Physical Sciences and Mathematics Commons^™

The Effects Of Climate Change On Competition And Recovery From Disturbance In An Iconic Marine Community, Lauren Tibbits

Senior Theses

This paper explores how the recovery of populations of Semibalanus balanoides is affected by climate variation. In intertidal marine ecosystems, larval recruitment is important in shaping all ecological processes. S. balanoides is a cold-water barnacle species that is competitively dominant over Chthamalus montagui and C. stellatus, two warm-water species. This competitive interaction is well studied, though it is not understood how that competition influences the recovery of S. balanoides after a disturbance nor how this process varies over a temperature gradient. 19 sites were studied across the United Kingdom, spanning from northern Scotland to southwestern England. At each site, a …

Conservation Communication In Web 2.0: A Mixed Methods Analysis Of Alveus Sanctuary's Strategies On Twitch.Tv, Hope Denison

Senior Theses

This thesis examines how environmental creators can effectively transmit conservation messages in the evolving Web 2.0 media environment. The case of Alveus Sanctuary, a virtual sanctuary and education center on Twitch.TV, is used to explore the strategies currently used to engage audiences and raise ecological awareness online. Thematic analysis of interviews with the sanctuary’s founder revealed that Alveus combines traditional environmental education with an influencer business model to engage online audiences, leveraging parasocial behavior, interactivity, and collaboration to garner support. A quantitative content analysis of social media posts revealed that these strategies effectively induce parasocial engagement in online spaces. These …

Crystallization And Utility Of Brominated Phenylethynylene Bis-Urea Macrocycles, Paras Srivastava

Senior Theses

Macrocycles are large, cyclic molecules with central cavities that can encapsulate other molecules and that have uses in supramolecular chemistry. The Shimizu group has previously synthesized Macrocycle 1, a phenylethynylene bis-urea macrocycle that crystallizes in a columnar form, enabling its utility as a host in host-guest systems. Reactions within these systems can be used to modify stereospecificity, kinetics, and other physical properties. Previously, photodimerization reactions between coumarins and chromones have been studied in systems of Macrocycle 1, resulting in the improved specificity of photodimerization products. Herein, we apply a dynamic covalent chemistry (DCC) strategy to synthesize Macrocycle 1 and its …

A Guide To Environmentally Sustainable Filmmaking In South Carolina, Christina Vera Dobrowolski

Senior Theses

The entertainment industry has a massive environmental impact. This information is not new in the filmmaking world, as multiple mainstream efforts exist to curb the material-intensive production of live action films and TV. One of these efforts is the use of voluntary sustainable guidelines, which serve as tools that outline sustainable best practices and resources that can help achieve them. However, current guidelines often lack practicality— focusing more on altruism rather than core economic factors that ultimately drive filmmaking. This project aims to address that gap for filmmakers in South Carolina by offering a website featuring attainable, state-specific resources for …

Fingerprinting Voice Commands Of Vpn-Protected Smart Speakers, Xiaoguang Guo, Keyang Yu, Qi Li, Dong Chen

Computer Science Faculty Research and Publications

Extensive recent research has shown that it is surprisingly easy to infer Amazon Alexa voice commands over their network traffic data. To prevent these traffic analytics (TA)-based inference attacks, smart home owners are considering deploying virtual private networks (VPNs) to safeguard their smart speakers. In this work, we design a new machine learning-powered attack framework—VoiceAttack that could still accurately fingerprint voice commands on VPN-encrypted voice speaker network traffic. We evaluate VoiceAttack under 5 different real-world settings using Amazon Alexa and Google Home. Our results show that VoiceAttack could correctly infer voice command sentences with a Matthews Correlation Coefficient (MCC) of …

Low-Complexity Structured Neural Networks And Their Usage In Image And Signal Processing, Adam Kuzmicki

