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Displaying 26 - 50 of 59

Forecasting Operation of a Chiller Plant Facility Using Data Driven Models

July 23, 2024

Author(s)

Behzad Salimian Rizi, Afshin Faramarzi, Amanda Pertzborn, Mohammad Heidarinejad

In recent years, data-driven models have enabled accurate prediction of chiller power consumption and chiller coefficient of performance (COP). This study evaluates the usage of time series Extreme Gradient Boosting (XGBoost) models to predict chiller

scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression

June 28, 2024

Author(s)

Ghulam Murtaza, Justin Wagner, Ritambhara Singh

Single-cell Hi-C (scHi-C) protocol helps identify cell-type-specific chromatin interactions and sheds light on cell differentiation and disease progression. Despite providing crucial insights, scHi-C data is often underutilized due to the high cost and the

Memory Efficient Segmentation of Large Microscopy Images Using Graph-based Neural Networks

June 18, 2024

Author(s)

Atishay Jain, David Laidlaw, Ritambhara Singh, Peter Bajcsy

We present a graph neural network (GNN) based framework applied to a large-scale image segmentation task. While deep learning models, like convolutional neural networks (CNNs), have become common for automating image segmentation tasks, they are limited by

Human-in-the-loop Technical Document Annotation: Developing and Validating a System to Provide Machine-Assistance for Domain-Specific Text Analysis

May 14, 2024

Author(s)

Juan Fung, Zongxia Li, Daniel Stephens, Andrew Mao, Pranav Goel, Emily Walpole, Alden A. Dima, Jordan Boyd-Graber

In this report, we address the following question: to what extent can machine learning assist a human with traditional text analysis, such as content analysis or grounded theory in the social sciences? In practice, such tasks require humans to review and

Measurement-driven neural-network training for integrated magnetic tunnel junction arrays

May 14, 2024

Author(s)

William Borders, Advait Madhavan, Matthew Daniels, Vasileia Georgiou, Martin Lueker-Boden, Tiffany Santos, Patrick Braganca, Mark Stiles, Jabez J. McClelland, Brian Hoskins

The increasing scale of neural networks needed to support more complex applications has led to an increasing requirement for area- and energy-efficient hardware. One route to meeting the budget for these applications is to circumvent the von Neumann

AI-Based Environment Segmentation Using a Context-Aware Channel Sounder

April 26, 2024

Author(s)

Anuraag Bodi, Samuel Berweger, Raied Caromi, Jihoon Bang, Jelena Senic, Camillo Gentile

We describe how the data acquired from the camera and Lidar systems of our context-aware radio-frequency (RF) channel sounder is used to reconstruct a 3D mesh of the surrounding environment, segmented and classified into discrete objects. First, the images

Context-Aware Channel Sounder for AI-Assisted Radio-Frequency Channel Modeling

April 26, 2024

Author(s)

Camillo Gentile, Jelena Senic, Anuraag Bodi, Samuel Berweger, Raied Caromi, Nada Golmie

We describe a context-aware channel sounder that consists of three separate systems: a radio-frequency system to extract multipaths scattered from the surrounding environment in the 3D geometrical domain, a Lidar system to generate a point cloud of the

Data-driven Simulations For Training AI-Based Segmentation of Neutron Images

March 19, 2024

Author(s)

Pushkar Sathe, Caitlyn M. Wolf, Youngju Kim, Sarah M. Robinson, Michael Daugherty, Ryan Murphy, Jacob LaManna, Michael Huber, David Jacobson, Paul A. Kienzle, Kathleen Weigandt, Nikolai Klimov, Daniel Hussey, Peter Bajcsy

Neutron interferometry is unique in its ability to measure atomic scale properties of materials that no other imaging modality can. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on

AIRI: Predicting Retention Indices and Their Uncertainties Using Artificial Intelligence

January 17, 2024

Author(s)

Lewis Geer, Stephen E. Stein, Gary Mallard, Douglas Slotta

The Kováts retention index (RI) is a quantity measured using gas chromatography and is commonly used in the identification of chemical structures. Creating libraries of observed RI values is a laborious task, so we explore the use of a deep neural network

A Comprehensive Evaluation of Generalizability of Deep Learning-Based Hi-C Resolution Improvement Methods

December 28, 2023

Author(s)

Ghulam Murtaza, Justin Wagner, Ritambhara Singh

Hi-C is a widely used technique to study the 3D organization of the genome. Due to its high sequencing cost, most of the generated datasets are of a coarse resolution, which makes it impractical to study finer chromatin features such as Topologically

Meta-model for ADMET Property Prediction Analysis

December 7, 2023

Author(s)

