Physical Sciences and Mathematics Commons^™

Relativistic Three Particle Scattering, Md Habib E Islam

Physics Theses & Dissertations

In this dissertation, we develop and apply a relativistic framework for studying three-body scattering amplitudes, their analytic structure, and the emergence of universal phenomena such as the Efimov effect. The work integrates three complementary components.

First, we introduce a systematically improvable numerical method for solving relativistic three-body integral equations in momentum space. By discretizing the continuum problem into finite matrix equations and extrapolating to the continuum limit, we obtain stable partial-wave amplitudes in the presence of a two-body bound state. Two complementary treatments of the pole contribution are implemented and shown to reproduce previous finite volume results, with controlled estimates …

(R2115) Analysis Of Single Server Queueing System With Differentiated Vacations And Differentiated Breakdowns, V. Karthick, V. Suvitha

Applications and Applied Mathematics: An International Journal (AAM)

This research work considers a single server queueing model with differentiated vacations. In addition there is a possibility of two types of failures when the server is in a busy period; namely hard failure and soft failure. In the time of soft failure server may work with a slow service rate. We analyzed as a Quasi-Birth-and-Death (QBD) process, using the matrix geometric method, the steady state probability vector of the number of customers in the queue and the stability conditions are produced. Busy period analysis of the proposed model in given. The effects of various parameters on the system performance …

(R2132) A Multi Server Markovian Working Vacation Queue With Randomly Varying Environment, A. Sundaramoorthy, R. Kalyanaraman

Applications and Applied Mathematics: An International Journal (AAM)

In this article, we consider a multi server Markovian queueing system with working vacation. During busy period, the arrival and service completion are generated by K distinct randomly varying environments. At a service completion epoch, if no customer in the system, the servers take vacation, the vacation policy is multiple vacation policy and the vacation period follows negative exponential distribution. In addition, during vacation period the servers serve customers if they arrive. Based on the vacation termination point we define two Models. For the two models, the steady state probability vector of number of customers in the queue, the stability …

(R2135) System Dynamical Analysis For Ann-Based Numerical Solutions Of A Compartmental Model: A Bio-Mathematical Model Of Drug Diffusion Through The Compartments Of Blood And Tissue, Rakesh Kumar, Sudarshan Dhua

Applications and Applied Mathematics: An International Journal (AAM)

This paper provides a considerably efficient numerical approach to acquire the solutions of a biomathematical model administrating oral and intravenous distribution of pharmaceuticals in the human body. The proposed numerical approach based on an artificial neural network is employed to extract numerical solutions for a detailed set of ordinary differential equations and analyze the change in concentration of drug diffusion via the compartments of blood and tissue medium. We primarily focus on analyzing three different models established on the diffusion process, exercising laws of mass action and Fick’s principle. In this work, the existing model is reformulated as an optimization …

(R2107) Analysis Of A Bivalent Vaccine Model With Peer Influence Effect On Testing, Manoj Kumar Singh, Anjali .

Applications and Applied Mathematics: An International Journal (AAM)

The coronavirus caused havoc around the world. There was a terrible situation in villages and cities, and no one knew how to deal with it. Although the governments of each country tried their best to save the common people, vaccination programs and testing centers were built everywhere. However, people were not utilizing it due to fear. Because of this, the infection spread rapidly. The qualitative study of the mathematical model here is in context with the situation when bivalent vaccination and testing are available for an epidemic. The mathematical model combines the exposed period and influenza model with vaccination included …

Topic Modeling And Culturomic Analysis Of 30,000 Books Over 100 Years Using Gensim, Michael A. Freeman

Electronic Theses and Dissertations

This thesis explores the cultural influence of historical events on English-language fiction published between 1820 and 1929. Using a corpus of 30,256 digitized books from Project Gutenberg, Latent Dirichlet Allocation (LDA) topic modeling was applied to identify recurring themes across eleven decades. The study sought to determine whether historically significant events could be detected within fictional narratives. One clear instance emerged: Napoleon Bonaparte and the Napoleonic Wars appeared explicitly in the 1820s corpus. Beyond this, several thematic patterns were observed—such as maritime language in the 1840s, national identity in the 1880s, and youth-oriented dialogue in the early 20th century—that plausibly …

