Merge pull request #265 from sathvikbhagavan/sb/paper

docs: fix references for the JOSS paper

.typos.toml

@@ -1 +1,2 @@
-[default.extend-words]
+[default.extend-words]
+Commun = "Commun"

joss/paper.bib

@@ -12,11 +12,23 @@ @article{Bezanson2017
   abstract = { Bridging cultures that have often been distant, Julia combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing. Julia is designed to be easy and fast and questions notions generally held to be “laws of nature" by practitioners of numerical computing: \beginlist \item High-level dynamic programs have to be slow. \item One must prototype in one language and then rewrite in another language for speed or deployment. \item There are parts of a system appropriate for the programmer, and other parts that are best left untouched as they have been built by the experts. \endlist We introduce the Julia programming language and its design---a dance between specialization and abstraction. Specialization allows for custom treatment. Multiple dispatch, a technique from computer science, picks the right algorithm for the right circumstance. Abstraction, which is what good computation is really about, recognizes what remains the same after differences are stripped away. Abstractions in mathematics are captured as code through another technique from computer science, generic programming. Julia shows that one can achieve machine performance without sacrificing human convenience. }
 }
 
-@article{gowda2021high,
-  title={High-performance symbolic-numerics via multiple dispatch},
-  author={Gowda, Shashi and Ma, Yingbo and Cheli, Alessandro and Gwozdz, Maja and Shah, Viral B and Edelman, Alan and Rackauckas, Christopher},
-  journal={arXiv preprint arXiv:2105.03949},
-  year={2021}
+@article{10.1145/3511528.3511535,
+  author = {Gowda, Shashi and Ma, Yingbo and Cheli, Alessandro and Gw\'{o}\'{z}zd\'{z}, Maja and Shah, Viral B. and Edelman, Alan and Rackauckas, Christopher},
+  title = {High-Performance Symbolic-Numerics via Multiple Dispatch},
+  year = {2022},
+  issue_date = {September 2021},
+  publisher = {Association for Computing Machinery},
+  address = {New York, NY, USA},
+  volume = {55},
+  number = {3},
+  issn = {1932-2240},
+  url = {https://doi.org/10.1145/3511528.3511535},
+  doi = {10.1145/3511528.3511535},
+  abstract = {As mathematical computing becomes more democratized in high-level languages, high-performance symbolic-numeric systems are necessary for domain scientists and engineers to get the best performance out of their machine without deep knowledge of code optimization. Naturally, users need different term types either to have different algebraic properties for them, or to use efficient data structures. To this end, we developed Symbolics.jl, an extendable symbolic system which uses dynamic multiple dispatch to change behavior depending on the domain needs. In this work we detail an underlying abstract term interface which allows for speed without sacrificing generality. We show that by formalizing a generic API on actions independent of implementation, we can retroactively add optimized data structures to our system without changing the pre-existing term rewriters. We showcase how this can be used to optimize term construction and give a 113x acceleration on general symbolic transformations. Further, we show that such a generic API allows for complementary term-rewriting implementations. Exploiting this feature, we demonstrate the ability to swap between classical term-rewriting simplifiers and e-graph-based term-rewriting simplifiers. We illustrate how this symbolic system improves numerical computing tasks by showcasing an e-graph ruleset which minimizes the number of CPU cycles during expression evaluation, and demonstrate how it simplifies a real-world reaction-network simulation to halve the runtime. Additionally, we show a reaction-diffusion partial differential equation solver which is able to be automatically converted into symbolic expressions via multiple dispatch tracing, which is subsequently accelerated and parallelized to give a 157x simulation speedup. Together, this presents Symbolics.jl as a next-generation symbolic-numeric computing environment geared towards modeling and simulation.},
+  journal = {ACM Commun. Comput. Algebra},
+  month = {jan},
+  pages = {92–96},
+  numpages = {5}
 }
 
 @misc{ma2021modelingtoolkit,
@@ -25,6 +37,8 @@ @misc{ma2021modelingtoolkit
   year={2021},
   eprint={2103.05244},
   archivePrefix={arXiv},
+  doi={10.48550/arXiv.2103.05244},
+  url={https://doi.org/10.48550/arXiv.2103.05244},
   primaryClass={cs.MS}
 }
 
@@ -38,30 +52,33 @@ @article{Tikhonov1943OnTS
   url={https://api.semanticscholar.org/CorpusID:202866372}
 }
 
-@article{Petters2021-aa,
-  title     = "Revisiting matrix-based inversion of scanning mobility particle
-               sizer ({SMPS}) and humidified tandem differential mobility
-               analyzer ({HTDMA}) data",
-  author    = "Petters, Markus D",
-  journal   = "Atmos. Meas. Tech.",
-  publisher = "Copernicus GmbH",
-  volume    =  14,
-  number    =  12,
-  pages     = "7909--7928",
-  month     =  dec,
-  year      =  2021,
-  copyright = "https://creativecommons.org/licenses/by/4.0/",
-  language  = "en"
+@Article{amt-14-7909-2021,
+  AUTHOR = {Petters, M. D.},
+  TITLE = {Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem
+  differential mobility analyzer (HTDMA) data},
+  JOURNAL = {Atmospheric Measurement Techniques},
+  VOLUME = {14},
+  YEAR = {2021},
+  NUMBER = {12},
+  PAGES = {7909--7928},
+  URL = {https://amt.copernicus.org/articles/14/7909/2021/},
+  DOI = {10.5194/amt-14-7909-2021}
 }
 
