Physical Sciences and Mathematics Commons^™

High-Resolution Event Stratigraphy Of Ostracods In The Ozan Formation Of Texas And Arkansas, Denali L. John

Master's Theses

This study focuses on the high-resolution event stratigraphic framework of the lower Campanian Ozan Formation in Northeast (NE) Texas and Southwest AR which is richly fossiliferous in ostracods. Little research has been published on the Ozan Formation and even less dealing with Ozan ostracods. Additionally, correlation of Upper Cretaceous units between Texas and AR is controversial due to the general lack of good exposures in both regions and extensive faulting in Northeast Texas despite similar depositional environment across the region. This study calibrates the ranges of ostracods and creates an event stratigraphic composite that is orders of magnitude higher in …

Assessment Of Estuarine Respiration And Benthic Nutrient Fluxes In Mississippi Sound, Nur Pasha Sufian

Master's Theses

This study quantified seasonal and spatial variability of both water column and benthic respiration rates as well as benthic nutrient fluxes along the Mississippi Sound. Respiration was measured in recirculating incubation chambers using Firesting optical DO sensors (PyroScience GmbH), and changes in nutrient concentrations were assessed. Mean water column respiration rates ranged from 0.35 and 2.76 µM O₂ hr⁻¹, with the lowest rates in the winter and the highest in the summer while detectable nutrient concentrations (ammonium, (NH₄⁺), and soluble reactive phosphate (PO₄³⁻)) showed only slight decreases (< 1 µM) over all seasons. Sediment incubation chambers consistently had much greater oxygen declines than the water incubations and showed seasonal trends with the highest mean respiration rates measured in the summer and the lowest mean in the winter, with a range from 107.4 to 1514.9 µmol O₂ m⁻² hr⁻¹ (2.6 to 36.3 mmol O₂ m⁻² d⁻¹). Sediment incubations consistently showed increases in NH₄⁺ concentrations in the overlying water, resulting in calculated NH₄⁺ benthic flux rates of 0.73 to 379.01 µM N m⁻² hr⁻¹ with the lowest rates in the winter and highest rates in the summer. Although PO₄³⁻ concentrations were often below detection in cooler seasons, PO₄³⁻ flux also peaked during the summer (< 28.8 µmol P m⁻² hr⁻¹). In this study, temperature was a key regulator for estuarine respiration and benthic nutrient flux rates, and these rates were positively correlated with porewater nutrient concentrations and sedimentary organic matter. These findings illustrate that benthic respiration can contribute to water column hypoxia and that sediments serve as a source of nutrients to the water column. Overall, these findings provide us with valuable insight about the seasonal coupling of oxygen demand and nutrient flux in the Mississippi Sound and provide valuable baseline data for future biogeochemical modelling of hypoxia, eutrophication, and productivity.

Contrastive Loss In Recommendation Systems, Maryam Aghamohammadghasem

Graduate Theses and Dissertations

A recommendation system is a bridge between users and products, which is widely used in e-commerce such as Amazon and Netflix. This study investigates the use of Graph Neural Networks (GNNs), Light Graph Convolution Network(LightGCN) and Graph Sample and Aggregate (GraphSAGE), in the recommendation system on two categories of Amazon review datasets ( "All Beauty" and "Tools and Home Improvement"). The novelty of this work includes combining supervised and self-supervised learning through Weighted Approximate Rank Pairwise (WARP) and Information Noise-Contrastive Estimation (InfoNCE) losses, to optimize the embeddings of users and recommended items in the shape of a ranking list. The …

Utilizing The Horseshoe Prior In Exploratory Factor Analysis And Gaussian Graphical Networks, James Thomas Roddy

Graduate Theses and Dissertations

High-dimensional data analysis frequently involves extracting meaningful structure from noisy, sparse signals. In recent years, Bayesian shrinkage priors—particularly global-local shrinkage priors—have emerged as powerful tools for inducing sparsity while preserving signal fidelity. Among these, the Horseshoe prior has gained notable attention for its capacity to simultaneously shrink irrelevant parameters and retain substantial signals. This dissertation explores the Horseshoe prior as a unified framework for sparse Bayesian inference across theory, simulation, and real-world application. The first component develops new theoretical results establishing the asymptotic Bayes optimality of the Horseshoe prior in Gaussian graphical models (GGMs). We consider sparse precision matrix estimation …

