Digital Commons Network^™

Quasielastic ³H⃗E (E⃗ , E′) Asymmetry In The Threshold Region With A Longitudinally Polarized Target, M. Nycz, W. Armstrong, T. Averett, C. Ayerbe Gayoso, X. Bai, J. Bane, S. Barcus, J. Benesch, H. Bhatt, D. Bhetuwal, D. Biswas, A. Camsonne, G. D. Cates, J-P. Chen, J. Chen, M. Chen, C. Cotton, M. M. Dalton, A. Deltuva, A. Deur, B. Dhital, B. Duran, S. C. Dusa, I. Fernando, E. Fuchey, B. R. Gamage, H. Gao, D. Gaskell, T. Gautam, N. Gauthier, J. Golak, J. -O. Hansen, F. Hauenstein, W. Henry, D. W. Higinbotham, G. M. Huber, C. Jantzi, S. Jia, K. Jin, M. Jones, S. Joosten, A. Karki, B. Karki, S. Katugampola, S. Kay, C. Keppel, E. King, P. King, W. Korsch, V. Kumar, R. Li, S. Li, W. Li, D. Mack, S. Malace, P. Markowitz, J. Matter, M. Mccaughan, Z-E. Meziani, R. Michaels, A. Mkrtchyan, C. Morean, V. Nelyubin, H. Nguyen, G. Niculescu, M. Niculescu, C. Peng, S. Premathilake, A. Puckett, A. Rathnayake, M. Rehfuss, P. Reimer, G. Riley, Y. Roblin, J. Roche, M. Roy, P. U. Sauer, S. Scopeta, M. Satnik, B. Sawatzky, S. Seeds, S. Širca, R. Skibiński, G. Smith, N. Sparveris, H. Szumila-Vance, A. Tadepalli, V. Tadevosyan, Y. Tian, W. A. Tobias, A. Usman, H. Voskanyan, H. Witala, S. Wood, B. Yale, C. Yero, A. Yoon, J. Zhang, Z. Zhao, X. Zheng, J. Zhou

Physics Faculty Publications

We report on the measurements of the double-spin asymmetry from electron-³He scattering in the threshold region of two- and three-body breakup of ³He for 4-momentum transfer values of 0.1 and 0.2 (GeV/c)². The measurement was done at Jefferson Lab using a polarized ³He target with its spin aligned parallel to the beam direction. This asymmetry is sensitive to a combination of spin-dependent response functions not previously measured and may highlight differences in two body forces, meson-exchange currents and final-state interactions. The results of this measurement serve as a test of our understanding of few-body systems. When compared with …

Real-Space Imaging Of The Electron-Pair Density Hole In Molecular Auger-Meitner Decay, Mats Simmermacher, Nathan Goff, Andres Moreno Carrascosa, Elke Fasshauer, Thomas Northey, Lingyu Ma, Haiwang Yong, Brian Stankus, Asami Odate, Xuan Xu, Wenpeng Du, Kyle Acheson, Joseph C. Cooper, Daniel Ratner, Mengning Liang, Ruaridh Forbes, Michael P. Minitti, Adam Kirrander, Peter M. Weber

Physics Faculty Publications

Electrons in matter can rearrange extremely quickly under external perturbations, underpinning subsequent structural and chemical transformations. Coulomb interactions between neighbouring electrons often shape this response, giving rise to correlated motion and strongly affecting the distribution of electrons in the system. Here we show that non-resonant hard X-ray scattering can directly access changes in the radial electron-pair density during the rapid rearrangement of core and valence electrons. We do this by studying sulfur hexafluoride molecules undergoing Auger–Meitner decay. We exploit a second-order interaction between the X-ray photons and the molecules to trigger and probe the decay dynamics with a single pulse, …

