Data Science Commons^™

Ai Assisted Workflows For Computational Electromagnetics And Antenna Design, Oameed Noakoasteen

Electrical and Computer Engineering ETDs

These days large volumes of data can be recorded and manipulated with relative ease. If valuable information can be extracted from them, these vast amounts of data can be a rich resource not just for the digital economy but also for scientific discovery and development of technology. When it comes to deriving valuable information from data, Machine Learning (ML) emerges as the key solution. To unlock the potential benefits of ML to science and technology, extensive research is needed to explore what algorithms are suitable and how they can be applied.

To shine light on various ways that ML can …

Understanding Collective Performance: Human Factors And Team Science, Joseph Keebler

Math Department Colloquium Series

This talk will focus on modern issues with team science. Joe will discuss a variety of projects he's been involved with aimed at improving teamwork in complex sociotechnical systems including military, aviation, and healthcare. He will discuss major theoretical facets of teamwork and provide evidence-based best practices that were utilized to improve teams in applied settings.

Data Quality Checks: Implementation With Popular Data Collection Crowdsourcing Platforms, James Down, Gregory Balkcom, Kristine Duncan, Ngan (An) Truong, Andrew Lewis

Symposium of Student Scholars

The utilization of online crowdsourcing platforms for data collection has increased over the past two decades in the field of public health due to the ease of use, the cost-saving benefits, the speed of the data collection process, and the accessibility of a potentially true representative population. Although these platforms offer many advantages to researchers, significant drawbacks exist, such as poor data quality, that threaten the reliability and validity of the study. Previous studies have examined data quality concerns, but differences in results arise due to variations in study designs, disciplinary contexts, and the platforms being investigated. Therefore, this study …

Machine Learning In Minecraft: Proof Of Concept For Object Detection Oriented Autonomous Bots In Minecraft, John Merkin

Symposium of Student Scholars

Machine learning provides new methods of problem solving through applied pattern recognition. An interesting challenge is to utilize machine learning in the automation of tasks and behaviors in virtual environments. Minecraft is an open-world, sandbox style game giving players nearly limitless freedom to alter a procedurally generated world. In the survival game mode, the player must collect resources to craft tools and build structures. The collection of resources can be tedious, so this project seeks to automate the standard initial task of collecting wood. By combining a convolutional neural network with API, a bot can collect resources while remaining scalable …

Repeated Games In The Presence Of Incomplete Information, Reza Habibi

The Journal of Economics and Politics

There are many strategic situations at which a game theoretical framework should be used to analyze the equilibrium decisions at which the incomplete information annoy the process of deriving the certain rules for making decisions. In these cases, players use signals of each other's to get proper decisions. For example, in economic environment, some macro-economic latent variables induce incomplete information. Morris and Shin (2000) referred this type of game as global game and studied one-shot type of it. However, in practical situations, it is a type of repeated game. In the current paper, following notations of Morris and Shin (2000), …

Improving Inferences About Exoplanet Habitability, Risinie D. Perera, Kevin H. Knuth

Physics Faculty Scholarship

Assessing the habitability of exoplanets (planets orbiting other stars) is of great importance in deciding which planets warrant further careful study. Planets in the habitable zones of stars like our Sun are sufficiently far away from the star so that the light rays from the star can be assumed to be parallel, leading to straightforward analytic models for stellar illumination of the planet’s surface. However, for planets in the close-in habitable zones of dim red dwarf stars, such as the potentially habitable planet orbiting our nearest stellar neighbor, Proxima Centauri, the analytic illumination models based on the parallel ray approximation …

Optimization Of Biomedical Imaging Filters For Use In Recaptured Identity Document Classification, John Magee, Stephen Sheridan Phd, Christina Thorpe Phd

Conference papers

As banks and online financial institutions move toward full remote onboarding services, the attack vectors for bad actors increases to include those of recaptured identity documents. This type of fraud opens banking customers to potential crimes of identity theft, as well as causing reputational damage to the institutions involved. In this paper we extend existing research focusing on the use of biomedical imaging filters and their usefulness when classifying recaptured identity documents. We perform a grid search and demonstrate that different filter configurations exist that dramatically reduce the classification error rates compared to those achieved using only the default filter …

Towards A Virtual Reality Visualization Of Hand-Object Interactions To Support Remote Physical Therapy, Trudi Di Qi, Louanne Boyd, Scott Fitzpatrick, Meghna Raswan, Franceli L. Cibrian

Engineering Faculty Articles and Research

Improving object manipulation skills through hand-object interaction exercises is crucial for rehabilitation. Despite limited healthcare resources, physical therapists propose remote exercise routines followed up by remote monitoring. However, remote motor skills assessment remains challenging due to the lack of effective motion visualizations. Therefore, exploring innovative ways of visualization is crucial, and virtual reality (VR) has shown the potential to address this limitation. However, it is unclear how VR visualization can represent understandable hand-object interactions. To address this gap, in this paper, we present VRMoVi, a VR visualization system that incorporates multiple levels of 3D visualization layers to depict movements. In …

Innovation Process And Industrial System Of Us Food And Drug Administration-Approved Software As A Medical Device: Review And Content Analysis

Faculty, Staff and Student Publications

BACKGROUND: There has been a surge in academic and business interest in software as a medical device (SaMD). SaMD enables medical professionals to streamline existing medical practices and make innovative medical processes such as digital therapeutics a reality. Furthermore, SaMD is a billion-dollar market. However, SaMD is not clearly understood as a technological change and emerging industry.

