Physical Sciences and Mathematics Commons^™

The Surface Water And Ocean Topography (Swot) Mission For River And Lake Ice Application, Sunwoo Yoon

Earth Sciences Theses and Dissertations

The Surface Water and Ocean Topography (SWOT) mission, launched in December 2022, is designed for global survey of Earth’s surface water. However, the seasonal freezing of lakes and rivers combined with SWOT’s unique interferometric radar characteristics presents a valuable opportunity to assess its potential for river and lake ice applications. In this dissertation, I first compare backscatter characteristics over open water and frozen lakes and rivers. I demonstrate strong contrast in backscatter between water and ice while accounting for incidence angle, highlighting SWOT’s capability to discriminate between surface cover types. However, overlapping backscatter signatures suggest further investigation of drivers of …

Electrochemistry Behind Pfas: Mechanistic And Analytical Approach For Sensing And Degradation Strategies, Jonathan Josue Calvillo Solis

Open Access Theses & Dissertations

Understanding the fundamental electrochemistry of perfluoroalkyl substances (PFAS) is key to developing effective water remediation and sensing strategies. This work explores the thermodynamics and kinetics of perfluorooctanoic acid (PFOA) electroreduction, focusing on C-F bond cleavage. These insights were applied to design a highly sensitive electrochemical sensor for detecting trace levels of PFOA in water. This dissertation focuses on the electrochemical investigation of the reduction reaction of PFOA in aqueous and organic media employing different electrode materials. This exploration allows to understand the defluorination reaction of PFAS to further propose potential strategies for water treatment and PFOA electrosensing. Through electrochemical, spectroscopical …

Advanced Study Of Nickel-Titanium Alloy: Effects Of Point Defects On Mechanical And Thermodynamic Properties, Diego Armando Juarez Rosales

Open Access Theses & Dissertations

High-throughput first-principles calculations of point defects are emerging as a powerful tool to accelerate materials discovery in applications [1]. Substitutional, antisite, and vacancy defects can play an important role in the mechanical and thermal properties of intermetallic alloys [2]. In this present work I compute the thermal and mechanical properties of shape-memory alloy nickel-titaniun (NiTi) in the B19â?? martensitic and B2 austenitic phases from molecular dynamics (MD), using a second nearest neighbor (2NN) modified embedded atom method (MEAM) [3] classical potential in the temperature range from 200K to 600K and composition range from 45 atomic percent to 55 atomic percent …

Evaluating Chatgpt To Answer Multi-Modal Exercises In Computer Science Education, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Benjamin Gan

Research Collection School Of Computing and Information Systems

This study investigates ChatGPT-4o's ability to answer multi-modal assessment exercises in computer science (CS) courses. While the use of large language models (LLMs) to answer text-based exercises are extensively researched, their ability to answer exercises involving artifacts of other modalities remains underexplored. To close this gap, we evaluate ChatGPT-4o's answers to 120 multi-modal CS exercises in programming, software design, human-computer interaction, statistical analysis, process analysis, and simulation. The multi-modal artifacts in these exercises include class diagrams, sequence diagrams, user interface images, analytical charts, workflow diagrams and object-flow diagrams. Our comparisons to the expected answers of these exercises show that ChatGPT-4o …

Position: Trustworthy Ai Agents Require The Integration Of Large Language Models And Formal Methods, Yedi Zhang, Yufan Cai, Xinyue Zuo, Xiaokun Luan, Kailong Wang, Zhe Hou, Yifan Zhang, Zhiyuan Wei, Meng Sun, Jun Sun, Jing Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing broad aspects of daily life. Despite their remarkable performance, LLMs exhibit a fundamental limitation: hallucination—the tendency to produce misleading outputs that appear plausible. This inherent unreliability poses significant risks, particularly in high-stakes domains where trustworthiness is essential. On the other hand, Formal Methods (FMs), which share foundations with symbolic AI, provide mathematically rigorous techniques for modeling, specifying, reasoning, and verifying the correctness of systems. These methods have been widely employed in mission-critical domains such as aerospace, defense, and cybersecurity. However, the broader adoption of FMs remains constrained …