Doctoral Dissertations and Master's Theses

Conventional neural networks face significant challenges due to high computational costs, large parameter counts, and reliance on backpropagation, which restricts their application in resource-constrained and real-time settings. To address these challenges, this thesis proposes three structured neural network (NN) architectures grounded in the theories of sparse and self-contained factorizations of transforms, with applications to image compression, reconstruction, classification, encryption, and also adaptive wideband multi-beam beamforming. The first neural network architecture, named DCTrix-Net, replaces conventional spatial con- volution with highly sparse factorization of the discrete Cosine transform (DCT) complemented by Toeplitz-structured weight initialization, achieving at least 97% FLOP reduction over CNNs, …

"Leaf It To The Trees": Assessing The Cooling Effects Of Tree Canopy On Campus Microclimates, Andrea Sophia Realyvasquez

Posters - 2026

Urban heat island (UHI) effects are becoming increasingly common worldwide, with lived experiences and media coverage highlighting the negative consequences of urban development for humans and the environment. Urbanization, like many other aspects of society, should not remain stagnant and outside the scope of innovation. Understanding the interplay between urban morphology and temperature distributions is crucial in informing effective policy development (AbbegCoproski et al. 2024). tion to a political approach to reducing surface temperatures, there is the social aspect of difficulty in evading UHI in microclimates. University campuses are among the many areas that struggle to overcome old infrastructure and …

Advancing Paleontologic Mapping: A Gis And Drone Photogrammetry Approach To The Lance Formation Beds Of Glenrock, Wyoming, William H. Steelman, Monika Angner, Norman S. Levine, W S. Persons

The Compass: Earth Science Journal of Sigma Gamma Epsilon

Small museums and volunteer-driven field programs often lack the resources required for detailed geological mapping, limiting the stratigraphic data associated with their fossil collections. To address this challenge, an affordable and replicable mapping workflow was developed that combined drone-based photogrammetry, GIS analysis, and targeted structural measurements using the Glenrock Exposure of the Lance Formation as a study site. The resulting 3D outcrop models, bedding-plane reconstructions, and 750-m-thick stratigraphic column provide the first comprehensive spatial framework for 35 fossil quarries curated by the Glenrock Paleon Museum. The results reveal tightly clustered fossil-bearing horizons, a long interval lacking preservation, and a notable …

Sigma Gamma Epsilon Student Research Poster Session, Geological Society Of America Annual Meeting 2025, San Antonio, Texas, Usa, Scott R. Beason, Lee S. Potter

The Compass: Earth Science Journal of Sigma Gamma Epsilon

The Society of Sigma Gamma Epsilon (SGE) sponsors a poster session at every annual meeting of the Geological Society of America. The 37th SGE undergraduate research poster session took place during the 2025 Geological Society of America Annual Meeting (GSA Connects) in San Antonio, Texas, USA, on Monday, October 20, 2025. Fifty-seven (57) posters were presented in Exhibit Hall 1 at the Henry B. Gonzalez Convention Center between 8:30 AM and 5:30 PM at the poster session, with authors present between 9:00 AM and 11:00 AM. Titles, authors (italics for the presenting author), affiliations, and abstracts for each poster are …

Draft Final Butte Priority Soils Operable Unit (Bpsou) Insufficiently Reclaimed Sites Sister Dump (Bres No. 38) Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Soliton Solutions To The Coupled Sasa–Satsuma Equation Under Mixed Boundary Conditions, Changyan Shi, Xiyao Chen, Guangxiong Zhang, Chengfa Wu, Bao-Feng Feng

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we derive general bright–dark soliton solutions to the coupled Sasa–Satsuma (CSS) equation using the Kadomtsev–Petviashvili reduction method. Since the CSS equation is a special case of the four-component Hirota equation, our approach begins with the construction of two-bright-two-dark soliton solutions for the four-component Hirota equation. By imposing specific parameter constraints, these solutions are subsequently reduced to the bright–dark soliton solutions of the CSS equation. Finally, the dynamical behaviours of the one- and two-bright–dark soliton solutions are thoroughly analysed and illustrated.

