVNm7cmGuWbV4CAgJIT0/PdWfovywtLXFwcMj1EPfW2NuJmm52JKfp+f3IDWOHU3iStBXCdOyYCQa9quvp0eDe+zcfreqppiXCr+NVrVVRfC7vgCM/ABp49HM141kUD78e6t9AarzMthWilNNoNAxv7cNvz7XB182Om3EpDF20h2l/nSI1vRSW73GrA82eVtvrXy/Zn53RV+H7vpB0Gyo3h4HLTLuRpY0zDP1VzRbWp8GacfDvJw9fI7KsWZ/VfKwtNOhv7GgeXLV2UL2j+rP+t+Cap6XF2rVrsbOzw87ODnt7e37//XdWrlyJNvNGycqVK9Hr9SxcuJAGDRpQp04dFi9ezNWrV9m6dSuxsbHExMTw6KOPUqNGDerUqcPw4cOpUqUK1tbW2NnZYWZmlj2b19r6/huFrVixguTkZL777jvqW9ygc+JvzO1hxfdHkglPtyswBoCrV68SGBhI7dq1qVmzJk888QSNGjXKPr6fnx+Ojo75nj8sLCzPVeNZrxXGrl27WLlyJWPHjr3rtcqVK2NpaUmzZs147rnnGD16dPZrFy9e5KuvvqJmzZr8/fffjB8/nueff56lS5cW6rwPRaOBXnPA1Q/iQuGXp+/v50F6as6NqsbSgKwwjJa0tbCwoGnTpmzevDn7Ob1ez+bNm2nVqvCFiPV6fa4ZslkJ23PnzrFp06bsu0EFOXz4MFqtFjc3t/v7EOKeNBoNA5vnNCQzGdKMTAjTEH015wd/u3vMss2i1ULveWBhDyF7IHhe8cVX3mWkwbqX1HbTEVC5mVHDKfP+W9tWZtsKUerV8XTgjwlteSqgCgYDfP3vRfrP38XliARjh3a3jlPByhHCj8GhZSVzzvhb8P3jEHcDKtaGp34CizLQQNrcCvp9C21eUN//8wH8PlH93BRwbpOa1a3RQQ8TK4ORl6zatkdWQMR548ZSCJ06deLw4cMcPnyYvXv3EhQURPfu3XOVFjh//jz29vbZyV1nZ2eSk5O5cOECzs7OjBgxgqCgIHr16sXs2bMJDS3amr6nTp2iUaNG2KbfhjUq6dmmz2j0ej1nzpy5ZwyTJ09m9OjRBAYGMn36dC5cyL367vTp0zz++ONFGvOdjh8/Tu/evXnnnXfo2rXrXa9v376d/fv3M3/+fGbNmsUPP/yQ/Zper6dJkyZ89NFHNG7cmLFjxzJmzBjmz59fbPHmYmkHA78Hc1u18uKfDwv/3rPrVWNLO3eoeffnFncz6pqSyZMns2DBApYuXcqpU6cYP348CQkJjBw5EoBhw4YxdWpOcflp06axceNGLl68yKlTp/jss8/4/vvvGTJkCKAStv3792f//v0sX76cjIwMwsLCCAsLIzU1FVDT2GfNmsWRI0e4ePEiy5cvZ9KkSQwZMqR4CzeXY32bVMZcp+HY9RjT6JILOTNtw0/I4EmI0mznHLU8p1oHVWepsJyqQLfMxipb3lfLLkXR2/0l3DoNNq4Q+I6xoykfavcE9waq4Z7ckBDCJFhb6Pjo8QbMH9IER2tzjl6Loeec7aw+eM3YoeVm65JzY2jL+5BczOP65FhY3k91K3esAkPXqFmqZYVWC4/8D3p8ChotHPoeVgyElDhjR2Zc6Snw1ytqu+V402o+lp/KzaBWd7UybOtHxo7mnmxtbfH19cXX15fmzZuzcOFCEhISspfgx8fH07Rp0+zEbtbj7NmzPPWUqlG6ePFigoODad26NStXrqRWrVrs3v2ANVDzYzDAqpFqFr5X45xSFJkKiuHdd9/lxIkT9OzZky1btlC3bl3WrFlT6FN7eHjkuWo867WCnDx5ki5dujB27FjefPPNPPepVq0aDRo0YMyYMUyaNIl33303+zVPT0/q1s3976JOnTpcvXq10PE/tIp+8Ngctb39MzizvnDvO5TZgKzRINCZFU9sZYxRk7YDBw7k008/5e2338bf35/Dhw+zfv367GnlV69ezXU3JCEhgWeffZZ69erRpk0bfvnlF5YtW5Y9Vfz69ev8/vvvXLt2DX9/fzw9PbMfu3btAlSZgx9//JEOHTpQr149PvzwQyZNmlRglz/xcJxtLXikrvoz/Wm/icy2da4Olo6QkaISDkKI0icuLKcm0r1q2eal8VB1hzcjFdY8Izdoilp0CGydrra7vq8aPIrip9FAx9fU9p6vZbatECakW31P/nqhHS2qOZOQmsHkn47w4o+HiEsuRT+fmo8G5xqQcEv9ol5c0pLhx6dUqTIbV5WwdfAqvvMZU4sx8OQK1Sj1wmZY3B1ii3ZWoknZNQeiLoKdB3R4zdjRFJ1Or6uvx3+BsOPGjeU+aTQatFotSUlJADRp0oRz587h5uaWndzNetxZUqBx48ZMnTqVXbt2Ub9+fVasWAGoVdcZGQ9XYqVOnTocObCHhIt71O/tTyxh5579aLVa/Pz87hkDQK1atZg0aRIbNmygb9++LF68uNDnb9WqFdu2bSMtLef/540bN+Ln51fgZMATJ07QqVMnhg8fzocfFm6G6n9Xl7dp04YzZ87k2ufs2bNUrVq10PEXiQb9VY1bULOdb18ueP/YG3B+k9qW0giFZvTq7RMmTODKlSukpKSwZ88eAgICsl/bunVrruLPH3zwAefOnSMpKYmoqCh27dqVq2i1j48PBoMhz0fHjh0B9R/M7t27iY6OJikpiZMnTzJ16lQsLS1L6iOXSwObq9oxaw5dJznNBOpHajTg2VBtS11bIUqn4Lnqxop3APi0u//3Z9VksnKC0MOw4/OijrB8Wz9F1Q2u0lrdTRclx68nuNdXs213f2nsaIQQ98HLyZofxrRk8iO10Gk1/Hr4Bj3n7ODQ1cI3tilWZhY53cN3fwlRl4r+HBnp8MsouLxdlTIa8gu4+hb9eUoTv+4wYi3YVoSwY7AwEMJPGjuqkhd9FbZl3gzo+oFpNh/Lj2dDqNtHbf9TumfbpqSkZK9YPnXqFBMnTiQ+Pp5evXoBqomXq6srvXv3Zvv27Vy6dImtW7fy/PPPc+3aNS5dusTUqVMJDg7mypUrbNiwgXPnzlGnTh1A5W0uXbrE4cOHiYiIKLAhfFJS0l0zei9cuMDg5q5YkcLwX5M4Xu9V/jl8iYkTJzJ06FDc3d0LjCEpKYkJEyawdetWrly5ws6dO9m3b192fAC1a9cucObtU089hYWFBaNGjeLEiROsXLmS2bNnM3ny5Ox91qxZQ+3atbO/P378OJ06daJr165Mnjw5+xrfupXTzGvevHn88ccfnDt3jnPnzvHtt9/y6aefZq8uB5g0aRK7d+/mo48+4vz586xYsYJvvvmG55577j7+lItI1w+hUjNIjoGfhqkbbvk5vFzNNq/Squz/n16EjJ60FeVDW19XvBytiElK4+8ThSvMbXTSjEyI0isxCvYtUtvtXn7wWmcOnmpZIsC/H8u/96Jy9u+cWnQ9PzP9WnSmRqvNmZ0ks22FMDk6rYbnu9Tkp3EtqeRkzdWoRJ6YH8yXW8+j15eCZlW1uqnGShmpsKmIS98YDLD2RfUzRGcJg34AL/+iPUdpVakpjN4ELjUh9hosCoKL/xo7qpJVVpqP5afT66oUxpl1cP2AsaPJ1/r167NXLAcEBLBv3z5WrVqVPRHOxsaGbdu2UaVKFfr27UudOnUYNWoUycnJODg4YGNjw+nTp+nXrx+1atVi7NixPPfcc4wbNw6Afv360a1bNzp16kTFihVz1Wv9r7Nnz9K4ceNcj3GjhmOz/kX+HmJDlIUXzQe+Qv/+/enSpQtz587NjjG/GHQ6HZGRkQwbNoxatWoxYMAAunfvznvvvZd93jNnzhATE5NvXI6OjmzYsIFLly7RtGlTXnrpJd5+++1cTcViYmJyzYj9+eefuXXrFsuWLcu1Krx585wSb3q9nqlTp+Lv70+zZs2YN28eH3/8Mf/73/+y92nevDlr1qzhhx9+oH79+rz//vvMmjWLwYMHF/JPuAiZWcCApWDtrH6PWp/P7Hi9PqcWusyyvS8ag0HaVD6I2NhYHB0diYmJwcGhDN0BLEYzN55lzuZztPF1YfnolsYO596OroLVo6FyCxi90djRCCHutOVD2DYDPBrCuG0PlxQ0GOCnoXDqD3CrB2P/ATNZffHA0pJgXgBEX4HWE9VMGVHy9HqY3xZunoD2r0LnN4wdUbGRMVnhybUyPTFJabyx5hhrj6rl8q1ruPD5QH/cHayMG1j4CfV/jEEPI/4EnzZFc9yN78DOWSqxNeB7qPNo0RzXlCRGqdIQV4NBaw6950KjJ40dVfE7txGW91c3fMfvBLc6936PKVrzDBz5AWp0gaGrjR2N6UlPhcXdVNK7UlMYuV4lDoVxXdgC3/cFDNDnK/B/Kvfrl7bB0l5q9cTLZ8pGQ8mHVNgxmcy0FSXmiaaV0Whg5/lIQqISjR3OvWXNtA07BnoTKOkgRHmRHAN7v1bb7R9ilm0WjQZ6fq5q5t08kVOHVTyY7TNVwtbeK6dhjSh5Wu0dtW3nqyYdQgiT42htzheDGjOjX0OszXXsuhBJt1nbeO+PE/x5LJSbcQUsRS1O7vWg6Qi1vX5K0YyVd85WCVtQ5YvKY8IWVLO1ob9Cvb6gT4M14+DfGeomc1mVngJ/vaq2W44vuwlbUCthtGaqfvGVXcaOxvRsekclbK0cof9iSdiWFjU6Q8epanvtpLvrNh/MbEBWv68kbO+TJG1FifF2tqGtrytgIg3JXHzBwk4t0Yk4a+xohBBZ9i1UiVtXP6jdq2iOaVcRHs2sabtzFlzbXzTHLW8izuf8wt19OljaGTWccq92L3CrCymxsPsrY0cjhHhAGo2GAc29Wft8W+p5OXA7MY3FOy/z7PKDtPhwMx0/+YeXVx1h5b6rXLgVT4ktpOz0Blg6QNhRNXPwYRxaBhvfVtuP/A+alPPls+ZW0O9baPOC+v6fD+H3iWW3aeqdzcc6lvEbvs7VcpaHb36/bCfji9qptTm1+vvMhwol3HhLFKz9K+AbCOnJahVjcmZ5iaRoOPW72m4yzGjhmSpJ2ooSNaCZNwA/H7hGRmmoyVUQrRY8GqhtqXMpROmQmgDB89R2u5fUv9OiUvcxaDBALfVcMw5STWBFQGliMMCfL6kah76BUOcxY0ck7qxtu3u+GjQLIUxWjYp2rH62NXOfasywVlWp4+mARgOXIxP5+cA1XvvlGF0++5emH2xi7Hf7WbDtIodDoknL0BdPQLau0CFzduTm/0FK3IMd59RalZAEaP18TqKyvNNqVQK7x6eqXMSh72HFQEiONXZkRev2lZzmY0EfgqW9ceMpCe1fUTWbr+5Sy8rFvd2+DL8+q7ZbTYDaPYwajsiDVgt9F4Cjt7oJ89tz6veD4z+rRG7FOqqkhbgvkrQVJaprPXecbMwJjUlm27lb936DsXn6q6+StBWidDiwFBIjoYIP1O9X9MfvMQPsPSHyPGx5v+iPX5adWAMXt6pfQrrPkOZjpUWdxzJn28aoMgmixMybNw8fHx+srKwICAhg7969+e67YMEC2rVrR4UKFahQoQKBgYF37T9ixAg0Gk2uR7du3Yr7Y4hSxtJMx6MNvfhf7/r89UI7jrzTlSUjm/Ncpxq0qOaMhZmWqIRUNpwM58M/T9Fn3k4avPs3g77ZzcwNZ9h29hZxyUU4W7PFOHCuDvHhsOPz+3//pe3w89PqhmnjISpJKXJrMQaeXAHmNmpJ/eIeEHvD2FEVnb9fVysbfdoVz9iuNHKsBM1Hqe0tH8hs23tJT4FVI9RYpnILCHzX2BGJ/Ng4q8ZkOgvVLyR4bk5phCZD5feDByBJW1GiLM10PN64EgAr95pAiYSsurY3Dhs1DCEEasC2a47abjsJdGZFfw7rCvDYF2p791dweUfRn6MsSo5VHZ8B2k0GlxrGjUfk0GpzZsIFfymzbUvIypUrmTx5Mu+88w4HDx6kUaNGBAUFcfPmzTz337p1K4MGDeKff/4hODgYb29vunbtyvXr13Pt161bN0JDQ7MfBXXcFuWDg5U5Hf3ceCWoNj+Na8Wxd7vyy/jWTO1em8A6bjjZmJOcpif4YiRztpxn2KK9NHpvAz3nbOfd30+w7mgoN2Mfoi6umUVOw8ldc9WsycK6cRh+GAQZKVD7UXh0tvxCnx+/7jBiHdhWhPBjML8d/PUaXN2tGk+aqnMb4fRaVeO1xyfl68+/7SSViL9xEM78ZexoSrcNb8GNQ2qc/sRi0JkbOyJRkEpNods0tb3xbQg9rJoqNiwHDRWLgcZQYkWPyhbpvvvgTofF0m3Wdsy0Gna/3gVXu1LcpT38JHzVStW2nRJStEuxhRD3Z/8iVdjeoRI8fwjMivH/jt8nwsHvwKkqjN8ltVnvZf3rsHuemm01PljV4hOlh14PX7WGW6eg4+s5DcrKiNI4JgsICKB58+bMnTsXAL1ej7e3NxMnTmTKlHvXa8zIyKBChQrMnTuXYcNU/bcRI0YQHR3Nr7/++sBxlcZrJYqXXm/gwq149l2+zf7LUey7EkVIVNJd+1VxtqGZTwWa+zjT3MeZGhVt0RQ2gWYwqK7gl7dDvcfhiSX3fk/EeVgUBIkRaobl4J/lZ0dh3L4MywdAxJmc5+y9VImnun3AO8B0fl9JS4YvW8LtS2q5e9CHxo6o5G16D3bMBPf6MG57if7ZnT17lvHjx/Paa6/RtWvXEjvvfTvxK6warraf+glqBRk1HFFIBgOsHgvHflLf1+0NA74zbkylTGHHZCbyP7ooS2p7ONCosiPpegNrDl6/9xuMybUWmFlDajxEXTB2NEKUXxlpOcsuWz9fvAlbgK4fqnpM0Vdg41vFey5TF3YsZ9l9j0/ll+7SSKuFDq+o7d3zchpDiGKRmprKgQMHCAwMzH5Oq9USGBhIcHBwoY6RmJhIWloazs7OuZ7funUrbm5u+Pn5MX78eCIjI4s0dlH2aLUaarrb81RAFWYO9Gf7q50JntqZLwY1ZnirqtTNrIt7NSqR1QevM3X1MQJn/kuT9zcy5rv9fLPtAgev3iY1vYDZnBpN5qwqjSqVc3V3wUHF3oDvH1cJW89GmUv/5WdHoVTwgWd2wKAf1aw1SweIu6F+Di/uBp/XVTNwrwSX/hm4u75QCVt7z7LffCw/rSeqP8Pw43ByTYmdNjk5mb59+7Jlyxa6devG0aNHS+zc9yXqYk696zYvSMLWlGg00GuWKtEF0Hy0UcMxZcWwtlSIexvQ3Jsj12JYuT+E0e2qFf5OfknTmYFHfbi2T9W1da1p7IiEKJ+O/QzRV9WywJLoOmrlAL3nwXePqRm+tR8F3y7Ff15To9fD2slgyFAzfOQalV51+0DFGXDrNOz5OqdkgihyERERZGRk4O7unut5d3d3Tp8+XahjvPbaa3h5eeVK/Hbr1o2+fftSrVo1Lly4wOuvv0737t0JDg5Gp9PleZyUlBRSUlKyv4+NLWMNjMQD8XS0plcja3o18gIgNjmNQ1ej2Xcpin2XozgcEs3txDQ2ngxn48lwAKzMtfh7O9Hcx5lmPs40qeKEvdUdS5Q9GqifzweXwvopMHpL3rMGE6Pg+74QcxWca8DgX9TPXFF4ZhaqXIJfd1U66sI/Kll+5k+IC1UJ3D3zVTK0zmNq9nNpm4F7+wpsz2w+1vWD8tF8LC82zmqW8daP4J+PoE7v4in/9R+TJk3ixIkTANSoUYMaNUphWau0ZPhpOKTEgndL6CyTKEyOhS2M2qBWCGQ1eBf3TZK2wigea+TFB2tPcf5mPAev3qZpVed7v8lYPP0zk7aHoUF/Y0cjRPmjz1BLxwBaPQcWNiVz3uodoMVY2PuNuss/fhdYO5XMuU3F4WVwba8qIZNVu0qUTlqd6lb9yyjVFCJgHFg5GjsqkYfp06fz448/snXrVqyscmYfPvlkTi24Bg0a0LBhQ2rUqMHWrVvp0iXvGybTpk3jvffeK/aYhWlzsDKnQ62KdKhVEYDUdD3Hb8SocgqZZRVuJ6ax+2IUuy9GAaDVqNVzzX0q0CyzpIJH5zfh+GpVe/LoSvAflPtEqQmwYoAq1WLvCUPXgF3Fkv64ZYuZJfh1U4+sBO7JX+H0OpXA3fu1emQncPuoBJixE7jlsflYflqOV0n2yPPq303jwcV6ulWrVjF/vlohZWFhwerVq7G1tS3Wcz6QDW9A2FGwcYH+i6SOramytJeE7UMqRbfbRHlib2VOjwaeAKzcV8obkkkzMiGM69TvEHFWJZiajSrZcwe+q+q0xl7PabQllIRI1VwAoONUcPAybjzi3uo9Dq5+qjzCnm+MHU2Z5erqik6nIzw8PNfz4eHheHh4FPjeTz/9lOnTp7NhwwYaNmxY4L7Vq1fH1dWV8+fP57vP1KlTiYmJyX6EhJTyMZcoFSzMtDSpUoGx7WuwYFgzDrz5CJsmt2da3wb0bVKJKs426A1wMjSWpcFXmPjDIVpO20y7L4/zh9NTAKRvfAd9clzOQdNTYeVQNRHCykklbCtUNc4HLKuyEriPz4dXzsOgldBoEFg65iRwF3eHmXXgz1fhyi7jlFA4u6H8Nh/Li5UDtH1Rbf87Xf1bKSYXLlxg5MiR2d/PmzePBg1KYULt+C+wb6HafvwbcKxk3HiEMCJJ2gqjGdjcG4C1R0OJT0k3cjQFyErahh5VBbWFECXHYIBtmcvnAsaX/BJKC1voMx80WjiyAk7/WbLnL802vwtJt8Gtnpq1KUo/rS6nLELwXEiWpfLFwcLCgqZNm7J58+bs5/R6PZs3b6ZVq1b5vm/GjBm8//77rF+/nmbNmt3zPNeuXSMyMhJPT89897G0tMTBwSHXQ4j7pdVq8HWzZ1CLKswc4M+2Vzux5/UuzH2qMSNa+1DPywGtBkKiknjpamuu6N0wSwhn4fQXGPf9fk6HRsOvz8CFzWBuo5qOudUx9scq23IlcM+pBk5ZCdz4MOMlcNOS4a/Mn0Mtx8vfgyzNx4CduyoFdqh4mjWlpKTQr18/EhISALV6Y9SoEp4MURiRF+D3F9R2u5egZmDB+wtRxknSVhhNc58KVHe1JTE1g3VHbxg7nPy51QGdBaTEqGL5QoiSc/ZvCD+mlt8bKzFYJUDVGwP44wU1w7S8C9kLBzN/qej5mSxZMyX1HldNNpOj1S/tolhMnjyZBQsWsHTpUk6dOsX48eNJSEjInuE0bNgwpk7Nmb3/8ccf89Zbb7Fo0SJ8fHwICwsjLCyM+Ph4AOLj43nllVfYvXs3ly9fZvPmzfTu3RtfX1+CgqQxiyh57g5WPNrQi3cfq8e659tx5J2ufPd0C8Z1rsPPLmMBGGb4neMnjrP/yzFw/BcMWnMY+D14Nzdy9OWMmaVq4JQrgftUPgncV4o3gXtn87EOrxXPOUyRhQ20e1ltb/sU0pKK/BSvvvoqR44cAaBatWp88803pa+vTFqSqmObGgdV20LH140dkRBGJ0lbYTQajYYBmbNtfyzNJRJ05uBeT22HHjFuLEKUJwYDbPtEbTcfpZo1GEunN6BibUi4CX++ZLw4SoOMdNV8DMB/CFTNf+agKIW0OmifNdt2nsy2LSYDBw7k008/5e2338bf35/Dhw+zfv367OZkV69eJTQ0NHv/r776itTUVPr374+np2f249NPPwVAp9Nx9OhRHnvsMWrVqsWoUaNo2rQp27dvx9LS0iifUYg72VuZ075WRV7q6sdLz7+MvkobrDRp/G77AUN0G9AbNMx2eIkQ59bGDrV8y07gfpVPAveb3AncyztVb4GicPsKbFf/p5Xr5mP5aTocHL1VKYt93z7QIbZs2UKtWrXYsGFDrud//fVX5syZA4C5uTmrV6/G3r4UXv/1U9RkDRtX6LewRJqyCVHaaQwGWe/9IGJjY3F0dCQmJkaWmj2Em3HJtJq2hQy9gf5NK9OvSWUCqjmj1Zayu35/vAgHFkObF+ERaeghRIm4uBW+6w1mVvDiMbBzM248Nw7Bgi5gyFANEcpr44zdX6lBtZUTTDwAtq7GjkjcL30GzAuAyHOqG3P7l40d0UORMVnhybUSJSb0CHzdAVC/av5P/zSLUgOxszTjnV516d+0cumb5VeepaeqcdeJNaqJWUpMzmt27lC3N9TtA1Vaqpt/D+KHp+DMOtV8bPgfUss2Lwe/U81vbVzghaNgaXdfbx8wYACrVq1Co9EwZcoU/ve//3Ht2jUaNmxIXJyqMf3ll18yfvz44oj+4RxdBatHAxoYuhpqdDZ2REIUq8KOyWSmrTAqN3srBgdUAeDnA9cYtGA37Wb8w4z1pzl/M+4e7y5B2XVtZaatECVmW+ZsjCbDjZ+wBfBqnJPcWvcSxIUXvH9ZFBsKWz5U24HvSsLWVP23tm1KKfp5K4QoGzwbQbOn1XanNxj+wgc0q1qB+JR0Xvn5KOO+P0BkfIpxYxQ5zCygVtfMGbjn4alV4D9YNYGND1czcJf0UDNw1718/zNwz25QCVutGfT4VBK2+Wk0SDXATYyEPfPv662JiYn88ccfABgMBqZPn0779u3p379/dsK2b9++PPPMM0Ue9kO7dVaVIANo/4okbIW4g8y0fUAyU6Ho6PUG9l2OYs2h66w7Fkpcck5TsoaVHXm8cSV6NfLC1c6Iy/+uH4QFncDaGV69KAMNIYrb1T2wqCtozeGFw+BY2dgRKempsLAzhB0Dvx7w5Iry9f/Bz0+rjr6VmsGojaCVe78mS58B81pA5Hno8rZq9mGiZExWeHKtRInSZ6il3pk/wzP0Br7edoHPN54lLcOAq50F0/s2JLCuu5EDFfnKmoF78lc4vRaS/zMDt85jUK8PVGmV/wzctGT4sqWqZdt6oiqNIPKXNePUylHNtrV2KtTb1qxZQ9++fXM9p9PpyMhQyXVvb2+OHTuGo6NjUUf8cFITYWEXuHlSzcIe9tuDz+YWwoTITFthMrRaDQHVXZjeryH73ghk3lNN6FLbDTOthqPXYnjvj5MEfLSZp5fsY+3RGySnFVFdpfvhXk/dGU6KgphSXH9XiLIiq+aZ/6DSk7AFNRPl8a9VMvnMn3DkB2NHVHIu/KMSthotPDpTEram7s7atrtktq0Qohhodbl+huu0Gp7t6Muvz7XBz92eiPhURn+3nym/HCU+Jb2AAwmjyZqB2+dLeDmPGbj7FsCSnnfMwN1x9wzcXXOk+dj9qN8XKtZRCfLguYV+208//YSZWe4asFkJW4B27dphY2NTZGEWmb9eVQlbWzfo960kbIX4D5lp+4BkpkLxi4xP4Y8jN1hz6DpHruXc1bW3NKNHA08eb1KJFj4lWP92fls1u27gMqjTq2TOKUR5FHoEvm6vkoMT9oNLDWNHdLftn8Hm/4GlAzwbXLoSy8UhPQW+aq1mZQY8A90/NnZEoihkpMOXAZmzbd+BdpONHdEDkTFZ4cm1EqVFcloGn204w8IdlzAYwNvZmpkD/GnuY8Smo6Lw0lPh0r+ZNXALmIHr4AVftoL05PLdD+B+nfwdfhoKFnbwwpF7lqNKTk7G2dmZpKSkfPfRaDQ0a9aMVatWUbVq1aKO+MEc+RHWjFNj/qG/QvUOxo5IiBIjM22FyXOxs2REm2r8NqEtmyZ3YEInXyo5WROXks7K/SE8+Y2qf/vp32e4cCu++APy9Fdfbxwu/nMJUZ5l1bKt3790JmwBWr+gSgSkxMJvE6Cs3//cOUcl9uzcodPrxo5GFBWdmaodB7DrC0gpgZ+lQggBWJnreKNnXX4Y05JKTtaERCUx4Otgpv91mpR0I6yqE/fHzAJqPpIzA3fwz+A/5O4ZuHObq4RttfZQr++9jyuUOr1UXejUeNjx+T1337BhQ4EJW1B1bg8ePEiDBg1Yu3ZtUUX64G6ehrWT1HaHKZKwFSIfkrQVJsHXzY6Xg/zY/monfhzbkoHNvLG3NON6dBJz/zlPl8/+pffcHSzZean4mhpIMzIhit/N03BKNVEo1bP+dGbw+Hwws4KL/8D+RcaOqPhEXcopVxH0kfqFTJQd9furpidJUeqXbCGEKEEtq7uw/sV29G9aGYMB5v97gd5zd3I6LNbYoYnCyk7gzvtPAtcJ9OmqpJQ0H7s/Gg10fktt71uoGsEWYNWqVXeVRshLRkYGcXFxvPPOO0UR5YNLTYBVwyEtEap3zGn0K4S4i5RHeECyvMz4ktMy2HgynDWHrvPv2Vtk6NVfZTOtho5+FXm8cWW61HHDyryI6uKE7INvA8G2Irx8TgYeQhSH1WPh6Eo1w2DgMmNHc2/BX8LfU8HcFsbvBOdqxo6oaBkMsGIAnNugZskM+13+7yuLDv8Avz4DNi6q6YmlnbEjui8yJis8uVaiNFt/PIzX1xwjKiEVC52Wl4NqMaptdXQlVQpNFK30VLiyE2yccya/iMIzGGBRNwjZDc1HQ8/P8twtJSUFV1dX4uMLt1qmS5cufPfdd3h5eRVltPdnzXg4skKt4HpmB9i5GS8WIYxEyiOIMs/KXEevRl4sGtGcPa934Z1edWlY2ZF0vYFNp27y3IqDNP9wE1N+Ocqei5Ho9Q95f8KjPmh0kHBLdcIVQhStqItw7Ge1bSqd7AOegaptIS0BfnsO9HpjR1S0Tq9TCVutOfT4TBK2ZVWDJ9Rs28RINaNHCCGMoFt9D9a/2I4utd1IzdDz0Z+nGbRgNyFRicYOTTwIMwuo0UkStg9Ko4HOb6rtA0vh9pU8d9u0adM9E7Y6nQ4LCwu++OILNm7caNyE7aFlKmGr0ao6x5KwFaJAkrQVZYKrnSUj21Tj9wlt2TS5Pc92rIGXoxVxyen8uC+Egd/spv0n//DZhjNcfND6t+bWUNFPbUuJBCGK3o5ZYMgA30fAq7GxoykcrRZ6z1Uzba/shD3zjR1R0UlNgL8yuzy3eR4q1jJuPKL4/Le2bWqCceMRQpRbbvZWLBzejOl9G2BjoWPvpSi6z97Oqv0hyAJRUe5UawfVOoA+Df6dkecuP/30U4GlETQaDfXr1+fIkSNMmDABjTFvwIefhHWZpRA6vQ4+bY0XixAmQpK2oszxdbPn1W612fFaZ34Y05IBzSpjZ2nGtdtJfLHlPJ0/+5fe83aydNdlohJS7+/g0owst5unYEFn2Pg26KVphHgIMdfh8Aq1bWp1rZyrQdAHanvzexBxzrjxFJV/Z0DsNXCsAu1M7M9E3L8GA6BCNUiMgH3fGjsaIUQ5ptFoeLJFFf56oR3NqlYgPiWdV34+yrjvDxRf7wohSqus2rZHVkDE+VwvpaamsmbNGtLT0+96m1arRaPRMHXqVPbu3Uvt2rVLItr8pcSrOrbpSVCjC7Q1kVV1QhiZJG1FmaXVamhVw4UZ/Rux741A5gxqTCe/iui0Go6ERPPO7ydo8eEmRi/dz5/HQklOK0TSUZqR5Yi9Acv6wfUDsHM2rBwis7PEg9s1R80iqNoWqrQ0djT3r+lIqNFZdUhe8wxk3D14Nik3T0PwXLXdYwZY2Bg3HlH87pxtu3O2/H8uhDC6qi62rBzXile7+WGu07DhZDhBs7ax6WS4sUMTouR4N4da3cCgh63Tcr20ZcsW4uLi7nqLTqejcuXK7Nixgw8//BALC4uSijZvBgOsmwwRZ8HeC/p+o1arCSHuSf6liHLB2kLHY428WDyyBbunduHtR+tSv5JDZv3bcJ5dfpAWH25i6upj7Lsclf/yKy9/9bW8J22TY2D5ExB7Xc3CM7OCM3/Ckkch/qaxoxOmJv6mqtUFpjfLNotGA499AZaOcH0/7Jpt7IgenMEA615SHZ/9eoJfd2NHJEpKw4FQwUfNtt2/yNjRCCEEOq2GZzv68utzbfBztyciPpXR3+3ntZ+PEp9i4jdIhSisTm+or8d/gfAT2U//tzRCVumDESNGcPz4cVq3bl2iYebr4Heq0bBGp+rY2roaOyIhTIYkbUW5U9HekqfbVmPtxHZsnNSe8R1r4OloRWxyOj/svcoT84Np/8k/zNxwhksR/5lp5F4f0EDcjfKbnExPhZVDIfy46vg5Yq3qKG/tDDcOwsLAsrM8XJSM4HlqqVSlplC9o7GjeXCOlaH7dLX9zzQIO27ceB7U0Z/gyg4ws875PKJ8uGu2rTT/EUKUDvW8HPltQhvGtKuGRgMr94fQffY29l2OMnZoQhQ/z4ZQtw9ggH8+AiAtLY1ffvkluzSCTqfDycmJX3/9lYULF2Jvb2+8eO8Udhz+elVtd3kLqrYybjxCmBiNQSq6P5DY2FgcHR2JiYnBwcHB2OGIh6TXG9h9KZLVB6/z17FQElJzSiU0ruJE38aVeLShFxVsLWBuc7W0Y/DPUPMRI0ZtBAYDrBmn7pSa28LIP3NmH0ech+X94PZlsK4Ag340zWXuomQlRsGsBpAar/7OmPqsToMBfnxKzTz3aACjt6juyaYiKRrmNoOEW9DlHWg32dgRiZKWkQZfNIXoK9D1Q2g9wdgR3ZOMyQpPrpUoC3ZfjOSln45wPToJjQbGta/BpEdqYmmmM3ZoQhSfW2fgy5aqTMKYLWw4EUlQUFD2yz169GDRokW4u7uXXEzpqZASBymxmV/veKRmfj2wBKIuQs2uMGillEUQIlNhx2SStH1AMugtu5JSM9hwMow1h66z/VwEGXr1T8Rcp6Gjnxvvpc/C6+of0OlN6PCKkaMtYZv/B9s/U0tbnvoJagbmfj3+FvzwpFoerrOEvl9DvceNE6swDVunq/pc7vXhmR2qzICpiwtXg+qkKGj/KnR+w9gRFd66l2DfQnCtBc/sNK2Esyg6B7+H3yeAbUV44Wipr2ksY7LCk2slyoq45DTe++MkPx+4BkBtD3tmPelPbQ/5ey3KsDXPwJEfoEYXxmxzYeHChVhZWTF79mzGjBmTXR6hQAaDqlufK8kam3fC9a5H1n7x6mtGIRsDOlSCcdvB1uXhPr8QZYgkbYuZDHrLh1txKfx+5AarD17jxI1YAEbp1vGW+XJOOLYn6fGlNK1aoXA/IE3dvm9VAXmAx+ZCk6F575eaCL+MhjPr1PddP4BWE8pGMk4UrZQ4+Lw+JEdD/8VQv6+xIyo6x1fDzyPVDY7Rm6BSE2NHdG/XD8KCzoABhv8B1dobOyJhLHfOtg36CFo9Z+yICiRjssKTayXKmvXHw3h9zTGiElKx0Gl5OagWo9pWR6eVcacog6IuqRVR+nTa/eZBcrqBFdMmUtPDrpBJ18wHRZwCsrBTD0v7/zwcwMYZmj0NLjWK9pxCmDhJ2hYzGfSWP2fD41h98DpXD27gy7S3uGZwpW3KHKo42/B440o83rgSPq62xg6zeJz5Sy35Nuih41ToOKXg/fUZsH4K7P1Gfd9iHHSbBlpZtibusGMWbHoHXGrCc3vK3t+PVSPhxGpw9YNx28DcytgR5U+fAQu7wI1D0GAA9Ftg7IiEsR1YCn88D7Zu8MKRUj3bVsZkhSfXSpRFN+OSmfrLMTafVv0mWlRz5rMnGuHtXHr/3xLigf3xIhxYTEq6AQsdDz55SKPLSazelWwt6PGf/S3syt4YXogSIEnbYiaD3vIrIzEa3YyqALTRL+B6ak6itkkVJx5vUpleDT1xsikjy4qvHYAlPVWjqMZD1Czbwi69CZ4HGzKXhvv1hH4LS/Uv/qIEpSWpWrYJt6DPV+D/lLEjKnqJUTAvABJuQuvnoev7xo4of/sWqtIIlg4wYT/Yl2A9NFE6pafC3KYQfRWCpkGrZ40dUb5kTFZ4cq1EWWUwGFi5L4T/rT1JYmoGdpZmvNOrLv2bVi4fK+JE+REXpibTJEXnkUi1yz+5eudzFnZgbi0rIYUwIknaFjMZ9JZzcxpD1EWSB/3M30l1WX3wOtvP3SKz/C3mOg2da7vxeOPKdKpd0XQbI0RegG+7QmIE+AaqRlE68/s7xok1sHqcqnlUqakqQG9XsXjiFaZjz9eqk6xTFZh48P7/XpmK03/Cj4MADTy9vnQ254u/CV80g5QY6P4JBIw1dkSitDiwBP54Aezc1Wxbc2tjR5QnGZMVnlwrUdZdiUzgpZ+OsP/KbQC61nVnWt8GuNhZGjkyIYQQIkdhx2TSuk+IB+HpD4DVzWP09q/E0qdbsHtqF97sWYe6ng6kZRj4+0Q4zyw7QIsPN/P+2pNEJaQaN+b7lRABy/urhK1nI3hi6YMl1uo9DsN+A+sKcP0AfBsIEeeLPl5hOtJTYedstd3mxbKbsAWo3QMaPQUYVPOI1ARjR3S3jW+rhK1HQ2g+ytjRiNKk0VPgWAXiw1UCVwghSrmqLrasHNeKV7v5Ya7TsOFkOEGztrHpZLixQxNCCCHumyRthXgQno3U19Aj2U+5OVgxul11/nyhHX+/2J5xHarj4WBFTFIa3+64RIdP/uGbbRdISc8wUtD3ITURVgyEqItqJuRTq9RymwdVtRWM2ghOVeH2Zfj2Ebi6p8jCFSbmyA8Qex3sPMB/sLGjKX7dpqmuubcvwaZ3jR1Nbpd3qD8PNPDoLKlJJnIzs4B2mQ0od8xSZU2EEKKU02k1PNvRl1+fa0Mtdzsi4lMZ/d1+Xvv5KPEp6cYOTwghhCg0oydt582bh4+PD1ZWVgQEBLB379589129ejXNmjXDyckJW1tb/P39+f7773PtYzAYePvtt/H09MTa2prAwEDOnTuXa5+oqCgGDx6Mg4MDTk5OjBo1ivj4+GL5fKKM8vJXX0MP5/myn4c9U7vXYeeUziwe2Zy6ng7EJafz0Z+nCZz5L+uOhlJqK5PoM+CXUXB9P1g5weBfiqa+pWtNGL0ZvJpAUhQs7QUnf3v44wrTkpEOOz5X222eL93NuYqKtRM89oXa3vsNXNxqzGhyZKSpOrYAzUZC5abGjUeUTv6DwdEb4sNUczIhhDAR9bwc+X1CW8a0q4ZGAyv3h9B99jb2XY4ydmhCCCFEoRg1abty5UomT57MO++8w8GDB2nUqBFBQUHcvHkzz/2dnZ154403CA4O5ujRo4wcOZKRI0fy999/Z+8zY8YM5syZw/z589mzZw+2trYEBQWRnJycvc/gwYM5ceIEGzduZO3atWzbto2xY6WGn7gPHg3V19uXIel2vrvptBo6+bnxx8S2fNK/Ie4OloREJfHcioP0nx/Moav5v9coDAb48xU48yfoLFUN24q1iu74dhVhxFrw66Fq3P40XDUrE+XHiTVqxqmNCzQdYexoSo5vF2j2tNr+bQIkxxo3HoDdX8Kt02DjCl3eNnY0orTKNdv2c0hLLnh/IYQoRazMdbzRsy4/jGlJJSdrQqKSGPB1MNP+OmUaq9+EEEKUa0ZtRBYQEEDz5s2ZO3cuAHq9Hm9vbyZOnMiUKVMKdYwmTZrQs2dP3n//fQwGA15eXrz00ku8/PLLAMTExODu7s6SJUt48sknOXXqFHXr1mXfvn00a9YMgPXr19OjRw+uXbuGl5dXoc4rjRwEsxqortrD/4Bq7Qv1lsTUdL7ZdpGv/71IUpoaKD7WyItXu/lRuYJNcUZbODs+z1y+rYEBS6Fu7+I5jz4D/noN9i1Q3wc8A0EfydLssk6vh69aqURh57eg/cvGjqhkpcTDV60h+go0Hgq95xovlphrMLc5pCVCn6/A/ynjxSJKv/RU1YAz9hp0nwEB44wdUS4yJis8uVaiPItLTuO9P07y84FrANT2sGfWk/7U9pB/C0IIIUpWqW9ElpqayoEDBwgMDMwJRqslMDCQ4ODge77fYDCwefNmzpw5Q/v2KmF26dIlwsLCch3T0dGRgICA7GMGBwfj5OSUnbAFCAwMRKvVsmeP1NgU9yGzGRk3Dhf6LTYWZrwYWIutr3TkiaaV0Wjg9yM36PzZv3y8/jRxyWnFEmqhHP0pp95mt2nFl7AFlZzt8Qk88r76fs98+GmYqqUryq4z61TC1tIRWowxdjQlz9JOJUjRwKHv4ewG48WyfopK2FZpDY0GGS8OYRpktq0QogywtzLn0ycaMX9IU5xtLTgdFsdjX+zkm20XyNCX0rJlQgghyjWjJW0jIiLIyMjA3T13rUx3d3fCwsLyfV9MTAx2dnZYWFjQs2dPvvjiCx555BGA7PcVdMywsDDc3NxyvW5mZoazs3OB501JSSE2NjbXQ5RzeTQjKyx3Bys+eaIRaye2pXUNF1LT9Xy19QIdP9nKst1XSM/QF3Gw93DxX/j1WbXdagK0HF/859RoVE3T/otBZwGn18J3j0FCRPGfW5Q8gwG2faK2W4wBK0fjxmMsPm2gZea/td8nQqIR6uqd3QCn/gCNDnp+pv4tCnEvjYeAQ2WIC4WD3xk7GiGEeGDd6nuw/sV2dKntRmqGno/+PM2gBbsJiZLJA0IIIUoXozciu1/29vYcPnyYffv28eGHHzJ58mS2bt1a7OedNm0ajo6O2Q9vb+9iP6co5e7RjKww6nk5snx0AAuHNaN6RVsiE1J589fjdJ+9nX/O3CyZZmXhJ2DlENCnQb3Hc2a/lpT6fWHYb6rp2bV9sDAQIi+UbAyi+J3frG5wmNvkJC3Lqy5vgUtN1djpr1dL9txpSfBnZlmKVs+Ce92SPb8wXWaW0G6S2pbZtkIIE+dmb8XC4c2Y3rcBNhY69l6KouOnW2kzfQtPzN/F8z8cYtpfp1i66zIbT4Zz/HoMUQmppbeRsBBCiDLJzFgndnV1RafTER4enuv58PBwPDw88n2fVqvF19cXAH9/f06dOsW0adPo2LFj9vvCw8Px9PTMdUx/f38APDw87mp0lp6eTlRUVIHnnTp1KpMnT87+PjY2VhK35Z1H5kzbyPOqqZDVg9XD0mg0BNZ1p4NfRX7Ye5XPN57l3M14Ri7eR7uarrzRs07x1dqKuQ7L+kNKrFom3Wc+aI1wL6dqaxi1EZb3U02qFgbCUyvBu0XJxyKK3p2zbJs9DbYuxo3H2Myt4fH58O0jcGwV1OlVvOVI7rR9pqqpa+8FHQpXO16IbI2Hqr9DsddViY/yWOZECFFmaDQanmxRhVY1XHh51RH2Xb7N9egkrkcnAXk3C7Yy1+LpaI2noxWejtZ4OVnh5aS+z/pqb2Vesh9ECCFEmWW0pK2FhQVNmzZl8+bN9OnTB1CNyDZv3syECRMKfRy9Xk9KSgoA1apVw8PDg82bN2cnaWNjY9mzZw/jx6vl3q1atSI6OpoDBw7QtGlTALZs2YJerycgICDf81haWmJpafkAn1SUWXYVwaGS+uU1/LhKPD4Ec52WYa186O1fiXn/nGfJzstsPxdBj9nbGdjcm0mP1MLN3qqIggeSY2B5f4i7Aa5+8ORyMC/C49+virVg9GZYMQBuHIKlvaDvAqj7mPFiEkXjyk4I2Q06S1V+Q0DlZtDmRdgxE9ZOUjdN7CoW7zkjzsPOWWq7+3RVY1eI+2FmCW0nqdnaOz6HJsPUc0IIYcKqutjy07hWhMYkExqTxI3onK83opOyn4+ITyU5Tc+liAQuRSTkezx7SzM8ne5I6jpa4+lkjZejFZ6ZiV0rc2m+K4QQ4t6MlrQFmDx5MsOHD6dZs2a0aNGCWbNmkZCQwMiRIwEYNmwYlSpVYtq0aYAqUdCsWTNq1KhBSkoKf/75J99//z1fffUVoO6Wvvjii3zwwQfUrFmTatWq8dZbb+Hl5ZWdGK5Tpw7dunVjzJgxzJ8/n7S0NCZMmMCTTz6Jl5eXUa6DMGGe/ippe+PwQydtszham/N6jzoMCajKx+tPs+5YKD/sDeH3wzcY37EGo9pWx9riIQd66anw42C4eRLs3GHIz2DjXCTxPxQ7NxixDn5+Gs6uV83Jgj5Sy7iF6cqaZdt4CDh4FrxvedJxCpz9G26egHWTYMD3xVdf1mCAP1+CjFTwDYQ6cjNEPKAmw3LPtm0+2tgRCSHEQ9NoNHg5WePlZE3Tqnnvk5yWQXhscnZSNzQmJ6l7IzqJG9FJxCanE5eSTlx4PGfD4/M9n7OtRfZs3UpOOcncrNm67g5WmOtMrpKhEEKIImbUpO3AgQO5desWb7/9NmFhYfj7+7N+/frsRmJXr15Fe8dS7YSEBJ599lmuXbuGtbU1tWvXZtmyZQwcODB7n1dffZWEhATGjh1LdHQ0bdu2Zf369VhZ5cwgXL58ORMmTKBLly5otVr69evHnDlzSu6Di7LDsxGcWfdAzcjupYqLDfMGN2Hk5Sg+WHeKwyHRfLrhLMv3XOWVID/6+FdCq32ABI9eD789B5e3g4UdDF4FTlWKPP4HZmELA5erWp/7v4W/p0L0VQj6ELQyK8HkXNsPF7eC1gzavGDsaEoXM0tVJmFBJ9UY7NgqaDigeM51Yo36c9BZQvcZ0nxMPDgzS2g3Wc223T5TlUyQ2bZCiHLAylxHVRdbqrrY5rtPQkr6XbN1/5vgTUzNICohlaiEVE7cyLu5tVYDFe0tM5O6meUY7pitW83FFkcbKcMghBBlncYg1dQfSGxsLI6OjsTExODgUEz1RkXpd/ZvtZy/Ym14bk+xncZgMPDH0VA+/ut0Zp0taFDJkTd71iGg+n3WB930rlrWqjWDp34C3y5FH3BRMBhg52zY9I76vk5muQRza+PGJe7Piifh7F/gPxj6fGnsaEqnf2fAPx+ClSM8u6foZyMnx8Lc5qrxWcfXoeNrRXt8Uf6kJcMcf4gLhZ4zofkoo4YjY7LCk2slhHEZDAZik9K5Hp2kkroxyYRG507qhsUkk5qhL/A4Wg0EVHOhRwMPgup54OZgxBJnQggh7lthx2SStH1AMugVAMSFwWd+oNHC1GtqlmgxSk7LYNHOS3z5zwXiU9IBCKrnzpTudajmWohz712Q0zm+95fQeHAxRltEjv0Mv45Xy7ort4BBP0ojK1MRdgzmtwU0MGE/uPoaO6LSKSNNNSW7cQh8H1Gz34tyJuz612H3PHCuDuODjVu7WpQde76Bv14Bh8rw/EGjzraVMVnhybUSovTT6w1EJqT+Z8Zu7gRvaExy9v4aDTSrWoHu9T3pVt8DLyeZ4CCEEKWdJG2LmQx6RbZP/dQMtqc3QJX8m9kVpYj4FD7feJYf9l5FbwBznYahLX14vosvTjYWeb/p9DpYOQQMeuj0BnR4tURiLRKXd8KPg1TzNOfqMPhncKlh7KjEvawaoZbl1+sLTyw2djSl283T8HV7yEiBXnOg6fCiOW7YMfi6AxgyYMjq0juzXpieO2fbPvo5NHvaaKHImKzw5FoJUTaERCWy/ngYfx4P5dDV6Fyv+Xs70aOBB93re+LtbGOcAIUQQhRIkrbFTAa9ItvyAXDub+j+CQSMLdFTnw2P46M/T7H1zC1ANTF7vktNhrasioXZHc0LQvbB0l6QnqSayPSaY3o1LW+dgWX9IeYq2LjAoJXg3dzYUYn8RJxTS/IxwDM7waO+sSMq/XbOgY1vqVrT43dBhXw6oRSWXg+Lu0HIHqjbBwYsLZIwhci252tVf9zRGyYeBLN8bhoWMxmTFZ5cKyHKnhvRSfx9Ioy/joWx70oUd/5236CSI93qe9CjgWfhVuUJIYQoEYUdk0lLSiEelmcj9bUYmpHdSy13e5aMbMF3T7fAz92emKQ03l97kq6f/8v642EYDAaIvAA/DFQJ25pdoefnppewBajoB6M3gac/JEbC0kdV8yZROm2fCRjAr8ddCdsRI0ag0WiyHy4uLnTr1o2jR48W2enfffdd/P39i2y/EtHqOfBuCanxqlmgXs/Ro0dp164dVlZWeHt7M2PGjAIPceTIEQYNGoS3tzfWNlbUmbKZ2fuBbtOy9/nv9c961KtXL9ex5s2bh4+PD1ZWVgQEBLB3797i+NTClDUZDnYeEBMCh5cbOxohhCiXvJysGdmmGj8904o9U7vwfu96tK7hglYDx67H8MnfZ+j06Va6zdrG7E3nOBceZ+yQhRBCFJIkbYV4WF7+6mvoYaOF0L5WRf58oR3T+jbA1c6Sy5GJPLPsAGO+Wk/K0sdVktPTH/ovBp2Z0eJ8aPbuMGId1AyC9GRYORR2zzd2VOK/bl+BoyvVdruX89ylW7duhIaGEhoayubNmzEzM+PRRx8twSBLIa1ONWszt4HL24nd+gVdu3alatWqHDhwgE8++YR3332Xb775Jt9DHDhwADc3N5Yt/JITz7vxRjsLpm5KYu53q7P3mT17dva1Dw0NJSQkBGdnZ5544onsfVauXMnkyZN55513OHjwII0aNSIoKIibN28W6yUQJsbcCtpOUtvbP4P0VOPGI4QQ5ZybgxVDW/mwYkxL9r0RyLS+DWhfqyJmWg2nw+L4fNNZHvl8G4Ez/2XmhjOcvBGLLLwVQojSS5K2QjysrJm2N0+pGn9GotNqGNSiCltf6ciETr44maUyIex1LGOvEGnuSdij34OlndHiKzKWdvDkCmg6EjDA+tdUoyV9wV12RQnaOUvVUK3eCSo3zXMXS0tLPDw88PDwwN/fnylTphASEsKtW7ey9wkJCWHAgAE4OTnh7OxM7969uXz5cvbrW7dupUWLFtja2uLk5ESbNm24cuUKS5Ys4b333uPIkSPZs0iXLFnyQB/l2LFjdO7cGWtra1xcXBg7dizx8fH3jAHUrNdOnTphb2+Pg4MDTZs2Zf/+/QWf0KUGPPI/AJbPeovUlGQWLVpEvXr1ePLJJ3n++eeZOXNmvm9/+umnmT17Nh1SNlDdJo4hgY0Z+fQoVq/OSdo6OjpmX3sPDw/279/P7du3GTlyZPY+M2fOZMyYMYwcOZK6desyf/58bGxsWLRo0YNcRlGWNR0Odu5qtu2RFcaORgghRCYXO0sGtajCd0+3YP+bgXzSvyGda7thodNy/mY8c7acp8ec7XT6dCsfrz/N0WvRksAVQohSRpK2Qjwsh0pg46qSVOEnjB0NdpZmvBxYg901l+Ovvchtgx1PxL9Eh69O8NmGMySkpBs7xIenM1ONbwLfVd/vngerhkNaklHDEkBsKBxaprbbv1Kot8THx7Ns2TJ8fX1xcXEBIC0tjaCgIOzt7dm+fTs7d+7Ezs6Obt26kZqaSnp6On369KFDhw4cPXqU4OBgxo4di0ajYeDAgbz00kvUq1cvezbpwIED7/ujJCQkEBQURIUKFdi3bx+rVq1i06ZNTJgwAaDAGAAGDx5M5cqV2bdvHwcOHGDKlCmYm5tnHz/fZHKzUVCtPcFXkmnvY4GFmS77paCgIM6cOcPt27fzDzxkLxz8Tm33/IyY2DicnZ3z3f3bb78lMDCQqlVVDd3U1FQOHDhAYGBg9j5arZbAwECCg4PvddlEeWNuDW1eVNvbZLatEEKURk42FjzRzJtFI5qz/61AZg30p2tddyzNtFyOTOSrrRd4bO5O2n78Dx+uO8mBK7fR6yWBK4QQxmbC66SFKCU0GjXb9sJmVSIhn5mFJcZggD9fxurSRjCzIrL7Ulz32XLxchRfbDnPj/tCeOmRWjzRzBud1gRr22bRaNSyXEdv+HU8nPodvguHJ38AWxdjR1d+Bc+FjFSo0gp82uS729q1a7GzUzO/ExIS8PT0ZO3atWi16l7iypUr0ev1LFy4MDsJunjxYpycnNi6dSvNmjUjJiaGRx99lBo1agBQp06d7OPb2dlhZmaGh4fHA3+UFStWkJyczHfffYetrWreMXfuXHr16sXHH3+Mubl5gTFcvXqVV155hdq1awNQs2bNXMf38/PD0dHx7hNrtdB7HmEf1KKaQ7S6pm1eAMDd3R2AsLAwKlSocPd7M9Jh7WS17T+EXdcNrFy5knXr1uX5GW/cuMFff/3FihU5MyQjIiLIyMjIPlcWd3d3Tp8+nc/VEuVas5Fqhn3MVTjyg5p9K4QQolRysDKnT+NK9GlciYSUdP45c5O/joWx5fRNrkcnsWD7JRZsv4SHg1V2E7OmVSuY9u8NQghhomSmrRBFwYjNyO6yYyYcWAxooO8CfJsGsnJcS+YPaUpVFxtuxaUwZfUxes7ZzvZzt+55uFKvQX8YugasHCFkD3z7CERdNHZU5VNCJOzPXD6fTy3bLJ06deLw4cMcPnyYvXv3EhQURPfu3XOVFjh//jz29vbY2dlhZ2eHs7MzycnJXLhwAWdnZ0aMGEFQUBC9evXKrtNalE6dOkWjRo2yE7YAbdq0Qa/Xc+bMmXvGMHnyZEaPHk1gYCDTp0/nwoULuY5/+vRpHn/88bxP7lQFXHzV9pYPVPmVwti3AMKPgZUTx72epHfv3rzzzjt07do1z92XLl2Kk5MTffr0KdzxhciLuXX2jQW2fwoZacaNRwghRKHYWprxaEMv5g1uwsG3HmH+kKb09vfCztKMsNhkluy6zICvgwn4aDNv/nqMXecjSM+QkmRCCFFSJGkrRFEoBc3IADjyI2xW9TDp/jHUfQxQy7C71fdg46QOvPVoXRytzTkdFsfQb/cycvFe0+8i69MWnt6gZt1GXYCFj8C1e9QOFUVv95eQlqia3vl2KXBXW1tbfH198fX1pXnz5ixcuJCEhAQWLFgAqJIJTZs2zU7sZj3Onj3LU089BaiZt8HBwbRu3ZqVK1dSq1Ytdu/eXdyfMpeCYnj33Xc5ceIEPXv2ZMuWLdStW5c1a9YU+tgetZoQrqmoZi6veQYy0ggPD1ev5TWDODYUtnwIwMkaY+nSqz9jx47lzTffzPP4BoOBRYsWMXToUCwsLLKfd3V1RafTZZ8rS3h4+EPNXBZlXNORYOsG0ZmzbYUQQpgUawsd3ep7MPvJxux/M5BvhzejX5PKOFiZERGfwrLdV3lq4R5afLSZKb8c5d+zt0iTBK4QQhQrSdoKURSyZtqGnzRePb8L/8Bvz6nt1hMhYNxdu1iYaRnVthr/vtKRp9tUw0yr4Z8zt+g2eztv/nqMiPiUEg66CLnVhtGb1J9FYgQseRRO570kXBSDpGjY+43abv+yKl9xHzQaDVqtlqQkVZe4SZMmnDt3Djc3t+zkbtbjzpICjRs3ZurUqezatYv69etnL/O3sLAgIyPjoT5SnTp1OHLkCAkJCdnP7dy5E61Wi5+f3z1jAKhVqxaTJk1iw4YN9O3bl8WLFxf6/K1atWLbhUTSzB3VDaHtM9m4cSN+fn55l0bY8AakxnFCU5tOE+YwfPhwPvzww3yP/++//3L+/HlGjRqV63kLCwuaNm3K5s2bs5/T6/Vs3ryZVq1aFTp+Uc5Y2OTMtt0ms22FEMKUWZnr6FLHnc8GNGL/m4+wZGRznmzuTQUbc6ISUvlxXwjDF+2l6fsbeemnI2w+FU5K+sONu4QQQtxNkrZCFAWnqmDlBPo0uHmy5M8fdgxWDgV9OtTrC4H/K3B3JxsL3u5Vlw2T2tO1rjsZegPLdl+l4ydb+XLreZLTTHTQZe8BI/6Eml0hPQl+HAx7vjZ2VOXDvgWQEgsV64Bfz3vunpKSQlhYGGFhYZw6dYqJEycSHx9Pr169ANXEy9XVld69e7N9+3YuXbrE1q1bef7557l27RqXLl1i6tSpBAcHc+XKFTZs2MC5c+eya8r6+Phw6dIlDh8+TEREBCkp+d+QSEpKumtG74ULFxg8eDBWVlYMHz6c48eP888//zBx4kSGDh2Ku7t7gTEkJSUxYcIEtm7dypUrV9i5cyf79u3LVfO2du3aBc68feqpp7CwtGTU7mqcuJnByi/fZ/asz5k8eXL2PmvWrFE1cy/8A8d/4fhNA52+OEfXrl2ZPHly9jW+devuUijffvstAQEB1K9f/67XJk+ezIIFC1i6dCmnTp1i/PjxJCQkMHLkyHv+2YpyrNnTYFsRoq+olR9CCCFMnoWZlo5+bkzv15B9bwSyfHQAgwOq4GpnSWxyOr8cvMaopftp+v4mXvjxEOuPh5nu7xJCCFHKSCMyIYpCVjOyS/+qurZZ5RJKQsw1WP4EpMZB1bbw+HzVyKgQqle045thzdh9MZIP1p3k+PVYZqw/w/LdV3mte216NfTMbgJlMiztVDOydZPh4FL461W1XPeR9wt9XcR9SomH4C/VdruXCnWd169fj6enJwD29vbUrl2bVatW0bFjRwBsbGzYtm0br732Gn379iUuLo5KlSrRpUsXHBwcSEpK4vTp0yxdupTIyEg8PT157rnnGDdOzTDv168fq1evplOnTkRHR7N48WJGjBiRZyxnz56lcePGuZ7r0qULmzZt4u+//+aFF16gefPm2NjY0K9fP2bOnJkdY34xpKenExkZybBhwwgPD8fV1ZW+ffvy3nvvZZ/jzJkzxMTE5HuNHB0d2bBhA8899xxN/07C1crA210rM/bpnCZPMTExnDlzBv5UNYR/jmnArchdLFu2jGXLlmXvV7VqVS5fvpzrfb/88guzZ8/O89wDBw7k1q1bvP3224SFheHv78/69evvak4mRC5Zs203vKlq2zZ6EnTmxo5KCCFEETHTaWnj60obX1f+17s++y9H8dfxMNYfDyMsNpnfDt/gt8M3sLHQ0cnPje4NPOhQqyL2VvKzQAghHoTGYDAYjB2EKYqNjcXR0ZGYmBgcHByMHY4oDTa8BbvmQLNR8OjMkjlnUjQs6ga3TkHF2vD0erDOY9l0Iej1Bn49fJ0Z688QFpsMgL+3E289WoemVZ2LMOgSYjDA9s9gy/vq+7p94PGvwdzKqGGVSbvmqqX5ztXhuX2gk/uBRS7+FnzZUpX+aDsZAt/JeW3bJ6pZmZ07TNinmvIJYSypCTDbH7xbQK/ZYOta7KeUMVnhybUSQhQHvd7AoZBo/joWyl/Hw7genZTr9coVrKntYY+fhz21PRyo7WGPj6st5jqZUCGEKJ8KOyaTpO0DkkGvuMvxX+Dnp6FSUxizpfjPl54Cy/rB5e1g56HquTp5P/Rhk1IzWLj9Il/9e4HEVLW0qWcDT17rVpsqLjYPffwSd/Qn+PVZVbrCuyUM+gFsTDAJXVqlJcPshhAfDo99AU2GGTuisuvk7/DTUNBoVeM97+YQdUklc9OTod+30KC/saMUApJuP/ANxAchY7LCk2slhChuBoOBY9dj+PNYGOuPh3I5MjHP/Sx0Wmq42eHnbodfZiLXz8MeT0cr01vpJ4QQ90mStsVMBr3iLpEX4IsmoLOE168X75JQvR5Wj1aJYgt7GPkneDYs0lPcjE3msw1n+elACAaDGliNaOPDc518cbQ2sSVOl7bBj0MgJQZcfGHwz+BczdhRlQ37FsK6l8ChMjx/CMwsjB1R2fbLGDj2k/p7PG47rBoO5zZAtfYw7Pf7bgAnRFkgY7LCk2slhChptxNSOR0Wx5mwWM6Ex3E6LI6zYXEkpOZd99bBygy/zATunclcBymxIIQoQyRpW8xk0CvuotfDx1VVM6ZndoLH3c19iszGt2HnbNCaweBVUKNzsZ3q5I1YPvrzFDvORwBQwcacFwNr8VRAFdNa0nTzFCzrD7HXwMYVnvoJKjc1dlSmLSMN5jSBmKvQ/RMIGGvsiMq+pNvwZSuICwXvAAjZA1pzGL8LKtYydnRCGIWMyQpPrpUQojTQ6w1cj07KTuaqr3FcjEggQ593esLL0UqVV/DMSeRWd7XDwsyEfh8RQohMkrQtZjLoFXla3BOu7IDeX0LjwcVzjj3fwF+vqO0+88F/UPGc5w4Gg4GtZ27x4Z+nOH8zHoDqFW15o0cdOtd2M50lTLGhsGIAhB0FM2vovwhq9zB2VKbr0HL47VmwdYMXj4K5tbEjKh/ObYTld5RBaPcSdHnbePEIYWQyJis8uVZCiNIsJT2DCzcTOBOek8g9ExZHaExynvubaTXUqGhHLQ97lch1V8ncyhWsTef3EyFEuSRJ22Img16Rp/Wvw+550GIc9JhR9Mc/tRZWDgEM0PlNaP9K0Z+jAOkZen7YF8LnG88SlZAKQOsaLjzZogq1PeypZgoNBVLiYNUIOL9J1QbtPgNajDF2VKZHnwFzm0PUBXjkf6pjvCg5v0+Eg9+BUxV4dg9YmGC9aSGKiIzJCk+ulRDCFMUkpnEmPPes3DNhccSlpOe5v52lGbX+Uyu3toc9TjZSxksIUToUdkwmLb6FKEpe/upr6OGiP3bIXvhlFGCApiOg3ctFf457MNNpGdqyKr39vfjynwss2nGJXRci2XUhEgBznYbqruput5+7HbUy73Z7V7BBqy0ld7st7WHQj7Buskp6/fkyRF+FwPdAW8oTzqXJyV9Vwta6AjR7Os9dZs6cSUJCAlOnTsXMTH7cFKluH0OFalCrmyRshRBCCFGmOdqY06KaMy2q5TQTNhgM3IhJviuRe+FWPPEp6Ry8Gs3Bq9G5juPuYJmTyM38PcXXzQ4rc10JfyIhhCgcmWn7gGSmgsjTrTMwrwWY28DUa6AtogFAxHn49hFIioKaQfDkCtAZPwkWEpXIwu0XOXY9hrPhaoCUFytzLbXc7anpZo+fR04y18PBiN1hDQbY/ils+UB9X+9xVW7C3Mo48ZgSvR7mt4WbJ6Dj69Dxtbt2Wb16Nf369QNg3LhxzJ8/v6SjFEKUEzImKzy5VkKIsi41Xc+liAROh8VmJ3JPh8VxPTopz/11Wg0+LjbU9nDA39uJtjVdqe1hL+UVhBDFSmbaCmEMLr5gbgtpCRBxDtxqP/wx42/B8n4qYevVGJ5YXCoStgDezja811s1XMu62302LI4z4XHZX8/djCc5Tc/RazEcvRaT6/32Vmb4udtnzsy1z07mOtuWwNIljUaVl3D0ht8mwIk1EBemEuI2zvd+f3l2dr1K2FrY59l87OzZswwdOjT7+0aNGpVkdEIIIYQQopyyMNPil1kS4U5xyWmcDY/LnpWb9TUmKY0LtxK4cCuBdcdCAahob0lbX1fa1XSlra8rbg4yqUMIYRwy0/YByUwFka9vgyBkNzz+DTQa+HDHSk2AJY/CjYNQwQdGbQQ7tyIJs6Rk6A1ciUzgbHgcZ8LiORsex9nwgrvDutpZ5szIzUzq1nSzw97KvHiCvPivqhWcEgsuNWHIz+p6i7sZDLCgs/o72XYSBL6b6+WEhASaNWvG6dOnAXjyySdZsWKFzFYQQhQbGZMVnlwrIYTIYTAYCI9N4XRYLKdC49hzKZLdFyNJTtPn2q+2h71K4taqSAsfZ6wtpJyCEOLhSCOyYiaDXpGvv16DPfOh5bPQbdqDHycjHVYOVrMarZ1VwtbVt+jiNLKU9AwuRSRwJiwuV0L3alRivu+p5GSNn0fWjFyV1K1RsYjqUIWfhOX9IfY62FaEp1ZCpaYPf9yy5sIW+P5xMLOGF4+BXcXslwwGA0OGDGHFihUA1KxZk4MHD2JnZ2esaIUQ5YCMyQpPrpUQQhQsJT2DA1dus/1cBDvORXD8Rgx3ZkwsdFqaV6tAW9+KtKvpSl1Ph9LTu0MIYTIkaVvMZNAr8nV4Bfw6Hqq2gZF/PtgxDAZY+yIcWAJmVjD8D/BuUZRRlloJKemcvxmfq8TC2fA4wmNT8txfqwEfF1tq3VFmwc/DDh8XW8x099lYLPYGLB8A4cdUXeL+i8CvexF8qjJkcQ+4shMCxkP36blemj9/PuPHjwfA2tqagwcPUrt2EZQIEUKIAsiYrPDkWgkhxP2JSkhl5/kItp+7xY5zEdyISc71uoutBW18XWlbU5VT8HS0NlKkQghTIknbYiaDXpGv8BPwVWtV73PKVdDeZ+IQYNunsOV9QAMDl0GdR4s8TFMTnZjK2XCVzD0XntkhNjyO6MS0PPe30GmpXtE2Z2ZuZr3cSk7WBd8NT46FVSPgwmbQaKHHJ9B8dPF8KFNzJRgWdwOtObxwBBwrZb+0d+9e2rRpQ3q6aka3cuVKBgwYYKxIhRDliIzJCk+ulRBCPDiDwcDFiAS2n73FjvMRBF+IJCE1I9c+vm52tPV1pX0tVwKquWBrWTp6kQghShdJ2hYzGfSKfGWkw7RKkJ4MEw7cf0mDwz/Ar8+o7R6fQosxRR9jGWEwGLgVn8LZsNwzc8+Fx901gMpiY6Gjprs9fu522Y3P/NztqWhvmVN3NSNNzXQ+tEx9X609tJoIvoEPloQvK5b1g/OboOkI6DU7++nIyEgaNmzIjRs3AHjhhReYNWuWcWIUQpQ7MiYrPLlWQghRdFLT9RwOiWb7uVtsPxfB0WvR3Nmyw1ynoUmVCrSr6Uq7mhWpX8kRnZRSEEIgSdtiJ4NeUaAFXeD6fuj3LTToX/j3XdgCy58AfTq0eQEe+V/xxViG6fUGrkcnqVq52cnceC7cjCc1Q5/ne5xszHM1PvNzs6PBxW+w3vkJGDITwK5+0OpZaPgkmJezLrLXD8KCTqDRwcQD4FwNAL1eT7du3di4cSMALVu2ZNu2bZibF1PTOCGE+A8ZkxWeXCshhCg+MYlp7LoQwbZzqpzCtdtJuV53tDanbWYphba+rng72xgpUiGEsUnStpjJoFcUaN1LsG8htJ4IXT8o3HvCjsGi7pAaB/X7Q98F5XtWZzFIz9BzOTJRlVcIz2qAFseliIRcd8Xv1Mg+huft/qF93DrM0xPUkzauqmRC89G5GnGVaT8OhtNrVcK679fZT7/77ru89957ADg7O3P06FEqVaqU31GEEKLIyZis8ORaCSFEyTAYDFyJTGT7+Qh2nLvFrvORxKWk59qnmqstbX1VLdxWNVywt5JJD0KUF5K0LWYy6BUFOvgd/D5RLasf/se9948OgW8fgbhQ8GkHQ34BM8vij1MAkJyWwcVbCf+ZmRuX6+64HYkM1G1ljMXfeBhuAWDQWaJpNBBaTYCKfkaKvgSEn4SvWgEaeG5P9mddv349PXr0wGAwoNFo2LRpE507dzZurEKIckfGZIUn10oIIYwjPUPPkWsx2Q3NDoVEk3HHrBGdVkNjb6fMhmYVaVTZ8f6bKgshTIYkbYuZDHpFgUKPwNftwcoRXrsCmgJqFyXdhkXd4NZpqFgHnl4P1k4lFqrIX3xKOieux7DrQiTBFyI5FHIbfUY63bT7GGO2Dn/thex9b3m0x7rDi9jV7lzwn7cp+mU0HFsFdXvDgO8AuHLlCo0aNSImJgaAadOmMWXKFGNGKYQop2RMVnhyrYQQonSITU5j94VItp+LYMf5CC5FJOR63d7KjNY1XGhbsyLta7pS1cXWSJEKIYqDJG2LmQx6RYHSU1UzsoxUeP5wdv3Pu/dLge/7wpUdYO8JozeBY+USDVUUXmJqOnsvRbHrQiS7zt/CKmw/o3V/0lW7H61G/Vd6UVedkz5DqdD8SZrWcMfKXGfkqB9S5AWY2wwMehi3DTwbkZKSQqtWrTh06BAAjz76KL/99htaKechhDACGZMVnlwrIYQonUKiEtlxXtXC3Xk+kpiktFyveztb09ZXJXBb13DF0UZKKQhhyiRpW8xk0Cvu6esOEHoYnlgK9frc/bpeD6tHw/FfwMJezbD1qF/SUYqHcDshld0XIzl18gjVzn9HUOombDQpAIQbnFim78apSv1oVLMarX1dTW6ZU1hYGB57PoBD30PNIBj8EwDjx49n/vz5AFSpUoUjR47g5ORkxEiFEOWZjMkKT66VEEKUfhl6A8evq1IK289FcPDqbdIyctI2Wg3YWpgVfJACFv4VtCZQU8CKwYIWE+b3kk6rpY2vC8NaVaVJlQoFHl+I8kSStsVMBr3inv54AQ4sgbaTIPDdu1/f8BbsmgNaM1XDtnrHEg5QFLWw8Bvc+udrqpxfhmN6BACJBktWZbRnUUZ3Ii0qE1DNmVY1XGjj64qfuz1abekcuKxatYoBAwbwShtLPupsgdmYTeDdgu+//55hw4YBYGFhwZ49e/D39zdusEKIck3GZIUn10oIIUxPQko6ey5Fsu2sKqVw/ma8sUN6IHU9HRjaqiq9/b2wuVfSWYgyTpK2xUwGveKe9i+CtZOgRmcYuib3a3u+hr9eVduPfw2Nniz5+ETxSU/FcPwXUrfPwTLyJAB6NGzMaMqC9B7sN/gBGlxsLWhZw4U2NVxp4+tCFWebUnP3uXv37vy9fj0AbWs589O/J7h16xbNmzcnJUXNJl60aBEjR440ZphCCCFjsvsg10oIIUzfrbgUElLS73o+v8ROQSmf/N9zf+/Ib//biWn8fCCE3w7fICVdD6h6vf2bVmZoy6pUr2iXb2xClGWStC1mMugV93T9ACzoDDYu8MqFnPUkp/6AlUMBA3R5G9q9ZNQwRTEyGODSNgieC+c2ZD990cKPucnd+C21GRnk1Lyt5GRN68xZuK1ruODmYGWMqImIiMDDw4OMjAwAdDodzs7OWFtbc/XqVQCefvppvv32W6PEJ4QQd5IxWeHJtRJCCGEM0YmprNp/jWV7rnAlMjH7+XY1XRnSsipdaruZVBk5IR6WJG2LmQx6xT2lJatmZPp0ePE4OHnD1T3w3WOQngzNnoaeMwsuDiTKjltnIHgeHPkRMtRM1RRbL3a7DWBRQjt2XU/NVasKwNfNjjY1XGjt60rL6i44WpdMw4H58+fz7Pjxue6ja7Va9Hp1d7xBgwbs2bMHa2vrEolHCCEKImOywpNrJYQQwpj0egPbzt3i++ArbDlzM3uGrpejFU8FVGFg8ypUtLc0bpBClIDCjsnkVoYQxcXcCirWUduhRyDiHPwwUCVsa3WH7p9IwrY8qegHj82BSSeg41SwccUy4QYdLs1iafQITrbZzo8DKzGufXXqV3JAo4HzN+NZGnyFcd8foPH/NvDY3B1M/+s028/dIik1o9hCXbZ08V1/NbMStgCVK1cmLS0NIYQQ+Zs3bx4+Pj5YWVkREBDA3r178913wYIFtGvXjgoVKlChQgUCAwPv2t9gMPD222/j6emJtbU1gYGBnDt3rrg/hhBCCFFktFoNHf3c+HZEc7a90olnOtSggo05N2KS+XTDWVpP38wLPx5i/+WoAss6CFFeGD1pW9QDWo1Gk+fjk08+yd7Hx8fnrtenT59ebJ9RlGNejdTXcxtgWT9Iug2VmkL/b0EnxdfLJbuK0HGKSt72mgOufpAah/neL2n5e2emJsxg7eM2HHrrEeYPacLQllWpUdEWvQGOXoth/r8XGPrtXhq9t4GBXwczZ/M5DlyJIi1Df+9zF0JISAi7du9FX8AYacOGDTRp0oQTJ04UyTmFEKKsWblyJZMnT+add97h4MGDNGrUiKCgIG7evJnn/lu3bmXQoEH8888/BAcH4+3tTdeuXbl+/Xr2PjNmzGDOnDnMnz+fPXv2YGtrS1BQEMnJySX1sYQQQogi4+1sw5TutQme2oWZAxrh7+1EWoaB3w7foP/8YHrM2cEPe6+SmHp3/V4hygujlkdYuXIlw4YNY/78+QQEBDBr1ixWrVrFmTNncHNzu2v/wYMH06ZNG1q3bo2VlRUff/wxa9as4cSJE1SqVAmAsLCwXO/566+/GDVqFOfPn6d69eqAStqOGjWKMWPGZO9nb2+Pra1toWOX5WWiUPYugD9fzvm+QjUYtVEl7oQA0OvhwmZV9/bi1pznq7SCVhPArztodYTFJLPrQgQ7z0ey60IEoTG5f0m3tdDRoppzZj1cV2p72KPV3v9M7k+nfcBrb7xVYNIWVJ1bc3Nzli1bRr9+/e77PEIIUVRK45gsICCA5s2bM3fuXECtVvD29mbixIlMmTLlnu/PyMigQoUKzJ07l2HDhmEwGPDy8uKll17i5ZfVuCImJgZ3d3eWLFnCk08WrqFpabxWQgghRJZj12L4fvdlaVwmyjyTqGlb1APavPTp04e4uDg2b96c/ZyPjw8vvvgiL7744gPHLoNeUSghe+HbR9S2jYtK2LrUMG5MovQKOwbBX8KxVaDPLD/gXB1aPgv+T4GFurFkMBi4HJnIzvMR7LoQQfCFSG4n5i5X4GxrQavqLrT2daF1DVd8XGzQFKIcRyNfL45eCC10yEFBQaxfv77wn1EIIYpYaRuTpaamYmNjw88//0yfPn2ynx8+fDjR0dH89ttv9zxGXFwcbm5urFq1ikcffZSLFy9So0YNDh06hL+/f/Z+HTp0wN/fn9mzZ+d5nJSUFFJSUrK/j42Nxdvbu9RcKyGEECIv+TUua+vrytBW0rhMmL7Cjl+Ntj47NTWVAwcOMHXq1OzntFotgYGBBAcHF+oYiYmJpKWl4ezsnOfr4eHhrFu3jqVLl9712vTp03n//fepUqUKTz31FJMmTcLMTJariyLmXh+sK6imZINWSsJWFMyjATz+FXR5G/YtgH3fQtRFNVt7yweqeV2LsWgcPKnmaks1V1uGtKyKXm/gVFgsu85HsvNCBHsvRRGVkMq6Y6GsO6YSsF6OVrT2daVNZhLX3cHqrtOfOXa4UAnbrKZkEydOZNq0aUV+GYQQwpRFRESQkZGBu7t7rufd3d05ffp0oY7x2muv4eXlRWBgIJCzkiyvY/53ldmdpk2bxnvvvXc/4QshhBBG52RjwZj21RnVtlquxmU7zkew43yENC4T5YbRspTFMaD9r6VLl2Jvb0/fvn1zPf/888/TpEkTnJ2d2bVrF1OnTiU0NJSZM2fme668ZioIcU8WNjBum9p2qmLcWITpcPBUidt2L8HhFbD7S5W83TETdn0BDfpDq+dUkhdV0L+elyP1vBwZ0746aRl6joREszMziXvo6m1uxCTz84Fr/HzgGgA1KtrSxteV5j7OVK9oS1UXW36Y/QY6DWQUsP5Cq9VSqVIlvvvuOzp27FgCF0MIIcqX6dOn8+OPP7J161asrO6+wXY/pk6dyuTJk7O/z5ppK4QQQpiCrMZlHf3cCIlKZPmeq6zcdzW7cdnszefo0cCToS2r0rRqhUKtLBTClJjs1NLCDGgXLVrE4MGD73r9zsFrw4YNsbCwYNy4cUybNg1Ly7zv0shMBfHAJFkrHpSFLbQYo2bYnl0Pu+bC1V1w5Af1qNZB1b31DQRtzvIgc52WZj7ONPNx5oXAmiSlZrDvchQ7M0spHLsew4VbCVy4lcB3wVfUewyppK3ekG/CNmt27bhx45gxYwZ2dlJPSggh8uLq6opOpyM8PDzX8+Hh4Xh4eBT43k8//ZTp06ezadMmGjZsmP181vvCw8Px9PTMdcw7yyX8l6WlZb5jWyGEEMKUZDUuezGwJn8eC+W74CscDonmt8M3+O3wDep4OjCsVVV6+3thY2GyqS4hcjFaEZCiGNBu2LAh14D2Ttu3b+fMmTOMHj36nrEEBASQnp7O5cuX891n6tSpxMTEZD9CQkLueVwhhCgSWh3U7glP/wVjtkD9fqDRwaV/YcUT8GVLOLBEleHIg7WFjva1KjK1ex1+n9CWw291Zf6QpgxrVZXGVZxwtrWg7a2fuXI7n86sGi1a2wq0njgLi/Zj+Cb4BqsPXuPAldtExqdgxNLoQghR6lhYWNC0adNc/RT0ej2bN2+mVatW+b5vxowZvP/++6xfv55mzZrleq1atWp4eHjkOmZsbCx79uwp8JhCCCFEWWNlrqNvk8r8+lwb/pjQlgHNKmNppuVUaCxTVx8j4KPNvPfHCS7eijd2qEI8NKM3ImvRogVffPEFoAa0VapUYcKECfk2IpsxYwYffvghf//9Ny1btsz32CNGjOD48ePs37//nnEsX76cYcOGERERQYUKFQoVe2lreiGEKGeiQ2DPfDj4HaRklmuxcYXmo9XDrmLhj5WRzkuBnszZFkFmk1ZFowGDAduGXXHuPBqtpU2eb7e3NKOqqw1VXWzxccn6qrYr2lvKMiUhRLEqjWOylStXMnz4cL7++mtatGjBrFmz+Omnnzh9+jTu7u4MGzaMSpUqZdcF//jjj3n77bdZsWIFbdq0yT6OnZ1d9sqGjz/+mOnTp7N06VKqVavGW2+9xdGjRzl58mShyyiUxmslhBBCPKzoxFR+PnCN73dL4zJhGkp9IzJQZQqGDx9Os2bNsge0CQkJjBw5EqDAAa2Pj09244U7B7SgPvyqVav47LPP7jpncHAwe/bsoVOnTtjb2xMcHMykSZMYMmRIoRO2QghhdE7eEPQhdHgNDn0Pu+dDzFX4dzrs+BwaDVSlEyr63fNQ+qM/sXxfZK6ErU6no2LFiixZsoS2HbtwNSqRK5EJXI7M/Bqhvt6ISSYuJZ3j12M5fv3uWt/W5jqqutjg42JLVdfMr5nfezhYodVKQlcIUfYMHDiQW7du8fbbbxMWFoa/vz/r16/P7uVw9epVtHeUtfnqq69ITU2lf//+uY7zzjvv8O677wLw6quvkpCQwNixY4mOjqZt27asX7/+oeveCiGEEKbOycaC0e2q83Qb1bhs2e4rbD4tjcuE6TPqTFuAuXPn8sknn2QPaOfMmUNAQAAAHTt2xMfHhyVLlgDg4+PDlStX7jrGnQNagG+++YYXX3yR0NBQHB0dc+178OBBnn32WU6fPk1KSgrVqlVj6NChTJ48+b5qfslMBSFEqZKRDqd+h+C5cP1AzvO+j0DrCar+bV4zXvV6/p1Ul45zzgCg0WgwGAyMHDmSzz///K7/Q/8rOS2DkKjEnGRuZAJXIhO5HJnA9dtJ6Av4CWNhpqWq8x0zdF3VVx8XWzwdreRuuBCiUGRMVnhyrYQQQpQXIVGJrNh7lZX7QohKSAXAXKehe31PhrWSxmXCuAo7JjN60tZUyaBXCFEqGQwQskclb0+tBTL/i3dvAK2eU/VwzSxy9j/xK+OGD+CbA2nodDpcXFxYvHgxPXr0eOhQUtP1XLudmJ3EvfNrSFQi6QVkdM11Grwr2FDV5b9JXVsqOVljYSYJXSGEImOywpNrJYQQorxJTsvI1bgsSx1PB4a2rEqfxtK4TJQ8SdoWMxn0CiFKvaiLsPsrOLQM0jJrO9l5QMBYaDoSrCuQ/mVbXF/aRUwKDBkyhDlz5pRIqZj0DD03opMzk7h3lF2ITORqZCKpGfp836vTavBysspVaiErsevtbIOVua7Y4xdClB4yJis8uVZCCCHKs2PXYvh+92V+O3yDlMzacPZWZvRvWpnBAVXxdbO7xxGEKBqStC1mMugVQpiMpNuwfzHs/QbiQtVz5jZQvRPpp9Yy9Nc0Br21kMeeGGzcODNl6A2ExSZzJSIhz7ILyWn5J3Q1GvB0sFJJXFcb3OytsLcyy3yYY2eZs531vLW5TpZGCWHCZExWeHKthBBCiPwbl9lbmlGpgjWVK9hQuYJ15kNte1ewwcHaTH5vEEVCkrbFTAa9QgiTk54KJ1ar0glhx3Keb/08dH3feHHdB4PBwM24FC5HJORZdiE+Jf2+j6nTanInc7O3zbC7I9nrUEDi187STGrwCmEkMiYrPLlWQgghRA693sD28xF8H3yZLadvFtiPA/JL6uZ872htLkldUSiStC1mMugVQpgsgwEub4fgLyE5GgYuA1tXY0f10AwGA5EJqVzJSuRGJBCRkEp8cjpxyWnEp6QTl5z1UN/fa2B2P6zNdXcke81xyEzm/neWr4OVeWYyOHdC2E5m/QrxQGRMVnhyrYQQQoi8JaVmcD06kZDbSVy7ncS124mZX5O4fjuRiPjUex7DztLsrkSuJHVFXiRpW8xk0CuEEKbNYDCQkJqRndSNS7kjoXtHcjfr+fjkdOJS0rK3YzNfz6qHVRTMtBrsspO9mTN570z8ZiV7Lc1wsDbHy8maqi42VLSzlAGgKLdkTFZ4cq2EEEKIB5OV1L2WndTNndiNiE+55zHsLM2o5HT3DN2sr042ktQtLwo7JpMWeUIIIcoljUaVRbCzNMPD0eqBj5Oaric+JSuRm5nUTck9uzf2P4ng/Gb9pusNRCemEZ2YBiQVOgY7S7Pspmw+ruprNVfVoM3VzkIGf0IIIYQQQjwEawsdvm72+LrZ5/m6Sur+Z4buHd/fikshPiWdM+FxnAmPy/MYtha6XDN0K/0nsVtBkrrljiRthRBCiIdgYabF2cwCZ1uLBz6GwWAgMTUj79m9mYnguJSc7fjkdGKS0gi5ncj16CTiU9I5cSOWEzdi7zq2naUZPq42VHWxpZqLLT6utvi42ODjaouLrSR0hRBCCCGEeFgqqWuHr5tdnq8np2UldXMndq/dTuT67SRuxqWQkJpRYFLXxkJ3V3M0b2f1vXcFGxxtzIvzIwojkKStEEIIYWQajQZbSzNsH2DWb0p6BiFRSVyOSOByZOYjIpFLEQnciFEJ3ePXYzl+/e6Err2lGVXvmJl750xdZ0noCiGEEEIIUSSszHXUqGhHjYr5J3VvROdVekF9vRmXQmJqBmfD4zkbHp/nMeytzP6TyLXG29kGb2eV5LWxkBSgqZE/MSGEEMKEWZrlf1c/OS2Da7cTuRSReFdS90ZMEnEFJXStzDKTuLZUc1EzdX1cVXJXlmYJPnXargABAABJREFUIYQQQghRdKzMdVSvaEf1QiZ1QzKTuSFRiVzLbJQWl5zOydBYTobePbYHcLG1oLKzSuZWzkzuqiSvDV5OVlia6YrzI4oHIElbIYQQooyyMs+/9lZyWgYhUWpG7pXIRC5FJnA5c/tGTBJxyekcux7Dsesxd73Xwcoss8xC7nIL1VxspYGCEEIIIYQQRexeSd3E1HSuZyZzQ6KykrlZ3ycSm5xOZEIqkQmpHAmJvuv9Gg2421tlJ3IrV7DOTPCq5K6HgxVmOm0xf0rxXxqDwWAwdhCmSLrvCiGEKKuS0zK4mp3QTcieqXslMoEbMckFvtfBykyVWshshFbtjvILTjYPXvdXiPzImKzw5FoJIYQQ5VNMUhrXMhO61zITudfuSPImpWUU+H4zrQZPJyuVxM2qqeucM1vX1c4SrVYmbhRWYcdkMtNWCCGEELlYmeuo5W5PLfe8Z+heiUzMLLOQU27hcmQCoTHJxCanc+RaDEeu3T1D19HaPFe5hazkro+LjSR0hRBCCCGEKCaO1uY4WjtSz8vxrtcMBgORCanZ5RZC7kjuXrudxPXbSaRm6DNn8CYBkXcdw9JMS6U8mqNVdbHB180OK3MpvfAgJGkrhBBCiEKzMtfh52GPn8fdCd2k1AyuROUkce9M6obFJhOTlMaRkOg8l2Q52ZircguZpRZ8XGzxdLTC09EaNwdLGegJIYQQQghRDDQaDa52lrjaWeLv7XTX63q9gfC45JykblRWTV21HRqTREq6nou3Erh4K+Gu92s1UM3VltqeDtTxsKe2hwO1Pe2p5GQtZdXuQcojPCBZXiaEEEIUXmJqOlejVJmFSxGJmWUXVFI3PDblnu93trXAw8EKT0cr3B2t8HSwwiMzqevhaImHozV2lnIvujySMVnhybUSQgghRFFLy9ATFpN81yzdkNtJXLwVz+3EtDzfZ29php+HPbU9VSK3jqda6WdvZV7Cn6DkSXkEIYQQQpQaNhZm6q66x92DksTUdFVyISKBy5lfr0SpZG5oTBLJaXqiElKJSkjNtxsuqIGfh6NK5mYleD2ykroO1ng6WkmjNCGEEEIIIYqQuU6bWd/W5q7XDAYDt+JSOBUWx+nQWE6HxXE6LI7zN+OIS0ln/5Xb7L9yO9d7vJ2tM39vyJmV6+Nii64c1syVmbYPSGYqCCGEEMXPYDAQk5RGaEwyYbHJhMUkq+2YJMJiUwiLSSI0Jpm45PRCHc/STJsrqZsza9c6M8lrhaudZbkcFJoqGZMVnlwrIYQQQpQGaRmqnMLpsFhOhcZxOiyW06FxhMXm3fTYylxLLXf7XInc2h4OONuaZl+Mwo7JJGn7gGTQK4QQQpQe8SnphMUkEx57Z1L3ziRvMpEJqYU6lk6rwd3e8o5Zu9Y5Cd7MhK+7gxUWZtpi/lSiMGRMVnhyrYQQQghRmkUnpqrZuJmzck+FxXE2LI6ktIw893ezt8yplZuZyK1R0a7Uj9MlaVvMZNArhBBCmJaU9AxuxqYQGpNMaEzSHQle9TU8Vj30hRwZudpZ5Erq5i7LoB42FlKJqrjJmKzw5FoJIYQQwtRk6A1cjUrkdGhsdpmFM+FxXIlMzHN/M60GXzc7anvY45c5K7eOhwPuDpalpkyaJG2LmQx6hRBCiLInPUNPRHxqnkndrJm7YTHJpGboC3U8ByszPB2t/9M8zYoKthaYaTVotRrMtBp0Gg06rQYznQatRoOZVotWC2ZaLTot6LRatY/ujn3vfH/WQ6OeK09kTFZ4cq2EEEIIUVbEp6RzNjyO03eUVzgVFptv2TQnG/Ps8gp1Mmfl1nK3x9pCV8KRSyMyIYQQQoj7ZqbTZs+SzY/BYCAqIfWu8gs53ycRFpNMQmoGscnpxCbHcSY8rsQ+g0ZDdmI36/HfxO6dyV/1uDM5nJUs1tz9uOO9eSaNM/extTRj0iO1SuwzCyGEEEKI8sXO0owmVSrQpEqF7OcMBgOhMcl31MpVM3MvRiQQnZjG7otR7L4Ylb2/RgM+LrbZydz+zSpTycnaGB8nT5K0FUIIIYS4DxqNBhc7S1zsLKnn5ZjvfnHJaXcldbPq7UYnpaHXG8gwGEjPMKA3GEjXG9Dr//M18/mMjMx973gtPwYDpBsK3qe4VbAxl6StEEIIIYQoURqNBi8na7ycrOlc2z37+eS0DM7fjM9VL/d0WBwR8SlcikjgUkQCfx0PI7CumyRthRBCCCHKOnsrc+ytzKnpbl8sx78rsfufR7pej16P+prPPtmP/ySHC9rnv4nlDL2eDD3qa+Y+VmYlv8xMCCGEEEKIvFiZ66hfyZH6lXJPuLgVl8KZsLjsmbm+bnZGijBvkrQVQgghhDBBWq0Gi3JWv1YIIYQQQoiiUtHekor2lrSt6WrsUPKkNXYAQgghhBBCCCGEEEIIIXJI0lYIIYQQQgghhBBCCCFKEUnaCiGEEEIIIYQQQgghRCkiSVshhBBCCCGEEEIIIYQoRSRpK4QQQgghhBBCCCGEEKWIJG2FEEIIIYQQQgghhBCiFJGkrRBCCCGEEEIIIYQQQpQikrQVQgghhBBCCCGEEEKIUkSStkIIIYQQQgghhBBCCFGKSNJWCCGEEEIIIYQQQgghShFJ2gohhBBCCCGEEEIIIUQpIklbIYQQQgghhBBCCCGEKEUkaSuEEEIIIYQQQgghhPg/e/cdHVW59XH8O+k9oaRCKAkldBAIAtJBEEHhoiCCItcu2Hgt2BFFrFwsiKIIKip2xAZqVJSOIkjvNZCEAOl95rx/nGRIIIEASSbl91nrrMycumcSws6e5+xHKhEVbUVEREREREREREQqERVtRURERERERERERCoRF0cHUFUZhgFASkqKgyMRERERqbkKcrGC3ExKpvxVRERExPFKm7+qaHuBUlNTAQgPD3dwJCIiIiKSmpqKv7+/o8Oo1JS/ioiIiFQe58pfLYaGJVwQm83GkSNH8PX1xWKxlPv1UlJSCA8P59ChQ/j5+ZX79aTs6HtXdel7V7Xp+1d16XtXtVX0988wDFJTUwkLC8PJSZ2/zkb5a/Wg97Xs6T0tH3pfy4fe1/Kh97Xs6T0tWWnzV420vUBOTk7Ur1+/wq/r5+enH/YqSt+7qkvfu6pN37+qS9+7qq0iv38aYVs6yl+rF72vZU/vafnQ+1o+9L6WD72vZU/vafFKk79qOIKIiIiIiIiIiIhIJaKirYiIiIiIiIiIiEgloqJtFeHu7s5TTz2Fu7u7o0OR86TvXdWl713Vpu9f1aXvXdWm758U0M9C+dD7Wvb0npYPva/lQ+9r+dD7Wvb0nl48TUQmIiIiIiIiIiIiUolopK2IiIiIiIiIiIhIJaKirYiIiIiIiIiIiEgloqKtiIiIiIiIiIiISCWioq2IiIiIiIiIiIhIJaKibRUwa9YsGjVqhIeHB126dGHt2rWODklKYfr06XTu3BlfX1+CgoIYNmwYO3bscHRYcgGef/55LBYL9913n6NDkVKKjY1l7Nix1KlTB09PT9q0acNff/3l6LDkHKxWK0888QSNGzfG09OTyMhInnnmGTRnauX0xx9/MHToUMLCwrBYLCxatKjIdsMwePLJJwkNDcXT05P+/fuza9cuxwQrFU75a9lSXlkxlPOVHeViZUs5UtlQ7lI+zva+5ubm8vDDD9OmTRu8vb0JCwvjxhtv5MiRI44LuApR0baS+/TTT5k0aRJPPfUU69evp127dgwcOJCEhARHhybnsGzZMiZMmMDq1av5+eefyc3N5fLLLyc9Pd3Rocl5WLduHW+//TZt27Z1dChSSidPnqR79+64urry448/snXrVl555RVq1arl6NDkHF544QVmz57NG2+8wbZt23jhhRd48cUXef311x0dmhQjPT2ddu3aMWvWrGK3v/jii7z22mu89dZbrFmzBm9vbwYOHEhWVlYFRyoVTflr2VNeWf6U85Ud5WJlTzlS2VDuUj7O9r5mZGSwfv16nnjiCdavX89XX33Fjh07uOqqqxwQadVjMfTRTKXWpUsXOnfuzBtvvAGAzWYjPDycu+++m8mTJzs4Ojkfx44dIygoiGXLltGzZ09HhyOlkJaWxiWXXMKbb77Js88+S/v27Zk5c6ajw5JzmDx5MitWrODPP/90dChynoYMGUJwcDBz5861rxsxYgSenp4sWLDAgZHJuVgsFr7++muGDRsGmCNVwsLC+L//+z8eeOABAJKTkwkODmb+/Plcd911DoxWypvy1/KnvLJsKecrW8rFyp5ypLKn3KV8nP6+FmfdunVER0dz4MABGjRoUHHBVUEaaVuJ5eTk8Pfff9O/f3/7OicnJ/r378+qVascGJlciOTkZABq167t4EiktCZMmMCVV15Z5N+gVH6LFy+mU6dOXHvttQQFBdGhQwfeeecdR4clpdCtWzdiYmLYuXMnABs3bmT58uVcccUVDo5Mzte+ffuIi4sr8vvT39+fLl26KIep5pS/VgzllWVLOV/ZUi5W9pQjlT/lLhUnOTkZi8VCQECAo0Op9FwcHYCULDExEavVSnBwcJH1wcHBbN++3UFRyYWw2Wzcd999dO/endatWzs6HCmFhQsXsn79etatW+foUOQ87d27l9mzZzNp0iQeffRR1q1bxz333IObmxvjxo1zdHhyFpMnTyYlJYWoqCicnZ2xWq1MmzaNMWPGODo0OU9xcXEAxeYwBdukelL+Wv6UV5Yt5XxlT7lY2VOOVP6Uu1SMrKwsHn74YUaPHo2fn5+jw6n0VLQVqQATJkxg8+bNLF++3NGhSCkcOnSIe++9l59//hkPDw9HhyPnyWaz0alTJ5577jkAOnTowObNm3nrrbf0h0Il99lnn/HRRx/x8ccf06pVKzZs2MB9991HWFiYvnciIvmUV5Yd5XzlQ7lY2VOOJNVBbm4uI0eOxDAMZs+e7ehwqgS1R6jE6tati7OzM/Hx8UXWx8fHExIS4qCo5HxNnDiR7777jt9++4369es7Ohwphb///puEhAQuueQSXFxccHFxYdmyZbz22mu4uLhgtVodHaKcRWhoKC1btiyyrkWLFhw8eNBBEUlpPfjgg0yePJnrrruONm3acMMNN3D//fczffp0R4cm56kgT1EOU/Mofy1fyivLlnK+8qFcrOwpRyp/yl3KV0HB9sCBA/z8888aZVtKKtpWYm5ubnTs2JGYmBj7OpvNRkxMDF27dnVgZFIahmEwceJEvv76a3799VcaN27s6JCklPr168emTZvYsGGDfenUqRNjxoxhw4YNODs7OzpEOYvu3buzY8eOIut27txJw4YNHRSRlFZGRgZOTkVTE2dnZ2w2m4MikgvVuHFjQkJCiuQwKSkprFmzRjlMNaf8tXworywfyvnKh3Kxsqccqfwpdyk/BQXbXbt28csvv1CnTh1Hh1RlqD1CJTdp0iTGjRtHp06diI6OZubMmaSnpzN+/HhHhybnMGHCBD7++GO++eYbfH197X1w/P398fT0dHB0cja+vr5n9Ijz9vamTp066h1XBdx///1069aN5557jpEjR7J27VrmzJnDnDlzHB2anMPQoUOZNm0aDRo0oFWrVvzzzz/MmDGD//73v44OTYqRlpbG7t277c/37dvHhg0bqF27Ng0aNOC+++7j2WefpWnTpjRu3JgnnniCsLCws84mLNWD8teyp7yyfCjnKx/KxcqecqSyodylfJztfQ0NDeWaa65h/fr1fPfdd1itVvv/YbVr18bNzc1RYVcNhlR6r7/+utGgQQPDzc3NiI6ONlavXu3okKQUgGKXefPmOTo0uQC9evUy7r33XkeHIaX07bffGq1btzbc3d2NqKgoY86cOY4OSUohJSXFuPfee40GDRoYHh4eRkREhPHYY48Z2dnZjg5NivHbb78V+//cuHHjDMMwDJvNZjzxxBNGcHCw4e7ubvTr18/YsWOHY4OWCqP8tWwpr6w4yvnKhnKxsqUcqWwodykfZ3tf9+3bV+L/Yb/99pujQ6/0LIZhGOVeGRYRERERERERERGRUlFPWxEREREREREREZFKREVbERERERERERERkUpERVsRERERERERERGRSkRFWxEREREREREREZFKREVbERERERERERERkUpERVsRERERERERERGRSkRFWxEREREREREREZFKREVbERERERERERERkUpERVsRETkni8XCokWLHB2GiIiIiEipKH8VkapORVsRkUrupptuwmKxnLEMGjTI0aGJiIiIiJxB+auIyMVzcXQAIiJyboMGDWLevHlF1rm7uzsoGhERERGRs1P+KiJycTTSVkSkCnB3dyckJKTIUqtWLcC89Wv27NlcccUVeHp6EhERwRdffFHk+E2bNtG3b188PT2pU6cOt912G2lpaUX2ee+992jVqhXu7u6EhoYyceLEItsTExMZPnw4Xl5eNG3alMWLF9u3nTx5kjFjxhAYGIinpydNmzY9I0kXERERkZpD+auIyMVR0VZEpBp44oknGDFiBBs3bmTMmDFcd911bNu2DYD09HQGDhxIrVq1WLduHZ9//jm//PJLkaR29uzZTJgwgdtuu41NmzaxePFimjRpUuQaTz/9NCNHjuTff/9l8ODBjBkzhhMnTtivv3XrVn788Ue2bdvG7NmzqVu3bsW9ASIiIiJSpSh/FRE5O4thGIajgxARkZLddNNNLFiwAA8PjyLrH330UR599FEsFgt33HEHs2fPtm+79NJLueSSS3jzzTd55513ePjhhzl06BDe3t4A/PDDDwwdOpQjR44QHBxMvXr1GD9+PM8++2yxMVgsFh5//HGeeeYZwEykfXx8+PHHHxk0aBBXXXUVdevW5b333iund0FEREREqgrlryIiF089bUVEqoA+ffoUSWoBateubX/ctWvXItu6du3Khg0bANi2bRvt2rWzJ7wA3bt3x2azsWPHDiwWC0eOHKFfv35njaFt27b2x97e3vj5+ZGQkADAnXfeyYgRI1i/fj2XX345w4YNo1u3bhf0WkVERESk6lP+KiJycVS0FRGpAry9vc+43auseHp6lmo/V1fXIs8tFgs2mw2AK664ggMHDvDDDz/w888/069fPyZMmMDLL79c5vGKiIiISOWn/FVE5OKop62ISDWwevXqM563aNECgBYtWrBx40bS09Pt21esWIGTkxPNmzfH19eXRo0aERMTc1ExBAYGMm7cOBYsWMDMmTOZM2fORZ1PRERERKov5a8iImenkbYiIlVAdnY2cXFxRda5uLjYJ0v4/PPP6dSpE5dddhkfffQRa9euZe7cuQCMGTOGp556inHjxjFlyhSOHTvG3XffzQ033EBwcDAAU6ZM4Y477iAoKIgrrriC1NRUVqxYwd13312q+J588kk6duxIq1atyM7O5rvvvrMn3SIiIiJS8yh/FRG5OCraiohUAUuWLCE0NLTIuubNm7N9+3bAnBl34cKF3HXXXYSGhvLJJ5/QsmVLALy8vFi6dCn33nsvnTt3xsvLixEjRjBjxgz7ucaNG0dWVhb/+9//eOCBB6hbty7XXHNNqeNzc3PjkUceYf/+/Xh6etKjRw8WLlxYBq9cRERERKoi5a8iIhfHYhiG4eggRETkwlksFr7++muGDRvm6FBERERERM5J+auIyLmpp62IiIiIiIiIiIhIJaKirYiIiIiIiIiIiEglovYIIiIiIiIiIiIiIpWIRtqKiIiIiIiIiIiIVCIq2oqIiIiIiIiIiIhUIiraioiIiIiIiIiIiFQiKtqKiIiIiIiIiIiIVCIq2oqIiIiIiIiIiIhUIiraioiIiIiIiIiIiFQiKtqKiIiIiIiIiIiIVCIq2oqIiIiIiIiIiIhUIiraioiIiIiIiIiIiFQiKtqKiIiIiIiIiIiIVCIq2oqIiIiIiIiIiIhUIiraioiIiIiIiIiIiFQiKtqKiIiIiIiIiIiIVCIq2oqIiIiIiIiIiIhUIiraiohUsD179nD77bcTERGBh4cHfn5+dO/enVdffZXMzEz7fo0aNcJisXD33XefcY7ff/8di8XCF198YV83f/58LBYLHh4exMbGnnFM7969ad269XnFOnLkSCwWCw8//HCx2wuu+ddffxW7fciQITRq1OiM9VlZWfzvf/+jS5cu+Pv74+HhQbNmzZg4cSI7d+48rxhFREREpPQK8reCxcXFhXr16nHTTTcVm0OWlSlTpmCxWAgODiYjI+OM7Y0aNWLIkCEXdO4333yT+fPnn/dxSUlJeHh4YLFY2LZtW7H7nC2HTkxMxGKxMGXKlDO2lTbnFxEpiYq2IiIV6Pvvv6dNmzZ89tlnDB06lNdff53p06fToEEDHnzwQe69994zjnnnnXc4cuRIqa+RnZ3N888/f9GxpqSk8O2339KoUSM++eQTDMO46HOCmdxedtllTJo0iaCgIKZOncqsWbMYNmwYixcvPu/CsoiIiIicv6lTp/Lhhx/y1ltvccUVV7BgwQJ69epFVlZWuV43ISGB2bNnl+k5L7Ro+/nnn2OxWAgJCeGjjz4qs3guJOcXETmdi6MDEBGpKfbt28d1111Hw4YN+fXXXwkNDbVvmzBhArt37+b7778vckyrVq3YsWMHzz//PK+99lqprtO+fXveeecdHnnkEcLCwi443i+//BKr1cp7771H3759+eOPP+jVq9cFn6/ATTfdxD///MMXX3zBiBEjimx75plneOyxxy76GiIiIiJydldccQWdOnUC4JZbbqFu3bq88MILLF68mJEjR5bbddu3b89LL73EXXfdhaenZ7ldpzQWLFjA4MGDadiwIR9//DHPPvvsRZ/zQnJ+EZHiaKStiEgFefHFF0lLS2Pu3LlFkrcCTZo0OeNT90aNGnHjjTee12jbRx99FKvVetGjbT/66CMGDBhAnz59aNGiRZmMPlizZg3ff/89N9988xkFWwB3d3defvnli76OiIiIiJyfHj16AOZt/YVt376da665htq1a+Ph4UGnTp1YvHhxkX1yc3N5+umnadq0KR4eHtSpU4fLLruMn3/++YzrPPnkk8THx5dqtK3NZmPmzJm0atUKDw8PgoODuf322zl58qR9n0aNGrFlyxaWLVtmb/nQu3fvc5774MGD/Pnnn1x33XVcd9117Nu3j5UrV57zuHO5kJxfRKQ4KtqKiFSQb7/9loiICLp163Zexz322GPk5eWVugjbuHHj8y70nu7IkSP89ttvjB49GoDRo0fzxRdfkJOTc0HnK1CQ4N9www0XdR4RERERKVv79+8HoFatWvZ1W7Zs4dJLL2Xbtm1MnjyZV155BW9vb4YNG8bXX39t32/KlCk8/fTT9OnThzfeeIPHHnuMBg0asH79+jOu06NHD/r27cuLL754zt6ut99+Ow8++KC9F+z48eP56KOPGDhwILm5uQDMnDmT+vXrExUVxYcffsiHH35Yqju3PvnkE7y9vRkyZAjR0dFERkaWySCFC835RUROp6KtiEgFSElJITY2ljZt2pz3sREREdxwww288847HD16tFTHFBR6X3jhhfO+HphJrLu7O1dffTUA1113HSdPnuSHH364oPMVKJjg4ULeBxEREREpO8nJySQmJnL48GG+/PJLnn76adzd3YtMBnbvvffai68PPfQQEyZM4Pfff6dr165FJqr9/vvvGTx4MHPmzOGWW25h0qRJfPzxxyVOZvvUU08RHx/PW2+9VWJ8y5cv59133+X9999nzpw53H777Tz//PN8+eWXrFu3js8//xyAYcOG4e/vT3BwMGPHjmXs2LEMGDDgnK//o48+4uqrr7a3aBg1ahSfffYZeXl5pXr/inMxOb+IyOlUtBURqQApKSkA+Pr6XtDxjz/++HmNti0o9M6ZM6fUhd7CPvroI6688kp7vE2bNqVjx44XPfrgYt8HERERESkb/fv3JzAwkPDwcK655hq8vb1ZvHgx9evXB+DEiRP8+uuvjBw5ktTUVBITE0lMTOT48eMMHDiQXbt2ERsbC0BAQABbtmxh165dpbp2z5496dOnz1lH237++ef4+/szYMAA+7UTExPp2LEjPj4+/Pbbbxf82v/99182bdpkv6sMzDvLEhMTWbp06QWfV7muiJQlFW1FRCqAn58fAKmpqRd0/IUUYc+30Ftg27Zt/PPPP3Tv3p3du3fbl969e/Pdd9/Zk9HSslgs9scX+z6IiIiISNmYNWsWP//8M1988QWDBw8mMTERd3d3+/bdu3djGAZPPPEEgYGBRZannnoKgISEBACmTp1KUlISzZo1o02bNjz44IP8+++/Z73+lClTiIuLK3G07a5du0hOTiYoKOiM66elpdmvfSEWLFiAt7c3ERER9lzXw8ODRo0aXdAghYJ8V7muiJQlF0cHICJSE/j5+REWFsbmzZsv+ByPPfYYH374IS+88ALDhg075/4RERGMHTuWOXPmMHny5FJfZ8GCBQDcf//93H///Wds//LLLxk/fjwAHh4eACWOkMjIyLDvAxAVFQXApk2b7JNdiIiIiEjFi46OplOnToDZYuCyyy7j+uuvZ8eOHfj4+GCz2QB44IEHGDhwYLHnaNKkCWCOnN2zZw/ffPMNP/30E++++y7/+9//eOutt7jllluKPbZnz5707t2bF198kTvuuOOM7TabjaCgoBKLqIGBgef9mgEMw+CTTz4hPT2dli1bnrE9ISGBtLQ0fHx8ADPfPVuuW7APlE3OLyJSQEVbEZEKMmTIEObMmcOqVavo2rXreR8fGRnJ2LFjefvtt+nSpUupjnn88cdZsGBBqXvbGobBxx9/TJ8+fbjrrrvO2P7MM8/w0Ucf2Yu2DRs2BGDHjh3FFmF37txJ69at7c+HDh3K9OnTWbBggYq2IiIiIpWEs7Mz06dPt08kNnnyZCIiIgBwdXWlf//+5zxH7dq1GT9+POPHjyctLY2ePXsyZcqUEou2YI627d27N2+//fYZ2yIjI/nll1/o3r27ve9sSQrf2XUuy5Yt4/Dhw0ydOpUWLVoU2Xby5Eluu+02Fi1axNixYwEz3/3111/JzMw8I44dO3bY9ylwsTm/iEgBtUcQEakgDz30EN7e3txyyy3Ex8efsX3Pnj28+uqrZz3H448/Tm5uLi+++GKprlm40BsXF3fO/VesWMH+/fsZP34811xzzRnLqFGj+O233zhy5AgAHTt2JCgoiHfffZfs7Owi51q0aBGxsbFcccUV9nVdu3Zl0KBBvPvuuyxatOiM6+fk5PDAAw+U6rWJiIiISNnp3bs30dHRzJw5k6ysLIKCguwF1eLacx07dsz++Pjx40W2+fj40KRJkzPyw9P16tWL3r1788ILL5CVlVVk28iRI7FarTzzzDNnHJeXl0dSUpL9ube3d5HnZ1PQGuHBBx88I9e99dZbadq0aZHRvYMHDyY3N/eMwrLNZmP27Nm4ubnRr18/+/qyyPlFREAjbUVEKkxkZCQff/wxo0aNokWLFtx44420bt2anJwcVq5cyeeff85NN910znOMHTuW999/v9TXLWirsGPHDlq1anXWfT/66COcnZ258sori91+1VVX8dhjj7Fw4UImTZqEm5sbL7/8MuPGjaNz586MGjWKOnXq8M8///Dee+/Rtm1bbrvttiLn+OCDD7j88sv5z3/+w9ChQ+nXrx/e3t7s2rWLhQsXcvToUV5++eVSvz4RERERKRsPPvgg1157LfPnz+eOO+5g1qxZXHbZZbRp04Zbb72ViIgI4uPjWbVqFYcPH2bjxo0AtGzZkt69e9OxY0dq167NX3/9xRdffMHEiRPPec2nnnqKPn36nLG+V69e3H777UyfPp0NGzZw+eWX4+rqyq5du/j888959dVXueaaawBzIMHs2bN59tlnadKkCUFBQfTt2/eMc2ZnZ/Pll18yYMCAIi28Crvqqqt49dVXSUhIICgoiKFDh3L55Zdz//33s3btWrp160ZGRgaLFy9mxYoVPPvss0VaNZRFzi8iAoAhIiIVaufOncatt95qNGrUyHBzczN8fX2N7t27G6+//rqRlZVl369hw4bGlVdeecbxu3btMpydnQ3A+Pzzz+3r582bZwDGunXrzjhm3LhxBmC0atWqxLhycnKMOnXqGD169Dhr/I0bNzY6dOhQZN2PP/5o9OnTx/Dz8zNcXV2Nxo0bG5MmTTJOnjxZ7DkyMjKMl19+2ejcubPh4+NjuLm5GU2bNjXuvvtuY/fu3We9voiIiIhcuLPljFar1YiMjDQiIyONvLw8wzAMY8+ePcaNN95ohISEGK6urka9evWMIUOGGF988YX9uGeffdaIjo42AgICDE9PTyMqKsqYNm2akZOTY9/nqaeeMgDj2LFjZ1y3V69eBlBs7jtnzhyjY8eOhqenp+Hr62u0adPGeOihh4wjR47Y94mLizOuvPJKw9fX1wCMXr16Ffvav/zySwMw5s6dW+L78/vvvxuA8eqrr9rXZWVlGVOmTDGioqIMd3d3w9vb27j00kuNBQsWlHie0ub8IiIlsRiGYTisYiwiIiIiIiIiIiIiRainrYiIiIiIiIiIiEgloqKtiIiIiIiIiIiISCWioq2IiIiIiIiIiIhIJaKirYiIiIiIiIiIiEgloqKtiIiIiIiIiIiISCWioq2IiIiIiIiIiIhIJaKirYiIiIiIiIiIiEgl4uLoAKoqm83GkSNH8PX1xWKxODocERERkRrJMAxSU1MJCwvDyUnjEc5G+auIiIiI45U2f1XR9gIdOXKE8PBwR4chIiIiIsChQ4eoX7++o8Oo1JS/ioiIiFQe58pfVbS9QL6+voD5Bvv5+Tk4GhEREZGaKSUlhfDwcHtuJiVT/ioiIiLieKXNX1W0vUAFt5T5+fkp6RURERFxMN3uf27KX0VEREQqj3Plr2r8JSIiIiIiIiIiIlKJqGgrIiIiIiIiIiIiUomoaCsiIiIiUsZmzZpFo0aN8PDwoEuXLqxdu7bEfXNzc5k6dSqRkZF4eHjQrl07lixZUmQfq9XKE088QePGjfH09CQyMpJnnnkGwzDK+6WIiIiIiAOop205stls5OTkODoMqUZcXV1xdnZ2dBgiIiJyFp9++imTJk3irbfeokuXLsycOZOBAweyY8cOgoKCztj/8ccfZ8GCBbzzzjtERUWxdOlShg8fzsqVK+nQoQMAL7zwArNnz+b999+nVatW/PXXX4wfPx5/f3/uueeeMo3farWSm5tbpueUms3NzQ0nJ40XEhEROR8WQx/PX5CUlBT8/f1JTk4udiKHnJwc9u3bh81mc0B0Up0FBAQQEhKiCVdEREQ4d07mCF26dKFz58688cYbgPlBfnh4OHfffTeTJ08+Y/+wsDAee+wxJkyYYF83YsQIPD09WbBgAQBDhgwhODiYuXPnlrjPuZzrvTIMg7i4OJKSks7n5Yqck5OTE40bN8bNzc3RoYiIiDhcafNXjbQtB4ZhcPToUZydnQkPD9enylImDMMgIyODhIQEAEJDQx0ckYiIiJwuJyeHv//+m0ceecS+zsnJif79+7Nq1apij8nOzsbDw6PIOk9PT5YvX25/3q1bN+bMmcPOnTtp1qwZGzduZPny5cyYMaPEWLKzs8nOzrY/T0lJOWvsBQXboKAgvLy89AGxlAmbzcaRI0c4evQoDRo00M+ViIhIKaloWw7y8vLIyMggLCwMLy8vR4cj1YinpycACQkJBAUFqVWCiIhIJZOYmIjVaiU4OLjI+uDgYLZv317sMQMHDmTGjBn07NmTyMhIYmJi+Oqrr7BarfZ9Jk+eTEpKClFRUTg7O2O1Wpk2bRpjxowpMZbp06fz9NNPlypuq9VqL9jWqVOnVMeIlFZgYCBHjhwhLy8PV1dXR4cjIiJSJWgIaDkoSLB1+4+Uh4IPAtRrTkREpHp49dVXadq0KVFRUbi5uTFx4kTGjx9f5G6tzz77jI8++oiPP/6Y9evX8/777/Pyyy/z/vvvl3jeRx55hOTkZPty6NChEvctyCs04EDKQ8HfRYU/iBAREZGz00jbcqRbf6Q86OdKRESk8qpbty7Ozs7Ex8cXWR8fH09ISEixxwQGBrJo0SKysrI4fvw4YWFhTJ48mYiICPs+Dz74IJMnT+a6664DoE2bNhw4cIDp06czbty4Ys/r7u6Ou7v7ecWvPEPKg36uREREzp9G2kq5atSoETNnziz1/r///jsWi0UTYIiIiEiV5ObmRseOHYmJibGvs9lsxMTE0LVr17Me6+HhQb169cjLy+PLL7/k6quvtm/LyMg4Y54EZ2dnTXpbDpS/ioiISGWgoq0A5qffZ1umTJlyQeddt24dt912W6n379atG0ePHsXf3/+CrnchoqKicHd3Jy4u7oxtJSXtU6ZMoX379kXWxcXFcffddxMREYG7uzvh4eEMHTq0yB9tIiIiUv1NmjSJd955h/fff59t27Zx5513kp6ezvjx4wG48cYbi0xUtmbNGr766iv27t3Ln3/+yaBBg7DZbDz00EP2fYYOHcq0adP4/vvv2b9/P19//TUzZsxg+PDhFf76Kgvlr8pfRUREqrMqUbSdNWsWjRo1wsPDgy5durB27doS983NzWXq1KlERkbi4eFBu3btWLJkyRn7xcbGMnbsWOrUqYOnpydt2rThr7/+Ks+XUakdPXrUvsycORM/P78i6x544AH7voZhkJeXV6rzBgYGnldvNDc3N0JCQirsFqrly5eTmZnJNddcc9aecOeyf/9+OnbsyK+//spLL73Epk2bWLJkCX369GHChAllGLGIiIhUdqNGjeLll1/mySefpH379mzYsIElS5bYJyc7ePAgR48ete+flZXF448/TsuWLRk+fDj16tVj+fLlBAQE2Pd5/fXXueaaa7jrrrto0aIFDzzwALfffjvPPPNMRb+8SkP5q/JXERGR6qzSF20//fRTJk2axFNPPcX69etp164dAwcOJCEhodj9H3/8cd5++21ef/11tm7dyh133MHw4cP5559/7PucPHmS7t274+rqyo8//sjWrVt55ZVXqFWrVkW9rEonJCTEvvj7+2OxWOzPt2/fjq+vLz/++CMdO3bE3d2d5cuXs2fPHq6++mqCg4Px8fGhc+fO/PLLL0XOe/on/RaLhXfffZfhw4fj5eVF06ZNWbx4sX376beXzZ8/n4CAAJYuXUqLFi3w8fFh0KBBRf7QycvL45577iEgIIA6derw8MMPM27cOIYNG3bO1z137lyuv/56brjhBt57770Lfv/uuusuLBYLa9euZcSIETRr1oxWrVoxadIkVq9efcHnFRERkapp4sSJHDhwgOzsbNasWUOXLl3s237//Xfmz59vf96rVy+2bt1KVlYWiYmJfPDBB4SFhRU5n6+vLzNnzuTAgQNkZmayZ88enn322Ro98a3yV+WvIiIi1VmlL9rOmDGDW2+9lfHjx9OyZUveeustvLy8SkxQPvzwQx599FEGDx5MREQEd955J4MHD+aVV16x7/PCCy8QHh7OvHnziI6OpnHjxlx++eVERkaWy2swDIOMnDyHLIZhlNnrmDx5Ms8//zzbtm2jbdu2pKWlMXjwYGJiYvjnn38YNGgQQ4cO5eDBg2c9z9NPP83IkSP5999/GTx4MGPGjOHEiRMl7p+RkcHLL7/Mhx9+yB9//MHBgweLjJx44YUX+Oijj5g3bx4rVqwgJSWFRYsWnfP1pKam8vnnnzN27FgGDBhAcnIyf/75Z6nfjwInTpxgyZIlTJgwAW9v7zO2Fx4lIyIiIlIVKH8tSvmriIiIVDQXRwdwNjk5Ofz9999Fen45OTnRv39/Vq1aVewx2dnZeHh4FFnn6enJ8uXL7c8XL17MwIEDufbaa1m2bBn16tXjrrvu4tZbby0xluzsbLKzs+3PU1JSSv06MnOttHxyaan3L0tbpw7Ey61svs1Tp05lwIAB9ue1a9emXbt29ufPPPMMX3/9NYsXL2bixIklnuemm25i9OjRADz33HO89tprrF27lkGDBhW7f25uLm+99Za9qD5x4kSmTp1q3/7666/zyCOP2Hu6vfHGG/zwww/nfD0LFy6kadOmtGrVCoDrrruOuXPn0qNHj3MeW9ju3bsxDIOoqKjzOk5ERGoYmw0Sd0Ls3+DqCS2vBidnR0clUizlr0UpfxUREaleDMMgMS2HPcfSzCUhnYcGNcfDtfLk55W6aJuYmIjVarX3/yoQHBzM9u3biz1m4MCBzJgxg549exIZGUlMTAxfffUVVqvVvs/evXuZPXs2kyZN4tFHH2XdunXcc889uLm5MW7cuGLPO336dJ5++umye3FVUKdOnYo8T0tLY8qUKXz//fccPXqUvLw8MjMzzzlSoW3btvbH3t7e+Pn5ldjuAsDLy6vIKOjQ0FD7/snJycTHxxMdHW3f7uzsTMeOHc85m/J7773H2LFj7c/Hjh1Lr169eP311/H19T3rsYWV5WgQERGpRtIS4PBfEPuX+fXIP5Bd6EPf8Eth+GyoHeG4GEWqOeWvxVP+KiIiNUVOno2DJ9LZcyzdXpwtKNSmZhXtdz+yc32iQvwcFOmZKnXR9kK8+uqr3HrrrURFRWGxWIiMjGT8+PFF2inYbDY6derEc889B0CHDh3YvHkzb731VolF20ceeYRJkybZn6ekpBAeHl6qmDxdndk6deBFvKoL51mGnxCcfuvUAw88wM8//8zLL79MkyZN8PT05JprriEnJ+es53F1dS3y3GKxnDVBLW7/i000t27dyurVq1m7di0PP/ywfb3VamXhwoX2Udd+fn4kJyefcXxSUpJ9huCmTZtisVhK/CBBRERqgJwMiPu3UJH2b0gupgjk4glh7SFuMxxaDbMvg4HToONNUEGTGImUhvLXopS/ioiIVG5JGTmnFWXT2XssjQMnMrDaiv8/2GKB8FpeRAZ6Exnog5dr5SqTVq5oTlO3bl2cnZ2Jj48vsj4+Pp6QkJBijwkMDGTRokVkZWVx/PhxwsLCmDx5MhERp0axhIaG0rJlyyLHtWjRgi+//LLEWNzd3XF3d7+g12GxWMrsFq/KZMWKFdx0003227rS0tLYv39/hcbg7+9PcHAw69ato2fPnoCZuK5fv5727duXeNzcuXPp2bMns2bNKrJ+3rx5zJ071570Nm/enL///vuM49evX0/z5s0B8za7gQMHMmvWLO65554z/jhISkpSXzARkerEZoPju4qOoo3fAob1tB0tENgc6nWC+h3Nr0EtwdkFkg7Cortg/5/w3X2w4we46nXwLT6/Ealoyl/Lj/JXERGRC2O1GRw+mXHGiNm9x9I5nl7yB7Debs5EBPrYi7ORQT5EBvrQsI5XpWqHcLpKnYm5ubnRsWNHYmJi7DOp2mw2YmJiztpzCsDDw4N69eqRm5vLl19+yciRI+3bunfvzo4dO4rsv3PnTho2bFjmr6E6a9q0KV999RVDhw7FYrHwxBNPnPOWrvJw9913M336dJo0aUJUVBSvv/46J0+exFLCiKXc3Fw+/PBDpk6dSuvWrYtsu+WWW5gxYwZbtmyhVatW3H///fTo0YNp06bxn//8B6vVyieffMKqVat488037cfNmjWL7t27Ex0dzdSpU2nbti15eXn8/PPPzJ49m23btpXreyAiIuUo7dip4mzsXxD7D2SfOYoN7yCo3wnqdTS/hl0CHiXcXhXQAG5cDKvfhJipsOsneLMrDPkftBpWri9HpCZT/qr8VUREKr/UrFz2Hktnb2LR4uz+xAxyrCX/vx3m72EvyEYUFGgDfQj2cy/x/9jKrFIXbQEmTZrEuHHj6NSpE9HR0cycOZP09HTGjx8PwI033ki9evWYPn06AGvWrCE2Npb27dsTGxvLlClTsNlsPPTQQ/Zz3n///XTr1o3nnnuOkSNHsnbtWubMmcOcOXMc8hqrqhkzZvDf//6Xbt26UbduXR5++OHzmqCtrDz88MPExcVx44034uzszG233cbAgQNxdi7+05LFixdz/Phx+wiLwlq0aEGLFi2YO3cuM2bMoFu3bvz4449MnTqVV155BScnJ9q0aUNMTEyRhDkiIoL169czbdo0/u///o+jR48SGBhIx44dmT17drm9dhERKWO5mXB0Y36B9m+zSJt0ljYHBQXaep3Av/75tThwcoJuE6FJP/jqNrO9wufjYPtIGPwSeAaU1asSkXzKX5W/iohI5WCzGRxNyWJPQlqREbN7jqURn5Jd4nHuLk40ruttL84WjJ5tXNcbb/dKX+Y8LxajCnShf+ONN3jppZeIi4ujffv2vPbaa3Tp0gWA3r1706hRI+bPnw/AsmXLuPPOO9m7dy8+Pj4MHjyY559/nrCwsCLn/O6773jkkUfYtWsXjRs3ZtKkSfZbikojJSUFf39/kpOT8fMrOoomKyuLffv20bhxYzw8PC7uxct5s9lstGjRgpEjR/LMM884Opwyp58vEZEyYrPB8d1FR9HGbwFb3mk7WqBus6KjaINagrNrsae9IHk5sOwFWD4DDBv41YOrZ0Fkn7K7RjV1tpxMilL+WnkpfxURkeoqM8fK3sRTBdk9x9LZk5DG3sQ0snJLHjVb18fdLMieVpwNC/DE2anqjZotrLT5a5UoQU+cOLHEdgi///57kee9evVi69at5zznkCFDGDJkSFmEJw524MABfvrpJ3r16kV2djZvvPEG+/bt4/rrr3d0aCIiUpmkJxbtQxu7/txtDup1hHqXgId/+cbm4gb9noBmg+Dr2+HEHvhwGETfDv2ngJtX+V5fRCqU8lcREakuUrNyOZKUxZHkTI4mZXEkKZMjyZkcScrk0IlMYpMySzzWxclCo7reRAZ65/ec9bE/9vcswwESVVSVKNqKnI2TkxPz58/ngQcewDAMWrduzS+//EKLFi0cHZqIiDhKbpbZ5iA2v83B4b8g6cCZ+7l4QGj7oqNo/cPPr81BWQrvDHf8CT8/CevehbVvw55f4T9vm/FVcoZhkJVrIz0nj7o+FzaBq0hNoPxVRESqguw8K3HJWWZRNimTo8mZHEnOf5y/LjX79LvUzuTv6UqToFOjZQsmBQuv7YWrs1MFvJKqSUVbqfLCw8NZsWKFo8MQERFHsdnMkamFR9HGby6mzQFmm4N6naB+R/NrcKuybXNQFty84cpXoPkV8M1EOL4L3h0APR+Eng+Ue7w2m0FaTh7JGbkkZ+aSkpVLSqb5ODkzl5TMPPvjgu3menNbjtVGiJ8Hqx/tV65xilRlyl9FRMTRbDaDY2nZ5sjYpCyOJpujYo/aH2eRmFZyb9nC/D1dCfX3oF6AJ6EBHoT6e1IvwJN6tTyJqOtNbW+3KjkRmKOpaCsiIiJVS3riqdGzBSNps4prcxBYtEBbEW0OylKT/nDnSvjhAdj8JSx7HnYtheFzILDZWQ/Ns9pIycorVGjNPaPQWlIhNjUrF9tFzniQXooRFyIiIiJSPgzDICUzz96m4NTo2Ex7K4P4lCxyredO+txdnAgL8CQsvxgbFuBJmL8HoQGe1MtfV90mAKss9K6KiIhI5ZWbBXH/Fh1FW2Kbg3ZFi7QBDRzX5qAMZOVaScnzIrnHqzjV7kWD1U/geuQf8mZfxoqGE/mj9n9IzrIWKcwWFGLTc6wXfX13Fyf8PV3x83TFv9Di5+FSZP0Z2z1d8XZzLoN3QERERESKk5VrzW9XkHXmSNn8dRmlyAedLBDiZxZg7SNl/T3yi7TmUsvLVaNkHURFWxEREak8bFbYtwx2/AiH10HcZrDlnrlfQZuDepeYfWiDW1eqNgc2m0FGrpW0rDxSs3JJzc4jLSuPtPyvhVsKFB0Be2rEa05e4dl06xLMNF50nUMv/qXXvldw3r2EB3Pv4Ch1SozDx/1UgbWg2Fp8obXoNj8PVzxcVXgVERERqWg2m0Fiera9j+yRJLMYe+prFifSc0p1rtrebqeKsPmjYwsehwV4EuTrjot6ylZaKtqKiIiIYxkGxG2Cfz+FTV9AWlzR7V518ycKyx9FG3YJeAaUSygFxdbUrFyz4Fqo2JqalUtqocKrua5gn1z7+tSsPNJy8jAussUAmKMfThVY/XnH/SWO5vzIfxLf4jLnLfzu9gh/t3qEE5HD8fN0K1KI9fVwURIuIiIiUslk5ljtbQtiTxYUY/MLtMlmT9kcq+2c5/Fycy5UkDWLsaEBp0bLhvp74qm7n6o0FW1FRETEMZJjYdNn8O9nkLD11HrPWtByGDS6zCzWBjQ8Z5uDkoqtZpG1aLG14HHhYmtqVn4htoyKrQVcnCz4erjg4+GCj7srvu7mY9+CFgMeRdsKFB7x6u/pirebC05Op7/2S+H4jfD17bgfXke3fx+D3NUw5FXwDii74EVERETkvNhsBolp2fYRsYVHyZqF2tKNknWyQLBf4TYFZjE2zN8szNYP8MLP00VtC6o5FW1FRESk4mSlwLbF5qjafX8C+RVSZzdoNojc1iPZ5deV/UnmhFipm/NIy951qqhaqNhaZNRrBRRbTz3PL7y6u+Dr4Xrm8/x9fD1ccHdxKp9kuk4kjF8CK/4Hvz8P276Fg2vgqteh+aCyv56IiIiIkJGTV6RtwYWOkvV2c6ZeLbMgWy+g6NewAA+C/Txw1R1TNZ6KtlKmevfuTfv27Zk5cyYAjRo14r777uO+++4r8RiLxcLXX3/NsGHDLuraZXUeEREpY9Zc2POrWajd/j3kZdk3JQV24p9aA1liu5QNR2DPxjTybGsu+FKFi62+7q72YuqpQqrZNsDHvVDxtdC+5V5sLUvOLtDzQWh6OXx1GxzbDp+MgktuhIHPgbuvoyMUqRKUv4qICJQ8Sja2UIH2ZEYxcy2cpmByr8KTedULKPrcz0OjZOXcVLQVAIYOHUpubi5Lliw5Y9uff/5Jz5492bhxI23btj2v865btw5vb++yChOAKVOmsGjRIjZs2FBk/dGjR6lVq1aZXqskmZmZ1KtXDycnJ2JjY3F3dy+yvaQE/KabbiIpKYlFixbZ1+3evZtp06bx888/c+zYMcLCwrj00kv5v//7Pzp16lQBr0ZEpBwYBhxZD/9+hrHpCywZifZNsc71+cp6GZ9md+XwoUA4BJBq3+7r4ULTIB9qebnZi6o+7kWLracXZqtUsbWshbaD25bBr8/Aqlmw/gPYuwyGvw0Nuzo6OpFyo/z1/Ch/FRE5U2pWLh+sOsCXfx/m0MkMcq3nvnXLx90lf1SsR6GibP7XWp4Ea3IvKSMq2goAN998MyNGjODw4cPUr1+/yLZ58+bRqVOn8054AQIDA8sqxHMKCQmpsGt9+eWXtGrVCsMwWLRoEaNGjbqg8/z111/069eP1q1b8/bbbxMVFUVqairffPMN//d//8eyZcvKOHIRkfJjtRkc3redrL8/IXDfImpnHgDAAiQafnxr7cpX1h5sMhoDFlycLDQL8iYqxI/mIb60CPUlKsSPUH+Pmld4vViuHjBwGjQbBIvugqQDMO8K6H4P9HkMXNzPfQ6RKkb56/lR/ioickpyZi7zV+xn7vK9pGTl2dcXHiVb0L7g9JGyfh6uDoxcahKV/gWAIUOGEBgYyPz584usT0tL4/PPP+fmm2/m+PHjjB49mnr16uHl5UWbNm345JNPznreRo0a2W81A9i1axc9e/bEw8ODli1b8vPPP59xzMMPP0yzZs3w8vIiIiKCJ554gtxc8xaE+fPn8/TTT7Nx40YsFgsWi8Ues8ViKTICYNOmTfTt2xdPT0/q1KnDbbfdRlpamn37TTfdxLBhw3j55ZcJDQ2lTp06TJgwwX6ts5k7dy5jx45l7NixzJ0795z7F8cwDG666SaaNm3Kn3/+yZVXXklkZCTt27fnqaee4ptvvrmg84qIVITjadms3J3I3OX7eHLhcma9/Bjrn76Uhh9eSvOtr1I78wBZhiuLrV25KedBhrq+w28RD9CtR39mjGzPD/f0YMvUgfx0fy9eG92BCX2a0DcqmLAATxVsL0bjHnDnCmg/FjBgxaswpw/EbXJ0ZCJlTvmr8lcRkfOVlJHDjJ93ctkLv/K/X3aSkpVHkyAf/jeqHSsm92Xns1ew8pF+fHFnN169rgMPD4rihksb0jcqmKgQPxVspUJppG1FMAzIzXDMtV29zjnjNoCLiws33ngj8+fP57HHHrP/wfz5559jtVoZPXo0aWlpdOzYkYcffhg/Pz++//57brjhBiIjI4mOjj7nNWw2G//5z38IDg5mzZo1JCcnF9srzNfXl/nz5xMWFsamTZu49dZb8fX15aGHHmLUqFFs3ryZJUuW8MsvvwDg7+9/xjnS09MZOHAgXbt2Zd26dSQkJHDLLbcwceLEIon9b7/9RmhoKL/99hu7d+9m1KhRtG/fnltvvbXE17Fnzx5WrVrFV199hWEY3H///Rw4cICGDRue8z0obMOGDWzZsoWPP/4YJ6czPz8JCAg4r/OJnJNhQOpRs3gTtwniN0PaMfDwA3c/8PDPXwo9LrI+/7mLm6NfiVSgrFwruxPS2BGXyva4FLbHpbI9LpWk1HT6OP3DMOcVjHVaj7slDyxgMyxscmvLjqDBZDe9ksjwUGaE+FHbWz83FcbDD4bNgqjBsPgeSNhiFm77PArd7wUnZ0dHKFWB8ldA+evplL+KSFV1Ij2Hucv38v7KA6RlmyNrmwf7cne/JlzROhRnJw0akMpHRduKkJsBz4U55tqPHgG30vXk+u9//8tLL73EsmXL6N27N2DeWjZixAj8/f3x9/fngQcesO9/9913s3TpUj777LNSJb2//PIL27dvZ+nSpYSFme/Hc889xxVXXFFkv8cff9z+uFGjRjzwwAMsXLiQhx56CE9PT3x8fHBxcTnr7WQff/wxWVlZfPDBB/aeZG+88QZDhw7lhRdeIDg4GIBatWrxxhtv4OzsTFRUFFdeeSUxMTFnTXrfe+89rrjiCnv/sYEDBzJv3jymTJlyzvegsF27dgEQFRV1XseJlEpejjkpUfxmiNsM8ZvMr5knLv7crl6lL/IWt97Vs1R/jEvFMgyD2KTM/OJsKtuOprAjLpW9ielYbQW9vQwuseziHuflDHFfTS3LqdFfqX5NyW09Ev/o62kXUJ92jnkZUljUlVA/Gr69F3Z8DzFPw86lMHw21I5wdHRS2Sl/BZS/nk75q4hUNcfTsnnnz318sGo/GTlWAKJCfLm3X1MGtgrBScVaqcRUtBW7qKgounXrxnvvvUfv3r3ZvXs3f/75J1OnTgXAarXy3HPP8dlnnxEbG0tOTg7Z2dl4eXmV6vzbtm0jPDzcnvACdO165gQpn376Ka+99hp79uwhLS2NvLw8/Pz8zuu1bNu2jXbt2hWZRKJ79+7YbDZ27NhhT3pbtWqFs/OpEUehoaFs2lTyLaRWq5X333+fV1991b5u7NixPPDAAzz55JPFjjgoiWGcu8G5SKmkJ54aORu32XycuANseWfua3GGuk0huDWEtAb/cMhOhaxkyE4xv2YlQ1bKmety8gt0uRnmkhZ3YfE6uZRQ5PUDj4AS1hfa390PzuPfmpwpNSuXnfGpbDtqjp7dEZfK9qOppGYX8zMDtPFIZJzPGvrm/EbtnCOnNviEQJtroN11+Ia0qaDo5bz4BMJ1H8GGj+HHh+HQaph9mdn/tuNN+gBFqjzlr8pfRUSKcyw1mzl/7GHB6oNk5prF2tb1/Linb1P6twhWsVaqBBVtK4KrlzliwFHXPg8333wzd999N7NmzWLevHlERkbSq1cvAF566SVeffVVZs6cSZs2bfD29ua+++4jJyenzMJdtWoVY8aM4emnn2bgwIH4+/uzcOFCXnnllTK7RmGurkX70VgsFmw2W4n7L126lNjY2DMmbrBarcTExDBgwADAvEUuOTn5jOOTkpLst8M1a9YMgO3bt9OhQ4eLeh1SQ1jz4MSeou0N4jaXXDx19zcLswUF2pA2EBhljnS90OtnF1PMzSr0uMj6YtYZNrOYnHHcXC6IJb+oW9II30KPfcMgsBn41a+Rhd48q439x9PNlgZHT7U3OHwys9j9XZwsNAnyISrEl3a1rXTP/oNGR77D7ejfUDCo1tUbWl4FbUdC41661b4qsFigwxhodJk5SdmB5fDdfbDjB7jqdfCtuImQpApR/lpqyl9FRCqP+JQs3l62l4/WHCA7z/zd2K6+P/f0a0rfqCDNnSBVioq2FcFiKfUtXo42cuRI7r33Xj7++GM++OAD7rzzTvsvtRUrVnD11VczduxYwOzxtXPnTlq2bFmqc7do0YJDhw5x9OhRQkNDAVi9enWRfVauXEnDhg157LHH7OsOHDhQZB83NzesVus5rzV//nzS09PtoxVWrFiBk5MTzZs3L1W8xZk7dy7XXXddkfgApk2bxty5c+1Jb/Pmzfn7778ZN26cfR+r1crGjRu55ZZbAGjfvj0tW7bklVdeYdSoUWeMckhKSlJfsJosKzm/rcHmUwXahG2Ql1X8/rUam0XZkDZFR9GWZVLi7AJetc3lQhgG5KSXvsBbXDE4LwswIDvZXJIPle7arl5QtxkENj/1NTDKfN+cq8d/hcdSs+19Z7cdTWVHfAo749PIySv+D/kQPw+iQn2JCvEjKsSXqFBfIgJccNuzFDa+Cat/PjVa2+IEkX2h7Sjzlvsq8n+anKZWQxj3Lax+E2Kmwq6f4M2uMOR/0GqYo6OTykb5K6D8VfmriFQVR5Mzeev3PXyy7pA9/+3QIIB7+zWlV7NAFWulSqoef6lKmfHx8WHUqFE88sgjpKSkcNNNN9m3NW3alC+++IKVK1dSq1YtZsyYQXx8fKmT3v79+9OsWTPGjRvHSy+9REpKyhnJY9OmTTl48CALFy6kc+fOfP/993z99ddF9mnUqBH79u1jw4YN1K9fH19fX9zd3YvsM2bMGJ566inGjRvHlClTOHbsGHfffTc33HCD/day83Xs2DG+/fZbFi9eTOvWrYtsu/HGGxk+fDgnTpygdu3aTJo0iZtvvpmoqCgGDBhAeno6r7/+OidPnrQnvRaLhXnz5tG/f3969OjBY489RlRUFGlpaXz77bf89NNPLFu27IJilSrEZoOk/YUKtPn9Z5MOFr+/qxcEtzpVmA1uA8Etwd23QsO+IBYLuPuYi3+9CztHXvZpxdyzFHgzk8z38fhus53D0Q3mUpiTK9SJzC/mNs8v5jaHOk0ufERyObLZDOJTszh4PIMDxzPYEZ9qL9QmphU/aszT1ZnmIb60CPWlebAvUaFmkTbAy63gpHBwJaxZCFsXm+9pgdB20PY6aD0CfC/sd6dUMk5O0G2iWYT/+naI+xc+HwfbR8Lgl8AzwNERipw35a8lU/4qItVdbFImb/62m8//OkyO1SzWdmpYi3v7N+WyJnVVrJUqTUVbOcPNN9/M3LlzGTx4cJH+XY8//jh79+5l4MCBeHl5cdtttzFs2LBib6MqjpOTE19//TU333wz0dHRNGrUiNdee41BgwbZ97nqqqu4//77mThxItnZ2Vx55ZU88cQTRSZJGDFiBF999RV9+vQhKSmJefPmFUnOAby8vFi6dCn33nsvnTt3xsvLixEjRjBjxowLfl8KJoXo16/fGdv69euHp6cnCxYs4J577mH06NEYhsGMGTOYPHkyXl5edOzYkT/++KNI0h0dHc1ff/3FtGnTuPXWW0lMTCQ0NJRu3boxc+bMC45VKqmcdHO0rL21wSaI3wo5qcXv71f/tPYGbc2RoTXwNn87F3ezR6dPYOmPsebByf3mxGyJO+DYzvzHuyA33Xx8bPtpB1nMUYmFC7l1m5utFjzOnPG7LGXk5HHoRCYHjqdz8EQGh05kcDB/OXQys8SRsxYLNKrjbY6aDfGzF2rDa3kV37Pr2A7YuBA2fV50xLJffbP1QdtREKSJZqqt4JZwSwwsewGWz4BNn8GBFXD1LIjs4+joRM6b8tfiKX8Vkerq0IkM3vx9N1/8fZhcq9lvu0vj2tzbvyldI+qoWCvVgsVQN/kLkpKSgr+/P8nJyWdMMpCVlcW+ffto3LgxHh4eDopQqiv9fFUBhgEpR/KLsptOjaI9vgco5leus5t5q35I21NF2uBWF96G4DxkZWUxevT1TJv2bKlHHVUbNhukxJrFy8Qd5teCx5knSz7ONzS/xUKUWcStm99qwbtuqdpR2GwGCanZ9kJsQWHWLNJmkpiWfdbjXZws1KvlSXgtL5oG+9iLtE2DffByO8dnsWkJsOkL+PfToqOO3f2g5dVmobZh95r9wUBNdGgdfH0bnNhrPo++HfpPAbfz6yvqKGfLyaQo5a/iKPr5EpGycuB4OrN+281X62PJs5l/W3VvUoe7+zbl0og6Do5OpHRKm79qpK2IyMXIyzZHaZ7ef7akop93UKHRs/n9Z+s2BWfX4vcvZ7t27WLRoq85duw4f/75e836RNrJCQLCzaVp/1PrDQPSE/MLudvNkbkFRd3Uo6eWfafd/ulZyz4aN6d2MxLcG7Kf+uzM8ufgyawiBdrsEkbLFvD3dKVBbS9zqeN16nFtL0L9PXBxPo+iak46bP8B/l0Ie34DI7+nopMLNBkA7UZBs0GVsh2EVJDwznDHcvjpCfhrLqx9G/b8Cv95G+p1dHR0IiIiIuw9lsYbv+3mmw1HsOYXa3s0rcu9/ZrSqVH5D3YRcQQVbUVESivtWNGRs3GbIHHnqcmaCrM4m6MxCxdoQ9qAT1DFx30Whw6Zt8WvWPEHn3322RkzS9dIFsupFgyNLiu6LSsZEndhJGwj48g2cuO24XJiF94Zh7FknoRDq+HQatyA+vlLB8OdPUYYu4167LaZX/c61cfq34h6dXwJL1SQbVjHi/BaXvh7XWQR32aFfX+YI2q3fQs5aae21esE7a6DVsPN0cEiYE44NWQGRA2GRRPg+C54dwD0fBB6PuCwD5ZERESkZtudkMobv+5m8cYj5Ndq6dM8kLv7NeWSBrUcG5xIOVPRVkSkJIm7YcMCOPqvWaRNiy9+Pw9/c0KwkDanirSBUeBa+W//O3jwIBaLM3AF99//IEOGDLHPWF3TZeVai/STPXgig4PHC3rL1iUrtzvQHQB3coiwHKWJJZYmTrE0scTSzPkojTiKtyWbtpZ9tGUfOBe6QLYrZDeBvGZgaw6W5uDcHFybABdYIIvbbI6o3fSFORq4QK1GZuuDtqPMiddEStKkP9y1Cn54ADZ/Ccueh11LYfgcsx2IiIiISAXYEZfK67/u4vtNRylo6tm/RRD39GtK2/oBDo1NpKKoaCsicrrUOPj9eVj/walbyQGwQO2I/MJsoQKtf/1S9TKtjA4dOoSLSz1yc18lPr4lL7zwAlOnTnV0WBXCMAyOpWXbC7MHjmcUmfgrPuXsvWWdLBAW4EnD/PYF4bXbmKNla3vToHb+aFlrbv4kaKf3zS2YBG2buRSRPwlaYFR+79zmpx57FNPvKOWIOZnYxk8hYcup9R4B0Po/0PY6CI+usj