Physical Sciences and Mathematics Commons^™

Contested: Consistency-Aided Tested Code Generation With Llm, Jinhao Dong, Jun Sun, Wenjie Zhang, Jinsong Dong, Dan Hao

Research Collection School Of Computing and Information Systems

Recent advancements in large language models (LLMs) have significantly improved code generation, which generates code snippets automatically based on natural language requirements. Despite achieving state-of-the-art performance, LLMs often struggle to generate accurate and reliable code, requiring developers to spend substantial effort debugging and evaluating the generated output. Researchers have proposed leveraging Consistency to select code that passes more tests (inter-consistency) and demonstrates consistent behavior across more counterparts (intra-consistency). However, since the tests themselves are also generated by LLMs, relying on majority voting based on incorrect tests leads to unreliable results. To address this, we propose a lightweight interaction framework that …

Hd-Epic: A Highly-Detailed Egocentric Video Dataset, Toby Perrett, Ahmad Darkhalil, Saptarshi Sinha, Omar Emara, Sam Pollard, Kranti Kumar Parida, Kaiting Liu, Prajwal Gatti, Siddhant Bansal, Kevin Flanagan, Jacob Chalk, Zhifan Zhu, Rhodri Guerrier, Fahd Abdelazim, Bin Zhu, Davide Moltisanti, Michael Wray, Hazel Doughty, Dima Damen

Research Collection School Of Computing and Information Systems

We present a validation dataset of newly-collected kitchenbased egocentric videos, manually annotated with highly detailed and interconnected ground-truth labels covering: recipe steps, fine-grained actions, ingredients with nutritional values, moving objects, and audio annotations. Importantly, all annotations are grounded in 3D through digital twinning of the scene, fixtures, object locations, and primed with gaze. Footage is collected from unscripted recordings in diverse home environments, making HDEPIC the first dataset collected in-the-wild but with detailed annotations matching those in controlled lab environments. We show the potential of our highly-detailed annotations through a challenging VQA benchmark of 26K questions assessing the capability to …

Dupin: A Parallel Framework For Densest Subgraph Discovery In Fraud Detection On Massive Graphs, Jiaxin Jiang, Siyuan Yao, Yuchen Li, Qiange Wang, Bingsheng He, Min Chen

Research Collection School Of Computing and Information Systems

Detecting fraudulent activities in financial and e-commerce transaction networks is crucial. One effective method for this is Densest Subgraph Discovery (DSD). However, deploying DSD methods in production systems faces substantial scalability challenges due to the predominantly sequential nature of existing methods, which impedes their ability to handle large-scale transaction networks and results in significant detection delays. To address these challenges, we introduce Dupin, a novel parallel processing framework designed for efficient DSD processing in billion-scale graphs. Dupin is powered by a processing engine that exploits the unique properties of the peeling process, with theoretical guarantees on detection quality and efficiency. …

Collaborative Tree Search For Enhancing Embodied Multi-Agent Collaboration, Lizheng Zu, Lin Lin, Song Fu, Na Zhao, Pan Zhou

Research Collection School Of Computing and Information Systems

Embodied agents based on large language models (LLMs) face significant challenges in collaborative tasks, requiring effective communication and reasonable division of labor to ensure efficient and correct task completion. Previous approaches with simple communication patterns carry erroneous or incoherent agent actions, which can lead to additional risks. To address these problems, we propose Cooperative Tree Search (CoTS), a framework designed to significantly improve collaborative planning and task execution efficiency among embodied agents. CoTS guides multi-agents to discuss long-term strategic plans within a modified Monte Carlo tree, searching along LLMdriven reward functions to provide a more thoughtful and promising approach to …

Final Silver Bow Creek Conservation Area (Sbcca) Butte Reduction Works (Brw) Air Monitoring For Interim Demolition Activities – Phase I Data Summary Report (Dsr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Unveiling The Vital Role Of Blue Carbon In Mangroves: Understanding Environmental Influences, Jahnelle Howe

Dissertations, Theses, and Capstone Projects

Mangrove ecosystems provide essential ecological services, including carbon sequestration, coastal protection, and heavy metal retention. However, their resilience and functionality are increasingly influenced by hurricanes, climate variability, and anthropogenic contamination. This dissertation integrates three research efforts examining mangrove canopy dynamics, carbon storage, and heavy metal contamination in two Puerto Rican mangrove systems: La Parguera and Laguna Grande.