Optimization Of Niobium Film For Particle Accelerators And Quantum Applications, Bektur Abdisatarov

Electrical & Computer Engineering Theses & Dissertations

Niobium (Nb) films play a central role in superconducting technologies used in particle accelerators and superconducting quantum circuits. Optimizing the physical properties of Nb films is therefore critical for improving both radiofrequency (RF) performance in superconducting radiofrequency (SRF) cavities and coherence in superconducting qubits. This thesis investigates the relationship between Nb film microstructure, impurity content, and electromagnetic response across these two application domains.

For particle accelerator applications, we studied Nb films deposited using high-power impulse magnetron sputtering (HiPIMS) with DC bias onto a 1.3 GHz elliptical SRF cavity. Nb film cavities exhibit a pronounced medium-field Q-slope, limiting their achievable accelerating …

Radicals Transforming Organic Matter: Implications For Non-Pyrogenic Black Carbon And Nitrogen Formation, Molecular Dynamics In Tropical Soils, And Lignin Valorization, João Vitor Dos Santos

Chemistry & Biochemistry Theses & Dissertations

Radicals drive the transformation of organic matter in natural and engineered systems, altering its structure, reactivity, and stability. Condensed aromatic carbon (ConAC) and condensed aromatic nitrogen (ConAN) are among the most persistent forms of organic matter in soils and are traditionally interpreted as products of biomass burning. This dissertation challenges the long-standing assumption that these materials are exclusively pyrogenic, showing that iron-mediated radical chemistry can generate condensed aromatic structures under ambient conditions.

Chapters II and III show that Fenton-like radical oxidation of lignin-rich biomass produces condensed aromatic structures compositionally similar to pyrogenic black carbon and black nitrogen when assessed with …

Quantifying Nutrient Fluxes Across The Sediment-Water Interface In The Lafayette River Estuary, Adriana E. Amrhein

OES Theses and Dissertations

The Lafayette River, a shallow, eutrophic tributary of the Chesapeake Bay, experiences seasonal harmful algal blooms (HABs). While management efforts focus on reducing external nutrient inputs, internal loading from benthic sediments remains poorly constrained. This study quantifies spatial and seasonal variability in nutrient and radium isotope fluxes across the sediment–water interface at three sites where HABs are known to initiate and represent contrasting sediment textures and hydrodynamic conditions. Sediment cores and porewater samples were collected in summer 2023 and winter 2024 to measure grain size, porosity, dissolved nutrients (NH₄⁺, NOₓ⁻, PO₄³⁻), and radionuclide activities (²²⁴Ra and ²²⁸Th). Radium isotope disequilibrium …

Personality Predictors Of Cybersecurity Vulnerability: Insights From Self-Reports And Stimulated Threat Scenarios, Saroja Roy Grandhi

Psychology Theses & Dissertations

In this cyber dependent and enabled era, understanding the role of human factors in digital security is essential. This study investigates the relationship between Big-Five personality traits and cybersecurity behaviors by examining both self-reported and stimulated behaviors in security threat scenarios. Participants completed validated questionnaires to report their personality traits, cybersecurity practices and engage in task-based stimulations to capture behaviors such as phishing detection, password creation, and response to security alerts. The study tested whether higher conscientiousness, openness, and agreeableness would be associated with stronger cybersecurity practices and smaller discrepancies between self-reported and observed behaviors. And, whether greater extraversion and …

Think First, Chatgpt Later: Guiding Human-Ai Collaboration For Learning Gains In Independent Human Creativity, Sarah Shi Hui Wong, Sophia Xuefei Qiu

Research Collection School of Social Sciences

Generative artificial intelligence (AI) tools such as ChatGPT can boost creative performance, but do these boosts translate into learning gains? This study examined whether the benefits of ChatGPT for creativity persist even when its assistance is removed, and how people can effectively use ChatGPT to enhance their learning and independent creativity. University students (N = 196) solved a creative product improvement task either independently (human-only group) or using ChatGPT freely (general-AI group) or using ChatGPT in a guided way (regulated-AI group). Specifically, the regulated-AI group used a novel “think first, ChatGPT later” approach—they first generated their own ideas, then collaborated …

