Physical Sciences and Mathematics Commons^™

Ivyapc: Auditable Generalized Payment Channels, Ming Li, Yuxian Li, Jian Weng, Yingjiu Li, Jiasi Weng, Junzuo Lai, Robert H. Deng

Research Collection School Of Computing and Information Systems

Payment channels are a cornerstone of a scalable blockchain infrastructure that enables transacting parties to lock assets on the blockchain and perform rapid off-chain updates with minimal latency and overhead. These protocols dramatically reduce on-chain interaction and improve throughput, with blockchain consensus only invoked in the event of disputes or final closure. While widely adopted in single-chain settings—such as in the Lightning Network for Bitcoin—existing constructions have several limitations, in particular they suffer from at least one of the following limitations: 1. No cross-chain. They do not enable fast trading of assets that reside on multiple isolated blockchains. 2. Non-optimal …

Rethinking Light Decoder-Based Solvers For Vehicle Routing Problems, Ziwei Huang, Jianan Zhou, Zhiguang Cao, Yixin Xu

Research Collection School Of Computing and Information Systems

Light decoder-based solvers have gained popularity for solving vehicle routing problems (VRPs) due to their efficiency and ease of integration with reinforcement learning algorithms. However, they often struggle with generalization to larger problem instances or different VRP variants. This paper revisits light decoder-based approaches, analyzing the implications of their reliance on static embeddings and the inherent challenges that arise. Specifically, we demonstrate that in the light decoder paradigm, the encoder is implicitly tasked with capturing information for all potential decision scenarios during solution construction within a single set of embeddings, resulting in high information density. Furthermore, our empirical analysis reveals …

Rethinking Neural Multi-Objective Combinatorial Optimization Via Neat Weight Embedding, Jinbiao Chen, Zhiguang Cao, Jiahai Wang, Yaoxin Wu, Hanzhang Qin, Zizhen Zhang, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Recent decomposition-based neural multi-objective combinatorial optimization (MOCO) methods struggle to achieve desirable performance. Even equipped with complex learning techniques, they often suffer from significant optimality gaps in weight-specific subproblems. To address this challenge, we propose a neat weight embedding method to learn weight-specific representations, which captures weight-instance interaction for the subproblems and was overlooked by most current methods. We demonstrate the potentials of our method in two instantiations. First, we introduce a succinct addition model to learn weight-specific node embeddings, which surpassed most existing neural methods. Second, we design an enhanced conditional attention model to simultaneously learn the weight embedding …

Dps: Design Pattern Summarisation Using Code Features, Najam Nazar, Sameer Sikka, Christoph Treude

Research Collection School Of Computing and Information Systems

Automatic summarisation has been used efficiently in recent years to condense texts, conversations, audio, code, and various other artefacts. A range of methods, from simple template-based summaries to complex machine learning techniques -- and more recently, large language models -- have been employed to generate these summaries. Summarising software design patterns is important because it helps developers quickly understand and reuse complex design concepts, thereby improving software maintainability and development efficiency. However, the generation of summaries for software design patterns has not yet been explored.Our approach utilises code features and JavaParser to parse the code and create a JSON representation. …

Alphaedit: Null-Space Constrained Knowledge Editing For Language Models, Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Jie Shi, Xiang Wang, Xiangnan He, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Large language models (LLMs) often exhibit hallucinations, producing incorrector outdated knowledge. Hence, model editing methods have emerged to enabletargeted knowledge updates. To achieve this, a prevailing paradigm is the locatingthen-editing approach, which first locates influential parameters and then edits themby introducing a perturbation. While effective, current studies have demonstrated that this perturbation inevitably disrupt the originally preserved knowledge within LLMs, especially in sequential editing scenarios. To address this, we introduce AlphaEdit, a novel solution that projects perturbation onto the null space of the preserved knowledge before applying it to the parameters. We theoretically prove that this projection ensures the output …

Open-Set Graph Anomaly Detection Via Normal Structure Regularisation, Qizhou Wang, Guansong Pang, Mahsa Salehi, Xiaokun Xia, Christopher Leckie

Research Collection School Of Computing and Information Systems

This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (referred to as *seen anomalies*) to detect both seen anomalies and *unseen anomalies* (*i.e*., anomalies that cannot be illustrated the training anomalies). Those labelled training data provide crucial prior knowledge about abnormalities for GAD models, enabling substantially reduced detection errors. However, current supervised GAD methods tend to over-emphasise fitting the seen anomalies, leading to many errors of detecting the unseen anomalies as normal nodes. Further, existing open-set AD models were introduced …

