Digital Commons Network^™

Full-Wave Modeling Of Rf Waves In Fusion Plasmas With Finite Element Method: Progress In Past Decades And Its Future Role, Syun'ichi Shiraiwa, Nicola Bertelli, Eun-Hwa Kim

Faculty Publications

No abstract provided.

Prophetic Preaching As A Tool For Social Justice, Trevor A. O'Reggio

Faculty Publications

No abstract provided.

Lost In Instructions: Study Of Blind Users' Experiences With Diy Manuals And Ai-Rewritten Instructions For Assembly, Operation, And Troubleshooting Of Tangible Products, Monalika Padma Reddy, Aruna Balasubramanian, Jiawei Zhou, Xiaojun Bi, Iv Ramakrishnan, Vikas Ashok

Computer Science Faculty Publications

AI tools like ChatGPT and Be-My-AI are increasingly being used by blind individuals. Although prior work has explored their use in some Do-It-Yourself (DIY) tasks by blind individuals, little is known about how they use these tools and the available product-manual resources to assemble, operate, and troubleshoot physical/tangible products – tasks requiring spatial reasoning, structural understanding, and precise execution. We address this knowledge gap via an interview study and a usability study with blind participants, investigating how they leverage AI tools and product manuals for DIY tasks with physical products. Findings show that manuals are essential resources, but product-manual instructions …

Explainable Convolutional Neural Network Model Provides An Alternative Genome-Wide Association Perspective On Mutations In Sars-Cov-2, Parisa C. Hatami, Richard Annan, Luis Miranda, Jane L. Gorman, Mengjun Xie, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Identifying informative genomic features in SARS-CoV-2 can help clarify patterns of viral evolution. In this study, we developed an explainable convolutional neural network (CNN) model to classify SARS-CoV-2 genomic sequences into the WHO-designated Variants of Concern (VOCs), Alpha, Beta, Gamma, Delta, and Omicron. Using a balanced dataset of genomes, the classification CNN achieved 99.96% accuracy on the held-out test set. To interpret the model’s predictions, we applied SHapley Additive exPlanations (SHAP) to estimate the contribution of each nucleotide position to VOC-label prediction and compared aggregated attributions with a chi-square GWAS baseline applied to the same categorical labels. SHAP prioritized several …

Variational Autoencoder Inverse Mapper For Extraction Of Compton Form Factors: Benchmarks And Conditional Learning, Douglas Adams, Md Fayaz Bin Hossen, Joshua Bautista, Gia-Wei Chern, Simonetta Liuti, Marie Boër, Marija Čuić, Michael Engelhardt, Gary R. Goldstein, Huey-Wen Lin, Yaohang Li

Computer Science Faculty Publications

Deeply virtual exclusive scattering processes (DVES) serve as precise probes of nucleon quark and gluon distributions in coordinate space. These distributions are derived from generalized parton distributions (GPDs) via Fourier transform relative to proton momentum transfer. QCD factorization theorems enable DVES to be parameterized by Compton form factors (CFFs), which are convolutions of GPDs with perturbatively calculable kernels. Accurate extraction of CFFs from DVCS, benefiting from interference with the Bethe–Heitler (BH) process and a simpler final state structure, is essential for inferring GPDs. This paper focuses on extracting CFFs from DVCS data using a variational autoencoder inverse mapper (VAIM) and …

Open Scholarly Information Systems: Status Quo, Challenges, Opportunities, Hannah Bast, Guillaume Cabanac, Paolo Manghi, Jian Wu, Marcel R. Ackermann

Computer Science Faculty Publications

Over the past 30 years, a rich ecosystem of scholarly information systems has developed that openly provide their services to the scientific community. These systems include aggregators of bibliographic metadata (e.g., DBLP, OpenCitations, OpenAIRE Graph, OpenAlex, ORKG, Semantic Scholar, CiteSeerX, and CORE); publication, data, and software repositories (e.g., Arxiv.org, Figshare, Zenodo, Software Heritage, and Dataverse); and PID authorities (e.g., ORCID, ROR, Crossref, and DataCite). This interdisciplinary Dagstuhl Seminar "Open Scholarly Information Systems: Status Quo, Challenges, Opportunities" (25381) was the first of its kind to bring together practitioners from this ecosystem, as well as researchers investigating related questions or relying on …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

