Physical Sciences and Mathematics Commons^™

A Comprehensive Study And Performance Optimization Of Shared Virtual Memory, Bennett Cooper

All Dissertations

High performance computing (HPC) is dominated by heterogeneous systems that mainly derive performance from GPU accelerators. A majority of HPC applications have gravitated towards GPUs, which require explicit programming. Historically, programming explicitly to take advantage of GPUs has proven a significant barrier in fully utilizing GPU acceleration. Unified Memory (UM) is a technology designed to lower the barrier of entry to GPU programming by merging all memory domains of a system. While UM provides easier access to the capabilities of heterogeneous systems, the performance cost of UM greatly detracts from the benefit. Additionally, UM is implemented on a vendor-by-vendor basis …

Alaska Earthquake Center Quarterly Review October-December 2025, Heather Mcfarland, Beth Grassi, Austin Holland, Nate Murphy, Elisabeth Nadin, Carolyn Parcheta, Michael West

Alaska Earthquake Center Reports

This series of technical quarterly reports from the Alaska Earthquake Center (AEC) includes detailed summaries and updates on Alaska seismicity, the AEC seismic network and stations, fieldwork, our online presence, public outreach, and lists publications and presentations by AEC staff. Multiple AEC staff members contributed to this report.

Learning Latent Structure In High-Dimensional Data Via Geometry And Graphs, Haozhe Chen

All Graduate Theses and Dissertations, Fall 2023 to Present

Modern datasets often contain many measured variables for each observation, such as gene-expression levels, brain activity signals, or features in tabular data. These data are also often noisy, meaning that useful patterns are mixed with measurement error or irrelevant variation. Although such datasets can appear complex, they are frequently represented by simpler hidden structures, such as trajectories, clusters, or relationships between observations. This dissertation develops methods for uncovering these hidden structures by learning geometric and graph-based representations directly from data. The first part introduces Functional Information Geometry, which represents local patterns in high-dimensional data using functional features and constructs a …

Understanding And Predicting Precipitation Characteristics In The United States Through Machine Learning, Numerical Modeling And Measurements, Cody Luther Ratterman

All Graduate Theses and Dissertations, Fall 2023 to Present

Precipitation is one of the most important yet uncertain variables in the climate system. It varies dramatically in terms of timing, accumulation, rate, and phase, depending on location, season, circulation patterns, and atmospheric conditions. Precipitation forecasts, especially snowfall, are essential to supporting drought mitigation and water management in the Intermountain West. Because rainfall and snowfall lead to opposite effects on snowpack, accurately partitioning rain and snow is important to estimate snowpack levels, winter recreation, mountain ecosystems and runoff. The research findings in this dissertation have advanced the understanding and prediction of precipitation and snowpack in the U.S. by addressing the …

Llm-As-A-Judge For Software Engineering: Literature Review, Vision, And The Road Ahead, Junda He, Jieke Shi, Terry Yue Zhuo, Christoph Treude, Jiamou Sun, Zhenchang Xing, Xiaoning Du, David Lo

Research Collection School Of Computing and Information Systems

The rapid integration of Large Language Models (LLMs) into software engineering (SE) has revolutionized tasks from code generation to program repair, producing a massive volume of software artifacts. This surge in automated creation has exposed a critical bottleneck: the lack of scalable and reliable methods to evaluate the quality of these outputs. Human evaluation, while effective, is very costly and time-consuming. Traditional automated metrics like BLEU rely on high-quality references and struggle to capture nuanced aspects of software quality, such as readability and usefulness. In response, the LLM-as-a-Judge paradigm, which employs LLMs for automated evaluation, has emerged. This approach leverages …

Efficient Test-Time Retrieval Augmented Generation, Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Although Large Language Models (LLMs) demonstrate significant capabilities, their reliance on parametric knowledge often leads to inaccuracies. Retrieval Augmented Generation (RAG) mitigates this by incorporating external knowledge, but these methods may introduce irrelevant retrieved documents, leading to inaccurate responses. While the integration methods filter out incorrect answers from multiple responses, but lack external knowledge like RAG methods, and their high costs require balancing overhead with performance gains. To address these issues, we propose an Efficient Test-Time Retrieval-Augmented Generation Framework named ET2RAG to improve the performance of LLMs while maintaining efficiency. Specifically, ET2RAG is a training-free method, that first retrieves the …

Continuous Query For Top-K Maximal Sum Intervals Over Streaming Data, Zhongshuai Zhang, Xiaochun Yang, Baihua Zheng, Rui Zhu, Haomin Li, Bin Wang

