Synthetic training and test datasets for experiments on deep generative modeling of noise time series. Consists of data for the following noise types: 1) band-limited thermal noise, i.e., bandpass filtered white Gaussian noise, 2) power law noise, including fractional Gaussian noise (FGN), fractional Brownian motion (FBM), and fractionally differenced white noise (FDWN), 3) generalized shot noise, 4) impulsive noise, including Bernoulli-Gaussian (BG) and symmetric alpha stable (SAS) distributions.

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.

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.

These images, videos, and tables show experimental data, where single lines of viscoelastic inks were extruded into moving viscoelastic support baths. Lines were printed at varying angles relative to the camera, such that videos and images captured the side of horizontal lines, cross-sections of horizontal lines, and the side of vertical lines. Metadata including pressure graphs, programmed speeds, toolpaths, and rheology data are also included.

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

One additively manufactured (AM) laser powder bed fusion (PBF-L) Inconel 625 mesoscale tensile specimen (gauge dimensions approximately 0.2mm x 0.2 mm x 1mm) was extracted from build AMB2022-CBM-B1 specimen TH1 and tested at room temperature using a quasistatic strain rate of 0.001/s to failure.  Microstructure was measured using x-ray computed tomography (XRCT) and scanning electron microscopy (SEM) techniques on the specimen gauge section or adjacent material.  Large-area electron backscatter diffraction was used to measure crystallographic texture and grain size/morphology of the entire

Additively manufactured (AM) laser powder bed fusion (PBF-L) Inconel 625 blocks were built with two different scan strategies: XY and X-only.  96 tensile specimens were extracted from blocks at different tensile axis orientations with respect to the build direction to yield the following conditions: XY scan strategy (0, 30, 45, 60, and 90 degree orientation w.r.t. build direction) and X-only scan strategy (0, 60, 90 degree orientation w.r.t.