Digital Commons Network^™

Complete Arcs From Plane Curves, Desmond West-Hedlund

IdeaFest 2026

We discuss the conditions under which the Fq-rational points of a smooth degree-d projective plane curves are maximal under inclusion with respect to the property that no d+1 are collinear in the plane.

Reclaiming Agency: Ai Hallucinations And Translingual Interrogations In The City Tech Writing Center,, Joseph Franklin, Anna Laura Falvey

Publications and Research

No abstract provided.

Strategic Peer Region Selection For Economic Development, Xiaobing Shuai, Riley Ford

School of Professional and Continuing Studies Faculty Publications

Benchmarking against peer and aspirational regions is essential for economic development because it provides meaningful context for evaluating performance and shaping strategy. These comparisons help highlight regional advantages that attract new investments, as well as uncover gaps that may represent opportunities for future growth. ...

This article provides a practical, data-driven framework that economic development organizations can use to identify peer and aspirational regions, strengthen benchmarking efforts, and support strategy development. A case study illustrates how this can be applied to better interpret economic indicators and inform decision-making. For EDOs, this approach offers a more consistent way to evaluate competitiveness, …

Alternative English Language Development Scheduling In High School: Long-Term English Learners’ Attitudes And Perceived Control, Kira Rashada

University of the Pacific Theses and Dissertations

This explanatory sequential mixed-methods study examined whether an alternative English Language Development (ELD) scheduling model influenced ninth-grade Long-Term English Learners’ (LTELs) attitudes toward school and perceived control at a California high school operating on a block schedule. Guided by Self-Determination Theory (Ryan & Deci, 2000), the study explored how differing ELD scheduling models may shape students’ experiences related to autonomy, competence, and relatedness. Specifically, the study compared ninth-grade LTEL students who participated in a traditional 90-minute ELD course during the 2023-24 school year with students who participated in an alternative scheduling model during the 2024-25 school year in which designated …

Statistics 103a Instructor Guide, Elizabeth R. Wentworth

Open Educational Resources

This set of slides contains reading, original videos, activities and instructions for instructors to run a complete 12 week course in any modality. These resources can be used to supplement in-person instruction or can be used for either a hybrid or asynchronous course.

Integrated Seismic And Sequence Stratigraphic Analysis Of The Abu Madi Formation, Baltim Field, Nile Delta, Egypt, Nouran Mostafa Eissa, Mostafa Toni, Alhussein Adham Basheer, Amr Talaat, Amir Ismail

Trends in advanced sciences and technology

Despite the economic significance of the Messinian Abu Madi Formation as a primary gas reservoir target in the Nile Delta Basin, a high-resolution, field-scale integration of seismic sequence stratigraphy with quantitative petrophysical characterization remains lacking for the Baltim area. This study addresses this gap by presenting an integrated seismic sequence stratigraphic and petrophysical analysis of the Upper Miocene Abu Madi Formation within the prolific Baltim gas fields, offshore Nile Delta, Egypt. The objectives are to characterize its depositional architecture, delineate reservoir distribution, and assess hydrocarbon potential. The workflow synthesizes well log data from five key wells with a grid of …

Early Stem Impressions, Student Engagement, And Readiness For Digitalization, Myron Sheu

Journal of International Technology and Information Management

This study examines how early impressions of science, technology, engineering, and mathematics (STEM) shape business students’ learning behaviors and, ultimately, their readiness for organizational digitalization. Focusing on gender differences, subgroup identities, and perceived obstacles, the analysis uses survey data processed through correlation matrices, regression models, and subgroup heatmaps to trace the relationship between initial attitudes toward STEM and subsequent engagement patterns. The findings reveal consistent links between positive early impressions and active participation in structured STEM activities, along with gender-based distinctions in action preferences. Subgroup analyses further uncover nuanced patterns where stereotypes or perceived barriers correspond with reduced engagement. Collectively, …

Exploring Resiliency, Identity, And Access: Educational Narratives Of Adults Who Experienced Youth Homelessness, Taylor Mestres