Research Collection School Of Computing and Information Systems

The continuous identification of top-k maximal sum intervals using a sliding window over a data stream is a critical operation for applications in IoT and beyond. A maximal sum interval is a non-overlapping, contiguous subsequence with the maximal sum in a sequence of signed values. Existing algorithms are ill-suited for streaming contexts: they either exhaustively enumerate all intervals even for small k values, or depend on indexes that require frequent and costly restructuring. We propose a novel partition-based strategy. Our core insight is a partitioning scheme that guarantees that any maximal sum interval is fully contained within a single partition, …

Success Of New Ideas In Online Platforms: An Idea Network Perspective, Yimei Zhou, Qian Tang, Vincent Z.W. Mack Mack, Shao Yi Liaw

Research Collection School Of Computing and Information Systems

On online platforms, new ideas often emerge by recombining existing ones within idea networks. Unlike traditional knowledge networks, idea networks represent curated, meaning-based associations among ideas, offering a distinct lens on recombination. Drawing upon a hypergraph perspective, we investigate how new idea success depends on their structural and content attributes, and how collaborative participation shapes these attributes. Using data from an ideation platform, we find that both structural embeddedness and bridging benefit new idea success. Content diversity has no direct effect, but it amplifies the benefits of bridging while constraining those of embeddedness. Both crowd contributions and ideator expertise strengthen …

Approximation And Learning-Based Algorithms For Influence Maximization In Multilayer Social Networks, Xueqin Chang, Ruize Liu, Qing Liu, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Motivated by the observation that users in the real world often engage across multiple social networks simultaneously, we study the problem of influence maximization in multilayer social networks (Mlim), aiming to select a small set of nodes that maximizes the total influence spread across all layers. To this end, we introduce a hybrid propagation model that jointly captures layer-specific diffusion dynamics and probabilistic cross-layer propagation. Based on this model, we formally define the Mlim problem and establish its NP-hardness, monotonicity, and submodularity. To address the Mlim problem, we first propose a greedy baseline Mlim-Greedy, which achieves a (1-1/e) approximation. Since …

Left: Learnable Fusion Of Tri-View Tokens For Unsupervised Time Series Anomaly Detection, Dezheng Wang, Tong Chen, Guansong Pang, Congyan Chen, Shihua Li, Hongzhi Yin

Research Collection School Of Computing and Information Systems

As a fundamental data mining task, unsupervised time series anomaly detection (TSAD) aims to build a model for identifying abnormal timestamps without assuming the availability of annotations. A key challenge in unsupervised TSAD is that many anomalies are too subtle to exhibit detectable deviation in any single view (e.g., time domain), and instead manifest as inconsistencies across multiple views like time, frequency, and a mixture of resolutions. However, most cross-view methods rely on feature or score fusion and do not enforce analysis–synthesis consistency, meaning the frequency branch is not required to reconstruct the time signal through an inverse transform, and …

Timeradar: A Domain-Rotatable Foundation Model For Time Series Anomaly Detection, Hui He, Hezhe Qiao, Yutong Chen, Kun Yi, Guansong Pang

Research Collection School Of Computing and Information Systems

Current time series foundation models (TSFMs) primarily focus on learning prevalent and regular patterns within a predefined time or frequency domain to enable supervised downstream tasks (\eg, forecasting). Consequently, they are often ineffective for inherently unsupervised downstream tasks—such as time series anomaly detection (TSAD), which aims to identify rare, irregular patterns. This limitation arises because such abnormal patterns can closely resemble the regular patterns when presented in the same time/frequency domain. To address this issue, we introduce TimeRadar, an innovative TSFM built in a fractional time–frequency domain to support generalist TSAD across diverse unseen datasets. Our key insight is that …

Evaluation Of High Temperature Thermal Energy Storage At A Field Site, Samuel R. Jennings

All Theses

A pilot-scale high-temperature thermal energy storage site has been developed at Clemson University and began operation in April 2025. High-temperature heat transfer fluid is circulated through a hexagonal array of borehole heat exchangers, which are installed up to 35 ft deep in the subsurface. The thermal energy is stored in the ground and is extracted by circulating the heat transfer fluid at a lower temperature. This test was expected to last more than six months and there was no way to evaluate the temperature data being measured. Since only temperature was being monitored, it was difficult to predict when key …

Implementation Of A Practical Sharp Interface Saltwater Intrusion Model To Assess Future Sea-Level Impacts, Alexandra G. Defalco

All Theses

Rising sea levels caused by global warming exacerbate saltwater intrusion, the inland movement of seawater into coastal aquifers. These processes increase susceptibility to freshwater contamination, surface flooding, and infrastructure damage. Numerical models are utilized to assess hazards to coastal aquifers on a regional scale.