Llmscan: Causal Scan For Llm Misbehavior Detection, Mengdi Zhang, Kai Kiat Goh, Peixin Zhang, Jun Sun, Lin Xin Rose, Hongyu Zhang

Research Collection School Of Computing and Information Systems

Despite the success of Large Language Models (LLMs) across various fields, their potential to generate untruthful and harmful responses poses significant risks, particularly in critical applications. This highlights the urgent need for systematic methods to detect and prevent such misbehavior. While existing approaches target specific issues such as harmful responses, this work introduces LLMSCAN, an innovative LLM monitoring technique based on causality analysis, offering a comprehensive solution. LLMSCAN systematically monitors the inner workings of an LLM through the lens of causal inference, operating on the premise that the LLM’s ‘brain’ behaves differently when generating harmful or untruthful responses. By analyzing …

Foodlmm: A Versatile Food Assistant Using Large Multi-Modal Model, Yuehao Yin, Huiyan Qi, Bin Zhu, Jingjing Chen, Yu-Gang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. Nevertheless, the performance of general LMMs in specific domains is still far from satisfactory. This paper proposes FoodLMM, a versatile food assistant based on LMMs with various capabilities, including food recognition, ingredient recognition, recipe generation, nutrition estimation, food segmentation and multi-round conversation. To facilitate FoodLMM to deal with tasks beyond pure text output, we introduce a series of novel task-specific tokens and heads, enabling the model to predict food nutritional values and multiple segmentation masks. We adopt a two-stage training strategy. In the first stage, we utilize multiple …

Query Understanding In Llm-Based Conversational Information Seeking, Yifei Yuan, Zahra Abbasiantaeb, Mohammad Aliannejadi, Yang Deng

Research Collection School Of Computing and Information Systems

Query understanding in CIS involves accurately interpreting user intent through context-aware interactions. This includes resolving ambiguities, refining queries, and adapting to evolving information needs. LLM enhance this process by interpreting nuanced language and adapting dynamically, improving the relevance and precision of search results in real-time. In this tutorial, we explore advanced techniques to enhance query understanding in LLM-based CIS systems. We delve into LLM-driven methods for developing robust evaluation metrics to assess query understanding quality in multi-turn interactions, strategies for building more interactive systems, and applications like proactive query management and query reformulation. We also discuss key challenges in integrating …

An Incentive Mechanism For Privacy Preserved Data Trading With Verifiable Data Disturbance, Man Zhang, Xinghua Li, Bin Luo, Yanbing Ren, Yinbin Miao, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

To motivate data owners’ (DOs’) trading willingness, the existing incentive mechanisms allow DOs to independently disturb data following data consumer's (DC’s) availability requirement. However, they cannot motivate DOs’ honest disturbance, which is attributed to DOs’ independent disturbance without any supervision. Thus, we implement an incentive mechanism for privacy preserved data trading with verifiable data disturbance where an honest-but-curious disturbance generator (DG) is additionally introduced to supervise DOs’ local disturbance and assist disturbance verification between DOs and DC. Specifically, DG generates the disturbance strategies and secretly distributes to DOs following private information retrieval, guaranteeing DOs's local disturbance's privacy and verifiability with …

Instruct2see: Learning To Remove Any Obstructions Across Distributions, Junhang Li, Yu Guo, Chuhua Xian, Shengfeng He

Research Collection School Of Computing and Information Systems

Images are often obstructed by various obstacles due to capture limitations, hindering the observation of objects of interest. Most existing methods address occlusions from specific elements like fences or raindrops, but are constrained by the wide range of real-world obstructions, making comprehensive data collection impractical. To overcome these challenges, we propose Instruct2See, a novel zero-shot framework capable of handling both seen and unseen obstacles. The core idea of our approach is to unify obstruction removal by treating it as a soft-hard mask restoration problem, where any obstruction can be represented using multi-modal prompts, such as visual semantics and textual instructions, …