Sarala Padi, Antonio Cardone, Ram Sriram

In drug discovery analysis ADMET properties, such as chemical absorption, distribution, metabolism, excretion, and toxicity, play a critical role. They allow the quantitative evaluation of a designed drug's efficacy. Several machine learning models have

KPI Extraction from Maintenance Work Orders-A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines

December 6, 2023

Author(s)

Marc-Alexander Lutz, Bastian Schafermeier, Rachael Sexton, Michael Sharp, Alden A. Dima, Stefan Faulstich, Jagan Mohini Aluri

Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the

Extending Explainable Boosting Machines to Scientific Image Data

November 30, 2023

Author(s)

Daniel Schug, Sai Yerramreddy, Rich Caruana, Craig Greenberg, Justyna Zwolak

As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a focus of great concern. Driven by the pressing need for interpretable models in science, we propose

Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial Networks

August 21, 2023

Author(s)

Adam Wunderlich, Jack Sklar

Random noise arising from physical processes is an inherent characteristic of measurements and a limiting factor for most signal processing and data analysis tasks. Given the recent interest in generative adversarial networks (GANs) for data-driven

Cluster Association for 3D Environment Based on 60 GHz Indoor Channel Measurements

May 31, 2023

Author(s)

Raied Caromi, Jian Wang, Anuraag Bodi, Camillo Gentile

In this paper, we present a ray tracing (RT) assisted multipath cluster association method. This work is based on an indoor channel measurement at 60 GHz, where a light detection and ranging (LiDAR) sensor was co-located with channel sounder and time

LabelVizier: Interactive Validation and Relabeling for Technical Text Annotations

March 30, 2023

Author(s)

Xiaoyu Zhang, Xiwei Xuan, Rachael Sexton, Alden A. Dima

With the rapid accumulation of text data brought forth by advances in data-driven techniques, the task of extracting "data annotations"—concise, high-quality data summaries from unstructured raw text—has become increasingly important. Researchers in the

Advances and Trends in Machine Learning and Artificial Intelligence: A Polymer Perspective

January 18, 2023

Author(s)

Tyler Martin, Debra Audus

Here we highlight recent advancements in informatics, machine learning, and artificial intelligence as applied to the fields of polymer physics and chemistry. In particular, we focus on the unique challenges presented by polymers and how the field is

A review of machine learning-based zero-day attack detection: Challenges and future directions

January 15, 2023

Author(s)

Yang Guo

Zero-day attacks exploit unknown vulnerabilities so as to avoid being detected by cybersecurity detection tools. The studies Bilge and Dumitraş (2012), Google (0000) and Ponemon Sullivan Privacy Report (2020) show that zero-day attacks are wide spread and

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

Building Interpretable Machine Learning Models to Identify Chemometric Trends in Seabirds of the North Pacific Ocean

October 5, 2022

Author(s)

Nathan Mahynski, Jared Ragland, Stacy Schuur, Vincent K. Shen

Marine environmental monitoring efforts often rely on the bioaccumulation of persistent anthropogenic contaminants in organisms to create a spatiotemporal record of the ecosystem. Intercorrelation results from the origin, uptake, and transport of these

Hysteresis Compensation in Temperature Response of Fiber Bragg Grating Thermometers Using Dynamic Regression

September 23, 2022

Author(s)

Zeeshan Ahmed

In recent years there has been considerable interest in using photonic thermometers such as Fiber Bragg grating (FBG) and silicon ring resonators as an alternative technology to resistance-based legacy thermometers. Although FBG thermometers have been

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

A Spatial Temporal Graph Neural Network Model for Predicting Flashover in Arbitrary Building Floorplans

August 3, 2022

Author(s)

Wai Cheong Tam, Eugene Yujun Fu, Jiajia Li, Xinyan Huang, Jian Chen, Michael Xuelin Huang

Rapid fire progression, such as flashover, has been one of the leading causes for firefighter deaths and injuries in residential building environments. Due to long computational time of and the required prior knowledge about the fire scene, existing models

Survey of Graph Neural Networks and Applications

July 28, 2022

Author(s)

Fan Liang, Cheng Qian, Wei Yu, David W. Griffith, Nada T. Golmie

The advance of deep learning has shown great potential in applications (speech, image and video classification). In these applications, deep learning models are trained by datasets in Euclidean space with fixed dimensions and sequences. Nonetheless, the

Interpretable modeling of genotype-phenotype landscapes with state-of-the-art predictive power

June 21, 2022

Author(s)

Peter Tonner, Abe Pressman, David Ross

Large-scale measurements linking genetic background to biological function have drivena need for models that can incorporate these data for reliable predictions and insightinto the underlying biophysical system. Recent modeling efforts, however, prioritize