+j4gBeteGa96D5YPj+/+DIP/B2D7PPbfTt6nksIiIi5WbrkRTe+G0XP2yKs68b2CqYu/s2pXW98p0QWKSyUdFWRKRAVgqsfA1WzYLcDHNd08vNfp8hbSGoBbj7ODbGMnbw4EFstnAgApvt/3j++RcZP348jRs3dnRoZSIr18rhk5kcPJGeP0o2M3/UbDqHTmSSmWs96/G+7i5Fe8oWehwW4InruXrLOruaPYvrNgWGnFpvs0HK4UL9cvN75x7bDllJZqH35H7YueS0gELNIm7d5uBfD3bHmG0QCia4c3aDZgPNQm3TAeDifn5vmEhhba6Bht3gm4mwJwaWTIYdP8KwN80Pq0RERETKyObYZF6L2cVPW0/d3Ti4TQgT+zSlZZgmGpWaSUXbcmQYxcwSL3KR9HNVDvKy4a958MeLkHHcXFevEwyYCo26Oza2crZ//yGs1gb5zx7BMN5n0qQH+PrrLx0a1/nKzLGyOyGNnfGp7IxPZUd8Krvi04hNyjzrcU4WCPX3PNVP9rT+sv6eruUzOZuTEwQ0MJczJkE7Vmhkbn4hN3Fn0UnQ9v5e9HwNupqtD1oNMydEEykrfmEw9ktzgrKfnjAn4HuzGwx+0fyZ0wjuakd5hpQH/VyJSEk2HkritZhdxGxPAMzUYkjbMCb2aULzEF8HRyfiWCralgNnZ7NpYU5ODp6emo1bylZGhjkC1NVVk8JcNJvN7Nn46zOQdMBcV6cJ9HsKWgytEcWI/fsPAV3yn/mQl/cSixZdzy+//EL//v3PdqhD5OTZ2JuYxo44syhrFmdTOXAig5L+HvRxdykyUja8thcNC42WdXOpRLd6WyzmZHU+QdC4R9FtWcmFRubuMEfihrSFtteaPWtFyovFAp1vgYg+8NVtEPsXfH07bP8ehswE7zqOjlDKQEFekZGRofxVylxOTg5w6u8kEZH1B0/yWswuft9xDDAHU1zdvh4T+kTSJEjFWhFQ0bZcuLi44OXlxbFjx3B1dcVJvd+kDBiGQUZGBgkJCQQEBCjpvVh7foWfn4K4f83nPsHQezJ0uKHGzJJus9mIizsENCi09jqcnN5kwoR72bx5g8M+HMiz2jhwIoOdcansjE+zj57dn5hOnq346mxtbzeaBfvQPNiXZiG+NAv2JTLQh1pe5TRatqJ5+EN4Z3MRcYQ6kfDfpbDif2bf722L4eBquOp1aD7I0dHJRXJ2diYgIICEBHOkk5eXV/X43SkOZ7PZOHbsGF5eXri46M9PkZrur/0neDVmF3/uSgTA2cnCsPxibURg9WpFJ3Kx9L9mObBYLISGhrJv3z4OHDjg6HCkmgkICCAkJMTRYVRdRzbAL1Ng72/mczdf6H4vdL0L3LwdGVmFS0hIIC8vFwgvtNaCzfYaO3d2ZPbs2dxzzz3lGoPNZhCblMmOuFR2JqSyMy6VHfFp7DmWRk6erdhjfD1caBZsFmWbB/uYj0N8qeuj/q0i5c7ZBXo+CE0GmKNtj22HT0bBJTfCwOfAXSNjqrKC/KKgcCtSVpycnGjQoIE+CBCpwVbvPc5rMbtYucdsR+fiZGHEJfW5q08kDevUrL/DRErLYqjB0AVJSUnB39+f5ORk/PyKb4pts9nstwKJlAVXV1eNsL1QJ/bBr8/C5i/M506u5u2+PR8A77qOjc1B1q1bR3R0NLAe6HDa1jvw8VnI3r27CAwMvOhrGYZBfEq2vZ3Bjjiz9+yuhDQycoqfDMzT1Zmm+UXZ5sG+NA32oXmILyF+HvqjT6QyyM0y28usmgUYENAQhr8NDbtWaBilycnEVNr3ymq1kpubW4GRSXXn5uamuw9FaiDDMFi15zgzY3axdt8JAFydLVzTMZy7ekcSXtvLwRGKOEZpczKNtC1HTk5OeHh4ODoMkZotPRH+eAnWzQVb/h+gba6FPo9B7caOjc3BDh48mP+oQTFbnyUz81MeffQx3nlnznmd93hatn0isB3xqfktDlJJycordn83ZyciAr1pnt/SoKBIW7+WJ05OKs6KVFquHjBwGjQbBIvuMnuDz7sCut9j/o510ej3qsrZ2VkfEouI1ACGYZBrNcix2sjOtZKdZyMnz0Z2no3sPGvxj3NtZOfvbx5nK3SctcjjI0lZbIpNBsycf2Tn+tzZuwn1AtQ7XaQ0VLQVkeopJx1WvQkrXoWcVHNdZF/oPwVC2zk0tMri0KFDODl5YrPVLmZrXazWqcydey933HE7HTt2PGOP5MxcdsUX6jkbl8quhFQS04q/w8DZyUKjOl5FirPNgn1pVMcLF2eNvhGpshr3gDtXwJLJsOEj8/furl9g7BfgF+bo6ERERKqNhNQs/tiZyPG0bHsRtaSCa7b9caGCau5p+1ttJU7mW1bcXJy4ProBt/eKINRfxVqR86GirYhUL9ZcWP8BLHsB0uLNdSFtYcBUiOzj2NgqmYMHD+Ls3ACbraTRrHfi5PQ2d9x5N+988T07E9LM1gbxaeyMSyUuJavEczeo7WWOmA3xsRdnIwK9cXfRyC2RasnDD4a9Cc0Hw7f3mqNwvYMcHZWIiEiVtz8xnaVb4vhpazzrD54s1yKrm7MT7i5OuLmYX91dnc11roXXOxfZ58x1px57ujpzWdO6BPvpDmSRC6GirYhUD4ZhzmQeMxWO7zbXBTSEfk9Cq/+A+qid4dChQ+TlhZ9lDxes1tf4a10/+k+Yhk+rM4veof4e+UXZ/N6zIb40CfLBy03/vYjUSC2GQHgXyE03Jy0TERGR82IYBptjU/hpaxxLt8SxMz6tyPZ29f2JDPIxi6qnFVDdXZwLFVydcHN2Ln0R1tlJrclEKhll0yJS9e1fAT8/CbF/mc+96kCvh6HjeHBxc2xslVSe1cambfswjLZn2SsdcAUak/zbfPpdcRUt6texj6BtEuSLv6drBUUsIlWGTyBw8RMYioiI1BR5Vhtr95/gpy3x/LQljiPJp+5oc3Gy0DWyDpe3DGZAyxBC/DVqVaSmUNFWRKqu+K3wyxTYtdR87uoFXSdCt7vNW3WliDyrjbX7TvD9pqMs2RzHrr37gSEFW4HNmIXapTg5L8Jm3QLYcHf3okfXnswf3xlPT/WhEhERERG5WJk5Vv7YdYyftsQTsz2epIxc+zZPV2d6Nw9kYKsQ+jQPwt9LAyVEaiIVbUWk6kk6BL9Phw0fAwZYnKHjOHN0rW+Io6OrVE4v1B5PNycJM6y52DJPAL/g5PQLBusxbJmAeUtUu7btueuut4mOjqZly5a4uOi/CxERERGRi5GUkUPMtgSWbonjj13HyMq12bfV9najf4sgLm8ZwmVN6+LhqrkgRGo6/RUuIlVHxglY/j9Y8zZYs811La+Gvk9C3SaOja0SKalQCxDg5crAliH0berPde/Wwtf3KN26RdOlywgaNGjANddcA0DdukHccsstjnoJIiIiIiLVQmxSJj/nTyS2Zt8JrLZTM4nVr+XJ5S1DGNgqmI4Na+HirHk4ROQUFW1FpPLLzTQLtctnQFayua5hdxgwFep3cmxslURpCrWD24bSLbIOrvnJ4IkTiVgspyYbMAyD2rWDOHEigdWr12IYRpHtIiIiIiJydoZhsCshjaWbzULtptjkItujQnwZ2CqEy1sF0zLUT/m2iJRIRVuR8mazgS1PE2JdCJsVNn4Cvz0HKbHmuqCW0P9paDoAaniCcyGF2sJOTxAtFguXXhrNDz98R2rqSfbs2UOTJhrBLCIiIiJyNjabwT+HTvLTlniWbolj//EM+zaLBTo3rM3lrYK5vGUIDep4OTBSEalKVLQVKS82K6x/3yw4ZiZBaDsI7wINuphf1Xu1ZIYBO5eak4wd22au86sPfR+DtqPAqeb2dypcqF26JY7EtPMr1J5LQdEWYO3atSraioiIiIgUIzvPyqo9x1m6JZ6ft8aTmJZt3+bm4sRlTeoysFUw/VoEU9fH3YGRikhVpaKtSHnY9wcseQTiN59aF/uXuayeZT4PaADhl0J4NDS41BxBWoOLkXaH1sHPT8LBleZzjwDo8X8QfRu4ejg0NEcp70JtYdHR0fbHa9eu5frrr7+o84mIiIiIVBepWbn8vuMYP22N57ftCaRl59m3+bq70Dd/IrFezQPxcVe5RUQujn6LiJSlE/vgp8dhuzlSEQ9/6P2oeSt/7N9waA0cXAMJWyDpoLls+szc183H7M9aUMit38k8vqZI3AUxT8O2b83nLh7Q5Q647D7wrOXQ0ByhIgu1hXXu3Nn+eOXKtWV2XhERERGRquhYajY/b43np61xrNx9nByrzb4tyNfd3vbg0og6uLloIjERKTsq2oqUhexU+ONlWP0mWHPA4gyd/gt9HgWv2uY+dSKh7UjzcVaKOer20FqzkHtoHeSkwt7fzQUACwS3Mgu4BYXcWo2qXx/X1Dj4/XlY/wEYVrA4Qfvrofcj4F/f0dFVKKvNYM3e4xVeqC2sdu3aNGzYhAMHdrNx4z/k5ubi6upaLtcSEREREamM9iem89PWOJZuiWf9wZMYxqltEXW9ubxVCANbBdOufgBOTtXs7zMRqTRUtBW5GDYbbPgIYqZCeoK5LqIPDJoOQS1KPs7DDyL7mguY/W8TtsGh1acKuSf3m+0V4jfDX++Z+/kE5xdxu5iF3NC24FJF+yNlpcCKV81Cd25+o/5mV0C/JyG4pWNjq0BWm8Gafcf5/l/HFWpP17VrZw4c2E1OThabN2+mQ4cOFXJdERERERFHMAyDLUdSWLoljp+2xLMjPrXI9nb1/e2F2iZBvg6KUkRqGhVtRS7UgVWw5GE4utF8XjsCBj4HzQad/2hYJ2cIaW0unW8x16XGFRqJuwaObIC0eLN9QEELAWd3COtwanKz8C7gXbfMXmK5yMs2i9B/vAQZx8119aNhwNPQsJtjY6sglbFQW1iXLtEsXPgJYPa1VdFWRERERKoLm81g//F0th5NYeuRFLYeTWFzbEqRicScnSxcGlGbga1CGNAymFB/TwdGLCI1lYq2Iucr6ZA5UdaWr8zn7n7Q6yGIvh1c3MruOr4h0PIqcwHIzYIj/+QXcdeao3IzjuePzl196rjakWbxtqCQW7c5OFWC3ko2G2z+En59BpIOmOvqNIX+T0HUkOrX9uE0lb1QW1jhycjWrFnL7bff7sBoREREREQuTGaOlR3xqfnF2WS2Hklhe1wqGTnWM/b1dHWmV7NABrYOpm/zYPy91CJMRBxLRVuR0spJh+UzYeVrkJcFWKDjOOjzOPgElv/1XT2gYVdzATAMOLEXDq4+Vcg9tg1O7DGXjR+b+3n4myNZCwq59TqCm3f5x1vY7hj45SmI22Q+9wmB3pOhww3gXH1/DVWlQm1hHTp0wMnJGZvNqsnIRERERKRKSEzLto+cLfi691gaNuPMfd1dnIgK9aNlqB8tw8yvrcL88HB1rvjARURKUH2rJSJlxWaDTZ/DL1Mg9Yi5ruFlZt/a0LaOi8tiMSc3qxMJHcaY6zJPwuG/ThVyY/+GrGTY/bO5gDlJWkib/HYK0dDg0vKb8OvIP+b7VjC5mrsfdL8XLr2z4gvHFaSqFmoL8/T0pGXLtmze/A87d24hNTUVX1/17hIRERERxyuuvcHWIykkpGYXu38dbzezMFuoONuojjculTQXFxEpoKKtyNkc/gt+fBhi/zKfBzSAy5+FFldVztv5PWtB0wHmAmDNg/hNp3rjHlwDKYfh6AZzWfu2uZ9fvfwJzi41v4a0AeeLuB3oxD749VnY/IX53MkVom+FHg+Ad52LeYWVUkGh9odNR1myuWoWak/XvXs0mzf/g2EYrF+/nl69ejk6JBERERGpYc6nvYHFAo3reNMi7NQI2lahfgT6umOpjH+7iYicg4q2IsVJOWKOEP33U/O5qzf0/D+4dILZpqCqcHYxJyoL6wBd8vuSJh8u1Bd3DRz9F1JiYcvX5gLg6mW2USgo5NbvBF61z3299ERzgrF1c8GWa65rMxL6Pga1GpXLS3SU6lioLSw6Opq33zaL+mvXrlXRVkRERETK1cW2N4gK8cXbXSUOEak+9BtNpLDcTFj5BiyfAbkZ5rr2Y6Dfk+bEYNWBf31zaT3CfJ6TDrHr8yc0yy/kZiXD/j/NpUDd5qcmNwu/1GzLUPCJdXYarH4TVrwGOanmush+5iRjoe0q9vWVo+peqC3s9MnIRERERETKgtobiIiUjoq2ImBO6rXla/j5KUg+aK4L7wKDnod6lzg2tvLm5g2Ne5gLmD18E3fmj8bNX47vhsQd5rL+A3M/z9rmexTYDDZ8AukJ5vrQ9jDgaYjo7YhXU+ZqUqG2sBYtWuDh4U1WVjqrVq1zdDgiIlXOrFmzeOmll4iLi6Ndu3a8/vrrRT4QKyw3N5fp06fz/vvvExsbS/PmzXnhhRcYNGiQfZ9GjRpx4MCBM4696667mDVrVrm9DhGRi6H2BiIiF05FW5EjG2DJI3Bwpfncr75ZdGw9onL2rS1vTk4QFGUuHceZ69ITT43CPbTGHJmbeQJ2/mguYLY/6PcktBxunqMKS8/O489dify2PYGY7fE1plBbmLOzMx07dmTFij84cuQA8fHxBAcHOzosEZEq4dNPP2XSpEm89dZbdOnShZkzZzJw4EB27NhBUFDQGfs//vjjLFiwgHfeeYeoqCiWLl3K8OHDWblyJR06dABg3bp1WK2nihybN29mwIABXHvttRX2ukRESsNmM/hpazzv/LmXfw6eVHsDEZELZDEMo5hfoXIuKSkp+Pv7k5ycjJ+fn6PDkQuRGg+/ToV/PgIMcPGEy+6HbneDm5ejo6vc8nIg7l84uBriN5v9by8ZBy5ujo7sgh08nsGv2+OJ2Z7Amr0nyLHa7NtqSqH2dA8++CAvv/wyAN9++y1DhgxxcEQiImeqjDlZly5d6Ny5M2+88QYANpuN8PBw7r77biZPnnzG/mFhYTz22GNMmDDBvm7EiBF4enqyYMGCYq9x33338d1337Fr165Sj0CrjO+ViFQfOXk2Fv0Ty1t/7GHvsXT7erU3EBEpqrQ5mT7GkponL9vsv/rHK6f6r7a5FvpPMXu9yrm5uJmTk9Xv5OhILlie1cbfB07y6/YEYrYnsDshrcj2hnW86BcVTN+oILpE1K4xhdrCCt/Gu3btWhVtRURKIScnh7///ptHHnnEvs7JyYn+/fuzatWqYo/Jzs7Gw6PoRKeenp4sX768xGssWLCASZMm6ZZhEXG4tOw8Fq49yLt/7iMuJQsAXw8XbuzakOu7NCTM30O/q0RELoCKtlJzGAZs/w5+ehxO7jfXhV0CV7wA4cX3mJPq5WR6Dst2HiNmewLLdiSQkpVn3+biZKFzo9r0axFEn6ggIup61/jksnDRdvVqTUYmIlIaiYmJWK3WM1rKBAcHs3379mKPGThwIDNmzKBnz55ERkYSExPDV199VaQdQmGLFi0iKSmJm2666ayxZGdnk519amKflJSU83sxIiJncTwtm/kr9/P+yv32vDrI151bejRmdHQDfD1cHRyhiEjVpqKt1Axxm2HJZNj/p/ncJ8QcWdt2VJXvvyolMwyDnfFpxGyP59dtCaw/radWbW83ejcLpG+LIHo0DcTfU4llYQ0aNKB27SBOnEhg9eq1GIZR4wvZIiLl4dVXX+XWW28lKioKi8VCZGQk48eP57333it2/7lz53LFFVcQFhZ21vNOnz6dp59+ujxCFpEa7NCJDN75cy+f/XWIrFyzpVhEXW9u7xXBsA71cHdxdnCEIiLVg4q2Ur2lJ8Jv0+Dv+WDYwNnd7Fl72f3g7uPo6KQcZOVaWbX3OL9uS+DX7QnEJmUW2R4V4ku/FkH0jQqmfXgAzk4qQpbEYrFw6aXR/PDDd6SmnmTPnj00adLE0WGJiFRqdevWxdnZmfj4+CLr4+PjCQkJKfaYwMBAFi1aRFZWFsePHycsLIzJkycTERFxxr4HDhzgl19+4auvvjpnLI888giTJk2yP09JSSE8PPw8X5GIiGl7XApv/b6Hb/89ijV/JETb+v7c2SuSy1uFKK8WESljKtpK9ZSXA+vegd9fgOxkc13LYTDgaajVyJGRSTmIS87i1+0J/Lo9nhW7j5OZe+p2UncXJ7o3qUvfKLPtQb0ATwdGWvUUFG3B7Guroq2IyNm5ubnRsWNHYmJiGDZsGGBORBYTE8PEiRPPeqyHhwf16tUjNzeXL7/8kpEjR56xz7x58wgKCuLKK688Zyzu7u64u7tf0OsQEQHzzrV1+08y+/fd/LbjmH19j6Z1ubNXJF0j6+hOLBGRclIlirazZs3ipZdeIi4ujnbt2vH6668X6bVYWG5uLtOnT+f9998nNjaW5s2b88ILLzBo0CD7PlOmTDnjVrHmzZuX2GdMqhDDgF0/wdJH4fhuc11IWxj0PDTq7tjYpMzYbAYbDyflF2oT2HKkaI++UH8P+kYF0TcqiG6RdfF00y1aF6rw79p169Zx/fXXOzAaEZGqYdKkSYwbN45OnToRHR3NzJkzSU9PZ/z48QDceOON1KtXj+nTpwOwZs0aYmNjad++PbGxsUyZMgWbzcZDDz1U5Lw2m4158+Yxbtw4XFyqRBovIlWUzWbw6/YEZi/bw98HTgLgZIEr2oRyR89I2tT3d3CEIiLVX6XP9j799FMmTZrEW2+9RZcuXZg5cyYDBw5kx44dBAUFnbH/448/zoIFC3jnnXeIiopi6dKlDB8+nJUrV9KhQwf7fq1ateKXX36xP1fiWw0c2wFLHoE9MeZz70Do9yS0HwNOKtpVdalZufy5K5GYbQks25lAYlqOfZvFAh3CA/ILtcG0CPXVJ/5lpFOnTvbHK1dqMjIRkdIYNWoUx44d48knnyQuLo727duzZMkS++RkBw8exKlQT/2srCwef/xx9u7di4+PD4MHD+bDDz8kICCgyHl/+eUXDh48yH//+9+KfDkiUoPkWm0s3nCEt5btYVdCGgBuzk6M6Fif23pG0Liut4MjFBGpOSyGYRjn3s1xunTpQufOnXnjjTcAc4RBeHg4d999N5MnTz5j/7CwMB577DEmTJhgXzdixAg8PT1ZsGABYI60XbRoERs2bLjguFJSUvD39yc5ORk/P78LPo+UgYwT8PvzsO5dMKzg5AqX3gk9HwQPfW+qsn2J6fa2B2v3nSDXeurXla+7Cz2bBdI3KojezQOp46PbP8tLw4ZNOHhwD25uHqSlpeDqqgnbRKTyUE5WenqvRKQkGTl5LFx7iHf/3MuR5CzAzLfHXNqQ/3ZvRJCfh4MjFBGpPkqbk1Xq4aU5OTn8/fffPPLII/Z1Tk5O9O/fn1WrVhV7THZ2Nh4eRf9D8fT0ZPny5UXW7dq1i7CwMDw8POjatSvTp0+nQYMGZf8ipPxY8+Cv9+D35yDTvGWH5lfC5c9AnUjHxiYXJNdqY93+E/ZJxPYmphfZHlHX2xxN2yKIzo1q4+rsVMKZpCx16xbNwYN7yMnJYvPmzUXuWhARERGRqutkeg7vr9rP+yv3czIjF4C6Pu7cfFljxlzaAD8PfVgvIuIolbpom5iYiNVqtd9KViA4OLjE/rMDBw5kxowZ9OzZk8jISGJiYvjqq6+wWk9NTNSlSxfmz59P8+bNOXr0KE8//TQ9evRg8+bN+Pr6Fnve7OxssrOz7c9TUlKK3U8qyJ5fzVYIx/J/DoJawqDpENHboWHJ+Tuels3vO47x6/YE/th5jNTsPPs2FycLXSJq0zcqmL5RQbody0G6dIlm4cJPAHMyMhVtRURERKq22KRM3v1zLwvXHrJP4tuwjhe39YxgxCX18XBVezkREUer1EXbC/Hqq69y6623EhUVhcViITIykvHjx/Pee+/Z97niiivsj9u2bUuXLl1o2LAhn332GTfffHOx550+ffoZk5eJAxzfA0sfg50/ms89a0Pfx+CSm8C52v04V0uGYbDtaCq/bo8nZnsCGw4lUbhJSx1vN/rkTyJ2WdO6+nS/Eig8GdmaNWu5/fbbHRiNiIiIiFyoXfGpvLVsL99siCXPZibhrcL8uLN3JFe0DsXZSfNCiIhUFpW6ylW3bl2cnZ2Jj48vsj4+Pp6QkJBijwkMDGTRokVkZWVx/PhxwsLCmDx5MhERESVeJyAggGbNmrF79+4S93nkkUeYNGmS/XlKSgrh4eHn+YrkgmUlw7IXYc3bYMsFJxeIvg16PQSetRwdnZxDZo6VlXsSidmewG/bEzia3yerQKswv/xJxIJoVz8AJyWLlUqHDh1wcnLGZrNqMjIRERGRKujvAyeZ/fseftl26m/rrhF1uLN3JD2a1tUkviIilVClLtq6ubnRsWNHYmJiGDZsGGBORBYTE8PEiRPPeqyHhwf16tUjNzeXL7/8kpEjR5a4b1paGnv27OGGG24ocR93d3fc3TXRUYWzWWH9B/Drs5CRaK5rMgAGPgeBzRwbm5xVbFKmOYnYtnhW7jlOdp7Nvs3D1YnLmtSlb1QwfaICCfX3dGCkci6enp60bNmWzZv/YefOLaSmppbYSkZEREREKgfDMPh9xzFmL9vD2n0nALBYYGDLEO7oHUn78ADHBigiImdVqYu2AJMmTWLcuHF06tSJ6OhoZs6cSXp6OuPHjwfgxhtvpF69ekyfPh2ANWvWEBsbS/v27YmNjWXKlCnYbDYeeugh+zkfeOABhg4dSsOGDTly5AhPPfUUzs7OjB492iGvUUqw70+zb238JvN53WZmsbbpAMfGJcWy2gw2HDpJTP4kYtvjUotsrxfgaZ9ErGtEHfXJqmK6d49m8+Z/MAyD9evX06tXL0eHJCIiIiLFyLPa+H7TUWb/vseek7s6W/hPh/rc1iuCyEAfB0coIiKlUemLtqNGjeLYsWM8+eSTxMXF0b59e5YsWWKfnOzgwYM4OZ2aQT4rK4vHH3+cvXv34uPjw+DBg/nwww8JCAiw73P48GFGjx7N8ePHCQwM5LLLLmP16tUEBgZW9MuT4pzYBz8/Adu+NZ97+EPvR6DzLeCs/qaVic1m8NPWOH7aEs9vOxLsM84COFngkga16NvCbHvQPNhXt11VYZ07d+btt98GYN26dSraioiIiFQymTlWPv/7EHP+2Mvhk5kAeLs5c32XBtx8WQQh/h4OjlBERM6HxTAKTwEkpZWSkoK/vz/Jycn4+fk5OpzqITsV/pwBq94Aaw5YnKDTf6H3o+Bdx9HRyWm2x6Xw6FebWH8wyb7O18OF3s2D6BsVSK9mQdT2dnNcgFKmNm3aRNu2bQG45ppr+fzzzxwckYiISTlZ6em9EqmekjNy+XD1fuat2M/x9BzAnNx3fPdG3HBpI/y9NPBFRKQyKW1OVulH2koNYLPBxk8g5mlIy2+MH9EbBk6H4JYODU3OlJGTx6u/7OLd5fuw2gy83ZwZHd2A/i2D6diwFq7OTuc+iVQ5LVu2xMPDm6ysdE1GJiIiIlIJxCVnMXf5Xj5ec5D0HCsA9Wt5clvPCK7tGI6nm9qRiYhUZSraimPlZMBnN8DuX8zntSPg8mnQ/AqzS75UKr9uj+eJRVuITTJvtxrUKoSnrmqpicRqAGdnZzp27MiKFX9w5MgB4uPj7W1qRERERKTi7DmWxpxle/nqn8PkWs0bZ6NCfLmzdyRXtgnFRYMoRESqBRVtxXGyU+Hj6+DAcnD1gt6Tocsd4OLu6MjkNHHJWTz97RZ+3BwHmJOKTb26Ff1aqGhXk3TtGs2KFX8AZl/bIUOGODgiERERkerFZjM4kZFDfEpW/pJd5HFcchbb4lIoaHIY3bg2d/aKpHfzQM0fISJSzahoK46RmQQfXQOH14G7H4z5HBpc6uio5DRWm8GHq/bz8k87ScvOw9nJws2XNea+/k3xctOvj5omOjra/njt2rUq2oqIiIiUkmEYpGbnkVCo+BqfmkXCaY8TUrPso2fPpn+LYO7sHUHHhrUrIHoREXEEVV2k4qUfhw+HQdy/4BEAN3wN9S5xdFRyms2xyTz69Sb+PZwMQPvwAJ4b3oaWYZq4pKYqXLRdvVp9bUVEREQAsnKtJKRkE5+aZRZgU7JISDVHyMYln3qckd939lwsFqjj7U6wnzvBfh75i/k4xM+DJkE+hNf2KudXJSIijqairVSs1HizYJuwFbzqwo3fQEhrR0clhaRl5/HKTzt4f+V+bAb4erjw8KAoro9ugJOTbrmqyRo0aEDt2kGcOJHAmjXrMAxDt+GJiIhItZVntZGYVrhVwal2BXEpWfZCbVJGbqnP6efhUqgQ61FsYTbQ112T+4qIiIq2UoGSY+GDq+D4bvANhRsXQ2AzR0cl+QzDYOmWeKYs3kJcShYAQ9uF8cSQFgT5ejg4OqkMLBYLXbp05scfvycl5QR79+4lMjLS0WGJiIiIXJQDx9NZvOEIcaf1kE1My8Z27k4FALi7OBHi70GwrwdBfu6E5BdiT3+sFmMiIlJa+h9DKsbJ/fD+VZB0APwbwLhvoHaEo6OSfIdPZjBl8RZ+2ZYAQIPaXjwzrDW9mgU6ODKpbC69NJoff/weMPvaqmgrIiIiVdmGQ0ncOHcNKVl5xW53drIQ5OtOkJ8Hwb75LQr8PQgq9DjY1wM/TxfdgSQiImVKRVspf4m7zRG2KbFmofbGxRAQ7uioBPOWr3kr9jPj551k5lpxdbZwe89IJvZtgoers6PDk0ro9MnIRo8e7cBoRERERC7cX/tPcNO8daRl59G6nh99mwcRnF+EDfE3R8bW8XbHWS3CRETEAVS0lfIVvxU+uBrSEyAwyuxh6xvi6KgE+OfgSR79ejPbjqYAEN2oNtOGt6ZpsK+DI5PKrHPnzvbHK1dqMjIRERGpmlbtOc7N768jI8fKpRG1mTuuM97u+vO4xkrcDalHwMkFLM7glL/YHxesdzptHxew5K+z71/wWMV+Ebk4+l9Jys+RDfDhcMg8AcFt4MZF4F3X0VHVeMmZuby0dDsfrTmIYUCAlyuPXtGCazrW10Rjck516tShQYNIDh7cw4YN68nNzcXV1dXRYYmIiIiU2h87j3HrB3+RnWejR9O6zLmhE55uususRjqyAZa9ADt+KIeTW4op5p5e9L2AwrCTizmpd0A4+Ifnf60PfvXBxa0cXoeIOIqKtlI+Dq2DBSMgOxnqdYSxX4JnLUdHVaMZhsF3/x5l6ndbOZaaDcCIS+rz6OAo6vi4Ozg6qUq6dYvm4ME95ORksXnzZjp06ODokERERERKJWZbPHcuWE+O1UbfqCDeHHOJ2oLVRKcXay1OUKcpGDaw5YFhBVv+YljNdTZbocf56w3bWS5igC3XXCqExbyr1V7IzS/mBjQ4tc5dd1WKVCUq2krZ278cPh4FOWnQoCtc/xl4+Dk6qhrt4PEMHv9mM3/sPAZARF1vnh3emm6RGvks569Ll2gWLvwEMPvaqmgrIiIiVcGSzXHc/cl6cq0GA1sF8/roS3BzcXJ0WFKRiivWtr4Gej4Igc3O/3yGcVphN/+rYSv0+PQCcEnr807b5/T1+QVlay6kJUDyQUg6BMmHIPkw5GVB6lFzOVxCGzOPgEIF3fDTRus2MO+MVVsHkUpDRVspW7tjYOEYyMuExr1g9Cfg5u3oqGqsnDwb7/y5l9didpGdZ8PN2YkJfZpwR+8I3F00okAuTOHJyNasWcvtt9/uwGhEREREzu3bjUe479MNWG0GQ9uFMWNkO1ydVbCtMcq6WFvAYgFnF8zSigPvXjQMSE8sWshNyi/mFqzLSjKXuCSI21T8eVw8zNG5hQu5Be0X/MPBr17+6xWRiqB/bVJ2dvwIn90I1hxoOhBGfgCuHo6OqsZat/8Ej361iV0JaQB0i6zDs8NaExHo4+DIpKrr0KEDTk7O2GxWTUYmIiIild6Xfx/mwS82YjPgP5fU46Vr2uGsuRxqhuKKtW2uNYu1dZs6NLQyZbGAT6C51OtY/D7ZqYVG5h46rbh7CFLjzNG6x3ebS7HXcQLfsNPaL5xW3NWgLZEyo6KtlI0tX8OXt5i3a7S4CkbMVRN0B0nKyGH6D9v59K9DANTxduPxIS0Y1r4eFt3qImXA09OTFi3asGXLBnbu3EpaWho+PvowQERERCqfT9Ye5NGvN2EYcF3ncJ4b3qZqTr6bcQI2LoR/PjQft7waOoyB0HaOjqxyOvIP/P4C7PzRfF5di7Xnw90XgluaS3HyciDlsDk6t0hBN3+kbkqsOUAr5bC5sKr483jVKTRatyFE9oXIPuYkaiJyXlS0lYu3cSEsutPssdPmWhj2lm6ZcADDMPhqfSzTftjGifQcAEZHh/PwoCgCvFRAl7LVvXs0W7ZswDBsrF+/np49ezo6JBEREZEiPli1nye/2QLAuK4NeWpoq6pVsDUMOLAS/p4PW78Ba/apbWvfNpfgNtD+emg70uxHWtOpWHvhXNygdoS5FMdmg/SEooXcIm0YDkF2CmQcN5ejG83jVs8CnxBoNwraXQ9BURX3mkSqOFXW5OL8NQ++ux8woMMNMPRVfYLmAHuPpfH4os2s3HMcgGbBPkwb3obOjWo7ODKprqKjo5kzZw5gTkamoq2IiIhUJu/+uZdnv98GwK09GvPo4BZV566z9OOw8ROzWHt816n1IW2g43hzBOPGT2D79xC/CZY+Aj8/Ac0GQfsx0HQAOLs6LHyHqGHF2rlz59K2bVs6d+5ccRd1cgLfEHMJL+G6mUlFWy4kbIOtiyAtDla8ai5hl5gfNLQeAV76e1XkbCyGYRiODqIqSklJwd/fn+TkZPz8/BwdjmOsng1LJpuPo2+DQS+Yv8ilwmTlWpn9+x5m/76HHKsND1cn7unXlFsui9BMuFKuNm3aRNu2bQG45ppr+fzzzxwckYjUVMrJSk/vldQUs37bzUtLdwAwsU8T/u/yZpW/YGsYsH+5Wajdtti8DR3A1RvaXAMdb4KwDmbv0gKZJ2Hzl/DPR3Bk/an13oHQdpRZGAtuVZGvouLVsGItmHdY+voGUL9+OFu2bMTZuZIPmsrLhp1LzQ8adv1ktlQEcHKF5leYHzQ06VfzPmiQGq20OZmKtheoxie9f86AmKfNx93ugQFTiyYQUu5W7k7k8UWb2ZuYDkCvZoE8c3VrGtTxcnBkUhNYrVZ8fPzJykonLKwhsbH7HR2SiNRQNT4nOw96r6S6MwyD//28k9d+NSdRmjSgGff0q+SFu/RE2PAxrH+/6ORPoe3NQm2ba8xepOeSsA02fAQbPzVvYS98ng5jq9+oxmKLtSPzi7VNHBtbOYuNjaV+/foALFiwgDFjxjg4ovOQdgw2fQ4bP4a4TafWewea37/210NIa8fFJ1JBVLQtZzU26TUM+O05+ONF83mvydB7sgq2Feh4WjbTvt/GV//EAhDo685TQ1tyZZvQyj+CQKqVyy7rxYoVfwAQFxdHcHCwgyMSkZqoxuZkF0DvlVRnhmHw/JLtvL1sLwCTr4jijl6RDo6qBDYb7P8zf1Ttt2DLNde7+ZijRDuOM0fVXghrHuz+BTYsgB1LTp3b2Q2aDzZHNUb2rbpzkNTgYm2Bn376iYEDBwLtadgwlV27tuHqWgVHqcZtgg2fwL+fQkbiqfUhbcyf0zbXqk+zVFulzcmq6G9qcQjDMHslrXzdfN5/Clx2v0NDqklsNoPP/jrE9B+3k5yZi8UCN1zakAcGNsfPowr+Jy1VXteu0fai7bp16xgyZIiDIxIREZGayDAMpn63lXkr9gPw1NCWjO/e2LFBFSftmDkadv37cGLvqfVhl5ijaluPAHefi7uGsws0H2Qu6cfNUY0bPoK4f83eolsXnZoUqv0YCGx+cderKLHrYdkLsHOJ+bwGFmsLbN26FScnD2y2eRw4cAnz58/n1ltvdXRY5y+kDQxqAwOezv+g4WPY8aNZzF0yGX56HJpebo6+bTrQnChNpIZR0VZKx2aDHx+Ede+az694Ebrc7tiYapCd8ak89vUm1u0/CUDLUD+e+08b2ocHODYwqdGio6Ptj9euXauirYiIiFQ4m83giW8289GagwBMG96aMV0aOjiqQmw22LfMHFW7/ftCo2p9oe1Ic1RtaLvyubZ3Hbj0DnOJ22T2vt30WdFJoep1gg5joNV/wDOgfOK4GCrWnsEs2rbAZmuPxTKKJ5+cyg033ICHh4ejQ7swzvm9bZtfARknzD7NGz4yR1Xv+MFcPGubrULaX2+2/NAdplJDqD3CBapRt5fZrPDtPfDPAsACQ2eanwRLucvMsfL6r7uY88de8mwGXm7OTBrQjJu6NcLFWRONiWMdOHCARo0aATBgwEB++mmJYwMSkRqpRuVkF0nvlVQ3VpvB5C//5fO/D2OxwIsj2nJtp3BHh2VKjT81qvbk/lPr63XKH1X7H3Dzrvi48nJg11JzVOPOpWBYzfUuHhA1xCzgNu4FTg6e3ErF2hJdeullrFnTCFgA7MRiacmMGS9z3333OTawspawzfw5/Tf/g4YCgS3M4m3bkeAb4rj4RC6CetqWsxqT9Fpz4es7YPMX5n+Uw94yb6WRcvf7jgSe+GYzh05kAjCgZTBTrmpFvQBPB0cmYjIMg7p1QzhxIgFf31okJx9XX2URqXA1JicrA3qvpDrJs9r4v8838s2GIzg7WZgxsh1Xt6/n2KBsNtj7mzmqdscPYMsz17v7QdtR5qjakDYODbGItASzILbhI0jYemq9Xz1oN9osjNWp4L7AKtaelWEY+PnVJi3tQeDR/LW3UKvWYg4e3IuPz0W216iMrHmw93fz53T792DNNtdbnCCyn/lz2nwwuFbRkcZSI6loW85qRNKblwNfjIft34GTC4yYC62GOTqqai8hJYup323lu3+PAhDq78GUq1oxsJU+RZTK58orh/LDD98BsGvXLpo0UTItIhWrRuRkZUTvlVQXuVYb9y78hx82xeHiZOG10R0Y3CbUcQGlxpl3Ja7/AJIOnFpfP9ocVdtqmGNG1ZaWYZi3om/42OyBm5V0aluDrmZRrNVwcPctvxiKK9a2HQU9HlCxtpCjR48SFhYGLAKuzl97ECenpjzzzFM8+uijJR9cHWQmwZavzAnMDq89td7D3+wJ3e56qN9J7ROk0lPRtpxV+6Q3NxM+uxF2/WTONDryQ7OZvZQbq83g4zUHeHHJDlKz83CywPjujbl/QDN83NV+WiqnqVOn8tRTTwHw8ccfM3r0aAdHJCI1TbXPycqQ3iupDrLzrEz46B9+2RaPm7MTb465hP4tgys+EJsN9vwKf88zJ08qaDPg7g/trjNH1Qa3qvi4LlZuFuz80ex/uycGDJu53tULWl5tFnAbXgZOZdSqraRibc8HK36UbxUQExND//79gZ1A00Jb7sHX90MOHNhLrVq1HBRdBUvcBRs/gY0LISX21Po6TaH9aGh7Hfg7ePS9SAlKm5OpEiRnykmHT66DfX+AiyeM/hgi+zo6qmpty5FkHv16MxsPJQHQrr4/04a3oXU9f8cGJnIOp09GpqKtiIiIlJesXCu3f/g3y3Yew93Fibdv6Ejv5kEVG0TK0VOjapMPnloffqk5qrbl1eDmVbExlSVXD3NUbavh5mv9d6FZwD1eUCD7BAIamCMa24+GWo0u7Doq1l6QLVu24OTkjs0WcdqWR0lPf5eXX36ZadOmOSS2Cle3KfR7Evo8ZtYuNn4CWxebP6sxUyHmGYjobX7QEDWkav+7lBpLI20vULUdqZCVAh9dC4dWg5sPXP8ZNOru6KiqrfTsPGb+spP3VuzHajPwcXfhwYHNGXtpQ5yddEuHVH7Hjx+nbt26AERHd2PNmhUOjkhEappqm5OVA71XUpVl5ORxy/t/sXLPcTxdnZk7rhPdmtStmIvbrLA7xuxVu3PJqVG1HgFm79eO4yCoRcXE4giGAYf/gg0LYPNXkJ1yalujHtB+DLS8qnQtIGL/ht9fMCdDAxVrz9Mdd9zBe++tJjd3QzFbH8HD43UOHNhLUFAFf5hRWWSlwNZvzALugUJ/l7j5mm1K2o+BBpeqfYI4nNojlLNqmfRmnIAFI+DIerMnzNivzH4wUi5+2RrPk99s5khyFgBXtgnlyaEtCfZTA3WpWho2bMLBg3twc/MgLS0FV1dXR4ckIjVItczJyoneK6mq0rLz+O+8dazdfwJvN2fmjY8munHt8r9wcqw5qvafDyH50Kn1Dbrlj6q9Clxr2CTBuZmw7TtzUqi9vwP55QQ3n/yi2Njii2Iq1paJbt16smpVfeDjYraewNm5MRMn/peZM/9X0aFVPif2ma0TNn4MSYVGxddqbH7Y0u46qNXQcfFJjaaibTmrdklv2jH4cBjEbwbP2nDjIght5+ioqqWjyZlMWbyFpVviAahfy5Nnrm5Nn6ga+mmoVHmjR1/PwoWfALB+/Xo6dOjg4IhEpCapdjlZOdJ7JVVRcmYuN81byz8Hk/D1cOH9/0ZzSYNy7Nlps8Kun81RtbuWnurp6lnLbAnQcRwENi+/61clSYfMotiGj+DkvlPra0eYt6S3Gw1p8cUUa6+Dng+oWHueDMMgIKAuKSn3A4+XsNczuLpOY8+eXYSHh1dkeJWXzQYHV5oT7W1ZBLnpp7Y16mH+rLa4Ctx9HBai1Dwq2pazapX0phyFD66GxB3gHQTjFlfv23scJM9q4/1VB5jx0w7Sc6y4OFm4pUcE9/Zriqebs6PDE7lgM2fO5P777wfgrbfe4vbbb3dwRCJSk1SrnKyc6b2SCpccC9ZswJI/8tJiFu2KfZz/3P7YQlJGHrct+JutR1Px9XTjnRujzTkfznHchcV6GNZ/aI6qLTypUcPLzFG1LYaa/V7lTIYBB1eZxdstiyAnLX+DBftIXBVrL1p8fDwhISHAV8DwEvZKxdk5gvHjh/POO3MqMLoqIicdtn1r/qzu+xP7z6ert/lvPLSdOfo2oAEENAQP/V8p5UNF23JWbZLepIPw/lXmJ6N+9eDGxVC3iaOjqnb+PZzEI19tYssRs/9Tx4a1mDa8NVEhVfhnRyTfypUr6d7d7H09fvx/ee+9uQ6OSERqkmqTk1UAvVdSYWw2+GaCeVuyQxRTzD1rwRjITi00qra2Ofqu403mZEdSetlpp4pi+/9UsbYM/fbbb/Tt2xfYDpxttPcrODk9zI4d22nSRH/blyjpIGz81Pw9dWJv8ft4BJgF3FoNzSJuQMNTz/3DNTr3XHLSAYsmgStGaXMylwqMSSqbE3vNgm3yIfOXz7jFFz77pxQrOTOX//28kw9W7cdmgJ+HC48MbsGoTuE4aaIxqSY6dOiAk5MzNpuVlSvXOjocERERcSTDgB8fyi/YWszJqQwDMMyiqP1x/vOCx5TlWKLC5z8PjXqcGlXr4l6G8dQg7j7QfrS5pBwBizP4Bjs6qmphy5YtWCyuGEbh4rcVyAB+BLYB23Bx2URenpV3332X559/3iGxVgkBDaDXg+YHCofWwo7v4eR+OHnALOhmnoCsJIhLgrh/iz+HV52ihdyABhDQKP9rePXseZ2XA+kJZuuTtGP5Xwue5z9OTzC/5qSBsxtcNgl6/B+4uDk6+ipHRdua6tgOs2CbFgd1mpgjbP3rOTqqasMwDL7+J5bnfthOYlo2AMPah/HYlS0J9FUCKNWLp6cnLVu2ZfPmf9i5cwupqan4+vo6OiwRERFxhN+fh3XvABYY8S60uab0xxoGR5MzGPvOag4cTyPUz50Pbo6mcW2vMwu8ZxSAz1IMLrJvCce5+4JvSFm/GzWbX5ijI6hWtm7disXii2E8CmzD1XUbubn7gXBgP7VqBdKyZQtat+5Bixa3ce211zo24KrCYoEGXcylsOxUs3ibdPBUITfpQP5yELKSIeO4uRxZX/y5fYJPtVooUthtCP71K8+HQzar+TrshddiirHp+esyT57fua05sOx52P4dXP0GhGn+k/Ohom1NFLcJPhgGGYkQ1BJu/AZ8NAlWWdkel8ITizazbr/5yywi0JupV7XmsqZ1HRyZSPnp3j2azZv/wTAM1q9fT69evRwdkoiIiFS0NW+bf5wDXPny+RVsgUMnM7n+3TUcOpFF/Vq+fHzrpYTX1m21IgCZmVkYxknq1/+C1q1b0LLl1Xz22ZccPrwfJycnjhw5iIeH+i6XGXdfCG5lLsXJTCpUyC2muJuTdqrgeXhdMSewgG9o0UJu4cKuX31wvoiSnWGYI4XthdeE0x7HnxoRm37s/O5McHI1a0g+QWZhuuCr9+nrgmDXT/DDg+ak9+/0g+73Qq+H1SO8lFS0rWli18OHw81/vKHtYOzX4P3/7d15XFT1/sfx97CjbOICLijuiAsqKJmVWRZmmtqilqV5y27ebKNNu1217dryy/S2WJlLWaYtamZlmrm0uKIiCrjkvuDOKuvM+f0xMkkCogIzA6/n4zEPhjPnnPmcwfLL2+/5fGvbu6oqISMnX28v26VP1uyT2WLI291Vj93YUg9c01Qebi72Lg+oUF27dtWHH34oSdqwYQOhLQAA1c3WL61tESSp57+lLg9e0uH7TmbpnmlrdSQtR6G1a+jzkVepYUAVvLUYuEzTp3+s999/T97ef/13kZKSojlz9stisWjXrl1q3769HSusZrwDrI/6HS58zTCsM1LP7Psr0C0MdwuD3YJsKeOI9XFgzYXnMLla1x36+wzdgMZSzbrW9g0lhrHnZsWa8y7hgkxSzTp/C2Hrnvs+qGgY612r7Is+trtDatrDGtxuny/9NklK/l4a8L7UKOoS6queCG2rkwNrpc/vknLTpUZdpKFfW/8ngytiGIYWxR/RK98n6USGtRXCLe2C9ULfcAaaqDa6dOlie75uHX1tAQCoVnb+JC0cZX0e/bB03TOXdPju45m6Z9paHc/IVfO6NTVn5FUK8mMWFnA+FxeXIoGtJLVp08b2PCkpidDWUZhMUo1A66Nh5wtfNwwp6+Rf7RaKtF84F/Ka86S0A9bH/t8uvxYv/6LBa5HZsOfNiK1R58pm9pamZh3prplSu9ulxbHSyR3S9Jukq/4l3fBC1ez9W04IbauLPaukL4ZI+WelJtdI98y1TvfHFdl5LEPjvt2mtXtOS5Ka1qmpCbe1VY9Wde1cGVC5wsPD5eVVUzk5WSxGBgBAdbJ/jfTlMMlSIHUYLMVMLPsMLEk7UjI09OO1OpmZp9ZBvvrswWjWgADK6PzQNjk52Y6V4JKYTJJPXeujuNmmFot1puzf++gWhrtZJ613TJc4GzbIeu6a9RyrDUGbflKT7tKSsdLWudKad6UdP0r935OadLN3dQ6J0LY62LVMmnevVJAjNb9BGvy55EFvqCuRmVug/y3fpRm/7VWBxZCXu4sevaGlHry2qTzdXO1dHlDpXF1dFRkZqd9/X60jR/br2LFjCgpipWAAAKq0lARpzmDr7xktY6y/eLuUvS3YtsNpum/6Op05m6+2Dfw0+4FoBdZkdXGgrP4+0xZVhIuL5Fff+vj7AmnOrkagdPuHUtuB0uInpNN/SjNvkbo+JPUaL3nUtHeFDoVGm1Vd0nfSF3dbB1Kt+0h3zyWwvQKGYei7+CO68a2V+mj1HhVYDN0cHqRlT/bQIz1bENiiWuvWravt+YYNxTXbBwAAVcbpPdLs26XcNKlxN+muWZKre5kP33IwVfdMW6szZ/MVERKgOQ9eRWALXKIWLVrIxcX6O+jWrYS2cCKte0v/Wit1uk+SIa3/UHq/m/UucdgQ2lZlCV9LXw6XLPlS+ABp0KeSG7caXa7dxzN17/R1evSLzTqWnqsmtWto5v1d9NGwKFa1BWRdjKzQ+vW0SAAAoMrKSJE+HWBdeTyo/SVPDNm477Tu/Xid0nMKFNWklj57oKv8a5Q98AVg5eHhoSZNmkuSdu/eIbPZbOeKgEvgHSD1f1e6d77kH2JtA/HpbdJ3T0g56fauziEQ2lZVmz+TvnlQMsxSxN3SHdMv6V++8Zes3AK99mOybpmyWr/vPiVPNxfF3tRKPz1xnXqG1bN3eYDDOD+0XbuW0BYAgCop+4x1hm3qfqlWU+neby5pceM1f57SsBnrlZlboG7NauuTf3SVrxe/pwCXq317a4uEvLwcHThwwM7VAJehxY3Sv9ZIUQ9Yv4+baZ11u/tn+9blAAhtq6L106RvH5FkSJEjpP7vV9wqgFWYYRj6IeGoek1apQ9W/al8s6Febepp2ZM99NiNLeXlTisE4HyNGzdWYKD1HzLWrl0vwzDsXBEAAChXeVnWHrbHt0s+wdKwhZJv2XvYr955QvfPXK+zeWZd27KOZtzfRTU9+T0FuBLh4fS1RRXg6Sv1nSQN/06qFSqlH5I+u0Na+IiUnWrv6uyG0Laq+eNd6Yenrc+jR0l9376kxQBgtedEpobNWK9/fb5JR9Ny1KiWtz4eFqWPh3dR49q0QgCKYzKZdNVV1tm2GRln9Oeff9q5IgAAUG4K8qQvh0kH10le/tJ9862/WJfR8qRjevCTjcotsOjGsHqaNixK3h5MggCuFIuRoUppep006g9rniWTtOUz6f2rpB1L7F2ZXZDmVSWr3pSW/tv6/JpYqfdEyWSyb01O5mxegd78KVkxk1fr110n5eHmosdubKmfY3uoV3jZZxEA1VVhaCvR1xYAgCrDYpEWjrLequpeQxr6tRTUtsyHL9l2VA9/Fqc8s0W92wZr6r2R3LUGlJOwsDDbc0JbVAkeNaVbXpP+sUSq3ULKOCp9MVj6ZqR09rS9q6tUhLZVgWFIy1+SVrxi/b7nC1Kv8QS2l8AwDP20PUU3TVqt91ZYWyFc37qulj5xnWJvasWgEigjFiMDAKCKMQzpx2elbV9LLm7SoNlSSNeLH3fOovgjemTOZuWbDd0W0UDv3tNJHm78GgqUl/ND24QEQltUIY2vkh7+Tbr6McnkIiV8Kb0XLSUusndllYYGQs7OMKQlY6V1U63f3/yqdPVo+9bkZPadzNKE77Zr5Y4TkqSGAd4a1y9cN4cHyUTwDVySLl262J6vWbPBjpUAAIBysfI1acM0SSZp4IdSy15lPvSbuEN65ut4WQzpjs6N9MadHeTqwvgaKE9+fn6qV6+hjh8/rKSkJBmGwe+xqDrcvaWbX5bC+1vXbjqRLH15nxQ+QOrzf5JPXXtXWKH4J05nZrFIi5/4K7C99S0C20uQk2/WpKU7dPPbq7Vyxwl5uLpodM8W+jm2h2LaBvMXHXAZAgMD1bhxc0nSli2blJ+fb+eKAADAZVv3obTqNevzPm9K7e+86CGGYWjroVT9e0GCnj4X2N7dNURvEtgCFaZdO2tf24yMMzpx4oSdqwEqQKMo6Z+rpWuflkyuUuJC6f1oKeFr62TGKoqZts7KXGD9V4atc63TxG97V+o01N5VOY2fE49pwnfbdehMtiTp2pZ19OJtbdWsro+dKwOc39VXd9WBA38qLy9H27ZtU6dOnexdEgAAuFRbv7S2RZCknv+Wuo4sdffjGTlauPmwvo47pJ3HMm3b7786VOP7hTMhAqhA4eFh+uWXnyVZ+9rWq1fPzhUBFcDNU7rxP1L4bdLCR6RjCdI3D0jb5kt9J0m+wfausNwR2jojc770zYPWf1kwuUq3f1Smf/WGdODUWb343XYtTz4uSarv76VxfcPVux0za4HyEh3dVXPnfiHJ2teW0BYAACezc6l14TFJin5Yuu6ZYnfLLTBredJxfR13SKt2npDZYp3t5OnmolvaBeuuqBBd3bw242yggrVp08b2PCkpST169LBjNUAFqx8hjfxF+u1tafWb0o7vpf2/S71fkyKGVKn1nQhtnU1+jvTV/dLOHyUXd+muWVKbvvauyuHl5Jv14ao9en/lbuUWWOTuatKD1zbToze0UA0P/jMAytP5i5GtW7de//znP+1YDQAAuCT711j7BVoKpA6DpZiJRX4BNgxD2w6n6+u4g/o2/ohSz/7VCimySS3dGdlIt3aoLz8vd3tUD1RL54e2ycnJdqwEqCRuHtL1z1nzsIX/ko5ukRY+LG2fL/WdLPk3tHeF5YK0ypnknZXmDZX+/EVy85IGf35JCwFUVyuSj2v8ou06cPqsJKl7i9p68bZ2alGPVghARejUqZNcXFxlsZj1xx/r7V0OAAAoq5Rt0pzBUkGO1DJG6v+e5GJdBuV4Ro6+3XxEX8cd0o5jGbZDgv28dEdkQ93RuRGtxgA7OT+03b49yY6VAJUsqK304HLpj/9JKydKu5ZK719lXbys83Cnn3VLaOsscjOkOUOk/b9J7jWlu7+QmnHLQ2kOnj6rlxYnalniMUlSkJ+n/tM3XLe2r88tWkAF8vb2Vnh4B23btlk7d25XRkaGfH197V0WAAAozek90uyBUm6a1LibdNcs5Rmu+mXbUX0dd0grdhRtfxDTNlh3RjZS9xZ1WGAMsLOgoCD5+AQoMzNV27YR2qKacXWTro2Vwm61rv10aIP03ePS9gVSv/9JtZrYu8LL5mLvAlAG2anWAdT+3yRPP+m++QS2pcgtMOvdX3bpprdXaVniMbm5mPTQdc20/Knr1bdDAwJboBJ0725tkWAYhjZt2mTnagCg8r333nsKDQ2Vl5eXoqOjtX59yXce5Ofn66WXXlLz5s3l5eWliIgILVmy5IL9Dh8+rHvvvVe1a9eWt7e32rdvr40bN1bkZaC6yEiRPh0gZR2XEdRWidd/pAlL9ir6vz/r4c826eek4zJbDHVqHKBXB7bT+n/30v/u7qTrWtUlsAUcgMlkUlhYmCQpJeWgMjMzL3IEUAXVbS394yfp5letd6fvWSm9301aP02yWOxd3WUhtHUGCx62/kuBV4A07Fup8VX2rshhrdp5QjFvr9b/Ld2pnHyLrmoWqB8ev1bP92kjH08mlgOV5fy+tqUFFQBQFc2bN0+xsbEaP368Nm3apIiICMXExOj48ePF7v/CCy/oww8/1DvvvKPExEQ9/PDDGjhwoDZv3mzb58yZM+revbvc3d31448/KjExUW+99ZZq1apVWZeFqir7jDT7dil1v9K8Q3RP9nPq81GCZv2xT2fO5ivIz1Ojrm+un2N7aMG/umtodBP5e9OvFnA07dv/1SJhx44ddqwEsCMXV+nq0dKoP6TGV0v5WdIPT0uf9JNO/Wnv6i4ZKZYzuOklKXW/dPs0KbidvatxSIdTs/XK4kT9uC1FklTP11P/vrWNbotgZi1gD39fjAwAqpNJkyZp5MiRGjFihCTpgw8+0Pfff68ZM2ZozJgxF+w/e/Zs/fvf/1afPn0kSaNGjdLPP/+st956S5999pkk6fXXX1dISIhmzpxpO65p06aVcDWoyvKyM5X18UDVOrVdx4wA3ZH6lA4ZbvJwc9HN4UG6M7KRrm3JbFrAGZzf1zYpKUmRkZF2rAaws9rNpfu/lzZ8LP08wXrn+tTu0o3jpOh/WsNdJ0Bo6wzqtpIe/t22CAD+kldg0ce/7dE7y3crO98sVxeT7r86VE/0ailfVqwF7KZNmzby8qqpnJwsFiMDUK3k5eUpLi5OY8eOtW1zcXFRr169tGbNmmKPyc3NlZeXV5Ft3t7e+u2332zfL1q0SDExMbrrrru0atUqNWzYUP/61780cuTIirkQVGnbj6Rp/oa96rn5CV2jzUozamhY3hjVadRKD0c2Ur8ODeRfg7E04Ez+HtoC1Z6LixT9kNTqZmnRo9Le1dJPY6XEhdJt71qzNgdHaOssCGwv8Nuukxq3aJv2nMiSJHUNDdRLA9oqLNjPzpUBcHV1VVRUlH77bZWOHj2gY8eOKSgoyN5lAUCFO3nypMxm8wX/zwsKClJycnKxx8TExGjSpEm67rrr1Lx5cy1fvlzz58+X2Wy27bNnzx5NnTpVsbGxev7557VhwwY99thj8vDw0PDhw4s9b25urnJzc23fp6enl8MVwlmdyszVwi1H9HXcISUfTdVk9/d1jetm5chD37adrHd79FHLIBYOBZxVYU9bSUpMJLQFbGqFSsMWSXGzpKX/kQ6ukz64Rur5vNRttHUhMwfluJUBJTialq1Xvk/S91uPSpLq+Hjq37eGaUDHhrRCABzIVVd10W+/rZIkbdiwQX379rVzRQDgmKZMmaKRI0cqLCxMJpNJzZs314gRIzRjxgzbPhaLRVFRUfrvf/8rSerUqZO2bdumDz74oMTQduLEiXrxxRcr5RrgmPLNFq1IPq6v4w7pl+TjKrAYkgy97D5b/V3/kMXkJvchn2tY65vtXSqAK9S0aVO5u3sqPz9XCQmEtkARJpMUNUJqeZP03ePS7p+ln8dLid9K/d+TgsLtXWGxmL4Jp5FXYNGHq/7UjW+t0vdbj8rFJN1/daiWP9VDAzs1IrAFHAyLkQGojurUqSNXV1cdO3asyPZjx44pODi42GPq1q2rhQsXKisrS/v371dycrJ8fHzUrFkz2z7169dXeHjRXyjatGmjAwcOlFjL2LFjlZaWZnscPHjwCq4MziTxSLpe+i5RV/13uR6aHaelicdUYDEU0chf37b9Vfe5/iTJJJfbP5QrgS1QJbi6uqp5c+vt3vv27VZ+fr6dKwIckH8jaejXUv/3JS9/6cgm6cPrpFVvSmbH+2/GKULb9957T6GhofLy8lJ0dHSpv/zn5+frpZdeUvPmzeXl5aWIiAgtWbKkxP1fe+01mUwmPfHEExVQOcrLH3+eVJ///aqJPybrbJ5ZkU1qafGj12rCbW1ZvRZwUOeHtmvXEtoCqB48PDwUGRmp5cuX27ZZLBYtX75c3bp1K/VYLy8vNWzYUAUFBfrmm2/Uv39/22vdu3e/YDXwnTt3qkmTJiWez9PTU35+fkUeqLpOZ+Vp5u971WfKr+rzv1814/e9OpWVpzo+nnroumZa+uR1+rbLdkX8+YH1gD5vSu3vtG/RAMpVhw7WvrZmc4H+/PNPO1cDOCiTSeo0VPrXOql1H8mSL614RZrWUzqWaO/qinD49gjz5s1TbGysPvjgA0VHR2vy5MmKiYnRjh07VK9evQv2f+GFF/TZZ59p2rRpCgsL008//aSBAwfqjz/+UKdOnYrsu2HDBn344Yfq0KFDZV0OLtGx9By9+n2SFsUfkSTVrumhMbeE6Y7OjeTCKraAQ2vcuLECA+vp9OnjWrt2vQzDYEY8gGohNjZWw4cPV1RUlLp27arJkycrKytLI0aMkCQNGzZMDRs21MSJEyVJ69at0+HDh9WxY0cdPnxYEyZMkMVi0bPPPms755NPPqmrr75a//3vfzVo0CCtX79eH330kT766CO7XCMcQ77ZopU7TujruIP6Jfm48s2GJMnD1UW9wuvpzshGuq5lXbm5ukhbv5R+PPdn6vrnpa4sYgdUNX9fjOz8PrcA/savvjRkjpTwtfTjM9LxJMlSYO+qinD40HbSpEkaOXKkbZD7wQcf6Pvvv9eMGTM0ZsyYC/afPXu2/v3vf6tPnz6SpFGjRunnn3/WW2+9pc8++8y2X2ZmpoYOHapp06bplVdeqZyLQZnlmy365I99envZTmXlmeViku69qomeuqk1K9kCTsJkMumqq7rqhx8WKyPjjP7880+1aNHC3mUBQIUbPHiwTpw4oXHjxiklJUUdO3bUkiVLbIuTHThwQC7nLTKbk5OjF154QXv27JGPj4/69Omj2bNnKyAgwLZPly5dtGDBAo0dO1YvvfSSmjZtqsmTJ2vo0KGVfXlwAMkp6fpq4yEt3HxYp7LybNvbN/TXnZGNdFtEA9Wq6fHXATuXSgtHWZ9HPyz1eFYAqp7zQ9qkpCQNHDjQjtUATsBkkjrcJTXrIe3/XarvWJM6HTq0zcvLU1xcnMaOHWvb5uLiol69emnNmjXFHpObmysvL68i27y9vfXbb78V2fbII4/o1ltvVa9evcoU2rL6buVZt+eUxn27XTuOZUiSOoYE6JUB7dSuob+dKwNwqQpDW8k6k4zQFkB1MXr0aI0ePbrY11auXFnk+x49eigx8eK34/Xt25dFHau5hENpen5BghIOp9m21fHx0MBODXVHZCOFBRfTAmP/GunLYdbZQ+0HSTETrb+kAqhyzp9pm5ycbMdKACfjU09q63j/yOHQoe3JkydlNpttsxIKBQUFlfg/oJiYGE2aNEnXXXedmjdvruXLl2v+/Pkym822febOnatNmzZpw4YNZa6F1Xcr3vGMHE38IVkLNh+WJNWq4a4xt4TprsgQWiEATuqGG27QuHHjVL9+iEJCQuxdDgAATmvb4TQN/Xit0nMK5O5q0o1hQbozspF6tK4rd9cSlipJ2SbNGSwVZEstY6QB70suTrGsCYDL0KpVK5lMJhmGoa1bk+xdDoAr5NCh7eWYMmWKRo4cqbCwMJlMJjVv3lwjRozQjBkzJEkHDx7U448/rmXLll0wI7c0Y8eOVWxsrO379PR0AohyUmC2aPba/Zq0dKcycgtkMkn3dG2sZ2JaK6CGx8VPAMBhde/eXVlZWapRo4a9SwEAwGklp6Tr3unrlJ5ToKgmtfThfZGq7eNZ+kGn90if3S7lpkmNu0l3zZJcaTMGVGXe3t5q1KipDh7co507k1lTAnByDh3a1qlTR66urjp27FiR7ceOHVNwcHCxx9StW1cLFy5UTk6OTp06pQYNGmjMmDFq1qyZJCkuLk7Hjx9X586dbceYzWatXr1a7777rnJzc+Xq6nrBeT09PeXpeZGBES7LuEXbNWfdAUlSh0b+erl/O0WEBNi3KADlhsAWAIDLt/t4hoZOW6fUs/mKCAnQzBFd5Ot1kfA1I0WaPVDKPCYFtZPunit58PcxUB20axemgwf3KDs7U4cOHWKyGeDEHPreGA8PD0VGRmr58uW2bRaLRcuXL1e3bt1KPdbLy0sNGzZUQUGBvvnmG/Xv31+SdOONNyohIUFbtmyxPaKiojR06FBt2bKl2MAWFcdsMbRoyxFJ0gu3ttGCf3UnsAUAAAAk7T2ZpXumrdOprDy1beCnT0d0vXhgm31Gmn27dGafVKupdO98yTugMsoF4ADCw+lrC1QVDj3TVpJiY2M1fPhwRUVFqWvXrpo8ebKysrI0YsQISdKwYcPUsGFDTZw4UZJ1oZvDhw+rY8eOOnz4sCZMmCCLxaJnn7WukOrr66t27doVeY+aNWuqdu3aF2xHxUs8kq7M3AL5erlpRPemcqV3LQAAAKCDp8/qnmlrdTwjV2HBvvrsgWj517hIYJt31trD9vh2ySdYum+B5BtU+jEAqpTzFyNLSkrSTTfdZMdqAFwJhw9tBw8erBMnTmjcuHFKSUlRx44dtWTJEtviZAcOHJDLec30c3Jy9MILL2jPnj3y8fFRnz59NHv2bAUEBNjpClCadXtPSZK6hAYS2AIAAACSDqdma8hHa3U0LUct6vnoswejVavmRdZ6MOdLXw6TDq6TvPyl++ZLgU0rp2AADuPvoS0A5+Xwoa0kjR49WqNHjy72tZUrVxb5vkePHkpMTLyk8//9HKg86/eeliR1bRpo50oAAAAA+0tJy9E909bqcGq2mtapqTkPRqvOxRYds1ikhaOk3cskN2/pnq+koLaVUzAAhxIWFmZ7vm0boS3gzBy6py2qNovF0IZ9hLYAAACAJB3PyNE9H6/V/lNnFRLorTkjo1XPz6v0gwxDWvKclPCV5OImDf5MahxdOQUDcDiBgYEKDKwnSUpMJLQFnBmhLexm1/FMnTmbL293V7Vv6G/vcgAAAAC7OZWZq3s/Xqc9J7LUMMBbcx68SvX9vS9+4KrXpfUfSTJJAz+UWvaq8FoBOLbCxchOnz6u06dP27kaAJeL0BZ2s/5cP9vIJrXk7sofRQAAAFRPqWfzdO/09dp5LFNBfp6aMzJaIYE1Ln7gug+lldYFmdXnTan9nRVbKACn0K7dX31tk5OT7VgJgCtBUga7WUc/WwAAAFRz6Tn5GjZjvZKOpquOj6fmjLxKTWrXvPiBW7+UfnzW+vz656WuIyu2UABOg8XIgKqB0BZ2YRgGoS1Qza1Zs0aurq669dZbL3ht5cqVMplMSk1NveC10NBQTZ48uci2FStWqE+fPqpdu7Zq1Kih8PBwPfXUUzp8+PBF6zh16pQmTJigLl26qG7dumrcuLFuvfVWzZ07V4ZhXPT4+++/XyaTyfaoXbu2evfura1bt1702LKaMGGCOnbsWG772cvWrVt17bXXysvLSyEhIXrjjTcueszy5ct19dVXy9fXV8HBwXruuedUUFBge33fvn1FPv/Cx9q1a237XH/99cXuU9yfPQCoTJm5BRo+Y722HkpTYE0PzRkZreZ1fS5+4M6l1oXHJCn6YanHsxVbKACncv5iZIS2gPMitIVd7Dt1VicycuXh6qKOIQH2LgeAHUyfPl2PPvqoVq9erSNHjlz2eT788EP16tVLwcHB+uabb5SYmKgPPvhAaWlpeuutt0o9dunSpWrVqpU2bNigp59+WkuXLtX8+fPVt29fvfzyy4qJiVFWVtZFa+jdu7eOHj2qo0ePavny5XJzc1Pfvn0v+5qqovT0dN18881q0qSJ4uLi9Oabb2rChAn66KOPSjwmPj5effr0Ue/evbV582bNmzdPixYt0pgxYy7Y9+eff7b9DI4eParIyEjba/Pnzy/y2rZt2+Tq6qq77rqrQq4VAMribF6B/jFzgzYfSJW/t7s+eyBarYJ8L37ggbXSl8MkS4HUfpAUM1EymSq+YABOo+hMW9ojAM6K0BZ2UdjPtmNIgLzcXe1cDYDKlpmZqXnz5mnUqFG69dZbNWvWrMs6z6FDh/TYY4/pscce04wZM3T99dcrNDRU1113nT7++GONGzeuxGM3btyou+++W7NmzdL333+vwYMHq1OnToqKitKoUaMUHx+vRo0a6e67775oHZ6engoODlZwcLA6duyoMWPG6ODBgzpx4oRtn4MHD2rQoEEKCAhQYGCg+vfvr3379tleX7lypbp27aqaNWsqICBA3bt31/79+zVr1iy9+OKLio+Pt80QvdzPKyEhQTfccIO8vb1Vu3ZtPfTQQ8rMzLxoDZI1QO3Zs6d8fX3l5+enyMhIbdy4sczv/fnnnysvL08zZsxQ27ZtNWTIED322GOaNGlSicfMmzdPHTp00Lhx49SiRQv16NFDb7zxht577z1lZGQU2bd27dq2n0FwcLDc3d1trwUGBhZ5bdmyZapRowahLQC7yck368FPNmr9vtPy9XTT7Ae6KryBX+kHZRyTNnwszRkkFWRLLWOkAe9LLvxKB6CoRo0aydvbOms/IYGZtoCz4m942AWtEYDq7csvv1RYWJhat26te++9VzNmzChTK4K/++qrr5SXl6dnny3+ttCAgIASj3300Uf16quvql+/fkpMTFSPHj1Ut25dDRo0SLGxsXrjjTf0wQcfKDExUStWrChzTZmZmfrss8/UokUL1a5dW5KUn5+vmJgY+fr66tdff9Xvv/8uHx8f9e7dW3l5eSooKNCAAQPUo0cPbd26VWvWrNFDDz0kk8mkwYMH66mnnlLbtm1tM0UHDx58SZ+TJGVlZSkmJka1atXShg0b9NVXX+nnn3/W6NGjJanUGiRp6NChatSokTZs2KC4uDiNGTOmSDB6sTB5zZo1uu666+Th4WHbFhMTox07dujMmTPFHpObmysvL68i27y9vZWTk6O4uLgi22+77TbVq1dP11xzjRYtWlTqZzF9+nQNGTJENWuWoWckAJSz3AKz/jk7Tn/8eUo1PVz1yQNd1aFRQPE7px2S1k6VZvSW3motff+UlJMmNe4m3TVLcnUv/jgA1ZrJZFKrVtYWCYcO7VV2dradKwJwOdzsXQCqp3V7rKFtdDNCW6A6mj59uu69915J1tYCaWlpWrVqla6//vpLOs+uXbvk5+en+vXrX/Jx+/bt04MPPiiz2ayBAwfq+uuv15QpU/Trr78qNjZW//73v+Xh4aG7775bP/30k3r27Fni+RYvXiwfH+tshqysLNWvX1+LFy+Wy7nZT/PmzZPFYtHHH39sC0FnzpypgIAArVy5UlFRUUpLS1Pfvn3VvHlzSUVva/Px8ZGbm5uCg4Mv6TrPN2fOHOXk5OjTTz+1hZXvvvuu+vXrp9dff13u7u6l1nDgwAE988wzth5pLVu2LHL+1q1by9/fv8T3T0lJUdOmTYtsCwoKsr1Wq1atC46JiYnR5MmT9cUXX2jQoEFKSUnRSy+9JEk6evSoJOtn89Zbb6l79+5ycXHRN998owEDBmjhwoW67bbbLjjn+vXrtW3bNk2fPr30DwwAKkBegUWPfL5Jq3aekLe7q2aO6KrOjf/2/7/Te6WkRVLit9Lhov9ApYZRUnh/KeofkkeNyiscgNPp0KGN4uM3yjAM7dy5UxEREfYuCcAlIrRFpTt05qwOp2bL1cV04SAVQJW3Y8cOrV+/XgsWLJAkubm5afDgwZo+ffolh7aGYdhC0EuRkJCgLl26yM3NTYmJiTp8+LDeffddubu7q2PHjkVmatavX1/x8fGlnq9nz56aOnWqJOnMmTN6//33dcstt2j9+vVq0qSJ4uPjtXv3bvn6Fu1VmJOToz///FM333yz7r//fsXExOimm25Sr169NGjQoEsOo0uTlJSkiIiIIrNLu3fvLovFoh07dui6664rtYbY2Fg9+OCDmj17tnr16qW77rrLFu5KUnJy+fdLu/nmm/Xmm2/q4Ycf1n333SdPT0/95z//0a+//moLxOvUqaPY2FjbMV26dNGRI0f05ptvFhvaTp8+Xe3bt1fXrl3LvV4AKE2B2aLH527Wz0nH5enmounDo/666+zETinpW2tQm5Jw3lEm66za8P5Sm76SfyO71A7A+Zy/GFlycjKhLeCEaI+ASrdhn3WWbbuG/qrpyb8bANXN9OnTVVBQoAYNGsjNzU1ubm6aOnWqvvnmG6WlpUmS/Pysff0Kvz9famqqbUZnq1atlJaWZpt1WVYFBQXy9vaWJOXl5cnd3b3Irf6Fs2YladOmTWrRokWp56tZs6ZatGihFi1aqEuXLvr444+VlZWladOmSbK2TIiMjNSWLVuKPHbu3Kl77rlHknXm7Zo1a3T11Vdr3rx5atWqldauXXtJ13WlSqthwoQJ2r59u2699Vb98ssvCg8PtwXvZREcHKxjx44V2Vb4fWkziGNjY5WamqoDBw7o5MmT6t+/vySpWbNmJR4THR2t3bt3X7A9KytLc+fO1QMPPFDmugGgPJgthp78Ml4/bkuRh6uLProvUlf7pEgr/iu9Fy2910X65RVrYGtylZr2kG6dJD21Q/rHj9JVDxPYArgkRRcjo68t4IwIbVHpbK0R6GcLVDsFBQX69NNP9dZbbxUJL+Pj49WgQQN98cUXkqy33ru4uFzQt3TPnj1KS0tTq1atJEl33nmnPDw89MYbbxT7fqmpqcVub9GihRISrDOZWrduLXd3d7377rsym81au3atfvrpJ+Xn5+uTTz7Rjz/+qPvvv/+SrtNkMsnFxcXWP6xz587atWuX6tWrZwt3Cx/ntxTo1KmTxo4dqz/++EPt2rXTnDlzJEkeHh4ym82XVMPftWnTRvHx8crKyrJt+/333+Xi4qLWrVtftAbJGpI/+eSTWrp0qW6//XbNnDmzzO/frVs3rV69Wvn5+bZty5YtU+vWrYttjXA+k8mkBg0ayNvbW1988YVCQkLUuXPnEvffsmVLsbOUv/rqK+Xm5tpacwBAZbBYDD379VZ9F39YHV336Kf2v6jHT72lD7pLq16XTiRLLu5Si5uk296Vnt4lDV8kdXlA8g2yd/kAnNT5oW1iIqEt4IyY5ohKt34voS1QXS1evFhnzpzRAw88cEH/0zvuuEPTp0/Xww8/LF9fXz344IN66qmn5Obmpvbt2+vgwYN67rnndNVVV+nqq6+WJIWEhOjtt9/W6NGjlZ6ermHDhik0NFSHDh3Sp59+aut3+nedOnVSdna2VqxYoZ49e2rWrFm699579cQTT6hly5YaMGCAXn/9dV1zzTVaunSp6tatW+p15ebmKiUlRZK1PcK7776rzMxM9evXT5J1Ea8333xT/fv310svvaRGjRpp//79mj9/vp599lnl5+fro48+0m233aYGDRpox44d2rVrl4YNGyZJCg0N1d69e7VlyxY1atRIvr6+8vT0LLaW7Oxsbdmypcg2X19fDR06VOPHj9fw4cM1YcIEnThxQo8++qjuu+8+BQUFae/evSXWkJ2drWeeeUZ33nmnmjZtqkOHDmnDhg264447bO8RFhamiRMnauDAgcXWdc899+jFF1/UAw88oOeee07btm3TlClT9Pbbb9v2WbBggcaOHVuk1cKbb76p3r17y8XFRfPnz9drr72mL7/8Uq6urpKkTz75RB4eHurUqZMkaf78+ZoxY4Y+/vjjC2qYPn26BgwYYFsgDgAqmsVs1odz5ilsx3f6zXO9GplOSoX/i3P1lFr0srY+aBUjeQfYs1QAVUzz5s3l6uoms7lACQmEtoBTMnBZ0tLSDElGWlqavUtxKsfSs40mzy02QscsNlKz8uxdDoBK1rdvX6NPnz7FvrZu3TpDkhEfH28YhmFkZ2cb48ePN8LCwgxvb2+jadOmxkMPPWScOHHigmOXLVtmxMTEGLVq1TK8vLyMsLAw4+mnnzaOHDlSYi3z5s0zQkJCjL179xqGYRgFBQXGoUOHDIvFYpw5c8bIyMgo0zUNHz7ckGR7+Pr6Gl26dDG+/vrrIvsdPXrUGDZsmFGnTh3D09PTaNasmTFy5EgjLS3NSElJMQYMGGDUr1/f8PDwMJo0aWKMGzfOMJvNhmEYRk5OjnHHHXcYAQEBhiRj5syZxdYyfvz4IrUUPm688UbDMAxj69atRs+ePQ0vLy8jMDDQGDlypO06S6shNzfXGDJkiBESEmJ4eHgYDRo0MEaPHm1kZ2fb3ru0ugrFx8cb11xzjeHp6Wk0bNjQeO2114q8PnPmTOPvQ5OePXsa/v7+hpeXlxEdHW388MMPRV6fNWuW0aZNG6NGjRqGn5+f0bVrV+Orr7664L2Tk5MNScbSpUtLrRHOhzFZ2fFZVRJzgWHs/dWwLH7KSHulmWGM9/vr8UqwYcwbZhgJ3xhGTtn+ngGAy9W8eZghyXB39zQKCgrsXQ6Ac8o6JjMZhmFUdlBcFaSnp8vf319paWm23ou4uMVbj2j0nM1qU99PPz5+rb3LAVDN/fe//9WkSZM0duxYDR48WI0aNVJubq5WrVqll19+WbGxsSXOHAXgGBiTlR2fVQUy50v7frUuJJb8vZR1wvZSuuGt1EY3qvE1Q6TmN0oeNexYKIDqZODA27VwoXUNgt27dxdZRBaA/ZR1TEZ7BFQqWiMAcCTPP/+8rr32Wr3yyit6/vnnZRiGCgoKFB4erscee8y26BUAABcoyJX2rJQSF0k7vpeyz9heynbz03c5nfSjpatu6TdEg7qVvqAlAFSE8PA2ttA2KSmJ0BZwMoS2qFSEtgAczbXXXquffvpJubm5On78uHx9fRUQEGDvsgAAjig/W9r9szWo3blEyk3/67WadaWwvpqfE6ln4/xUIDe91L+tBnULtVu5AKq38xcjS0pKUt++fe1YDYBLRWiLSpN6Nk/JKRmSpC6EtgAcjKenp0JCQuxdBgDA0eRmSrt+sga1u5ZK+Wf/es23vtTmNin8NqlxN72/eq/e+H2HJOmFW9toGIEtADv6e2gLwLkQ2qLSFM6ybV63pur4FL/qOQAAAGB32anWmbSJi6Q/l0sFOX+95t/YGtKG95caRkkuLpKkj3/dozeWWAPbZ3u31oPXNrND4QDwl9atW9ueb9uWbMdKAFwOQltUGltrhGa17VwJAAAA8DdZp6y9aRMXWXvVWvL/ei2w+V9Bbf2OkslU5NBP1+zTK99bZ7E90aul/nU9PWwB2J+Pj4+Cg0OUknJQSUlJMgxDpr/9/wuA4yK0RaVZv49+tgAAAHAwZ09LC0dJu5ZJhvmv7XXbWEPa8NukeuEXBLWFvlh/QOO+3S5J+tf1zfX4jS0ro2oAKJN27dooJeWgMjNTdezYMQUHB9u7JABlRGiLSpGRk69th9MkSV0JbQEAAOAofp9ibYUgScEdrCFtm/5S3VYXPfTruEN6fkGCJOnBa5rqmZjWzGID4FDatm2jn39eKsna15bQFnAehLaoFHH7z8hiSI0Da6i+v7e9ywEAAAAki1na+qX1+e0fSx3uKvOhi+KP6Nmv42UY0vBuTfTvW9sQ2AJwOGFhYbbnycnJ6tmzpx2rAXApXOxdAKqHwn62zLIFAACAw9j3q5RxRPIKsM6wLaMfE47qyXlbZDGku7s21oTb2hLYAnBIbdq0sT1PSkqyYyUALhWhLSoFoS0AAAAcTvw869d2t0tunmU65OfEY3r0i80yWwzdGdlIrw5oR2ALwGGdH9pu305oCziTcg1tjxw5oqefflrp6ekXvJaWlqZnnnlGx44dK8+3hBPIzjMr/lCqJOmqprXtWwwAAMB5GL9WY3lZUuK31ucdhpTpkJU7jutfn29SgcVQ/44N9PodHeTiQmALwHHVrVtXfn7WyVPbthHaAs6kXEPbSZMmKT09XX5+fhe85u/vr4yMDE2aNKk83xJOYPPBM8o3Gwr281JIIP1sAQCA42D8Wo0lfy/lZ0m1mkohXS+6+++7T+qfs+OUZ7aoT/tgvXVXhFwJbAE4OJPJZJtte/z4YWVkZNi5IgBlVa6h7ZIlSzRs2LASXx82bJgWL15cnm8JJ3B+awRuHQMAAI6E8Ws1Fj/X+jViiHSRMer6vaf14CcblVtgUa82QZoypJPcXOk0B8A5tGtXdDEyAM6hXEcae/fuVePGjUt8vVGjRtq3b195viWcwLo99LMFAACOifFrNZWRIu1ZYX3eYVCpu8btP6MRM9crO9+s61vX1XtDO8mdwBaAE2ExMsA5letow9vbu9RB7b59++Ttze3x1UlegUWbDpyRJF3VjNAWAAA4Fsav1VTCV5JhkUKipcBmJe629VCq7p+xXll5Zl3Too4+uDdSnm6ulVgoAFw5QlvAOZVraBsdHa3Zs2eX+Pqnn36qrl0v3i8KVUfC4VTlFlgUWNNDzev62LscAACAIhi/VlPx86xfI0pegGz7kTTdN329MnIL1LVpoKYNi5KXO4EtAOdzfmibmEhoCzgLt/I82dNPP62bbrpJ/v7+euaZZxQUFCRJOnbsmN544w3NmjVLS5cuLc+3hINbV9jPNpR+tgAAwPEwfq2GUrZJxxIkVw+p7cBid9mRkqH7pq9XWna+OjcO0Iz7u8jbg8AWgHNq3LixPDy8lJeXo4QEetoCzqJcQ9uePXvqvffe0+OPP663335bfn5+MplMSktLk7u7u9555x3dcMMN5fmWcHCF/WyjaY0AAAAcEOPXamjruQXIWsVI3rUuePnPE5ka+vE6nc7KU0Qjf836R1f5eJbrr00AUKlcXV3VokVrJSbGa//+3crLy5OHh4e9ywJwEeU++vjnP/+pvn376ssvv9Tu3btlGIZatWqlO++8U40aNSrvt4MDKzBbFLff2s+WRcgAAICjYvxajVjM0tavrM8j7r7g5X0ns3TPtLU6mZmr8Pp++vQf0fLzcq/kIgGg/HXo0EaJifGyWMzavXu3wsPD7V0SgIuokH8ybtiwoZ588smKODWcSNLRDGXmFsjXy01hwX72LgcAAKBEjF+riT0rpcwUyTtQanFTkZcOnj6re6at1bH0XLUO8tVnD0bLvwaBLYCq4e+LkRHaAo6vXEPb//3vf8Vu9/f3V6tWrdStW7fyfDs4uHV7T0mSuoQGytWFfrYAAMDxMH6tZraeW4Cs3R2S21+3Bh9JzdY9H6/VkbQcNa9bU589GK3Amtw6DKDqOD+0TU6mry3gDMo1tH377beL3Z6amqq0tDRdffXVWrRokQIDuVW+OihchCya1ggAAMBBMX6tRnIzpaTvrM8jhtg2n8nK09CP1+ng6WyF1q6hOSOvUl1fTzsVCQAVIywszPY8KSnJjpUAKCuX8jzZ3r17i32cOXNGu3fvlsVi0QsvvFCebwkHZbEY2rDPGtrSzxYAADgqxq/VSNJ3Uv5ZKbC51DDStvnruEPaezJLDQO8NWfkVQry87JjkQBQMVq1aiUXF2sEtHUroS3gDMo1tC1Ns2bN9Nprr2np0qWV9Zawo53HM5R6Nl81PFzVrqG/vcsBAAC4ZIxfq5itc61fI+6WTH+17iqcaDCsWxM1CPC2R2UAUOE8PT0VEtJMkrRrV7IsFoudKwJwMZUW2kpS48aNlZKSUplvCTtZf641QmSTWnJ3rdQ/ZgAAAOWG8WsVkXZY2rPK+rzDINtmwzC06cAZSVJUaC17VAYAlaZ9e2tf25ycszp06JCdqwFwMZWapiUkJKhJkyaV+Zawk8J+tl1DaY0AAACcF+PXKiLhK0mG1PhqqdZfP8/9p87qZGaePFxd1LYBd4cBqNratKGvLeBMynUhsvT09GK3p6WlKS4uTk899ZSGDx9enm8JB2QYhm2mLf1sAQCAI2P8Wg0YhhRf2BphSJGX4vZbZ9m2b+QvL3fXyq4MACpVmzZtbM+TkpIUExNjx2oAXEy5hrYBAQEyndcf6nwmk0kPPvigxowZU55vCQe092SWTmTkysPNRREhAfYuBwAAoESMX6uBlK3SiSTJ1VMK71/kpY3nQtvIJrRGAFD1/T20BeDYyjW0XbFiRbHb/fz81LJlS/n4+Gjbtm1q165deb4tHEzhLNuOIQHMWAAAAA6N8Ws1ED/P+rX1LZJ3QJGXNhHaAqhGzg9tExIIbQFHV66hbY8ePYrdnpGRoTlz5mj69OnauHGjzGZzeb4tHExhaBtNawQAAODgGL9WceaCc/1sJUXcXeSltOx87TyeIUnq3JjQFkDV5+/vrzp16uvkyaNKTk62dzkALqJCFyJbvXq1hg8frvr16+v//u//1LNnT61du7Yi3xIOYB39bAEAgJNi/FrF7FkhZR2XatSRWtxY5KXNB87IMKTQ2jVU19fTTgUCQOUKD7cuRnbmzAmdOnXKztUAKE25zrSVpJSUFM2aNUvTp09Xenq6Bg0apNzcXC1cuFDh4eHl/XZwMIfOnNXh1Gy5uZi4zQwAADgFxq9VWOECZO3ukFzdi7xUuAhZZ8asAKqRdu3aaPVqa2ugpKQkXXPNNXauCEBJynWmbb9+/dS6dWtt3bpVkydP1pEjR/TOO++U51vAwRW2RmjX0F81PMr93wQAAADKFePXKiwnXUpebH0eMeSClwtD26gm3B0GoPpgMTLAeZRrqvbjjz/qscce06hRo9SyZcvyPDWcBP1sAQCAM2H8WoUlLZIKcqQ6raQGnYq8VGC2aMvBVEksQgagejk/tKWvLeDYynWm7W+//aaMjAxFRkYqOjpa7777rk6ePFmebwEHV9jPNroZoS0AAHB8jF+rsMLWCB0GSyZTkZeSUzJ0Ns8sXy83taznY4fiAMA+wsLCbM+3b2emLeDIyjW0veqqqzRt2jQdPXpU//znPzV37lw1aNBAFotFy5YtU0ZGRnm+HRzM8fQc7T2ZJZNJiuQ2MwAA4AQYv1ZRqQelfb9an3cYfMHLG/dZJxp0blxLLi6mC14HgKqqQYMGqlHDV5K0bRuhLeDIyjW0LVSzZk394x//0G+//aaEhAQ99dRTeu2111SvXj3ddtttFfGWcADrzw1+2wT7yd/b/SJ7AwAAOA7Gr1VMwpfWr6HXSgEhF7wcdyBVkhRFawQA1YzJZFJYmLVFwpEj+3X27Fk7VwSgJBUS2p6vdevWeuONN3To0CF98cUXFf12sKN1e6yhbVf62QIAACfG+NXJGYYUP8/6vJhZtpIUd26yAf1sAVRH7dtbQ1vDMLRz5047VwOgJBUe2hZydXXVgAEDtGjRosp6S1SywkXIrqKfLQAAqAIYvzqpI5ulkzskNy8pvP+FL6dm60hajlxdTIoICaj8+gDAzs5fjCwpiRYJgKOqtNAWVduZrDztOGbt+dYllNAWAAAAdrL13CzbsFslL78LXo7bf0aS1Ka+r2p6ulVmZQDgEM5fjIzQFnBchLYoFxvO3WLWop6Pavt42rkaAAAA+3rvvfcUGhoqLy8vRUdHa/369SXum5+fr5deeknNmzeXl5eXIiIitGTJkiL7TJgwQSaTqcjj/F+6cY45X0r42vq8w5BidykMbaNYOBdANXX+TNvEREJbwFER2qJcrDvXGiGafrYAAKCamzdvnmJjYzV+/Hht2rRJERERiomJ0fHjx4vd/4UXXtCHH36od955R4mJiXr44Yc1cOBAbd68uch+bdu21dGjR22P3377rTIux7nsXi6dPSnVrCs1v6HYXQpD2870swVQTTVr1kxubh6SpIQEQlvAURHaolwU9rNlETIAAFDdTZo0SSNHjtSIESMUHh6uDz74QDVq1NCMGTOK3X/27Nl6/vnn1adPHzVr1kyjRo1Snz599NZbbxXZz83NTcHBwbZHnTp1KuNynMvWudav7e+SXC9sfXA2r0CJR9MlSVGEtgCqKTc3NzVr1lKStGfPLhUUFNi5IgDFIbTFFcvIydf2I2mSCG0BAED1lpeXp7i4OPXq1cu2zcXFRb169dKaNWuKPSY3N1deXl5Ftnl7e18wk3bXrl1q0KCBmjVrpqFDh+rAgQPlfwHOLDtVSv7B+jyi+NYIWw6mymwxVN/fSw0CvCuvNgBwMO3aWVvsFBTkae/evXauBkBxCG1xxTbuPyOLITUOrKH6/gx+AQBA9XXy5EmZzWYFBQUV2R4UFKSUlJRij4mJidGkSZO0a9cuWSwWLVu2TPPnz9fRo0dt+0RHR2vWrFlasmSJpk6dqr179+raa69VRkZGibXk5uYqPT29yKNKS/xWMudKddtIwR2K3WXTudYIkcyyBVDNhYf/1deWxcgAx0Roiyu2nn62AAAAl23KlClq2bKlwsLC5OHhodGjR2vEiBFycflrqH7LLbforrvuUocOHRQTE6MffvhBqamp+vLLL0s878SJE+Xv7297hISEVMbl2M/WedavEYMlk6nYXTYS2gKApKKLkRHaAo7JKULb8l59d+rUqerQoYP8/Pzk5+enbt266ccff6zoy6iy6GcLAABgVadOHbm6uurYsWNFth87dkzBwcHFHlO3bl0tXLhQWVlZ2r9/v5KTk+Xj46NmzZqV+D4BAQFq1aqVdu/eXeI+Y8eOVVpamu1x8ODBy7soZ3Bmv7T/d0kmqf2gYnexWAzbTNuoJoxbAVRv54e2ycnJdqwEQEkcPrStiNV3GzVqpNdee01xcXHauHGjbrjhBvXv31/bt2+vrMuqMrLzzNp6KFWSFN20tn2LAQAAsDMPDw9FRkZq+fLltm0Wi0XLly9Xt27dSj3Wy8tLDRs2VEFBgb755hv179+/xH0zMzP1559/qn79+iXu4+npaZukUPiosraem3Hc9DrJv2Gxu+w+kan0nAJ5u7sqrL5vJRYHAI6ndevWtucJCcy0BRyRw4e2FbH6br9+/dSnTx+1bNlSrVq10quvviofHx+tXbu2si6ryth84IzyzdbFHEIC6WcLAAAQGxuradOm6ZNPPlFSUpJGjRqlrKwsjRgxQpI0bNgwjR071rb/unXrNH/+fO3Zs0e//vqrevfuLYvFomeffda2z9NPP61Vq1Zp3759+uOPPzRw4EC5urrq7rvvrvTrcziGIW2da31ewgJkkhR3bpZtx5AAubs6/K9BAFChatSooQYNmkiSkpOTZBiGnSsC8Hdu9i6gNIWr754/qC2v1XcLmc1mffXVV8rKyip19kNubq5yc3Nt31f5hRzKaN15rRFMJfQOAwAAqE4GDx6sEydOaNy4cUpJSVHHjh21ZMkS2+JkBw4cKNKvNicnRy+88IL27NkjHx8f9enTR7Nnz1ZAQIBtn0OHDunuu+/WqVOnVLduXV1zzTVau3at6tatW9mX53gOx0mndkvuNaQ2/UrcbeM++tkCwPkiIzvqyJH98vLylNlslpubQ0dEQLXj0P9Flrb6bkk9VwpX373uuuvUvHlzLV++XPPnz5fZbC6yX0JCgrp166acnBz5+PhowYIFCg8PL7GWiRMn6sUXX7zyi6pi6GcLAABwodGjR2v06NHFvrZy5coi3/fo0UOJiYmlnm/u3LnlVVrVE3/uswnrK3mW3PZg04FzoW0ooS0ASNInn8zU0aNH1aJFCwJbwAFVufuCyrL6rmTt37JlyxatW7dOo0aN0vDhw0sdLFerhRzKKLfAbBv80s8WAAAAla4gT9r2tfV5xOASdzuZmau9J7MkSZ1DCG0BQJJq1aql8PBweXh42LsUAMVw6NC2Ilff9fDwUIsWLRQZGamJEycqIiJCU6ZMKbGWarWQQxklHEpTboFFtWt6qHndmvYuBwAAANXN7mVS9hnJJ1hqen2Ju20618+2VZCP/Gu4V05tAAAAV8ChQ9vKWn238Lzn96zFxdHPFgAAAHZV2Bqh/Z2Sa8m39hYuQkY/WwAA4CwcvmlJbGyshg8frqioKHXt2lWTJ0++YPXdhg0bauLEiZKsq+8ePnxYHTt21OHDhzVhwoQLVt8dO3asbrnlFjVu3FgZGRmaM2eOVq5cqZ9++sku1+is1tHPFgAAAPaSfUbaucT6PGJIqbv+FdoybgUAAM7B4UPbilh99/jx4xo2bJiOHj0qf39/dejQQT/99JNuuummyr48p1VgtihunzW0pZ8tAAAAKt32BZI5TwpqJwW3L3G33AKzth5Ok8RMWwAA4DwcPrSVyn/13enTp5dXadVW4tF0ZeWZ5eflptbBJa/SCwAAAFSI+HnWrx1KXoBMkrYdTlfeuXUYQmvXqITCAAAArpxD97SF41p/rjVCl9BAubrQzxYAAACV6PQe6eBayeQitb+r1F3j9lvHrZ2b1GIdBgAA4DQIbXFZ1u451xqhGX3BAAAAUMm2fmn92ux6ya9+qbsW9rONojUCAABwIoS2uGQWi6EN+woXIaOfLQAAACqRYUjxc63PO5S+AJlhGOctQkZoCwAAnAehLS7ZzuMZSsvOVw0PV7Vt4GfvcgAAAFCdHFwvndkrudeU2vQtddcDp8/qZGaePFxd1K6hfyUVCADObc2aNXJ1ddWtt956wWsrV66UyWRSamrqBa+FhoZq8uTJRbatWLFCffr0Ue3atVWjRg2Fh4frqaee0uHDhy9ax6lTpzRhwgR16dJFdevWVePGjXXrrbdq7ty5Mgzjosfff//9MplMtkft2rXVu3dvbd269aLHltWECRPUsWPHctvPXrZu3aprr71WXl5eCgkJ0RtvvHHRY5YvX66rr75avr6+Cg4O1nPPPaeCgoIi+xiGof/7v/9Tq1at5OnpqYYNG+rVV1+1vf73n1Hho23btuV+jc6I0BaXbN251giRTWrJ3ZU/QgAAAKhEW8/Nsg2/TfKoWequG/dZZ9m2a+gnL3fXiq4MAKqE6dOn69FHH9Xq1at15MiRyz7Phx9+qF69eik4OFjffPONEhMT9cEHHygtLU1vvfVWqccuXbpUrVq10oYNG/T0009r6dKlmj9/vvr27auXX35ZMTExysrKumgNvXv31tGjR3X06FEtX75cbm5u6tu39H/wq27S09N18803q0mTJoqLi9Obb76pCRMm6KOPPirxmPj4ePXp00e9e/fW5s2bNW/ePC1atEhjxowpst/jjz+ujz/+WP/3f/+n5ORkLVq0SF27drW9PmXKFNvP5+jRozp48KACAwN1112l96uvLkjccMkKFyGLbko/WwAAAFSiglxp23zr8w6DL7p73IFz/WxDGbcCQFlkZmZq3rx5GjVqlG699