Memebuddy: Dialog-Style Audio Representations For Engaging Non-Visual Meme Experiences, Chirag Bhansali, Vikas Ashok, Hae-Na Lee

Computer Science Faculty Publications

Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a …

Privacy-Preserving Federated Learning With Optimized Ensemble Weighting And Knowledge Distillation For Covid-19 Detection From Non-Iid Medical Imaging Data, Richard Annan, Hong Qin, Robert Newman, Madhuri Siddula, Letu Qingge

Computer Science Faculty Publications

Medical imaging enables rapid and accurate diagnosis of COVID-19, with CT scans proving especially effective. However, data privacy concerns limit collaborative model development across hospitals. To address this issue, we introduce a novel federated learning framework. It is referred to as Independent Knowledge Distillation with post-Ensemble Federated Learning (IKDEFL). Differential Privacy (DP) is integrated into the framework to improve privacy guarantees. Three DP mechanisms are evaluated. These include Fixed Gaussian, Gaussian Adaptive, and Tree Adaptive. The evaluation has been conducted on heterogeneous and Non-Independent and Identically Distributed (Non-IID) datasets. These datasets reflect real-world hospital scenarios. Results show that IKDEFL significantly …

Toward An Event-Level Analysis Of Hadron Structure Using Differential Programming, Kevin Braga, Markus Diefenthaler, Steven Goldenberg, Daniel Lersch, Yaohang Li, Jian-Wei Qiu, Kishansingh Rajput, Felix Ringer, Nobuo Sato, Malachi Schram

Computer Science Faculty Publications

Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon degrees of freedom is a central goal in nuclear and particle physics. This effort lies at the core of major experimental programs, such as the Jefferson Lab 12 GeV program and the upcoming Electron-Ion Collider. A primary challenge is the inherent inverse problem: converting large-scale observational data from collision events into the fundamental quantum correlation functions (QCFs) that characterize the microscopic structure of hadronic systems within the theory of QCD. Recent advances in scientific computing and machine learning have opened new avenues for addressing this challenge using …

Attf-Gnn: An Attention-Based Multi-Omics Graph Neural Network With Modality Learning For Disease Subtyping, Sovon Chakraborty, Eleni Adam, Terry Stilwell, Harold Riethman, Desh Ranjan, Pratip Rana

Computer Science Faculty Publications

We propose AttF-GNN, an attention-based graph fusion strategy for diseases classification and subtyping. In multiomics analysis, not all types of molecular data are equally relevant for disease subtyping and considering all modalities equally may obscure discriminative signals and limit the effectiveness of predictive models by overlooking modality-specific contributions. Therefore, we design an attention-based multimodal GraphSAGE framework that can automatically emphasize the modalities providing the most relevant information for classification. At first, we have constructed three graphs using mRNA, RNA-seq and DNA methylation modalities, and train each omics with individual GraphSAGE encoders. Next, a unified intersection graph is formed using an …

An Investigation Of Federated Gnns Under Aggregation, Data Poisoning, And Differential Privacy For Icu Length-Of-Stay Prediction, Shakib Mahmud Dipto, Soumya Banerjee, Sandip Roy, Ahmad F. Al Musawi, Preetam Ghosh, Sachin Shetty, Pratip Rana

Computer Science Faculty Publications

Accurate prediction of ICU Length of Stay (LoS) is essential for clinical decision-making and healthcare resource management. Graph Neural Networks (GNNs), such as GraphSAGE, offer a natural fit by capturing patient data from Electronic Health Records (EHRs) through graph structures. However, the distributed and sensitive nature of this data raises both privacy and legal concerns regarding the aggregation and training of GNN models. This additionally leads to issues with data imbalance and model robustness. In this study, we perform an analysis of the Federated Graph Neural Network (GNN-FL) framework to enable decentralized learning on EHRs derived from the MIMIC-III dataset. …