Isolation Of The 2019 Bonnet Carré Spillway Effects On The Habitat Suitability Of Brown Shrimp (Farfantepenaeus Aztecus) And Blue Crab (Callinectes Sapidus) Using A Modeling Approach, Mallory Dyson

Master's Theses

In 2019, an unprecedented flood year, the Bonnet Carré Spillway (BCS) was opened on two separate occasions (February 27^th to April 11^th, and May 10^th through July 27^th) diverting a volume 6 times the volume of Lake Pontchartrain into the surrounding coastal waters of Louisiana, Mississippi, and Alabama. The goal of this research was to determine the spatio-temporal influence of these freshwater releases on two economically and ecologically important species (Farfantepenaeus aztecus and Callinectes sapidus) with variable responses to salinity and temperature depending on the age class. The Habitat Suitability Index (HSI) …

Early Career Setback And Future Achievement In Professional Sports, Suman Kalyan Maity, Yang Wang, Nima Dehmamy, Victoria Medvec, Brian Uzzi, Dashun Wang

Computer Science Faculty Research & Creative Works

A central tenet of human performance posits that past success is a key predictor of future outcomes. This principle underpins selection processes in various human endeavors, shaping opportunity, wage, and winner-take-all inequalities. Here we systematically examine the future performance of previous winners and non-winners across two sports contexts using two different empirical strategies. First, we track young athletes participating in world-class track and field competitions and compare the future performance of bronze medalists to those finishing just shy of the podium. Next, we study a novel natural experiment in tennis, where we compare future performances of 'lucky losers'—players who advanced …

Understanding Bias And Fairness In Large Language Models: An Empirical Study, Joshua Johnson

Electrical Engineering and Computer Science Undergraduate Honors Theses

This thesis investigates demographic bias in large language models (LLMs) through the use of evaluating outcome disparities when utilized in decision making tasks as well as underlying associations that could contribute to furthering these disparities. Using profiles from the Adult dataset, we analyze how Gemini 2.0 Flash performs in an income prediction task using zero-shot and few-shot prompting methods. Our findings show that models exhibit measurable differences in demographic parity and false positive rates, with the use of few-shot prompting reducing these disparities. Alongside this line of testing, we tested associational bias in Qwen 2.5 using probability based association tests …

Geochemical Analysis Of The Eagle Ford Shale In Webb And Dimmit Counties, Tx, Natalie G. Girlinghouse

Electronic Theses and Dissertations

The Late Cretaceous Eagle Ford Shale of South Texas is a mixed carbonate–siliciclastic succession that serves as both a prolific hydrocarbon source and reservoir. This study integrates chemostratigraphy, mineralogical analysis, and elemental ratio proxies to reconstruct depositional environments and assess controls on detrital influx, paleoproductivity, and redox conditions across multiple cores in Webb and Dimmit counties. Major, trace, and redox-sensitive elements were evaluated alongside Total Organic Content (TOC) and carbonate content to identify stratigraphic trends and environmental shifts.

Results reveal distinct geochemical signatures between the Upper and Lower Eagle Ford Shale, with the Lower Eagle Ford characterized by elevated Al-rich …

Structural Geology Of The Brady Mountain Area, Ouachita Mountains, Arkansas: Integrating Field Mapping, Geospatial Techniques, Photogrammetry And 3d Modeling For Structural Analysis, Emmanuel Darko, Zachariah Fleming

Electronic Theses and Dissertations

Much of the Ouachita orogenic belt, representing the deformed Paleozoic rocks flanking the southern margin of the North American craton, is buried beneath post-orogenic Mesozoic and Tertiary sediments composing the Gulf Coastal Plain. The Ouachita Mountains of Arkansas and Oklahoma are the largest areas of outcrop for the Pennsylvanian-age Ouachita orogenic belt, making it an ideal place to study the orogeny. This study focuses on the Brady Mountain area within the Benton Uplift, the orogenic core of the Ouachita Mountains in western Arkansas. The region is comprised of numerous ridges trending nearly east-west and are for the most part densely …

Ice Surface Change And Flow Dynamics Analysis Of Sít’ Tlein (Malaspina Glacier) Through Remote Sensing, Victor Devaux-Chupin

Geosciences

Sít’ Tlein, located in southeast Alaska, is the largest piedmont glacier in the world. Periglacial lake expansion, low-lying bed, and proximity to the Pacific Ocean put its glacier lobe at risk of rapid retreat. Its disappearance would equate to removing an area of land about half the size of Rhode Island. Sít’ Tlein exhibits complex ice flow responsible for its iconic folded moraines, and for its heterogeneous ice resupply in its lobe, which occasionally surges. This lobe is gradually mantled by debris and vegetation as the ice flow fades towards its margins, protecting it from melting. This the­ sis is …

Security Vulnerabilities And Defense Tactics For Generative Ai Application Development, Kyle Klein

University Honors Theses

Generative AI (GenAI) applications such as OpenAI's ChatGPT leverage large language models (LLMs) trained on enormous amounts of data to accomplish tasks such as document editing, summarization, and query response. Chatbots and LLM programs that are equipped with retrieval-augmented generation (RAG) have the ability to draw upon data provided by developers and users to improve the quality of the program's responses. LLM technology has even expanded to generate images, audio, and video from user instructions. Designed around unpredictable user input and typically composed of many opaque components, LLM software products face a paradigm shift of new, constantly evolving security challenges. …

Content And Consequences: Impact Of Representation In Stem Higher Education Instructional Content On Marginalized Students, Nichole Ventura

Doctorate in Education

This qualitative study examined representation of historically marginalized students in STEM instructional content at the higher education level and its impact on their learning experiences. Despite growing diversity initiatives in STEM enrollment, curricular materials often fail to reflect the identities of underrepresented students. Using critical theory and interpretivist approaches, this research investigated how representation—or its absence—shapes students' sense of belonging, academic identity formation, and persistence. Through semi-structured interviews with undergraduate students from historically marginalized backgrounds, and purposeful sampling, this study captured the lived experiences of students engaging with STEM instructional materials. Interview protocols explored how students perceive their representation in …

Changepoint Detection As Model Selection: A General Framework, Michael A. Grantham

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation presents a general framework for changepoint detection based on ℓ₀ model selection. The core method, Iteratively Reweighted Fused Lasso (IRFL), improves upon the generalized lasso by adaptively reweighting penalties to enhance support recovery and minimize criteria such as the Bayesian Information Criterion (BIC). The approach allows for flexible modeling of seasonal patterns, linear and quadratic trends, and autoregressive dependence in the presence of changepoints.

Simulation studies demonstrate that IRFL achieves accurate changepoint detection across a wide range of challenging scenarios, including those involving nuisance factors such as trends, seasonal patterns, and serially correlated errors. The framework is …

Gradient-Based Model Shortcut Detection For Time Series Classification, Salomon Ibarra, Frida Cantu, Kaixiong Zhou, Li Zhang

Computer Science Faculty Publications

Deep learning models have attracted lots of research attention in time series classification (TSC) task in the past two decades. Recently, deep neural networks (DNN) have surpassed classical distance-based methods and achieved state-of-the-art performance. Despite their promising performance, deep neural networks (DNNs) have been shown to rely on spurious correlations present in the training data, which can hinder generalization. For instance, a model might incorrectly associate the presence of grass with the label "cat" if the training set have majority of cats lying in grassy backgrounds. However, the shortcut behavior of DNNs in time series remain under-explored. Most existing shortcut …

A Software Framework For Translating Onnx Models Onto The Lace-C3a Hardware, Shawn Jones

All Graduate Theses and Dissertations, Fall 2023 to Present

Modern computers are powerful, but they are not always efficient enough for small, low power systems like those used on satellites and scientific instruments. To solve this problem, engineers often turn to FPGAs—reconfigurable computer chips that can be customized to run specific tasks much faster and with far less energy than ordinary processors. However, finding the best possible design for an FPGA program is extremely difficult because there are millions of ways a design could be built, and only a small fraction of them actually perform well.

