Digital Commons Network^™

Targeting The Exonic Circular Ogt Rna/O-Glcnac Transferase/Forkhead Box C1 Axis Inhibits Asparagine- And Alanine-Mediated Ferroptosis Repression In Neuroblastoma Progression, Qilan Li, Yang Cheng, Chunhui Yang, Minxiu Tian, Xiaojing Wang, Dan Li, Xinyue Li, Jiaying Qu, Shunchen Zhou, Liduan Zheng, Qiangsong Tong

Faculty, Staff and Students Publications

The disruption of ferroptosis, an emerging form of programmed cell death, is crucial in the development and aggressiveness of tumors. Meanwhile, the mechanisms and treatments that control ferroptosis in neuroblastoma (NB), a prevalent extracranial cancer in children, are still unknown. In this study, forkhead box C1 (FOXC1) and O-GlcNAc transferase (OGT) are identified as regulators of asparagine- and alanine-mediated ferroptosis repression in NB. Mechanistically, OGT facilitates FOXC1 stabilization via inducing O-GlcNAcylation in liquid condensates to increase the expression of asparagine synthetase (ASNS) and glutamate pyruvate transaminase 2 (GPT2), resulting in asparagine and alanine biogenesis, and subsequent …

Bearing Witness: Témoignage As A Tool For Child Advocacy During Armed Conflict, Anik Patel, Amber Alayyan, Heather A Haq, Parminder S Suchdev, Naji Hattar, Jeffrey Goldhagen, David J Schonfeld, Tanya Haj-Hassan, Lia Harris, Seema Jilani, Ayesha Kadir, Paul H Wise, Lisa Umphrey

Faculty, Staff and Students Publications

Children affected by armed conflict suffer devastating physical, emotional, and social harm. War uproots families, forcing many to flee as refugees or internally displaced persons, while others remain trapped in dangerous environments. In these crises, children face disproportionate risks-violence, exploitation, disrupted education, and collapsed healthcare systems. Their unique vulnerabilities require urgent, targeted action to protect their health, rights, and development. Beyond immediate care, the humanitarian principle of témoignage-bearing witness-is essential. Rooted in humanitarian ethics, témoignage means speaking out about injustice, amplifying the voices of those affected, and driving systemic change. It challenges traditional notions of neutrality and calls on humanitarian …

Refinement Of A Published Gene-Physical Activity Interaction Impacting Hdl-Cholesterol: Role Of Sex And Lipoprotein Subfractions, Kenneth E Westerman, Tuomas O Kilpeläinen, Magdalena Sevilla-Gonzalez, Margery A Connelly, Alexis C Wood, Michael Y Tsai, Kent D Taylor, Stephen S Rich, Jerome I Rotter, James D Otvos, Amy R Bentley, Samia Mora, Hugues Aschard, D C Rao, Charles Gu, Daniel I Chasman, Alisa K Manning

Faculty, Staff and Students Publications

Large-scale gene-environment interaction (GxE) discovery efforts often involve analytical compromises for the sake of data harmonization and statistical power. Refinement of exposures, covariates, outcomes, and population subsets may be helpful to establish often-elusive replication and evaluate potential clinical utility. Here, we used additional datasets, an expanded set of statistical models, and interrogation of lipoprotein metabolism via nuclear magnetic resonance (NMR)-based lipoprotein subfractions to refine a previously discovered GxE modifying the relationship between physical activity (PA) and HDL-cholesterol (HDL-C). We explored this GxE in the Women’s Genome Health Study (WGHS; N=23,294; the strongest cohort-specific signal in the original meta-analysis), the UK …

The Process And Significance Of Convening A Community Advisory Board With Individuals With Severe Mental Illness, Jacelyn Biondo, Nia Johnson

Department of Medicine Faculty Papers

Individuals with lived experience of being diagnosed with severe mental illness and/or substance use disorders have deep and profound knowledge regarding the culture and needs of their community. Historically, these and other vulnerable populations have been excluded from care access, treatment insights, research development and participation, and often society in general. In this paper, we share our experience of convening a community advisory board with vulnerable populations and highlight the expertise these community members provide. We outline our processes of convening the community advisory board, engaging in collaborative meetings, revising the research protocol, and exploring future research needs. Using meeting …

A New Interpolated Pseudodifferential Preconditioner For The Helmholtz Equation In Heterogeneous Media, Sebastian Acosta, Tahsin Khajah, Benjamin Palacios

Faculty, Staff and Students Publications

This paper introduces a new pseudodifferential preconditioner for the Helmholtz equation in variable media with absorption. The pseudodifferential operator is associated with the multiplicative inverse to the symbol of the Helmholtz operator. This approach is well suited for the intermediate and high-frequency regimes. The main novel idea for the fast evaluation of the preconditioner is to interpolate its symbol, not as a function of the (high-dimensional) phase-space variables, but as a function of the wave speed itself. Since the wave speed is a real-valued function, this approach allows us to interpolate in a univariate setting even when the original problem …

Evidence-Based Evaluation To Promote High-Value Care In Pediatric Critical Care: It Is Time To Fully Adopt, Maya Dewan, Rajit K Basu, Erin Bennett, Lily B Glater-Welt, Mark Hall, Benson S Hsu, Lia Lowrie, Venessa Lynn Pinto, Alexandre T Rotta, Edward Seferian, Linda Siegel, Heather A Wolfe, Vijay Srinivasan

Faculty, Staff and Students Publications

Introduction: Evidence-based recommendations for high-value care in pediatric critical care are needed. We sought to develop recommendations to identify unnecessary interventions, enhance outcomes, and promote efficient, patient-centered practices.

Methods: Using a modified Delphi process, a multi-institutional panel of pediatric intensivists from a national pediatric critical care quality improvement group identified 30 potential topics and selected 20 high- and medium-priority topics for further evaluation, and narrowed them to 10 by consensus voting. A structured scoping review of literature published between 2014 and 2023 identified 10 practices, which were critically assessed using the PICOST framework. The panel selected 5 priority recommendations based …

Hymate: A Hybrid Mamba And Transformer Model For Ehr Representation Learning, Md Mozaharul Mottalib, Thao-Ly Phan, Rahmatollah Beheshti

Department of Medicine Faculty Papers

Electronic health Records (EHRs) have become a cornerstone in modern-day healthcare. They are a crucial part for analyzing the progression of patient health; however, their complexity, characterized by long, multivariate sequences, sparsity, and missing values-poses significant challenges in traditional deep learning modeling. While Transformer-based models have demonstrated success in modeling EHR data and predicting clinical outcomes, their quadratic computational complexity and limited context length hinder their efficiency and practical applications. On the other hand, State Space Models (SSMs) like Mamba present a promising alternative offering linear-time sequence modeling and improved efficiency for handling long sequences, but focus mostly on mixing …

The Iterative Process Of Developing A School-Based Intervention To Take Actions Against Racism, Lindsay Poole, Danika Perry, Zumana Noor, Kim Graham, Tia Barnes, Danielle Hatchimonji, Kira Branch

Department of Medicine Faculty Papers

Racism threatens the mental and physical health of Black and Brown K-8 students and may lead to re-occurring harm without intervention. We developed ‘Actions Against Racism’ professional development trainings using a trauma-informed racial socialization lens to provide K-8 educators with proactive strategies to disrupt the negative psychosocial developmental outcomes associated with racism. This paper is a descriptive account of how we iteratively developed this intervention through four phases over the course of four years. The primary takeaway from this work is that developing and implementing interventions to disrupt racism and promote healing must take into account the implementation context, with …

Impact Of Elevated Serum Triglycerides On Children With Acute Recurrent Or Chronic Pancreatitis From Insppire-2, Zachary M Sellers, Matthew J Giefer, Fuchenchu Wang, Gretchen A Cress, Maisam A Abu-El-Haija, Ankur Chugh, Reuven Z Cohen, Elissa M Downs, Douglas S Fishman, A Jay Freeman, Cheryl E Gariepy, Tanja Y Gonska, Amit S Grover, Doug Lindblad, Quin Y Liu, Asim Maqbool, Jacob A Mark, Brian A Mcferron, Megha S Mehta, Veronique D Morinville, Kenneth Ng, Robert A Noel, Chee Y Ooi, Emily R Perito, Madhura Y Phadke, Wenly Ruan, Sarah Jane Schwarzenberg, David M Troendle, Michael Wilschanski, Yuhua Zheng, Ying Yuan, Mark E Lowe, Aliye Uc, Consortium For The Study Of Chronic Pancreatitis, Diabetes And Pancreatic Cancer

Faculty, Staff and Students Publications

Objective: To determine if mild-moderate hypertriglyceridemia (HTG) is associated with increased development of chronic pancreatitis (CP) or pancreatitis-associated complications in children with acute recurrent or CP.

Study design: Longitudinal data from the INternational Study group of Pediatric Pancreatitis: In search for a cuRE-2 (INSPPIRE-2) cohort of children with acute recurrent or CP (n = 559) were analyzed. Subjects were divided into normal triglycerides (< 150 mg/dL; 1.7 mmol/L), any HTG (≥150 mg/dL; ≥1.7 mmol/L), mild-moderate HTG (150-499 mg/dL; 1.7-5.6 mmol/L), moderate HTG (500-999 mg/dL; 5.6-11.3 mmol/L), and severe HTG groups (≥1000 mg/dL; ≥11.3 mmol/L), based on highest serum triglyceride value. Laboratory, imaging, pancreatitis and hospital events, complications, and quality of life data were analyzed.

Results: In children with acute recurrent or CP and HTG, there was no increase in the number of pancreatitis attacks per person-years, nor an increase in CP prevalence. However, HTG severity was associated with increased pancreatic …

Self-Directed Behavior Reflects Social Stress In Captive Asian Elephants, Sofia Vilela, Peini Chen, So Murakami, Yusuke Tanabe, Shinya Yamamoto

Faculty, Staff and Students Publications

Self-directed behaviors (SDBs), such as scratching, self-grooming, and body shaking, have been widely used as indicators of anxiety and social stress in non-human primates. However, research focused on SDB outside the primate range is still in infancy. Expanding this topic to other species can support animal welfare assessments and enhance comparative social studies. This study investigates whether SDB levels can reflect the social stress experienced in Asian elephants (Elephas maximus). Using all-occurrences and focal sampling on four captive individuals, we compared post-conflict SDB levels in victims to their baseline levels. Furthermore, changes in group composition during the study …

Substrate Preference And Foraging Behavior Of Two Declining Warblers In Appalachian Shrublands, Samantha Fishman

Theses and Dissertations

Abstract Shrubland and early successional habitats (ESH) support a diversity of wildlife, including many declining songbirds. However, nonnative shrubs increasingly dominate these habitats, potentially altering habitat quality by influencing prey availability. Habitat quality can be assessed by observing foraging behaviors of songbirds and identifying preferred and avoided substrates as well as testing the predictions of optimal foraging theory; specifically that birds will maximize their energy gain relative to the costs. We examined foraging behaviors of Golden-winged Warblers (Vermivora chrysoptera; GWWA) and Chestnut-sided Warblers (Setophaga pensylvanica; CSWA) in ESH on private lands in Virginia with varying amounts …

Relational Discipleship In Ministry With Youth: Mapping The Past, Confronting The Gaps, Shaping The Future, Rogelio Paquini

Faculty Publications

No abstract provided.

Effectiveness Of Immersive Virtual Reality Combined With Task-Specific Training On Hemiplegic Arm Function Following Stroke: A Single Case Series Study, Rahsaan J. Holley

Theses and Dissertations

Motor recovery of the arm following a neurological injury is a common goal of the rehabilitation process for individuals impacted by the condition . Due to the long-term functional implications of impaired arm movement, individuals are often trained to use compensatory strategies to complete functional tasks without incorporating the weakened limb. While compensatory strategies offer stroke survivors the opportunity to regain a level of independence, such approaches fail to directly address the underlying causes of the disability (i.e., limited range of motion, decreased strength, poor coordination, motor planning difficulties) which can unfortunately result in long-term impairment and functional disability.

Motor …

Macular Pucker And Cataract Treated With Phacoemulsification And Iol Implantation Combined With Small-Gauge Pars Plana Vitrectomy: A Comparison Of Outcomes With And Without Femtosecond Laser Assistance, Howard Wen-Haur Chao, Cheng-Kuo Cheng, Shiow-Wen Liou, Hsiao-Ming Chao

Faculty, Staff and Students Publications

Objectives: Age-related cataracts and macular pucker are increasingly common. Standard treatment combines phacoemulsification, IOL implantation and small gauge vitrectomy. Recent advancements and acceptance of femtosecond laser (FSL) assistance in cataract surgery have improved precision and outcomes. However, evidence regarding the efficacy and safety of FSL-assisted phacovitrectomy, particularly in Oriental patient populations with distinct anatomical and genetic characteristics, remain limited. This study aims to address this critical gap by comparing the safety and post-operative outcomes of 23- or 25-gauge phacovitrectomy for stage 3 macular pucker and medium density cataract with versus without FSL-assistance (FSLA), in an Oriental patient cohort.

Methods: Patients …

Heterogeneous Clustering Of Multiomics Data For Breast Cancer Subgroup Classification And Detection, Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene …

Prompt-Based Learning For Few-Shot Class-Incremental Learning, Jicheng Yuan, Hang Chen, Songsong Tian, Wenfa Li, Lusi Li, Enhao Ning, Yugui Zhang

Computer Science Faculty Publications

Few-Shot Class-Incremental Learning (FSCIL) aims to enable deep neural networks to incrementally learn new tasks from a limited number of labeled samples, while retaining knowledge of previously learned tasks, mimicking the way humans learn. In this paper, we introduce a novel approach called Prompt Learning for FSCIL (PL-FSCIL), which leverages the power of prompts alongside a pre-trained Vision Transformer (ViT) model to effectively tackle the challenges of FSCIL. Our approach explores the feasibility of directly applying visual prompts in FSCIL, using a simplified model architecture. PL-FSCIL integrates two key prompts: the Domain Prompt and the FSCIL Prompt. Both are tensors …

A Data-Driven Sliding-Window Pairwise Comparative Approach For The Estimation Of Transmission Fitness Of Sars-Cov-2 Variants And The Construction Of The Evolution Fitness Landscape, Md Jubair Pantho, Richard Annan, Landen Alexander Bauder, Sophia Huang, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Estimating the transmission fitness of SARS-CoV-2 variants and understanding their evolutionary fitness trends are important for epidemiological forecasting. Existing methods are often constrained by their parametric natures and do not satisfactorily align with the observations during COVID-19. Here, we introduce a sliding-window data-driven pairwise comparison method, the differential population growth rate (DPGR) that uses viral strains as internal controls to mitigate sampling biases. DPGR is applicable in time windows in which the logarithmic ratio of two variant subpopulations is approximately linear. We apply DPGR to genomic surveillance data and focus on variants of concern (VOCs) in multiple countries and regions. …

Not Here, Go There: Analyzing Redirection Patterns On The Web, Kritika Garg, Sawood Alam, Dietrich Ayala, Michele C. Weigle, Michael L. Nelson

Computer Science Faculty Publications

URI redirections are integral to web management, supporting structural changes, SEO optimization, and security. However, their complexities affect usability, SEO performance, and digital preservation. This study analyzed 11 million unique redirecting URIs, following redirections up to 10 hops per URI, to uncover patterns and implications of redirection practices. Our findings revealed that 50% of the URIs terminated successfully, while 50% resulted in errors, including 0.06% exceeding 10 hops. Canonical redirects, such as HTTP to HTTPS transitions, were prevalent, reflecting adherence to SEO best practices. Non-canonical redirects, often involving domain or path changes, highlighted significant web migrations, rebranding, and security risks. …

Gramseq-Dta: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information, Kasul Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance …

The Invisible Influencer In Information Infrastructure, Herbert Van De Sompel, Michael L. Nelson

Computer Science Faculty Publications

The UPS Prototype was a proof-of-concept web portal built in preparation for the Universal Preprint Service Meeting held in October 1999 in Santa Fe, New Mexico. The portal provided search functionality for a set of metadata records that had been aggregated from a range of repositories that hosted preprints, working papers, and technical reports. Every search result was overlaid with a dynamically generated menu, called an SFX-menu, that provided a selection of value-adding links for the described scholarly work. The meeting eventually led to the Open Archives Initiative and its Protocol for Metadata Harvesting (OAI-PMH), which remains widely used in …

Github Repository Complexity Leads To Diminished Web Archive Availability, David Calano, Michael Nelson, Michele Weigle

Computer Science Faculty Publications

Software is often developed using versioned controlled software, such as Git, and hosted on centralized Web hosts, such as GitHub and GitLab. These Web hosted software repositories are made available to users in the form of traditional HTML Web pages for each source file and directory, as well as a presentational home page and various descriptive pages. We examined more than 12,000 Web hosted Git repository project home pages, primarily from GitHub, to measure how well their presentational components are preserved in the Internet Archive, as well as the source trees of the collected GitHub repositories to assess the extent …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Uncertainty-Aware Deep Learning Framework For Forecasting Coastal Water Level In Virginia Beach, Md Mahmudul Hasan, Malachi Schram, Sridhar Katragadda, Diana Mcspadden, Alisa N. Udomvisawakul, Heather Richter, Frank Liu

Computer Science Faculty Publications

Coastal areas like Virginia Beach, USA, are increasingly vulnerable to flooding. To mitigate the impact of flooding, it is crucial for the City of Virginia Beach to have reliable 72-hour-ahead (3 days) forecasts of water levels at key gauge locations. To support this effort, several sensors have been installed throughout the city to monitor water levels and other environmental parameters such as wind speed, precipitation, and atmospheric pressure. Leveraging sensor data from one of these locations, we developed an uncertainty-aware deep learning model to forecast water levels. We employed deep quantile regression (DQR) to quantify variability in the predictions and …

Cath-Ddg: Towards Robust Mutation Effect Prediction On Protein-Protein Interactions Out Of Cath Homologous Superfamily, Guanglei Yu, Xuehua Bi, Teng Ma, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Protein-protein interactions (PPIs) are fundamental aspects in understanding biological processes. Accurately predicting the effects of mutations on PPIs remains a critical requirement for drug design and disease mechanistic studies. Recently, deep learning models using protein 3D structures have become predominant for predicting mutation effects. However, significant challenges remain in practical applications, in part due to the considerable disparity in generalization capabilities between easy and hard mutations. Specifically, a hard mutation is defined as one with its maximum TM-score < 0.6 when compared to the training set. Additionally, compared to physics-based approaches, deep learning models may overestimate performance due to potential data leakage.

Results: We propose new training/test splits that mitigate data leakage according to the CATH homologous superfamily. Under the constraints of physical …

Multi-Modal Mri Based Segmentation Of Brain Metastases Using Adaptive Self-Attention, Evan Savaria

Computer Science Faculty Publications

Brain metastases (BMs) are the most common adult central nervous system malignancy, affecting 20–40% of cancer patients. Accurate segmentation of metastatic lesions in multi-modal MRI is essential for treatment planning and prognosis however, manual delineation is time consuming and prone to variability. Traditional deep learning models such as U-Net, have improved segmentation accuracy but capture limited long-range dependencies and struggle with variations in metastasis size, shape, and distribution. This study introduces the Adaptive Integrated Multi-modal Segmentation (AIMS) model, an adaptive self-attention framework within a hybrid U-Net and Transformer architecture to enhance BM segmentation by leveraging multi-modal MRI integration. The proposed …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Optiselect And Enshap: Integrating Machine Learning And Game Theory For Ischemic Stroke Prediction, Pritam Chakraborty, Anjan Bandyopadhyay, Sricheta Parul, Sujata Swain, Partha Sarathy Banerjee, Tapas Si, Hong Qin, Saurav Mallik

Computer Science Faculty Publications

Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing …

Sting: A Stealthy Backdoor Attack On Gnn-Based Malicious Domain Detection Via Dns Perturbations, Muhammad Anan, Mahmoud Nazzal, Abdallah Khreishah, Issa Khalil, Nhathai Phan, Ahmad Sawalmeh

Computer Science Faculty Publications

Detecting malicious Internet domains is essential for safeguarding against various online threats. The current approach to detecting malicious domains (MDD) employs a graph neural network (GNN) method, which uses DNS logs to construct heterogeneous graphs for determining the maliciousness of unknown domains. Despite its success, this method is vulnerable to data poisoning attacks where an adversary can manipulate specific graph nodes to implant a backdoor into the model during training. To showcase the vulnerability, we propose a stealthy trigger injection attack on node features and graph structure in MDD, dubbed (STING). The attacker carefully manipulates selected features and edges of …

Ai For Nuclear Physics: The Exclaim Project, S. Liuti, D. Adams, M. Boër, G. W. Chern, M. Cuic, M. Engelhardt, G. R. Goldstein, B. Kriesten, Y. Li, H. W. Lin, M. Sievert, D. Sivers

Computer Science Faculty Publications

An overview of the recent activity of the newly funded EXCLusives with AI and Machine learning (EXCLAIM) collaboration is presented. The main goal of the collaboration is to develop a framework to implement AI and machine learning techniques in problems emerging from the phenomenology of high energy exclusive scattering processes from nucleons and nuclei, maximizing the information that can be extracted from various sets of experimental data, while implementing theoretical constraints from lattice QCD. A specific perspective embraced by EXCLAIM is to use the methods of theoretical physics to understand the working of ML, beyond its standardized applications to physics …

Normalizing Images In Various Weather And Lighting Conditions Using Colorpix2pix Generative Adversarial Network, Sanjida Tasnim, Ashif Mahmud Mostafa, Azmain Morshed, Namreen Shaiyaz, Shakib Mahmud Dipto, Saad Aloteibi, Mohammad Ali Moni, Md. Golam Rabiul Alam, Md. Ashraful Alam

Computer Science Faculty Publications

Autonomous vehicles (AVs) are widely regarded as the future of transportation due to their tremendous benefits and user comfort. However, the AVs have been struggling with very crucial challenges, such as achieving reliable accuracy in object detection as well as faster computation required for quick decision-making. In recent years, perception systems in driverless cars have been significantly enhanced, mainly due to advances in deep-learning-based object detection systems. However, these perception systems are still heavily affected by environmental variables, such as changes in illumination, refractive interference, and adverse weather conditions, which may compromise their reliability and safety. This research proposes an …