This template presents a structured methodology for describing and assessing automated vehicle (AV) features, with a focus on ensuring comprehensive standardization and reproducibility in testing across various scenarios. The document delineates a systematic approach to integrating operational design domains (ODD), levels of automation, and behaviors into AV feature descriptions.

The Tip-of-the-Tongue (ToT) Track focuses on the known-item identification task where the searcher has previously experienced or consumed the item (e.g., a movie) but cannot recall a reliable identifier (i.e., It's on the tip of my tongue...). Unlike traditional ad-hoc keyword-based search, these information requests tend to be natural-language, verbose, and complex containing a wide variety of search strategies such as multi-hop reasoning, and frequently express uncertainty and suffer from false memories.

This dataset contains data used in the investigation of Universally Unique Identifiers (UUIDs) that will enable a standards-based digital thread of product data in ISO 10303-242. Included are EXPRESS schema used for implementation and .stp files that were exported from native CAD (CATIA V5, Creo, and NX). UUIDs were assigned to CAD features during .stp export for each of the four design iterations.

The Authoring Tools for Multimedia Content (AToMiC) Track aims to build reliable benchmarks for multimedia search systems. The focus of this track is to develop and evaluate IR techniques for text-to-image and image-to-text search problems.

The CrisisFACTS track focuses on temporal summarization for first responders in emergency situations. These summaries differ from traditional summarization in that they order information by time and produce a series of short updates instead of a longer narrative.

The Deep Learning track focuses on IR tasks where a large training set is available, allowing us to compare a variety of retrieval approaches including deep neural networks and strong non-neural approaches, to see what works best in a large-data regime.

This dataset hosts the code required to replicate the findings discussed in the research paper entitled: "A Simulation Study of mmWave 5G-enabled Medical Extended Reality (MXR)" by Tanguy Ropitault, Yongkang Liu, Richard Rouil, and Mohamad Omar Al Kalaa. Our study explores a 5G Medical Extended Reality (MXR) setup, in which a surgeon uses a headset to perform surgeries, leveraging the capabilities of MXR technology.

Datasets for the four published figures in the Paper "AC Metrology Applications of the Josephson Effect ".
This paper is being submitted to Applied Physics Letters Special Topic, "Advances in Quantum Metrology,"
https://publishing.aip.org/publications/journals/special-topics/apl/adv… .

Metadata for all final NIST Technical Series publications available online, created and maintained by the NIST Research Library.

Dataset for publishable figures in a "On-Chip Frequency Conversion Using Kinetic Inductance Within a Coplanar Waveguide" manuscript to be submitted to IEEE Transactions on Applied Superconductivity. This work presents a method of frequency conversion which exploits the kinetic inductance of a superconducting coplanar waveguide. Both frequency doubling and tripling are examined, with attention paid to conversion efficiency. This approach allows up-conversion to be implemented in a cryogenic environment, which can simplify the design of cryogenic systems.