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Information Technology

Computational science

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Displaying 26 - 50 of 153

Baseline Pruning-Based Approach to Trojan Detection in Neural Networks

May 7, 2021
Author(s)
Peter Bajcsy, Michael Majurski
This paper addresses the problem of detecting trojans in neural networks (NNs) by analyzing how NN accuracy responds to systematic pruning. This study leverages the NN models generated for the TrojAI challenges. Our pruning-based approach (1) detects any

Optoelectronic Intelligence

May 7, 2021
Author(s)
Jeff Shainline
To design and construct hardware for general intelligence, we must consider principles of both neuroscience and very-large-scale integration. For large neural systems capable of general intelligence, the attributes of photonics for communication and

Challenge Design and Lessons Learned from the 2018 Differential Privacy Challenges

April 12, 2021
Author(s)
Diane Ridgeway, Mary Theofanos, Terese Manley, Christine Task
The push for open data has made a multitude of datasets available enabling researchers to analyze publicly available information using various statistical and machine learning methods in support of policy development. An area of increasing interest that is

Designing Trojan Detectors in Neural Networks Using Interactive Simulations

February 20, 2021
Author(s)
Peter Bajcsy, Nicholas J. Schaub, Michael P. Majurski
This paper addresses the problem of designing trojan detectors in neural networks (NNs) using interactive simulations. Trojans in NNs are defined as triggers in inputs that cause misclassification of such inputs into a class (or classes) unintended by the

The membership problem for constant-sized quantum correlations is undecidable

January 26, 2021
Author(s)
Carl A. Miller, Honghao Fu, William Slofstra
When two spatially separated parties make measurements on an unknown entangled quantum state, what correlations can they achieve? How difficult is it to determine whether a given correlation is a quantum correlation? These questions are central to problems

Fast Methods for Finding Multiple Effective Influencers in Real Networks

December 31, 2020
Author(s)
Fern Y. Hunt, Roldan Pozo
We present scalable first hitting time methods for finding a collection of nodes that enables the fastest time for the spread of consensus in a network. That is, given a graph G = (V,E) and a natural number k, these methods find k vertices in G that

Object Measurements from 2D Microscopy Images

December 11, 2020
Author(s)
Peter Bajcsy, Joe Chalfoun, Mylene Simon, Mary C. Brady, Marcin Kociolek
This chapter addresses object measurements from 2D microscopy images. Object measurements (called image features) vary in terms of theoretical formulas for the same image feature, the physical units used to represent pixel-based measurements, the

Reducing the Measurement Time of Exact NOEs by Non-Uniform Sampling

September 3, 2020
Author(s)
Parker J. Nichols, Alexandra Born, Morkos A. Henen, Dean Strotz, David N. Jones, Frank Delaglio, Beat Vogeli
We have previously reported on the measurement of exact NOEs (eNOEs), which yield a wealth of additional information in comparison to conventional NOEs. We have used these eNOEs in a variety of applications, including calculating high-resolution structures

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

Notes on Interrogating Random Quantum Circuits

May 29, 2020
Author(s)
Luis Brandao, Rene C. Peralta
Consider a quantum circuit that, when fed a constant input, produces a fixed-length random bit- string in each execution. Executing it many times yields a sample of many bit-strings that contain fresh randomness inherent to the quantum evaluation. When the

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

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

A Science Gateway for Atomic and Molecular Physics

January 7, 2020
Author(s)
Barry I. Schneider, Klaus Bartschat, Oleg Zatsarinny, Igor Bray, Fernando Martin, Armin Scrinzi, Sudhakar Pamidighantam, Jonathan Tennyson, Jimena Gorfinkiel, Markus Klinker
We describe the creation of a new Atomic and Molecular Physics science gateway (AMPGateway). The gateway is designed to bring together a subset of the AMP community to work collectively to make their codes available and easier to use by the partners as

Cognitive Information Measurements: A New Perspective

December 1, 2019
Author(s)
Hamid Gharavi
From a traditional point of view, the value of information does not change during transmission. The Shannon information theory considers information transmission as a statistical phenomenon for measuring the communication channel capacity. However, in

Design of an intelligent PYTHON code to run coupled and license-free finite-element and statistical analysis software for calibration of near-field scanning microwave microscopes

October 2, 2019
Author(s)
Jeffrey T. Fong, N. Alan Heckert, James Filliben, Pedro V. Marcal, Samuel Berweger, Thomas Mitchell (Mitch) Wallis, Pavel Kabos
To calibrate near-field scanning microwave microscopes (NSMM) for defect detection and characterization in semiconductors, it is common to develop a parametric finite element analysis (FEA) code to guide the microscope user on how to optimize the settings

DESIGN OF AN INTELLIGENT PYTHON CODE FOR VALIDATING CRACK GROWTH EXPONENT BY MONITORING A CRACK OF ZIG-ZAG SHAPE IN A CRACKED PIPE

July 27, 2019
Author(s)
Jeffrey T. Fong, Pedro V. Marcal, Robert B. Rainsberger, Nathanael A. Heckert, James J. Filliben
When a small crack is detected in a pressure vessel or piping, we can estimate the fatigue life of the vessel or piping by applying the classical law of fracture mechanics for crack growth if we are certain that the crack growth exponent is correct and the

An Overset Mesh Framework for an Isentropic ALE Navier-Stokes HDG Formulation

January 6, 2019
Author(s)
Justin A. Kauffman, William L. George, Jonathan S. Pitt
Fluid-structure interaction simulations where solid bodies undergo large deformations require special handling of the mesh motion for Arbitrarily Lagrangian-Eulerian (ALE) formulations. Such formulations are necessary when body-fitted meshes with certain
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