Digital Commons Network^™

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 …

Hierarchical Learning-Based Graph Partition For Large-Scale Vehicle Routing Problems, Yuxin Pan, Ruohong Liu, Yize Chen, Zhiguang Cao, Fangzhen Lin

Research Collection School Of Computing and Information Systems

Neural solvers based on the divide-and-conquer approach for Vehicle Routing Problems (VRPs) in general, and capacitated VRP (CVRP) in particular, integrates the global partition of an instance with local constructions for each subproblem to enhance generalization. However, during the global partition phase, misclusterings within subgraphs have a tendency to progressively compound throughout the multi-step decoding process of the learning-based partition policy. This suboptimal behavior in the global partition phase, in turn, may lead to a dramatic deterioration in the performance of the overall decomposition-based system, despite using optimal local constructions. To address these challenges, we propose a versatile Hierarchical Learning-based …

Worldcuisines: A Massive-Scale Benchmark For Multilingual And Multicultural Visual Question Answering On Global Cuisines, Genta Indra Winata, Et. Al

Research Collection School Of Computing and Information Systems

Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date. It includes tasks for identifying dish names and their origins. We provide evaluation datasets in two sizes (12k and 60k instances) alongside …

Exploring The Potential Of Large Language Models For Heterophilic Graphs, Yuxia Wu, Shujie Li, Yuan Fang, Chuan Shi

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have presented significant opportunities to enhance various machine learning applications, including graph neural networks (GNNs). By leveraging the vast open-world knowledge within LLMs, we can more effectively interpret and utilize textual data to better characterize heterophilic graphs, where neighboring nodes often have different labels. However, existing approaches for heterophilic graphs overlook the rich textual data associated with nodes, which could unlock deeper insights into their heterophilic contexts. In this work, we explore the potential of LLMs for modeling heterophilic graphs and propose a novel two-stage framework: LLM-enhanced edge discriminator and LLM-guided edge reweighting. In the first …

On Learning Informative Trajectory Embeddings For Imitation, Classification And Regression, Zichang Ge, Changyu Chen, Arunesh Sinha, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

In real-world sequential decision making tasks like autonomousdriving, robotics, and healthcare, learning from observed state-action trajectories is critical for tasks like imitation, classification,and clustering. For example, self-driving cars must replicate humandriving behaviors, while robots and healthcare systems benefitfrom modeling decision sequences, whether or not they come fromexpert data. Existing trajectory encoding methods often focus onspecific tasks or rely on reward signals, limiting their ability togeneralize across domains and tasks.Inspired by the success of embedding models like CLIP andBERT in static domains, we propose a novel method for embeddingstate-action trajectories into a latent space that captures the skillsand competencies in the …

Correcting Sampling Bias With Privacy-Preserving Synthetic Data: Inference Stability Under The Da-Mi Framework, Hannah Jiang

Arts & Sciences Graduate Student Theses and Dissertations

With the rapid advancement into the Data Age, synthetic data has emerged as a promising avenue for sharing scientific information while protecting the original data. While existing research has primarily focused on generating synthetic data to accurately replicate the characteristics of observed data, we explore the potential of synthetic data to adjust for unrepresentative sampling. This study explores how sampling bias—specifically unbalanced subsets—impacts statistical inference, and whether synthetic data can help correct such bias. Using a Data Augmentation–Multiple Imputation (DA–MI) framework, we generate synthetic datasets from biased samples and evaluate parameter recovery under different correction strategies. Simulations under a Missing …

Analyzing Sleep Staging And Cognitive Transitions Using Fmrl Time Series Analysis, Qingwen Zeng

Arts & Sciences Graduate Student Theses and Dissertations

Understanding sleep stages and their underlying cognitive transitions is essential for advancing our knowledge of brain function. While sleep staging has traditionally relied on electroencephalogram (EEG), this study explores the feasibility of classifying sleep stages using only functional Magnetic Resonance Imaging (fMRI) data. We investigate the use of Hidden Markov Models (HMM) and Hierarchical Dirichlet Process Hidden Markov Models (HDP-HMM) to capture temporal brain-state dynamics from task-free fMRI recordings. A series of pipelines—including sliding window, two-stage majority filtering, and prototype-based mapping via the Hungarian algorithm—were implemented to align model-inferred states with EEG-defined sleep stages. Among the twelve tested pipelines, the …

Reducing Measurement Error In Three-Point Bend Test Using Linear Gradient Correction Model, Juan J. Becerra-Garcia