Reaching The Intrinsic Performance Limits Of Superconducting Nanowire Single-Photon Detectors Up To 0.1 Mm Wide, Kristen M. Parzuchowski, Eli Mueller, Bakhrom G. Oripov, Benedikt Hampel, Ravin A. Chowdhury, Sahil R. Patel, Daniel Kuznesof, Emma K. Batson, Ryan Morgenstern, Robert H. Hadfield, Varun B. Verma, Matthew D. Shaw, Jason P. Allmaras, Martina J. Stevens, Alex Gurevich, Adam N. Mccaughan

Physics Faculty Publications

Superconducting nanowire single-photon detectors combine high detection efficiency, low noise, and excellent timing resolution, making them a leading platform for photon-counting applications. However, despite decades of materials and fabrication research, detector performance has never been shown to match theoretical performance expectations. Here, we demonstrate in situ tuning of a detector from its typical, suboptimal operation, to a regime limited only by material quality, allowing the device to reach its intrinsic performance limit. Our approach is based on current-biased superconducting “rails” placed on either side of the detector that redistribute current across its width to achieve peak performance. This technique reduces …

Efficient Dynamic And Momentum Aperture Optimization For Lattice Design Using Multipoint Bayesian Algorithm Execution, Z. Zhang, I. Agapov, S. Gasiorowski, T. Hellert, W. Neiswanger, X. Huang, D. Ratner

Physics Faculty Publications

We demonstrate that multipoint Bayesian algorithm execution (multipointBAX) can overcome fundamental computational challenges in storage ring design optimization. Dynamic aperture and momentum aperture optimization is a multipoint, multiobjective design task for storage rings, ultimately informing the flux of x-ray sources and luminosity of colliders. Current production black-box optimization methods require extensive particle-tracking simulations for each trial configuration; the high computational cost restricts the extent of the search to ~ 10³ configurations and therefore limits the quality of the final design. We remove this bottleneck using multipointBAX, which selects, simulates, and models each trial configuration at the single-particle level. We demonstrate …

Gaze Transition Entropy And Automation Trust In Multitasking Workspace, Yusuke Yamani, Austin Jackson, Tetsuya Sato, Feyishola Ashimi, Michael S. Politowicz, Eric T. Chancey, Makoto Itoh

Psychology Faculty Publications

Safe flight operation requires visual scanning across multiple displays in a cockpit, which collectively represent the state of the aircraft and supporting automation. Trust is a crucial factor that drives human-automation interaction, and recent work has suggested a relationship between an operator's visual attention and automation trust. One index that captures predictability of eye movements between different areas of interest is gaze transition entropy. The current work reanalyzed data from Sato et al., which examined eye movement patterns and trust in automation associated with the system monitoring task of the Multi-Attribute Task Battery. Results showed credible positive correlations between the …

Artificial Intelligence And Machine Learning In Smart Vaginal Formulation Development, Deborah A. Ogundemuren, Vivek Agrahari, Andrew P. Wong, Carolina Herrera, Margaret O. Ilomuanya, Gustavo F. Doncel

CONRAD Publications

Vaginal drug delivery in women's health remains underutilized and insufficiently studied, largely due to the complexity and dynamic nature of the vaginal microenvironment. Variations in vaginal pH, hormonal levels, and microbiota composition introduce significant biological variability, complicating formulation design and contributing to inconsistent therapeutic outcomes and poor patient adherence. Conventional vaginal formulations often fail to account for these individual differences, highlighting the need for more adaptive and predictive approaches. Emerging advances in artificial intelligence (AI) and machine learning (ML) offer promising strategies to address these challenges by enabling multi-parameter, data-driven formulation development that explicitly considers biological variability. Despite their transformative …

Micrornas Regulated By Pregnancy Target Antiviral And Cancer Immunity Overlapping With The Hiv Interactome, Paula F. T. Cezar-De-Mello, Jonathan M. Dreyfuss, Pai-Lien Chen, Hidemi Yamamoto, Xiaoming Gao, Hui Pan, Charles Morrison, Gustavo F. Doncel, Robert L. Barbieri, Raina N. Fichorova