Runtime Anomaly Detection For Drones: An Integrated Rule-Mining And Unsupervised Learning Approach, Ivan Wei Han Tan, Wei Minn, Christopher M. Poskitt, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, have witnessed a remarkable surge in popularity due to their versatile applications. These cyber-physical systems depend on multiple sensor inputs, such as cameras, GPS receivers, accelerometers, and gyroscopes, with faults potentially leading to physical instability and serious safety concerns. To mitigate such risks, anomaly detection has emerged as a crucial safeguarding mechanism, capable of identifying the physical manifestations of emerging issues and allowing operators to take preemptive action at runtime. Recent anomaly detection methods based on LSTM neural networks have shown promising results, but three challenges persist: the need for models …

Diversity Optimization For Travelling Salesman Problem Via Deep Reinforcement Learning, Qi Li, Zhiguang Cao, Yining Ma, Yaoxin Wu, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Existing neural methods for the Travelling Salesman Problem (TSP) mostly aim at finding a single optimal solution. To discover diverse yet high-quality solutions for Multi-Solution TSP (MSTSP), we propose a novel deep reinforcement learning based neural solver, which is primarily featured by an encoder-decoder structured policy. Concretely, on the one hand, a Relativization Filter (RF) is designed to enhance the robustness of the encoder to affine transformations of the instances, so as to potentially improve the quality of the found solutions. On the other hand, a Multi-Attentive Adaptive Active Search (MA3S) is tailored to allow the decoders to strike a …

Surrogate Learning In Meta-Black-Box Optimization: A Preliminary Study, Zeyuan Ma, Zhiyang Huang, Jiacheng Chen, Zhiguang Cao, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Recent Meta-Black-Box Optimization (MetaBBO) approaches have shown possibility of enhancing the optimization performance through learning meta-level policies to dynamically configure low-level optimizers. However, existing MetaBBO approaches potentially consume massive function evaluations to train their meta-level policies. Inspired by the recent trend of using surrogate models for cost-friendly evaluation of expensive optimization problems, in this paper, we propose a novel MetaBBO framework which combines surrogate learning process and reinforcement learning-aided Differential Evolution algorithm, namely Surr-RLDE, to address the intensive function evaluation in MetaBBO. Surr-RLDE comprises two learning stages: surrogate learning and policy learning. In surrogate learning, we train a Kolmogorov-Arnold Networks …

Shield: Multi-Task Multi-Distribution Vehicle Routing Solver With Sparsity And Hierarchy, Yong Liang Goh, Zhiguang Cao, Yining Ma, Jianan Zhou, Mohammed Haroon Dupty, Wee Sun Lee

Research Collection School Of Computing and Information Systems

Recent advances toward foundation models for routing problems have shown great potential of a unified deep model for various VRP variants. However, they overlook the complex real-world customer distributions. In this work, we advance the Multi-Task VRP (MTVRP) setting to the more realistic yet challenging Multi-Task Multi-Distribution VRP (MTMDVRP) setting, and introduce SHIELD, a novel model that leverages both sparsity and hierarchy principles. Building on a deeper decoder architecture, we first incorporate the Mixture-of-Depths (MoD) technique to enforce sparsity. This improves both efficiency and generalization by allowing the model to dynamically select nodes to use or skip each decoder layer, …

Sparse-To-Dense: A Free Lunch For Lossless Acceleration Of Video Understanding In Llms, Xuan Zhang, Cunxiao Du, Sicheng Yu, Jiawei Wu, Fengzhuo Zhang, Wei Gao, Qian Liu

