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Articles 10711 - 10740 of 291657

Full-Text Articles in Physical Sciences and Mathematics

Simultaneous Selection Of Inflations And Variables In Multiple Inflations Poisson Model (Mip), John Koomson Aug 2025

Simultaneous Selection Of Inflations And Variables In Multiple Inflations Poisson Model (Mip), John Koomson

Open Access Theses & Dissertations

Count data frequently arise in biomedical, economic, and social science research and are often characterized by structural excesses at specific count levels. To accommodate such patterns, Su et al. (2013), among others, introduced the Multiple-Inflation Poisson (MIP) model, which allows for multiple inflated counts within the distribution. However, two critical challenges remain in modeling such data: (i) identifying the true inflation points where excess counts occur, and (ii) selecting the relevant covariates that explain variation in the inflation and count process. This dissertation addresses these issues by advancing the MIP model through a novel methodology that enables the simultaneous selection …

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Study On Self-Interaction Errors In Molecular Properties And Magnetic Behaviour Of 2d Materials Under External Stimuli Using Density Functional Theory, Prakash Mishra Aug 2025

Study On Self-Interaction Errors In Molecular Properties And Magnetic Behaviour Of 2d Materials Under External Stimuli Using Density Functional Theory, Prakash Mishra

Open Access Theses & Dissertations

Density Functional Theory (DFT) has established itself as a practical and versatile method for efficiently studying the properties of materials of solids and molecules. Despite being exact in principle, in practice, DFT calculations rely on an approximation for the exchange-correlation energy functional of the electron density. This results in an erroneous interaction of the electron with itself, known as the self-interaction error (SIE). It arises from the fact that the exchange self-interaction does not exactly cancel out the Coulomb self-interaction. Due to this error, the approximate one-electron potential decays exponentially in the asymptotic region rather than exhibiting the correct ?1 …

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Temporal Partitioning And Coexistence Among Four Sympatric Felid Species In The Northern Jaguar Reserve, Kylie Marie Rezendes Aug 2025

Temporal Partitioning And Coexistence Among Four Sympatric Felid Species In The Northern Jaguar Reserve, Kylie Marie Rezendes

Open Access Theses & Dissertations

Coexistence among sympatric carnivores is shaped by behavioral strategies that mediate competition and mortality risk in heterogeneous landscapes. Understanding how sympatric carnivores, such as felids, use space and time in relation to one another requires models that account for both spatial and temporal overlap across landscapes. Models incorporating time-to-event probabilities provide a powerful framework for detecting behavioral dynamics across shared landscapes. This study evaluated the spatiotemporal responses of two apex predators â?? the jaguar (Panthera onca) and mountain lion (Puma concolor) - and two mesopredators - bobcat (Lynx rufus) and ocelot (Leopardus pardalis) - across five years of camera trap …

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Experimental Investigation Of Photovoltaic Soiling Losses And Aerosol Absorption And Scattering Related To Dust In The Chihuahuan Desert Region, German Rodriguez Ortiz Aug 2025

Experimental Investigation Of Photovoltaic Soiling Losses And Aerosol Absorption And Scattering Related To Dust In The Chihuahuan Desert Region, German Rodriguez Ortiz

Open Access Theses & Dissertations

This dissertation explores the interaction between mineral dust and solar radiation in the Chihuahuan Desert, one of the dustiest areas in North America. I focused on two main aspects: (1) the characterization of atmospheric aerosol optical properties during dust events, and (2) the effects of dust deposition (soiling) on the performance of photovoltaic (PV) modules. I characterized the intrinsic optical properties of aerosols in El Paso, Texas during the peak of the 2021 dust season. Ground-level, high-resolution optical and meteorological data were collected at the Black and Brown Carbon (BC^2) study site, whereas particulate matter and additional meteorological parameters were …

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A Digital Engineering Framework For Ai-Driven Trade-Off Evaluation And Predictive Component Classification, Alejandro Silva Au Aug 2025

A Digital Engineering Framework For Ai-Driven Trade-Off Evaluation And Predictive Component Classification, Alejandro Silva Au