Using remote sensing techniques (LiDAR and NDVI analysis), we assessed the impact of Hurricane Maria (Category 4, 2017) on mangrove canopy structure and vegetation health. Results revealed significant canopy height loss, with greater damage at Laguna Grande, where pre-storm canopy height and human …

Exploring The Enstatite Chondrites: Modal Abundances, Metamorphic History, And Volatile Challenges, Mabel Gray

Dissertations, Theses, and Capstone Projects

This thesis investigates the formation conditions, thermal histories, and volatile inventories of enstatite chondrites (ECs). A diverse set of ECs is analyzed to capture the broad variability within this meteorite class. Chapter 1 presents a comparative analysis of mineral abundances using automated point counting, X-ray diffraction analysis, and XMapTools-based phase mapping. The results reveal significant variations in silicate and sulfide abundances across EC groups, challenging the conventional binary classification and suggesting distinct formation conditions. Chapter 2 explores the thermal histories of two highly equilibrated EH chondrites, proposing a novel formation mechanism on the parent asteroid: contact metamorphism driven by an …

Excitons And Polaritons In Two-Dimensional Materials Heterostructures - Applications As Qubits, Time Crystals, And Superfluids, Gabriel Pimenta Martins

Dissertations, Theses, and Capstone Projects

This dissertation is concerned with exploring the properties and applications of excitons and polaritons in two-dimensional (2D) materials and heterostructures. It focuses on their potential to form qubits, time crystals, and superfluids. The work is motivated by the unique electronic and optical properties of 2D materials—specifically, their ability to host strongly bound excitons and hybrid light-matter quasiparticles known as polaritons. By exploiting a combination of theoretical modeling and numerical simulations, this work examines the behavior of these quasiparticles under various physical conditions, including strain-induced pseudomagnetic fields, optical microcavities, and periodic external potentials.

The first part of this dissertation is devoted …

Fast Transients From Magnetic Disks Around Non-Spinning Collapsar Black Holes, Justin A. Bopp

Dissertations, Theses, and Capstone Projects

Most black holes (BHs) formed in collapsing stars have low spin, though some are expected to acquire a magnetic accretion disk during the collapse. While such BH disks can launch magnetically driven winds, their physics and observational signatures have remained unexplored. We present global 3D general relativistic magnetohydrodynamic simulations of collapsing stars that form slowly spinning BHs with accretion disks. As the disk transitions to a magnetically arrested state, it drives mildly relativistic, wobbling, collimated magnetic outflows through two mechanisms: steady outflows along vertical magnetic field lines (“Blandford-Payne jets”) and magnetic flux eruptions. With isotropic-equivalent energy of E_iso ≈10 …

Tracing My Roots: An Exploratory Data Visualization And Analysis Of Jewish Immigration And Assimilation To New York City, Jamie E. Gelberg

Dissertations, Theses, and Capstone Projects

As my Capstone, I explored the complex process of immigrant assimilation to New York City from the late 19th century and beyond through a personal lens, using my Ashkenazi Jewish family as a case study.

I outlined and analyzed relevant demographic data from the US Census Bureau, Berman Jewish DataBank, and other sources to understand New York City during this period and how Jewish immigrants fit into the story. I focused on my family history, immigration and settlement, social assimilation, and economic status. I also incorporated personal narratives from my family history from 3 generations. These narratives help provide context …

Ideal Query Expansion Using Reinforcement Learning, Madhuchchhanda Das

Master’s Dissertations

Information retrieval (IR) systems often struggle with short, ambiguous, or underspecified queries, leading to suboptimal document retrieval. Traditional query reformulation methods, such as those based on the Rocchio algorithm, rely on heuristic term selection and relevance feedback but typically apply fixed or manually tuned weights to expanded terms. This limits their adaptability and generalization across diverse query-document contexts. In this thesis, we propose a novel reinforcement learning (RL)-based framework to dynamically optimize term weighting in reformulated queries. We model the problem as a Markov Decision Process (MDP), where each state represents a query as a vector of term weights. An …