Biogenic Modification Of Tidally Modified Substrates From The Micro- To Macroscopic Scale, Rogers Clark Caldwell Buntin Ii

OES Theses and Dissertations

Digital outcrop models generated by high-resolution photogrammetric imagery permit investigations into organism-substrate interactions at the centimeter to sub-millimeter scale. This dissertation uses this three-dimensional technology to study two sedimentary successions— the Archaean Dresser Formation and Cretaceous Dakota Sandstone— by capturing images via unmanned aircraft systems and single lens reflex cameras. The ~3.48 Ga Dresser Formation provides evidence for some of the earliest life on Earth in the form of stromatolites and microbially induced sedimentary structures. Aerial imagery of the 3.5 km2 Buick Geoheritage reserve capture the site’s geologic structures and close-range imagery documents stromatolites and several microbially induced sedimentary structures …

Synchrotron Radiation Interferometry At Cebaf, Sankalp Rajendra Shinde

Physics Theses & Dissertations

Synchrotron radiation interferometry is a method to measure transverse beam size in electron accelerators. By using synchrotron radiation interferometry at a dispersive location in CEBAF at Jefferson Lab, we measure the electron beam size and energy spread. This interferometry technique allows us to monitor the energy spread non-invasively, to measure precise electron beam rms energy spread for hypernuclear experiments, and verify that they meet experiment requirements in real time. This dissertation discusses the design, simulations, construction, and commissioning of this Synchrotron Radiation Interferometer (SRI) in the Hall C line at Jefferson Lab in 2024–2025.

The Structure Function Of The Free Neutron At High X-Bjorken, Madhusudhan Pokhrel

Physics Theses & Dissertations

Understanding the internal structure of nucleons is one of the primary goals of nuclear physicists. As protons and neutrons are only the bound state solution of the QCD lagrangian (at least inside atomic nuclei), studying protons and neutrons helps uncover nuclear structure. Due to its easy availability, many studies on protons have been done on a wide range of kinematics. However, free neutron targets are not readily achievable. So, any information on neutrons has to be extracted from neutron-rich nuclei, and some nuclear models have to be used to subtract the contributions from other nucleons to extract the information …

Ground-Arthropod Assemblage Response To Groundwater As A Metric Of Flooding In The Middle Rio Grande, Wesley J. Noe

Water Resources Professional Project Reports

Riparian zones are dynamic ecosystems, crucial for maintaining biodiversity and ecological balance, particularly in arid environments like the Middle Rio Grande (MRG). Ground arthropods, with their sensitivity to environmental changes, serve as bioindicators for assessing the impacts of water regime changes in the MRG, specifically flooding which was historically the main driver of the MRG ecosystem. This study investigated the responses of ground arthropod communities to fluctuations in groundwater and land management practices within MRG riparian habitats. Using a combination of pitfall trapping and habitat assessments, arthropod abundance was assessed in relation to depth to groundwater (DTG). Findings revealed shifts …

Ground-Arthropod Assemblage Response To Groundwater As A Metric Of Flooding In The Middle Rio Grande, Wesley Noe

Water Resources Professional Project Reports

Riparian zones are dynamic ecosystems, crucial for maintaining biodiversity and ecological balance, particularly in arid environments like the Middle Rio Grande (MRG). Ground arthropods, with their sensitivity to environmental changes, serve as bioindicators for assessing the impacts of water regime changes in the MRG, specifically flooding which was historically the main driver of the MRG ecosystem. This study investigated the responses of ground arthropod communities to fluctuations in groundwater and land management practices within MRG riparian habitats. Using a combination of pitfall trapping and habitat assessments, arthropod abundance was assessed in relation to depth to groundwater (DTG). Findings revealed shifts …

Spatial And Equity Analysis Of The Xeriscape Conversion Rebate Program In Albuquerque, Nm., Claire Jordy

Water Resources Professional Project Reports

With increasing water scarcity concerns threatening water supplies, many cities in the Western U.S. are focusing more effort on their water conservation programs to reduce consumptive water use. The Albuquerque Bernalillo County Water Utility Authority (Water Authority) in Bernalillo County, NM has had several water conservation programs in place since the 1990s, and still, they aim to further reduce water consumption. The Xeriscape Conversion Rebate Program is one of the primary tools to achieve this goal, encouraging customers to convert their high water-use turfgrass lawns to desert-friendly xeriscaping by providing a one-time rebate on their water bill. This rebate program …