University of the Pacific Theses and Dissertations

This study explored the educational experiences of adults who experienced homelessness during their youth, focusing on how instability, stigma, and resilience shaped their K-12 schooling. Guided by Maslow's hierarchy of needs and resiliency theory, the research employed a narrative inquiry approach with seven adult collaborators who reflected on their schooling while experiencing housing instability. Seven themes emerged: living between places, school as sanctuary, concealment and stigma, resilience and survival, the role of educators and support systems, thriving despite adversity, and retrospective recognition. Findings reveal that while housing instability and shame created significant barriers to learning and belonging, schools also functioned …

Inspiring Voices: The Impact Of Poetry Education, Nancy Aide Gonzalez St.Clair

University of the Pacific Theses and Dissertations

Poetry education is frequently viewed as an enrichment activity rather than a critical practice that supports academic, emotional, and social development. This qualitative exploratory case study examined how participation in a nonprofit creative writing program in northern California influenced the cognitive development, emotional well-being, communication skills, empathy, and perspective taking of young adults. The study was guided by constructivism, radical constructivism, and symbolic interactionism. It centered on the lived experiences of six poetry program alumni aged 18 to 25. The researcher collected data through semi-structured Zoom interviews and poetry portfolios containing three poems selected by each participant. I used inductive …

Near Real-Time Adaptive Isotropic And Anisotropic Image-To-Mesh Conversion For Cerebral Aneurysm Simulations, Kevin Garner, Chander Sadasivan, Nikos Chrisochoides

Computer Science Faculty Publications

This paper presents two performance optimization techniques for a mesh adaptation method that is designed to help streamline the discretization of complex vascular geometries within the numerical modeling process. This method is integrated into a pipeline with an image-to-mesh conversion tool to generate adaptive anisotropic meshes from segmented medical images. The pipeline is shown to satisfy quality, fidelity, smoothness, and robustness requirements while providing near real-time performance for medical image-to-mesh conversion. Tested with two brain aneurysm cases and utilizing up to 96 CPU cores within a single, multicore node on Purdue University’s Anvil supercomputer, the parallel adaptive anisotropic meshing method …

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 …

Adaptive Self-Attention For Enhanced Segmentation Of Adult Gliomas In Multi-Modal Mri, Evan P. Savaria, Jiangwen Sun

Computer Science Faculty Publications

Every year there are an estimated 80,000–90,000 new glioma cases, highlighting the need for reliable imaging-based decision support. Although deep learning has improved tumor sub-region segmentation, many state-of-the-art models fail to fully capture complementary information across T1, T1Gd, T2, and FLAIR MRI modalities and often operate as “black boxes,” limiting physician trust when precise delineation is critical for surgical planning, radiation targeting, and treatment monitoring. To address these limitations, we propose AIMS, an Adaptive Integrated Multi-Modal Segmentation framework that maintains modality-specific feature streams and employs adaptive self-attention within a hierarchical CNN-Transformer architecture to prioritize and fuse multi-modal MRI features. We …

Voxvista: Enhancing Screen Reading Experience For Online User Comments, Yash Prakash, Akshay Kolgar Nayak, Mohammed Shoaib Alyaan, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Online discussions have become integral to how people exchange ideas, form opinions, and participate in collective deliberation. While sighted users can comfortably engage with online discussions, blind users who are dependent on screen readers are forced to listen to long threads narrated in a single, monotonic voice that lacks prosodic variation, rhythm, or emotion. This robotic auditory experience not only deteriorates the user engagement with the content but also increases cognitive strain, by making it difficult to remain attentive and discern meaning beyond literal words. In an interview study, most blind participants reported that monotonous narration hindered their ability to …

Modeling Joint Visual Attention In Naturalistic Dyadic Interactions, Kuushini Thennakoon, Yasasi Abeysinghe, Bhanuka Mahanama, Vikas Ashok, Sampath Jayarathna

