Digital Commons Network^™

Enhancing Expressive Power Of Graph Neural Networks Using Geometric Transformations, Suranjan Dey

Master’s Dissertations

Graph Neural Networks (GNNs) are highly effective in many real-world tasks, such as molecular property prediction, modeling protein structures, analyzing user-item relationships, and making link predictions. What sets them apart is their ability to learn meaningful representations by capturing not just the features of individual nodes, but also the overall structure of the graph they belong to. This expressive strength allows GNNs to model complex relationships more accurately. In this work, we take a step further by introducing geometric transformations aimed at improving how GNNs handle spatial information. In particular, we focus on angular aggregation methods that maintain rotational consistency, …

Universally Consistent Hyperbolic Deep Neural Networks, Sagar Ghosh

Master’s Dissertations

The ubiquitous pertinence of Deep Neural Networks has made it pivotal in modern Computer Science Applications, ranging from Computer Vision to Pattern Recognition and Machine Translation. Although these deep architectures are primarily based on Euclidean Spaces, Hyperbolic Neural Networks (HNN) gained traction in recent times to tackle more complex non-Euclidean data having inherent hierarchical structures. These HNN architectures have shown commendable improvements in test results on tree or graph-like data by exploiting the inherent exponential metric distances of hyperbolic spaces, making them more suitable to embed non-Euclidean data. Although HNNs surpass their conventional Euclidean counterparts by commendable margins, little to …

Cutting Through The Infodemic Efficiently: News Claims Surveillance And Llm-Based Lightweight Fact Verification, Xuan Zhang

Dissertations and Theses Collection (Open Access)

In the context of the current infodemic, the rapid spread of misinformation poses a severe threat to social stability and public health. Recently, the rise of deep learning technologies has offered the potential for accelerating the development of automated misinformation detection and verification. However, current technological capabilities and computational resources often prove inadequate for the exhaustive scrutiny required, rendering the enhancement of processing efficiency a critical imperative. Given the vast amount of data on the internet, current technology and computational power often fall short in timely and accurate scrutiny of each piece of information, making the improvement of processing efficiency …

On-Demand Scenario Generation For Testing Automated Driving Systems, Songyang Yan, Xiaodong Zhang, Kunkun Hao, Haojie Xin, Yonggang Luo, Jucheng Yang, Ming Fan, Chao Yang, Jun Sun, Zijiang Yang

Research Collection School Of Computing and Information Systems

The safety and reliability of Automated Driving Systems (ADS) are paramount, necessitating rigorous testing methodologies to uncover potential failures before deployment. Traditional testing approaches often prioritize either natural scenario sampling or safety-critical scenario generation, resulting in overly simplistic or unrealistic hazardous tests. In practice, the demand for natural scenarios (e.g., when evaluating the ADS's reliability in real-world conditions), critical scenarios (e.g., when evaluating safety in critical situations), or somewhere in between (e.g., when testing the ADS in regions with less civilized drivers) varies depending on the testing objectives. To address this issue, we propose the On-demand Scenario Generation (OSG) Framework, …

Dupin: A Parallel Framework For Densest Subgraph Discovery In Fraud Detection On Massive Graphs, Jiaxin Jiang, Siyuan Yao, Yuchen Li, Qiange Wang, Bingsheng He, Min Chen

Research Collection School Of Computing and Information Systems

Detecting fraudulent activities in financial and e-commerce transaction networks is crucial. One effective method for this is Densest Subgraph Discovery (DSD). However, deploying DSD methods in production systems faces substantial scalability challenges due to the predominantly sequential nature of existing methods, which impedes their ability to handle large-scale transaction networks and results in significant detection delays. To address these challenges, we introduce Dupin, a novel parallel processing framework designed for efficient DSD processing in billion-scale graphs. Dupin is powered by a processing engine that exploits the unique properties of the peeling process, with theoretical guarantees on detection quality and efficiency. …

Predictors Of Nursing Students' Stress, Anxiety, And Depression During The Covid-19 Pandemic In A Hispanic-Serving University In South Texas: A Cross-Sectional Study, Maria I. Diaz, Eleftherios Gkioulekas, Nancy Nadeau

School of Mathematical & Statistical Sciences Faculty Publications

Background: In nursing education, there have been several studies on the impact of the COVID-19 pandemic on the ability of nursing students to cope while in nursing school.

