Physical Sciences and Mathematics Commons^™

Draft Final 2025 Bpsou Subdrain Data Summary Report (Dsr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Learning Techniques In Prediction Of Functional Epigenomic Events, Mohammad Shiri

Computer Science Theses & Dissertations

Accurately predicting functional epigenomic events from DNA sequences is critical to understanding gene regulation and the functional impact of non-coding variants. Despite considerable progress, critical challenges hamper the effectiveness and efficiency of existing deep learning approaches. These challenges include negative transfer in multi-task learning (MTL), suboptimal network architectures, and pervasive label noise, particularly the positive-unlabeled problem arising from data sparsity in single-cell assays. This dissertation presents a cohesive framework of novel learning techniques to effectively address these challenges. First, a highly scalable task grouping framework is presented to mitigate negative transfer in deep MTL. This method clusters tasks based on …

Pushing High-Performance Private Inference Towards Resource-Constrained Edge Clients, Xiangrui Xu

Computer Science Theses & Dissertations

The widespread adoption of Machine Learning as a Service (MLaaS) has enabled resource constrained edge clients, such as mobile and IoT devices, to leverage powerful deep learning mod els hosted on the cloud. However, this paradigm introduces critical privacy challenges regarding the client’s sensitive input data and the server’s proprietary model parameters. While cryptographic techniques like Homomorphic Encryption (HE) and Multi-Party Computation (MPC) enable Private Inference (PI), existing frameworks impose prohibitive computational and communication overheads that render them impractical for edge deployment. This dissertation introduces three novel frameworks—SPOT, LUTless, and PrivShap—to systematically address the efficiency bottlenecks of PI in edge …

Radicals Transforming Organic Matter: Implications For Non-Pyrogenic Black Carbon And Nitrogen Formation, Molecular Dynamics In Tropical Soils, And Lignin Valorization, João Vitor Dos Santos

Chemistry & Biochemistry Theses & Dissertations

Radicals drive the transformation of organic matter in natural and engineered systems, altering its structure, reactivity, and stability. Condensed aromatic carbon (ConAC) and condensed aromatic nitrogen (ConAN) are among the most persistent forms of organic matter in soils and are traditionally interpreted as products of biomass burning. This dissertation challenges the long-standing assumption that these materials are exclusively pyrogenic, showing that iron-mediated radical chemistry can generate condensed aromatic structures under ambient conditions.

Chapters II and III show that Fenton-like radical oxidation of lignin-rich biomass produces condensed aromatic structures compositionally similar to pyrogenic black carbon and black nitrogen when assessed with …

Draft Final Silver Bow Creek Conservation Area Materials Management Plan Revision 1, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Silver Bow Creek Conservation Area (Sbcca) Construction Quality Assurance Plan (Cqap), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Advancing Paleontologic Mapping: A Gis And Drone Photogrammetry Approach To The Lance Formation Beds Of Glenrock, Wyoming, William H. Steelman, Monika Angner, Norman S. Levine, W S. Persons

The Compass: Earth Science Journal of Sigma Gamma Epsilon

Small museums and volunteer-driven field programs often lack the resources required for detailed geological mapping, limiting the stratigraphic data associated with their fossil collections. To address this challenge, an affordable and replicable mapping workflow was developed that combined drone-based photogrammetry, GIS analysis, and targeted structural measurements using the Glenrock Exposure of the Lance Formation as a study site. The resulting 3D outcrop models, bedding-plane reconstructions, and 750-m-thick stratigraphic column provide the first comprehensive spatial framework for 35 fossil quarries curated by the Glenrock Paleon Museum. The results reveal tightly clustered fossil-bearing horizons, a long interval lacking preservation, and a notable …

The Compass - Volume 95 Issue 1 - Complete Issue, Scott R. Beason

The Compass: Earth Science Journal of Sigma Gamma Epsilon

No abstract provided.