Humanist Copyright, Jane C. Ginsburg

Faculty Scholarship

This exploration of the role of authorship in copyright law proceeds in three parts: historical, doctrinal, and predictive. First, I will review the development of author-focused property rights in the pre-copyright regimes of printing privileges and in early Anglo-American copyright law through the 1909 U.S. Copyright Act. Second, I will analyze the extent to which the present U.S. copyright law does (and does not) honor human authorship. Finally, I will consider the potential responses of copyright law to the claims of proprietary rights in AI-generated outputs. I will explain why the humanist orientation of U.S. copyright law validates the position …

Dual Quaternions For Gravity Recovery Missions, Ryan Kinzie

Doctoral Dissertations and Master's Theses

A dual quaternion-based modeling, state estimation and control approach is introduced as a better alternative to the traditional methods which are currently utilized for gravity recovery missions. The proposed modeling and control approach was verified against and compared to the tangent bundle to Special Euclidean Group 3 through MATLAB simulations. The dual quaternion-based approach shows superior performance over traditional linearized and uncoupled methodologies, in both modeling accuracy of spacecraft translational position, and the ability to control the pose of a test mass relative to its host spacecraft. Utilizing data products from the Gravity Recovery and Climate Experiment Follow-On mission, a …

Applying Software Engineering Black-Box Methods For Testing Machine Learning Models, Timothy Elvira

Doctoral Dissertations and Master's Theses

This dissertation proposes researching an approach to incorporate and align Software black-box testing methods into Machine Learning (ML) applications, specifically in the context of computer vision models. Typically, testing methods within Software Engineering (SE) encompass a range of test types that assess levels of a software system, such as Unit, Integration, Functional, and System testing [1]. The testing spectrum offers two perspectives on the system: black-box, where the system’s code is hidden, and white-box, where the system's code is exposed for testing. Software Quality pairs testing with requirements, in a many-to-one relationship, to ensure proper validation of the software system. …

High-Latitude Ionospheric Irregularities Characterized Through Machine Learning Methods, Anna-Marie Bals

Doctoral Dissertations and Master's Theses

This study uses Machine Learning and data-driven techniques to understand plasma irregularities in high-latitude regions better. By combining observations and recent findings from modeling, the goal is to identify and classify scintillation signatures caused by different types of irregularities in the ionosphere. The focus is on irregularities from electron precipitation in the auroral oval and ExB drifts in the polar cap. Using Machine Learning tools, the study aims to distinguish between different scintillation signatures and link them to their sources, improving our ability to detect and characterize these events. Using multiple instruments and advanced filtering, the aim is to enhance …

Developing Fixed-Bias Langmuir Probes For Multi-Point Constellation Deployments, Henry Carter Valentine

Doctoral Dissertations and Master's Theses

Since their initial development in the early 20th century, electrostatic Langmuir probes have proved invaluable in terrestrial and interplanetary ionospheric sounding applications. When deployed aboard rocket and satellite platforms, these probes yield high-cadence, in-situ measurements of key plasma parameters such as electron density, ion density, and electron temperature. Furthermore, the efficacy of Langmuir probes in characterizing the three-dimensional structure and dynamics of ionospheric plasmas can be augmented by the technique of multi-payload deployments. In this work, we discuss the design, development, and analysis of fixed-bias Langmuir probes constructed for two multi-point science campaigns: the Mars-bound, dual-satellite Escape and Plasma Acceleration …

Acoustic-Gravity Wave Propagation Based On Solutions To The Generalized Multi-Component Transport Equations, Benedict PiñEyro

Doctoral Dissertations and Master's Theses

Nonlinear atmospheric models have provided important insight into acoustic waves generated by natural and man-made hazards, which may steepen into shocks or N-waves while also dissipating when propagating in the thermosphere. Although models have yielded results that agree with observations of ionospheric perturbations, dynamical models for the diffusive and stratified lower thermosphere often use single gas approximations with height-dependent physical properties that omit the dynamics of the major and minor constituents. Thus, the inter-species diffusion associated with these flows (e.g. variations of mean molecular weight, and specific heat) are not accounted for. This approximation is simpler and less computationally expensive …

Solitons, Breathers And Rogue Waves Of The Yajima–Oikawa-Newell Long Wave–Short Wave System, Marcos Caso-Huerta, Bao-Feng Feng, Sara Lombardo, Ken-Ichi Maruno