Purpose statement: The goal of this study is to assess undergraduate nursing students' support mechanisms as predictors of stress, anxiety, and depression during the COVID-19 pandemic within a Hispanic-serving institution in South Texas.

Methods: Across-sectional design was used in this study. An online survey using self-reported questionnaires was used to gather data from an undergraduate nursing student cohort during the Fall 2021 semester. Linear regression was used to identify the predictors of …

Impact Of Seed Moisture And Temperature On Hemp Seed Germination, Paul Cockson, Andrea Webb, Natalia Martinez-Ochoa, Lindsey Moffitt, Robert Pearce, Manohar Chakrabarti

School of Integrative Biological & Chemical Sciences Faculty Publications

Germination rates of commercial lots of hemp have been highly variable, resulting in poor stand establishment. Germination rates in some seed lots have decreased by 50% after only 1 year of storage. The objective of this trial was to investigate the impact of seed storage conditions on seed germination over time. Industrial hemp (IH) seeds (cv. NWG2730) were harvested from the field. The seeds were cleaned, sorted, and dried to specific moisture contents (MC) 6%, 8%, 10%, and 14%. Seeds were subdivided, placed in hermetically sealed packets, and stored at temperatures of −20°C, 4°C, 10°C, or 21°C for 3, 6, …

A Digital Dive: Redesigning The Cabrillo High School Aquarium Website, Jacob V. Cacho

Graphic Communication

Tucked away on the Central Coast in Lompoc, you’ll find the Cabrillo High School (CHS) Aquarium. Started in 1986, the CHS Aquarium is the only high school aquarium of its kind in the nation run entirely by high school students. This 10,000+ square foot aquarium serves an underserved community at a Title I school, where students manage all aspects of animal care, nutrition, breeding, educational curriculum development, and visitor tours.

This program is truly one-of-a-kind and deserves the spotlight for just how unique it is. As a CHS graduate, I felt the current website lacked in many areas and could …

Raising The Roof For All: Integrating Companion Planting Ecology, Policy And Community In Portland's Green Roof Future, Zoe Edelman

University Honors Theses

Green roofs, or ecoroofs, provide environmental and social benefits in urban areas. Ecoroofs manage stormwater by absorbing rainfall, reducing rooftop temperatures, supporting pollinators, and offering green space in densely developed cities. Portland, Oregon has adopted progressive policies that encourage green roof installation through incentives and building requirements. However, many ecoroofs in the city are underperforming due to poor maintenance, low public awareness, and limited access to rooftop spaces. This thesis explores whether companion planting can improve the ecological performance and long- term viability of extensive green roofs. A rooftop experiment at Portland State University tested the growth of radishes, with …

Data Driven Analysis Of Samara Seed Kinematics And Dynamics, Shashwat Sparsh

Master's Theses

Samara Seeds are a class of fruit most famously belonging to the Acer species and are characterized by their single-bladed geometry and their auto-rotation response during descent. This steady-state auto-rotation response is the subject of aerodynamic analysis which aim to quantify the performance. The period prior to the beginning of steady-state auto-rotation is classified as the transition regime and has not been the subject of intense scrutiny.

This thesis employs a data-driven approach to analyzing the kinematic and dynamic response of these seeds during both the transition and auto-rotation stages of flight to quantify the performance with respect to the …

Filling Gaps In Scientific Data Sets Using Physics Informed Neural Networks: A Case Study In Velocity Fields, Ellen Saunders

Master's Theses

Gaps in scientific data sets are a persistent issue for researchers in a variety of fields, and while nothing makes up for missing out on real data, well-simulated synthetic data can be a useful tool. In the world of image processing, machine learning techniques have become quite sophisticated at taking an image with a missing component and filling in that space with something believable. The aim of this thesis is to take machine learning techniques similar to what gets used in image processing and repurpose them to infill gaps in scientific data sets in a realistic manner. This thesis compares …

Vertical And Latitudinal Variability Of Marine Heatwaves In The California Current, Gavin M. Plume

