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Displaying 126 - 150 of 444

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

Gauging the difficulty of image segmentation

May 19, 2022

Author(s)

Marek Franaszek

Image segmentation is the first step in a complex process of object recognition. This report presents a method to gauge the difficulty of segmentation by calculating a scalar parameter Q for an image. This parameter depends on a distribution of the

Machine learning enabling high-throughput and remote operations at large-scale user facilities

May 18, 2022

Author(s)

Bruce D. Ravel, Tatiana Konstantinova, Phillip Michael Maffettone, Stuart Campbell, Andi Barbour, Daniel Olds

Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher

Machine Learning-Based Algorithmically Generated Domain Detection

May 1, 2022

Author(s)

Zheng Wang, Yang Guo, Douglas Montgomery

Malware like botnets typically uses domain generation algorithms (DGAs) to dynamically produce a large number of random algorithmically generated domains (AGDs) and use a few of them to communicate with the command and control servers. AGD detection

Use of Supervised Machine Learning to Detect Abuse of COVID-19 Related Domain Names

May 1, 2022

Author(s)

Zheng Wang, Douglas Montgomery

A comprehensive evaluation of supervised machine learning models for the COVID-19 related domain name detection is presented. One representative conventional machine learning implementation and nineteen state-of-the-art deep learning implementations are

Data-Driven RRAM Device Models using Kriging Interpolation

April 8, 2022

Author(s)

Imtiaz Hossen, Mark Anders, Lin Wang, Gina Adam

The future of collaborative human-AI decision-making for mission planning

April 4, 2022

Author(s)

Simon Su

In an increasingly complex military operating environment, next generation wargaming platforms can reduce risk, decrease operating costs, and improve overall outcomes. Novel Artificial Intelligence (AI) enabled wargaming approaches, based on software

Development of a Flashover Prediction Model for Compartment Fires Using Support Vector Regression

April 1, 2022

Author(s)

Wai Cheong Tam, Jun Wang, Richard D. Peacock, Paul A. Reneke, Eugene Yujun Fu, Thomas Cleary

This report provides additional technical details to an article entitled P-Flash – A Machine Learning-based Model for Flashover Prediction using Recovered Temperature Data. Research was conducted to examine the use of Support Vector Regression (SVR) to

Towards a Standard for Identifying and Managing Bias in Artificial Intelligence

March 15, 2022

Author(s)

Reva Schwartz, Apostol Vassilev, Kristen K. Greene, Lori Perine, Andrew Burt, Patrick Hall

As individuals and communities interact in and with an environment that is increasingly virtual they are often vulnerable to the commodification of their digital exhaust. Concepts and behavior that are ambiguous in nature are captured in this environment

Toward Robust Autotuning of Noisy Quantum Dot Devices

February 25, 2022

Author(s)

Joshua Ziegler, Thomas McJunkin, Emily Joseph, Sandesh Kalantre, Benjamin Harpt, Donald Savage, Max Lagally, Mark Eriksson, Jacob Taylor, Justyna Zwolak

The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this

Feasibility of Modeling Orthogonal Frequency-Division Multiplexing Communication Signals with Unsupervised Generative Adversarial Networks

February 14, 2022

Author(s)

Jack Sklar, Adam Wunderlich

High-quality recordings of radio frequency (RF) emissions from commercial communication hardware in realistic environments are often needed to develop and assess spectrum-sharing technologies and practices, e.g., for training and testing spectrum sensing

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

February 4, 2022

Author(s)

Ira Monarch, Jacob Collard, Sangjin Shin, Eswaran Subrahmanian, Talapady N. Bhat, Ram D. 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

Can you tell? SSNet - a Biologically-inspired Neural Network Framework for Sentiment Classifiers

February 2, 2022

Author(s)

Apostol Vassilev, Munawar Hasan, Honglan Jin

When people try to understand nuanced language they typically process multiple input sensor modalities to complete this cognitive task. It turns out the human brain has even a specialized neuron formation, called sagittal stratum, to help us understand

A Convolutional Neural Networks-Based Approach for Texture Directionality Detection

January 12, 2022

Author(s)

Marcin Kociolek, Michal Kozlowski, Antonio Cardone

The perceived texture directionality is an important, not fully explored image characteristic. In many applications texture directionality detection is of fundamental importance. Several approaches have been proposed, such as the fast Fourier-based method