Physical Sciences and Mathematics Commons^™

Development Of Lineal Energy Spectrum-Based Biological Effects Models For Protons, Joseph M. Decunha, Fada Guan, David Grosshans, Zhongxing Liao, Dragan Mirkovic, Oleg Vassiliev, Radhe Mohan

Dissertations and Theses (Open Access)

In this dissertation, methods are developed and described to allow for the rapid calculation of microdosimetric spectra (specifically, lineal energy) for protons. SuperTrack, a GPU-accelerated tool for calculation of microdosimetric spectra was developed and is capable of computing lineal energy spectra up to 5000x faster than using Geant4 directly. Proton lineal energy spectra generated by SuperTrack are indistinguishable from those generated by Geant4. With SuperTrack, large libraries of lineal energy spectra for monoenergetic protons spanning 0-300 MeV have been developed. The proton lineal energy spectra calculated by SuperTrack have been compared to experimental measurements made by a tissue equivalent proportional …

Novel Statistical Methods For Mediation Analysis With High-Dimensional Omics Mediators, Zhichao Xu

Dissertations and Theses (Open Access)

Mediation analysis is a widely used statistical method for examining how molecular traits, such as gene or protein expression, act as intermediaries linking an exposure to a health outcome. For example, it can help explain how smoking affects disease risk through molecular changes. The rapid progress in high-throughput omics profiling technologies and large-scale epidemiology consortia, such as the Trans-Omics for Precision Medicine (TOPMed) program from the National Heart, Lung and Blood Institute (NHLBI) and UK Biobank, now has resulted in an extensive accumulation of genomic data for biomedical research and analysis. At the same time, it poses significant methodological challenges, …

Identifying And Characterizing Transition Cells In Developmental Processes From Scrna-Seq Data, Yuanxin Wang

Dissertations and Theses (Open Access)

During the development of multicellular organisms, individual cells make distinct decisions about their cell types and states. Understanding the molecular mechanisms underlying cellular state transitions at different developmental stages provides deep insights into physiology, morphology and the etiology of diseases. Single-cell RNA-sequencing (scRNA-seq), which is widely used to study complex cell states and dynamic gene expression patterns, enables us to investigate molecular mechanisms of cellular state transitions. Currently, however, computational tools available for identifying cellular states and state transitions remain limited.

Although trajectory-based methods such as Monocle and Slingshot assume that state transitions generate continuous expression profiles, they cannot distinguish …

Towards Reliable Ml: Data Attribution And Adversarial Robustness, Xiaosen Zheng

Dissertations and Theses Collection (Open Access)

Modern machine learning (ML) models achieve remarkable success, but face critical reliability challenges. This thesis advances two pillars of reliable ML systems: interpretability through data attribution and robustness against adversarial threats.

In the first part, we develop novel data attribution methods to elucidate the data-model relationship. We establish the critical role of memorization in model generalization through token-level influence analysis, extend sample-level attribution to diffusion models with effective approximation techniques, and introduce REGMIX, a group-level approach that predicts data mixture performance using small-scale experiments. These contributions provide practitioners with scalable tools to audit training data impacts across modalities.

The second …

Tailoring Transformer-Based Deep Learning For Code Generation And Translation, Imam Nur Bani Yusuf

Dissertations and Theses Collection (Open Access)

Software is increasingly pervasive in modern society, making the effective translation of human intent into code essential. Novice programmers often struggle with domain-specific code due to limited background knowledge, while experienced developers face challenges in maintaining evolving largescale codebases. Traditional pattern-based approaches address these issues, but such approaches are task-specific and require significant adaptation for different tasks. Transformer-based models offer a more flexible alternative, as the same architecture can be tailored for diverse programming tasks.

This dissertation investigates how Transformer-based models can be customized for various code generation and translation tasks. First, it introduces Transformer-based approaches that assist end-users with …

2024 Annual Operations And Maintenance Report, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

The Stem Gender Gap: Can It Be Closed?, Makenna Roberts

Undergraduate Honors Capstone Projects

Despite increasing gender parity in many professional fields, women remain underrepresented in STEM industries, particularly in math-intensive areas such as engineering, computer science, and physics. This report explores the persistent gender gap in STEM through analysis of two data sources: gendered differences in the US national Scholastic Aptitude Test (SAT) performance and post-graduate employment patterns among scientists and engineers.

The first portion of the analysis examines SAT score data from 2018 to 2024, with a focus on high-performing test-takers. Using Python and R, data was extracted and visualized to compare male and female performance on the Math and Evidence-Based Reading …

Towards Multimodal Scene Graph Generation Approaches To Video Understanding, Trong-Thuan Nguyen

Graduate Theses and Dissertations

This thesis advances video understanding by enhancing Video Scene Graph Generation (VidSGG) through improved temporal modeling, the integration of long-range temporal dependencies via continuous updates to interaction histories, and the utilization of Large Language Models (LLMs) for scene graph reasoning. To this end, three novel datasets and corresponding approaches are introduced. First, the ASPIRe dataset incorporates interactivity annotations and leverages the Hierarchical Interlacement Graph (HIG) for hierarchical temporal modeling, providing deep insights into scene changes and effectively capturing intricate interactions. Next, the AeroEye dataset, focusing on drone videos, is paired with the Cyclic Graph Transformer (CYCLO), which establishes circular connectivity …

Achieving Flexible Fairness And Privacy In Federated Learning, Alycia N. Carey

Graduate Theses and Dissertations

Having access to large, high-quality datasets is crucial for training machine learning models that achieve satisfactory performance. Unfortunately, it is common that a single entity (e.g., mobile device or organization) does not have access to such datasets due to monetary or resource constraints. Traditional machine learning requires that all training data reside in a centralized location during the entire duration of model training, however, in many circumstances it is difficult or even impossible (e.g., due to governmental regulations) for multiple parties to combine their data to meet this constraint. Federated learning is a machine learning paradigm that facilitates the joint …

Conservation Agriculture Practice Effects On Greenhouse Gas Emissions From Fine-Textured Soils In Arkansas, Lauren Gwaltney

Graduate Theses and Dissertations

Biochar application and reduced tillage (RT) are both practices within the conservation agriculture framework, but specific impacts of these practices on direct greenhouse gas (GHG) emissions still require investigation in varied agricultural systems. This research aimed to evaluate the effects of biochar source (i.e., powder- and pellet-sized) and application rate (i.e., 0, 2.5, and 5 Mg ha^-1) on GHG production in simulated furrow-irrigated rice (Oryza sativa) in a greenhouse experiment, and to evaluate the effects of RT relative to conventional tillage (CT) on GHG production in soybean (Glycine max) in southeast Arkansas. Both studies …

Insights From Automated Mineralogic Analysis Of Modern Sand, Quinten Jones

Graduate Theses and Dissertations

Methods for determining and categorizing the modal compositions of sand and sandstone have long been a subject of debate in the field of sedimentary geology. Point counting is the most commonly used technique for determining modal compositions from petrographic slides, which are then categorized based on relative proportions of quartz, feldspar, and rock (or lithic) fragments. However, this approach fails to adequately preserve relevant textural data, such as grain size and sorting, which play an important role in diagenesis and in influencing reservoir quality. Additionally, the categorical nature of point counting results in a lack of specificity and loss of …

The Future Of Fashion: A Systematic Literature Review On Consumer Willingness To Pay For Green Apparel, Emma Clark

Graduate Theses and Dissertations

In recent years, consumers have become highly aware of the environmental impact of their purchases, specifically of apparel products. Accordingly, apparel brands have increasingly focused on enhancing the sustainability of their product offerings to attract environmentally conscious consumers and elevate their brand image (Dangelico et al., 2022). Furthermore, most sustainably produced products are priced higher for consumers compared to standard alternatives (Elmanadily & El-Deeb, 2022). Research has shown that consumers can accept higher prices for products that do less harm to the environment (Gomes et al., 2023), but it has yet to be determined in which cases this is consistently …

Latent Variable Dyadic Regression Models For Predicting Over/Under Bets In Sports Betting, Alexcia Trejo

Graduate Theses and Dissertations

This thesis explores the use of latent factor models to uncover hidden structures in pair wise outcomes derived from Over/Under betting markets in sports betting. Specifically, we implement and evaluate the Eigen model, a latent space model that represents dyadic data using node-specific vectors whose inner product govern edge probabilities. By modeling relationships between teams as adjacency matrices of binary outcomes, we investigate the extent to which the Eigen model captures both homophily, the tendency of similar teams to yield consistent betting results, and stochastic equivalence, where different teams exhibit indistinguishable patterns of Over/Under outcomes. A Bayesian formulation of the …

Koszul Cohomology Of Canonical Products, Alexander Scott Duncan

Graduate Theses and Dissertations

In this thesis, we give a complete classification of the Koszul cohomology groups K_p,₁(C, B, ω_C ⊗ B) on a smooth curve C of genus g ≥ 2 with B p-very ample. Such a classification in the case B = ω_C has been incomplete until [5]. The classification follows easily from our main result which precisely calculates K_p,1(C, B, B ω_C ⊗ B) in terms of h⁰(C, B). This result also handles the case B = ω_C. A straightforward …

Application Of Ordinal Regression Models To Acquired Stress Resistance In Wild Strains Of Saccharomyces Cerevisiae, Carson Stacy

Graduate Theses and Dissertations

This thesis explores the application of ordinal regression to the analysis of semi-quantitative growth assays often used when comparing fitness for different strains of the model yeast Saccharomyces cerevisiae. For stress survival assays, yeast stress resistance is measured using an ordered survival score that ranges from 0 (no growth) to 4 (confluent growth). Traditional approaches to analyze this type of data either treats data as a nominal categorical variable or as a continuous numerical variable. These approaches risk loss of information or violation of testing assumptions. In contrast, cumulative logit ordinal regression uses the information contained in the order …

Designing Accessible And Dependable Tools For Vocational Rehabilitation Data Analysis, Ruth E. Taylor

All Graduate Theses and Dissertations, Fall 2023 to Present

Since 1973, the U.S. Rehabilitation Services Administration (RSA) has partnered with state vocational rehabilitation (VR) agencies to help individuals with disabilities achieve meaningful employment and independence. RSA-911 datasets play a crucial role in this effort by capturing detailed participant data, but their complexity can hinder effective analysis.

To simplify this process, we present an R software package to streamline the cleaning and analysis of RSA-911 and Transition Readiness Toolkit (TRT) data, a new measure of program effectiveness. We also deliver a user-friendly dashboard, empowering both VR researchers and counselors with the opportunity to conduct analyses. Using our developed tools, we …

Park Cool Island Modifications To Assess Radiative Cooling Of A Tropical Urban Park, Graces N. Y. Ching, Sin Kang Yik, Su Li Heng, Beatrice H. Ho, Peter J. Crank, Moshe Eliezer Mandelmilch, Xiang Tian Ho, Winston T. L. Chow

Research Collection College of Integrative Studies

Many cities experience urban overheating from climate change and the urban heat island phenomenon. Previous studies demonstrate that parks are a potential nature-based solution to mitigate urban overheating through the ‘Park Cool Island’ (PCI) effect. PCI intensity can be measured through field measurements (FM) or remote sensing. This FM study used a network of meteorological sensors within a park and in its surrounding urban area to ascertain its PCI intensity in Singapore from January to December 2022. Consistently cooler air temperatures were found throughout a 24-h period in the park area, with mean daytime (nighttime) PCI intensity measured ~ 2.21 …

Sensors And Sensibilities: Exploring Interactions For Habitat Comfort With An Environmental-Physiological Sensing Eyewear In The Wild, Sailin Zhong, Patrick Chwalek, Nathan Perry, David Ramsay, Clayton Miller, Denis Lalanne, S. Hamed Alavi, A. Joseph Paradiso

Research Collection College of Integrative Studies

Buildings increasingly incorporate sensing and actuation techniques to automate the regulation of temperature, lighting, ventilation, and more. This trend seeks to minimize human intervention, justified by the promise of enhancing energy optimization. However, it has been widely acknowledged that loss of control over environmental conditions can lead to a diminished perception of comfort and compromised long-term user awareness and satisfaction. How can we envision building systems that can interact with building inhabitants and engage them at the “right” time and place? In this work, we address this challenge through three key contributions: 1) AirSpecs, a novel smart glasses-based system that …

The Efficacy Of Incorporating Artificial Intelligence (Ai) Chatbots In Brief Gratitude And Self-Affirmation Interventions: Evidence From Two Exploratory Experiments, Jing Wen Hung, Andree Hartanto, Adalia Y.H. Goh, Zoey K.Y. Eun, K. T. A. Sandeeshwara Kasturiratna, Zhi Xuan Lee, Nadyanna M. Majeed

