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Active Evaluation Software for Selection of Ground Truth Labels
Data provided by National Institute of Standards and Technology
This software repository contains a python package Aegis (Active Evaluator Germane Interactive Selector) package that allows us to evaluate machine learning systems's performance (according to a metric such as accuracy) by adaptively sampling trials to label from an unlabeled test set to minimize the number of labels needed. This includes sample (public) data as well as a simulation script that tests different label-selecting strategies on already labelled test sets. This software is configured so that users can add their own data and system outputs to test evaluation.
Tags: active evaluation,machine learning,ar,
Modified: 2024-02-22
Views: 0
Simulated Radar Waveform and RF Dataset Generator for Incumbent Signals in the 3.5 GHz CBRS Band
Data provided by National Institute of Standards and Technology
This software tool generates simulated radar signals and creates RF datasets. The datasets can be used to develop and test detection algorithms by utilizing machine learning/deep learning techniques for the 3.5 GHz Citizens Broadband Radio Service (CBRS) or similar bands. In these bands, the primary users of the band are federal incumbent radar systems. The software tool generates radar waveforms and randomizes the radar waveform parameters.
Tags: 3.5 GHz,CBRS,LTE,ESC,radar,radio frequency signals,spectrum,machine learning,deep learning,detection,
Modified: 2024-02-22
Views: 0
Optical scattering measurements and simulation data for one-dimensional (1-D) patterned periodic sub-wavelength features
Data provided by National Institute of Standards and Technology
This data set consists of both measured and simulated optical intensities scattered off periodic line arrays, with simulations based upon an average geometric model for these lines. These data were generated in order to determine the average feature sizes based on optical scattering, which is an inverse problem for which solutions to the forward problem are calculated using electromagnetic simulations after a parameterization of the feature geometry.
Tags: electromagnetic simulations,simulations,experimental,angle-resolved scattering,scattering,gratings,patterned semiconductors,semiconductors,scatterfield microscopy,bright-field microscopy,microscopy,inverse problems,machine learning,
Modified: 2024-02-22
Views: 0
Baseline Tailor
Data provided by National Institute of Standards and Technology
Baseline Tailor is a software tool for using the United States government's Cybersecurity Framework and for tailoring the NIST Special Publication (SP) 800-53 security controls. Baseline Tailor generates output in an Extensible Markup Language (XML) format capturing a user's Framework Profile and tailoring choices.
Tags: cybersecurity framework,security control,framework profile,manufacturing profile,tailored baseline,industrial control system,xml,
Modified: 2024-02-22
Views: 0
QFlow 2.0: Quantum dot data for machine learning
Data provided by National Institute of Standards and Technology
Using a modified Thomas-Fermi approximation, we model a reference semiconductor system comprising a quasi-1D nanowire with a series of five depletion gates whose voltages determine the number of quantum dots (QDs), the charges on each of the QDs, as well as the conductance through the wire. The original dataset, QFlow lite, consists of 1 001 idealized simulated measurements with gate configurations sampling over different realizations of the same type of device.
Tags: machine learning,quantum dots,simulated data,
Modified: 2024-02-22
Views: 0
Simple Knowledge Organization System (SKOS) version of Materials Data Vocabulary
Data provided by National Institute of Standards and Technology
A version of the Materials Data Vocabulary structured as Simple Knowledge Organization System (SKOS). The XML was originally created by the TemaTres software. This vocabulary describes the applicability to material science of records in the NIST Materials Resource Registry (NMRR - https://materials.registry.nist.gov/). The NMRR allows for the registration of materials resources, bridging the gap between existing resources and the end users.
Tags: materials science,controlled vocabulary,xml,vocabularies,
Modified: 2024-02-22
Views: 0
Nestor: a toolkit for quantifying tacit maintenance knowledge, for investigatory analysis in smart manufacturing
Data provided by National Institute of Standards and Technology
There is often a large amount of maintenance data already available for use in Smart Manufacturing systems, but in a currently-unusable form: service tickets and maintenance work orders (MWOs). Nestor is a toolkit for using Natural Language Processing (NLP) with efficient user-interaction to perform structured data extraction with minimal annotation time-cost.
Tags: information,communication,maintenance,tribal knowledge,event sequences,training,machine learning,data cleaning,prognostics,diagnostics,visualization,decision guidance,CMMS,scheduling,investigations,nestor,smart manufacturing,manufacturing operations,manufacturing performance,
Modified: 2024-02-22
Views: 0
REMI: Resource for Materials Informatics
Data provided by National Institute of Standards and Technology
The REsource for Materials Informatics (REMI) will host a diverse collection of scripting notebooks (Jupyter, Matlab LiveScripts, etc.) for collecting, pre-processing, analyzing, and visualizing materials data. Notebooks are curated using tags aligned to Materials Science and Data Science topics. REMI emerged from the realization that both experts and novices wanted examples of using machine learning for science. Meanwhile, lots of experts are developing digital notebooks (e.g. Jupyter) to demonstrate step-by-step data collection, pre-processing, analysis and visualization.
Tags: machine learning,data analysis,data processing,materials science,materials genome initiative,
Modified: 2024-02-22
Views: 0
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: 2024-02-22
Views: 0