Open Access Theses & Dissertations

This thesis introduces a digital engineering tool designed to help engineers make smarter decisions when choosing actuators. At its core, the system brings together machine learning (specifically XGBoost) and a decision-making method called Multi-Utility Attribute Theory (MUAT). The goal is to support engineers in picking components based on what really matters for their designs, whether that's speed, cost, durability, or any other performance factor. What makes this tool stand out is its user-friendly interface that lets people interact with the system directly. It takes a set of actuator performance data, classifies each one into a relevant use category, and then …

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Laser Scan Path Design For Controlled Microstructure In Additive Manufacturing With Integrated Reduced-Order Phase-Field Modeling And Deep Reinforcement Learning, Augustine Twumasi Aug 2025

Laser Scan Path Design For Controlled Microstructure In Additive Manufacturing With Integrated Reduced-Order Phase-Field Modeling And Deep Reinforcement Learning, Augustine Twumasi

Open Access Theses & Dissertations

Laser Powder Bed Fusion (L-PBF) is a well-established additive manufacturing technique for fabricating intricate metal components with exceptional precision. A significant challenge in L-PBF is the formation of complex microstructures that influence final material properties. We propose a physics-guided, machine learning-aided approach to optimize scan paths for desired microstructure outcomes, such as equiaxed grains. We employed a phase-field method (PFM) to model the evolution of the crystalline grain structure. To reduce computational costs, we trained a surrogate machine learning model, a 3D U-Net convolutional neural network, using single-track phase-field simulations with varying laser powers to predict crystalline grain orientations based …

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Evaluating And Mitigating Linguistic Discrimination In Large Language Models: Perspectives On Safety Equity And Knowledge Equity, Guoliang Dong, Haoyu Wang, Jun Sun, Xinyu Wang Aug 2025

Evaluating And Mitigating Linguistic Discrimination In Large Language Models: Perspectives On Safety Equity And Knowledge Equity, Guoliang Dong, Haoyu Wang, Jun Sun, Xinyu Wang

Research Collection School Of Computing and Information Systems

By training on text in various languages, large language models (LLMs) typically possess multilingual support and demonstrate remarkable capabilities in solving tasks described in different languages. However, LLMs can exhibit linguistic discrimination due to the uneven distribution of training data across languages. That is, LLMs are hard to keep the consistency of responses when faced with the same task but depicted in different languages. In this study, we first explore the consistency in the LLMs’ outputs responding to queries in various languages from two aspects: safety and quality. We conduct this analysis with two datasets (AdvBench and NQ) based on …

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Evowiki: Evaluating Llms On Evolving Knowledge, Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao Aug 2025

Evowiki: Evaluating Llms On Evolving Knowledge, Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao

Research Collection School Of Computing and Information Systems

Knowledge utilization is a critical aspect of LLMs, and understanding how they adapt to evolving knowledge is essential for their effective deployment. However, existing benchmarks are predominantly static, failing to capture the evolving nature of LLMs and knowledge, leading to inaccuracies and vulnerabilities such as contamination. In this paper, we introduce EvoWiki, an evolving dataset designed to reflect knowledge evolution by categorizing information into stable, evolved, and uncharted states. EvoWiki is fully auto-updated, enabling precise evaluation of continuously changing knowledge and newly released LLMs. Through experiments with Retrieval-Augmented Generation (RAG) and Continual Learning (CL), we evaluate how effectively LLMs adapt …

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Fact-Audit: An Adaptive Multi-Agent Framework For Dynamic Fact-Checking Evaluation Of Large Language Models, Hongzhan Lin, Yang Deng, Yuxuan Gu, Wenxuan Zhang, Jing Ma, See-Kiong Ng, Tat-Seng Chua Aug 2025

Fact-Audit: An Adaptive Multi-Agent Framework For Dynamic Fact-Checking Evaluation Of Large Language Models, Hongzhan Lin, Yang Deng, Yuxuan Gu, Wenxuan Zhang, Jing Ma, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have significantly advanced the fact-checking studies. However, existing automated fact-checking evaluation methods rely on static datasets and classification metrics, which fail to automatically evaluate the justification production and uncover the nuanced limitations of LLMs in fact-checking. In this work, we introduce FACT-AUDIT, an agent-driven framework that adaptively and dynamically assesses LLMs’ fact-checking capabilities. Leveraging importance sampling principles and multi-agent collaboration, FACT-AUDIT generates adaptive and scalable datasets, performs iterative model-centric evaluations, and updates assessments based on model-specific responses. By incorporating justification production alongside verdict prediction, this framework provides a comprehensive and evolving audit of LLMs’ factual reasoning …

