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Search Publications

NIST Authors in Bold

Displaying 51 - 75 of 219

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

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

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

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

Gradient Decomposition Methods for Training Neural Networks with Non-Ideal Synaptic Devices

November 22, 2021
Author(s)
Junyun Zhao, Siyuan Huang, Osama Yousuf, Yutong Gao, Brian Hoskins, Gina Adam
While promising for high capacity machine learning accelerators, memristor devices have non-idealities that prevent software-equivalent accuracies when used for online training. This work uses a combination of Mini-Batch Gradient Descent (MBGD) to average

HYBRID MODELING OF MELT POOL GEOMETRY IN ADDITIVE MANUFACTURING USING NEURAL NETWORKS

November 17, 2021
Author(s)
Kevontrez Jones, Zhuo Yang, Ho Yeung, Paul Witherell, Yan Lu
Laser powder-bed fusion is an additive manufacturing (AM) process that offers exciting advantages for the fabrication of metallic parts compared to traditional techniques, such as the ability to create complex geometries with less material waste. However

Theoretical Bounds on Data Requirements for the Ray-Based Classification

November 10, 2021
Author(s)
Brian Weber, Sandesh Kalantre, Thomas McJunkin, Jacob Taylor, Justyna Zwolak
The problem of classifying high-dimensional shapes in real-world data grows in complexity as the dimension of the space increases. For the case of identifying convex shapes of different geometries, a new classification framework has recently been proposed

Open Media Forensics Challenge (OpenMFC) 2020-2021: Past, Present, and Future

September 29, 2021
Author(s)
Haiying Guan, Yooyoung Lee, Lukas Diduch, Jesse Zhang, Ilia Ghorbanian Bajgiran, Timothee Kheyrkhah, Peter Fontana, Jonathan G. Fiscus
This document describes the online leaderboard public evaluation program, Open Media Forensics Challenge (OpenMFC) 2021-2022. In the report, first, the introduction, objectives, challenges, contributions, and achievements of the evaluation program are

Baseline Control Systems in the Intelligent Building Agents Laboratory

September 22, 2021
Author(s)
Amanda Pertzborn, Daniel Veronica
The goal of the Embedded Intelligence in Buildings program at the National Institute of Standards and Technology (NIST) is to develop and deploy advances in measurement science that will improve building operations to achieve lower operating costs

Reservoir computing leveraging the transient non-linear dynamics of spin-torque nano-oscillators

August 6, 2021
Author(s)
Mathieu Riou, Jacob Torrejon, Flavio Abreu Araujo, Sumito Tsunegi, Guru Khalsa, Damien Querlioz, Paolo Bortolotti, Nathan Leroux, Danijela Markovic, Vincent Cros, K. Yakushiji, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Mark D. Stiles, Julie Grollier
Present artificial intelligence algorithms require extensive computations to emulate the behavior of large neural networks, operating current computers near their limits, which leads to high energy costs. A possible solution to this problem is the
Displaying 51 - 75 of 219