Weakly Supervised Video Anomaly Detection And Localization With Spatio-Temporal Prompts, Peng Wu, Xuerong Zhou, Guansong Pang, Zhiwei Yang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Current weakly supervised video anomaly detection (WSVAD) task aims to achieve frame-level anomalous event detection with only coarse video-level annotations available. Existing works typically involve extracting global features from full-resolution video frames and training frame-level classifiers to detect anomalies in the temporal dimension. However, most anomalous events tend to occur in localized spatial regions rather than the entire video frames, which implies existing frame-level feature based works may be misled by the dominant background information and lack the interpretation of the detected anomalies. To address this dilemma, this paper introduces a novel method called STPrompt that learns spatio-temporal prompt embeddings …

Trace: Securing Smart Contract Repository Against Access Control Vulnerability, Chong Chen, Lingfeng Bao, David Lo, Yanlin Wang, Zhenyu Shan, Ting Chen, Guangqiang Yin, Jianxing Yu, Zibin Zheng, Jiachi Chen

Research Collection School Of Computing and Information Systems

Smart contract vulnerabilities have led to billions of dollars in economic losses. Among these, improper Access Control, which allows unauthorized users to execute restricted functions, is particularly prevalent and has caused significant financial damage. Smart contract repositories contain source code, documentation, configuration files, and other artifacts necessary for building and deploying smart contracts. GitHub hosts numerous open-source repositories of this kind, which serve as intermediate artifacts in development and require compilation and packaging to produce deployable contracts. Third-party developers often reference, reuse, or fork code from these repositories during custom development. However, if the referenced code contains vulnerabilities, it can …

Semat: Semantic Enhanced Natural Image Interactive Matting, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Qianru Sun, Yang Tang, Bo Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Recent approaches attempt to adapt powerful interactive segmentation models, such as SAM, to interactive matting and fine-tune the models based on synthetic matting datasets. However, models trained on synthetic data fail to generalize to complex and occlusion scenes. We address this challenge by proposing a new matting dataset based on the COCO dataset, namely COCO-Matting. It selects real-world complex images from COCO and converts semantic segmentation masks to matting labels. The built COCO-Matting comprises an extensive collection of 36,980 human instance-level alpha mattes in complex natural scenarios. Furthermore, existing SAM-based matting methods extract intermediate features and masks from a frozen …

Distributional Vision-Language Alignment By Cauchy-Schwarz Divergence, Wenzhe Yin, Zehao Xiao, Pan Zhou, Shujian Yu, Jiayi Shen, Jan-Jakob Sonke, Stratis Gavves

Research Collection School Of Computing and Information Systems

Vision-language alignment is crucial for various downstream tasks such as cross-modal generation and retrieval. Previous multimodal approaches like CLIP utilize InfoNCE to maximize mutual information, primarily aligning pairwise samples across modalities while overlooking distributional differences. In addition, InfoNCE has inherent conflict in terms of alignment and uniformity in multimodality, leading to suboptimal alignment with modality gaps. To overcome the limitations, we propose CS-Aligner, a novel framework that performs distributional vision-language alignment by integrating Cauchy-Schwarz (CS) divergence with mutual information. CS-Aligner captures both the global distribution information of each modality and the pairwise semantic relationships. We find that the CS divergence …

Dragging With Geometry: From Pixels To Geometry-Guided Image Editing, Xinyu Pu, Hongsong Wang, Jie Gui, Pan Zhou