Research Collection School of Social Sciences

Numerous studies have demonstrated that positive psychology interventions, including brief interventions, can significantly improve well-being outcomes. These findings are particularly important given that many of these interventions are brief and self-administered, making them both accessible and scalable for large populations. However, the efficacy of positive psychology interventions is often constrained by small effect sizes. In light of advancements in generative Artificial Intelligence (AI), this study explored whether integrating AI chatbots into positive psychology interventions could enhance their efficacy compared to traditional self-administered approaches. Study 1 examined the efficacy of a gratitude intervention delivered through Snapchat's My AI, while Study 2 …

Greening Intelligence: Why Ai Infrastructure And Governance Must Evolve Together, Heng Wang, Poh Seng Lee

Research Collection Yong Pung How School Of Law

AI infrastructure is evolving faster than the regulation and governance needed to ensure it serves public and planetary interests.

Oscar: Object Status And Contextual Awareness For Recipes To Support Non-Visual Cooking, Franklin Mingzhe Li, Kaitlyn Ng, Bin Zhu, Patrick Carrington

Research Collection School Of Computing and Information Systems

Following recipes while cooking is an important but difficult task for visually impaired individuals. We developed OSCAR (Object Status Context Awareness for Recipes), a novel approach that provides recipe progress tracking and context-aware feedback on the completion of cooking tasks through tracking object statuses. OSCAR leverages both Large-Language Models (LLMs) and Vision-Language Models (VLMs) to manipulate recipe steps, extract object status information, align visual frames with object status, and provide cooking progress tracking log. We evaluated OSCAR’s recipe following functionality using 173 YouTube cooking videos and 12 real-world non-visual cooking videos to demonstrate OSCAR’s capability to track cooking steps and …

Sans: Efficient Densest Subgraph Discovery Over Relational Graphs Without Materialization, Yudong Niu, Yuchen Li, Jiaxin Jiang, Laks V. S. Lakshmanan

Research Collection School Of Computing and Information Systems

How can we efficiently identify the densest subgraph over relational graphs? Existing dense subgraph discovery (DSD) approaches assume that a relational graph H is already derived from a heterogeneous data source and they focus on efficient discovery of the densest subgraph on the materialized H. Unfortunately, materializing relational graphs can be resource-intensive, which thus limits the practical usefulness of existing algorithms over large datasets. To mitigate this, we propose a novel Summary-bAsed deNsest Subgraph discovery (SANS) system. Our unique summary-based peeling algorithm forms the core of SANS. Following the peeling paradigm, it utilizes summaries of each node's neighborhood to efficiently …

Robust Threshold Ecdsa With Online-Friendly Design In Three Rounds, Guofeng Tang, Haiyang Xue

Research Collection School Of Computing and Information Systems

Threshold signatures, especially ECDSA, enhance key protection by addressing the single-point-of-failure issue. Threshold signing can be divided into offline and online phases, based on whether the message is required. Schemes with low-cost online phases are referred to as “online-friendly”. Another critical aspect of threshold ECDSA for real-world applications is robustness, which guarantees the successful completion of each signing execution whenever a threshold number t of semi-honest participants is met, even in the presence of misbehaving signatories. The state-of-the-art online-friendly threshold ECDSA with-out robustness was developed by Doerner et al. in S&P'24, requiring only three rounds. Recent work by Wong et …

Acccred: Improved Accountable Anonymous Credentials With Dynamic Triple-Hiding Committees, Sijiang Xie, Rui Shi, Yang Yang, Huiqin Xie, Yingjiu Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

Accountable anonymous credentials protect user privacy while holding the accountability of ill-intentioned individuals, which is a critical feature for applications such as online payments and other financial services. Existing accountable anonymous credentials rely on a public committee of trustworthy members who are assumed not to collude and are well protected to perform privacy revocation. However, this assumption is unsound in blockchain-based cryptocurrency systems because the selected committees may involve nodes with significant stakes, and public nodes serving as committee members are vulnerable against targeted attacks from high-computing power adversaries. In this paper, we propose an improved accountable anonymous credential called …

Gamba: Marry Gaussian Splatting With Mamba For Single-View 3d Reconstruction, Qiuhong Shen, Zike Wu, Xuanyu Yi, Pan Zhou, Hanwang Zhang, Shuicheng Yan, Xinchao Wang