Research Collection School Of Computing and Information Systems

Due to the auto-regressive nature of current video large language models (Video-LLMs), the inference latency increases as the input sequence length grows, posing challenges for the efficient processing of video sequences that are usually very long. We observe that during decoding, the attention scores of most tokens in Video-LLMs tend to be sparse and concentrated, with only certain tokens requiring comprehensive full attention. Based on this insight, we introduce Sparse-to-Dense (StD), a novel decoding strategy that integrates two distinct modules: one leveraging sparse top-K attention and the other employing dense full attention. These modules collaborate to accelerate Video-LLMs without loss. …

Breaking The Reasoning Barrier: A Survey On Llm Complex Reasoning Through The Lens Of Self-Evolution, Tao He, Hao Li, Jingchang Chen, Runxuan Liu, Yixin Cao, Lizi Liao, Zihao Zheng, Zheng Chu, Jiafeng Liang, Ming Liu, Bing Qin

Research Collection School Of Computing and Information Systems

The release of OpenAI’s O1 and subsequent projects like DeepSeek R1 has significantly advanced research on complex reasoning in LLMs. This paper systematically analyzes existing reasoning studies from the perspective of self-evolution, structured into three components: data evolution, model evolution, and self-evolution. Data evolution explores methods to generate higher-quality reasoning training data. Model evolution focuses on training strategies to boost reasoning capabilities. Self-evolution research autonomous system evolution via iterating cycles of data and model evolution. We further discuss the scaling law of self-evolution and analyze representative O1-like works through this lens. By summarizing advanced methods and outlining future directions, this …

Simulating Before Planning: Constructing Intrinsic User World Model For User-Tailored Dialogue Policy Planning, Tao He, Lizi Liao, Ming Liu, Bing Qin

Research Collection School Of Computing and Information Systems

Recent advancements in dialogue policy planning have focused on optimizing system agent policies to achieve predefined goals, emphasizing strategy design, trajectory acquisition, and training efficiency. However, these approaches often overlook the critical role of user characteristics, which are essential in real-world scenarios like conversational search and recommendation, where interactions must adapt to individual user traits such as personality, preferences, and goals. To address this gap, we conduct a comprehensive study using task-specific user personas to evaluate dialogue policy planning under diverse user behaviors. Our analysis, based on these user profiles, reveals significant shortcomings in existing approaches, underscoring the necessity for …

Generalization Analysis For Supervised Contrastive Representation Learning Under Non‑Iid Settings, Minh Hieu Nong, Antoine Ledent

Research Collection School Of Computing and Information Systems

Contrastive Representation Learning (CRL) has achieved impressive success in various domains in recent years. Nevertheless, the theoretical understanding of the generalization behavior of CRL has remained limited. Moreover, to the best of our knowledge, the current literature only analyzes generalization bounds under the assumption that the data tuples used for contrastive learning are independently and identically distributed. However, in practice, we are often limited to a fixed pool of reusable labeled data points, making it inevitable to recycle data across tuples to create sufficiently large datasets. Therefore, the tuple-wise independence condition imposed by previous works is invalidated. In this paper, …

Cracking Aegis: An Adversarial Llm-Based Game For Raising Awareness Of Vulnerabilities In Privacy Protection, Jiaying Fu, Yiyang Lu, Zehua Yang, Fiona Fui-Hoon Nah, Ray Lc

Research Collection School Of Computing and Information Systems

Traditional methods for raising awareness of privacy protection often fail to engage users or provide hands-on insights into how privacy vulnerabilities are exploited. To address this, we incorporate an adversarial mechanic in the design of the dialogue-based serious game Cracking Aegis. Leveraging LLMs to simulate natural interactions, the game challenges players to impersonate characters and extract sensitive information from an AI agent, Aegis. A user study (n=22) revealed that players employed diverse deceptive linguistic strategies, including storytelling and emotional rapport, to manipulate Aegis. After playing, players reported connecting in-game scenarios with real-world privacy vulnerabilities, such as phishing and impersonation, and …

Using Radium Isotopes To Compare Nutrient Inputs Via Submarine Groundwater Discharge To Offshore Export On The West Florida Shelf, Andrew L. Lindgren