Computer Science Faculty Publications

Joint visual attention (JVA) provides important insight into how individuals coordinate attention during social interaction. Egocentric eye tracking enables the study of JVA in natural, multi-user settings. This work presents a multi-stage framework to identify and analyze JVA using egocentric video and gaze data. The approach consists of three steps: spatiotemporal tube-based visual similarity, gaze-guided object detection, and attention pattern analysis using the ambient–focal coefficient K. Results show that object-focused collaborative activities exhibit high JVA, with object detection capturing higher joint attention than visual similarity, whereas conversation-based or independent activities show lower and more fragmented joint attention. Analysis of K …

The Privacy Paradox Of Llms: User Perceptions And The Reality Of Pii Leakage, Shuai Cheng, Haitao Xu, Shu Meng, Shuai Hao, Chuan Yue, Zhao Li

Computer Science Faculty Publications

Large language models (LLMs) are increasingly deployed, yet they introduce significant privacy risks by disclosing personally identifiable information (PII) during interactions. Although prior work has demonstrated the feasibility of extracting PII from LLMs, no comprehensive study has evaluated the actual extent of PII leakage across mainstream LLMs or investigated user perceptions, literacy, and behavioral responses to these risks. To address these gaps, we conduct a large-scale evaluation of PII leakage in popular LLMs, demonstrating that attackers can extract email addresses and phone numbers with high success rates. Through a mixed-methods study involving 20 interviews and 204 survey participants, we identify …

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 …

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 …

Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis, Yasasi Abeysinghe, Gavindya Jayawardena, Enkelejda Kasneci, Sampath Jayarathna

Computer Science Faculty Publications

With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across …

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 …

Larval Crowding In Culex Pipiens Generates Cumulative Cascading Effects Under Changing Environmental Conditions, Sengul Talay, Zafer Sakaci, Michele C. Weigle, Bugrahan Regaip Kilinc, Jenah Parman, Holly Gaff, Deniz Sirin, Bulent Alten, Dennis Bente, Sirri Kar

Computer Science Faculty Publications

Density-dependent population regulation is a fundamental driver in the population dynamics of living systems, ensuring a balanced and sustainable maintenance in nature. It has been suggested that the adverse developmental and morphological effect of high larval density in container mosquitoes represents a significant limiting factor for population density, and the need to investigate this possible importance within the framework of field-based principles has been emphasized. This semi-field study, conducted under the natural thermal regime and using a natural population in Turkish Thrace, aimed to examine the effects of non-food-limiting larval density (crowding effect) in the mosquito species, Culex pipiens ( …

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 …

Attention-Based Multi-Omics Fusion For Drug Synergy Prediction, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug combination therapy in disease management gained popularity in the last few decades. Computational modeling of such combinations is an active area of research in the drug discovery domain. While earlier approaches solely emphasized on the structural features of participating drugs for designing synergistic models, they lack other crucial factors directly linked with drug administration - omics expressions. As differential omics expression is a downstream consequence of the administered drug combinations, utilizing such expressions while designing synergistic models promises robust and dynamic modeling. In this work, we propose SynergyLM that fuses multi-omics features with drug embeddings to build an omics-aware …

A Sustainable & Universal Redesign Of Hickory Hill Park In Richmond, Virginia, Ren Hollis, Cynthia Lin, Kira Miller, Jackson Nivera, Patrick O’Shea, Corinne Tsou, Hayston Wines

UROP Posters

Urban green spaces provide essential environmental, psychological, and physical benefits, yet access to these resources is often inequitable. Cities are increasingly vulnerable to climate change, highlighting the need for adaptive design. This study focuses on the redesign of Hickory Hill Park in Southside Richmond, selected through a combination of site suitability analysis and qualitative survey. The guiding research question is: How can a park in Richmond be designed to optimize environmental resilience, accessibility, and community benefits?