-@article{Curry1966OnPF,
-  title={On P{\'o}lya frequency functions IV: The fundamental spline functions and their limits},
-  author={Haskell B. Curry and I. J. Schoenberg},
-  journal={Journal d’Analyse Math{\'e}matique},
-  year={1966},
-  volume={17},
-  pages={71-107},
-  url={https://api.semanticscholar.org/CorpusID:120740205}
+@Inbook{Curry1988,
+  author="Curry, H. B.
+  and Schoenberg, I. J.",
+  editor="de Boor, Carl",
+  title="On P{\'o}lya Frequency Functions IV: The Fundamental Spline Functions and their Limits",
+  bookTitle="I. J. Schoenberg Selected Papers",
+  year="1988",
+  publisher="Birkh{\"a}user Boston",
+  address="Boston, MA",
+  pages="347--383",
+  abstract="The present paper was written in 1945 and completed by 1947 (see the abstract [3]) but for no good reason has so far not been published. It appears now in a somewhat revised and improved form.",
+  isbn="978-1-4899-0433-1",
+  doi="10.1007/978-1-4899-0433-1_17",
+  url="https://doi.org/10.1007/978-1-4899-0433-1_17"
 }
 
 @article{DEBOOR197250,
@@ -72,7 +89,7 @@ @article{DEBOOR197250
   pages = {50-62},
   year = {1972},
   issn = {0021-9045},
-  doi = {https://doi.org/10.1016/0021-9045(72)90080-9},
+  doi = {10.1016/0021-9045(72)90080-9},
   url = {https://www.sciencedirect.com/science/article/pii/0021904572900809},
   author = {Carl {de Boor}}
 }
@@ -96,19 +113,24 @@ @article{10.1145/321607.321609
   numpages = {14}
 }
 
-@article{Schoenberg1946ContributionsTT,
-  title={Contributions to the problem of approximation of equidistant data by analytic functions. Part A. On the problem of smoothing or graduation. A first class of analytic approximation formulae},
-  author={I. J. Schoenberg},
-  journal={Quarterly of Applied Mathematics},
-  year={1946},
-  volume={4},
-  pages={45-99},
-  url={https://api.semanticscholar.org/CorpusID:125923957}
+@Inbook{Schoenberg1988,
+  author="Schoenberg, I. J.",
+  editor="de Boor, Carl",
+  title="Contributions to the Problem of Approximation of Equidistant Data by Analytic Functions",
+  bookTitle="I. J. Schoenberg Selected Papers",
+  year="1988",
+  publisher="Birkh{\"a}user Boston",
+  address="Boston, MA",
+  pages="3--57",
+  isbn="978-1-4899-0433-1",
+  doi="10.1007/978-1-4899-0433-1_1",
+  url="https://doi.org/10.1007/978-1-4899-0433-1_1"
 }
 
-@misc{lagrange,
-  title={Lectures on Elementary Mathematics},
-  author={Lagrange, J. L, and Thomas J McCormack},
+@book{lagrange1898lectures,
+  title={Lectures on elementary mathematics, by Joseph Louis Lagrange; tr. by Thomas J. McCormack},
+  author={Lagrange, J.L.},
+  url={https://books.google.co.in/books?id=7h9G0AEACAAJ},
   year={1898},
-  url={https://lccn.loc.gov/98000373},
+  publisher={Open court publishing Company}
 }

joss/paper.md

@@ -26,7 +26,7 @@ bibliography: paper.bib
 
 # Summary
 
-Interpolations are used to estimate values between known data points using an approximate continuous function.DataInterpolations.jl is a Julia [@Bezanson2017] package containing 1D implementations of some of the most commonly used interpolation functions. These include Constant Interpolation, Linear Interpolation, Quadratic Interpolation, Lagrange Interpolation [@lagrange], Quadratic Splines, Cubic Splines [@Schoenberg1946ContributionsTT], Akima Splines [@10.1145/321607.321609], B-Splines [@Curry1966OnPF] [@DEBOOR197250] and Regression based B-Splines. Along with these, the package also has methods to fit parameterized curves with the data points and Tikhonov regularization [@Tikhonov1943OnTS] [@Petters2021-aa] for obtaining smooth curves. The package also provides functionality to compute integrals and derivatives upto second order for those interpolations methods.
+Interpolations are used to estimate values between known data points using an approximate continuous function.DataInterpolations.jl is a Julia [@Bezanson2017] package containing 1D implementations of some of the most commonly used interpolation functions. These include Constant Interpolation, Linear Interpolation, Quadratic Interpolation, Lagrange Interpolation [@lagrange], Quadratic Splines, Cubic Splines [@Schoenberg1988], Akima Splines [@10.1145/321607.321609], B-Splines [@Curry1988] [@DEBOOR197250] and Regression based B-Splines. Along with these, the package also has methods to fit parameterized curves with the data points and Tikhonov regularization [@Tikhonov1943OnTS] [@amt-14-7909-2021] for obtaining smooth curves. The package also provides functionality to compute integrals and derivatives upto second order for those interpolations methods.
 
 # Statement of need
 
@@ -62,3 +62,5 @@ DataInterpolations.derivative(A1, 1.0, 2)
 # integral
 DataInterpolations.integral(A1, 1.0, 5.0)
 ```
+
+# References