The U.S. Navy is currently developing a robust, reduced-order groundwater modeling platform, SWI6, that uses the sharp interface approach to approximate the location of the freshwater-saltwater interface to define the extent of saltwater intrusion within an aquifer. Featuring compatibility with MODFLOW 6 unstructured grid formats and the Newton-Raphson formulation for better solution stability, SWI6 improves upon …

Insect Abundance And Bat Use Of Fields For Foraging In The Cumberland Plateau, Caroline M. Abramowitz

All Theses

Bat populations and their insect prey are declining, making it urgent to understand how habitat features and management support both groups. Early successional habitat (ESH), grasslands and shrublands with reduced vegetative clutter and abundant flowering resources, may benefit foraging bats and nocturnal insects. This study examined how landscape context, patch characteristics, microhabitat features, and management (mechanical mastication and prescribed burns) affected insects and bats across 29 managed fields in Big South Fork National River and Recreation Area (TN/KY) from 2024–2025. Flight-intercept traps collected 25,297 insects across five orders. Though results were order-specific, vegetative composition and height were the strongest predictors …

Fixed Points For Fitted Lines, David L. Farnsworth

Articles

For many fitting methods for lines in the plane, each observation possesses an associated point in the plane, or at infinity for particular observations. These associated points are called “fixed points,” because properly chosen replacement points for the originally selected observation create other fitted lines that contain the same fixed point. Simple linear regression and orthogonal least-squares regression are investigated as examples. The phenomenon has been previously noted for simple regression, where the properly selected replacement points are related to regression diagnostics and the determination of outliers. Orthogonal regression lines are fundamental to principal component analysis. Mechanical devices for simple …

Some Results About Orthogonal Least-Squares Lines, Steven J. Kilner, David L. Farnsworth

Articles

A set of quantitative bivariate data is augmented with one additional point. Orthogonal least-squares fitting is used for the original set and the augmented set. The possible locations of the two lines, the points of means of the two sets, and the augmenting point are examined.

Draft Final Butte Priority Soils Operable Unit Insufficiently Reclaimed Sites Temporary Best Management Practices Work Plan: Bres No. 16 – Curry And Bres No. 50 – Zelia, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Clark Tailings Consolidated Waste Management Area Site Investigation Quality Assurance Project Plan (Qapp), Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Graph Perturbation Analysis For Subgraph Counting, Hanhua Xiao, Yuchen Li, Kyriakos Mouratidis

PhD Student’s Publications Collection

Subgraph counting, which involves determining the frequency of a query graph within a data graph, has numerous applications such as query optimization, fraud detection, and evaluating the expressiveness of graph neural networks. Despite its importance, there has been no systematic study on the impact of adversarial graph perturbations on subgraph counts. In this work, we examine the kSub problem, which aims to identify k edge additions that maximize the count of a query graph. We prove that kSub is intractable due to its NP-hardness, even for constant approximation. To address this, we relax the problem into a top-k selection, termed …

Odyssey To The Extraordinary: Living Our Land Grant Mission And Beyond, Jeffrey P. Gold, Katherine S. Ankerson, Tiffany Heng-Moss

Institute of Agriculture and Natural Resources: Institute Publications

Presents a strategy intended to guide future directions for the University of Nebraska-Lincoln's (UNL) Institute of Agriculture and Natural Resources (IANR) including issues related to governance, funding, organizational structure, and strategic alignment within UNL and the University of Nebraska system.

The Role Of Local Winds And A Semi-Persistent Coastal Front On The Diurnal Heat Budget In An Upwelling Bay, April P. Thibodeau, Ryan K. Walter, Piero L.F. Mazzini, Thomas P. Connolly, Christopher A. Edwards, Ian C. Robbins

Physics

Coastal embayments in nearshore upwelling systems (“upwelling bays”) play a disproportionately large role in regional oceanography. In these systems, local diurnal wind forcing and thermal gradients associated with an upwelling shadow front that forms between warmer sheltered waters inside the bay and colder recently upwelled waters outside the bay strongly influence local temperature dynamics. Despite their importance to local ecosystems, there are no long-term studies that assess the heat budget inside upwelling bays. In this study, we analyzed approximately two years of water temperature throughout the water column using an autonomous profiler in a small upwelling bay in central California …

Book Review: Radioactive Dixie: A Nuclear History Of The American South, Will Jones

Georgia Library Quarterly

No abstract provided.

Summary Report: Second Conference On Attribution Science And Climate Law, Jessica A. Wentz

Sabin Center for Climate Change Law

The Sabin Center for Climate Change Law and the Columbia Climate School hosted the Second Conference on Attribution Science and Climate Law at Columbia University on June 10–11, 2026. This event brought together an interdisciplinary group of researchers and practitioners to discuss recent developments in attribution science and the field’s relevance to climate law and policy. Participants included physical scientists, public health researchers, economists, social scientists, legal scholars, advocates, and policy experts. This report provides a written summary of the conference proceedings.