A Simple Guide For Describing Soils, 2nd Edition, Angela Stuart-Street, Nicolyn Short Dr, Paul Galloway, Noel R. Schoknecht

Natural resources published reports

Soils are enormously diverse and can be very confusing to understand and talk about. This simple guide for describing soils helps to identify the most important parts of a soil profile and provide an easy way to understand and explain what you see. It gives you a step-by-step guide of what soil properties to describe and how to describe them, along with the tools to make basic soil classifications. The soil descriptors help you to identify the soil type and aid in assigning a simple and standardised name to the soil. While this guide is designed to link with a …

Enhanced Point Cloud Generation From A Novel 360° Underwater Lidar, Olagoke E. Daramola

Dissertations

This dissertation presents novel algorithms to improve the mapping capabilities of a 360-degree underwater Pulsed Laser Line Scanner LiDAR (PLLS-360°). Due to its 360° field-of-view (FOV), the PLLS-360° is a compact full-waveform omnidirectional imager suitable for seafloor mapping, underwater asset inspection, object detection, ice-sheet mapping, and construction progress monitoring. The proposed methodology includes an improved waveform fitting technique for saturated waveform recovery, detection array response correction, radiometric corrections, and fusion of LiDAR and sonar bathymetric datasets. The first part of this dissertation assesses the LiDAR’s performance and describes how the data for this unique 360° FOV architecture is processed. The …

Mazur’S Intersection Property And Its Variants, Deepak Gothwal

Doctoral Theses

We discuss various differentiability notions in connection with ball separation prop- erties. We characterise the uniform Mazur’s intersection property (UMIP) in terms of w*-semidenting points in attempt to resolve a long standing open question: “Does UMIP imply uniformly smooth renorming?” Further, we discuss a stronger version of UMIP called the hyperplane uniform Mazur intersection property (HUMIP) which is shown to characterise uniform smoothness. Similar ball separation char- acterisations are obtained for Fr´echet smoothness and asymptotic uniform smoothness (AUS). These ball separation properties are then shown to be residual properties. Thus, we obtain that norms which have UMIP or norms which …

12co Ro-Vibrational Spectroscopy Of Protoplanetary Disks: Inferring Planet-Disk Interactions, Janus Kozdon

All Dissertations

Observing and studying planets around young stars during their initial formation stages is essential for understanding the physics of their formation. Protoplanetary disks are the host sites of planets, and they exhibit substructures that hint towards the planets' presence, but the planet itself has rarely been detected. Analysis of the substructures in protoplanetary disks, especially towards the inner regions where most planets seem to lie, will provide information that can be used to refine theories about planet-disk interactions and, ultimately, planet formation. To probe the high-velocity inner regions, this study utilizes high-resolution spectroscopy, which has increased sensitivity with increasing velocities. …

How Developers Use Type-System Related Programming Language Features, Samuel W. Flint

School of Computing: Dissertations, Theses, and Student Research

Optional type annotations are a popular feature of programming languages that allow developers to omit explicit type information in code while, in some cases, retaining many of the benefits of static typing, such as in-code documentation, improved detection of type errors, or enforcement of code properties. However, how developers use and understand optional type annotations is not clear. The focus of this dissertation is to understand the use and comprehension of optional type annotations.

Optional type annotations are examined through four lenses: first, by examining the evolution of usage in a statically typed programming language (Kotlin, the default language for …

Preservation Of The Bernstein Property For Sums Of Independent Random Variables, Iosif Pinelis

Michigan Tech Publications

It is shown that Bernstein-type conditions on independent random variables are preserved by their sum. Some optimality properties of such preservation are proved.

