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Training in machine learning necessarily involves more operations than inference only, with higher precision, more memory, and added computational complexity. In hardware, many implementations side-step this issue by designing "inference-only" hardware
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
Bruce D. Ravel, Phillip Michael Maffettone, Daniel Allan, Andi Barbour, Thomas Caswell, Dmitri Gavrilov, Marcus Hanwell, Thomas Morris, Daniel Olds, Maksim Rakitin, Stuart Campbell
Simon Taylor, Charles Macal, Andrea Matta, Markus Rabe, Susan Sanchez, Guodong Shao
Simulations are used to investigate physical systems. A digital twin goes beyond this by connecting a simulation with the physical system with the purpose of analyzing and controlling that system in real-time. In the past five years, there has been a
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
Yooyoung Lee, Jonathan Fiscus, Lukas Diduch, Jeffery Byrne
This document describes an evaluation of the 2022 Open Fine-grained Activity Detection (OpenFAD) challenge. The evaluation plan covers resources, task definitions, task conditions, file formats for system inputs and outputs, evaluation metrics, scoring
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
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
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
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
Superconducting Optoelectronic Networks (SOENs) combine pho- tonics and superconductors to instantiate computing systems that approach the fundamental limits of information processing in terms of speed and scalability. Overcoming the engineering challenges
We present the development of a bias compensating reinforcement learning (RL) algorithm that optimizes thermal comfort (by minimizing tracking error) and control utilization (by penalizing setpoint deviations) in a multi-zone heating, ventilation, and air
ryan muddiman, Kevin O' Dwyer, Charles Camp, Bryan Hennelly
Broadband coherent anti-Stokes Raman scattering (BCARS) is capable of producing high-quality Raman spectra spanning broad bandwidths, 400–4000 cm−1, with millisecond acquisition times. Raw BCARS spectra, however, are a coherent combination of vibrationally
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
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
Adam McCaughan, Bakhrom Oripov, Natesh Ganesh, Sae Woo Nam, Andrew Dienstfrey, Sonia Buckley
We show that model-free perturbative methods can be used to efficiently train modern neural network architectures in a way that can be directly applied to emerging neuromorphic hardware. These methods were investigated for training VLSI neural networks
In this paper, we present a ray tracing (RT) assisted multipath cluster association method. This work is based on an indoor channel measurement at 60 GHz, where a light detection and ranging (LiDAR) sensor was co-located with channel sounder and time
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
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
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
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
Hyunwoong Ko, Zhuo Yang, Yande Ndiaye, Paul Witherell, Yan Lu
Data analytics with Machine Learning (ML) and Artificial Intelligence (AI) offers high potential to continuously transform AM data to newfound knowledge of Process-Structure-Property (PSP) relationships. In AM, however, realizing the potential is still
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
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
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