This thesis presents SNOW, a new framework that helps automate the search for …

An Investigation Of Paleoclimate Through A Sedimentological Lens In The Gulf Of Mexico And Western North Pacific, Sarah Monica

Dissertations

This dissertation explores the use of sediment cores as proxy data for the reconstruction of paleoclimatic and environmental conditions. Human-caused climate change is leading to dramatic shifts in the global climate. As the instrumental record of climate is relatively short compared to the amount of time Earth has experienced weather, this work aims to extend our knowledge of climate beyond the instrumental record, thereby improving our holistic understanding of the climate system. In Chapter one, sediment cores from the central Texas inner shelf are used to produce a record of intense tropical cyclone (TC) activity over a ~4500-year period. X-Ray …

Advancements In Sinkhole Remediation: Field Data-Driven Sinkhole Grout Volume Prediction Model Via Machine Learning-Based Regression Analysis, Bubryur Kim, Yuvaraj Natarajan, K. R. Sri Preethaa, V. Danushkumar, Ryan Shamet, Jiannan Chen, Rui Xie, Timothy Copeland, Boo Hyun Nam, Jinwoo An

Civil Engineering Faculty Publications

Sinkhole formation poses a significant geohazard in karst regions, where unpredictable subsurface erosion often necessitates costly grouting for stabilization. Accurate estimation of grout volume remains a persistent challenge due to spatial variability, site-specific conditions, and the limitations of traditional empirical methods. This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test (CPT)-derived Sinkhole Resistance Ratio (SRR) values, spatial correlations between CPT and grouting points (GPs), and field-recorded grout volumes from six sinkhole sites in Florida. Three data transformation methods, the Proximal Allocation Method (PAM), the Equitable Distribution Method (EDM), and the Threshold-based …

Upright-Net+: Enhanced Learning Of Upright Orientation For 3d Point Clouds, Xufang Pang, Feng Li, Hongjie Zhuang, Ning Ding, Xiaopin Zhong, Shengfeng He, Wenxi Liu, Bo Jiang

Research Collection School Of Computing and Information Systems

Automatic 3D shape analysis is heavily influenced by the pose of input 3D models, as the continuous nature of pose space introduces complexities that usually exceed the encoding capacities of standard deep learning frameworks. To tackle this challenge, we present Upright-Net+, an enhancement of our previous model, Upright-Net, specifically developed for estimating upright orientation in 3D point clouds. Our approach is grounded in the design principle that ”form ever follows function,” treating the natural base of an object as a functional structure that stabilizes it in its typical pose, influenced by physical laws and geometric properties. We reformulate the continuous …

Safe-Sora: Safe Text-To-Video Generation Via Graphical Watermarking, Zihan Su, Xuerui Qiu, Hongbin Xu, Tangyu Jiang, Jun-Hao Zhuang, Chun Yuan, Ming Li, Shengfeng He, Fei Yu

Research Collection School Of Computing and Information Systems

The explosive growth of generative video models has amplified the demand for reliable copyright preservation of AI-generated content. Despite its popularity in image synthesis, invisible generative watermarking remains largely underexplored in video generation. To address this gap, we propose Safe-Sora, the first framework to embed graphical watermarks directly into the video generation process. Motivated by the observation that watermarking performance is closely tied to the visual similarity between the watermark and cover content, we introduce a hierarchical coarse-to-fine adaptive matching mechanism. Specifically, the watermark image is divided into patches, each assigned to the most visually similar video frame, and further …

