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.

This tool is a Python client that can interact with the NIST Entropy Source Validation Test System.

Sim-PROCESD 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. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices/components that can be configured by the user to model various real-world manufacturing systems.

Python scripts and Python+Qt graphical user interface for calculating Feldman-Cousins confidence intervals for low-count Poisson processes in the presence of a known background and for Gaussian processes with a physical lower limit of 0.

The purpose of this software and data is to enable reproduction and facilitate extension of the computational results associated with the following referenceDeJaco, R. F.; Majikes, J. M.; Liddle, J. A.; Kearsley, A. J. Binding, Brightness, or Noise? Extracting Temperature-dependent Properties of Dye Bound to DNA. Biophysical Journal, 2023, https://doi.org/10.1016/j.bpj.2023.03.002.The software and data can also be found at https://github.com/usnistgov/dye_dna_plates.

Round 9 Test DatasetThis is the test data used to 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.

Round 9 Holdout DatasetThis is the holdout data used to 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.

Round 10 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of object detection AIs trained on the COCO dataset. 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 144 AI models using a small set of model architectures.

A python package to analyze ``lnPi`` data from Transition Matrix Monte Carlo(TMMC) simulation. The main output from TMMC simulations, ``lnPi``, provides a means to calculate a host of thermodynamicproperties. Moreover, if ``lnPi`` is calculated at a specific chemical potential, it can be reweighted to providethermodynamic information at a different chemical potential. The python package``tmmc-lnpy`` provides a wide array of routines to analyze ``lnPi`` data.

Polarized Resonant Soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool to measure structure in complex, chemically heterogeneous systems. P-RSoXS combines principles of X-ray scattering and X-ray spectroscopy; this combination provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials.