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Articles 181 - 210 of 69087

Full-Text Articles in Physical Sciences and Mathematics

Scaling Logical Reasoning On Modern Hpc Hardware, Yihao Sun Jun 2026

Scaling Logical Reasoning On Modern Hpc Hardware, Yihao Sun

Dissertations - ALL

Deductive logic reasoning has evolved from a theoretical symbolic artificial intelligence tool into a massive computational workload. Modern domains, ranging from static program analysis and binary reverse engineering to knowledge-graph reasoning, rely on Datalog, a logical query language, to express deeply recursive, declarative specifications. However, scaling logic reasoning to these industrial workload exposes two interlocking ceilings in traditional engines. Architecturally, the memory bandwidth and parallel throughput of single-node CPUs fall drastically short of the read- and write-heavy demands of semi-naive evaluation. Asymptotically, traditional query processing algorithms, such as binary join algorithms generate massive intermediate relations that exhaust device memory on …

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On The Extension Conjecture For Koszul Algebras, Benjamin Kaufman Jun 2026

On The Extension Conjecture For Koszul Algebras, Benjamin Kaufman

Dissertations - ALL

We investigate questions related to the extension conjecture for finite-dimensional algebras. Suppose $\Lambda = \kk Q/I$ is a finite-dimensional algebra given by a quiver with relations. The extension conjecture asserts that if $S$ is a simple right $\Lambda$-module corresponding to a vertex with a loop, that is, $\Ext_\Lambda^1(S,S)\neq 0$, then $\Ext^n_\Lambda(S,S)\neq 0$ for infinitely many $n$. We show that one can obtain information about the non-vanishing of the extensions $\Ext^n_\Lambda(S,S)$ by looking at the graded Cartan matrix and its inverse. We use this connection to prove the extension conjecture for standardly graded $\kk$-algebras on two vertices with Loewy length at …

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Using Low-Frequency Vibrational Spectroscopy To Develop Analytical Profiles For Polymorphic Organic Molecular Crystals, Salvatore Zarrella Jun 2026

Using Low-Frequency Vibrational Spectroscopy To Develop Analytical Profiles For Polymorphic Organic Molecular Crystals, Salvatore Zarrella

Dissertations - ALL

Low-frequency vibrational spectroscopy provides a sensitive approach for characterizing polymorphism and intermolecular dynamics in organic molecular crystals. Terahertz time-domain spectroscopy (THz-TDS) and low-frequency Raman spectroscopy probe collective lattice vibrations arising from weak intermolecular interactions, offering direct insight into crystal packing, phase stability, and structural organization that is often inaccessible to conventional diffraction and mid-infrared techniques. To interpret these modes, experimental measurements are combined with periodic solid-state density functional theory (ss-DFT), which enables vibrational assignments to specific intermolecular motions, and crystal structure prediction (CSP), which maps accessible polymorphic energy landscapes. Together, these methods form a unified experimental–computational framework for understanding molecular …

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From Detection To Segmentation: A Foundation Model Approach To Organoid Brightfield Image Analysis Using Sam 3, Hiren Manani Jun 2026

From Detection To Segmentation: A Foundation Model Approach To Organoid Brightfield Image Analysis Using Sam 3, Hiren Manani

Theses - ALL

This thesis presents the first systematic application of SAM 3, a unified foundation model for promptable segmentation, to mouse small intestinal organoid brightfield microscopy image analysis. The work spans the complete pipeline from zero-shot baseline evaluation through domain-specific fine-tuning on a GPU cluster, and documents the full engineering process required to adapt a state-of-the-art foundation model to a novel biomedical imaging domain. A comprehensive literature review of over 20 papers spanning detection-based, classical segmentation, foundation model, and morphological analysis approaches identified a clear research gap that this thesis addresses. Five critical compatibility patches were developed to deploy SAM 3 on …

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Constrained Neural Intelligence: Investigations Into Deep Multi-Objective Optimization, Naveed Tahir Jun 2026

Constrained Neural Intelligence: Investigations Into Deep Multi-Objective Optimization, Naveed Tahir

Dissertations - ALL

Neural intelligence is identified with complex learning problems optimized over the high-dimensional, non-convex parameter spaces of deep neural networks. Solving such problems generally requires handling competing objectives and conflicting constraints. This is traditionally dealt with using heuristic methods that collapse such complexity into unconstrained, singular objectives. While computationally convenient, this invites a tradeoff against the precision and stability afforded by non-heuristic, geometry-aware approaches. This dissertation explores constrained multi-objective learning in various applied and theoretical contexts and highlights the feasibility of approximate methods as well as the necessity of exact methods. We propose a framework for equality-constrained deep learning via approximate …

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Thermal Aberrations And Optical Design For Current And Future Gravitational Wave Detectors, Matthew Richard Todd Jun 2026

Thermal Aberrations And Optical Design For Current And Future Gravitational Wave Detectors, Matthew Richard Todd