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Browsing Like Human: A Multimodal Web Agent With Experiential Fast-And-Slow Thinking, Haohao Luo, Jiayi Kuang, Wei Liu, Ying Shen, Jian Luan, Yang Deng Aug 2025

Browsing Like Human: A Multimodal Web Agent With Experiential Fast-And-Slow Thinking, Haohao Luo, Jiayi Kuang, Wei Liu, Ying Shen, Jian Luan, Yang Deng

Research Collection School Of Computing and Information Systems

Automating web navigation which aims to build a web agent that follows user instructions to complete tasks like booking flights by interacting with websites, has received increasing attention due to its practical value. Although existing web agents are mostly equipped with visual perception, planning, and memory abilities, their reasoning process are still deviate from human cognition. In this work, we study the human thought pattern to empower agent with more human-like abilities in web navigation. To tackle this problem, we propose a novel multimodal web agent framework called WebExperT, which is designed to emulate the human planning process of “thinking …

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A Review: The Beauty Of Serendipity Between Integrated Circuit Security And Artificial Intelligence, Chen Dong, Decheng Qiu, Bolun Li, Yang Yang, Chenxi Lyu, Dong Cheng, Hao Zhang, Zhenyi. Chen Aug 2025

A Review: The Beauty Of Serendipity Between Integrated Circuit Security And Artificial Intelligence, Chen Dong, Decheng Qiu, Bolun Li, Yang Yang, Chenxi Lyu, Dong Cheng, Hao Zhang, Zhenyi. Chen

Research Collection School Of Computing and Information Systems

Integrated circuits are the core of a cyber-physical system, where tens of billions of components are integrated into a tiny silicon chip to conduct complex functions. To maximize utilities, the design and manufacturing life cycle of integrated circuits rely on numerous untrustworthy third parties, forming a global supply chain model. At the same time, this model produces unpredictable and catastrophic issues, threatening the security of individuals and countries. As for guaranteeing the security of ultra-highly integrated chips, detecting slight abnormalities caused by malicious behavior in the current and voltage is challenging, as is achieving computability within a reasonable time and …

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Leveraging Reviewer Experience In Code Review Comment Generation, Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Michael W. Godfrey, Chunhua Liu, Wachiraphan Charoenwet Aug 2025

Leveraging Reviewer Experience In Code Review Comment Generation, Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Michael W. Godfrey, Chunhua Liu, Wachiraphan Charoenwet

Research Collection School Of Computing and Information Systems

Modern code review is a ubiquitous software quality assurance process aimed at identifying and resolving potential issues (e.g., functional, evolvability) within newly written code. Despite its effectiveness, the process demands large amounts of effort from the human reviewers involved. To help alleviate this workload, researchers have trained various deep learning based language models to imitate human reviewers in providing natural language code reviews for submitted code. Formally, this automation task is known as code review comment generation. Prior work has demonstrated improvements in code review comment generation by leveraging machine learning techniques and neural models, such as transfer learning and …

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Eformer: An Effective Edge-Based Transformer For Vehicle Routing Problems, Dian Meng, Zhiguang Cao, Yaoxin Wu, Yaqing Hou, Hongwei Ge, Qiang Zhang Aug 2025

Eformer: An Effective Edge-Based Transformer For Vehicle Routing Problems, Dian Meng, Zhiguang Cao, Yaoxin Wu, Yaqing Hou, Hongwei Ge, Qiang Zhang

Research Collection School Of Computing and Information Systems

Recent neural heuristics for the Vehicle Routing Problem (VRP) primarily rely on node coordinates as input, which may be less effective in practical scenarios where real cost metrics--such as edge-based distances--are more relevant. To address this limitation, we introduce EFormer, an Edge-based Transformer model that uses edge as the sole input for VRPs. Our approach employs a precoder module with a mixed-score attention mechanism to convert edge information into temporary node embeddings. We also present a parallel encoding strategy characterized by a graph encoder and a node encoder, each responsible for processing graph and node embeddings in distinct feature spaces, …