OBJECTIVE: This study aims to review the landscape of SaMD in response to increasing interest in SaMD within health systems and regulation. The objectives of the study are to (1) clarify the innovation process of SaMD, (2) identify the prevailing typology of such …

The Transcription Factor Irf4 Determines The Anti-Tumor Immunity Of Cd8+ T Cells, Xiang Xiao, Jinfei Fu, Dawei Zou, Anze Yu, Tao Jiang, Xian C Li, Zhongming Zhao, Wenhao Chen

Faculty, Staff and Student Publications

Understanding the factors that regulate T cell infiltration and functional states in solid tumors is crucial for advancing cancer immunotherapies. Here, we discovered that the expression of interferon regulatory factor 4 (IRF4) was a critical T cell intrinsic requirement for effective anti-tumor immunity. Mice with T-cell-specific ablation of IRF4 showed significantly reduced T cell tumor infiltration and function, resulting in accelerated growth of subcutaneous syngeneic tumors and allowing the growth of allogeneic tumors. Additionally, engineered overexpression of IRF4 in anti-tumor CD8+ T cells that were adoptively transferred significantly promoted their tumor infiltration and transition from a naive/memory-like cell state into …

An Open Natural Language Processing (Nlp) Framework For Ehr-Based Clinical Research: A Case Demonstration Using The National Covid Cohort Collaborative (N3c), Sunyang Fu, Robert Miller, Andrew Williams, Daniel Harris, Ramakanth Kavuluru, Mei Liu, Noor Abu-El-Rub, Dalton Schutte, Rui Zhang, Masoud Rouhizadeh, John D Osborne, Yongqun He, Umit Topaloglu, Stephanie S Hong, Joel H Saltz, Thomas Schaffter, Emily Pfaff, Christopher G Chute, Tim Duong, Melissa A Haendel, Rafael Fuentes, Peter Szolovits, Hua Xu, Hongfang Liu

Faculty, Staff and Student Publications

Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both …

Domain Specific Feature Representation Learning For Diverse Temporal Data, Farhan Asif Chowdhury

Computer Science ETDs

Humans can leverage domain context to recognize novel patterns and categories based on limited known examples. In contrast, computational learning methods are not adept at exploiting context and require sufficient labeled examples to achieve similar accuracy. Many temporal data domain, for example, seismic signals and oil mining sensor data, requires domain expert annotation, which is both costly and time-consuming. The dependency on training data limits the applicability of machine learning algorithms for domains with limited labeled data. This dissertation aims to address this gap by developing temporal mining algorithms that exploit domain context to learn discriminative feature representation from limited …

Usp38 Exacerbates Atrial Inflammation, Fibrosis, And Susceptibility To Atrial Fibrillation After Myocardial Infarction In Mice, Jingjing Zhang, He Huang

Faculty, Staff and Student Publications

BACKGROUND: Inflammation plays an important role in the pathogenesis of atrial fibrillation (AF) after myocardial infarction (MI). The role of USP38, a member of the ubiquitin-specific protease family, on MI-induced atrial inflammation, fibrosis, and associated AF is unclear.

METHODS: In this study, we surgically constructed a mouse MI model using USP38 cardiac conditional knockout (USP38-CKO) and cardiac-specific overexpression (USP38-TG) mice and applied biochemical, histological, electrophysiological characterization and molecular biology to investigate the effects of USP38 on atrial inflammation, fibrosis, and AF and its mechanisms.

RESULTS: Our results revealed that USP38-CKO attenuates atrial inflammation, thereby ameliorating fibrosis, and abnormal electrophysiologic properties, …

Uavs And Deep Neural Networks: An Alternative Approach To Monitoring Waterfowl At The Site Level, Zachary J. Loken

LSU Master's Theses

Understanding how waterfowl respond to habitat restoration and management activities is crucial for evaluating and refining conservation delivery programs. However, site-specific waterfowl monitoring is challenging, especially in heavily forested systems such as the Mississippi Alluvial Valley (MAV)—a primary wintering region for ducks in North America. I hypothesized that using uncrewed aerial vehicles (UAVs) coupled with deep learning-based methods for object detection would provide an efficient and effective means for surveying non-breeding waterfowl on difficult-to-access restored wetland sites. Accordingly, during the winters of 2021 and 2022, I surveyed wetland restoration easements in the MAV using a UAV equipped with a dual …

Rotational Symmetry Breaking In Superconducting Nickelate Nd08sr02nio2 Films, Zheyuan Xie, Chengcheng Ji, Shichao Qi, Xiaoyue Gao, Minghui Xu, Peng Gao, Liang Qiao, Yi-Feng Yang, Guang-Ming Zhang, Jian Wang

Faculty, Staff and Student Publications

The infinite-layer nickelates, isostructural to the high-Tc cuprate superconductors, have emerged as a promising platform to host unconventional superconductivity and stimulated growing interest in the condensed matter community. Despite considerable attention, the superconducting pairing symmetry of the nickelate superconductors, the fundamental characteristic of a superconducting state, is still under debate. Moreover, the strong electronic correlation in the nickelates may give rise to a rich phase diagram, where the underlying interplay between the superconductivity and other emerging quantum states with broken symmetry is awaiting exploration. Here, we study the angular dependence of the transport properties of the infinite-layer nickelate Nd0.8Sr0.2NiO2 superconducting …

Utilizing Non-Negative Least Squares For Data-Driven Discovery Of Dynamics, Tracey G. Oellerich

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Incorporating Adaptive Human Behavior Into Epidemiological Models Using Equation Learning, Austin Barton, Jordan Klein, Jonathan Greer, Kevin Flores, Patrick Haughey

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Parameter Estimation In Epidemiological And Climate Models Using Ensemble Smoothing With Multiple Data Assimilation, Emmanuel Fleurantin

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Critical Transitions In Mental Health: Van Gogh Case Study, Anna Singley

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Bayesian Adaptive Smoothing For Activation Detection In Fmri, Juan Florez

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.