Dissertations - ALL

Since the first detection of gravitational waves in 2015, the LIGO-Virgo-KAGRA collabo- ration has detected over 300 gravitational-wave events. Planned upgrades to each detector will further improve gravitational wave sensitivity limits, pushing the frontier of multi- messenger astronomy and paving the way for next-generation detectors, like Cosmic Ex- plorer and Einstein Telescope. Though many challenges to reach sensitivity goals are being addressed, there are still cru- cial aspects of the detectors that are poorly understood and will inhibit gravitational wave detection commissioners from bringing future detectors to their design sensitivity. In par- ticular, increased circulating arm power targets will exacerbate …

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Peptide And Peptide Bioconjugate Based Drug Development For The Treatment Of Metabolic Diseases, Nancy Cham Jun 2026

Peptide And Peptide Bioconjugate Based Drug Development For The Treatment Of Metabolic Diseases, Nancy Cham

Dissertations - ALL

The Doyle group has coined the term ‘corrination’ to describe the conjugate modification of a peptide, protein, small molecule, or radionuclide with a corrin ring-containing molecule such as cobalamin, also known as vitamin B12 (B12), or cobinamides. By exploiting the innate chemico-physical properties of corrin ring-containing compounds, both in general and specifically via the innate dietary B12 uptake pathway in mammals, corrination has been explored for drug development and targeted/localized delivery of probes and therapeutics. Most recently, it is in the field of peptide-based therapeutics that corrination is generating significant interest, no doubt driven in part by the recent successes …

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Image Restoration Regularized By Structured Sparsity Promoting Functions: Theory, Algorithms, And Applications, Jianchen Wei Jun 2026

Image Restoration Regularized By Structured Sparsity Promoting Functions: Theory, Algorithms, And Applications, Jianchen Wei

Dissertations - ALL

Image restoration aims to recover an unknown clean image from observations degraded by blur, subsampling, missing pixels, or noise. This dissertation develops and analyzes variational image restoration models based on structured sparsity promoting functions (SSPFs) and their Moreau-enveloped regularization. The main objective is to design nonconvex regularization models that promote structured sparsity in transformed image representations while remaining analytically tractable and computationally effective. The first part of the dissertation studies an SSPF regularized image restoration model. Under the identifiability condition $\ker(A)\cap \ker(B)=\{\bm0\}$, where $A$ is a linear forward degradation operator and $B$ is a transform operator used to extract image …

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An Analysis Of Heat Spread Through Smoldering Pine Needles, Timothy Keith Jun 2026

An Analysis Of Heat Spread Through Smoldering Pine Needles, Timothy Keith

Theses and Dissertations

The goal of this thesis is to calculate the average speed of flame spread through smoldering pine needles. We present two different models for doing this: the first is a very large system of ODEs meant to represent the spread of flame through individual needles, the second is a macroscopic model based on the porous medium PDE. Simulations are run for each of these models and compared to each other as well as to existing models and experimental results.

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A Mathematical Model Of The Interactions Between A Collagen Lattice, Fibroblasts, And Fixed Surfaces With Varying Topographies, Mary Jenkins Jun 2026

A Mathematical Model Of The Interactions Between A Collagen Lattice, Fibroblasts, And Fixed Surfaces With Varying Topographies, Mary Jenkins

Theses and Dissertations

Collagen is an important structural protein in the body, which plays a role in wound healing, particularly the contraction process. Collagen lattices have been studied for nearly 50 years to provide insight into wound contraction. In some cases, collagen interacts with surfaces that have fixed shapes, such as medical implants. Our model focuses on the interactions of a collagen lattice with such a fixed surface. We mathematically model a collagen lattice as a network of nodes connected by springs. We also model fixed surfaces that have various topographies. Our model includes fibroblast cells that connect to both surfaces and remodel …

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Non-Euclidean Geometries And Fairness Constraints In Advanced Clustering, Arnab Seal Jun 2026

Non-Euclidean Geometries And Fairness Constraints In Advanced Clustering, Arnab Seal

Master’s Dissertations

A fundamental challenge in modern unsupervised learning is adapting classical clustering algorithms to handle complex, real-world data constraints. Traditional models often assume data resides in a flat, Euclidean space and optimize strictly for cluster cohesion, thereby failing to capture intrinsic hierarchical structures and ignoring sociotechnical demographic biases. This thesis addresses these critical limitations by extending generalized mean-shift dynamics into two novel clustering frameworks. First, to natively accommodate data with tree-like structures (e.g., taxonomies and social networks), we propose Hyperbolic Gaussian Blurring Mean Shift (HypeGBMS). By projecting data into the Poincar´e ball model and utilizing M¨obius vector space operations, HypeGBMS successfully …

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Leveraging Spatial Statistics For Domain Adaptation Of Vision Language Models In Medical Vqa, Himanshu Raj Jun 2026

Leveraging Spatial Statistics For Domain Adaptation Of Vision Language Models In Medical Vqa, Himanshu Raj