Master's Theses

Marine heatwaves (MHWs) are increasing in frequency and intensity globally, with grave effects on marine ecosystems and their dependent industries. There are significant gaps in knowledge of the subsurface behavior and geographic distribution of MHWs, especially in eastern boundary upwelling systems like the California Current. Here, two decades of full water-column temperature observations from a shallow autonomous profiler in San Luis Obispo Bay (~10m) revealed that while MHWs have similar average durations and intensities across all depths, one-third of bottom MHWs occur without a concurrent surface signal. MHWs across the water column initiated during anomalously weak upwelling or downwelling conditions …

Myceli-Yum: Elucidating Structure-Property Relationships For Polymer Degradation By Mycelial Digestion, Jordan Scott Ford

Master's Theses

Since the industrial entrance of polymer plastic materials, plastic has become ubiquitous in both everyday use and waste. Due to inefficiencies and knowledge gaps, current recycling methods are not able to account for the high scale of plastic waste, resulting in the bulk of this waste being landfilled, mishandled, and deposited in the environment. Mycelium, the microorganism responsible for fruiting mushroom bodies and mold growth, holds potential to reduce plastic waste and can potentially be utilized as a method of industrial recycling. Following a drug-design approach, the active site of mycelial enzymes responsible for natural biopolymer degradation have been assessed …

Status And Distribution Of Diseases Caused By Phytoplasmas In Africa, Shakiru Adewale Kazeem, Agnieszka Zwolińska, Joseph Mulema, Akindele Oluwole Ogunfunmilayo, Shina Salihu, Joy Oluchi Nwogwugwu, Inusa Jacob Ajene, Justina Folasayo Ogunsola, Adedapo Olutola Adediji, Olubusola Fehintola Oduwaye, Kouamé Daniel Kra, Mustafa Ojonuba Jibrin, Wei Wei

All Peer-Reviewed Publications

Phytoplasma (“Candidatus Phytoplasma” species) diseases have been reported globally to severely limit the productivity of a wide range of economically important crops and wild plants causing different yellows-type diseases. With new molecular detection techniques, several unknown and known diseases with uncertain etiologies or attributed to other pathogens have been identified as being caused by Phytoplasmas. In Africa, Phytoplasmas have been reported in association with diseases in a broad range of host plant species. However, the few reports of Phytoplasma occurrence in Africa have not been collated together to determine the status in different countries of the continent. Thus, this paper …

A Novel Fractional Order Model For Analyzing Counterterrorism Operations And Mitigating Extremism, Mutaz Mohammad, Isa Abdullahi Baba, Evren Hincal, Fathalla A. Rihan

All Works

This study examines the profound impact of terrorism on individuals and society by developing a fractional-order mathematical model to analyze and enhance counterterrorism efforts. The model accounts for the persistent and complex nature of extremist behavior, particularly emphasizing the importance of preventing violent extremism before it escalates into terrorism. Real-world data on terrorist activities in Nigeria – specifically from the Boko Haram insurgency – was used to calibrate and validate the model, ensuring its relevance and accuracy. The model reveals that the basic reproduction number (R0) plays a decisive role in determining the long-term success of counterterrorism strategies. Numerical simulations …

An Exposition Of "Probabilistic Polynomials And Hamming Nearest Neighbors", Vivek Srirama

University Honors Theses

This paper is an exposition of the paper Probabilistic Polynomials and Hamming Nearest Neighbors by Josh Alman and Ryan Williams. It presents the findings of this paper in a more accessible format for Computer Science students earlier in their career who may not be as familiar with Computational Theory and its concepts as their PhD counterparts are. The paper assumes that the reader has a basic understanding of Algorithms and Complexity, typically obtained in an introductory level Algorithms course.