Deep Incomplete Multi-View Clustering Via Hierarchical Imputation And Alignment, Yiming Du, Ziyu Wang, Jian Li, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) aims to discover shared cluster structures from multi-view data with partial observations. The core challenges lie in accurately imputing missing views without introducing bias, while maintaining semantic consistency across views and compactness within clusters. To address these challenges, we propose DIMVC-HIA, a novel deep IMVC framework that integrates hierarchical imputation and alignment with four key components: (1) view-specific autoencoders for latent feature extraction, coupled with a view-shared clustering predictor to produce soft cluster assignments; (2) a hierarchical imputation module that first estimates missing cluster assignments based on cross-view contrastive similarity, and then reconstructs missing features using …

Distributed Semi-Speculative Parallel Anisotropic Mesh Adaptation, Kevin Garner, Polykarpos Thomadakis, Nikos Chrisochoides

Computer Science Faculty Publications

This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore cc-NUMA-based (shared memory) mesh generation software and a parallel runtime system that is designed to help applications leverage the concurrency offered by emerging high-performance computing (HPC) architectures. First, an initial mesh is decomposed and its interface elements (subdomain boundaries) are adapted on a single multicore node (shared memory). Subdomains are then distributed among the …

A Survey Of Ai Methods For Geometry Preparation And Mesh Generation In Engineering Simulation, Steven Owen, Nathan Brown, Nikos Chrisochoides, Rao Garimella, Xianfeng Gu, Franck Ledoux, Na Lei, Roshan Quadros, Navamita Ray, Nick Winovich, Yongjie Jessica Zhang

Computer Science Faculty Publications

Artificial intelligence is beginning to reduce the manual effort in the CAD-to-mesh pipeline. Written for meshing and geometry practitioners with limited AI background, this survey organizes recent work by workflow step. We cover part classification and segmentation, mesh quality prediction, and defeaturing. We review AI guidance for unstructured meshing, block-structured meshing in 2D and 3D, and volumetric parameterization, including reconstruction from implicit or sampled geometry. We also discuss parallel mesh generation and scripting automation via reinforcement learning and large language models. Across these topics, AI complements established geometry and meshing algorithms rather than replacing them. We conclude with practical lessons …

What’S Sleep Got To Do With It? A Mixed Methods Exploration Of The Feasibility, Acceptability, And Initial Outcomes Of Group Cbt For Insomnia In Parkinson’S Disease, Claire M. Williams

Theses and Dissertations

Parkinson’s disease is a progressive neurodegenerative disease that is commonly associated with sleep disturbances, affecting up to 80 percent of individuals with Parkinson’s. Effective and accessible treatments for sleep problems in Parkinson’s disease remain limited. Cognitive Behavioral Therapy for Insomnia (CBTi) is a well-established, non-pharmacological treatment for insomnia but has been minimally studied in Parkinson’s populations. This study evaluated the feasibility, acceptability, and preliminary outcomes of a group-based CBTi intervention adapted for individuals with Parkinson’s disease. Participants engaged in a six-week group intervention that incorporated Parkinson’s-specific adaptations. Preliminary findings indicated that the adapted group CBTi intervention was generally feasible and …

Use Of Transcranial Magnetic Stimulation As An Assessment Of Corticospinal Excitability In Individuals With Persistent Postural Perceptual Dizziness, Elizabeth Cornforth

Theses and Dissertations

Persistent postural-perceptual dizziness (PPPD) is a common, disabling, chronic vestibular disorder whereby individuals experience symptoms of non-vertiginous dizziness, unsteadiness, and sway or rocking that are present most hours of the day, most days of the week. Individuals with PPPD have been noted to have an increased burden of dizziness and reduced quality of life compared to other dizziness diagnoses. The cause of PPPD is not well understood which can lead to years of diagnostic testing, failed therapies, and poor symptom management. It is important to understand PPPD pathophysiology so that optimal treatment interventions can be developed for this disorder to …

Proof Of Recovery: A Model To Enhance Data Integrity In Data Management Systems, Gustaf Barkstrom

Theses and Dissertations

Businesses lose millions of dollars every year when they can’t restore data from backups. Research shows that Disaster Recovery Plan (DRP) testing is not conducted frequently enough, nor are records maintained that demonstrate full data recovery from backups. This work introduces a design science artifact called PRTOK that aims to increase DRP testing. The design science artifact is a software solution that integrates with Data Management Systems (DMS)

such as iRODS and DSpace, and can work with formats such as HDF5 and BagIt. Proof-of- recovery records, or tokens, are recorded in a replicated, resilient, and indelible proof-of- authority blockchain data …