CONRAD Publications

Innate immunity predictors of HIV-1 risk and pathogenesis vary with reproductive hormones, pregnancy, and lactation, yet the underlying mechanisms remain unclear. We hypothesized that pregnancy-associated physiological adaptations alter systemic microRNA (miRNA) expression, thereby regulating immunity, pathogenesis and susceptibility to infection. We analyzed 174 serum samples from 88 participants in a longitudinal cohort from Uganda and Zimbabwe across pre-pregnancy (PP), pregnancy (P), and postpartum breastfeeding (BF). Cell-free peripheral blood miRNAs (n = 2083) were profiled using HTG EdgeSeq. Pregnancy-specific miRNAs were identified by intersecting differentially expressed (DE) miRNAs from P vs. PP and P vs. BF comparisons. miRNA targets and pathways …

Large Language Model-Assisted Research Question Development In Public Health: A Case Study In The Special Supplemental Nutrition Program For Women, Infants, And Children (Wic), Qi Zhang, Bidusha Neupane, Priyanka Patel, Futun N. Alkhalifah, Yi He, Leslie Hodges

Health Behavior, Policy & Management Faculty Publications

Objective:

To assess the feasibility of using large language models (LLMs) to develop research questions about changes to the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) food packages.

Design:

We conducted a controlled experiment using ChatGPT-4 and its plugin, MixerBox Scholarly, to generate research questions based on a section of the USDA summary of the final public comments on the WIC revision. Five questions weekly for three weeks were generated using LLMs under two conditions: fed with or without relevant literature. The experiment generated 90 questions, which were evaluated using the FINER criteria (Feasibility, Innovation, Novelty, Ethics, …

Pilot Study On The Use Of Multimorbidity Index In Patients Receiving Home Parenteral Nutrition, Michael M. Rothkopf, Mohan Pant, Zachary Rothkopf, Rebecca Brown Rdn, Jaime Haselhorst Rdn, Debbie L. Stevenson Rdn, Andrew Depalma Mba, Michael Saracco

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background

Home parenteral nutrition (HPN) patients often have multiple comorbidities. A validated multimorbidity index (MMI) could help determine resource needs and risks with HPN. We evaluated MMI scoring to determine if it predicted HPN resource use and outcomes.

Methods

We performed a secondary analysis of 60 HPN patients from a previous study based on Cumulative Illness Rating Scale (CIRS) scoring. We examined three variables: PN formula changes, hospitalizations, and hospital length of stay (LOS). Density plots were produced to select a CIRS score cutoff value. Spearman correlations among the three variables of interest were computed. The data were then subjected …

A Critical Perspective On The Society Of Environmental Toxicology And Chemistry's Adherence To Founding Principles— Opportunities For The Future, Barnett A. Rattner, Annegaaike Leopold, Carys L. Mitchelmore, Glenn W. Suter, Mark S. Johnson, Adriana C. Bejarano, Lawrence A. Kapustka, Niranjana Krishnan, Derek C. G. Muir, Beatrice O. Opeolu, Martha Georgina Orozco-Medina, April Reed, Bruce W. Vigon, Adam R. Wronski

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

The Society of Environmental Toxicology and Chemistry (SETAC) is a global organization whose mission is the advancement of environmental science and management through collaboration, leadership, communication and education. On SETAC's 45th anniversary, the following question was raised: Are the 1979 founding principles of SETAC, multidisciplinary approaches to solving environmental problems, multisector engagement and scientific objectivity, still useful, adequate and effective in fulfilling its mission? In a special session held at the 45th Annual Meeting in Fort Worth, Texas, United States, a critical evaluation of the founding principles was initiated by reviewing SETAC's history and ongoing activities, and recommendations were made …

Vitamin D Status During Pregnancy Modulates The Effect Of Pre-Pregnancy Obesity On Gestational Diabetes Mellitus Risk: A Birth Cohort Study, Ali H. Ziyab, Abdullah Al-Taiar, Reem Al-Sabah, Majeda S. Hammoud, Saeed Akhtar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Objective:

To determine whether gestational vitamin D status modulates the effect of pre-pregnancy obesity on gestational diabetes mellitus (GDM) risk while stratifying by maternal age.

