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Artificial intelligence

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Displaying 201 - 225 of 273

Overview of the TREC 2019 Deep Learning Track

July 27, 2020
Author(s)
Ellen Voorhees, Nick Craswell, Bhaskar Mitra, Daniel Campos, Emine Yilmaz
The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime. It is the first track with large human-labeled training sets, introducing two sets corresponding to two tasks, each with rigorous TREC

Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm

July 14, 2020
Author(s)
Brian L. DeCost, Jason R. Hattrick-Simpers, Zachary T. Trautt, Aaron G. Kusne, Martin L. Green, Eva Campo
Recent years have seen an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific

TREC-COVID: Rationale and Structure of an Information Retrieval Shared Task for COVID-19

July 8, 2020
Author(s)
Ellen Voorhees, Ian Soboroff, Tasmeer Alam, Kirk Roberts, William Hersh, Dina Demner-Fushman, Steven Bedrick, Kyle Lo, Lucy L. Wang
TREC-COVID is an information retrieval (IR) shared task initiated to support clinicians and clinical research during the COVID-19 pandemic. IR for pandemics breaks many normal assumptions, which can be seen by examining nine important basic IR research

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

July 2, 2020
Author(s)
Werickson Fortunato de Carvalho Rocha, Charles Prado, Niksa Blonder
Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such

Detection of Dense, Overlapping, Geometric Objects

July 1, 2020
Author(s)
Adele P. Peskin, Boris Wilthan, Michael P. Majurski
Using a unique data collection, we are able to study the detection of dense geometric objects in image data where object density, clarity, and size vary. The data is a large set of black and white images of scatterplots, taken from journals reporting

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
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