Research Collection School Of Computing and Information Systems

Interactive point-based image editing serves as a controllable editor, enabling precise and flexible manipulation of image content. However, most drag-based methods operate primarily on the 2D pixel plane with limited use of 3D cues. As a result, they often produce imprecise and inconsistent edits, particularly in geometry-intensive scenarios such as rotations and perspective transformations. To address these limitations, we propose a novel geometry-guided drag-based image editing method—GeoDrag, which addresses three key challenges: 1) incorporating 3D geometric cues into pixel-level editing, 2) mitigating discontinuities caused by geometry-only guidance, and 3) resolving conflicts arising from multi-point dragging. Built upon a unified displacement …

Dreamcs: Geometry-Aware Text-To-3d Generation With Unpaired 3d Reward Supervision, Xiandong Zou, Ruihao Xia, Hongsong Wang, Pan Zhou

Research Collection School Of Computing and Information Systems

While text-to-3D generation has attracted growing interest, existing methods often struggle to produce 3D assets that align well with human preferences. Current preference alignment techniques for 3D content typically rely on hardly-collected preference-paired multi-view 2D images to train 2D reward models, when then guide 3D generation — leading to geometric artifacts, such as the Janus face problem and geometric incompleteness, due to their inherent 2D bias. To address these limitations, we construct 3D-MeshPref, the first large-scale unpaired 3D preference dataset, featuring diverse 3D meshes annotated by a large language model and refined by human evaluators. We then develop RewardCS, the …

From Spatial To Actions: Grounding Vision-Language-Action Model In Spatial Foundation Priors, Zhengshen Zhang, Hao Li, Yalun Dai, Zhengbang Zhu, Lei Zhou, Chenchen Liu, Dong Wang, Francis E. H. Tay, Sijin Chen, Ziwei Liu, Yuxiao Liu, Xinghang Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptability. Recent 3D integration techniques for VLAs either require specialized sensors and transfer poorly across modalities, or inject weak cues that lack geometry and degrade vision-language alignment. In this work, we introduce FALCON (From Spatial to Action), a novel paradigm that injects rich 3D spatial tokens into the action head. FALCON leverages spatial foundation models to deliver strong geometric priors from RGB alone, and includes an Embodied Spatial Model that can optionally fuse depth, or pose …

Where Did It Go Wrong? Attributing Undesirable Llm Behaviors Via Representation Gradient Tracing, Zhe Li, Wei Zhao, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their deployment is frequently undermined by undesirable behaviors such as generating harmful content, factual inaccuracies, and societal biases. Diagnosing the root causes of these failures poses a critical challenge for AI safety. Existing attribution methods, particularly those based on parameter gradients, often fall short due to prohibitive noisy signals and computational complexity. In this work, we introduce a novel and efficient framework that diagnoses a range of undesirable LLM behaviors by analyzing representation and its gradients, which operates directly in the model's activation space to provide a semantically meaningful signal linking …

Reducing Class-Wise Performance Disparity Via Margin Regularization, Beier Zhu, Kesen Zhao, Jiequan Cui, Qianru Sun, Yuan Zhou, Xun Yang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Deep neural networks often exhibit substantial disparities in class-wise accuracy, even when trained on class-balanced data—posing concerns for reliable deployment. While prior efforts have explored empirical remedies, a theoretical understanding of such performance disparities in classification remains limited. In this work, we present Margin Regularization for performance disparity Reduction (MR2 ), a theoretically principled regularization for classification by dynamically adjusting margins in both the logit and representation spaces. Our analysis establishes a margin-based, class-sensitive generalization bound that reveals how per-class feature variability contributes to error, motivating the use of larger margins for “hard” classes. Guided by this insight, MR2 optimizes …

Managing Reproducibility Debt In Scientific Software: A Practical Framework, Zara Hassan, Christoph Treude, Graham Williams, Michael Norrish, Alex Potanin

Research Collection School Of Computing and Information Systems

Scientific software includes end-user applications, modelling tools, research software for publications, and production systems for real users. It plays a key role across various scientific disciplines by enabling large-scale computation, simulation, and data analysis. Unlike commercial software, scientific software is often developed in dynamic research environments with limited engineering practices, documentation, or testing. This makes it fragile and difficult to reproduce results, even when code and data are available, conditions in which Reproducibility Debt (RpD) accumulates. This paper presents the Reproducibility Debt Management Framework (RpD-MF), which is grounded in evidence from a systematic literature review, practitioner interviews, and a global …