Dissipation Physics And Absorption Features In Black Hole X-Ray Binaries, Theodore Dezen Phd, Benjamin R. Cavallari

McNair Summer Research Program

As matter falls closer toward the center of the accretion disk it loses gravitational potential energy, part of which becomes radiation primarily in X-ray wavelengths. Accretion disk models often invoked to fit observed spectra predict relativistically smeared absorption features that are not present in data from black hole X-ray systems such as GX 339-4 and LMC-X3. Informed by recent local and global simulations, we conduct new disk structure and radiative transfer calculations with increased dissipation rates of gravitational potential energy into thermal energy in disk upper layers. We find a noticeable reduction of the absorption features compared to older models …

Ed-Filter: Dynamic Feature Filtering For Eating Disorder Classification, Mehdi Naseriparsa, Suku Sukunesan, Zhen Cai, Osama Alfarraj, Amr Tolba, Saba Fathi Rabooki, Feng Xia

Research outputs 2022 to 2026

Eating disorders (ED) are critical psychiatric problems that have alarmed the mental health community. Mental health professionals are increasingly recognizing the utility of data derived from social media platforms such as Twitter. However, high dimensionality and extensive feature sets of Twitter data present remarkable challenges for ED classification. To overcome these hurdles, we introduce a novel method, an informed branch and bound search technique known as ED-Filter. This strategy significantly improves the drawbacks of conventional feature selection algorithms such as filters and wrappers. ED-Filter iteratively identifies an optimal set of promising features that maximize the eating disorder classification accuracy. In …

Enhancing Gaas Solar Cell Efficiency Through Nanostructured Features: A Comprehensive Review Of Recent Advances, Challenges And Future Outlook, Mohammad Nur-E-Alam, Boon Kar Yap, Tiong Sieh Kiong, Mohammad Khairul Basher, Tarek Abedin, Mohammad Aminul Islam, Mohd Adib Ibrahim, Mayeen Uddin Khandaker, Narottam Das

Research outputs 2022 to 2026

This article aims to provide a synopsis of recent advances in the use of nanostructured features to augment the conversion efficiency of gallium arsenide (GaAs) solar cells. GaAs are a very well-known and highly advantageous material for solar cells because they possess an extraordinary bandgap (i.e., approximately 1.4 eV), wide spectral absorption coefficient, and favorable carrier mobility. Numerous studies have explored the use of nanostructures, such as antireflection (AR) nanofilms/nanocoatings, nanoparticles (NPs), and nanogratings (NG), to improve the conversion efficiency of GaAs-based solar cells. These structures are generally designed to enhance light transmission capacity and absorption while reducing light reflection …

Urbanisation And Specifically Impervious Cover Alter Riparian Plant Communities In A Rapidly Urbanising Landscape In The Himalayas, Karma Jamtsho, Mark A. Lund, David Blake, Eddie Van Etten

Research outputs 2022 to 2026

Impervious covers, such as roads, pavements, buildings, and parking lots, prevent water infiltration, thereby increasing surface runoff. The expansion of impervious cover along riverbanks in urban areas poses a significant threat to riparian ecosystems by altering species diversity and composition through hydrological changes. As dynamic ecosystems at the interface between aquatic and terrestrial environments, riparian areas play a crucial role in aquatic ecology, particularly in terms of biodiversity, bank stability, nutrient dynamics, and hydrological processes. Employing plot-based floristic sampling, this study investigated the effects of urbanisation, particularly impervious cover (quantified as PTIA, Percentage of Total Impervious Area), on riparian plant …

Incorporating Ecosystem Service Assessments Into Development Planning − Impact From A Dredging Project In South Australia On Seagrass, Sam Gaylard, Rachel Colella, Matt Nelson, Paul Lavery, Michelle Waycott

Research outputs 2022 to 2026

Major infrastructure development is required for economic development and to improve human well-being, however conflict exists between developers and the community. Environmental impact assessment (EIA) is used in over 100 countries to evaluate potential impacts of major developments across environment, economy, and social benchmarks. However, EIA has been criticized for a lack of transparency and accountability, lack of consultation or participation and inadequate science. An ecosystem service assessment (ESA) recognizes the links between the environment and the socio-economic environment, resulting in a more holistic evaluation of potential impacts and effective community consultation. Despite this, its inclusion within EIA's is rare. …