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we consider the recently-introduced Yajima–Oikawa–Newell (YON) system describing the nonlinear resonant interaction between a long wave and a short wave. It extends and generalises the Yajima–Oikawa (YO) and the Newell (N) systems, which can be obtained from the YON system for special choices of the two non-rescalable, arbitrary parameters that it features. Remarkably, for any choice of these latter constants, the YON system is integrable, in the sense of possessing a Lax pair. New families of solutions, including the bright and dark multi-solitons, as well as the breathers and the higher-order rogue waves are systematically derived by …

Approximations Of Koopman Operator Semigroups, Ibrahem Al Jabea

LSU Doctoral Dissertations

The main purpose of this dissertation is to study approximation methods for nonlinear systems using Bernhard Koopman's Global Linearization Method or Sophus Lie's method of continuous transformation groups. This approach enables the application of linear semigroup methods to a nonlinear system by focusing on the dynamics of the observables of the states, rather than directly studying the dynamics of the states. In this dissertation, we studied the pointwise semigroup and introduce the modified space $C_m(\Omega)$ and the modified Koopman-Lie semigroups. We use a splitting operator and outline a systematic approach for approximating the pointwise Koopman-Lie semigroup flows \begin{equation*} t\to T(t)g(x) …

19ne Excited States In Explosive Nucleosynthesis, Khang Pham

LSU Doctoral Dissertations

Classical novae and Type I X-ray bursts are stellar explosions that occur in binary systems, where nuclear reactions involving ¹⁹Ne play a crucial role in nucleosynthesis. In novae, the destruction of ¹⁸F via the ¹⁸F(p,alpha)¹⁵O reaction significantly affects the amount of ¹⁸F remaining in the ejecta, directly influencing the detectability of predicted 511-keV gamma rays. However, discrepancies between observed and predicted ¹⁸F abundances highlight uncertainties in the reaction rates, primarily due to unknown properties of resonances in the compound nucleus ¹⁹Ne. In Type I X-ray bursts, the ¹⁵O(alpha,gamma)¹⁹Ne reaction …

Intersectional Predictors Of Early Mathematics Identity Among Underrepresented Engineering-Interested Students, Douglas D. Havard, Adriana Quirós-Arauz

Education Faculty Articles and Research

This study examines the intersectional factors influencing early mathematics identity development among underrepresented secondary students (grades 9-10) with aspirations in engineering. Mathematics identity is a well-established predictor of long-term persistence in engineering, making its early formation critical to understanding student retention in the engineering pipeline. Grounded in Bronfenbrenner's bioecological framework, this study situates learning within nested layers of influence. Using data form the nationally representative High School Longitudinal Study of 2009 (HSLS:09), which includes over 23,000 9th-graders, a hierarchical multiple regression analysis was conducted. The analysis examined intersections of race and gender identity across 16 variables spanning individual, micro-, meso-, …

Exploring Transfer Learning For Deep Learning Polyp Detection In Colonoscopy Images Using Yolov8, Fabian Vazquez Jr., Jose Angel Nuñez, Xiaoyan Fu, Pengfei Gu, Bin Fu

Computer Science Faculty Publications

Deep learning methods have demonstrated strong performance in object detection tasks; however, their ability to learn domain-specific applications with limited training data remains a significant challenge. Transfer learning techniques address this issue by leveraging knowledge from pre-training on related datasets, enabling faster and more efficient learning for new tasks. Finding the right dataset for pre-training can play a critical role in determining the success of transfer learning and overall model performance. In this paper, we investigate the impact of pre-training a YOLOv8n model on seven distinct datasets, evaluating their effectiveness when transferred to the task of polyp detection. We compare …

Understanding The Growth Mechanism Of Thiol-Conjugated Au25 Cluster, Chunhui Liu, Haiying He, Ravindra Pandey, Shashi P. Karna

Michigan Tech Publications

The synthesis of ligand-conjugated gold nanoclusters has attracted significant attention due to its ability to achieve precise control over cluster size selectivity. Among these, Au₂₅(SR)₁₈-, where R represents an alkyl group, is one of the earliest being synthesized with a very high yield, although its growth mechanism is yet to be fully understood. Using density functional theory, we present the results of a theoretical investigation on the growth process of Au₂₅(SR)₁₈-, beginning from Au₁₃(SR)₁₂-. Our findings indicate that the sulfur atoms in the core structure of Au_{13 …}