McKelvey School of Engineering Graduate Student Theses & Dissertations

Endovascular devices like catheters feature non-uniform flexural rigidity (EI) profiles (flexi- ble tip, stiff shaft) crucial for navigation and pushability. Standard three-point bend analysis (assuming uniform stiffness) yields inaccurate apparent rigidity (B0) for these graded devices, especially in transition zones. This inaccuracy hinders device comparison, clinical selection, and predictive modeling. This thesis develops and validates a correction model for three-point bend tests aimed at improving local flexural rigidity accuracy for devices with varying stiffness, particularly those exhibiting transition zones similar to logistic profiles. The model assumes a linear gradient in rigidity (B(x) = Bm(1 + 2mx/L)) across the test span …

Top-Down Control And Bottom-Up Salience Guide Attention During Visual Search, Christopher Ahn

Arts & Sciences Graduate Student Theses and Dissertations

We search the visual world using different strategies, some of them relying on bottom-up salience, others on top-down expectations. However, little is known about how we arbitrate between them. Here, we report a study investigating whether people are sensitive to environmental regularities that incentivize different strategies with differential effort requirements. To test this, we used a visual search task in which people need to search a target in a field of items, each of which is printed in one of two colors. This task allows us to parametrically manipulate bottom-up salience by varying the number of items with the less …

The Little Diagram That Could: Geometric Properties And Statistical Applications Of Persistence Diagrams In Topological Data Analysis, Eugene Kler

McKelvey School of Engineering Graduate Student Theses & Dissertations

Topological Data Analysis (TDA) is a collection of techniques for data analysis that leverages topological invariants of spaces formed from data points. These methods excel at extracting useful information from noisy or sparse data, making them attractive to many mathematicians, statisticians, and scientists. In this thesis, we explore TDA on three fronts: algebraic foundations, statistical applications, and metric properties. Throughout, the central object of study is the Persistence Diagram (PD), a summary of the changes in homology that occur as one builds simplicial complexes from the data by increasing a parameter.

Animal Burrow Detection In Levee Systems Using Res-Net 34, Christopher D. Moore

LSU New Orleans Theses and Dissertations

Animal burrow detection is a time-consuming and costly task for levee inspectors. Annual budgets run up to approximately $16 million per state. The inspectors typically will have to travel to the inspection sites using government-assigned transportation. Depending on the distance to the site, it may take minutes or hours to arrive before any productive inspections occur. Once at the site, the inspectors were subject to human error, overgrown foliage, severe weather, or prohibitive landscaping that would make any human inspection impossible. Also, animal burrows could be small enough or overgrown, so the human inspector misses the problem areas. We aimed …

Effects Of Increased Use Of Technology In Early Years Of Child Development, Candy Guadalupe Pena Romero

Capstone Projects and Master's Theses

This senior capstone research project examines how the increased use of technology in early years can have a significant effect on child development, both positive and negative. In this research project, besides conducting an extensive literature review, interviews with three teachers from an early childhood education center and an administrator were interviewed and parent surveys were conducted. The result findings revealed that the increased use of technology in early years affects children’s development. The project also suggests that finding a way to balance technology with traditional play and face-to-face interactions could be a good way to promote healthy development in …

Developing Self-Regulation Skills In Early Childhood, Elizabeth Alvarez

Capstone Projects and Master's Theses

Some elementary-school-age children have not yet developed emotional self-regulation skills. When children have trouble with self-regulation, they have challenges focusing, retaining information, and socializing, which can result in poor academic performance and poor social skills. When children learn emotional self-regulation skills, children can gain emotional intelligence which helps them to recognize, understand, and manage their own emotions. To address this issue, I created a three-day lesson for students in a kindergarten classroom at Sherwood Elementary in Salinas, California.

The Benefits Of Experiential Outdoor Learning For Elementary School Students, Jillian R. Witt

Capstone Projects and Master's Theses

This senior capstone research project aims to delve deeper into the benefits of experiential outdoor learning that contributes to the growing body of knowledge and practical applications of this dynamic educational approach of elementary school students. Through the use of literature review, interviews with program workers and school administrators and survey with educators and past students, the result findings indicated that experiential outdoor learning benefits elementary school students despite some cost challenges, but provided them with more opportunities to learn from outdoor activities to increase their academic achievement, improve their cognition, and enhance their social skills with a deeper sense …

Impact Of Private Early Childhood Education On Academic And Social Preparedness Of Preschoolers, Destiny E. Rizo

Capstone Projects and Master's Theses

Early childhood education plays a crucial role in shaping a child's academic and social development. This senior capstone examines how private preschool programs influence kindergarten readiness in both academic and social domains. Through a literature review and interviews with early childhood educators, this project explores the specific practices within private programs that contribute to children's preparedness. The result findings indicated that smaller class size, individualized instruction, and enriched learning environments are the key factors that could really support early learning success for children. As a future educator, this research highlights the value of incorporating these strategies into public school curriculum …

Exploring The Perception Of Spanish-Speaking Students On Language And Cultural Identity, Andrea Martinez

