Round 5 Holdout DatasetThis is the holdout 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 text sentiment classification 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 6 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 text sentiment classification 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.

Supporting datasets and algorithms (R-based) for the manuscript entitled "Development of a Tool to Determine the Variability of Consensus Mass Spectra", including an R Markdown script to reproduce the manuscript's figures.

Round 6 Test DatasetThis is the test 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 text sentiment classification 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 6 Train Dataset part2This 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 text sentiment classification 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 6 Holdout DatasetThis is the holdout 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 text sentiment classification 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.

The effectiveness and the failure mechanism of fire barriers in residential upholstered furniture (RUF) were investigated by full-scale flaming tests on single upholstered chair mock-ups. Six fire barriers were tested. The fire barriers were screened for (1) the presence of elements that are typically used in fire retardants and, (2) the presence of targeted fire retardants.

This study looks at fire growth in a furnished compartment intended to mimic a living room where a couch, either with or without a barrier fabric, served as the primary fuel load.

ThermoML is an XML-based IUPAC standard for the storage and exchange of experimental thermophysical and thermochemical property data. The ThermoML archive is a subset of Thermodynamics Research Center (TRC) data holdings corresponding to cooperation between NIST TRC and five journals: Journal of Chemical Engineering and Data (ISSN: 1520-5134), The Journal of Chemical Thermodynamics (ISSN: 1096-3626), Fluid Phase Equilibria (ISSN: 0378-3812), Thermochimica Acta (ISSN: 0040-6031), and International Journal of Thermophysics (ISSN: 1572-9567).

Combustion calorimetry is the main method for the determination of enthalpies of formation for organic compounds. Rigorous application of the method uses a 100-step procedure, sometimes called Washburn corrections, to convert the experimental results into standard thermodynamic quantities. Because every laboratory uses its own in-house software implementing this procedure, which is often not available for verification or testing, it is difficult to fully assess experimental results. Furthermore, these programs often use obsolete reference values of thermodynamic properties.