The paper by Alman and Williams analyzes a specific problem known as the Hamming Nearest Neighbor problem. All known solutions for …

Surface Temperature Analysis Report: Kellogg Creek Restoration & Community Enhancement Project, Dalton Palin

University Honors Theses

Kellogg Creek is located in the Kellogg-Mt Scott watershed which flows through downtown Milwaukie, Oregon into the Willamette River. At the confluence of Kellogg Creek and the Willamette River is the Kellogg Dam. This dam impedes salmon, steelhead, and lamprey movement up Kellogg Creek, which is known historically as a salmon and steelhead rearing and migrating habitat. A plan to remove Kellogg Dam and restore 14 acres of Kellogg Creek is in progress by North Clackamas Watersheds Council (NCWC) and partners. Water temperature studies in Kellogg Creek have been conducted for the past several years, this report analyzed thermal surface …

Event-Based Eye Tracking: Event-Based Vision Workshop 2025, Qinyu Chen, Et. Al.

Research Collection School Of Computing and Information Systems

No abstract provided.

On The Design Of A Framework For Large-Scale Exploratory Graph Analytics, Oliver Andres Alvarado Rodriguez

Dissertations

Large-scale exploratory graph analytics merges data science with high-performance computing to extract critical insights from network-representable data. Data scientists routinely analyze data from the natural, social, and computing sciences by representing it as networks, or graphs, where objects become vertices and their relationships become edges. This representation allows data scientists to add graph analytics to their toolbox. However, designing tools for large-scale exploratory graph analytics is challenging due to the complexities of graph algorithms, such as high communication in distributed systems and large memory demands. These challenges can lead to overly complex software, which limits usability and development to a …

An Inquiry Into The Physics Of Mixing And Floc Filtration, Andrew P. Pennock

Dissertations

Flocculation and clarification are two essential processes to deliver safe water at a reasonable cost to consumers. There are two major thrusts to the research presented in this dissertation. The first is to better characterize the physics and mixing parameters used for the design of hydraulic flocculators in the context of drinking water treatment plants. The second major thrust is to investigate floc filtration as a mechanism for the removal of primary particles during floc blanket clarification.

The intensity of mixing in environmental and chemical engineering applications is often characterized by the Camp and Stein velocity gradient. This parameter has …

Fact-Checking As A Multi-Step Process: From Ambiguity Resolution To Claim Validation, Wenbo Wang

Dissertations

The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. Fact-checking—the process of verifying claims against credible evidence—has emerged as a critical safeguard against misinformation. Yet, the task is fraught with challenges: claims are often ambiguous, context-dependent, or composed of multiple intertwined assertions, while automated systems struggle to replicate the nuanced reasoning of human experts. This dissertation addresses these challenges by reimagining fact-checking as a multi-step, knowledge-guided process that systematically resolves ambiguity, decomposes complexity, and validates claims through structured reasoning. Additionally, the proposed …

Towards Explainable Ai On Graph Neural Networks: Xaig, Jiaxing Zhang

Dissertations

In the evolving landscape of artificial intelligence (AI), Graph Neural Networks (GNNs) have garnered growing prominence for their adeptness in processing graph-structured data. Despite this, the interpretability of their predictions often remains elusive. The demand for transparency and explainability in complex prediction models has reached unprecedented levels. To address this, post-hoc instance-level explanation techniques have emerged, aiming to unveil the rationale behind GNN predictions. These techniques endeavor to unearth substructures that elucidate the predictive behavior of trained GNNs.

This dissertation embarks on an exploration of Explainable AI (XAI) technologies within the realm of GNNs. Amid the challenges posed by the …

Tree Story, Jia Hu

Masters Theses

What is Nature?

Nature is a system of intelligence. It means designing for efficiency—often by learning from strategies that have evolved over time. In my research, I use patterns to interpret and decode nature.

To explore nature, I began with the red cedar tree, aiming to simulate and predict its growth patterns—forms shaped by both internal biology and external forces. By analyzing its geometry, I sought to understand how trees embody the dynamic relationship between organism and environment. These patterns reveal the adaptive logic of life.

