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Artificial intelligence

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Displaying 201 - 225 of 267

MSEC: A QUANTITATIVE RETROSPECTIVE

June 25, 2020
Author(s)
Rachael Sexton, Michael Brundage, Alden A. Dima, Michael Sharp
The Manufacturing Science and Engineering Conference (MSEC) in 2020 is the 15th annual conference put on by the Manufacturing Engineering Division (MED) of ASME. MED and ASME MSEC focuses on manufacturing sciences, technology, and applications, including

NIST Pilot Too Close for Too Long (TC4TL) Challenge Evaluation Plan

June 18, 2020
Author(s)
Seyed Omid Sadjadi, Craig S. Greenberg, Douglas A. Reynolds
One of the keys to managing the current (and future) epidemic is notifying people of possible virus exposure so they can isolate and seek treatment to limit further spread of the disease. While manual contact tracing is effective for notifying those who

{A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al

June 4, 2020
Author(s)
Howard L. Joress, Brian L. DeCost, Suchismita Sarker, Trevor M. Braun, Logan T. Ward, Kevin Laws, Apurva Mehta, Jason R. Hattrick-Simpers
Based on a set of machine learning predictions of glass formation in the Ni-Ti-Al system, we have undertaken a high-throughput experimental study of that system. We utilized rapid synthesis followed by high- throughput structural and electrochemical

Learning to predict crystal plasticity at the nanoscale: Deep residual networks and size effects in uniaxial compression discrete dislocation simulations

May 19, 2020
Author(s)
Zijiang Yang, Stefanos Papanikolaou, Andrew C. Reid, Wei-keng Lao, Alok Choudhary, Carelyn E. Campbell, Ankit Agrawal
The increase of dislocation density in a metallic crystal undergoing plastic deformation influences the mechanical properties of the material. This effect can be used to examine the related inverse problem of deducing the prior deformation of a material

The 2019 NIST Audio-Visual Speaker Recognition Evaluation

May 18, 2020
Author(s)
Seyed Omid Sadjadi, Craig S. Greenberg, Elliot Singer, Douglas A. Reynolds, Lisa Mason, Jaime Hernandez-Cordero
In 2019, the U.S. National Institute of Standards and Technology (NIST) conducted the most recent in an ongoing series of speaker recognition evaluations (SRE). There were two components to SRE19: 1) a leaderboard style Challenge using unexposed

The 2019 NIST Speaker Recognition Evaluation CTS Challenge

May 18, 2020
Author(s)
Seyed Omid Sadjadi, Craig S. Greenberg, Elliot Singer, Douglas Reynolds, Lisa Mason, Jaime Hernandez-Cordero
In 2019, the U.S. National Institute of Standards and Technology (NIST) conducted a leaderboard style speaker recognition challenge using conversational telephone speech (CTS) data extracted from the unexposed portion of the Call My Net 2 (CMN2) corpus

Approaches to Training Multi-Class Semantic Image Segmentation of Damage in Concrete

May 14, 2020
Author(s)
Peter Bajcsy, Steven B. Feldman, Michael P. Majurski, Kenneth A. Snyder, Mary C. Brady
This paper addresses the problem of creating a large quantity of high-quality training image segmentation masks from scanning electron microscopy (SEM) images of concrete samples that exhibit progressive amounts of degradation resulting from alkali-silica

Prevention of Cooktop Ignition Using Detection and Multi-Step Machine Learning Algorithms

May 8, 2020
Author(s)
Wai Cheong Tam, Eugene Yujun Fu, Amy E. Mensch, Anthony P. Hamins, Christina Yu, Grace Ngai, Hong va Leong
This paper presents a study to examine the potential use of machine learning models to build a real-time detection algorithm for prevention of kitchen cooktop fires. Sixteen sets of time- dependent sensor signals were obtained from 60 normal/ignition

Streaming Batch Gradient Tracking for Neural Network Training

April 3, 2020
Author(s)
Siyuan Huang, Brian D. Hoskins, Matthew W. Daniels, Mark D. Stiles, Gina C. Adam
Faster and more energy efficient hardware accelerators are critical for machine learning on very large datasets. The energy cost of performing vector-matrix multiplication and repeatedly moving neural network models in and out of memory motivates a search

Auto-tuning of double dot devices it in situ with machine learning

March 31, 2020
Author(s)
Justyna Zwolak, Thomas McJunkin, Sandesh Kalantre, J. P. Dodson, Evan MacQuarrie, D. E. Savage, M. G. Lagally, S N. Coppersmith, Mark A. Eriksson, Jacob Taylor
The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time- consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the \it in situ} implementation of a

Summary: Workshop on Machine Learning for Optical Communication Systems

March 26, 2020
Author(s)
Joshua A. Gordon, Abdella Battou, Michael P. Majurski, Dan Kilper, Uiara Celine, Massimo Tonatore, Joao Pedro, Jesse Simsarian, Jim Westdorp, Darko Zibar
Optical communication systems are expected to find use in new applications that require more intelligent and automated functionality. Optical networks are needed to address the high speeds and low latency of 5G wireless networks. The analog nature of

Workshop on Machine Learning for Optical Communication Systems: a summary

March 8, 2020
Author(s)
Joshua A. Gordon, Abdella Battou, Dan Kilper
A summary and overview of a public workshop on machine learning for optical Communication systems held on August 2nd 2019, by the Communications Technology Laboratory at the National Institute of Standards and Technology in Boulder, CO.

Energy-efficient stochastic computing with superparamagnetic tunnel junctions

March 5, 2020
Author(s)
Matthew W. Daniels, Advait Madhavan, Philippe Talatchian, Alice Mizrahi, Mark D. Stiles
Stochastic computing has been limited by the inaccuracies introduced by correlations between the pseudorandom bitstreams used in the calculation. We hybridize a stochastic version of magnetic tunnel junctions with basic CMOS logic gates to create a

Ray-based classification framework for high-dimensional data

February 3, 2020
Author(s)
Justyna Zwolak, Jacob Taylor, Sandesh Kalantre, Thomas McJunkin, Brian Weber
While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative information about the boundaries defining the structures can suffice. Rather than
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