VbNmjXrss5z6NAhPfbYY3rsscc0Y8YMXX/99QoNDdV1112njz/+WOPGjSvx2I0bN+ruu+/WrFmz9P3332vw4MHq1KmToqKiNGrUKMXHx6tRo0a6++67L1qHp6engoODFRwcrI4dO2rMmDE6ePCgTpw4Ydvn4MGDGjRokAICAhQYGKj+/ftr3759ttdXrlyprl27qmbNmgoICFD37t21f/9+zZo1Sy+++KLi4+NtM0Qv9/NKSEjQDTfcIG9vb9WuXVsPPfSQMjMzL1qDZA1Qe/bsKV9fX/n5+SkyMlIbN24s83t//vnnysvL04wZM9S2bVsNGTJEjz32mCZNmlTiMfPmzVOHDh00btw4tWjRQj169NAbb7yh9957TxkZGZKkpKQkTZ06Vd9++61uu+02NW3aVJGRkbrpppts5/H397f9fIKDg7Vx40adOXNGI0aMuNSPsEoitMUlMQxD6/bSzxYAAAB2sPMnKSdV8q0vNb3uorvHnZtp27kx/WwBoCy+/PJLhYWFqXXr1rr33ns1Y8aMMrUi+LuvvvpKeXl5evbZZ4t9PSAgoMRjH330Ub366qvq16+fEhMT1aNHD9WtW1eDBg1SbGys3njjDX3wwQdKTEzUihUrylxTZmamPvvsM7Vo0UK1a1vzjPz8fMXExMjX11e//vqrfv/9d/n4+Kh3797Ky8tTQUGBBgwYoB49emjr1q1as2aNHnroIZlMJg0ePFhPPfWU2rZta5spOnjwxf9B8e+ysrIUExOjWrVqacOGDfrqq6/0888/a/To0ZJUag2SNHToUDVq1EgbNmxQXFycxowZI3d3d9v5LxYmr1mzRtddd508PDxs22JiYrRjxw6dOXOm2GNyc3Pl5eVVZJu3t7dycnIUFxcnSfruu+/UrFkzLV68WE2bNlVoaKgefPBBnT59usRapk+frl69eqlJkyalf2jVhJu9C4Bz2XsySyczc+Xh5qIOjegLBgAAgEq0dZ71a4dBkkvp7Q7SsvO187h1tg+LkAFA2UyfPl333nuvJGtrgbS0NK1atUrXX3/9JZ1n165d8vPzU/369S/5uH379unBBx+U2WzWwIEDdf3112vKlCn69ddfFRsbq3//+9/y8PDQ3XffrZ9++kk9e/Ys8XyLFy+Wj4+PJGs4Wr9+fS1evFguLtY5jPPmzZPFYtHHH39sC0FnzpypgIAArVy5UlFRUUpLS1Pfvn3VvHlzSVKbNm1s5/fx8ZGbm5uCg4Mv6TrPN2fOHOXk5OjTTz9VzZrWtj/vvvuu+vXrp9dff13u7u6l1nDgwAE988wzCgsLkyS1bNmyyPlbt24tf/+S85uUlBQ1bdq0yLagoCDba7VqXfh3aExMjCZPnqwvvvhCgwYNUkpKil566SVJ0tGjRyVJe/bs0f79+/XVV1/p008/ldls1pNPPqk777xTv/zyywXnPHLkiH788UfNmTOn9A+sGmGmLS5J4SzbTiEB9AUDAABA5Tl72jrTVpI6DLno7psPnJFhSE1q11BdX88KLg4AnN+OHTu0fv16W9sBNzc3DR48WNOnT7/kcxmGYQtBL0VCQoK6dOkiNzc37dixQ4cPH9a7776rjh076tFHHy0SHtevX7/EmaCFevbsqS1btmjLli1av369YmJidMsttxRpLbB79275+vrKx8dHPj4+CgwMVE5Ojv78808FBgbq/vvvV0xMjPr162frwVqekpKSFBERYQtsJal79+6yWCzasWPHRWuIjY3Vgw8+qF69eum1117Tn3/+WeT8ycnJGjhwYLnWfPPNN+vNN9/Uww8/LE9PT7Vq1Up9+vSRJFsgbrFYlJubq08//VTXXnutrr/+ek2fPl0rVqzQjh07LjjnJ598ooCAAA0YMKBca3VmhLa4JPSzBQAAgF1s+0ay5EvBHaSg8Ivuvmm/9Rd5ZtkCQNlMnz5dBQUFatCggdzc3OTm5qapU6fqm2++UVpamiTJz89Pkmzfny81NdU2o7NVq1ZKS0u75ICzoKBA3t7ekqS8vDy5u7sXudW/cNasJG3atEktWrQo9Xw1a9ZUixYt1KJFC3Xp0kUff/yxsrKyNG3aNEnWlgmRkZG2YLfwsXPnTt1zzz2SrDNv16xZo6uvvlrz5s1Tq1attHbt2ku6ritVWg0TJkzQ9u3bdeutt+qXX35ReHi4FixYUOZzBwcH69ixY0W2FX5f2gzi2NhYpaam6sCBAzp58qT69+8vSWrWrJkka6ju5uamVq1a2Y4pnCF84MCBIucyDEMzZszQfffdV6RNQ3VHaItLsp5+tgAAALCHwtYIERefZStJGwltAaDMCgoK9Omnn+qtt94qEl7Gx8erQYMG+uKLLyRZb713cXGx9S0ttGfPHqWlpdkCujvvvFMeHh564403in2/1NTUYre3aNFCCQkJkqy39bu7u+vdd9+V2WzW2rVr9dNPPyk/P1+ffPKJfvzxR91///2XdJ0mk0kuLi7Kzs6WJHXu3Fm7du1SvXr1bOFu4eP8lgKdOnXS2LFj9ccff6hdu3a2W/g9PDxkNpsvqYa/a9OmjeLj45WVlWXb9vvvv8vFxUWtW7e+aA2SNSR/8skntXTpUt1+++2aOXNmmd+/W7duWr16tfLz823bli1bptatWxfbGuF8JpNJDRo0kLe3t7744guFhISoc+fOkqyzhQsKCorM/N25c6ckXdCzdtWqVdq9e7ceeOCBMtddHRDaoswOnj6rw6nZcnMxqXOTAHuXAwAAgOri5G7p0AbJ5CK1u/OiuxeYLdpyMFWSFNWEO8QA4GIWL16sM2fO6IEHHlC7du2KPO644w5biwRfX189+OCDeuqpp7Ro0SLt3btXq1ev1tChQ3XVVVfp6quvliSFhITo7bff1pQpU/TAAw9o1apV2r9/v37//Xf985//1Msvv1xsHZ06dVJ2drZWrFghb29vzZo1S+PGjZOnp6dGjBihAQMG6PXXX9fMmTO1dOlS1a1bt9Trys3NVUpKilJSUpSUlKRHH31UmZmZ6tevnyTrIl516tRR//799euvv2rv3r1auXKlHnvsMR06dEh79+7V2LFjtWbNGu3fv19Lly7Vrl27bDNGQ0NDtXfvXm3ZskUnT55Ubm5uibVkZ2dfMKP3zz//1NChQ+Xl5aXhw4dr27ZtWrFihR599FHdd999CgoKKrWG7OxsjR49WitXrrR9vhs2bCjS8zYsLKzUmbf33HOPPDw89MADD2j79u2aN2+epkyZotjYWNs+CxYssPXMLfTmm28qISFB27dv18svv6zXXntN//vf/+Tqam2l2atXL3Xu3Fn/+Mc/tHnzZsXFxemf//ynbrrppiKzbyXrLO/o6Gi1a9eu1J9ntWPgsqSlpRmSjLS0NHuXUmm+3njQaPLcYmPAe7/ZuxQAAADDMKrnmOxyOfVntfwVwxjvZxiz7yjT7gmHUo0mzy022o1fYpjNlgouDgCcX9++fY0+ffoU+9q6desMSUZ8fLxhGIaRnZ1tjB8/3ggLCzO8vb2Npk2bGg899JBx4sSJC45dtmyZERMTY9SqVcvw8vIywsLCjKeffto4cuRIibXMmzfPCAkJMfbu3WsYhmEUFBQYhw4dMiwWi3HmzBkjIyOjTNc0fPhwQ5Lt4evra3Tp0sX4+uuvi+x39OhRY9iwYUadOnUMT09Po1mzZsbIkSONtLQ0IyUlxRgwYIBRv359w8PDw2jSpIkxbtw4w2w2G4ZhGDk5OcYdd9xhBAQEGJKMmTNnFlvL+PHji9RS+LjxxhsNwzCMrVu3Gj179jS8vLyMwMBAY+TIkbbrLK2G3NxcY8iQIUZISIjh4eFhNGjQwBg9erSRnZ1te+/S6ioUHx9vXHPNNYanp6fRsGFD47XXXivy+syZM42/R4g9e/Y0/P39DS8vLyM6Otr44YcfLjjv4cOHjdtvv93w8fExgoKCjPvvv984depUkX1SU1MNb29v46OPPiq1xqqkrGMyk2EYRuVHxc4vPT1d/v7+SktLs/V0qeqe+3qr5m08qH/2aKaxt7S5+AEAAAAVrDqOyS6X035WFov0vwgp9YB0x3Sp/cVn2n7yxz6NX7RdPVrV1Sf/6FoJRQIAytN///tfTZo0SWPHjtXgwYPVqFEj5ebmatWqVXr55ZcVGxtb7otrAZWlrGMy2iOgzNbtPSWJRcgAAABQiQ6utQa2Hr5S6z5lOoR+tgDg3J5//nktWLBAS5cuVfPmzeXh4SFvb2/Fxsbqvvvusy16BVRlbvYuAM7hWHqO9p06K5NJigoltAUAAEAliZ9r/RreX/KoUaZDNp0LbaMIbQHAaV177bX66aeflJubq+PHj8vX11cBAQH2LguoNIS2KJP1e09LksLr+8nPy93O1QAAAKBayM+Rti+0Po8YXKZDjqZl63BqtlxdTIoICaiw0gAAlcPT01MhISH2LgOodLRHQJkUhrZdaY0AAACAyrLzRyk3TfJrJDW5pkyHxJ2bZdumvq9qejJHBQAAOCdCW5TJX/1sa9u5EgAAAFQb8fOsXzsMklzK9qvLxn3n+tk2pjUCAABwXoS2uKjTWXnaeSxTktQllMEvAAAAKkHWSWn3MuvziCFlPmzTgXOhLeswAAAAJ0Zoi4vasM/aGqFlPR/V9vG0czUAAACoFrZ9I1kKpPodpbqty3TI2bwCbT+SLkmKZBEyAADgxAhtcVHr9tDPFgAAAJUsfq71a8TdZT/kYJrMFkP1/b3UMMC7ggoDAACoeIS2uKj1+871s21GP1sAAABUghM7pSObJJOr1O6OMh8Wt9862aAzs2wBAICTI7RFqdJz8pV47hazrvQFAwAAQGXYem6WbYtekk/dMh8Wt9/azzaK0BYAADg5QluUKm7/GVkMqUntGgr297J3OQAAAKjqLBZp65fW55ewAJnFYthCW/rZAgAAZ0doi1IV9rONpp8tAAAAKsP+36W0g5Knn9T6ljIf9ueJTKXnFMjb3VVt6vtVYIEAAAAVj9AWpVq/19rPtmtT+tkCAACgEhS2RgjvL7mXfTGxjedm2UaE+MvdlV9zAACAc2M0gxJl55m19VCaJGbaAgAAoBLknZW2f2t9HnH3JR36Vz9bxq0AAMD5EdqiRJsOnFGBxVADfy81qlX2WQ4AAADAZdnxg5SXIfk3lhp3u6RD6WcLAACqEkJblGjdXms/265NA2UymexcDQAAAKq8rfOsXzsMklzK/qvKqcxc7T2ZJUnq3JjQFgAAOD9CW5SIfrYAAACoNJnHpd3Lrc8jhlzSoYWzbFvW85F/DffyrgwAAKDSEdqiWLkFZm0+kCrJOtMWAAAAqFAJX0uGWWoYKdVpeUmHxh041882lFm2AACgaiC0RbG2HkpTboFFdXw81LxuTXuXAwAAgKpu61zr10tcgEyS4vZZQ1taIwAAgKqC0BbFWk8/WwAAAFSW40nS0XjJxU1qe/slHZpbYNbWw2mSpKhQ7hADAABVA6EtimVbhIyBLwAAACpa/LlZti1vlmpe2noK2w6nK6/Aoto1PRRau0YFFAcAAFD5CG1xgQKzRXH7rKFtdDMWIQMAAEAFspilhK+szy9xATJJ2nRuEbLOTWpxhxgAAKgyCG1xge1H0pWVZ5afl5taB/nauxwAAABUZft+ldIPS17+Uqvel3z4xv3WyQaRTehnCwAAqg5CW1zg/H62Li7MVgAAAEAFip9n/dp2oOTmeUmHGoahuP2pkqQoQlsAAFCFENriAuv2npJkDW0BAACACpOXJSV+a30ecfclH37g9FmdzMyVh6uL2jX0L+fiAAAA7IfQFkVYLIZtpm10U/rZAgAAoAIlfy/lZ0m1QqWQ6Es+PO5cP9t2Df3k5e5azsUBAADYD6EtithxLEPpOQWq4eGqtg387F0OAAAAqrL4udavHQZLl7GI2MZzoS39bAEAQFVDaIsiCmfZRjapJTdX/ngAAACggmSkSHtWWJ93GHxZp9hkC21p6wUAAKoWUjkUUdjP9qpmtEYAAABABUr4SjIsUqOuUu3ml3x4Wna+dhzLkMRMWwAAUPUQ2sLGMP7qZ8siZAAAAKhQ8fOsXyMub5btloOpMgypSe0aquvrWY6FAQAA2B+hLWz2nMzSycw8ebi5qEMjVt8FAABABUnZJh1LkFzcpba3X9Yp4vada+vVmFm2AACg6iG0hc26PdaBb6eQAHm6sfouAAAAKsjWcwuQtYqRalzeHV5xB871sw0ltAUAAFUPoS1s1p/rZxtNP1sAAABUFItZ2vqV9XnEkMs6RYHZos0HUiXRzxYAAFRNhLaQZO1nu+5cP9to+tkCAACgouxZKWWmSN61pJY3X9YpklMydDbPLF9PN7Wq51u+9QEAADgAQltIkg6dydbRtBy5uZjUqXGAvcsBAABAVbX13AJkbW+X3C5vAbG4/dbWCJ2a1JKLi6m8KgMAAHAYhLaQJNss2w6N/FXDw83O1QAAAKBKys2Ukr6zPr/M1gjSX6FtFK0RAABAFUVoC0l/9bPt2pR+tgAAAKggSd9J+WelwGZSoy6XfZrC0JZ+tgAAoKoitIUkaT39bAEAAFDRts61fu0wRDJdXluDo2nZOpyaLReT1DEkoPxqAwAAcCCEttCx9BztO3VWLiYpMpTZCgAAAKgAaYelPauszzsMuuzTFM6ybVPfTzU9aesFAACqJkJb2PrZhjfwk5+Xu52rAQAAQJWU8JUkQ2rcTQpsetmnoZ8tAACoDght8Vc/21D62QIAAKACGIYUX9gaYfAVnaowtO1MaAsAAKowQlvY+tl2pZ8tAAAAKkLKVulEkuTqKbUdcNmnOZtXoO1H0iVJUaGMXQEAQNVFaFvNnc7K085jmZIIbQEAAMrLe++9p9DQUHl5eSk6Olrr168vcd/8/Hy99NJLat68uby8vBQREaElS5aUuP9rr70mk8mkJ554ogIqryDx86xfW/eWvC9/hmz8wTSZLYaC/bzUwN+rnIoDAABwPIS21VzhLNtWQT4KrOlh52oAAACc37x58xQbG6vx48dr06ZNioiIUExMjI4fP17s/i+88II+/PBDvfPOO0pMTNTDDz+sgQMHavPmzRfsu2HDBn344Yfq0KFDRV9G+TEXnOtnK6nDkCs61aYD1tYIkaG1ZDKZrrQyAAAAh0VoW83RGgEAAKB8TZo0SSNHjtSIESMUHh6uDz74QDVq1NCMGTOK3X/27Nl6/vnn1adPHzVr1kyjRo1Snz599NZbbxXZLzMzU0OHDtW0adNUq5YT9XPds0LKOi7VqC216HVFp9q4zzp2jWzsRNcPAABwGQhtq7l15xYhi27KImQAAABXKi8vT3FxcerV669w0sXFRb169dKaNWuKPSY3N1deXkVv9ff29tZvv/1WZNsjjzyiW2+9tci5S5Obm6v09PQiD7soXICs3R2S2+Xf2WWxGNp0IFWSFBVKaAsAAKo2pwhty7sn2MSJE9WlSxf5+vqqXr16GjBggHbs2FHRl+Fw0nPylXjUOnhnpi0AAMCVO3nypMxms4KCgopsDwoKUkpKSrHHxMTEaNKkSdq1a5csFouWLVum+fPn6+jRo7Z95s6dq02bNmnixIllrmXixIny9/e3PUJCQi7voq5ETrqUvNj6/ApbI/x5IlNp2fnydndVm/p+5VAcAACA43L40LYieoKtWrVKjzzyiNauXatly5YpPz9fN998s7KysirrshxC3L4zMgwptHYNBfmxkAMAAIA9TJkyRS1btlRYWJg8PDw0evRojRgxQi4u1qH6wYMH9fjjj+vzzz+/YEZuacaOHau0tDTb4+DBgxV1CSVLWiQV5Ei1W0oNO1/RqeL2W/vZRoT4y93V4X+NAQAAuCIOP9qpiJ5gS5Ys0f3336+2bdsqIiJCs2bN0oEDBxQXF1dZl+UQ1p5rjcAsWwAAgPJRp04dubq66tixY0W2Hzt2TMHBwcUeU7duXS1cuFBZWVnav3+/kpOT5ePjo2bNmkmS4uLidPz4cXXu3Flubm5yc3PTqlWr9L///U9ubm4ym83FntfT01N+fn5FHpWusDVCxGDpChcO23gutI1sQmsEAABQ9Tl0aFuRPcHOl5aWJkkKDKxe4WXhImT0swUAACgfHh4eioyM1PLly23bLBaLli9frm7dupV6rJeXlxo2bKiCggJ988036t+/vyTpxhtvVEJCgrZs2WJ7REVFaejQodqyZYtcXV0r9JouW+pBad+v1uftB13x6TadC22jmlSvMTsAAKie3OxdQGlK6wmWnJxc7DGFPcGuu+46NW/eXMuXL9f8+fNLnIFgsVj0xBNPqHv37mrXrl2JteTm5io3N9f2vd0WcignZ/MKlHDIGlYz0xYAAKD8xMbGavjw4YqKilLXrl01efJkZWVlacSIEZKkYcOGqWHDhrb+tOvWrdPhw4fVsWNHHT58WBMmTJDFYtGzzz4rSfL19b1gnFqzZk3Vrl271PGr3SV8af3a5BqpVpMrOtWpzFztOWltZdapccAVFgYAAOD4HDq0vRxTpkzRyJEjFRYWJpPJpObNm2vEiBEltlN45JFHtG3btlJn4krWhRxefPHFiijZLjYfSFWBxVADfy81quVt73IAAACqjMGDB+vEiRMaN26cUlJS1LFjRy1ZssQ2EeHAgQO2frWSlJOToxdeeEF79uyRj4+P+vTpo9mzZysgIMBOV1AODEOKn2d9HjH4ik+36UCqJKllPR8F1PC44vMBAAA4OocOba+kJ1hOTo5OnTqlBg0aaMyYMbaeYOcbPXq0Fi9erNWrV6tRo0al1jJ27FjFxsbavk9PT7fPCrzlZN0eaz/b6Ga1ZbrC/mIAAAAoavTo0Ro9enSxr61cubLI9z169FBiYuIlnf/v53A4RzZLJ3dIbl5SeP8rPt3G/da2XvSzBQAA1YVD97StiJ5gkmQYhkaPHq0FCxbol19+UdOmTS9ai0Ms5FCO1p3rZ0trBAAAAJS7zGOSbwOpdR/Jy/+KT7eJRcgAAEA149AzbaXy7wkmWVsizJkzR99++618fX2VkpIiSfL395e3d9VvFZBbYNbmg6mSCG0BAABQAVrfIrW8WcpJu+JT5RaYFX9uLQZCWwAAUF04fGhbET3Bpk6dKkm6/vrri7zXzJkzdf/991f0Jdld/ME05RVYVMfHU83q1LR3OQAAAKiKXFylGlc+QWD7kXTlFVgUWNNDTRm7AgCAasLhQ1up/HuCGYZRXqU5pfV7z/WzbRpIP1sAAAA4tLh91tYInRvXYuwKAACqDYfuaYuKQT9bAAAAOIu4c/1so0JpjQAAAKoPQttqpsBssQ18CW0BAADgyAzD0EYWIQMAANUQoW01s+1Ius7mmeXv7a7WQb72LgcAAAAo0cHT2TqZmSt3V5PaN/S3dzkAAACVhtC2minsZ9slNFAuLvQEAwAAgOPauN/a1qtdQ395ubvauRoAAIDKQ2hbzaw/1882mtYIAAAAcHC2fra0RgAAANUMoW01YrYYf4W2zQhtAQAA4Nji6GcLAACqKULbamRHSobScwpU08NV4fX97F0OAAAAUKL0nHztOJYhSepMaAsAAKoZQttqpLCfbWRooNxc+dEDAADAcW0+kCrDkBoH1lA9Xy97lwMAAFCpSO6qkXX0swUAAICToJ8tAACozghtqwnDMFiEDAAAAE4jbr917EprBAAAUB0R2lYTf57I0qmsPHm6uah9I397lwMAAACUqMBs0ZYDqZKkqFBCWwAAUP0Q2lYThbNsOzUOkKebq52rAQAAAEqWnJKhrDyzfD3d1LKer73LAQAAqHSEttXEunOLkEU3rW3nSgAAAIDSbTpg7WfbqUktubqY7FwNAABA5SO0rQYMw9C6PfSzBQAAgHPYuM8a2kY2pjUCAAConghtq4FDZ7KVkp4jd1eTOjHwBQAAgIOL228NbelnCwAAqitC22pg7R5ra4QOjQLk7UE/WwAAADiulLQcHU7NlotJiggJsHc5AAAAdkFoWw0ULkLWldYIAAAAcHCFs2zb1PeTj6ebnasBAACwD0LbamD9PkJbAAAAOIeN+61j18gmtEYAAADVF6FtFZeSlqP9p87KxSRFMfAFAACAg9t0bqYtoS0AAKjOCG2ruHV7rf1s2zbwl6+Xu52rAQAAAEqWnWfW9iPpkghtAQBA9UZoW8XRzxYAAADOIv5QqgoshoL9vNQwwNve5QAAANgNoW0VR2gLAAAAZxF3XmsEk8lk52oAAADsh9C2CjuVmatdxzMlSV1DCW0BAADg2OLoZwsAACCJ0LZK27DPOsu2dZCvatX0sHM1AAAAQMksFoPQFgAA4BxC2ypsHa0RAAAA4CT2nMxUWna+vNxdFN7Az97lAAAA2BWhbRVGP1sAAAA4i437rLNsIxoFyN2VX1MAAED1xmioikrLzlfi0XRJUjShLQAAABxcYWuEqFBaIwAAABDaVlFx+0/LMKSmdWqqnp+XvcsBAAAASkU/WwAAgL8Q2lZRtn62ocyyBQAAgGM7nZWnPSezJEmdGxPaAgAAENpWUev2WEPb6GaEtgAAAHBshbNsW9TzUUANDztXAwAAYH+EtlVQVm6Bth1Ok8QiZAAAAHB8tn62tEYAAACQRGhbJW0+kKoCi6GGAd5qVKuGvcsBAAAAShW333qXWGdCWwAAAEmEtlXSur2nJDHLFgAAAI4vr8Ci+EPWu8SYaQsAAGBFaFsFFS5CFk1oCwAAAAe37Uia8gosCqzpoaZ1atq7HAAAAIdAaFvF5OSbteVgqiRm2gIAAMDxbTrXz7Zz41oymUx2rgYAAMAxENpWMVsPWWcq1PHxZKYCAAAAHN7GfdbQNpLWCAAAADaEtlXMuj3WfrbRzQKZqQAAAACHZhiG4g5YQ9uoUEJbAACAQoS2Vcz6ffSzBQAAgHM4eDpbJzJy5e5qUvuG/vYuBwAAwGEQ2lYh+WaL4s71BKOfLQAAABxd3AHrhIN2Df3l5e5q52oAAAAcB6FtFbLtcJrO5pkVUMNdrer52rscAAAAoFS2fraNaY0AAABwPkLbKmT9XutMhS6hgXJxoZ8tAAAAHFvhXWL0swUAACiK0LYKKQxt6WcLAAAAR5eek68dxzIkSZ2bENoCAACcj9C2ijBbDNsiZPSzBQAAgKPbciBVhiE1Dqyher5e9i4HAADAoRDaVhHJKenKyCmQj6ebwuv72bscAAAAoFQbz7VGiGSWLQAAwAUIbauIwtYIkU1qyc2VHysAAAAc2yZCWwAAgBKR7lURhaEtrREAAADg6ArMFm0+QGgLAABQEkLbKsAwDFtoe1UzQlsAAAA4th3HMpSVZ5avp5taBfnauxwAAACHQ2hbBfx5IlOnsvLk6eai9g0D7F0OAAAAUKq4c60ROjYOkKuLyc7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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_loss_and_auc(ax, history, title_prefix):\n", + " ax[0].plot(history[\"loss\"], label=\"Training Loss\")\n", + " ax[0].plot(history[\"val_loss\"], label=\"Validation Loss\")\n", + " best_val_loss_idx = np.argmin(history[\"val_loss\"])\n", + " best_val_loss = history[\"val_loss\"][best_val_loss_idx]\n", + " best_val_auc = history[\"val_auc\"][best_val_loss_idx]\n", + "\n", + " ax[0].annotate(\n", + " f\"Best Loss: {best_val_loss:.3f}\",\n", + " xy=(best_val_loss_idx, best_val_loss),\n", + " xytext=(best_val_loss_idx + 0.5, best_val_loss + 0.02),\n", + " arrowprops=dict(facecolor=\"black\", shrink=0.05, width=1, headwidth=8),\n", + " )\n", + " ax[0].set_title(f\"{title_prefix} Loss\")\n", + " ax[0].set_xlabel(\"Epochs\")\n", + " ax[0].set_ylabel(\"Loss\")\n", + " ax[0].legend()\n", + "\n", + " ax[1].plot(history[\"auc\"], label=\"Training AUC\")\n", + " ax[1].plot(history[\"val_auc\"], label=\"Validation AUC\")\n", + "\n", + " ax[1].annotate(\n", + " f\"AUC @ Best Loss: {best_val_auc:.3f}\",\n", + " xy=(best_val_loss_idx, best_val_auc),\n", + " xytext=(best_val_loss_idx + 0.5, best_val_auc - 0.02),\n", + " arrowprops=dict(facecolor=\"blue\", shrink=0.05, width=1, headwidth=8),\n", + " )\n", + "\n", + " ax[1].set_title(f\"{title_prefix} AUC\")\n", + " ax[1].set_xlabel(\"Epochs\")\n", + " ax[1].set_ylabel(\"AUC\")\n", + " ax[1].legend()\n", + "\n", + "\n", + "fig, ax = plt.subplots(2, 2, figsize=(14, 10))\n", + "\n", + "plot_loss_and_auc(ax[:, 0], cnn_history, \"CNN\")\n", + "plot_loss_and_auc(ax[:, 1], resnet_history, \"ResNet\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3f4f91b0-f407-416f-b3dc-da136ac7e576", + "metadata": {}, + "source": [ + "For both cases, while I tried different methods for reducing overfitting, such as dropout, L2 regularization and early stopping, the model tended to perform well even with overfitting. The ResNet model performed better overall, with a validation loss of 0.236 compared to 0.270 for the CNN." + ] + }, + { + "cell_type": "markdown", + "id": "c77cef1d-40d2-40a0-b886-02a1c3955cf5", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "### Hyperparameter search" + ] + }, + { + "cell_type": "markdown", + "id": "1b11bf52-fbf8-499e-81c0-227b5bd8c1fe", + "metadata": {}, + "source": [ + "I performed a Bayesian search using Optuna on the following 4 parameters:\n", + "\n", + "- Window Size: The length of the sliding window used to segment the ECG signal into smaller, more manageable parts. A larger window size allows the model to capture more temporal information from each heartbeat cycle, which can be critical for detecting certain patterns. However, too large a window can increase computational cost and risk overfitting by including irrelevant portions of the ECG signal.\n", + "\n", + "- Step Size: The interval at which the window moves across the ECG signal. A smaller step size results in more overlap between windows, potentially capturing finer details in the signal. However, it also increases the number of windows, which can lead to higher computational load and data redundancy.\n", + "\n", + "- Dropout Rate: The proportion of units randomly dropped during training to prevent overfitting. By randomly setting some of the connections to zero during training, the model is forced to learn more robust features that generalize better to unseen data, especially important in a clinical setting where data variance can be high.\n", + "\n", + "- L2 Regularization: A technique to prevent overfitting by penalizing large weights in the model. This helps to ensure that the model doesn't become too complex and overfit to the training data, which is crucial when dealing with noisy and diverse ECG signals.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2a67f234-4333-4dda-9a59-98be758b6c4b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of trials: 33\n", + "Best parameters: {'window_size': 196, 'step_size': 75, 'l2_reg': 3.6206124274583214e-11, 'dropout_rate': 0.43035840459539587}\n" + ] + } + ], + "source": [ + "print(f\"Number of trials: {len(tscnn_study.trials)}\")\n", + "print(f\"Best parameters: {tscnn_study.best_trial.params}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "422aeea0-d873-4d3c-a070-bfb15a64a0a5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/wt/hkz7f97s4jl11qlwpkm6ry4r0000gn/T/ipykernel_69775/1843262676.py:1: ExperimentalWarning: plot_param_importances is experimental (supported from v2.2.0). The interface can change in the future.\n", + " vis.matplotlib.plot_param_importances(tscnn_study)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vis.matplotlib.plot_param_importances(tscnn_study)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "67fe883a-a473-4bac-8635-29fbfcfef799", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/wt/hkz7f97s4jl11qlwpkm6ry4r0000gn/T/ipykernel_69775/2039773073.py:1: ExperimentalWarning: plot_contour is experimental (supported from v2.2.0). The interface can change in the future.\n", + " vis.matplotlib.plot_contour(tscnn_study)\n", + "[W 2024-08-27 18:14:40,947] Output figures of this Matplotlib-based `plot_contour` function would be different from those of the Plotly-based `plot_contour`.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vis.matplotlib.plot_contour(tscnn_study)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1225c6b1-1462-47d2-892a-f9805f12631d", + "metadata": {}, + "source": [ + "The search performed 33 trials over a total of 5 hours, and the best parameters were:\n", + "\n", + "- Window Size: 196\n", + "- Step Size: 75\n", + "- Dropout Rate: 0.430\n", + "- L2 Regularization: 3.62e-11\n", + "\n", + "The hyperparameter importance analysis shows that the window size had the most significant impact on the model's performance, followed by dropout rate and L2 regularization This suggests that the model's performance is highly dependent on the amount of contextual information provided by the window size, and regularization plays a critical role in controlling overfitting.\n", + "\n", + "The contour plot further illustrates how these parameters interact and influence the objective value, with optimal regions marked by darker areas on the plot. The search space was effectively explored, leading to the discovery of a well-performing combination of hyperparameters.\n" + ] + }, + { + "cell_type": "markdown", + "id": "8a38478b-d582-4df4-babf-d53667ed19bd", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "### Classification analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a47d68b9-6fd9-4ed0-a7d6-4e5ce879cbb4", + "metadata": {}, + "outputs": [], + "source": [ + "cnn_fpr, cnn_tpr, _ = roc_curve(y_test, cnn_preds)\n", + "resnet_fpr, resnet_tpr, _ = roc_curve(y_test, resnet_preds)\n", + "\n", + "cnn_auc = auc(cnn_fpr, cnn_tpr)\n", + "resnet_auc = auc(resnet_fpr, resnet_tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8918ca05-7fab-402f-a939-9de2e837d718", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.plot(cnn_fpr, cnn_tpr, label=f\"CNN (AUC = {cnn_auc:.3f})\")\n", + "plt.plot(resnet_fpr, resnet_tpr, label=f\"ResNet (AUC = {resnet_auc:.3f})\")\n", + "plt.plot([0, 1], [0, 1], \"k--\")\n", + "plt.xlabel(\"False Positive Rate\")\n", + "plt.ylabel(\"True Positive Rate\")\n", + "plt.title(\"ROC Curves\")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8c1cd885-1f1c-4712-9d4d-dfe52d5acedb", + "metadata": {}, + "source": [ + "The ROC curves for both models show excellent performance, with AUC values of 0.954 and 0.960, respectively. The ResNet slightly outperforms the CNN, indicating its superior ability to distinguish between the two classes." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "68391a55-b69c-4fdd-b5bb-c1109bbb8845", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cnn_cm = confusion_matrix(y_test, (cnn_preds > 0.5).astype(int))\n", + "resnet_cm = confusion_matrix(y_test, (resnet_preds > 0.5).astype(int))\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n", + "sns.heatmap(cnn_cm, annot=True, fmt=\"d\", ax=ax[0], cmap=\"Blues\")\n", + "ax[0].set_title(\"CNN Confusion Matrix\")\n", + "ax[0].set_xlabel(\"Predicted\")\n", + "ax[0].set_ylabel(\"Actual\")\n", + "\n", + "sns.heatmap(resnet_cm, annot=True, fmt=\"d\", ax=ax[1], cmap=\"Blues\")\n", + "ax[1].set_title(\"ResNet Confusion Matrix\")\n", + "ax[1].set_xlabel(\"Predicted\")\n", + "ax[1].set_ylabel(\"Actual\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ba6b98ba-41ef-4f99-b5e2-3cac638a92ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MetricCNNResNet
0AUC0.9540.960
1Sensitivity0.8360.868
2Specificity0.9380.920