OES Theses and Dissertations

Radium isotopes have been used to trace submarine groundwater discharge (SGD) and associated nutrient and metal inputs into the coastal ocean. They have also been used to estimate rates of diffusive mixing and export fluxes of dissolved constituents offshore. However, use of both approaches in tandem to determine the fate of groundwater inputs after they enter the coastal ocean is still relatively unexplored. Radium isotopes and nutrients were measured in two cross-shelf cruises on the West Florida Shelf in different seasons and in quarterly sampling of offshore wells. Through 226Ra mass balance and 223Ra-derived diffusive mixing rates, inputs of nutrients …

Unraveling Nonperturbative Qcd With Transverse Momentum Hadronic Structures, Tommaso Rainaldi

Physics Theses & Dissertations

The main purpose of this thesis is to investigate the theoretical foundations of the factorization theorems that involve transverse quark and gluon momentum distributions, and to better understand the interface between foundational questions and phenomenological applications. Many of these applications involve using transverse parton momentum dependent (TMD) correlation functions as tools for probing the complex nonperturbative structures of the hadrons.

We first introduce the concepts of collinear and TMD factorization in a toy-model theory and, by leveraging the comparative simplicity of such a model, we test the range of validity and limitations of the factorization approach. At the same time, …

Towards Efficient Privacy-Preserving Deep Learning: He-Friendly Structures, Flexible Pruning, He-Efficient Architectures, And Secure Transformer Token Drop, Yifei Cai

Electrical & Computer Engineering Theses & Dissertations

Deep learning (DL) has become a powerful tool for solving complex problems, but developing DL models typically requires vast datasets, high computational resources, and expert knowledge—barriers that limit accessibility. Machine Learning as a Service (MLaaS) addresses this challenge by allowing resource-rich providers to deliver pre-trained DL models as services. However, privacy concerns arise: clients hesitate to share sensitive data, while providers protect their proprietary models. To address this, privacy-preserving MLaaS integrates cryptographic techniques into DL computations, as seen in frameworks like Cryptonets, SecureML, GAZELLE, CrypTFlow2, Cheetah, and BOLT. Among them, Homomorphic Encryption (HE) enables computation on encrypted data but remains …

Expanding And Mainstreaming Sociohydrology Toward Transdisciplinary Praxis, Shinichiro Nakamura, Heidi Kreibich, Melissa Haeffner, Jenia Mukherjee, Giuliano Di Baldassarre, Maiko Sakamoto, Mikiko Sugiura, Günter Blöschl, Taikan Oki, Murugesu Sivapalan

Environmental Science and Management Faculty Publications and Presentations

Since its development in the early 2010s, sociohydrology has deepened our understanding of the long-term coevolution of humans and water by integrating insights from both the natural and social sciences, while also fostering an interdisciplinary community. Its modus operandi to date has been to focus on emergent phenomena, manifesting as unintended consequences, in a variety of contexts. The compound disaster that struck Japan’s Noto Peninsula in 2024, and similar experiences in other parts of the world, underscore the urgent need for systemic approaches that are co-developed by academia and practitioners and focus on context-specific solutions. This perspective piece thus calls …

Atmospheric Teleconnection Patterns And Hydrological Whiplashes In The Western U.S., Wenzhao Li, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Western U.S. is undergoing notable transformations in its hydrological patterns, distinguished by rising variability and recurrent “whiplash” shifts between extreme wet and dry phases. Our comprehensive analysis of 469 streamflow stations from 1981 to 2023 reveals a substantial increase in hydrological whiplash events, with a peak of 206 stations experiencing dry-to-wet whiplash in the early 1990s. We establish strong links between these streamflow extremes and sub-seasonal to seasonal teleconnection factors, particularly the Western Pacific Oscillation (WP) and Eastern Pacific/North Pacific Oscillation (EPO). Additionally, we demonstrate the combined impacts of the El Niño-Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO), …

P-Adic Cellular Neural Networks With Delay, Baboucarr Dibba

Theses and Dissertations

This dissertation presents a novel framework for p-adic reaction-diffusion cellular neural networks (CNNs) with delay, offering new insights into the stability and dynamic behavior of these networks. Through numerical simulations, we explore their response to various conditions, highlighting their capability to model complex systems. Additionally, this work reviews cutting-edge developments in p-adic CNNs, particularly their application to advanced image processing tasks such as edge detection and noise filtering, demonstrating their effectiveness in preserving critical image features while filtering out noise. This dissertation is written in collaboration with my Ph.D supervisor and Dr. Zambrano-Luna, Brian.