Microphysics Regimes Due To Haze–Cloud Interactions: Cloud Oscillation And Cloud Collapse, Fan Yang, Hamed F. Sadi, Raymond Shaw, Fabian Hoffmann, Pei Hou, Aaron Wang, Mikhail Ovchinnikov

Michigan Tech Publications

It is known that aqueous haze particles can be activated into cloud droplets in a supersaturated environment. However, haze–cloud interactions have not been fully explored, partly because haze particles are not represented in most cloud-resolving models. Here, we conduct a series of large-eddy simulations (LESs) of a cloud in a convection chamber using a haze-capable Eulerian-based bin microphysics scheme to explore haze–cloud interactions over a wide range of aerosol injection rates. Results show that the cloud is in a slow microphysics regime at low aerosol injection rates, where the cloud responds slowly to an environmental change and droplet deactivation is …

Construction Of Stock Portfolios By Machine Learning Methods, Alfan Gehad Abulehia

Theses

We study the theory and application of two machine learning (ML) algorithms: Long-Short Term Memory Network (LSTM) and Random Forest. The study begins by providing an overview of mathematical foundation, highlighting the significance of mathematics in machine learning. We study and explain the mathematical details involved in these two algorithms. We also study closely related subjects which include but not limited to gradient descent, automatic differentiation, and recurrent neural network. The main ideas behind these topics form the foundation of any ML algorithms.

As an application of the ML algorithms, we focus on the U.S. stock market. We build stock …

Collaborative Network Traffic Management Strategies Using Distributed Reinforcement Learning And Large Language Models, Saeed Rashed Alkuwaiti

Theses

The focus of this research is to explore collaborative network traffic management strategies using the Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs) approaches. It emphasizes exploring a new tool for addressing network traffic by utilizing Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs). This is achieved by utilizing self-organizing and self-directing techniques to optimize the network performance. Using the NF-TON-IOT dataset, various classifiers such as Random Forest, AdaBoost, C4. 5, Multi-Layer Perceptron (MLP), and SVM with an RBF kernel were tested for traffic classification and intrusion detection. Research recommends that DRL optimizes the complexity of the network …

Quadratic Stochastic Processes: Algebraic Structures And Their Applications, Taimun Saleh Qaisar

Dissertations

This research focuses on the algebraic structures of the Quadratic Stochastic Processes (𝑄𝑆𝑃𝑠). In this work, we first study 𝜉𝑎- Quadratic Stochastic Operators (𝑄𝑆𝑂𝑠) linked to the partition P3. We simultaneously discuss the dynamics of the obtained 𝑄𝑆𝑂𝑠. Moreover, algebraic structure of the associated genetic algebra is studied. Further, we build Quadratic Stochastic Processes (𝑄𝑆𝑃𝑠) using the given Markov processes. Consequently, we obtain an ordinary differential equation for the resultant Quadratic Stochastic Processes (𝑄𝑆𝑃𝑠). Besides, we apply the solution of this ordinary differential equation for the option pricing problem. Thereafter, we construct Quadratic Stochastic Processes (𝑄𝑆𝑃𝑠) in three-dimensional space by …

Monitoring Sand Migration In Al Ain City Utilizing Remote Sensing Techniques, Sanad Salem Mohammad Fareaa

Theses

Sand migration significantly impacts urban development, infrastructure, and ecosystems in arid regions such as Al Ain city, United Arab Emirates (UAE). This study employs advanced remote sensing techniques to monitor and predict sand dune migration across the Sieh Al Hama dune field, a critical area west of Al Ain. The research objectives include quantifying dune migration rates over three years (2018–2020) using monthly Sentinel-2 satellite imagery, identifying distinct dune fields, and analyzing the textural and mineralogical properties of dune sediments to infer their provenance. Field sampling from four dunes (Large Sabra Dune, Dune 1, Dune 2, and Dune 3) was …

Study Of Falāj Pathways And Their Unknown Extension In Al Ain City Using Geophysical Methods, Saud Mohammed Alsenaani

Theses

Historically, the city of Al Ain has relied on its Falāj systems as historic water resources for freshwater, which is vital for drinking and irrigating its oases and essential for supporting life in the region for centuries, providing the foundation for communities and farming. The remarkable rainfall in 2024 uncovered several hidden ancient Falāj systems and newly enlarged versions of previously recorded ones, highlighting the importance of ongoing research for their preservation. This research project aims to examine the current Falāj systems in Al Ain, particularly Falāj Mezyad, using geophysical methods to identify uncharted extensions and to examine current Falāj …