Design:

Birth cohort.

Setting:

A major maternity hospital in Kuwait.

Participants:

Pregnant women in their second/third trimester of gestation were enrolled. Pre-pregnancy body mass index (BMI, kg/m²) was categorized as under/normal weight (< 25.0), overweight (25.0 to < 30.0), and obesity (≥30.0). Gestational 25-hydroxyvitamin D concentrations were categorized as deficiency (< 50 nmol/L) or insufficiency/sufficiency (≥50 nmol/L). GDM status was ascertained according to international guidelines. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were estimated using logistic regression.

Results:

Data from 957 pregnant women were analyzed, with GDM affecting 166 (17.4%) pregnancies. Pre-pregnancy obesity and gestational vitamin D deficiency was ascertained in 275 (28.7%) and 533 (55.7%) pregnant women, respectively. The association between pre-pregnancy obesity and GDM risk differed according …

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …

Multiple Changepoint Detection For Non-Gaussian Time Series, Robert Lund, Thomas J. Fisher, Norou Diawara, Michael Wehner

Mathematics & Statistics Faculty Publications

This article combines methods from existing techniques to identify multiple changepoints in non‐Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non‐Gaussian series, enabling penalized likelihood methods to handle non‐Gaussian scenarios. When the marginal distribution of the data is continuous, the methods essentially reduce to the change of variables formula for probability densities. When the marginal distribution is count‐oriented, Hermite expansions and particle filtering techniques are used to quantify the scenario. Simulations demonstrating the efficacy of the methods are given and two data sets are analyzed: 1) the proportion of home runs hit by …

Adaptive Multi-Grade Deep Learning For Highly Oscillatory Fredholm Integral Equations Of The Second Kind, Jie Jiang, Yuesheng Xu

Mathematics & Statistics Faculty Publications

This paper studies the use of Multi-Grade Deep Learning (MGDL) for solving highly oscillatory Fredholm integral equations of the second kind. We provide rigorous error analyses of continuous and discrete MGDL models, showing that the discrete model retains the convergence and stability of its continuous counterpart under sufficiently small quadrature error. We identify the DNN training error as the primary source of approximation error, motivating a novel adaptive MGDL algorithm that selects the network grade based on training performance. Numerical experiments with highly oscillatory (including wavenumber 500) and singular solutions confirm the accuracy, effectiveness and robustness of the proposed approach.

Changepoint Analyses Confirms Global Tropical Cyclone Frequency Decline, Michael Wehner, Thomas Fisher, Norou Diawara, Robert Lund

Mathematics & Statistics Faculty Publications

Changes in tropical cyclone frequencies as the climate warms is a topic of significant current debate [1, 2]. There is no accepted theory of how tropical cyclogenesis might respond to a warmer ocean-atmosphere system as multiple controlling factors exist [3–7]. Anthropogenic warming of surface ocean temperatures due to increased greenhouse gas concentrations [8] increases the potential for tropical cyclogenesis [9–11]; however, realized cyclogenesis also requires an initial local disturbance [12–16] to develop. Most multi-decadal tropical cyclone permitting climate models (i.e. resolutions of 15-50km) exhibit frequency decreases in warmer climates, despite the increase in tropical cyclogenesis potential [17–25]. In this paper, …

From Screens To Stress: Public Health Implications Of Cancer Worry In A Digitally Connected World, Shreya Mathur, Ethan Burns, Michael Pokojovy, Tzu-Liang Bill Tseng, Sunil Mathur

Mathematics & Statistics Faculty Publications

Aims:

This study examines how digital information environments, genetic testing experiences, health behaviors, and psychological distress influence cancer-related worry among adults in the United States. It further considers the public health implications of elevated or reduced cancer worry for prevention and risk communication.