Who Said Cve? How Vulnerability Identifiers Are Mentioned By Humans, Bots, And Agents In Pull Requests, Pien Rooijendijk, Christoph Treude, Mairieli Wessel

Research Collection School Of Computing and Information Systems

Vulnerability identifiers such as CVE, CWE, and GHSA are standardised references to known software security issues, yet their use in practice is not well understood. This paper compares vulnerability ID use in GitHub pull requests authored by autonomous agents, bots, and human developers. Using the AIDev pop dataset and an augmented set of pull requests from the same repositories, we analyse who mentions vulnerability identifiers and where they appear. Bots account for around 69.1% of all mentions, usually adding few identifiers in pull request descriptions, while human and agent mentions are rarer but span more locations. Qualitative analysis shows that …

Iron Deficiency And Depressive Symptoms: Effects Of Racial And Ethnic Disparity In Two Reproductive-Aged Us Women, Alexis Monkhouse

USF Tampa Graduate Theses and Dissertations

Problem: Reproductive- aged women are one of the populations most at risk for iron deficiency and depression in the United States. While many studies have looked at the relationship between these two illnesses, none have explored it from a biocultural framework, examining how race/ethnicity affects this relationship. Using data from NHANES, this thesis examined the relationship between iron status (total body iron) and depressive symptoms (PHQ-9) among reproductive-aged women in the U.S. and how race/ethnicity affects it. Methods. NHANES survey data from 2005-2010 were pulled. Demographic and laboratory data were used to determine eligibility, total body iron, and depressive scores. …

Draft Final Butte Priority Soils Operable Unit (Bpsou) Insufficiently Reclaimed Sites Sister Dump (Bres No. 38) Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

High-Resolution Monitoring Of Intra-Seasonal Agricultural Drought Using Sentinel-2 And Machine Learning Across Bimodal Growing Seasons In Kenya, S. Mohammad Mirmazloumi, Harison Kipkulei, Rose Waswa, Tobias Landmann, Tom Dienya, Maximilian Schwarz, Fabrizio Ramoino, Clément Albergel, Gohar Ghazaryan

All Peer-Reviewed Publications

Drought presents significant challenges to agriculture, threatening food security and livelihoods, across many regions. In Kenya, recurrent droughts across diverse agro-ecological zones emphasize the urgent need for reliable and scalable drought assessment methods. Although drought assessment with various datasets has been carried out for this region, many of them often use course or moderate resolution data. This study uses high-resolution Sentinel-2 observations and machine learning to monitor intra-seasonal crop conditions and assess drought impacts across bimodal growing seasons. Using pixel-based supervised random forest models trained with multiple vegetation indices as input, we classify croplands into drought-affected and unaffected areas. The …

Plasma N6 Polyunsaturated Fatty Acid Levels And Risk For Dementia: A Prospective Observational Study From The United Kingdom Biobank, Aleix Sala-Vila, Nathan Tintle, Jason Westra, Evan Dejong, Tom J. Clark, Paige Miller, Martha A. Belury, William S. Harris

Faculty Work Comprehensive List

Background

Some recent observational studies have linked higher blood linoleic acid (LA) concentration with lower risk of cardiometabolic diseases. However, these associations have not been observed for other n-6 polyunsaturated fatty acids (PUFAs), which, in some settings, have been linked to higher disease risk, and thus the controversy over the health effects of n-6 PUFAs remains. Dementia is a paradigmatic case of it.

Objectives

To investigate the association of LA and non-LA n-6 PUFAs with the risk of incident dementia.

Methods

We included 273,795 participants from the UK Biobank free of dementia at baseline for whom plasma n-6 PUFAs data …

Aiw26s: Machine Learning Of Structured Data, Moumita Saha

Paul English Applied Artificial Intelligence (AI) Institute Publications

This workshop introduces the fundamentals of machine learning for structured data, focusing on tabular datasets and real-world applications. Participants explore key concepts such as data types, data preprocessing, feature engineering, and supervised learning methods. The session covers commonly used models, including linear regression, logistic regression, decision trees, and neural networks, along with evaluation metrics such as RMSE, accuracy, and confusion matrices. By the end of the workshop, participants will have gained a practical understanding of how to build, interpret, and evaluate machine learning models for structured data.