Iostom: Offline Imitation Learning From Observations Via State Transition Occupancy Matching, Quang Anh Pham, Brahmanage Janaka Chathuranga Thilakarathna, Tien Mai, Akshat Kumar

Research Collection School Of Computing and Information Systems

Offline Learning from Observations (LfO) focuses on enabling agents to imitate expert behavior using datasets that contain only expert state trajectories and separate transition data with suboptimal actions. This setting is both practical and critical in real-world scenarios where direct environment interaction or access to expert action labels is costly, risky, or infeasible. Most existing LfO methods attempt to solve this problem through state or state-action occupancy matching. They typically rely on pretraining a discriminator to differentiate between expert and non-expert states, which could introduce errors and instability—especially when the discriminator is poorly trained. While recent discriminator-free methods have emerged, …

Cssa-Fusion: Channel Selective And Spatial Alignment Infrared-Visible Image Fusion, Zhen Li, Zhi Zeng, Zhongrui Xiao, Ming Wen, Zhiyuan Zhang, Yibin Tian

Research Collection School Of Computing and Information Systems

Infrared-visible image fusion aims to integrate complementary information from two modalities to generate images with enriched semantic content. However, existing methods often neglect two critical aspects: the design of a local–global feature enhancement architecture and spatial alignment. To address these challenges, we propose Channel Selective and Spatial Alignment Fusion (CSSA-Fusion), a novel framework composed of two synergistic modules. The first is a selective channel and redundancy suppression module, which introduces a dual-branch selective channel attention mechanism to jointly capture local saliency and global channel importance for enhanced feature representation, and an informativeness–redundancy separation strategy to suppress redundant information while preserving …

The Rise Of Parameter Specialization For Knowledge Storage In Large Language Models, Yihuai Hong, Yiran Zhao, Wei Tang, Yang Deng, Yu Rong, Wenxuan Zhang

Research Collection School Of Computing and Information Systems

Over time, a growing wave of large language models from various series has been introduced to the community. Researchers are striving to maximize the performance of language models with constrained parameter sizes. However, from a microscopic perspective, there has been limited research on how to better store knowledge in model parameters, particularly within MLPs, to enable more effective utilization of this knowledge by the model. In this work, we analyze twenty publicly available open-source large language models to investigate the relationship between their strong performance and the way knowledge is stored in their corresponding MLP parameters. Our findings reveal that …

Contx: Scene Context Prediction Via Context Bank And Layout Perception, Jingxin Liang, Yangyang Xu, Haorui Song, Yuan Lu, Yuhui Deng, Yiyi Long, Yan Huang, Shengxin Liu, Jianbo Jiao, Shengfeng He

Research Collection School Of Computing and Information Systems

Scene context prediction, which seeks to infer unknown contextual information from isolated object properties, currently faces limitations due to predominant reliance on pixel-wise supervision that overlooks real-world context priors. To address this, we present ContX, a context-prior-driven, coarse-to-fine model. ContX distinctively integrates explicit linguistic-contextual knowledge in two key ways. First, it proposes a linguistic guided context bank, leveraging linguistic-statistical contextual data to guide the rationality of segmentation shapes and foster meaningful inter-class contextual interactions. Second, ContX augments contextual comprehension by correlating layouts with linguistic descriptions, enhancing layout perception through a multi-modal strategy. Comprehensive experiments demonstrate ContX's superiority and versatility, outperforming …

Pilot-C: Physics-Informed Low-Distortion Optimal Trajectory Compression, Kefei Wu, Baihua Zheng, Weiwei Sun

Research Collection School Of Computing and Information Systems

Location-aware devices continuously generate massive volumes of trajectory data, creating demand for efficient compression. Line simplification is a common solution but typically assumes 2D trajectories and ignores time synchronization and motion continuity. We propose PILOT-C, a novel trajectory compression framework that integrates frequency-domain physics modeling with error-bounded optimization. Unlike existing line simplification methods, PILOT-C supports trajectories in arbitrary dimensions, including 3D, by compressing each spatial axis independently. Evaluated on four real-world datasets, PILOT-C achieves superior performance across multiple dimensions. In terms of compression ratio, PILOT-C outperforms CISED-W, the current state-of-the-art SED-based line simplification algorithm, by an average of 19.2%. For …