Methods:

Data were drawn from a nationally representative survey of 6,252 U.S. adults. Measures included reliance on social media for health decision-making, smoking status, history of genetic testing, and psychological distress. Multivariable analyses assessed associations between these factors and levels of cancer worry.

Results/Findings:

Reliance on social media for health decisions was associated with greater cancer worry, …

A Composite Narxnn Approach To Photovoltaic Power Forecasting With Integrated Weather Inputs And Uncertainty Quantification, Denisse Urenda Castañeda, Sharmin Abdullah, Jackson Morgan, Honglun Xu, Michael Pokojovy, Tzu-Liang Tseng

Mathematics & Statistics Faculty Publications

Solar photovoltaics (PV) are a major source of sustainable energy. Yet, their power output is highly sensitive to environmental variability, particularly solar irradiance, cloud cover, wind, and temperature. Accurate forecasting of PV power is essential for efficient grid integration and energy planning, especially in applications requiring reliable longer-term forecasting rather than one-step-ahead predictions. This study presents a PV power forecasting approach using Nonlinear Autoregressive models with Exogenous Inputs (NARX), integrating large-scale numerical weather historical data as exogenous variables. Although NARX models effectively capture temporal dependencies, they can become overly dependent on historical power values, reducing responsiveness to real-time weather changes. …

A New Parallel-In-Time Direct Inverse Method For Nonlinear Differential Equations, Nail K. Yamaleev, Subhash Paudel

Mathematics & Statistics Faculty Publications

We propose a new method for parallelization of the first-order backward difference discretization (BDF1) of the first-order time derivative in nonlinear partial differential equations, such as conservation law equations. The time derivative term is discretized by using the method of lines based on the implicit BDF1 scheme, while the inviscid and viscous terms are approximated by conventional 2nd-order central discretizations of the 1st- and 2nd-order derivatives in each spatial direction. The global system of nonlinear discrete equations in the space-time domain is solved by the Newton method for all time levels simultaneously. For the BDF1 discretization, this all-at-once system at …

The Adaptive Solution Of High-Frequency Helmholtz Equations Via Multi-Grade Deep Learning, Peiyao Zhao, Rui Wang, Tingting Wu, Yuesheng Xu

Mathematics & Statistics Faculty Publications

The Helmholtz equation is fundamental for modeling wave propagation in acoustics, electromagnetics, and geophysics; however, high-frequency regimes remain notoriously difficult due to the severe numerical “pollution effect.” We propose FD-MGDL, an adaptive framework that synergizes finite difference discretizations with Multi-Grade Deep Learning (MGDL) to efficiently resolve high-frequency wavefields. Unlike standard physics-informed neural networks (PINNs), which frequently suffer from spectral bias and heavy automatic differentiation overhead, FD-MGDL employs a progressive, grade-wise training strategy that incrementally incorporates shallow sub-networks to refine residual errors. By leveraging ReLU activations in refinement grades, the framework reformulates the highly non-convex global optimization problem into a sequence …

Ocular Surface Disease Following Lasik And Cataract Surgery: A Review Of Their Interrelated Complications, Matthew D. Spangler, Nila Kirupaharan, John D. Sheppard

Department of Ophthalmology Faculty Publications

Background: Ocular surface disease is a multifactorial condition that is very commonly caused by dry eye disease (DED). Ophthalmic procedures intended to improve visual outcomes, laser-assisted in situ keratomileusis (LASIK) and cataract surgery, can paradoxically cause or exacerbate underlying ocular surface disease. This results in worsening vision and quality of life.