Onair: Applications Of The Nasa On-Board Artificial Intelligence Research Platform, Evana Gizzi, Conner Firth, Caleb Adams, James Berck, P. Timothy Chase Jr., Christian Cassamajor-Paul, Rachael Chertok, Lily Cloug, Jonathan Davis, Melissa De La Cruz, Matthew Dosberg, Alan Gibson, Jonathan Hammer, Ibrahim Haroon, Michael A. Johnson, Brian Kempa, James Marshall, Patrick Maynard, Brett Mckinney, Leyton Mckinney, Michael Monaghan, Robin Onsay, Hayley Owens, Sam Pedrotty, Daniel Rogers, Mahmooda Sultana, Jivko Sinapov, Bethany Theiling, Aaron Woodard, Caroline Zouloumian

Computer Science: Student Work

Infusing artificial intelligence algorithms into production aerospace systems can
be challenging due to costs, timelines, and a risk-averse industry. We introduce
the Onboard Artificial Intelligence Research (OnAIR) platform, an open-source
software pipeline and cognitive architecture tool that enables full life cycle AI
research for on-board intelligent systems. We begin with a description and user
walk-through of the OnAIR tool. Next, we describe four use cases of OnAIR for
both research and deployed onboard applications, detailing their use of OnAIR
and the benefits it provided to the development and function of each respective scenario. Lastly, we describe two upcoming planned deployments …

Investigating Global Lightning Observed From Ground And Space, And Its Relationship To Solar Activity, Megan Diane Mark

Theses and Dissertations

The extremes of lightning, specifically lightning with long-lasting continuing currents and lightning with extremely high peak currents, are investigated from both ground- and space-based observations. Additionally, the potential solar influence on lightning is investigated on large spatial scales.

Continuing currents may occur following the impulsive flow of current during a cloud-to-ground (CG) return stroke and are usually low amplitude (from a few amperes to a few kiloamperes) and long duration (several to hundreds of milliseconds). Remotely estimating their duration from the electromagnetic fields measured by existing ground-based lightning locating systems (LLSs) is not possible, but some space-based lightning detection systems …

Dreamanime: Learning Style-Identity Textual Disentanglement For Anime And Beyond, Chenshu Xu, Yangyang Xu, Huaidong Zhang, Xuemiao Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Text-to-image generation models have significantly broadened the horizons of creative expression through the power of natural language. However, navigating these models to generate unique concepts, alter their appearance, or reimagine them in unfamiliar roles presents an intricate challenge. For instance, how can we exploit language-guided models to transpose an anime character into a different art style, or envision a beloved character in a radically different setting or role? This paper unveils a novel approach named DreamAnime, designed to provide this level of creative freedom. Using a minimal set of 2-3 images of a user-specified concept such as an anime character …

Exploring Rehabilitation Therapists' Knowledge And Perspectives On The Use Of Artificial Intelligence And Machine Learning For Persons Poststroke, Hannah Clark, Mia Delvecchio, Min Hun Lee, Elena D. Brown, Kaia Mikula, Robert Halyama, Kasey Stepansky

Research Collection School Of Computing and Information Systems

Research Objectives: The use of technology such as robotics, gaming systems, self-monitoring apps, or other sensor-based devices in standard practice is infrequent. Due to the rapid development of artificial intelligence (AI) and machine learning (ML) applications, it is important to look at how therapists perceive AI/ML, and design applications with potential barriers in mind. to support future integration into practice. The purpose of this research project is to gain rehabilitation therapists’ perspectives on AI/ML in post-stroke assessment and intervention.Design: This ongoing study uses a mixed methods design with surveys and focus groups. Participants engaged in a 30-minute webinar to learn …

Learning Frame-Level Classifiers For Video-Based Real-Time Assessment Of Stroke Rehabilitation Exercises From Weakly Annotated Datasets, Ana Rita Cóias, Min Hun Lee, Alexandre Bernardino, Asim Smailagic, Mariana Mateus, David Fernandes, Sofia Trapola

Research Collection School Of Computing and Information Systems

Autonomous rehabilitation support solutions, such as virtual coaches, should provide real-time feedback to improve motor function and maintain patient engagement. However, fully annotated dataset collection for real-time exercise assessment is time-consuming and costly, posing a barrier to evaluating proposed methods. In this work, we present a novel framework that learns a frame-level classifier using weakly annotated videos for real-time assessment of compensatory motions in stroke rehabilitation exercises by generating pseudo-labels at a frame level. We consider three approaches: 1) a baseline approach that uses a source dataset to train a frame-level classifier, 2) a transfer learning approach that uses target …