Catalytic Synthesis Of Iron-Doped Graphitic Aerogels From Poly(Phloroglucinol-Terephthalaldehyde – Urethane) Precursors, Stephen Yaw Owusu, Rushi U. Soni, Chariklia Sotiriou-Leventis

Chemistry Faculty Research & Creative Works

We report a new class of graphitic carbon aerogel precursors based on iron oxide-doped poly(phloroglucinol-terephthalaldehyde–urethane) (T-POL/PU-FeOx) networks. The hybrid polymeric network incorporates a rigid aromatic triisocyanate, tris(4-isocyanatophenyl)methane, which reacts in situ with the hydroxyl groups of phloroglucinol to form a polyurethane-containing framework. Monolithic aerogels derived from this system undergo catalytic graphitization at significantly reduced temperatures (800–1500 °C) compared to conventional graphitization (2500–3300 °C). An oxidative ring fusion aromatization step (240 °C, air) prior to pyrolysis enhanced the degree of graphitization. The resulting graphitic aerogels were characterized by XRD, Raman spectroscopy, TGA, TEM, SEM, XPS, and N₂ sorption porosimetry. Compared to …

General Test-Time Backdoor Detection In Split Neural Network-Based Vertical Federated Learning, Shunjie Yuan, Xinghua Li, Xuelin Cao, Haiyan Zhang, Robert H. Deng

Research Collection School Of Computing and Information Systems

As a new distributed machine learning framework, vertical federated learning (VFL) has been widely applied in the industry. However, recent studies have demonstrated that VFL faces serious challenges from backdoor attacks, which significantly hinder its further development. Although a few studies have focused on defending against VFL backdoor attacks, these defenses either do not consider the latest attack methods or show limited effectiveness. Moreover, most existing backdoor defense efforts primarily focus on backdoor attacks in horizontal federated learning (HFL) and centralized learning. Due to the unique architecture of VFL models, these methods cannot be directly applied to backdoor defense in …

Kpiroot+: An Efficient Integrated Framework For Anomaly Detection And Root Cause Analysis In Large-Scale Cloud Systems, Wenwei Gu, Renyi Zhong, Guangba Yu, Xinying Sun, Jinyang Liu, Yintong Huo, Zhuangbin Chen, Jianping Zhang, Jiazhen Gu, Yongqiang Yang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

To ensure the reliability of cloud systems, their runtime status reflecting the service quality is periodically monitored with monitoring metrics, i.e., KPIs (key performance indicators). When performance issues happen, root cause localization pinpoints the specific KPIs that are responsible for the degradation of overall service quality, facilitating prompt problem diagnosis and resolution. To this end, existing methods generally locate root-cause KPIs by identifying the KPIs that exhibit a similar anomalous trend to the overall service performance. While straightforward, solely relying on the similarity calculation may be ineffective when dealing with cloud systems with complicated interdependent services. Recent deep learning-based methods …

Fl-Cdf: Collaborative Defense Framework For Backdoor Mitigation In Federated Learning, Haiyan Zhang, Xinghua Li, Yinbin Miao, Shunjie Yuan, Mengyao Zhu, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated learning (FL) is vulnerable to backdoor attacks due to its distributed nature. Existing unilateral defense mechanisms often fail against persistent attack strategies, primarily due to their limited perspectives. To address the challenge of model misclassification on the server side caused by overlooked model similarity drift, and gradient misjudgment on the client side caused by semantic learning imbalances across classes, this paper proposes a collaborative defense framework for federated learning, termed FL-CDF. FL-CDF establishes an end-to-end defense through a bidirectional client-server collaboration mechanism. Specifically: (1) On the client side, an adversarial perturbation-based malicious neuron detection module is introduced. This module …