On The Deployment Of Ris-Mounted Uav Networks, Anupam Mondal

Master’s Dissertations

Reconfigurable intelligent surfaces (RIS) enable smart wireless environments by dynamically controlling signal propagation to enhance communication and localization. Unmanned aerial vehicles (UAVs) can act as flying base stations and thus, improve system performance by avoiding signal blockages. In this paper, we propose a gradient ascent and coordinate search based method to determine the optimal location for a system that consists of a UAV and a RIS, where the UAV serves cellular users (CUs) and the RIS serves device-to-device (D2D) pairs. In particular, by optimizing the net throughput for both the D2D pairs and the CUs, the suggested method establishes the …

Modeling And Verification Of Sigma Delta Neural Networks, Sirshendu Das

Master’s Dissertations

In the context of modern day embedded safety-critical systems and low-resource edge devices in particular, Sigma-Delta Neural Networks (SDNNs) offer a promising alternative to traditional Artificial Neural Networks (ANNs) by leveraging eventdriven, sparse computations inspired by biological neural processing. This energyefficient paradigm makes SDNNs well-suited for neuromorphic hardware and realtime applications, particularly in scenarios with temporal redundancy, such as video processing. However, as neural networks become integral to safety-critical systems, ensuring their robustness against adversarial perturbations is an absolute necessity. In this work, we propose an end-to-end framework for formal modeling and verification of SDNNs using Satisfiability Modulo Theory (SMT). …

Addressing Class Imbalance Problems To Improve Animal Detection Through Aerial Image Data, Suryang Koushal

Master’s Dissertations

Monitoring animal populations in wildlife reserves is essential for conservation, especially for endangered species, but manual censuses are costly, risky, and logistically challenging due to vast, inaccessible terrains. Unmanned Aerial Vehicles (UAVs) with digital cameras provide a safer, scalable solution for collecting aerial imagery to estimate animal populations. However, semi-automated processing of these images faces significant challenges due to class imbalance in datasets, including foreground-background disparities, where background terrain dominates over sparse animal instances, and inter-class imbalances from uneven species representation and varied visual appearances (e.g., species, sizes, fur patterns) against diverse backgrounds like deserts or forests. These imbalances hinder …

Energy-Efficient Uav Movement And User-Uav Association In Multi-Uav Networks, Subhadip Ghosh

Master’s Dissertations

These days, unmanned aerial vehicle (UAV)-based millimeter wave (mmWave) communication systems have drawn a lot of attention due to the increasing demand for faster data rates. Given the susceptibility of mmWave signals to obstacles and high propagation loss of mmWaves, ensuring line-of-sight (LoS) connectivity is critical for maintaining robust and efficient communication. Furthermore, UAVs have limited power resource and limited capacity in terms of number of users it can serve. Most significantly di↵erent users have di↵erent delay requirements and they keep moving while interacting with the UAVs. In this paper, first, we have provided an efficient solution for the optimal …

Enhancing Expressive Power Of Graph Neural Networks Using Geometric Transformations, Suranjan Dey

Master’s Dissertations

Graph Neural Networks (GNNs) are highly effective in many real-world tasks, such as molecular property prediction, modeling protein structures, analyzing user-item relationships, and making link predictions. What sets them apart is their ability to learn meaningful representations by capturing not just the features of individual nodes, but also the overall structure of the graph they belong to. This expressive strength allows GNNs to model complex relationships more accurately. In this work, we take a step further by introducing geometric transformations aimed at improving how GNNs handle spatial information. In particular, we focus on angular aggregation methods that maintain rotational consistency, …