Nanomagnet Based Straintronic Devices For Unconventional Computing: Simulation And Performance Analysis, Rahnuma Rahman

Theses and Dissertations

Nanomagnetic devices are of great interest in digital hardware because of their non-volatility and dynamic ability to change magnetization but suffer from high switching error rates and temperature sensitivity. Magnetostrictive nanomagnets that utilize strain to switch between stable magnetization states encoding bit information are of interest since they are extremely energy efficient as piezoelectric layers can be used to rotate magnetization that have switching energies in the range of attojoules. Their stochasticity can also be useful in probabilistic, analog, neuromorphic, and collective computing systems, where occasional switching errors are not devastating. The dissertation extends spintronics beyond conventional computing schemes by …

Local Lung Delivery Of Ginkgetin For Therapeutic Intervention In Lung Fibrosis, Cimone L. Richardson

Theses and Dissertations

Lung fibrosis (LF) is a progressive, irreversible pathological condition in the lungs that is ultimately fatal. Current drug treatments (nintedanib, pirfenidone, and nerandomilast) slow disease progression but cause significant systemic adverse effects due to oral administration. Transient receptor potential vanilloid 4 (TRPV4), a mechanosensitive ion channel, may play a role in LF progression and represent a novel anti-LF target. Ginkgetin (GKT), a natural biflavonoid, is a potent TRPV4 inhibitor with anti-inflammatory and antioxidative properties. Local lung delivery could achieve high local lung concentrations while minimizing systemic exposure. Accordingly, this project evaluates GKT's therapeutic intervention efficacy in LF via local lung …

Ontological Runtime Monitoring For Mission Engineering, Alexander R. Will

Theses and Dissertations

The advent of the Urban Air Mobility (UAM) concept will bring low-altitude aviation to civilians through passenger and cargo transport. However, the prospective vehicles in UAM studies are predominantly autonomous, raising questions about their efficacy and stability in densely populated urban areas. At the same time, extensive work has been performed to define a new branch of systems engineering that focuses on runtime behavior, synchronization, communication, and task allocation.This field is known as "mission engineering". Mission engineering has been deployed in military scenarios to model human-autonomy cooperation. By applying the concepts from this field to UAM, full systems-of-systems can be …

Real-Life Health Applications Of Wireless Sensing Using Edge Devices, Md Touhiduzzaman

Theses and Dissertations

Wireless sensing has emerged as a transformative paradigm that leverages ubiquitous radio-frequency signals for environmental perception and human activity recognition beyond their primary role in data communication. Unlike wearable sensors and camera-based systems, wireless sensing offers a non-intrusive, privacy-preserving, and cost-effective alternative that operates seamlessly under low-lighting conditions, through walls, and in non-line-of-sight (NLoS) scenarios. The growing availability of edge computing devices further enables real-time on-device data processing, making wireless sensing solutions highly suitable for in-home health monitoring and smart living applications. This dissertation investigates real-life health applications of wireless sensing through four completed systems, each addressing a distinct challenge …

Dielectric Grating Integrated Hg1-Xcdxte (Mercury Cadmium Telluride) Infrared Photodetectors For High Operating Temperature, Mohammad Ariful Hoque Sojib

Theses and Dissertations

Mercury cadmium telluride (Hg1-xCdxTe, MCT) infrared photodetectors typically require cryogenic cooling to suppress thermal noise, limiting their adoption in size, weight, power, and cost (SWaP-C) constrained military and commercial systems. This dissertation investigates dielectric grating array integration as an extrinsic route to high operating temperature (HOT) detection in Auger-suppressed MCT architectures. Finite element modeling shows that a grating assisted light coupling into a subwavelength (1.5 µm) Hg0.762Cd0.238Te mid-wave infrared layer acting as an absorptive waveguide, achieves photocarrier generation rate comparable to a conventional bulk (e.g., 5 µm) layer. For long-wave infrared detectors, coupled FDTD and drift-diffusion modeling, together with a …