Areas covered: This review examines the pathophysiological mechanisms contributing to ocular surface disease development following LASIK and cataract surgery. Both procedures are associated with the transection of corneal nerves, leading to decreased tear production, surface instability, altered neurotrophin production, and impairment of the blink reflex. Furthermore, these incisional procedures …

Steady As She Goes: Remote Work Benefits Daily Goal Progress Through Reduced Energy Variability, Andrew A. Bennett, Kathleen R. Keeler, Emily D. Campion, Sheila K. Keener

Management Faculty Publications

As the prevalence and allowance of remote and hybrid work evolves, research has shown consistently mixed findings about how work location is related to worker energy and perceptions of daily performance. We aim to resolve this ambiguity by integrating affective events theory with ideas about emotion dynamics to develop new theorizing explaining why energy variability, rather than mean energy level, is an important predictor of daily goal progress. We test our hypotheses using a sample of 219 employees from one organization allowing remote and hybrid work, capturing individual hourly energy levels for five consecutive workdays (4,812 hourly observations). Our …

The Emerging Social Media Water Cooler? Motivations For Reddit Use In A Niche League, Jan Boehmer, Brendan O'Hallarn

Communication & Theatre Arts Faculty Publications

Social media play a key role in sport marketing—with athletes, teams, and leagues directly engaging with their audiences. While Facebook, Instagram, and Twitter have received much attention in the sport marketing and communication literature, research on Reddit is scant. This is particularly noteworthy, as the platform is growing rapidly and promises utility for users with nonmainstream interests to find and engage with each other. This study employs a uses and gratifications approach to investigate how prevalent Reddit use is among fans of the United Football League (UFL), what motivates users to engage with the platform, and whether Reddit use leads …

Stages Of Being Foreign As Portrayed In Daughter Of Keltoum And Lost Birds, Fran Hassencahl

Communication & Theatre Arts Faculty Publications

In this paper, I use the intercultural communication model, The Stages of Being Foreign, developed by anthropologist Oberg (1960) and modified by Gullahorn & Gullahorn (1963), Lewis & Jungman (1986), and Ward, Bochner, and Furnham (2001) to explain the experience of culture shock. This model describes the stages that individuals experience when they leave, either voluntarily or involuntarily, their home country/culture and must adapt to a new culture. These stages as described by Oberg are a preliminary stage (a spectator stage), an increasing participation stage, the pit of the culture shock stage, the adaptation stage, and for some migrants a …

Enlem: Ensemble Learning-Based Model To Detect Phishing Websites, Most Nilufa Yeasmin, Md Abu Rumman Refat, Bikash Chandra Singh, Zulfikar Alom, Zeyar Aung, Mohammad Azim

School of Cybersecurity Faculty Publications

Phishing involves manipulating individuals into revealing private data, e.g., user IDs, bank details, and passwords. The observed surge in fraud is related to increased deception, impersonation, and advanced online attacks. Thus, effective phishing detection methods are required to mitigate escalating global phishing threats. Existing methods (e.g., heuristics-based, signature-based, and visual similarity-based methods) attempt to detect phishing sites, and machine learning (ML) and deep learning (DL) methods are effective in the cybersecurity context in terms of learning from data, offering insights, and forecasting. However, independent ML algorithms are limited when handling complex data, and DL techniques surpass traditional ML methods in …

Toward Secure And Practical Machine Learning-Based Access Control: A Framework With Real-World Constraints And Adversarial Analysis, Olusesi Balogun, Mohammad Ghasemigol, Zhipeng Cai, Daniel Takabi

School of Cybersecurity Faculty Publications

Attribute-Based Access Control (ABAC) frameworks coordinate access requests based on subject, object, and environment attributes, as well as policy rules, and are widely used in corporate security systems. Recently, machine learning has been applied to ABAC to address policy-generation imbalances, misassigned privileges, and attribute leakages. However, existing MLBAC techniques do not consider the structural constraints and attribute interdependencies present in traditional ABAC systems. Moreover, these frameworks have not been extensively evaluated under black-box attack scenarios. To address these gaps, we propose extensions to MLBAC that integrate structural constraints, attribute dynamism, and attribute weighting into the MLBAC objective function. Additionally, we …

A Phenomenological Analysis Of Sense Of Belonging Among Community College Educators, Muhammad Owais Aziz