The selection of a research location and scope used a mixed-methods approach integrating spatial analysis, environmental assessment, and community engagement. A site suitability analysis …

The Impact Of Test-Optional Policy On Private College Admissions During The Covid-19, Anna Kye, Meng-Jia Wu

Journal of College Access

The COVID-19 pandemic disrupted many aspects of higher education, including college admissions processes. Since 2020, numerous universities and colleges have adopted test-optional policies, allowing applicants to decide whether to submit standardized test scores. Although such policies have been in place at some institutions since 1998, research on their associations with student diversity and enrollment patterns has produced mixed findings. The widespread adoption of test-optional policies during the pandemic presents an opportunity to further examine their role in admissions. This study draws on admission data from a four-year, nonprofit, private university in the Midwest and uses logistic regression to explore patterns …

Saliency Guided Reinforcement Leaming For Multi Step Mathematical Reasoning In Large Language Models, Ji Dung Lo

Computer Science and Engineering Master's Theses

Large language models perform strongly on many tasks but remain unreliable on complex multi step mathematical reasoning. Existing reinforcement learning methods treat reasoning trajectories as uniformly informative, ignoring that some intermediate steps matter more than others. This thesis argues that reasoning is structurally uneven and that optimization should reflect this internal credit distribution.

We adopt Group Relative Policy Optimization (GRPO) to construct a fully automated reinforcement learning framework. To provide intermediate supervision without human annotation, we automatically generate short quiz questions that test key reasoning checkpoints. Quiz rewards complement final answer rewards, enabling structured feedback while keeping the pipeline scalable. …

Ethnonationalism By Algorithm, Spencer A. Overton

GW Law Faculty Publications & Other Works

In the United States, artificial intelligence (“AI”) policy has become a critical arena for ethnonationalism—an ideology that defines national belonging through shared ancestry, culture, and language. Amid rapid demographic change and cultural anxiety, the second Trump Administration has harnessed federal AI governance to advance its broader agenda of dismantling diversity—most notably through Executive Order 14,179, “Removing Barriers to American Leadership in Artificial Intelligence” and related legal directives. By eliminating safeguards against algorithmic bias and recasting equity as an ideological threat to innovation, the policies facilitate exclusion under the guise of neutrality. These moves are not merely deregulatory; they represent a …

Topic Modeling The Cuny Graduate Center's Dissertations And Theses, Michael Mandiberg

Open Educational Resources

This 4 week module is designed for Data Analysis, Data Visualization, and Digital Humanities courses at the MA/MS or advanced 400-level undergraduate level. It introduces students to textual analysis with topic modeling and requires a solid foundation in Python. The module uses Gensim and a Colab notebook to introduce a standard text analysis workflow used in Digital Humanities, archival research, and exploratory data analysis.

Students build a topic model describing 19,000 CUNY Graduate Center dissertations and theses. They work with an unexplored dataset to load and explore the data, prepare the corpus, train and evaluate a topic model, and interpret, …

Creative Destruction For The Patent System? Impact Of Generative Ai, Henry H. Perritt Jr.

Minnesota Journal of Law, Science & Technology

AI raises the provocative possibility that patents could one day become obsolete. AI has the ability both to generate inventions and to simulate the perspective of a person having ordinary skill in the art (PHOSITA). This article explores the transformative impact of generative artificial intelligence (AI) on the United States patent system. The article supports a scenario in which everything becomes invented—and simultaneously, everything becomes obvious—thus challenging a foundational criterion for patentability. These developments can occur within established principles of inventorship, obviousness, and subject matter eligibility under U.S. patent law. Using a narrative case study and a test invention, the …

Sentinels Of Security -- A Case For State Leadership In The Quantum Era, J.T. Ayotte

Minnesota Journal of Law, Science & Technology

No abstract provided.

Scalar Source Localization Using Multi-Sensor Domains Of Dependence In Turbulent Channel Fow, Zejian You, Xiaowei Zhu, Qi Wang

Mechanical and Materials Engineering Faculty Publications and Presentations

Tracking hazardous events such as wildfire smoke, coastal oil spills, or chemical releases in natural environments is complicated by turbulent dispersion and molecular diffusion. This study presents a method to localize pollutant sources rapidly and accurately using infinite time-averaged measurements from a multi-sensor network, leveraging the duality between mean forward and adjoint scalar fields. The forward-adjoint duality states that measurements at the sensor (forward feld) are equal to the adjoint field at the source, providing crucial spatial information about the source. Consequently, the adjoint scalar fields can be interpreted as the domain of dependence for the sensor observations [1, 2]. …