Round 11 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of image classification AIs trained on synthetic image data build from Cityscapes. 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 288 AI models using a small set of model architectures.

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.

This set corresponds to a (pending) publication, where we attempt to model spectral features appearing in the lab by calculating many segments of an inhomogeneous field. Every data set here is a transmission value, either normalized to 1 in modeled data, or an arbitrary-scaled voltage reading from a photodiode onto an oscilloscope. These are given in scans over coupling photon detuning, delta_C, which is divided by 2 pi, and given in MHz. Arrays are scans over delta_C, and position/fieldstrength, for figure 3.

On-resonance Rydberg atom-based radio-frequency (RF) electric field sensing methods remain limited by the narrow frequency signal detection bands available by resonant transitions. The use ofan additional RF tuner field to dress or shift a target Rydberg state can be used to return a detuned signal field to resonance and thus dramatically extend the frequency range available for resonantsensing.

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.

Contains Artificial Intelligence Patent Landscape data classifying 13,244,037 granted patents and PGPubs published from 1976 through 2021 in eight AI component technologies using state-of-the art machine learning based models.

The Bulk Data Storage System (BDSS) API allows the public to discover, search by date, and download patent and trademark data within BDSS in bulk form.

The Bulk Search and Download API allows searching published patent applications (pre-grant publications, pgpubs) and issued patents (patent grants) across various fields from 2001 to present, and to request a custom zip package for the given patent application or patent ids. Download up to 100 document numbers at a time. Wait time can vary based on the number of requests being processed.

This curated dataset consists of 269,353 patent documents (published patent applications and granted patents) spanning the 1976 to 2016 period and is intended to help identify promising R&D on the horizon in diagnostics, therapeutics, data analytics, and model biological systems.