EVMS School of Health Professions Theses & Dissertations

This phenomenological study explored the complex phenomenon of sense of belonging (SB) among diverse health professions educators (HPEs) and non-health professions educators (non-HPEs) at a Canadian community college. SB, as a dynamic construct shaped by an individual's perception of being valued and respected, is influenced by personal preferences and by complex interactions with the individual's surroundings. To better understand SB and foster a more inclusive environment for diverse educators, it is necessary to investigate SB among these educators. This qualitative study used eleven semi-structured interviews and seven steps of data analysis. The results of this study can inform strategies, policies, …

A Comprehensive Overview Of Neurophysiological Correlates Of Cognitive Impairment In Amyotrophic Lateral Sclerosis, Seyyed Bahram Borgheai, Brie E. Achorn, Alyssa H. Zisk, Sarah M. Hosni, Karl E. G. Richter, Frank S. Menniti, Yalda Shahriari

Computer Science Faculty Publications

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that leads to the gradual loss of motor control, typically resulting in paralysis and death within 3 to 5 years of diagnosis. ALS shares neuropathological and genetic associations with fronto-temporal dementia (FTD), a neurodegenerative condition primarily impacting cognitive functions. These two conditions are increasingly viewed as manifestations of a single molecular disease process that affects distinct brain systems, impacting motor neuronal pathways in ALS and fronto-cortical functions in FTD. However, this simple dichotomy belies the complexity of these conditions. In particular, patients with primary motor ALS can also experience significant cognitive …

Sage: Spatially Aware Gene Selection And Dual-View Embedding Fusion For Domain Identification In Spatial Transcriptomics, Yi He, Yunpei Xu, Liqing Ding, Hong-Dong Li, Yaohang Li, Shaokai Wang

Computer Science Faculty Publications

Despite enabling high-resolution mapping of gene expression within tissues, spatial transcriptomics (ST) still faces challenges in accurately segmenting spatial domains due to complex tissue architecture and limitations of current methods. Most approaches rely on local spatial priors, lack gene-level interpretability, and fall short in capturing structure-discriminative genes or long-range functional relationships, limiting their ability to resolve biologically meaningful architectures. We present Spatially Aware Gene selection and dual-view Embedding fusion (SAGE), a unified and reproducible framework for domain identification in spatial transcriptomics that combines topic-driven gene selection with dual-view embedding fusion to address these gaps. SAGE integrates non-negative matrix factorization (NMF)-based …

Quantum Machine Learning Models: Principles, Frameworks, And Computational Challenges, K. A. Jayabalaji, S. Venkata Anand, Dineshkumar Rajendran, Prasanta Chatterjee Biswas, Sardor Omonov, Rubaid Ashfaq

Computer Science Faculty Publications

Quantum machine learning (QML) has become an optimistic avenue of harnessing quantum computation in data-driven modeling, especially of issues with high dimensionality and complicated correlations. Current methods are generally based on fixed or over-parameterized quantum circuits, and hence restricted to scalability as well as unproductive optimization in real-world hardware. This chapter introduces a hybrid quantum-classical learning system that is adaptive and provides principled quantum data encoding, architecture-conscious variational circuit design and resource-optimal optimization. The technique is based on the concepts of quantum architecture search and subspace-preserving transformations to trade expressiveness with trainability, and discretize the quantum model into a classical …

Examining Inclusive Computing Education For Blind Students In India, Akshay Kolgar Nayak, Yash Prakash, Md Javedul Ferdous, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

The growing demand for computer professionals, driven by the expanding Information Technology industry, has led to numerous inclusive computing education efforts. These efforts have even included blind or visually-impaired (BVI) students, who are being increasingly encouraged to pursue education and a career in computing, despite the visually-oriented nature of the discipline. Extant literature has predominantly focused on identifying and addressing the accessibility barriers faced by BVI students to promote more inclusive learning environments. While few studies have also investigated the accessibility of computing education from the perspectives of BVI learners and instructors, these have been primarily situated in the Global …