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Theory aware Machine Learning (TaML)
Data provided by National Institute of Standards and Technology
A code repository and accompanying data for incorporating imperfect theory into machine learning for improved prediction and explainability. Specifically, it focuses on the case study of the dimensions of a polymer chain in different solvent qualities. Jupyter Notebooks for quickly testing concepts and reproducing figures, as well as source code that computes the mean squared error as a function of dataset size for various machine learning models are included.For additional details on the data, please refer to the README.md associated with the data.
Tags: polymers,machine learning,transfer learning,theory,
Modified: 2025-04-06
Dark solitons in BECs dataset 2.0
Data provided by National Institute of Standards and Technology
Atomic Bose-Einstein condensates (BECs) are widely investigated systems that exhibit quantum phenomena on a macroscopic scale. For example, they can be manipulated to contain solitonic excitations including conventional solitons, vortices, and many more. Broadly speaking, solitonic excitations are solitary waves that retain their size and shape and often propagate at a constant speed. They are present in many systems, at scales ranging from microscopic, to terrestrial and even astronomical.
Tags: machine learning,Bose-Einstein condensates,dark solitons,
Modified: 2025-04-06
Entropy Source Validation Client
Data provided by National Institute of Standards and Technology
This tool is a Python client that can interact with the NIST Entropy Source Validation Test System.
Tags: python,Entropy Source Validation Test System,NIST,CMVP,ESV Client,
Modified: 2025-04-06
Dataset for paper Y. Ma, S. Mosleh and J. Coder, "Analyzing 5G NR-U and WiGig Coexistence with Multiple-Beam Directional LBT," 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), 2022, pp. 272-275, doi: 10.1109/CCNC49033.2022.9700690
Data provided by National Institute of Standards and Technology
This project produces synthetic datasets of spectrum sharing simulation results (I/Q data, metadata, and KPIs).
Tags: wireless coexistence,Spectrum sharing,machine learning,wireless communications and networks,4G,5G,6G,Wi-Fi,
Modified: 2025-04-06
Python tools for measuring filament defects in embedded 3D printing
Data provided by National Institute of Standards and Technology
In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. This repository includes Python code for analyzing and managing images and videos of the printing process during extrusion of single filaments. The zip file contains the state of the code when the associated paper was submitted. The link to the GitHub page goes to version 1.0.0, which is the same as the code attached here. From there, you can also access the current state of the code.Associated with: L. Friedrich, R. Gunther, J.
Tags: python,digital image analysis,computer vision,3D printing,additive manufacturing,openCV,
Modified: 2025-04-06
Trojan Detection Software Challenge - nlp-summary-jan2022-train
Data provided by National Institute of Standards and Technology
Round 9 Train DatasetThis is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.
Tags: Trojan Detection,Artificial Intelligence,ai,machine learning,Adversarial Machine Learning,
Modified: 2025-04-06
Data for Modeling OFDM Communication Signals with Generative Adversarial Networks
Data provided by National Institute of Standards and Technology
This repository contains results for experiments on generative modeling of synthetic Orthogonal-Frequency Division Multiplexing (OFDM) communication signals. (This record supersedes Software and Data for Modeling OFDM Communication Signals with Generative Adversarial Networks, formerly at https://doi.org/10.18434/mds2-2428)
Tags: generative adversarial network,machine learning,wireless communications,
Modified: 2025-04-06
Simantha: Simulation for Manufacturing
Data provided by National Institute of Standards and Technology
Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems.
Tags: discrete-event simulation,manufacturing,production,maintenance,python,
Modified: 2025-04-06
multicomplex: C++ and Python code for multicomplex arithmetic
Data provided by National Institute of Standards and Technology
The library multicomplex is an implementation of multicomplex algebra in C++ to allow for higher-order derivatives of numerical functions. Many (though not all) mathematical functions are implemented, allowing for calculation of derivatives (straight and mixed) to approximately numerical precision, which is difficult or impossible to achieve in conventional double precision
Tags: mathematics,multicomplex,derivatives,C++,python,
Modified: 2025-04-06
ANDiE: the Autonomous Neutron Diffraction Explorer.
Data provided by National Institute of Standards and Technology
ANDiE the Autonomous Neutron Diffraction Explorer is a tool for autonomously discovering the magnetic transition temperature and transition dynamics of a material from neutron diffraction experiments. The Jupyter notebooks used to implement ANDiE can be found here: https://github.com/usnistgov/ANDiE-v1_0 The Jupyter notebooks contained therein are of ANDiE as implemented at the WAND2 instrument at the HB-2C beamline at the High-Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL).
Tags: Autonomous Experiments,neutron diffraction,machine learning,Active Learning,Artificial Intelligence,
Modified: 2025-04-06