Patterns are central to understanding nature. While tree geometry may appear chaotic, it follows …

Field Journal: The Excluded Middle, Carrie E. Kouts

Masters Theses

What does it look like to engage with organisms and landscapes at the periphery of anthropocentric value structures? How does one break the internalized myth that the “built” environment is excluded from the natural world? When does a hyper-mobile and hyper-commodified society confront the exponential crisis of animal death? Within this series of journal entries, field notes, collection observations, weird prose, and sensory musings, one will find questions on the nature of being human and the complex narratives of care we encounter in a world shared with more-than-humans. Each handwritten vignette and photograph from daily life weaves a non-linear and …

The Limits Of Knowing: Determinism, Uncertainty, And What’S Beyond The Human Gaze, Xilong T. Zhang

Masters Theses

This essay traces a personal philosophical and artistic journey from rigid belief in scientific determinism to an evolving embrace of uncertainty, subjectivity, and computational perception. Raised in an atheist, scientifically grounded household in China, the author initially adopted Newtonian determinism and Laplace’s thought experiment of a fully predictable universe as guiding principles. These beliefs informed early artistic practices rooted in Constructivism, geometry, and rule-based aesthetics. However, a failed attempt to fully optimize life through deterministic control led to physical and mental collapse, prompting deeper exploration into Cartesian dualism, quantum mechanics, and the limits of reason. Through Heisenberg’s Uncertainty Principle and …

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …

A Novel Framework For Dynamic Graph Representation Learning With Mamba, Ashish Pandey

Theses

Dynamic graph embedding is a key technique for modeling temporal dependencies in evolving networks. While transformer-based models perform well, their quadratic complexity limits scalability on long graph sequences. This thesis compares transformer approaches with the Mamba architecture-a linear-complexity state-space model—for temporal graph embedding.

Two frameworks are proposed: DG-Mamba and GDG-Mamba. DG-Mamba uses standard GCN-based spatial encoding, while GDG-Mamba incorporates domain-aware edge features using Graph Isomorphism Network with Edge Convolution (GraphGINE). Experiments on UCI, Reality Mining, Slashdot, Bitcoin-OTC, and SBM datasets show that Mamba-based models match or exceed transformer performance, especially on graphs with high temporal variability.

The thesis also applies …

Plm-Dbps: Enhancing Plant Dna-Binding Protein Prediction By Integrating Sequence-Based And Structure-Aware Protein Language Models, Suresh Pokharel, Kepha Barasa, Pawel Pratyush, Dukka B. Kc

Michigan Tech Publications

DNA-binding proteins (DBPs) play a crucial role in gene regulation, development, and environmental responses across plants, animals, and microorganisms. Existing DBP prediction methods are largely limited to sequence information, whether through handcrafted features or sequence-based protein language models (PLMs), overlooking structural cues critical to protein function. In addition, most existing tools are trained for general DBP predictions, which are often not accurate for plant-specific DBPs due to the unique structural and functional properties of plant proteins. Our work introduces PLM-DBPs, a deep learning framework that integrates both sequence-based and structure-aware representations to enhance DBP prediction in plants. We evaluated several …

Agent-Based Modeling: Introduction And Actuarial Applications, Rick Gorvett

Mathematics and Economics Faculty Working Papers

Agent-based modeling (ABM) has become an important and valued approach to modeling complex systems. In this paper, I advocate for actuaries to recognize the complex systems-nature of socioeconomic and risk processes and for ABM models to become a regular resource in our actuarial toolkits. These models allow for the observation of potential macro-behavior emerging from the underlying agent-level micro-activity and characteristics. Therefore, ABM models can provide significant insight into the quantification of risk and the identification of optimal strategies. This paper is an introduction and guide to ABM models, and it includes several case studies to illustrate their utility.

Preparation And Modification Of Mxene Composites For Application In Electrochemical Energy Storage, Zhang-Hai You, Ding-Ze Lu, Kiran Kumar Kondamareddy, Wen-Ju Gu, Peng-Fei Cheng, Jing-Xuan Yang, Rui Zheng, Hong-Mei Wang

Journal of Electrochemistry

With the acceleration of advanced industrialization and urbanization, the environment is deteriorating rapidly, and non-renewable energy resources are depleted. The gradual advent of potential clean energy storage technologies is particularly urgent. Electrochemical energy storage technologies have been widely used in multiple fields, especially supercapacitors and rechargeable batteries, as vital elements of storing renewable energy. In recent years, two-dimensional material MXene has shown great potential in energy and multiple application fields thanks to its excellent electrical properties, large specific surface area, and tunability. Based on the layered materials of MXene, researchers have successfully achieved the dual functions of energy storage and …