Quantizing Text-Attributed Graphs For Semantic-Structural Integration, Jianyuan Bo, Hao Wu, Yuan Fang

Research Collection School Of Computing and Information Systems

Text-attributed graphs (TAGs) have emerged as a powerful representation for modeling complex relationships across diverse domains. With the rise of large language models (LLMs), there is growing interest in leveraging their capabilities for graph learning. However, current approaches face significant challenges in embedding structural information into LLM-compatible formats, requiring either computationally expensive alignment mechanisms or manual graph verbalization techniques that often lose critical structural details. Moreover, these methods typically require labeled data from source domains for effective transfer learning, significantly constraining their adaptability. We propose STAG, a novel self-supervised framework that directly quantizes graph structural information into discrete tokens using …

Akma+: Security And Privacy-Enhanced And Standard-Compatible Akma For 5g Communication, Guomin Yang, Guomin Yang, Yingjiu Li, Minming Huang, Zilin Shen, Imtiaz Karim, Ralf Sasse, David Basin, Elisa Bertino, Jian Weng, Hwee Hwa Pang, Deng, Robert H.

Research Collection School Of Computing and Information Systems

The Authentication and Key Management for Applications (AKMA) protocol is a fundamental building block for security and privacy of 5G cellular networks. Therefore, it is critical that the protocol is free of vulnerabilities that can be exploited by attackers. Unfortunately, based on a detailed analysis of AKMA, we show that AKMA has several vulnerabilities that may lead to security and privacy breaches.We define AKMA+, an enhanced protocol for 5G communication that protects against security and privacy breaches while maintaining compatibility with existing standards. AKMA+ includes countermeasures for protecting communication between the user equipment (UE) and application functions (AFs) from attackers, …

Solving Two-Stage Stochastic Integer Programs Via Representation Learning, Yaoxin Wu, Zhiguang Cao, Wen Song, Yingqian Zhang

Research Collection School Of Computing and Information Systems

Solving stochastic integer programs (SIPs) is extremely intractable due to the high computational complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) for scenario representation learning. A graph convolutional network (GCN) based VAE embeds scenarios into a low-dimensional latent space, conditioned on the deterministic context of each instance. With the latent representations of stochastic scenarios, we perform two auxiliary tasks: objective prediction and scenario contrast, which predict scenario objective values and the similarities between them, respectively. These tasks further integrate objective information into the representations through gradient backpropagation. Experiments show that the learned scenario representations can …

Coleclip: Open-Domain Continual Learning Via Joint Task Prompt And Vocabulary Learning, Yukun Li, Guansong Pang, Wei Suo, Chenchen Chen, Yuling Xi, Lingqiao Liu, Hao Chen, Guoqiang Liang, Peng Wang

Research Collection School Of Computing and Information Systems

This article investigates the problem of continual learning (CL) of vision-language models (VLMs) in open domains, where models are required to perform continual updating and inference on a stream of datasets from diverse seen and unseen domains with novel classes. Such a capability is crucial for various applications in open environments, e.g., AI assistants, autonomous driving systems, and robotics. Current CL studies mostly focus on closed-set scenarios in a single domain with known classes. Large pretrained VLMs such as CLIP have showcased exceptional zero-shot recognition capabilities, and several recent studies have leveraged the unique characteristics of VLMs to mitigate catastrophic …

Optimal Transport Alignment Of User Preferences From Ratings And Texts, Nhu Thuat Tran, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Modeling hidden factors driving user preferences is crucial for recommendation yet challenging due to sparse rating data. While aligning preference factors from ratings and texts, as a solution, shows improvements, existing methods impose restrictive one-to-one factor correspondences and underutilize cross-modal interest signals. We propose an optimal transport (OT) approach to address these gaps. By modeling rating- and text-based preference factors as distributions, we compute an OT plan that captures their probabilistic relationships. This plan serves dual roles: 1) to regularize cross-modal preference factors without rigid correspondence assumptions, and 2) to blend preference signals across modalities through barycentric mapping. Experiments on …

