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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: 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
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: 2024-02-22
Views: 0
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: 2024-02-22
Views: 0
Enriched Citation API (Version 1)
Data provided by United States Patent and Trademark Office
The Enriched Citation API provides the Intellectual Property 5 (IP5 - EPO, JPO, KIPO, CNIPA, and USPTO) and the Public with greater insight into the patent evaluation process. It allows users to quickly view information about which references, or prior art, were cited in specific patent application Office Actions, including: bibliographic information of the reference, the claims that the prior art was cited against, and the relevant sections that the examiner relied upon.
Tags: uspto,ip5,enriched,citation,prior art,office action,claims,machine learning,ai,api,statutes,references,
Modified: 2024-02-22
Views: 0
Enriched Citation API (Version 2)
Data provided by United States Patent and Trademark Office
The Enriched Citation API provides the Intellectual Property 5 (IP5 - EPO, JPO, KIPO, CNIPA, and USPTO) and the Public with greater insight into the patent evaluation process. It allows users to quickly view information about which references, or prior art, were cited in specific patent application Office Actions, including: bibliographic information of the reference, the claims that the prior art was cited against, and the relevant sections that the examiner relied upon.
Tags: uspto,ip5,enriched,citation,prior art,office action,claims,machine learning,ai,api,statutes,references,
Modified: 2024-02-22
Views: 0
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: 2024-02-22
Views: 0
Trojan Detection Software Challenge - nlp-question-answering-sep2021-test
Data provided by National Institute of Standards and Technology
Round 8 Test 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 extractive question answering. 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. This dataset consists of 360 QA AI models using a small set of model architectures.
Tags: Trojan Detection,Artificial Intelligence,ai,machine learning,Adversarial Machine Learning,
Modified: 2024-02-22
Views: 0
Trojan Detection Software Challenge - nlp-question-answering-sep2021-holdout
Data provided by National Institute of Standards and Technology
Round 8 Holdout 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 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: 2024-02-22
Views: 0
Data Supporting "Seabird Tissue Archival and Monitoring Project (STAMP) Data from 1999-2010"
Data provided by National Institute of Standards and Technology
Here we provide curated analytical chemistry data for eggs collected from 1999 to 2010 on a subset of species and analytes that were measured regularly and reasonably systematically. Included in this publication are 487 samples analyzed for 174 ubiquitous environmental contaminants such as (poly)brominated diphenyl ethers (BDEs), mercury, organochlorine pesticides (OCPs), and polychlorinated biphenyls (PCBs). Data were collated to form a dataset useful in chemometric and related analyses of the marine ecosystem in the north Pacific Ocean.
Tags: NIST Biorepository,eggs,tissues,BDEs,PBDEs,PCBs,Pesticides,mercury,trace elements,heavy metals,organic,inorganic,chemistry,stable isotopes,genetics,Environment and Climate,chemometric,machine learning,ML,seabird,bird,Pacific Ocean,
Modified: 2024-02-22
Views: 0