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The 6th International Workshop On Talent And Management Computing (Tmc 2025), Hengshu Zhu, Yong Ge, Hui Xiong, Ee-Peng Lim Aug 2025

The 6th International Workshop On Talent And Management Computing (Tmc 2025), Hengshu Zhu, Yong Ge, Hui Xiong, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with talent and management-related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for leaders to deliver intelligence for effective management for organizations. In the past few years, talent and management computing have increasingly attracted attention from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 6th International Workshop on Talent and Management Computing …

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Ai-Assisted Triage And Decision Support Of Head And Neck Cancer Screening And Diagnosis In Low-Resourced Settings, Min Hun Lee, Sean Shao Wei Lam, Shaun Xin Hong Liew, Michael Dorosan, Nicholas Graves, Jonas Karlström, Hiang Khoon Tan, Walter Tsong Lee Aug 2025

Ai-Assisted Triage And Decision Support Of Head And Neck Cancer Screening And Diagnosis In Low-Resourced Settings, Min Hun Lee, Sean Shao Wei Lam, Shaun Xin Hong Liew, Michael Dorosan, Nicholas Graves, Jonas Karlström, Hiang Khoon Tan, Walter Tsong Lee

Research Collection School Of Computing and Information Systems

The mortality burden of head and neck cancer (HNC) is increasing globally and disproportionately affects people in low-and middle-income countries with limited medical workforce. To address this issue, artificial intelligence (AI) algorithms are increasingly being explored to process medical imaging data, demonstrating competitive performance. However, the clinical adoption of AI remains challenging as clinicians struggle to understand how complex AI works and trust it to use in practice. In addition, AI may not perform well on varying data qualities of endoscopy videos for HNC screening and diagnosis from multiple sites.In this project, our international and interdisciplinary team will collaborate with …

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Graph Positional Autoencoders As Self-Supervised Learners, Yang Liu, Deyu Bo, Wenxuan Cao, Yuan Fang, Yawen Li, Chuan Shi Aug 2025

Graph Positional Autoencoders As Self-Supervised Learners, Yang Liu, Deyu Bo, Wenxuan Cao, Yuan Fang, Yawen Li, Chuan Shi

Research Collection School Of Computing and Information Systems

Graph self-supervised learning seeks to learn effective graph representations without relying on labeled data. Among various approaches, graph autoencoders (GAEs) have gained significant attention for their efficiency and scalability. Typically, GAEs take incomplete graphs as input and predict missing elements, such as masked node features or edges. Although effective, our experimental investigation reveals that traditional feature or edge masking paradigms primarily capture low-frequency signals in the graph and fail to learn expressive structural information. To address these issues, we propose Graph Positional Autoencoders (GraphPAE), which employ a dual-path architecture to reconstruct both node features and positions. Specifically, the feature path …

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Inference-Time Gaze Refinement For Micro-Expression Recognition: Enhancing Event-Based Eye Tracking With Motion-Aware Post-Processing, Panahetipola Mudiyanselage Nuwan Bandara, Thivya Kandappu, Archan Misra Aug 2025

Inference-Time Gaze Refinement For Micro-Expression Recognition: Enhancing Event-Based Eye Tracking With Motion-Aware Post-Processing, Panahetipola Mudiyanselage Nuwan Bandara, Thivya Kandappu, Archan Misra

Research Collection School Of Computing and Information Systems

Event-based eye tracking holds significant promise for fine-grained cognitive state inference, offering high temporal resolution and robustness to motion artifacts, critical features for decoding subtle mental states such as attention, confusion, or fatigue. In this work, we introduce a model-agnostic, inference-time refinement framework designed to enhance the output of existing event-based gaze estimation models without modifying their architecture or requiring retraining. Our method comprises two key post-processing modules: (i) Motion-Aware Median Filtering, which suppresses blink-induced spikes while preserving natural gaze dynamics, and (ii) Optical Flow-Based Local Refinement, which aligns gaze predictions with cumulative event motion to reduce spatial jitter and …