Semat: Semantic Enhanced Natural Image Interactive Matting, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Qianru Sun, Yang Tang, Bo Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Recent approaches attempt to adapt powerful interactive segmentation models, such as SAM, to interactive matting and fine-tune the models based on synthetic matting datasets. However, models trained on synthetic data fail to generalize to complex and occlusion scenes. We address this challenge by proposing a new matting dataset based on the COCO dataset, namely COCO-Matting. It selects real-world complex images from COCO and converts semantic segmentation masks to matting labels. The built COCO-Matting comprises an extensive collection of 36,980 human instance-level alpha mattes in complex natural scenarios. Furthermore, existing SAM-based matting methods extract intermediate features and masks from a frozen …

Bridging Bug Localization And Issue Fixing: A Hierarchical Localization Framework Leveraging Large Language Models, Jianming Chang, Xin Zhou, Lulu Wang, David Lo, Bixin Li

Research Collection School Of Computing and Information Systems

Automated issue fixing is a critical task in software debugging and has recently garnered significant attention from academia and industry. However, existing fixing techniques predominantly focus on the repair phase, often overlooking the importance of improving the preceding bug localization phase. As a foundational step in issue fixing, bug localization plays a pivotal role in determining the overall effectiveness of the entire process. To enhance the precision of issue fixing by accurately identifying bug locations in large-scale projects, this paper presents BugCerberus, the first hierarchical bug localization framework powered by three customized large language models. First, BugCerberus analyzes intermediate representations …

Trade Liberalization Under Safta And Bimstec: Evidence From A Cge-Gtap Case Study Of A Small Open Economy, Gita Bhushal, Pankaj Lal

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Regional trade liberalization via preferential agreements increasingly shapes economic outcomes in small open economies embedded in overlapping regional frameworks. This study evaluates the short-run economy-wide effects of tariff and non-tariff measure (NTM) reforms under the South Asian Free Trade Area (SAFTA) and the Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) using a Computable General Equilibrium (CGE) model calibrated to the GTAP 10 database. Gravity-based estimates of ad valorem equivalents (AVEs) of NTMs are integrated into the CGE framework, enabling explicit modeling of regulatory barriers alongside tariff reductions. Policy simulations examine scenarios involving a 90 percent tariff …

Scenarioxp: A Complete Scenario-Based Testing Framework For The Exploration And Exploitation Of Autonomous Vehicle Validation Scenarios, Quentin Goss

Doctoral Dissertations and Master's Theses

Today is an age of exciting emerging technology where cutting-edge research in autonomous vehicles (AVs) reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, improving driving safety and quality of the user experience as a result. The ever-increasing complexity of these autonomous systems poses many challenges towards the validation and verification (V\&V) of these complex systems under time and resource constraints, as the use of artificial intelligence and also the intricacy of the operating environment means that these systems are also black-box and non-deterministic. Scenario-based V\&V testing of such systems, which involves …

Study Of Output And Behavior Of Llms Using Confidence Framing In Prompt Engineering, Micah Parrilla

Doctoral Dissertations and Master's Theses

While prompt engineering is pivotal for shaping Large Language Model (LLM) outputs, the impact of confidence framing on behavioral calibration remains underexplored. This study investigates the ways in which psychological framing, utilizing techniques such as capability praise, role amplification, and doubt induction, affects linguistic tone, objective accuracy, and internal calibration. A 1,080-trial experimental matrix evaluated six diverse models across factual, logical, coding, and cyber security domains. Analysis using the Kruskal-Wallis H-test revealed highly significant behavioral shifts across all measured dimensions, providing conclusive evidence that the applied frames exert a substantial influence on model performance.