Faithfulrag: Fact-Level Conflict Modeling For Context-Faithful Retrieval-Augmented Generation, Qinggang Zhang, Zhishang Xiang, Yilin Xiao, Le Wang, Junhui Li, Xinrun Wang, Jinsong Su

Research Collection School Of Computing and Information Systems

Large language models (LLMs) augmented with retrieval systems have demonstrated significant potential in handling knowledge-intensive tasks. However, these models often struggle with unfaithfulness issues, generating outputs that either ignore the retrieved context or inconsistently blend it with the LLM’s parametric knowledge. This issue is particularly severe in cases of knowledge conflict, where the retrieved context conflicts with the model’s parametric knowledge. While existing faithful RAG approaches enforce strict context adherence through well-designed prompts or modified decoding strategies, our analysis reveals a critical limitation: they achieve faithfulness by forcibly suppressing the model’s parametric knowledge, which undermines the model’s internal knowledge structure …

Debate, Reflect, And Distill: Multi-Agent Feedback With Tree-Structured Preference Optimization For Efficient Language Model Enhancement, Xiaofeng Zhou, Heyan Huang, Lizi Liao

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) continue to set new standards in knowledge-intensive and complex reasoning tasks, yet their high computational demands limit widespread adoption. While distilling large models into smaller ones offers a sustainable solution, current techniques—such as static knowledge distillation, resource-intensive reinforcement learning from human feedback, or limited self-reflection—struggle to yield substantial and lasting performance gains. In this paper, we present a novel Debate and Reflect (D&R) framework that orchestrates multi-turn debates between smaller models and stronger teacher models, eliciting actionable feedback (e.g., error analysis, corrective strategies) to guide student models. Further, we introduce Tree-structured Direct Preference Optimization (T-DPO) to …

R2dqg: A Quality Meets Diversity Framework For Question Generation Over Knowledge Bases, Yimeng Ren, Yanhua Yu, Lizi Liao, Yuhu Shang, Kangkang Lu, Mingliang Yan

Research Collection School Of Computing and Information Systems

The task of Knowledge-Based Question Generation (KBQG) involves generating natural language questions from structured knowledge sources, posing unique challenges in balancing linguistic diversity and semantic relevance. Existing models often focus on maximizing surface-level similarity to ground-truth questions, neglecting the need for diverse syntactic forms and leading to semantic drift during generation. To overcome these challenges, we propose Refine-Reinforced Diverse Question Generation (R2DQG), a two-phase framework leveraging a generation-then-refinement paradigm. The Generator first constructs a diverse set of expressive templates using dependency parse tree similarity, capturing a wide range of syntactic patterns and styles. These templates guide the creation of question …

Cooling Climate Across Last Interglacial High Stands On San Salvador And Great Inagua, The Bahamas, Ian Winkelstern, Sierra Petersen, H. Allen Curran, Cecilie Phillips, Alex Quizon, Bosiljka Glumac, David Griffing

Geosciences: Faculty Publications

The last interglacial (LIG) is the last time global climate was about as warm as today, with global sea-levels several metres higher. The LIG probably had a re- duced latitudinal temperature gradient, with warmer poles and cooler tropics than today. Well-constrained records from low latitudes can test this overall model. We used bivalve shells sampled from six localities thought to expose the LIG age Cockburn Town Member of the Grotto Beach Formation on both San Salvador and Great Inagua Islands, The Bahamas. Previous work described two LIG depositional intervals: older ‘Reef I’ and younger ‘Reef II’, separated by a disconformity. …

Detection Of Activity Cliffs Produced By Anti-Cancer Drugs And An Algorithm For Reliable Predictions In Affected Areas, Sarah Josephine Aurit

Department of Statistics: Dissertations, Theses, and Student Research

An activity cliff (AC) occurs when drugs close in chemical space produce dissimilar biological results. We focus on developing an inferential procedure to detect the presence of ACs in a chemical landscape. If detected, we provide a distance-based procedure that can be used to identify regions of stability in the chemical landscape of interest and generate prediction with higher precision in those areas of stability. We conceptualize the chemical landscape as a spatial random field and use spatial models for prediction of efficacy for new drugs based on “distance” in chemical space. We argue that an AC manifests itself by …