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Taclr: A Scalable And Efficient Retrieval-Based Method For Industrial Product Attribute Value Identification, Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen Aug 2025

Taclr: A Scalable And Efficient Retrieval-Based Method For Industrial Product Attribute Value Identification, Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen

Research Collection School Of Computing and Information Systems

Product Attribute Value Identification (PAVI) involves identifying attribute values from product profiles, a key task for improving product search, recommendation, and business analytics on e-commerce platforms. However, existing PAVI methods face critical challenges, such as inferring implicit values, handling outof-distribution (OOD) values, and producing normalized outputs. To address these limitations, we introduce Taxonomy-Aware Contrastive Learning Retrieval (TACLR), the first retrieval-based method for PAVI. TACLR formulates PAVI as an information retrieval task by encoding product profiles and candidate values into embeddings and retrieving values based on their similarity. It leverages contrastive training with taxonomy-aware hard negative sampling and employs adaptive inference …

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Starpose: 3d Human Pose Estimation Via Spatial-Temporal Autoregressive Diffusion, Haoxin Yang, Weihong Chen, Xuemiao Xu, Cheng Xu, Peng Xiao, Cuifeng Sun, Shaoyu Huang, Shengfeng He Aug 2025

Starpose: 3d Human Pose Estimation Via Spatial-Temporal Autoregressive Diffusion, Haoxin Yang, Weihong Chen, Xuemiao Xu, Cheng Xu, Peng Xiao, Cuifeng Sun, Shaoyu Huang, Shengfeng He

Research Collection School Of Computing and Information Systems

Monocular 3D human pose estimation remains a challenging task due to inherent depth ambiguities and occlusions. Compared to traditional methods based on Transformers or Convolutional Neural Networks (CNNs), recent diffusionbased approaches have shown superior performance, leveraging their probabilistic nature and high-fidelity generation capabilities. However, these methods often fail to account for the spatial and temporal correlations across predicted frames, resulting in limited temporal consistency and inferior accuracy in predicted 3D pose sequences. To address these shortcomings, this paper proposes StarPose, an autoregressive diffusion framework that effectively incorporates historical 3D pose predictions and spatialtemporal physical guidance to significantly enhance both the …

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From Risk To Resilience: Towards Assessing And Mitigating The Risk Of Data Reconstruction Attacks In Federated Learning, Xiangrui Xu, Zhize Li, Yufei Han, Bin Wang, Jiqiang Liu, Wei Wang Aug 2025

From Risk To Resilience: Towards Assessing And Mitigating The Risk Of Data Reconstruction Attacks In Federated Learning, Xiangrui Xu, Zhize Li, Yufei Han, Bin Wang, Jiqiang Liu, Wei Wang

Research Collection School Of Computing and Information Systems

Data Reconstruction Attacks (DRA) pose a significant threat to Federated Learning (FL) systems by enabling adversaries to infer sensitive training data from local clients. Despite extensive research, the question of how to characterize and assess the risk of DRAs in FL systems remains unresolved due to the lack of a theoretically-grounded risk quantification framework. In this work, we address this gap by introducing Invertibility Loss (InvLoss) to quantify the maximum achievable effectiveness of DRAs for a given data instance and FL model. We derive a tight and computable upper bound for InvLoss and explore its implications from three perspectives. First, …

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Anomalygfm: Graph Foundation Model For Zero/Few-Shot Anomaly Detection, Hezhe Qiao, Chaoxi Niu, Ling Chen, Guansong Pang Aug 2025

Anomalygfm: Graph Foundation Model For Zero/Few-Shot Anomaly Detection, Hezhe Qiao, Chaoxi Niu, Ling Chen, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years. Existing generalist graph models have achieved remarkable success in different graph tasks but struggle to generalize to the GAD task. This limitation arises from their difficulty in learning generalized knowledge for capturing the inherently infrequent, irregular and heterogeneous abnormality patterns in graphs from different domains. To address this challenge, we propose AnomalyGFM, a GAD-oriented graph foundation model that supports zero-shot inference and few-shot prompt tuning for GAD in diverse graph datasets. …