Ramsey Chains In Graphs, Ritabrato Chatterjee

Dissertations

The research in this work deals with decomposition of graphs and Ramsey theory, two popular areas of research in graph theory due to numerous fascinating problems and the sheer mathematical beauty of the subjects. A decomposition {G₁, G₂, ..., G_k} of a graph G into subgraphs of G is ascending if the subgraph G_i is isomorphic to a proper subgraph of G_i+1 for i = 1, 2, ..., k − 1. The well-known and long-standing of Ascending Subgraph Decomposition Conjecture states that every graph has an ascending subgraph …

Leading The Change: Staff-Driven Ai Transformation In Smu Libraries’ Collection Team, Siew Khim Lim, Fion Goh

Research Collection Library

No abstract provided.

Emergency Risk Dispatch For Integrated Electricity-Heat Systems Under Typhoon Disasters, Tongchui Liu, Lian Tan, Dongxuan Bao, Lanting Zeng, Pengfei Hou, Ronghua Ling

Journal of System Simulation

Abstract: The spatiotemporal randomness of typhoon movement paths leads to uncertain operational risks for integrated electricity-heat systems (IEHS), making it difficult to balance system risk controllability and dispatch economy. To tackle this problem, an emergency risk dispatch (ERD) method for IEHS under typhoon disasters is proposed. An ERD model for IEHS under typhoon disasters is established within the model predictive control framework. Based on the uncertainty of typhoon wind speed prediction, a moment-based ambiguity set for uncertain equipment component failures is constructed, and a distributionally robust chance-constrained ERD model is formulated. An approximation method based on worst-case conditional value-at-risk (WC-CVaR) …

Bi-Level Coordinated Scheduling And Optimization Of Power Systems Based On Stackelberg-Gmo, Yuanxing Zhang, Jianfeng Li, Taoyong Li, Linjuan Zhang, Jincheng Liu, Bin Li

Journal of System Simulation

Abstract: , To balance the interests of the power grid and the demand side, and achieve coordinated improvements in system economic efficiency, environmental friendliness, and renewable energy accommodation capacity, this paper proposes a bi-level coordinated scheduling model based on the Stackelberg game and the GMO. A leader-follower game model incorporating carbon emission constraints and multi-scenario stochastic constraints for photovoltaic generation is constructed, with the grid operator as the leader and EVs/V2G and energy storage as the followers, resolving the core contradiction between global optimization and individual rationality. The spatio-temporal stochastic characteristics of EV travel, the cycle life of energy storage …

Energy Management Method For Integrated Energy Driven By Users’ Social Attributes, Yankai Zhu, Yujing Huang, Qinghua Wang, Xiaoning Zhang, Fang Fang, Yuguang Niu

Journal of System Simulation

Abstract: To explore a new interaction mechanism between an energy service provider (ESP) and multiple users, this paper proposes a complex modeling and energy management method for integrated energy systems driven by users' social attributes. A multi-agent interaction framework comprising an ESP and user clusters is established. To maximize the ESP's operational benefit and minimize users' energy costs, a leader-follower game-based energy management model is established within a reinforcement learning framework, and a distributed collaborative solution algorithm combining Q-learning and quadratic programming is proposed. Simulation results show that, compared with the traditional integrated demand response method, consideration of users' social …

Missing-Data-Tolerant Diffusion-Based Wind Power Scenario Forecasting Method, Yingying Shi, Xiaochong Dong, Guobin Fu, Miaomiao Ma, Yanhe Li, Xuebin Wang

Journal of System Simulation

Abstract: To address the issue of error accumulation in traditional "imputation-then-forecasting" approaches, a missing data tolerant diffusion framework (MDTDF) is proposed. An XGBoost regression model is employed to map numerical weather prediction data into deterministic power forecasts. The encoder in the denoising network extracts temporal features, which are fused with the deterministic forecasts and fed into the decoder through a cross-attention mechanism to guide the denoising process. A historical constraint mechanism is introduced to directly utilize incomplete historical data and dynamically correct the denoising result at each step through sample gradient updates and noise injection guided by historical information. The …

Time-Step Relaxation Method For Simulation With Time-Sensitive Interactions, Zhaopeng Liu, Kaidi Jin, Xunyun Liu, Dongao Zhou, Xinhai Xu

Journal of System Simulation

Abstract: In time-driven military simulation, time-step setting is a key technology for balancing operational efficiency and simulation accuracy. Based on the current research on time-step setting in military simulation, a trajectory spatiotemporal intersection calculation model is designed to address the performance bottleneck caused by the minimum time step, thus removing the constraints imposed on the minimum time step by interactions such as high-speed target detection and jamming. Experiments involving detection interaction scenarios are designed to verify the effectiveness of the model in preventing missed interactions and improving simulation efficiency.