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Artificial intelligence

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Displaying 76 - 100 of 273

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

May 18, 2023
Author(s)
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
Author(s)
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
Author(s)
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
Author(s)
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
Author(s)
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

Discovery of digital forensic dataset characteristics with CASE-Corpora

February 13, 2023
Author(s)
Alexander Nelson, Eoghan Casey
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

Self-driving Multimodal Studies at User Facilities

January 22, 2023
Author(s)
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

Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research

December 21, 2022
Author(s)
Amilson R. Fritsch, Shangjie Guo, Sophia Koh, Ian Spielman, Justyna Zwolak
We establish a dataset of over 1.6 x 10^4 experimental images of Bose–Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About 33 % of this dataset has manually assigned and carefully

Making Semantic Structures Explicit: Developing and Evaluating Tools and Techniques to Support Understanding of Large Cybersecurity Corpora

December 1, 2022
Author(s)
Ira Monarch, Jacob Collard, Sangjin Shin, Eswaran Subrahmanian, Talapady N. Bhat, Ram Sriram
This report describes the adaptation, composition and use of natural language processing, machine learning and other computational tools to help make implicit informational structures in very large technical corpora explicit. The tools applied to the

Efficient Parameter Exploration of Simulation Studies

November 18, 2022
Author(s)
Megan Olsen, M S Raunak
Simulation is a useful and effective way to analyze and study complex, real-world systems. It allows researchers, practitioners, and decision makers to make sense of the inner working of a system that involves many factors often resulting in some sort of

GLFF: Global and Local Feature Fusion for Face Forgery Detection

November 16, 2022
Author(s)

Haiying Guan, Yan Ju, Shan Jia, Jialing Cai, Siwei Lyu

With the rapid development of the deep generative models (such as Generative Adversarial Networks and Auto-encoders), AI-synthesized images of human face are now of such high qualities that humans can hardly distinguish them from pristine ones. Although

Characterization of AI Model Configurations For Model Reuse

October 24, 2022
Author(s)
Peter Bajcsy, Daniel Gao, Michael Paul Majurski, Thomas Cleveland, Manuel Carrasco, Michael Buschmann, Walid Keyrouz
With the widespread creation of artificial intelligence (AI) models in biosciences, bio-medical researchers are reusing trained AI models from other applications. This work is motivated by the need to characterize trained AI models for reuse based on

Glycosylation and the global virome

October 10, 2022
Author(s)
Cassandra Pegg, Benjamin Schulz, Ben Neely, Gregory Albery, Colin Carlson
The sugars that coat the outsides of viruses and host cells are key to successful disease transmission, but they remain understudied compared to other molecular features. Understanding the comparative zoology of glycosylation - and harnessing it for

Towards Generative Adversarial Network on Industrial Internet of Things

October 1, 2022
Author(s)
Cheng Qian, Wei Yu, Chao Lu, David W. Griffith, Nada T. Golmie
Machine learning, as a viable way of conducting data analytics, has been successfully applied to a number of areas. Nonetheless, the lack of sufficient data is one critical issue for applying machine learning in Industrial Internet of Things (IIoT) systems

The Industrial Ontologies Foundry (IOF) Core Ontology

September 19, 2022
Author(s)
Boonserm Kulvatunyou, Milos Drobnjakovic, Farhad Ameri, Chris Will, Barry Smith
The Industrial Ontologies Foundry (IOF) has been formed to create a suite of interoperable ontologies that would serve as a foundation for data and information interoperability in all areas of manufacturing. To ensure that the ontologies are developed in a

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
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