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Gnncontext: Gnn-Based Code Context Prediction For Programming Tasks, Xiaoye Zheng, Zhiyuan Wan, Shun Liu, Kaiwen Yang, David Lo, Xiaohu Yang Aug 2025

Gnncontext: Gnn-Based Code Context Prediction For Programming Tasks, Xiaoye Zheng, Zhiyuan Wan, Shun Liu, Kaiwen Yang, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

A code context model comprises source code elements and their relations relevant to a programming task. The capture and use of code context models in software tools can benefit software development practices, such as code navigation and search. Prior research has explored approaches that leverage either the structural information of code or interaction histories of developers with integrated development environments to automate the construction of code context models. However, these approaches primarily capture shallow syntactic and lexical features of code elements, with limited ability to capture contextual and structural dependencies among neighboring code elements. In this paper, we propose GNNContext, …

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Tetd: Trusted Execution In Trust Domains, Zhanbo Wang, Jiaxin Zhan, Xuhua Ding, Fengwei Zhang, Ning Hu Aug 2025

Tetd: Trusted Execution In Trust Domains, Zhanbo Wang, Jiaxin Zhan, Xuhua Ding, Fengwei Zhang, Ning Hu

Research Collection School Of Computing and Information Systems

Intel TDX empowers cloud service providers to construct confidential virtual machines called trust domains (TDs) on x86 platforms. Similar to its counterparts from AMD and Arm, TDX's hardware based protection over integrity and secrecy of virtual machine memory and vCPU states inevitably hinders legitimate virtual machine management such as introspection. At the presence of compromised high-privileged software (e.g., the guest kernel), neither the cloud service provider nor the TD owner can securely carry out a task within the TD. To tackle this problem, we propose TETD, an in-TD trusted execution technique without trusting any TD system software. Our design does …

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Achilles: A Formal Framework Of Leaking Secrets From Signature Schemes Via Rowhammer, Junkai Liang, Zhi Zhang, Xin Zhang, Qingni Sheng, Yansong Gao, Xinliang Yuan, Haiyang Xue, Pengfei Wu, Zhonghai. Wu Aug 2025

Achilles: A Formal Framework Of Leaking Secrets From Signature Schemes Via Rowhammer, Junkai Liang, Zhi Zhang, Xin Zhang, Qingni Sheng, Yansong Gao, Xinliang Yuan, Haiyang Xue, Pengfei Wu, Zhonghai. Wu

Research Collection School Of Computing and Information Systems

Signature schemes are a fundamental component of cyber-security infrastructure. While they are designed to be mathematically secure against cryptographic attacks, they are vulnerable to Rowhammer fault-injection attacks. Since all existing attacks are ad-hoc in that they target individual parameters of specific signature schemes, it remains unclear about the impact of Rowhammer on signature schemes as a whole.In this paper, we present Achilles, a formal framework that aids in leaking secrets in various real-world signature schemes via Rowhammer. Particularly, Achilles can be used to find potentially more vulnerable parameters in schemes that have been studied before and also new schemes that …

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2-Adic Quantum Mechanics, Continuous-Time Quantum Walks, And The Space Discreteness, Wilson A. Zuniga-Galindo Aug 2025

2-Adic Quantum Mechanics, Continuous-Time Quantum Walks, And The Space Discreteness, Wilson A. Zuniga-Galindo

School of Mathematical & Statistical Sciences Faculty Publications

The authors show that a large class of 2-adic Schrödinger equations is the scaling limit of certain continuous-time quantum Markov chains (CTQMCs). Practically, a discretization of such an equation gives a CTQMC. As a practical result, new types of continuous-time quantum walks (CTQWs) on graphs using two symmetric matrices are constructed. The transport between nodes in one direction is described by one matrix, while the transport between nodes in the opposite direction. This construction includes, as a particular case, the CTQWs constructed using adjacency matrices. The final goal of this work is to contribute to the understanding of the foundations …

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Complex System Governance And Cyber Operations, Willie Gernard Mccallister Aug 2025