Research Collection School Of Computing and Information Systems

We tackle the challenge of efficiently reconstructing a 3D asset from a single image at millisecond speed. Existing methods for single-image 3D reconstruction are primarily based on Score Distillation Sampling (SDS) with Neural 3D representations. Despite promising results, these approaches encounter practical limitations due to lengthy optimizations and significant memory consumption. In this work, we introduce Gamba, an end-to-end 3D reconstruction model from a single-view image, emphasizing two main insights: (1) Efficient Backbone Design: introducing a Mamba-based GambaFormer network to model 3D Gaussian Splatting (3DGS) reconstruction as sequential prediction with linear scalability of token length, thereby accommodating a substantial number …

Building Bridges Across Papua New Guinea’S Digital Divide In Growing The Ict Industry, Marc Cheong, Sankwi Abuzo, Hideaki Hata, Priscilla Kevin, Winifred Kula, Benson Mirou, Christoph Treude, Dong Wang, Raula Gaikovina Kula

Research Collection School Of Computing and Information Systems

Papua New Guinea (PNG) is an emerging tech society with an opportunity to overcome geographic and social boundaries, in order to engage with the global market. However, the current tech landscape, dominated by Big Tech in Silicon Valley and other multinational companies in the Global North, tends to overlook the requirements of emerging economies such as PNG. This is becoming more obvious as issues such as algorithmic bias (in tech product deployments) and the digital divide (as in the case of non-affordable commercial software) are affecting PNG users. The Open Source Software (OSS) movement, based on extant research, is seen …

Iot In Sustainability And Iot In The Ai And Metaverse Age, Yuzhou Qian, Keng Siau

Research Collection School Of Computing and Information Systems

The Internet of Things (IoT) is a modern technology that has gained large popularity and is still developing. Connecting heterogeneous devices, such as phones, vehicles, and household appliances, IoT has brought convenience to our lives. Further, IoT plays a significant role in enhancing environmental sustainability. It provides timely data about different devices and enables users and managers to directly control the objects. IoT can optimize the existing energy systems and promote the usage of renewable technologies. In this paper, we discuss how IoT supports green initiatives (i.e., how it is applied in different sectors), how it can be "green" itself …

Sigscope: Detecting And Understanding Off‑Chain Message Signing‑Related Vulnerabilities In Decentralized Applications, Sajad Meisami, Hugo Dabadie, Song Li, Yuzhe Tang, Yue Duan

Research Collection School Of Computing and Information Systems

In Web 3.0, an emerging paradigm of building decentralized applications or DApps is off-chain message signing, which has advantages in performance, cost efficiency, and usability compared to conventional transaction-signing schemes. However, message signing burdens DApp developers with extra coding complexity and message designing, leading to new security risks.This paper presents the first systematic study to uncover and characterize the security issues in off-chain message signing schemes and the DApps built atop them. We present a holistic static-analysis framework, SigScope, that uniquely combines the insights extracted from DApp front-end code (HTML and Javascript) off-chain and back-end smart contracts on-chain. We evaluate …

Real-Time Rectifying Flight Control Misconfiguration Using Intelligent Agent, Ruidong Han, Shangzhi Xu, Juanru Li, Elisa Bertino, David Lo, Jianfeng Ma, Siqi Ma

Research Collection School Of Computing and Information Systems

Configurations are supported by most flight control systems, allowing users to control a flying drone adapted to complexities such as environmental changes or mission alterations. Such an advanced functionality also introduces a significant problem—misconfiguration settings. It may cause drone instability, threaten drone safety, and potentially lead to substantial financial loss. However, detecting and rectifying misconfigurations across different flight control systems is challenging because (1) (mis)configuration-related code snippets might be syntactically correct and thus hard to identify through traditional code analysis; (2) the response to each configuration varies under different flying scenarios.In this article, we propose and implement a novel rectification …

Unlocking The Planning Capabilities Of Llms Through Maximum Diversity Fine-Tuning, Wenjun Li, Changyu Chen, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have demonstrated impressive task-solving capabilities through prompting techniques and system designs, including solving planning tasks (e.g., math proofs, basic travel planning) when sufficient data is available online and used during pre-training. However, for planning tasks with limited prior data (e.g., blocks world, advanced travel planning), the performance of LLMs, including proprietary models like GPT and Gemini, is poor. This paper investigates the impact of fine-tuning on the planning capabilities of LLMs, revealing that LLMs can achieve strong performance in planning through substantial (tens of thousands of specific examples) fine-tuning. Yet, this process incurs high economic, time, …