Capstone Projects and Master's Theses

This senior capstone research project explores the perceptions of Spanish-speaking students regarding the intersection of language and cultural identity within educational settings in Monterey County, California. Spanish speakers represent a significant portion of the student population, therefore understanding their cultural/language experiences is essential for fostering inclusive and culturally responsive educational environments. Drawing on literature about bilingual education, cultural identity, and educational equity, interviews with teachers and surveys of students, the findings revealed how language and cultural identity shape students’ sense of belonging, self-perception, academic engagement, attitudes toward bilingualism, and their cultural expression. In addition, there are challenges these students face …

Impact Of Parental Involvement On Elementary School Student Development In Pursuit Of Higher Education, Valeria Guapo

Capstone Projects and Master's Theses

The purpose of this senior capstone is to examine the relationship between parental involvement and elementary school students' academic trajectories, with a focus on their pursuit of higher education. Through a comprehensive analysis of existing literature and through the use of literature reviews, interviews, and surveys with college students, teachers, and parents, the results revealed that parental involvement is a big part of students' educational performance and mental well-being in many aspects. This research investigates how parental involvement in elementary education influences students' academic achievement, motivation, and aspirations for higher education. The findings highlight the significance of parental involvement in …

Benefits Of Parental Involvement On Kindergarten Through Third Grade (K-3) Students’ Education, Kathie Trujillo-Suarez

Capstone Projects and Master's Theses

The purpose of this senior capstone project is to show the benefits of parental involvement in grades K-3 students’ education. Parental involvement plays an important role in the students’ academic success in the early years of elementary school. Schools, teachers and parents should be aware of the impact parent involvement does to K-3 students. Through the use of teacher interviews, and parent surveys, and literature review, the result findings revealed that parent involvement was a critical component of students' education, leading to higher test scores, and academic achievement in the students' education. Strengthening parental involvement in schools could increase K-3 …

Impact Of Bilingual Education On The Academics Of Elementary School Students, Angelica M. Lopez

Capstone Projects and Master's Theses

This research investigates the impact of bilingual education on the academic performance of elementary school students, with a particular focus on Hispanic students in the Soledad area. As bilingual education continues to grow in importance across the United States, understanding its influence on students' academic outcomes is crucial. This study explores the definition, types, and historical evolution of bilingual education, with emphasis on how bilingual programs affect elementary school students' cognitive development, academic performance, and overall school experience. Utilizing both quantitative and qualitative methods, the research includes surveys administered to teachers and parents, assessing their perspectives on the availability and …

2022 Annual Operations And Maintenance Report 2022 Bres Annual Summary Report For Work Completed In Quadrant 2, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Organizational Commitment Of Korean American Nurses: A Cross-Sectional Study, Myunghee Jun, Hye-Kyung Kim, Sungsu Kim, Michael Wreen, Yeon Hee Kim, Christine L. Vandenhouten, Soo Ii Shin

Philosophy Faculty Research and Publications

Background:

Studies on reducing the turnover of foreign-educated nurses (FENs) can help address the nursing shortage in the United States. Organizational commitment and associated factors have been explored in relation to lower turnover. Nursing researchers have identified job satisfaction, psychological contract breach, person-organization fit, and person-work setting fit as major factors in enhancing nurses’ organizational commitment. Research on the organizational commitment of Korean American (KA) nurses would provide insights into reducing turnover among FENs.

Objectives:

This study identifies some of the key factors that significantly enhance the organizational commitment of KA nurses.

Methods:

A cross-sectional study conducted with a survey …

Bioavailability Of Lead In Terre Haute Soils, Terre Haute, Indiana, Zach Kemp

All-Inclusive List of Electronic Theses and Dissertations

Contamination of soils from heavy metal pollution has been a global concern for decades. Heavy metals are released into the soil from natural and anthropogenic releases. The largest producers of heavy metal pollution include mining, industrial production, and the combustion of fossil fuels. Vigo County formed in 1818, and Terre Haute was established in 1832. After the discovery of coal in Clay County in 1867, Terre Haute became the third largest coal producer and fifth largest iron manufacturer in the state during that time. Lead, in particular, is a heavy metal of concern because of the harmful effects to humans …

Air And Missile Defense Threat Scenario Variation To Reduce Pretest Sensitization, Video Games As A Case Study, Julie Renee Szekerczes

All-Inclusive List of Electronic Theses and Dissertations

This study uses fixed and variable video game types to measure pretest sensitization as a proxy for repeated and varied threat test scenarios in system performance testing of air and missile defense systems. The pretest sensitization phenomenon exists when repeated exposure to a test condition influences the participant's response. Research shows air and missile defense development correlates with video games, resulting in similar interfaces and computer operating environments. Department of Defense acquisition test and evaluation results must reflect system performance without prior knowledge of the threat scenarios confounding the results. System performance results inform acquisition decisions, such as further funding …