Complex System Governance And Cyber Operations, Willie Gernard Mccallister

Engineering Management & Systems Engineering Theses & Dissertations

This dissertation examines the potential integration of Complex System Governance (CSG) within cybersecurity, emphasizing the development of a reference model for Cybersecurity Infrastructures. Traditional strategies for securing digital environments have struggled to address the intricate and dynamic layers inherent in modern cybersecurity systems. The purpose of this research is to explore the applicability of CSG as a framework to assess cybersecurity infrastructure using a case study research design. The research addresses two key questions: (1) How can the CSG reference model be adapted to explore cybersecurity infrastructure? (2) What results from CSG based exploration of cybersecurity infrastructure through a case …

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Enhancing The Design Of Strained Superlattice Gallium Arsenide Based Photocathodes With Distributed Bragg Reflector, Adam D. A. Masters Aug 2025

Enhancing The Design Of Strained Superlattice Gallium Arsenide Based Photocathodes With Distributed Bragg Reflector, Adam D. A. Masters

Electrical & Computer Engineering Theses & Dissertations

Particle accelerators play a crucial role in our understanding of matter and the universe and have numerous practical applications in various fields. These devices enable scientists to examine the smallest components of matter, study the forces that govern their interactions, and probe conditions from the early universe. Moreover, accelerators are valuable in medicine, industry, and research, enhancing imaging methods, cancer therapies, and manufacturing techniques. As the experiments conducted at these facilities evolve and require higher precision, improved particle sources must continue to advance to keep up with their requirements. To do that, we enhanced the design of spin polarized electron …

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Input Structure Based Optimization For Privacy Preserving Ai Systems, Feng Yizhou Aug 2025

Input Structure Based Optimization For Privacy Preserving Ai Systems, Feng Yizhou

Electrical & Computer Engineering Theses & Dissertations

As Artificial Intelligence (AI) systems become increasingly integrated into critical domains, ensuring privacy-preserving model design and system deployment has become a pressing priority. Safeguarding both sensitive user data and proprietary model parameters is critical throughout the AI model and system, from data acquisition and pre-processing to model inference and deployment. However, existing privacy-preserving frameworks face several limitations, including fragmented data ownership, incomplete protection across system stages, substantial computational overhead, and poor scalability to modern architectures such as large language models. This dissertation explores a unifying optimization strategy centered on input structure design to address these challenges. The core idea is …

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A Strict Physicality-Preserving Scheme For A 2d Q-Tensor Flow With A Singular Potential, Md Mashud Parvez Aug 2025

A Strict Physicality-Preserving Scheme For A 2d Q-Tensor Flow With A Singular Potential, Md Mashud Parvez

Mathematics & Statistics Theses & Dissertations

Nematic liquid crystals are a state of matter that exhibit properties between those of conventional liquids and solid crystals. Their unique ability to align molecules in specific directions makes them essential in various applications, including display technologies and advanced materials. To model their complex behavior, mathematical frameworks such as the Q-tensor model are used to describe the orientation and degree of molecular order. In this work, we introduce a numerical scheme for a two-dimensional (2D) dynamic Q-tensor model, which is formulated as an L2-gradient flow driven by the liquid crystal free energy and incorporates a singular potential to …

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Learning Regulatory Dna-Sequence Code Of Epigenetic Events Using Deep Neural Networks, Sanjeeva Reddy Dodlapati Aug 2025

Learning Regulatory Dna-Sequence Code Of Epigenetic Events Using Deep Neural Networks, Sanjeeva Reddy Dodlapati

Computer Science Theses & Dissertations

Epigenetic events, such as DNA methylation and histone modifications, arise from a complex interplay among genomic sequence, chromatin-remodeling factors, and environmental cues. These regulatory mechanisms can induce changes in gene expression without altering the underlying DNA sequence, playing critical roles in development, disease, and cellular differentiation. Among these events, DNA methylation is frequently profiled using bisulfite sequencing (e.g., whole-genome bisulfite sequencing [WGBS], reduced representation bisulfite sequencing [RRBS]). However, predictive modeling of epigenetic states—including methylation patterns and regulatory variant effects—remains challenging due to data sparsity, label noise, and limited uncertainty estimation in current deep learning approaches. This dissertation addresses these issues …

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