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OpenCLSim - Rule driven scheduling of cyclic activities for in-depth comparison of alternative operating strategies.
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public repo for ionospheric fluid electrodynamic model
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PyZFS: A Python package for first-principles calculations of zero-field splitting tensors
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Release for Digraphs
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  • Software/Code
Persistent Homology of Networks (PHN) is a python package for the functions developed in "Persistent Homology of Complex Networks for Dynamic State Detection." These functions use a time series to develop a complex network, which can be analyzed to detect dynamic state changes as well as complexity changes in the time series. Additionally, complex networks allow for a unique view into the shape of high dimension embedding that could not normally be visualized.
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This toolbox supports the results in the following publication: D. Florescu and M. England. A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs. The authors are supported by EPSRC Project EP/R019622/1: Embedding Machine Learning within Quantifer Elimination Procedures. The main script is ML_test_rand/pipeline1.py. More details can be found as comments in the script. The sotd heuristic is implemented in the file data_gen_sotd_rand_test.mw. The data is already generated in the repository. The dataset of polynomials can be found in folders entitled poly_rand_dataset (for training) and poly_rand_dataset_test (for testing). The CAD data is generated by running generate_CAD_data.py. The data is already generated in the repository. The CAD routine was run in Maple 2018, with an updated version of the RegularChains Library downloaded in February 2019 from http://www.regularchains.org. The library file is also available in this repository (RegularChains_Updated.mla) This updated library contains bug fixes and additional functionality. The training and evaluation of the machine learning models was done using the scikit-learn package v0.20.2 for Python 2.7. Some data files generated by the pipeline are included in this repository for consistency and for saving time. However, they can be generated again by the user should they wish so: - the predictions with the sotd heuristic (II(d) in the supported paper) - the ML hyperparameters, resulted from 5-fold cross-validation (I(d)i in the supported paper) - the files containing CAD runtimes (in the folders comp_times_rand_dataset and comp_times_rand_dataset_test, corresponding to I(a) and II(e) in the supported paper)
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Repository of Deliverable 7.1 "FAIR data and Open Science principles"
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itk.js 11.1.0 itk.js combines Emscripten and ITK to enable high-performance spatial analysis in a JavaScript runtime environment. itk.js provides tools to a) build C/C++ code to JavaScript (asm.js) and WebAssembly, b) bridge local filesystems, native JavaScript data structures, and traditional file formats, c) transfer data efficiently in and out of the Emscripten runtime, and d) asynchronously execute processing pipelines in a background thread. itk.js can be used to execute ITK, VTK or arbitrary C++ codes in the browser or on a workstation / server with Node.js. Installation npm install itk Usage For more information, see the itk.js documentation. 11.1.0 (2020-03-26) Features Docker: Bump ITK to 5.1 RC 2+ (71dbd8b) Emscripten: Bump to 1.39.4 (b1870ef) itk-js-cli: Update default Docker image for 11.1.0 (0284908) version: Bump NPM version to 11.1.0 (e52f36b)
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mhealthtools is an open-source, modular R package for preprocessing and extracting features from remote sensor data collected using mobile and wearable devices. The package is written in a functional style so that various components of the preprocessing and feature extraction pipeline can be inserted, omitted, moved around, extended, and shared between multiple libraries with minimal overhead. Depending on the data source, the package includes a default set of preprocessing and feature extraction behavior for convenient exploratory analysis.
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  • Software/Code
Histogram layer models from "Histogram Layers for Texture Analysis" (https://arxiv.org/abs/2001.00215). The repository is also available: https://github.com/GatorSense/Histogram_Layer.
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  • Software/Code
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