Testing The Limits Of Provenance Analysis From Basaltic Fluvial Sediment Near Sandvatn, Iceland, As A Mars Analog, Audrey R. Putnam, Kirsten L. Siebach, Michael T. Thorpe, Valerie M. Tu, Elizabeth B. Rampe, Candice C. Bedford, Gelu Costin, Joseph J. Tamborski

OES Faculty Publications

Detrital sediments that accumulate downstream and are preserved in sedimentary rocks can allow characterization of geologic formations that are inaccessible for spatial or temporal reasons. However, mixing, sorting, and alteration of sediment during transport may complicate reconstruction of protolith characteristics. We test the preservation of three key provenance signals in coarse fluvial sand at a Mars analog watershed in Iceland to determine whether detrital sediments capture (a) watershed magmatic chemical variation, (b) textural indicators of lava-water interaction during eruption and cooling, and (c) hydrothermal alteration. Specifically, we tested whether diagnostic variations in rock mineralogy, chemistry, and texture can be recovered …

Aquatic Macrophytes As Nature-Based Solutions: Challenges And Opportunities Across Inland And Coastal Waters, Morgan Botrel, Roxane Maranger, Lindsay L. Trottier, Mathieu Cusson, Lars L. Iverson, Fanny Noisette, Christian Nozais, Katrine Turgeon, Andrea Bertolo, Thibaud André-Alphonse, Simon Bélanger, Arthur De Grandpré, Jean-Olivier Goyette, Sabine Hilt, Ladd Johnson, Valérie Langlois, Martin Laporte, Félix Lauzon, Brigitte Légaré, Anne Ola, Marie-Pomme Presne-Poissant, Matthieu Prugne, Caroline Susini, Richard Zimmerman

OES Faculty Publications

Macrophytes are foundation species whose use as nature-based solution (NbS) in aquatic ecosystems can help reduce climate change impacts while mitigating biodiversity loss. The discrepancy in research and application of macrophytes as NbS across inland and marine coastal ecosystems is an opportunity to expand the concept and learn from different approaches. We report on an initiative uniting marine and freshwater scientists that compared knowledge on macrophytes as NbS across realms, identified advances and gaps in their use, and synthesized challenges and opportunities for application. We conducted a bibliometric analysis and an expert participatory exercise around recent review papers. We reveal …

Revisiting The Life Cycle Of Margalefidinium Polykrikoides Group Iii, Eduardo Pérez-Vega, Kenneth N. Mertens, Pjotr Meyvisch, Margaret R. Mulholland

OES Faculty Publications

Dinoflagellates produce cysts as a strategy to withstand environmental stressors, with nutrient depletion generally considered a key trigger for cyst production. Resting cysts are thick-walled, typically composed of one to several layers, and characterized by a prolonged dormancy period. In contrast, pellicle cysts possess a thin, single wall and exhibit no dormancy or a markedly shorter dormancy than resting cysts of the same species. Margalefidinium polykrikoides produces pellicle and resting cysts, whereas its congener, M. fulvescens, has been shown to produce pumpkin-like structures. Using phase-contrast microscopy, time-lapse microscopy, FlowCam, and attenuated total reflection Fourier transform infrared microspectroscopy (ATR μ-FTIR), …

Effect Of Density Irregularities On Radio Frequency Wave Propagation In Ionospheric Plasmas, Eun-Hwa Kim, Jay Johnson, Syun'ichi Shiraiwa, Nicola Bertelli, Masayuki Ono, Simon Wing

Faculty Publications

No abstract provided.

Effects Of Density Turbulence On Helicon Wave Propagation In The Core Plasmas, Eun-Hwa Kim, Syun'ichi Shiraiwa, Seung-Hoe Ku, A. Bortolon, Masayuki Ono, Nicola Bertelli, Robert I. Pinsker

Faculty Publications

No abstract provided.

Existence, Nonexistence And Uniqueness Of Positive Solution To A General Elliptic System With Fixed Or Perturbed Growth Rates, Joon Hyuk Kang, Noah Koliadko

Faculty Publications

No abstract provided.

Investigation Of Causal Relationships Of The Cross-Scale Wave Coupling Through Information Theoretical Approach, Elmer Rivera, J. Johnson, Simon Wing, Eun-Hwa Kim, Khan-Hyuk Kim, Katariina Nykyri

Faculty Publications

No abstract provided.