The findings identify a distinct cognitive …

Low-Complexity Structured Neural Networks And Their Usage In Image And Signal Processing, Adam Kuzmicki

Doctoral Dissertations and Master's Theses

Conventional neural networks face significant challenges due to high computational costs, large parameter counts, and reliance on backpropagation, which restricts their application in resource-constrained and real-time settings. To address these challenges, this thesis proposes three structured neural network (NN) architectures grounded in the theories of sparse and self-contained factorizations of transforms, with applications to image compression, reconstruction, classification, encryption, and also adaptive wideband multi-beam beamforming. The first neural network architecture, named DCTrix-Net, replaces conventional spatial con- volution with highly sparse factorization of the discrete Cosine transform (DCT) complemented by Toeplitz-structured weight initialization, achieving at least 97% FLOP reduction over CNNs, …

Information Theory Analysis Of The Solar Wind Magnetic Structures For Space Weather Prediction, Katherine Holland

Doctoral Dissertations and Master's Theses

Forecasting space weather at Earth is highly complicated, because of the limited measurements of the dynamic processes in the Sun that span multiple temporal, spatial, and energy-scales. The solar wind is a highly structured, multi-scale, evolving plasma and consists of coronal mass ejections (CMEs), stream interaction regions (SIRs), expanding flux tubes (Borovsky, 2008), and interplanetary magnetic field (IMF) discontinuities and fluctuations. The aim of this research is to improve our understanding of the evolution and dissipation of different scale-size solar wind magnetic structures as they move from the Sun-Earth Lagrange point 1 (L1) to Earth's bow shock and, ultimately, to …

Time-Correlated Photons From Spontaneous Parametric Down-Conversion (Spdc), Aaron Dubois , ‘26, Tanvir Islam , ‘26, Prashan Thapa Magar , ‘26

Senior Theses, Projects, and Awards

Time-correlated photons can be used to demonstrate emerging technical applications such as ghost imaging, and they can demonstrate quantum-based phenomena in order to interest undergraduate students in engineering applications involving optics and quantum mechanics. In this work, we use spontaneous parametric down-conversion to generate time-correlated photons. To achieve this, we simulated laboratory parameters with MATLAB and designed optical and electrical setups. Specifically, we used a 405 nm diode laser, various common optical components, and a nonlinear crystal designed to generate time-correlated photons, and we used single-photon detectors, time-to-amplitude converters, custom flash-conversion analog-to-digital converters, a Teensy 4.1 microcontroller, and the Arduino …

Modeling And Constraining Beyond Standard Model Neutrino Self-Interactions, Sabrina Hanning , ‘26

Senior Theses, Projects, and Awards

Neutrinos are the ideal subject for theories of Beyond Standard Model (BSM) physics. This is because small neutrino interaction cross-sections make them incredibly difficult to detect and result in many open questions regarding neutrino properties, particularly those that do not fit nicely into the Standard Model of particle physics, such as the existence of neutrino masses. BSM neutrino self-interactions are an interesting possibility with connections to dark matter candidates, neutrino mass generation, and in general any measurement that relies on accurate modeling of incident astrophysical neutrino flux.

These self-interactions may take various forms, with different mediator particles; we present a …

Unraveling The Intractable Trilemma In Urban Weather And Climate Modeling, Peiyuan Li, Ashish Sharma, Rao Kotamarthi, Alberto Martilli, Subimal Ghosh, Cristina Negri, Scott Collis, Lee Chapman, Fei Chen, Luis M. A. Bettencourt, Winston T. L. Chow

Research Collection College of Integrative Studies

Urban weather and climate modeling is constrained by the highly heterogeneous and dynamic nature of cities. It exhibits a persistent trilemma between spatial granularity, spatiotemporal coverage, and physical interpretability. We articulate this challenge and propose a hybrid framework that integrates physics -based models, urban observations, and machine learning. Framing urban modeling as an integration problem across methods and scales, we provide a structured guide for next -generation, decision -relevant urban climate capabilities.

Structural Silence: When Ai Infrastructure Fails Speakers Of Underrepresented Languages, Avijit Roy, Proma Roy

Publications and Research

Artificial intelligence tools for education and language support are increasingly framed as scalable responses to access gaps in under-resourced communities. Yet the infrastructure underlying these tools—training corpora, tokenization schemes, evaluation benchmarks, and deployment architectures—encodes a set of assumptions that systematically disadvantages speakers of underrepresented languages before a single model is trained. This paper examines those assumptions through the lens of Bengali, one of the world’s most widely spoken languages with roughly 285 million speakers (Ethnologue, 2025; International Communication and Leadership School, 2026), and the structural barriers that emerge when attempting to build AI-assisted educational tools for Bengali-speaking learners in low-connectivity …

