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)

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.

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.

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.

A linear axis testbed at the Center for Intelligent Maintenance Systems (IMS Center) at the University of Cincinnati was run to failure (the detection of backlash) over one year with periodic data collected from an inertial measurement unit (IMU) on the carriage, two triaxial accelerometers on the ball nut, and the controller.

Source code associated with the Inverted Library Search Algorithm (ILSA) for identifying mixture components using is-CID mass spectra. This source code is frozen to accompany the manuscript titled "Updates to the Inverted Library Search Algorithm for mixture analysis" by Moorthy et. al.

This project produces synthetic datasets of spectrum sharing simulation results (I/Q data, metadata, and KPIs).

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.

SolDet is an object-oriented package for solitonic feature detection in absorption images of Bose-Einstein condensate. with wider use for cold atom image analysis. Featured with classifier, object detector, and Mexican hat metric methods. Technical details are explained in https://arxiv.org/abs/2111.04881.

This tool uses techniques from ASTM E3200 for evaluating manufacturing investments from the perspective of environmentally sustainable manufacturing by pairing economic methods of investment analysis with environmental aspect of manufacturing. The economic techniques used include net present value, internal rate of return, payback period, and hurdle rate. These four techniques are deterministic, meaning that they deal with known values that are certain. The tool also conducts a sensitivity analysis using Monte Carlo techniques.