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" + ], + "text/plain": [ + " Metric CNN ResNet\n", + "0 AUC 0.954 0.960\n", + "1 Sensitivity 0.836 0.868\n", + "2 Specificity 0.938 0.920" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cnn_sensitivity, cnn_specificity = calc_sens_spec(y_test, (cnn_preds > 0.5).astype(int))\n", + "resnet_sensitivity, resnet_specificity = calc_sens_spec(y_test, (resnet_preds > 0.5).astype(int))\n", + "\n", + "summary_table = pd.DataFrame(\n", + " {\n", + " \"Metric\": [\"AUC\", \"Sensitivity\", \"Specificity\"],\n", + " \"CNN\": [cnn_auc, cnn_sensitivity, cnn_specificity],\n", + " \"ResNet\": [resnet_auc, resnet_sensitivity, resnet_specificity],\n", + " }\n", + ")\n", + "\n", + "display(summary_table.round(3))" + ] + }, + { + "cell_type": "markdown", + "id": "4d6e2175-15c7-4f25-b46a-657642a3bfd9", + "metadata": {}, + "source": [ + "The confusion matrices and sensitivity metrics indicate that the ResNet model correctly classifies more positive instances (570 vs. 549 for CNN) and has fewer false negatives (87 vs. 108 for CNN), resulting in a higher sensitivity (0.867 vs. 0.836 for CNN). However, this comes at the cost of slightly lower specificity (0.920 for ResNet vs. 0.939 for CNN), as it has a marginally higher rate of false positives. Overall, the ResNet shows better performance in detecting positive cases, while the CNN is slightly more effective at avoiding false positives." + ] + }, + { + "cell_type": "markdown", + "id": "d12567d7-1d15-4d56-96b9-4f906f119af6", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "### Subgroup analysis" + ] + }, + { + "cell_type": "markdown", + "id": "6a635662-420b-479b-b088-46b14cdc912c", + "metadata": {}, + "source": [ + "Let's look into the model's ability to perform on each class of age, gender and disease." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "51822785-a388-4885-9584-9acfefbb4041", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_group(y_true, y_pred_resnet, y_pred_cnn):\n", + " sens_resnet, spec_resnet, auc_resnet = calc_sens_spec_auc(y_true, y_pred_resnet)\n", + " sens_cnn, spec_cnn, auc_cnn = calc_sens_spec_auc(y_true, y_pred_cnn)\n", + " return [sens_resnet, spec_resnet, auc_resnet, sens_cnn, spec_cnn, auc_cnn]\n", + "\n", + "\n", + "def evaluate_diagnosis(y_true, y_pred_resnet, y_pred_cnn):\n", + " sens_resnet, spec_resnet = calc_sens_spec(y_true, y_pred_resnet)\n", + " sens_cnn, spec_cnn = calc_sens_spec(y_true, y_pred_cnn)\n", + " return [sens_resnet, spec_resnet, sens_cnn, spec_cnn]\n", + "\n", + "\n", + "def calc_sens_spec_auc(y_true, y_pred):\n", + " sensitivity, specificity = calc_sens_spec(y_true, y_pred)\n", + " auc = roc_auc_score(y_true, y_pred)\n", + " return sensitivity, specificity, auc" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d939cee4-154a-44c6-b78e-bdafc6366c9e", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(42)\n", + "sample_indices = np.random.choice(Y.shape[0], size=int(sample_size * Y.shape[0]), replace=False)\n", + "Y = Y.iloc[sample_indices]\n", + "y_test_meta = Y.loc[Y.strat_fold == 10]\n", + "diagnostic_dummies = y_test_meta[\"diagnostic_class\"].str.get_dummies(sep=\"|\")\n", + "y_test_meta = pd.concat([y_test_meta, diagnostic_dummies], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "04d8bac4-1129-4714-bffc-456136a14233", + "metadata": {}, + "outputs": [], + "source": [ + "male_indices = y_test_meta[\"sex\"] == 0\n", + "female_indices = y_test_meta[\"sex\"] == 1\n", + "y_test_male = y_test_meta[male_indices].LABEL.to_numpy()\n", + "y_test_female = y_test_meta[female_indices].LABEL.to_numpy()\n", + "\n", + "cnn_preds_male = cnn_preds[male_indices]\n", + "resnet_preds_male = resnet_preds[male_indices]\n", + "\n", + "cnn_preds_female = cnn_preds[female_indices]\n", + "resnet_preds_female = resnet_preds[female_indices]\n", + "\n", + "results_gender = [\n", + " [\"Male\"] + evaluate_group(y_test_male, resnet_preds_male.round(), cnn_preds_male.round()),\n", + " [\"Female\"] + evaluate_group(y_test_female, resnet_preds_female.round(), cnn_preds_female.round()),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a1ce2d8d-160b-4e9a-afbe-965b4f59b680", + "metadata": {}, + "outputs": [], + "source": [ + "age_groups = [(18, 30), (30, 50), (50, 70), (70, 100)]\n", + "results_age = []\n", + "\n", + "for min_age, max_age in age_groups:\n", + " age_indices = (y_test_meta[\"age\"] >= min_age) & (y_test_meta[\"age\"] < max_age)\n", + " y_test_age = y_test_meta[age_indices].LABEL.to_numpy()\n", + " cnn_preds_age = cnn_preds[age_indices]\n", + " resnet_preds_age = resnet_preds[age_indices]\n", + " results_age.append(\n", + " [f\"{min_age}-{max_age}\"] + evaluate_group(y_test_age, resnet_preds_age.round(), cnn_preds_age.round())\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "fcab3c74-f2cf-426d-84f6-f64125d3bc77", + "metadata": {}, + "outputs": [], + "source": [ + "diagnostic_classes = [\"NORM\", \"STTC\", \"CD\", \"HYP\", \"MI\"]\n", + "results_diagnosis = []\n", + "\n", + "for diagnosis in diagnostic_classes:\n", + " diag_indices = y_test_meta[diagnosis] == 1\n", + " y_test_diagnosis = y_test_meta[diag_indices].LABEL.to_numpy()\n", + " cnn_preds_diagnosis = cnn_preds[diag_indices]\n", + " resnet_preds_diagnosis = resnet_preds[diag_indices]\n", + " results_diagnosis.append(\n", + " [diagnosis] + evaluate_diagnosis(y_test_diagnosis, resnet_preds_diagnosis.round(), cnn_preds_diagnosis.round())\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4750bc96-07d6-41b4-8839-c8cad77d74b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gender-based Performance Comparison:\n" + ] + }, + { + "data": { + "text/html": [ + "
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GroupResNet SensitivityResNet SpecificityResNet AUCCNN SensitivityCNN SpecificityCNN AUC
0Male0.8500.9350.8930.8190.9490.884
1Female0.8840.9030.8940.8510.9270.889
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" + ], + "text/plain": [ + " Group ResNet Sensitivity ResNet Specificity ResNet AUC \\\n", + "0 Male 0.850 0.935 0.893 \n", + "1 Female 0.884 0.903 0.894 \n", + "\n", + " CNN Sensitivity CNN Specificity CNN AUC \n", + "0 0.819 0.949 0.884 \n", + "1 0.851 0.927 0.889 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Age Group-based Performance Comparison:\n" + ] + }, + { + "data": { + "text/html": [ + "
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GroupResNet SensitivityResNet SpecificityResNet AUCCNN SensitivityCNN SpecificityCNN AUC
018-300.8000.9660.8830.8000.9830.891
130-500.7040.9310.8170.7040.9540.829
250-700.8520.9190.8860.8300.9320.881
370-1000.9080.8710.8890.8620.8940.878
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" + ], + "text/plain": [ + " Group ResNet Sensitivity ResNet Specificity ResNet AUC \\\n", + "0 18-30 0.800 0.966 0.883 \n", + "1 30-50 0.704 0.931 0.817 \n", + "2 50-70 0.852 0.919 0.886 \n", + "3 70-100 0.908 0.871 0.889 \n", + "\n", + " CNN Sensitivity CNN Specificity CNN AUC \n", + "0 0.800 0.983 0.891 \n", + "1 0.704 0.954 0.829 \n", + "2 0.830 0.932 0.881 \n", + "3 0.862 0.894 0.878 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Diagnostic Class-based Performance Comparison:\n" + ] + }, + { + "data": { + "text/html": [ + "
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GroupResNet SensitivityResNet SpecificityCNN SensitivityCNN Specificity
0NORM0.0000.920.0000.938
1STTC0.9040.000.8730.000
2CD0.8670.000.8390.000
3HYP0.8620.000.7870.000
4MI0.9340.000.9230.000
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" + ], + "text/plain": [ + " Group ResNet Sensitivity ResNet Specificity CNN Sensitivity \\\n", + "0 NORM 0.000 0.92 0.000 \n", + "1 STTC 0.904 0.00 0.873 \n", + "2 CD 0.867 0.00 0.839 \n", + "3 HYP 0.862 0.00 0.787 \n", + "4 MI 0.934 0.00 0.923 \n", + "\n", + " CNN Specificity \n", + "0 0.938 \n", + "1 0.000 \n", + "2 0.000 \n", + "3 0.000 \n", + "4 0.000 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "columns_gender_age = [\n", + " \"Group\",\n", + " \"ResNet Sensitivity\",\n", + " \"ResNet Specificity\",\n", + " \"ResNet AUC\",\n", + " \"CNN Sensitivity\",\n", + " \"CNN Specificity\",\n", + " \"CNN AUC\",\n", + "]\n", + "columns_diagnosis = [\"Group\", \"ResNet Sensitivity\", \"ResNet Specificity\", \"CNN Sensitivity\", \"CNN Specificity\"]\n", + "\n", + "df_gender = pd.DataFrame(results_gender, columns=columns_gender_age)\n", + "df_age = pd.DataFrame(results_age, columns=columns_gender_age)\n", + "df_diagnosis = pd.DataFrame(results_diagnosis, columns=columns_diagnosis)\n", + "\n", + "print(\"Gender-based Performance Comparison:\")\n", + "display(df_gender.round(3))\n", + "\n", + "print(\"\\nAge Group-based Performance Comparison:\")\n", + "display(df_age.round(3))\n", + "\n", + "print(\"\\nDiagnostic Class-based Performance Comparison:\")\n", + "display(df_diagnosis.round(3))" + ] + }, + { + "cell_type": "markdown", + "id": "66ed06e1-bbb6-451e-b8ad-1d2f6921f185", + "metadata": {}, + "source": [ + "The ResNet model demonstrated higher sensitivity in both the male (0.850) and female (0.884) groups compared to the CNN, which had sensitivities of 0.819 and 0.851, respectively. However, the CNN excelled in specificity, particularly in males (0.935) and females (0.927), while the ResNet achieved specificities of 0.935 for males and 0.903 for females. The CNN also achieved slightly higher AUC values of 0.884 for males and 0.889 for females, compared to 0.893 and 0.894 for the ResNet.\n", + "\n", + "For age groups, the ResNet showed the highest sensitivity in the 50-70 and 70-100 age ranges. The CNN led in specificity across all age groups, particularly in the 18-30 group with a specificity of 0.983. The AUC values for the CNN were consistently high across age groups, with the highest being 0.891 in the 18-30 group. Both models underperformed in terms of sensitivity for the 30-50 age group (0.704).\n", + "\n", + "In the diagnostic class-based comparison, the ResNet model demonstrated higher sensitivity for all the abnormal subgroups within the dataset. On the other hand, the CNN model showed stronger specificity, particularly in correctly identifying Normal ECGs (NORM) with a specificity of 0.938." + ] + }, + { + "cell_type": "markdown", + "id": "c8babbe8-940c-45a8-8d67-346fe587467d", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "### Final analysis and conclusion" + ] + }, + { + "cell_type": "markdown", + "id": "ce34dd94-a370-4e29-b385-6e89fbda8e78", + "metadata": {}, + "source": [ + "In this analysis, I evaluated the performance of two models, CNN and ResNet, on ECG data, focusing on their ability to distinguish between normal and abnormal signals. Both models were optimized to minimize validation loss, with an extensive hyperparameter search conducted to fine-tune critical parameters such as window size, step size, dropout rate, and L2 regularization. The ResNet model generally outperformed the CNN model in terms of sensitivity across different subgroups, benefiting from its more complex architecture, which allows it to capture subtle patterns in the data. This advantage was evident across most diagnostic classes and age groups, although CNN showed better specificity, suggesting it was more conservative in making positive predictions.\n", + "\n", + "During model training, both models were subjected to strategies aimed at reducing overfitting, such as early stopping and regularization. Despite these efforts, the models still performed better when slightly overfitting, which is a common occurrence in deep learning models with complex data like ECG signals.\n", + "\n", + "While ResNet was better at identifying abnormalities across most subgroups, CNN's cautious approach resulted in fewer false positives. This balance between sensitivity and specificity highlights the strengths and limitations of each model depending on the specific requirements of the application. \n", + "\n", + "Notably, sensitivity performance could be further improved for patients under 50 years old, where both models showed room for enhancement. This may be partly due to the population in the dataset being skewed towards older ages, which could have influenced the models' ability to generalize to younger age groups." + ] + }, + { + "cell_type": "markdown", + "id": "0d16a689-6ee5-4a00-a331-68d154596dbc", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "### Next Steps" + ] + }, + { + "cell_type": "markdown", + "id": "909d0318-4648-4501-9eee-1ec7c04d224d", + "metadata": {}, + "source": [ + "As we think about what to do next with this project, here are some actions that could help improve and validate the models:\n", + "\n", + "- Tackle Ambiguous Labels: We could consult with medical professionals to clear up any ambiguous labels in the dataset. This would ensure the model is learning from more accurate data, which could boost its performance.\n", + "\n", + "- Expand to Multi-Class Classification: To make the model more versatile, we could explore moving from binary to multi-class classification. This would allow the model to recognize a wider range of ECG conditions, making it more useful in different scenarios.\n", + "\n", + "- Fine-Tune Hyperparameters: We could dive into further tuning of hyperparameters to get the most out of the model. For example, adding learning rate scheduling might help with better model convergence and reduce overfitting, while tweaking other hyperparameters could improve training stability and effectiveness. Additionally, spending time optimizing parameters for the ResNet, similar to the approach taken with the CNN, could further enhance its performance.\n", + "\n", + "- Improve Subgroup Performance: Given the variability in model performance across age subgroups, we could focus on improving model generalization for these groups, potentially through targeted data augmentation or subgroup-specific training strategies.\n", + "\n", + "- Explore Other Models: We could explore alternative model architectures, such as Transformer-based models or other advanced neural networks, to see if they provide better performance for this ECG classification task.\n", + "\n", + "- Deploy the Best Model: the model could be deployed on devices or used in clinical settings, making sure it's robust and reliable enough for real-world use." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ruben-solution/reports/cnn_history.pkl b/ruben-solution/reports/cnn_history.pkl new file mode 100644 index 0000000..8dbebe6 Binary files /dev/null and b/ruben-solution/reports/cnn_history.pkl differ diff --git a/ruben-solution/reports/resnet_history.pkl b/ruben-solution/reports/resnet_history.pkl new file mode 100644 index 0000000..5d14368 Binary files /dev/null and b/ruben-solution/reports/resnet_history.pkl differ diff --git a/ruben-solution/reports/tscnn_hyperparameter_study.pkl b/ruben-solution/reports/tscnn_hyperparameter_study.pkl new file mode 100644 index 0000000..345f74e Binary files /dev/null and b/ruben-solution/reports/tscnn_hyperparameter_study.pkl differ diff --git a/ruben-solution/requirements.txt b/ruben-solution/requirements.txt new file mode 100644 index 0000000..f79044a --- /dev/null +++ b/ruben-solution/requirements.txt @@ -0,0 +1,159 @@ +absl-py==2.1.0 +alembic==1.13.2 +anyio==4.4.0 +appnope==0.1.4 +argon2-cffi==23.1.0 +argon2-cffi-bindings==21.2.0 +arrow==1.3.0 +asttokens==2.4.1 +astunparse==1.6.3 +async-lru==2.0.4 +attrs==24.2.0 +babel==2.16.0 +beautifulsoup4==4.12.3 +black==24.8.0 +bleach==6.1.0 +certifi==2024.7.4 +cffi==1.17.0 +charset-normalizer==3.3.2 +click==8.1.7 +colorlog==6.8.2 +comm==0.2.2 +contourpy==1.2.1 +cycler==0.12.1 +debugpy==1.8.5 +decorator==5.1.1 +defusedxml==0.7.1 +-e . +exceptiongroup==1.2.2 +executing==2.0.1 +fastjsonschema==2.20.0 +flatbuffers==24.3.25 +fonttools==4.53.1 +fqdn==1.5.1 +gast==0.6.0 +google-pasta==0.2.0 +greenlet==3.0.3 +grpcio==1.66.0 +h11==0.14.0 +h5py==3.11.0 +httpcore==1.0.5 +httpx==0.27.0 +idna==3.7 +importlib_metadata==8.4.0 +importlib_resources==6.4.3 +ipykernel==6.29.5 +ipython==8.18.1 +ipywidgets==8.1.5 +isoduration==20.11.0 +isort==5.13.2 +jedi==0.19.1 +Jinja2==3.1.4 +joblib==1.4.2 +json5==0.9.25 +jsonpointer==3.0.0 +jsonschema==4.23.0 +jsonschema-specifications==2023.12.1 +jupyter-events==0.10.0 +jupyter-lsp==2.2.5 +jupyter_client==8.6.2 +jupyter_core==5.7.2 +jupyter_server==2.14.2 +jupyter_server_terminals==0.5.3 +jupyterlab==4.2.4 +jupyterlab_code_formatter==3.0.2 +jupyterlab_pygments==0.3.0 +jupyterlab_server==2.27.3 +jupyterlab_widgets==3.0.13 +kaleido==0.2.1 +keras==3.5.0 +kiwisolver==1.4.5 +libclang==18.1.1 +Mako==1.3.5 +Markdown==3.7 +markdown-it-py==3.0.0 +MarkupSafe==2.1.5 +matplotlib==3.9.2 +matplotlib-inline==0.1.7 +mdurl==0.1.2 +mistune==3.0.2 +ml-dtypes==0.3.2 +mypy-extensions==1.0.0 +namex==0.0.8 +nbclient==0.10.0 +nbconvert==7.16.4 +nbformat==5.10.4 +nest-asyncio==1.6.0 +notebook_shim==0.2.4 +numpy==1.26.4 +opt-einsum==3.3.0 +optree==0.12.1 +optuna==3.6.1 +optuna-integration==3.6.0 +overrides==7.7.0 +packaging==24.1 +pandas==2.2.2 +pandocfilters==1.5.1 +parso==0.8.4 +pathspec==0.12.1 +pexpect==4.9.0 +pillow==10.4.0 +platformdirs==4.2.2 +plotly==5.23.0 +prometheus_client==0.20.0 +prompt_toolkit==3.0.47 +protobuf==4.25.4 +psutil==6.0.0 +ptyprocess==0.7.0 +pure_eval==0.2.3 +pycparser==2.22 +Pygments==2.18.0 +pyparsing==3.1.2 +python-dateutil==2.9.0.post0 +python-json-logger==2.0.7 +pytz==2024.1 +PyYAML==6.0.2 +pyzmq==26.1.1 +referencing==0.35.1 +requests==2.32.3 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +rich==13.7.1 +rpds-py==0.20.0 +scikit-learn==1.5.1 +scipy==1.13.1 +seaborn==0.13.2 +Send2Trash==1.8.3 +six==1.16.0 +sniffio==1.3.1 +soundfile==0.12.1 +soupsieve==2.6 +SQLAlchemy==2.0.32 +stack-data==0.6.3 +tenacity==9.0.0 +tensorboard==2.16.2 +tensorboard-data-server==0.7.2 +tensorflow==2.16.2 +tensorflow-io-gcs-filesystem==0.37.1 +termcolor==2.4.0 +terminado==0.18.1 +threadpoolctl==3.5.0 +tinycss2==1.3.0 +tomli==2.0.1 +tornado==6.4.1 +tqdm==4.66.5 +traitlets==5.14.3 +types-python-dateutil==2.9.0.20240821 +typing_extensions==4.12.2 +tzdata==2024.1 +uri-template==1.3.0 +urllib3==2.2.2 +wcwidth==0.2.13 +webcolors==24.8.0 +webencodings==0.5.1 +websocket-client==1.8.0 +Werkzeug==3.0.4 +wfdb==4.1.2 +widgetsnbextension==4.0.13 +wrapt==1.16.0 +zipp==3.20.0 diff --git a/ruben-solution/results/cnn_preds.npy b/ruben-solution/results/cnn_preds.npy new file mode 100644 index 0000000..6387fd4 Binary files /dev/null and b/ruben-solution/results/cnn_preds.npy differ diff --git a/ruben-solution/results/resnet_preds.npy b/ruben-solution/results/resnet_preds.npy new file mode 100644 index 0000000..5ba6655 Binary files /dev/null and b/ruben-solution/results/resnet_preds.npy differ diff --git a/ruben-solution/setup.py b/ruben-solution/setup.py new file mode 100644 index 0000000..9503aae --- /dev/null +++ b/ruben-solution/setup.py @@ -0,0 +1,8 @@ +from setuptools import setup, find_packages + +setup( + name='ecg-analysis', + version='1.0', + packages=find_packages(where='src'), + package_dir={'': 'src'}, +) diff --git a/ruben-solution/src/__init__.py b/ruben-solution/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ruben-solution/src/models/__init__.py b/ruben-solution/src/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ruben-solution/src/models/resnet.py b/ruben-solution/src/models/resnet.py new file mode 100644 index 0000000..e1b0dbb --- /dev/null +++ b/ruben-solution/src/models/resnet.py @@ -0,0 +1,154 @@ +from typing import List, Tuple + +import tensorflow as tf +from tensorflow.keras import layers, metrics, models, regularizers + + +class ECGResNet: + def __init__(self, input_shape: Tuple[int], l2_reg: float, dropout_rate: float, num_classes: int = 1): + """ + Initialize ResNet model for ECG time series data. + + Parameters: + - input_shape (tuple): Shape of the input data (timesteps, channels). + - num_classes (int): Number of classes for classification. Defaults to 1 for binary classification. + - l2_reg (float): L2 regularization factor. Defaults to 0.001. + - dropout_rate (float): Dropout rate. Defaults to 0.5. + """ + self.input_shape = input_shape + self.num_classes = num_classes + self.l2_reg = l2_reg + self.dropout_rate = dropout_rate + self.model = self.build_model() + + def residual_block(self, x: tf.Tensor, filters: int, kernel_size: int = 3) -> tf.keras.models: + """ + Create a residual block with two Conv1D layers and a skip connection. + + Parameters: + - x (tf.Tensor): Input tensor. + - filters (int): Number of filters for the convolutional layers. + - kernel_size (int): Size of the convolutional kernel. Defaults to 3. + + Returns: + - tf.keras.models: A model containing the residual block. + """ + shortcut = x + x = layers.Conv1D( + filters=filters, + kernel_size=kernel_size, + padding="same", + activation="relu", + kernel_regularizer=regularizers.l2(self.l2_reg), + )(x) + x = layers.BatchNormalization()(x) + x = layers.Conv1D( + filters=filters, + kernel_size=kernel_size, + padding="same", + activation=None, + kernel_regularizer=regularizers.l2(self.l2_reg), + )(x) + x = layers.BatchNormalization()(x) + + if shortcut.shape[-1] != filters: + shortcut = layers.Conv1D(filters=filters, kernel_size=1, padding="same")(shortcut) + + x = layers.Add()([x, shortcut]) + x = layers.ReLU()(x) + return x + + def build_model(self) -> tf.keras.Model: + """ + Build and compile the ResNet model. + + Returns: + - tf.keras.Model: Compiled Keras model ready for training. + """ + inputs = layers.Input(shape=self.input_shape) + + x = layers.Conv1D(64, kernel_size=7, padding="same", activation="relu")(inputs) + x = layers.BatchNormalization()(x) + x = layers.MaxPooling1D(pool_size=2)(x) + + x = self.residual_block(x, filters=64) + x = self.residual_block(x, filters=128) + x = self.residual_block(x, filters=256) + + x = layers.GlobalAveragePooling1D()(x) + x = layers.Dense(128, activation="relu", kernel_regularizer=regularizers.l2(self.l2_reg))(x) + x = layers.Dropout(self.dropout_rate)(x) + + outputs = layers.Dense(self.num_classes, activation="sigmoid")(x) + + model = models.Model(inputs=inputs, outputs=outputs) + model.compile( + optimizer=tf.keras.optimizers.Adam(), + loss="binary_crossentropy", + metrics=["accuracy", metrics.AUC(name="auc")], + ) + return model + + def train( + self, + X_train: tf.Tensor, + y_train: tf.Tensor, + X_val: tf.Tensor, + y_val: tf.Tensor, + callbacks: List[tf.keras.callbacks.Callback] = [], + epochs: int = 10, + batch_size: int = 32, + verbose: int = 1, + ) -> tf.keras.callbacks.History: + """ + Train the ResNet model. + + Parameters: + - X_train (tf.Tensor): Training data of shape (num_samples, timesteps, channels). + - y_train (tf.Tensor): Training labels of shape (num_samples, 1). + - X_val (tf.Tensor): Validation data of shape (num_samples, timesteps, channels). + - y_val (tf.Tensor): Validation labels of shape (num_samples, 1). + - callbacks (list): List of Keras callbacks to apply during training. Defaults to an empty list. + - epochs (int): Number of training epochs. Defaults to 10. + - batch_size (int): Number of samples per gradient update. Defaults to 32. + - verbose (int): Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. Defaults to 1. + + Returns: + - tf.keras.callbacks.History: Training history object containing training and validation metrics. + """ + return self.model.fit( + X_train, + y_train, + validation_data=(X_val, y_val), + epochs=epochs, + batch_size=batch_size, + verbose=verbose, + callbacks=callbacks, + ) + + def evaluate(self, X_test: tf.Tensor, y_test: tf.Tensor, verbose: int = 0) -> Tuple[float, float]: + """ + Evaluate the ResNet model. + + Parameters: + - X_test (tf.Tensor): Test