Energy Forecasting Inaccuracies And Their Direct Impact On Grid Performance, Rashed Khalid Alboom

Theses

Accurate energy forecasting is critical for the stability, efficiency, and cost-effectiveness of modern power grids, particularly as renewable energy sources like solar and wind become more prominent. The variability of these sources presents challenges for traditional forecasting models, which struggle with non-linearity, external dependencies, and evolving grid conditions. These limitations lead to operational inefficiencies, grid instability, and increased financial risks. This study investigates the effectiveness of traditional statistical models, advanced machine learning techniques, and hybrid forecasting approaches to enhance prediction accuracy and grid performance. Using real-world datasets from Kaggle, including historical energy generation, consumption, pricing, and weather variables, this research …

Uvm Based Verification Of Ee621 Risc Processor Module, Collin Neidel

Theses

In the process of digital design, the majority of efforts are focused on verification. This is because a proper verification environment can help engineers discover bugs that otherwise would have flown under the radar. On the other hand, a poorly designed verification environment could fail to fully confirm the device’s functionality and leave the customer with a buggy mess. For obvious reasons, this should be avoided at all costs, hence the emphasis on functional verification and validation efforts. Throughout time, the complexity of the average design under test (DUT) has increased dramatically, making verification a significant challenge for today’s verification …

Enhancing Influence Estimation In Gradient Boosted Decision Trees Through Hierarchical Analysis, Anurag Choubey

Theses

This report presents a novel influence function for gradient-boosted decision trees (GBDTs), a widely-used class of predictive models. Influence estimation aims to quantify how individual training samples affect a model’s predictions, offering valuable insights for model debugging, data quality analysis, and in- terpretability. Existing influence functions for GBDTs—such as LeafInfluence, LeafInfluenceSP, and BoostIn—have shown varying degrees of success, with BoostIn currently recognized as the state-of-the-art in terms of estimation quality and computational efficiency. In this work, we propose BoostInLCA, a new influence function that extends BoostIn by incorporating information from non-leaf nodes via the Lowest Common Ancestor (LCA) path, thereby …

Classification Of Linear Systems Of Equations For Quantum Computing Implementation, Mark Danza

Theses

Drawing nearer to an error-corrected era of quantum computing, it is necessary to understand the suitability of certain post-NISQ algorithms for practical problems. One of the most promising, applicable, and yet difficult to implement in practical terms is the Harrow, Hassidim and Lloyd (HHL) algorithm for linear systems of equations. An enormous number of problems can be expressed as linear systems of equations, from machine learning to fluid dynamics to electrical circuit analysis. However, in most cases, HHL will not be able to provide a practical, reasonable solution to these problems. This work seeks to determine whether problems can be …

Sales Opportunities Lead Qualification In B2b Market, Hajar Hussain Ahli

Theses

This thesis addresses the challenge of inefficient lead qualification in the business-to-business (B2B) market by applying machine learning techniques to predict the likelihood of winning a sales opportunity. Using a real-world dataset of over 78,000 records and 17 variables, the study aims to improve how sales teams identify and prioritize high-conversion leads. A thorough data preparation process was conducted, including handling of missing values, outlier detection, and under-sampling to resolve class imbalance between won and lost opportunities. After thorough data cleaning, preprocessing, and under-sampling to address class imbalance, five machine learning models were developed: Logistic Regression, Random Forest, Neural Network, …

Intellimad: A Framework For Secure Machine Learning Models Evaluation And Fine-Tuning In Federated Setting, Dmitrii Korobeinikov

Theses

In contemporary artificial intelligence (AI) appli- cations, Machine Learning (ML) models are core components enabling AI-driven functionalities, yet selecting and fine-tuning a model and its hyperparameters remains challenging. ML model architecture, as well as key model training parameters, such as the number of training epochs, batch size, and learning rate, are highly dependent on both the dataset modalities and the specific task resolved in a particular application. More sophisticated execution setups may require determination of additional environment-related parameters, such as identifica- tion of computational capabilities required for execution of a particular AI-driven task, or discovery and establishment of desired security-related …

Developing Meld-Accelerated Molecular Dynamics Protocols To Simulate The Binding Of The P53-Derived Ligand To The Mdm-2, X Protein, Maria Ciko

Theses

In this study, we focus on developing computational methods to predict protein-ligand binding affinities, with applications in peptide drug discovery. Molecular Dynamics (MD) simulations can capture the complex conformational behavior of proteins, but their high computational cost limits their efficiency. MELD, or Modeling Employing Limited Data, is a Bayesian approach that integrates external information to accelerate sampling of low- energy, high-probability conformations. Building on previous work by Morrone et al., which successfully applied MELD to P53-MDM2 complexes, we hypothesize that we can effectively compute the relative binding affinities while reducing steric clashes and mitigating the effect of slowed diffusion on …