Llmqua: Practical Backdoor Injection On Large Language Model Quantization, Xiangxiang Chen, Peixin Zhang, Jun Sun, Jin Song Dong, Wenhai Wang, Jingyi Wang

Research Collection School Of Computing and Information Systems

Quantization is widely used to enable local deployment of large language models (LLMs) on resource-constrained devices. Recent work (e.g., QuRA) shows quantization can be exploited via rounding manipulation to implant backdoors. However, such an attack has been evaluated only on small models and does not directly apply to LLMs due to three key constraints: (1) limited poisoning data from small, task-agnostic calibration sets; (2) layer-wise quantization restricting adversarial access to global representations; and (3) lack of gradient access in quantization pipelines, blocking gradient-based attacks.We propose LLMQuA, a practical quantization-phase backdoor attack tailored to the LLM setting. LLMQuA (i) injects backdoors …

Who You Explain To Matters: Learning By Explaining To Conversational Agents With Different Pedagogical Roles, Zhengtao Xu, Junti Zhang, Anthony Tang, Yi-Chieh Lee

Research Collection School Of Computing and Information Systems

Conversational agents are increasingly used in education for learning support. An application is “learning by explaining”, where learners explain their understanding to an agent. However, existing research focuses on single roles, leaving it unclear how different pedagogical roles influence learners’ interaction patterns, learning outcomes and experiences. We conducted a between-subjects study (N=96) comparing agents with three pedagogical roles (Tutee, Peer, Challenger) and a control condition while learning an economics concept. We found that different pedagogical roles shaped learning dynamics, including interaction patterns and experiences. Specifically, the Tutee agent elicited the most cognitive investment but led to high pressure. The Peer …

A Model Intercomparison Study To Investigate Mixing Characteristics In Non-Precipitating Stratocumulus Clouds, Fan Yang, Kyoung Ock Choi, Kamal Kant Chandrakar, Fabian Hoffmann, Pei Hou, Steve Krueger, Chunsong Lu, Mikhail Ovchinnikov, Yangze Ren, Shin Ichiro Shima, Peng Wu, Chongzhi Yin, Zeen Zhu, Seong Soo Yum

Michigan Tech Publications

Recent aircraft observations of marine stratocumulus clouds consistently showed that cloud microphysical relationships vary with altitude, indicating inhomogeneous mixing characteristics near cloud top and homogeneous mixing characteristics in mid-levels of clouds. Here, we conduct model intercomparison of an idealized, non-precipitating stratocumulus cloud to evaluate model consistency and examine whether simulations can reproduce the observed mixing characteristics. The results show that eleven large-eddy simulations with various dynamics and microphysics schemes show good agreement on the thermodynamical, microphysical, and dynamical properties of the stratocumulus-topped boundary layer in a steady state. The inter-model spread in steady-state liquid water path is significantly reduced compared …

Unraveling The Intractable Trilemma In Urban Weather And Climate Modeling, Peiyuan Li, Ashish Sharma, Rao Kotamarthi, Alberto Martilli, Subimal Ghosh, Cristina Negri, Scott Collis, Lee Chapman, Fei Chen, Luis M. A. Bettencourt, Winston T. L. Chow

Research Collection College of Integrative Studies

Urban weather and climate modeling is constrained by the highly heterogeneous and dynamic nature of cities. It exhibits a persistent trilemma between spatial granularity, spatiotemporal coverage, and physical interpretability. We articulate this challenge and propose a hybrid framework that integrates physics -based models, urban observations, and machine learning. Framing urban modeling as an integration problem across methods and scales, we provide a structured guide for next -generation, decision -relevant urban climate capabilities.