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Search Publications by: Walid Keyrouz (Fed)

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Displaying 1 - 20 of 20

An Infrastructure for Curating, Querying, and Augmenting Document Data: COVID-19 Case Study

August 8, 2023
Author(s)
Eswaran Subrahmanian, Guillaume Sousa Amaral, Talapady N. Bhat, Mary C. Brady, Kevin G. Brady, Jacob Collard, Sarra Chouder, Philippe Dessauw, Alden A. Dima, John T. Elliott, Walid Keyrouz, Nicolas Lelouche, Benjamin Long, Rachael Sexton, Ram D. Sriram
With the advent of the COVID-19 pandemic, there was the hope that data science approaches could help discover means for understanding, mitigating, and treating the disease. This manifested itself in the creation of the COVID-19 Open Research Dataset (CORD

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

Computational Study of the Allosteric Effects of p5 on the CDK5-p25 Hyperactivity as Alternative Inhibitory Mechanisms in Neurodegeneration

June 30, 2022
Author(s)
Tejaswi Tammareddy, Walid Keyrouz, Ram D. Sriram, Harish C. Pant, Antonio Cardone, Jeffery B. Klauda
The cyclin-dependent kinase (CDK5) forms a stable complex with its activator p25, leading to the hyperphosphorylation of tau proteins and to the formation of plaques and tangles that are considered to be one of the typical causes of Alzheimer's disease (AD

Graph Convolutional Neural Network Applied to the Prediction of Normal Boiling Point

February 4, 2022
Author(s)
Chen Qu, Anthony J. Kearsley, Barry I. Schneider, Walid Keyrouz, Thomas C. Allison
In this article, we describe training and validation of a machine learning model for the prediction of organic compound normal boiling points. Data are drawn from the experimental literature as captured in the NIST Thermodynamics Research Center (TRC)

Trojan Detection Evaluation: Finding Hidden Behavior in AI Models

October 10, 2020
Author(s)
Michael Paul Majurski, Derek Juba, Timothy Blattner, Peter Bajcsy, Walid Keyrouz
Neural Networks are trained on data, learn relationships in that data, and then are deployed to the world to operate on new data. For example, a traffic sign classification AI can differentiate stop signs and speed limit signs. One potential problem is

Geometry Entrapment in Walk-on-Subdomains

November 19, 2019
Author(s)
Walid Keyrouz, Preston Hamlin, W. J. Thrasher, Michael V. Mascagni
One method of computing the electrostatic energy of a biomolecule in a solution combines the Walk-On-Spheres and Walk-On-Subdomains Monte Carlo algorithms. In the course of examining an implementation of this method, a performance issue was discovered in

A Design Tool for High Performance Image Processing on Multicore Platforms

March 23, 2018
Author(s)
Timothy J. Blattner, Jiahao Wu, Walid Keyrouz, Shuvra S. Bhattacharyya
Design and implementation of smart vision systems often involve the mapping of complex image processing algorithms into efficient, real-time implementations on multicore platforms. In this paper, we describe a novel design tool that is developed to address

Model-based Dynamic Scheduling for Multicore Implementation of Image Processing Systems

October 5, 2017
Author(s)
Timothy J. Blattner, Walid Keyrouz, Jiahao Wu, Shuvra S. Bhattacharyya
In this paper, we present a new software tool, called HTGS Model-based Engine (HMBE), for the design and implementation of multicore signal processing applications. HMBE provides complementary capabilities to HTGS (Hybrid Task Graph Scheduler), which is a

A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows

June 22, 2017
Author(s)
Timothy J. Blattner, Walid Keyrouz, Mary C. Brady, Shuvra S. Bhattacharyya, Milton Halem
Designing applications for scalability is key to improving their performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with data dependencies, memory management, data motion, and

Acceleration and Parallelization of ZENO/Walk-on-Spheres

June 1, 2016
Author(s)
Derek Juba, Walid Keyrouz, Michael V. Mascagni, Mary C. Brady, Michael Mascagni
This paper describes our on-going work to accelerate ZENO, a software tool based on Monte Carlo methods (MCMs), used for computing material properties at the nanoscale. ZENO employs three main algorithms: (1)Walk on Spheres (WoS), (2)interior sampling, and

A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows

December 16, 2015
Author(s)
Timothy J. Blattner, Walid Keyrouz, Milton Halem, Shuvra S. Bhattacharyya, Mary C. Brady
The scalability of applications is a key requirement to improving performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with dependencies, memory management, data motion, and processor

Scientific Software Sustainability: The Numerical Reproducibility Challenge

October 15, 2015
Author(s)
Walid Keyrouz, Michael V. Mascagni
Experimental reproducibility is a cornerstone of the scientific method. The ease of achieving its counterpart in computing, numerical reproducibility, was one of the core assumptions underpinning the growth of scientific computing over the past several

A Hybrid CPU-GPU System for Stitching Large Scale Optical Microscopy Images

September 12, 2014
Author(s)
Timothy Blattner, Walid Keyrouz, Joe Chalfoun, Bertrand C. Stivalet, Mary C. Brady, Shujia Zhou
Researchers in various fields are using optical microscopy to acquire very large images, 10K--200K of pixels per side. Optical microscopes acquire these images as grids of overlapping partial images (thousands of pixels per side) that are then stitched

A Hybrid CPU-GPU Approach to Fourier-Based Image Stitching of Optical Microscopy Images

March 3, 2013
Author(s)
Walid Keyrouz, Timothy J. Blattner, Bertrand C. Stivalet, Joe Chalfoun, Mary C. Brady, Shujia Zhou
We present a hybrid CPU-GPU approach for the Fourier-based stitching of optical microscopy images. This system achieves sub-minute stitching rates with large grids; it stitches a grid of 59x42 tiles in 26 seconds on a two-CPU (8 physical cores) & two-GPU