Application Of Deep Learning In Analysis Of Stellar Spectra, Piyush Yayati

Master’s Dissertations

In recent years, the analysis of high-resolution stellar spectra has become increasingly important for estimating key stellar parameters such as effective temperature (Teff ), surface gravity (log g), metallicity ([M/H]), and rotational velocity (v sin i). Traditional methods often rely on manual calibration or spectrum synthesis, which can be time-consuming and error-prone, especially for M dwarfs whose spectra are dense with molecular features. In this study, we investigate the use of convolutional neural networks (CNNs) to automate the estimation of stellar parameters using synthetic and observed data.We adopt a StarNet-like CNN architecture trained on synthetic spectra generated from the PHOENIX-ACES …

Causal Explanations In Deep Learning Systems, Dhruv Vansraj Rathore

Master’s Dissertations

Deep learning models often deliver high predictive accuracy; however, their lack of interpretability can hinder their adoption in critical fields such as healthcare and finance. This thesis explores the concept of Intrinsic Causal Contribution (ICC), a novel method for explaining neural network predictions by quantifying each input feature’s intrinsic causal influence on the output, independent of correlated effects. ICC models the network as a Structural Causal Model and employs Causal Normalizing Flows to handle complex dependencies, with efficient estimation via the Jansen Estimator. Analysis on both synthetic and real data sets provides evidence that ICC produces faithful, interpretable attributions, often …

Universally Consistent Hyperbolic Deep Neural Networks, Sagar Ghosh

Master’s Dissertations

The ubiquitous pertinence of Deep Neural Networks has made it pivotal in modern Computer Science Applications, ranging from Computer Vision to Pattern Recognition and Machine Translation. Although these deep architectures are primarily based on Euclidean Spaces, Hyperbolic Neural Networks (HNN) gained traction in recent times to tackle more complex non-Euclidean data having inherent hierarchical structures. These HNN architectures have shown commendable improvements in test results on tree or graph-like data by exploiting the inherent exponential metric distances of hyperbolic spaces, making them more suitable to embed non-Euclidean data. Although HNNs surpass their conventional Euclidean counterparts by commendable margins, little to …

Heat, Smoke, And Resilience In East Central Spokane: Community Insights On Climate Adaptation, Sarah Bliss Matousek, Amanda Gray, Dante Jester, Brian G. Henning

Climate, Water, and the Environment Research

This report presents findings from a multi-methods research initiative exploring the impacts of climate-related hazards—specifically extreme heat and wildfire smoke—on residents of Spokane’s East Central neighborhood. The project combined a community survey with a World Café-style symposium to gather both quantitative data and lived experiences from local participants. Key themes that emerged include the need for more cooling infrastructure, accessible health education, improved indoor air quality, and stronger neighborhood-level communication and support networks. The report highlights how systemic underinvestment in East Central intersects with climate vulnerability, underscoring the urgency of equitable, community-driven solutions. Recommendations include expanding tree canopy, increasing funding …

Building A Smoke-Resilient Spokane: Guidance From Community Conversations, Anna Reed, Hannah Mckinley, Dante Jester, Brian G. Henning, Tania Busch Isaksen

Climate, Water, and the Environment Research

Wildfire season has grown in length and severity in the western United States due to decades of fire suppression and climate change. This trend is projected to continue, worsening air quality and increasing the risk to public health. In response, the Gonzaga Institute for Climate, Water, and the Environment, in partnership with the University of Washington, hosted the Smoke Ready Spokane Symposium in July 2024 in Spokane, Washington. The event convened community partners across sectors to reflect on past wildfire smoke events and discuss locally-relevant wildfire smoke exposure reduction strategies. Symposium participants engaged in small group discussions using the World …

On-Site Water Management Field Sampling Plan (Fsp), 2025 Update, Mark Thompson, Rampart Solutions

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Revised Draft Final Butte Priority Soils Operable Unit (Bpsou) Unreclaimed Sites: Ur-03 Site Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Revised Draft Final Bpsou Unreclaimed Sites: Ur-05 Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Pm2.5 Forecasting At U.S. Embassies And Consulates Worldwide Using Nasa Model Powered By Machine Learning, Junhyeon Seo, Alqamah Sayeed, Seohui Park, John Kerekes, Stephanie Christel, Mary Tran, Pawan Gupta