data of shape (num_samples, timesteps, channels). + - y_test (tf.Tensor): Test labels of shape (num_samples, 1). + - verbose (int): Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. Defaults to 1. + + Returns: + - tuple: A tuple containing the test loss and test AUC score. + """ + return self.model.evaluate(X_test, y_test, verbose=verbose) + + def predict(self, X: tf.Tensor, verbose: int = 0) -> tf.Tensor: + """ + Make predictions using the trained model. + + Parameters: + - X (tf.Tensor): Input data of shape (num_samples, timesteps, channels). + - verbose (int): Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. Defaults to 1. + + Returns: + - tf.Tensor: Predicted probabilities for each sample. + """ + return self.model.predict(X, verbose=verbose) diff --git a/ruben-solution/src/models/tscnn.py b/ruben-solution/src/models/tscnn.py new file mode 100644 index 0000000..4554be6 --- /dev/null +++ b/ruben-solution/src/models/tscnn.py @@ -0,0 +1,200 @@ +from typing import List, Optional, Tuple + +import numpy as np +import tensorflow as tf +from tensorflow.keras import layers, metrics, models, regularizers +from tensorflow.keras.utils import Sequence + + +class TimeSeriesCNN: + def __init__( + self, + input_shape: Tuple[int], + window_size: int, + dropout_rate: float, + l2_reg: float, + filters: List[int], + num_classes: int = 1, + ): + """ + Initialize the CNN model for time series data with a sliding window approach. + + Parameters: + - input_shape (tuple): Shape of the input data (ecg_length, channels). + - window_size (int): Size of the sliding window (number of timesteps). + - num_classes (int): Number of classes for classification. Defaults to 1 for binary classification. + - dropout_rate (float): Dropout rate for regularization. + - l2_reg (float): L2 regularization factor. + - filters (list): Number of filters for each Conv1D layer. + """ + self.input_shape = input_shape + self.num_classes = num_classes + self.window_size = window_size + self.dropout_rate = dropout_rate + self.l2_reg = l2_reg + self.filters = filters + self.model = self.build_model() + + def build_model(self) -> models.Model: + """ + Build and compile the CNN model. + + Returns: + - tf.keras.Model: Compiled Keras model ready for training. + """ + model = models.Sequential() + model.add(layers.Input(shape=(self.window_size, self.input_shape[1]))) + + for i, filter_count in enumerate(self.filters): + model.add( + layers.Conv1D( + filter_count, + kernel_size=5 if i < 2 else 3, + activation="relu", + padding="same", + kernel_regularizer=regularizers.l2(self.l2_reg), + ) + ) + model.add(layers.MaxPooling1D(pool_size=2)) + model.add(layers.Dropout(self.dropout_rate)) + + model.add(layers.Flatten()) + model.add(layers.Dense(128, activation="relu", kernel_regularizer=regularizers.l2(self.l2_reg))) + model.add(layers.Dropout(0.5)) + model.add(layers.Dense(self.num_classes, activation="sigmoid")) + + model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy", metrics.AUC(name="auc")]) + return model + + def train( + self, + train_generator: Sequence, + val_generator: Sequence, + callbacks: List[tf.keras.callbacks.Callback], + epochs: int = 10, + batch_size: int = 32, + verbose: int = 1, + ) -> tf.keras.callbacks.History: + """ + Train the CNN model. + + Parameters: + - train_generator (Sequence): Data generator for training data. + - val_generator (Sequence): Data generator for validation data. + - callbacks (list[tf.keras.callbacks.Callback]): List of Keras callbacks to apply during training. + - epochs (int): Number of training epochs. Defaults to 10. + - batch_size (int): Number of samples per gradient update. Defaults to 32. + - verbose (int): Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. + + Returns: + - tf.keras.callbacks.History: Training history object containing training and validation metrics. + """ + return self.model.fit( + train_generator, + validation_data=val_generator, + epochs=epochs, + verbose=verbose, + callbacks=callbacks, + batch_size=batch_size, + ) + + def evaluate(self, test_generator: Sequence, verbose: int) -> Tuple[float, float, float]: + """ + Evaluate the CNN model. + + Parameters: + - test_generator (Sequence): Data generator for test data. + - verbose (int): Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. Defaults to 1. + + Returns: + - tuple: Test loss, test accuracy, and any other metrics specified in the model. + """ + return self.model.evaluate(test_generator, verbose=verbose) + + def predict(self, test_generator: Sequence, verbose: int) -> np.ndarray: + """ + Make predictions using the trained model. + + Parameters: + - test_generator (Sequence): Data generator for test data. + - verbose (int): Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. Defaults to 1. + + Returns: + - np.ndarray: Predicted probabilities for each class. + """ + return self.model.predict(test_generator, verbose=verbose) + + +class SlidingWindowGenerator(Sequence): + def __init__(self, X: np.ndarray, y: np.ndarray, window_size: int, step_size: int, batch_size: int): + """ + Initialize the sliding window generator. + + Parameters: + - X (np.ndarray): Input data of shape (n_rows, ecg_length, channels). + - y (np.ndarray): Binary labels corresponding to X. + - window_size (int): Size of the sliding window. + - step_size (int): Step size for the sliding window. + - batch_size (int): Number of windows per batch. + """ + super().__init__() + self.X = X + self.y = y + self.window_size = window_size + self.step_size = step_size + self.batch_size = batch_size + self.n_rows, self.ecg_length, self.channels = X.shape + self.indices = np.arange(0, self.ecg_length - window_size + 1, step_size) + self.on_epoch_end() + + def __len__(self) -> int: + """ + Calculate the total number of batches. + + Returns: + - int: Total number of batches. + """ + num_windows_per_row = (self.ecg_length - self.window_size) // self.step_size + 1 + total_windows = self.n_rows * num_windows_per_row + return int(np.ceil(total_windows / self.batch_size)) + + def __getitem__(self, index: int) -> Tuple[np.ndarray, np.ndarray]: + """ + Generate one batch of data. + + Parameters: + - index (int): Index of the batch. + + Returns: + - tuple: Batch of data (X_batch, y_batch). + """ + start_idx = index * self.batch_size + end_idx = min(start_idx + self.batch_size, len(self.indices) * self.n_rows) + + batch_indices = [(i // len(self.indices), i % len(self.indices)) for i in range(start_idx, end_idx)] + + return self.__data_generation(batch_indices) + + def on_epoch_end(self): + """Shuffle the indices after each epoch.""" + np.random.shuffle(self.indices) + + def __data_generation(self, batch_indices: List[Tuple[int, int]]) -> Tuple[np.ndarray, np.ndarray]: + """ + Generate data for one batch. + + Parameters: + - batch_indices (list): List of tuples containing row and window indices. + + Returns: + - tuple: Arrays of shape (batch_size, window_size, channels) and (batch_size,). + """ + X_batch = np.array( + [ + self.X[row, self.indices[window_idx] : self.indices[window_idx] + self.window_size, :] + for row, window_idx in batch_indices + ] + ) + y_batch = np.array([self.y[row] for row, window_idx in batch_indices]) + + return X_batch, y_batch diff --git a/ruben-solution/src/utils/__init__.py b/ruben-solution/src/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ruben-solution/src/utils/data_preprocessing.py b/ruben-solution/src/utils/data_preprocessing.py new file mode 100644 index 0000000..9afb9a2 --- /dev/null +++ b/ruben-solution/src/utils/data_preprocessing.py @@ -0,0 +1,110 @@ +from os.path import join +from typing import Optional, Tuple, Union + +import numpy as np +import pandas as pd +import wfdb +from models.tscnn import SlidingWindowGenerator + + +def load_raw_data(df: pd.DataFrame, sampling_rate: int, path: str) -> np.ndarray: + """ + Load raw ECG data from the PTB-XL dataset. + + Parameters: + - df (pd.DataFrame): DataFrame containing metadata of ECG records, including file paths. + - sampling_rate (int): The desired sampling rate for the ECG data. PTB-XL provides data at two sampling rates: 100 Hz and 500 Hz. + - path (str): The base path to the directory containing the ECG files. + + Returns: + - np.ndarray: A 3D array where each entry corresponds to the ECG signal of a single record. + The dimensions of the array are (number of records, number of samples, number of leads). + """ + if sampling_rate == 100: + data = [wfdb.rdsamp(join(path, f)) for f in df.filename_lr] + else: + data = [wfdb.rdsamp(join(path, f)) for f in df.filename_hr] + data = np.array([signal for signal, meta in data]) + return data + + +def aggregate_diagnostic(y_dic: dict, agg_df: pd.DataFrame, col: str, threshold: float = 80.0) -> str: + """ + Aggregate diagnostic codes based on their likelihoods, including only those with a likelihood of 80% or higher. + + Parameters: + - y_dic (dict): Dictionary containing diagnostic codes as keys and their respective probabilities or confidences as values. + - agg_df (pd.DataFrame): DataFrame that maps diagnostic codes to their corresponding superclasses, subclasses, or other aggregate categories. + - col (str): The column name in agg_df that specifies the category (e.g., superclass, subclass) to which the diagnostic codes should be aggregated. + - threshold (float): The minimum likelihood required for a diagnostic code to be included in the aggregation. + + Returns: + - str: A string containing the aggregated diagnostic categories separated by the "|" character. + """ + tmp = [] + for scp_code, likelihood in y_dic.items(): + if scp_code in agg_df.index and likelihood >= threshold: + tmp.append(agg_df.loc[scp_code][col]) + return "|".join(set(tmp)) + + +def create_generators_or_datasets( + y: pd.DataFrame, + window_size: Optional[int], + step_size: Optional[int], + batch_size: int, + sample_size: float, + sampling_rate: int, + data_path: str, + return_generators: bool, + valid_fold: int = 9, + test_fold: int = 10, + random_seed: int = 42, +) -> Union[ + Tuple[SlidingWindowGenerator, SlidingWindowGenerator, SlidingWindowGenerator], + Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray], +]: + """ + Sample data, load raw data, and either create generators or return datasets for training, validation, and testing. + + Parameters: + - y (pd.DataFrame): The DataFrame containing labels and fold information. + - window_size (int, optional): The size of the window for the sliding window generator. Required if return_generators is True. + - step_size (int, optional): The step size for the sliding window generator. Required if return_generators is True. + - batch_size (int): The batch size for the sliding window generator. + - sample_size (float): The fraction of the data to sample. + - sampling_rate (int): The sampling rate for loading the raw data. + - data_path (str): The path to the raw data directory. + - return_generators (bool): If True, return sliding window generators; if False, return raw datasets. + - valid_fold (int): The fold number to use for validation. Defaults to 9. + - test_fold (int): The fold number to use for testing. Defaults to 10. + - random_seed (Optional[int]): Seed for the random number generator. Defaults to 42. + + Returns: + - Union[Tuple[SlidingWindowGenerator, SlidingWindowGenerator, SlidingWindowGenerator], + Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]]: + Depending on return_generators, either a tuple of generators (train_generator, val_generator, test_generator) or a tuple of datasets (X_train, y_train, X_val, y_val, X_test, y_test). + """ + + np.random.seed(random_seed) + + sample_indices = np.random.choice(y.shape[0], size=int(sample_size * y.shape[0]), replace=False) + y_sub = y.iloc[sample_indices] + X_sub = load_raw_data(y_sub, sampling_rate, join(data_path, "raw")) + + X_train = X_sub[np.where(~y_sub.strat_fold.isin([valid_fold, test_fold]))] + y_train = y_sub.loc[(~y_sub.strat_fold.isin([valid_fold, test_fold]))].LABEL.to_numpy() + + X_val = X_sub[np.where(y_sub.strat_fold == valid_fold)] + y_val = y_sub.loc[y_sub.strat_fold == valid_fold].LABEL.to_numpy() + + X_test = X_sub[np.where(y_sub.strat_fold == test_fold)] + y_test = y_sub.loc[y_sub.strat_fold == test_fold].LABEL.to_numpy() + + if return_generators: + train_generator = SlidingWindowGenerator(X_train, y_train, window_size, step_size, batch_size) + val_generator = SlidingWindowGenerator(X_val, y_val, window_size, step_size, batch_size) + test_generator = SlidingWindowGenerator(X_test, y_test, window_size, step_size, batch_size) + return train_generator, val_generator, test_generator + else: + return X_train, y_train, X_val, y_val, X_test, y_test diff --git a/ruben-solution/src/utils/ecg_visualization.py b/ruben-solution/src/utils/ecg_visualization.py new file mode 100644 index 0000000..2ff07ff --- /dev/null +++ b/ruben-solution/src/utils/ecg_visualization.py @@ -0,0 +1,57 @@ +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from utils.data_preprocessing import load_raw_data + + +def load_and_plot_ecg(index: int, metadata: pd.DataFrame, path: str, sampling_rate: int = 100) -> None: + """ + Load and plot all 12 leads of an ECG signal in a single combined graph with a red grid. + + Parameters: + - index (int): The index of the ECG entry in the metadata DataFrame. + - metadata (pd.DataFrame): The metadata DataFrame containing ECG information, including file paths and annotations. + - sampling_rate (int): The sampling rate of the ECG data (100 or 500 Hz). Defaults to 100 Hz. + - path (str): The base path where ECG files are stored. + + Returns: + - None: The function displays a plot of the 12 ECG leads but does not return any values. + """ + ecg_data = load_raw_data(metadata.iloc[[index]], sampling_rate, path) + ecg_signals = ecg_data[0] + + patient_id = metadata.iloc[index]["patient_id"] + ecg_id = metadata.index[index] + + annotations = metadata.iloc[index]["scp_codes"] + print(f"Annotations (SCP Codes) for Patient ID {patient_id}, ECG ID {ecg_id}:", annotations) + + plt.figure(figsize=(8, 11)) + lead_names = ["I", "II", "III", "aVR", "aVL", "aVF", "V1", "V2", "V3", "V4", "V5", "V6"] + y_offsets = np.linspace(0, -40, 12) # Vertical offsets to separate each ECG lead + + # Plot each of the 12 leads + for i in range(12): + plt.plot(ecg_signals[:, i] + y_offsets[i], color="black", linewidth=0.7) + plt.text(-5, y_offsets[i], lead_names[i], fontsize=9, ha="right", va="center", color="black") + + # Configure the grid and axes for the ECG plot + plt.gca().xaxis.set_major_locator(plt.MultipleLocator(sampling_rate)) + plt.gca().xaxis.set_minor_locator(plt.MultipleLocator(sampling_rate / 5)) + plt.gca().yaxis.set_major_locator(plt.MultipleLocator(1.0)) + plt.gca().yaxis.set_minor_locator(plt.MultipleLocator(0.5)) + plt.grid(True, which="major", color="red", linestyle="-", linewidth=0.3) + plt.grid(True, which="minor", color="red", linestyle="-", linewidth=0.1) + + # Set up x-axis labels as time in seconds + x_ticks = np.arange(0, ecg_signals.shape[0] + 1, sampling_rate) + x_labels = x_ticks / sampling_rate + plt.xticks(x_ticks, labels=[f"{label:.1f}" for label in x_labels]) + + # Remove y-axis numerical labels + plt.gca().yaxis.set_major_formatter(plt.NullFormatter()) + + plt.title(f"ECG Signal - 12 Leads Combined (Patient ID: {patient_id}, ECG ID: {ecg_id})", fontsize=12) + plt.xlabel("Time (seconds)", fontsize=10) + + plt.show() diff --git a/ruben-solution/src/utils/example_physionet.py b/ruben-solution/src/utils/example_physionet.py new file mode 100644 index 0000000..6942df4 --- /dev/null +++ b/ruben-solution/src/utils/example_physionet.py @@ -0,0 +1,49 @@ +import pandas as pd +import numpy as np +import wfdb +import ast + + +def load_raw_data(df, sampling_rate, path): + if sampling_rate == 100: + data = [wfdb.rdsamp(path + f) for f in df.filename_lr] + else: + data = [wfdb.rdsamp(path + f) for f in df.filename_hr] + data = np.array([signal for signal, meta in data]) + return data + + +path = "path/to/ptbxl/" +sampling_rate = 100 + +# load and convert annotation data +Y = pd.read_csv(path + "ptbxl_database.csv", index_col="ecg_id") +Y.scp_codes = Y.scp_codes.apply(lambda x: ast.literal_eval(x)) + +# Load raw signal data +X = load_raw_data(Y, sampling_rate, path) + +# Load scp_statements.csv for diagnostic aggregation +agg_df = pd.read_csv(path + "scp_statements.csv", index_col=0) +agg_df = agg_df[agg_df.diagnostic == 1] + + +def aggregate_diagnostic(y_dic): + tmp = [] + for key in y_dic.keys(): + if key in agg_df.index: + tmp.append(agg_df.loc[key].diagnostic_class) + return list(set(tmp)) + + +# Apply diagnostic superclass +Y["diagnostic_superclass"] = Y.scp_codes.apply(aggregate_diagnostic) + +# Split data into train and test +test_fold = 10 +# Train +X_train = X[np.where(Y.strat_fold != test_fold)] +y_train = Y[(Y.strat_fold != test_fold)].diagnostic_superclass +# Test +X_test = X[np.where(Y.strat_fold == test_fold)] +y_test = Y[Y.strat_fold == test_fold].diagnostic_superclass diff --git a/ruben-solution/src/utils/hyperparameter_tuning.py b/ruben-solution/src/utils/hyperparameter_tuning.py new file mode 100644 index 0000000..efb86a4 --- /dev/null +++ b/ruben-solution/src/utils/hyperparameter_tuning.py @@ -0,0 +1,128 @@ +from typing import Any, Callable, List, Tuple + +import numpy as np +import pandas as pd +from models.tscnn import SlidingWindowGenerator +from sklearn.metrics import accuracy_score, roc_auc_score +from tensorflow.keras.callbacks import EarlyStopping +from tensorflow.keras.models import Model +from utils.data_preprocessing import create_generators_or_datasets + + +def evaluate_cnn_performance( + model: Model, data_generator: SlidingWindowGenerator, true_labels: np.ndarray +) -> Tuple[float, float, float, np.ndarray]: + """ + Evaluate the performance of a CNN model on a given dataset using a SlidingWindowGenerator. + + Parameters: + - model (Model): The trained Keras model to be evaluated. + - data_generator (SlidingWindowGenerator): A generator that generates batches of data using a sliding window approach. + - true_labels (np.ndarray): The true labels for the dataset. + + Returns: + - Tuple[float, float, float, np.ndarray]: Returns the loss, accuracy, AUC, and the predictions as a tuple. + """ + predictions = model.predict(data_generator, verbose=0) + num_windows_per_sample = len(predictions) // len(true_labels) + aggregated_predictions = predictions.reshape(-1, num_windows_per_sample).mean(axis=1) + predicted_classes = np.round(aggregated_predictions) + + accuracy = accuracy_score(true_labels, predicted_classes) + auc = roc_auc_score(true_labels, aggregated_predictions) + + loss = model.evaluate(data_generator, verbose=0) + if isinstance(loss, (list, tuple)): + loss = loss[0] + + return loss, accuracy, auc, aggregated_predictions + + +def evaluate_resnet_performance(model: Model, X: np.ndarray, y: np.ndarray) -> Tuple[float, float, float, np.ndarray]: + """ + Evaluate the performance of a ResNet model on a given dataset. + + Parameters: + - model (Model): The trained Keras model to be evaluated. + - X (np.ndarray): Input data of shape (num_samples, timesteps, channels). + - y (np.ndarray): True labels corresponding to X. + + Returns: + - Tuple[float, float, float, np.ndarray]: Returns the loss, accuracy, AUC, and the predictions as a tuple. + """ + predictions = model.predict(X, verbose=0) + predicted_classes = np.round(predictions) + + accuracy = accuracy_score(y, predicted_classes) + auc = roc_auc_score(y, predictions) + + loss = model.evaluate(X, y, verbose=0) + if isinstance(loss, (list, tuple)): + loss = loss[0] + + return loss, accuracy, auc, predictions + + +def objective( + trial: Any, + objective_metric: str, + model_class: Callable, + input_shape: Tuple[int, int], + Y: pd.DataFrame, + sampling_rate: int, + sample_size: float, + data_path: str, + batch_size: int, + num_classes: int, + epochs: int, + filters: List[int], +) -> float: + """ + Objective function for Optuna to optimize hyperparameters for a CNN model. + + Parameters: + - trial (Any): A trial object used to suggest values for the hyperparameters. + - objective_metric (str): The metric to optimize, e.g., 'val_loss'. + - model_class (Callable): The CNN model class to instantiate. + - input_shape (Tuple[int, int]): Shape of the input data (timesteps, channels). + - Y (pd.DataFrame): DataFrame containing the labels and other metadata. + - sampling_rate (int): The sampling rate for the ECG data. + - sample_size (float): The fraction of the data to sample. + - data_path (str): The path to the raw data directory. + - batch_size (int): Number of windows per batch. + - num_classes (int): Number of classes for the output layer. + - epochs (int): Number of training epochs. + - filters (List[int]): List of filter sizes for each convolutional layer. + + Returns: + - float: The objective_metric score for the current set of hyperparameters. + """ + window_size = trial.suggest_int("window_size", 50, 200) + step_size = trial.suggest_int("step_size", 10, window_size) + l2_reg = trial.suggest_float("l2_reg", 1e-12, 1e-3, log=True) + dropout_rate = trial.suggest_float("dropout_rate", 0.0, 0.5) + + train_generator, val_generator, test_generator = create_generators_or_datasets( + y=Y, + window_size=window_size, + step_size=step_size, + batch_size=batch_size, + sample_size=sample_size, + sampling_rate=sampling_rate, + data_path=data_path, + return_generators=True, + ) + + cnn = model_class( + input_shape=input_shape, + window_size=window_size, + num_classes=num_classes, + l2_reg=l2_reg, + dropout_rate=dropout_rate, + filters=filters, + ) + + early_stopping = EarlyStopping(monitor="val_loss", patience=5, restore_best_weights=True) + history = cnn.train(train_generator, val_generator, epochs=epochs, verbose=0, callbacks=[early_stopping]) + + return history.history[objective_metric][-1]