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Displaying 101 - 125 of 444

Advancing Discovery with Artificial Intelligence and Machine Learning at NSLS-II

September 16, 2022

Author(s)

Bruce D. Ravel

With the National Synchrotron Light Source II (NSLS-II) coming online in 2015 as the brightest source in the world, the imminent upgrades at the Advanced Photon Source, Advanced Light Source, and Linear Coherent Light Source, and advances in detector

Towards Resilient Manufacturing Ecosystems Through Artificial Intelligence - Symposium Report

September 14, 2022

Author(s)

Jim Davis, Stephan Biller, James A. St Pierre, Said Jahanmir

In 2020, the National Science and Technology Council (NSTC) Subcommittee on Advanced Manufacturing and Subcommittee on Machine Learning and Artificial Intelligence articulated cross-agency interest in the value and timeliness of organizing a symposium to

A general approach to fast online training of modern datasets on real neuromorphic systems without backpropagation

September 7, 2022

Author(s)

Sonia Buckley, Adam McCaughan

We present parameter-multiplexed gradient descent (PMGD), a perturbative gradient descent framework designed to easily train emergent neuromorphic hardware platforms. We show its applicability to both analog and digital systems. We demonstrate how to use

Leveraging Theory for Enhanced Machine Learning

August 26, 2022

Author(s)

Debra Audus, Austin McDannald, Brian DeCost

The application of machine learning to the materials domain has traditionally struggled with two major challenges: a lack of large, curated data sets and the need to understand the physics behind the machine-learning prediction. The former problem is

NIST Explainable AI Workshop Summary

August 25, 2022

Author(s)

P. Jonathon Phillips, Carina Hahn, Peter Fontana, Amy Yates, Matthew Smith

This report represents a summary of the National Institute of Standards and Technology (NIST) Explainable Artificial Intelligence (AI) Workshop, which NIST held virtually on January 26-28, 2021.

Face Recognition Vendor Test (FRVT) Part 4A: MORPH - Utility of 1:N Face Recognition Algorithms for Morph Detection

July 29, 2022

Author(s)

Mei Lee Ngan, Patrick J. Grother, Kayee Hanaoka

This report is a part of a series of studies on the topic of face morphing, its relevance and implications as a vulnerability to automated face recognition, and methods to aid in detecting morphs. Expanding on concepts introduced in a study conducted by

Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges

July 19, 2022

Author(s)

Axel Hoffmann, Shriram Ramanathan, Julie Grollier, Andrew Kent, Marcelo Rozenberg, Ivan Schuller, Oleg Shpyrko, Robert Dynes, Yeshaiahu Fainman, Alex Frano, Eric Fullerton, Giulia Galli, Vitaliy Lomakin, Shyue Ping Ong, Amanda K. Petford-Long, Jonathan A. Schuller, Mark Stiles, Yayoi Takamura, Yimei Zhu

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device

Associative memories using complex-valued Hopfield networks based on spin-torque oscillator arrays

July 14, 2022

Author(s)

Nitin Prasad, Prashansa Mukim, Advait Madhavan, Mark Stiles

Simulations of complex valued Hopfield networks based on spin-torque oscillators can recover phase-encoded images. Sequences of memristor-augmented inverters provide tunable delay elements that implement complex weights by phase shifting the oscillatory

Discovery of digital forensic dataset characteristics with CASE-Corpora

July 11, 2022

Author(s)

Alexander Nelson

The digital forensics community has generated training and reference data over the course of decades. However, significant challenges persist today in the usage pipeline for that data, from research problem formulation, through discovery of applicable

Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT based models

July 1, 2022

Author(s)

Sarala Padi, Omid Sadjadi, Dinesh Manocha, Ram Sriram

Automatic emotion recognition plays a key role in computer-human interaction as it has the potential to enrich the next generation artificial intelligence with emotional intelligence. It finds applications in customer and/or representative behavior

Spatiotemporal Monitoring of Melt-Pool Variations in Metal-Based Additive Manufacturing

July 1, 2022

Author(s)

Siqing Zhang, Yan Lu, Paul Witherell, Timothy Simpson, Soundar Kumara, Hui Yang

Additive manufacturing provides a higher level of flexibility to build customized products with complex geometries. However, AM is currently limited in its ability to ensure quality assurance and process repeatability. Advanced imaging provides unique

Interpretable modeling of genotype-phenotype landscapes withstate-of-the-art predictive power

June 21, 2022

Author(s)

Peter Tonner, Abe Pressman, David Ross

Large-scale measurements linking genetic background to biological function have drivena need for models that can incorporate these data for reliable predictions and insightinto the underlying biophysical system. Recent modeling efforts, however, prioritize

Evaluating Multiple Video Understanding and Retrieval Tasks at TRECVID 2021

June 15, 2022

Author(s)

George Awad, Asad Butt, Keith Curtis, Jonathan G. Fiscus, Afzal A. Godil, Yooyoung Lee, Andrew Delgado, Eliot Godard, Baptiste Chocot, Lukas Diduch, Jeffrey Liu, Yvette Graham, Gareth Jones, Georges Quenot

ARL Battlespace: A Platform for Developing Novel AI for Complex Adversarial Reasoning in MDO

June 6, 2022

Author(s)

Simon Su

A barrier to developing novel AI for complex reasoning is the lack of appropriate wargaming platforms for training and evaluating AIs in a multiplayer setting combining collaborative and adversarial reasoning under uncertainty with game theory and

Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons

May 31, 2022

Author(s)

Shangjie Guo, Sophia Koh, Amilson R. Fritsch, Ian Spielman, Justyna Zwolak

In ultracold-atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is particularly problematic when the processes of interest are complicated, such as