Articles

Air quality forecasting is crucial for public health, especially in rural, suburban, and developing areas lacking reliable monitoring data. Hybrid monitoring (surface, satellite, and models) offers a scalable, cost‐ effective solution for tracking pollution and trends. This work presents a machine learning model that integrates ground measurements with global model outputs assimilating satellite observations to forecast air quality. Ground measurements of fine particulate matter (PM2.5) from over 60 U.S. embassies and consulates were used to calibrate global model outputs for local air quality forecasting. Multi‐channel input data was prepared using the Goddard Earth Observing System forward processing for meteorology and …

Fused-Silica Microelectromechanical Systems For Relative Gravimetry, Ethan Doerstling

Theses and Dissertations

Gravimeters are devices that measure gravitational acceleration which can be used by the United States Air Force (USAF) in the areas of navigation and remote sensing. Fused-silica microelectromechanical systems (MEMS) devices offer capabilities to make inexpensive relative gravimeters with higher thermal stability than common silicon devices while maintaining good gravitational sensitivity. The fused-silica devices in this research were designed, simulated, fabricated, and tested to observe their performance as gravimeters. The devices exhibit properties of highly sensitive accelerometers but the current designs do not qualify as gravimeters. This study provides information to improve the sensitivity and stability of these fused-silica MEMS …

Enabling Automatic Solar Pv Array Identification Using Big Satellite Imagery, Qi Li, Keyang Yu, Carson Snow, Dong Chen

Computer Science Faculty Research and Publications

Recently, there has been a growing interest in automatically collecting distributed solar photovoltaic (PV) installation information in smart grid systems, including the quantity and locations of solar PV deployments, as well as their profiling information across a given geospatial region. Most recent approaches are still suffering low detection accuracy due to insufficient sample and principal feature learning when building their models and also separation of rooftop object segmentation and identification during their detection processes. In addition, they cannot report accurate multi-deployment results. To address these problems, we design a new system-SolarDetector+, which can automatically and accurately detect and profile distributed …

Impact Of Seed Moisture And Temperature On Hemp Seed Germination, Paul Cockson, Andrea Webb, Natalia Martinez-Ochoa, Lindsey Moffitt, Robert Pearce, Manohar Chakrabarti

School of Integrative Biological & Chemical Sciences Faculty Publications

Germination rates of commercial lots of hemp have been highly variable, resulting in poor stand establishment. Germination rates in some seed lots have decreased by 50% after only 1 year of storage. The objective of this trial was to investigate the impact of seed storage conditions on seed germination over time. Industrial hemp (IH) seeds (cv. NWG2730) were harvested from the field. The seeds were cleaned, sorted, and dried to specific moisture contents (MC) 6%, 8%, 10%, and 14%. Seeds were subdivided, placed in hermetically sealed packets, and stored at temperatures of −20°C, 4°C, 10°C, or 21°C for 3, 6, …

City & Town Perspectives On Water Management In Utah: Descriptive Report Of Survey Findings, Bailey M. Holdaway, Courtney G. Flint

Environment and Society Student Research

In an effort to better understand the water management practices of municipalities in Utah, surveys were conducted with city and town representatives. These surveys aimed to capture insights across a variety of key topics related to water resource management. A brief overview of survey methods is provided followed by findings organized by question topic.

Nonlinear Physics Of Parity-Broken Fluids, Sudheesh Srivastava

Dissertations, Theses, and Capstone Projects

This thesis explores parity-breaking mechanisms, nonlinear wave phenomena, and localization transitions across different physical contexts. First, we examine modulation instability in parity-breaking systems, deriving modified nonlinear Schr¨odinger equations that reveal direction dependent instabilities. Next, we investigate wave turbulence, demonstrating numerically that parity-breaking dispersion significantly alters turbulent cascades and modifies their statistical properties. Lastly, we analyze localization in quasiperiodic tight- binding model based on polariton condensate lattice. Collectively, these studies illustrate the universal role of symmetries and nonlinear interactions in shaping macroscopic behaviors, facilitating interdisciplinary insights.