A Study Of Quasigroups, A Computational Approach On Isotopism And Isomorphism Hierarchical Structure, Runaldo Montrose

Theses and Dissertations

Quasigroups morphism classification can present a real computational challenge. As of today, the number of quasigroups that can be generated from a finite set S of cardinality n is known for very small value of n. Though we know the number of quasigroups for small n, generating them in real time is a whole other issue that leads to a bigger challenge of building their isomorphy classes, because the algorithm used required large computing memory resources. In this paper we will expose the computational complexity of constructing their hierarchical morphism structure from isotopism to isomorphism. As an improvement, …

Electrocatalytic Degradation Of Methylene Blue Using Graphene Oxide, Antimony Oxide, And Graphene Oxide-Supported Antimony Oxide Ink-Based Electrodes, Maria Irene Myers Armas

Theses and Dissertations

This study investigates the electrocatalytic degradation of methylene blue (MB) using copper mesh electrodes coated with graphene oxide (GO), antimony oxide (Sb₂O₃), and their composite (GO/Sb₂O₃). These materials were evaluated across a pH range of 2 to 8 using sodium sulfate as the supporting electrolyte. UV-Vis spectroscopy at 665 nm confirmed dye degradation, with removal efficiencies reaching up to 95% at pH 2. However, degradation decreased at higher pH, with 40 – 60% removal at pH 8, depending on the electrode. Kinetic analyses revealed optimum performance under acidic conditions. GO/Sb₂O₃ electrodes demonstrated the most consistent and effective performance across all …

Predicting Enzyme-Substrate Association Using Heterogeneous Knowledge Graph, Jannatul Ferdaus

Theses and Dissertations

Phosphorylation and dephosphorylation are dynamic processes that control many aspects of cellular activity, such as metabolic pathways, cell cycle progression, and signal transduction. Protein activity and interactions are modulated by the reversible addition or removal of phosphate groups, which allows cells to react abruptly to evolving conditions. Although kinase-specific phosphorylation site prediction has advanced, phosphatase-specific dephosphorylation site computational prediction is still a major obstacle that prevents us from fully comprehending the extent of cellular regulation. In this study, we constructed a knowledge graph for the prediction of enzymes (kinases and phosphatases) and their associated substrates with specific phosphosites. As part …

Sequential Data Modeling Of Influenza A Via Traditional, Dwt-Gpr Hybrid, And Deep Learning Architectures, Edmund Fosu Agyemang

Theses and Dissertations

Influenza A is responsible for 290,000 to 650,000 respiratory deaths a year, though this estimate is an improvement from years past due to improved sanitation, healthcare practices, and vaccination programs. In this study, we perform a comparative analysis of traditional, deep-learning and discrete wavelet (DWT)-Gaussian Process (GP) hybrid models to predict Influenza A outbreaks. Using historical data from January 2009 to December 2023, we compared the performance of traditional ARIMA and ETS models, four variants of DWT-GPR models and six distinct deep learning architectures: Simple RNN, LSTM, GRU, BiLSTM, BiGRU and Transformer. The results reveal a clear superiority of all …

Advancing Food Nutrition Estimation Via Visual-Ingredient Feature Fusion, Huiyan Qi, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF2 ) method to enhance nutrition estimation by integrating visual and ingredient features. Ingredient robustness is improved through synonym replacement and resampling strategies during training. The ingredient-aware …