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NIST Authors in Bold

Displaying 1 - 25 of 217

An Adaptable AI Assistant for Network Management

April 12, 2024
Amar Abane, Abdella Battou, Mheni Merzouki
This paper presents a network management AI assistant built with Large Language Models. It adapts at runtime to the network state and specific platform, leveraging techniques like prompt engineering, document retrieval, and Knowledge Graph integration. The

AI Use Taxonomy: A Human-Centered Approach

March 26, 2024
Mary Frances Theofanos, Yee-Yin Choong, Theodore Jensen
As artificial intelligence (AI) systems continue to be developed, humans will increasingly participate in human-AI interactions. Humans interact with AI systems to achieve particular goals. To ensure that AI systems contribute positively to human-AI

Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations

January 4, 2024
Apostol Vassilev, Alina Oprea, Alie Fordyce, Hyrum Andersen
This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key types of ML

Facilitating Stakeholder Communication around AI-Enabled Systems and Business Processes

November 21, 2023
Edward Griffor, Matthew Bundas, Chasity Nadeau, Jeannine Shantz, Thanh Nguyen, Marcello Balduccini, Tran Son
Artificial Intelligence (AI) is often critical to the success of modern business processes. Leveraging it, however, is non-trivial. A major hurdle is communication: discussing system requirements among stakeholders with different backgrounds and goals

Labeling Software Security Vulnerabilities

October 1, 2023
Irena Bojanova, John Guerrerio
Labeling software security vulnerabilities would benefit greatly modern artificial intelligence cybersecurity research. The National Vulnerability Database (NVD) partially achieves this via assignment of Common Weakness Enumeration (CWE) entries to Common

Neural networks three ways: unlocking novel computing schemes using magnetic tunnel junction stochasticity

September 28, 2023
Matthew Daniels, William Borders, Nitin Prasad, Advait Madhavan, Sidra Gibeault, Temitayo Adeyeye, Liam Pocher, Lei Wan, Michael Tran, Jordan Katine, Daniel Lathrop, Brian Hoskins, Tiffany Santos, Patrick Braganca, Mark Stiles, Jabez J. McClelland
Due to their interesting physical properties, myriad operational regimes, small size, and industrial fabrication maturity, magnetic tunnel junctions are uniquely suited for unlocking novel computing schemes for in-hardware neuromorphic computing. In this

Tuning Arrays with Rays: Physics-Informed Tuning of Quantum Dot Charge States

September 28, 2023
Joshua Ziegler, Florian Luthi, Mick Ramsey, Felix Borjans, Guoji Zheng, Justyna Zwolak
Quantum computers based on gate-defined quantum dots (QDs) are expected to scale. However, as the number of qubits increases, the burden of manually calibrating these systems becomes unreasonable and autonomous tuning must be used. There has been a range

Comparison of Ice-on-Coil Thermal Energy Storage Models

September 13, 2023
Kalyan Ram Kanagala, Amanda Pertzborn
Data collected from the Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST) are used to develop a physics-based and four machine learning models of ice-on-coil thermal energy storage (TES): linear


July 14, 2023
Milos Drobnjakovic, Boonserm Kulvatunyou, Simon P. Frechette, Vijay Srinivasan
ISO and IEC have jointly initiated, and recently issued, a series of standards (the ISO/IEC 21838 series) for top-level ontologies. These standards have been used by industrial consortia to develop and disseminate standards for mid-level ontologies to ease

O-RAN with Machine Learning in ns-3

June 28, 2023
Wesley Garey, Richard A. Rouil, Evan Black, Tanguy Ropitault, Weichao Gao
The Open Radio Access Network (O-RAN) Alliance is the industry led standardization effort, with the sole purpose of evolving the Radio Access Network (RAN) to be more open, intelligent, interoperable, and autonomous to support the ever growing need of

2022 Cybersecurity & Privacy Annual Report

May 30, 2023
Patrick D. O'Reilly, Kristina Rigopoulos, Larry Feldman, Greg Witte
During Fiscal Year 2022 (FY 2022) – from October 1, 2021, through September 30, 2022 –the NIST Information Technology Laboratory (ITL) Cybersecurity and Privacy Program successfully responded to numerous challenges and opportunities in security and privacy

Automated extraction of capacitive coupling for quantum dot systems

May 24, 2023
Joshua Ziegler, Florian Luthi, Mick Ramsey, Felix Borjans, Guoji Zheng, Justyna Zwolak
Gate-defined quantum dots (QDs) have appealing attributes as a quantum computing platform. However, near-term devices possess a range of possible imperfections that need to be accounted for during the tuning and operation of QD devices. One such problem is

Low-Rank Gradient Descent for Memory-Efficient Training of Deep In-Memory Arrays

May 18, 2023
Siyuan Huang, Brian Hoskins, Matthew Daniels, Mark Stiles, Gina C. Adam
The movement of large quantities of data during the training of a Deep Neural Network presents immense challenges for machine learning workloads. To minimize this overhead, espe- cially on the movement and calculation of gradient information, we introduce

AI for Materials

April 25, 2023
Debra Audus, Kamal Choudhary, Brian DeCost, A. Gilad Kusne, Francesca Tavazza, James A. Warren
The application of artificial intelligence (AI) methods to materials re- search and development (MR&D) is poised to radically reshape how materials are discovered, designed, and deployed into manufactured products. Materials underpin modern life, and

Real-Time Flashover Prediction Model for Multi-Compartment Building Structures Using Attention Based Recurrent Neural Networks

March 17, 2023
Wai Cheong Tam, Eugene Yujun Fu, Jiajia Li, Richard D. Peacock, Paul A. Reneke, Thomas Cleary, Grace Ngai, Hong Va Leong, Michael Xuelin Huang
This paper presents the development of an attention based bi-directional gated recurrent unit model, P-Flashv2, for the prediction of potential occurrence of flashover in a traditional 111 m2 single story ranch-style family home. Synthetic temperature data

A Review of Machine Learning Control in Building Operations

March 14, 2023
Liang Zhang, Zhelun Chen, Xiangyu Zhang, Amanda Pertzborn
Machine learning control (MLC) is a highly flexible and adaptable method that enables the design, modeling, tuning, and maintenance of building controllers to be more accurate, automated, flexible, and adaptable. The research topic of MLC in building

Colloquium: Advances in automation of quantum dot devices control

February 17, 2023
Justyna Zwolak, Jacob Taylor
Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual

Self-driving Multimodal Studies at User Facilities

January 22, 2023
Bruce D. Ravel, Phillip Michael Maffettone, Daniel Allan, Stuart Campbell, Matthew Carbone, Brian DeCost, Howie Joress, Dmitri Gavrilov, Marcus Hanwell, Joshua Lynch, Stuart Wilkins, Jakub Wlodek, Daniel Olds
Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days to months. In this paper, we describe a unified multimodal measurement of a
Displaying 1 - 25 of 217