Watersmart Farms - Desalination: Community Desalination, Sharmina Begum, Wendell Ela, John K. Ruprecht

Natural resources commissioned reports

The Wheatbelt is a vast agricultural region in southwestern Western Australia, covering approximately 25 million hectares, of which 17 million are cleared. It is a key contributor to the state’s economy, supporting broadacre grain production, livestock (such as sheep, beef, and pigs) grazing, mixed farming, and numerous small rural communities. However, decades of extensive land clearing for agriculture have significantly altered the natural hydrological balance. These changes have disrupted local water cycles, increased recharge post-clearing, increased evaporation, and contributed to widespread soil salinity.

Compounding these land-use impacts, the region has also experienced a long-term drying trend due to climate change. …

Weakly Supervised Video Anomaly Detection And Localization With Spatio-Temporal Prompts, Peng Wu, Xuerong Zhou, Guansong Pang, Zhiwei Yang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Current weakly supervised video anomaly detection (WSVAD) task aims to achieve frame-level anomalous event detection with only coarse video-level annotations available. Existing works typically involve extracting global features from full-resolution video frames and training frame-level classifiers to detect anomalies in the temporal dimension. However, most anomalous events tend to occur in localized spatial regions rather than the entire video frames, which implies existing frame-level feature based works may be misled by the dominant background information and lack the interpretation of the detected anomalies. To address this dilemma, this paper introduces a novel method called STPrompt that learns spatio-temporal prompt embeddings …

Portrait Shadow Removal Via Self-Exemplar Illumination Equalization, Qian Huang, Cheng Xu, Guiqing Li, Ziheng Wu, Shengxin Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

We introduce the Self-Exemplar Illumination Equalization Network, designed specifically for effective portrait shadow removal. The core idea of our method is that partially shadowed portraits can find ideal exemplars within their non-shadowed facial regions. Rather than directly fusing two distinct classes of facial features, our approach utilizes non-shadowed regions as an illumination indicator to equalize the shadowed regions, generating deshadowed results without boundary-merging artifacts. Our network comprises cascaded Self-Exemplar Illumination Equalization Blocks (SExmBlock), each containing two modules: a self-exemplar feature matching module and a feature-level illumination rectification module. The former identifies and applies internal illumination exemplars to shadowed areas, producing …

Trace: Securing Smart Contract Repository Against Access Control Vulnerability, Chong Chen, Lingfeng Bao, David Lo, Yanlin Wang, Zhenyu Shan, Ting Chen, Guangqiang Yin, Jianxing Yu, Zibin Zheng, Jiachi Chen

Research Collection School Of Computing and Information Systems

Smart contract vulnerabilities have led to billions of dollars in economic losses. Among these, improper Access Control, which allows unauthorized users to execute restricted functions, is particularly prevalent and has caused significant financial damage. Smart contract repositories contain source code, documentation, configuration files, and other artifacts necessary for building and deploying smart contracts. GitHub hosts numerous open-source repositories of this kind, which serve as intermediate artifacts in development and require compilation and packaging to produce deployable contracts. Third-party developers often reference, reuse, or fork code from these repositories during custom development. However, if the referenced code contains vulnerabilities, it can …

Penforge: On-The-Fly Expert Agent Construction For Automated Penetration Testing, Huihui Huang, Jieke Shi, Junkai Chen, Ting Zhang, Yikun Li, Chengran Yang, Eng Lieh Ouh, Lwin Khin Shar, David Lo

Research Collection School Of Computing and Information Systems

Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has explored automating this process with Large Language Model (LLM)-powered agents, but existing approaches either rely on a single generic agent that struggles in complex scenarios or narrowly specialized agents that cannot adapt to diverse vulnerability types. We therefore introduce PenForge, a framework that dynamically constructs expert agents during testing rather than relying on those prepared beforehand. By integrating automated reconnaissance of potential attack surfaces with agents instantiated on the fly for context-aware exploitation, PenForge achieves a 30.0% exploit success rate (12/40) …