Master’s Dissertations

Recent advances in Vision–Language Models (VLMs) have demonstrated strong performance in Medical Visual Question Answering (Medical VQA) task. Although they perform very well within their domains, these models often experience issues with their generalization ability on unknown clinical distribution data because of different imaging technologies and patient groups used in various medical facilities. Generalization problems faced by these models make their practical application in the field of VLM-based medical VQA systems rather difficult. To overcome this limitation we proposed our method named Spatial Semantics Aware Domain Adaptation (SSADA), which is an integrated framework that combines both finetuning and prompt-based in-context …

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Predictive Importance Sampling Based Coverage Verification For Multi Uav Trajectory Planning, Snehashish Ghosh Jun 2026

Predictive Importance Sampling Based Coverage Verification For Multi Uav Trajectory Planning, Snehashish Ghosh

Master’s Dissertations

In next-generation wireless networks, unmanned aerial vehicle (UAV) networks are emerging as a promising solution for ultra-reliable low-latency communication (URLLC). A key challenge in millimeter-wave UAV networks is ensuring that mobile users are always in line-of-sight (LoS) coverage, since the current snapshot-based trajectory planning approach does not consider the mobility of the users during the decision interval, resulting in disastrous LoS gaps. For continuous coverage verification, standard uniform sampling is too computationally expensive, as it would need a large number of samples to estimate rare failure events that have latencies that are not suitable for real-time requirements. In this work, …

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Learning Trajectories Of Online Batch Selection Methods, Luke Green Jun 2026

Learning Trajectories Of Online Batch Selection Methods, Luke Green

Theses and Dissertations

Modern deep neural networks achieve strong performance on large-scale datasets, but often require substantial training time. Online batch selection methods seek to reduce this cost by updating models on informative subsets of each batch rather than on all available examples. Recently introduced methods leverage teacher models and report substantial speedups, particularly in noisy-label settings. However, comparisons are often based on the number of epochs required to reach a target test accuracy, a coarse metric that is sensitive to implementation details and may obscure important differences in learning dynamics. In this thesis, we implement several online batch selection methods in a …

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Advancing Data Usability, Activity Modeling, And Stability Optimization In Computational Enzyme Design, Spencer Gardiner Jun 2026

Advancing Data Usability, Activity Modeling, And Stability Optimization In Computational Enzyme Design, Spencer Gardiner

Theses and Dissertations

A grand challenge of computational biology is to computationally design, in a single pass, a protein sequence that catalyzes an arbitrary chemical reaction at a high rate under specified conditions [1]. This work details advances in three essential areas on the path to that goal: data quality, activity prediction and modeling, and stability optimization. The structure and implementation of the Allotrope Simple Model (a FAIR data format for many scientific instruments) was examined in [2], setting the stage for training deep learning models on high-quality experimental datasets from diverse sources. In [3], the limits of physics-based and deep learning tools …

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Beyond Single Images: A Comprehensive Benchmark For Album-Level Vision-Language Understanding, Shawn Huang Jun 2026

Beyond Single Images: A Comprehensive Benchmark For Album-Level Vision-Language Understanding, Shawn Huang

Theses and Dissertations

Automatic album organization has been studied extensively over the past decades due to significant progress in digital photography. Recent Vision-Language Models (VLMs) have shown strong performance on multi-image understanding, making them natural candidates for automating album organization workflows. While VLMs’ abilities in multi-image understanding have been widely studied, their performance on album organization remains underexplored. To bridge this gap, we introduce AlbumBench, the first comprehensive benchmark for automatic album organization. Specifically, we (1) define album organization tasks as photo selection for album-specific user objectives, photo rating according to how well user intents are fulfilled, and album-specific photo grouping given a …

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Galaxy Morphology Classification Using Deep Learning, Dipanwita Kundu Roy Jun 2026

Galaxy Morphology Classification Using Deep Learning, Dipanwita Kundu Roy

Master’s Dissertations

Galaxy morphology is the study of the shape and visual appearance of galaxies, such as spiral, smooth, edge-on, and other morphological types. Morphological classification plays an important role in understanding how galaxies form and evolve over cosmic time. Most existing machine learning approaches for galaxy morphology classification rely solely on RGB galaxy images, which primarily capture spatial information and lack the physical spectral context of galaxies. In contrast, astronomical spectral datacubes contain rich information across multiple wavelengths, providing insights into the internal and physical properties of galaxies. However, such spectral observations are available for only a limited number of objects. …

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Relay Selection And User Scheduling In Reconfigurable Intelligent Surface Assisted Millimeter-Wave D2d Communication, Lakshmikanta Sau Jun 2026

Relay Selection And User Scheduling In Reconfigurable Intelligent Surface Assisted Millimeter-Wave D2d Communication, Lakshmikanta Sau

Doctoral Theses

Reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) device to device (D2D) communication has recently been proposed as a viable solution to support the overwhelming data traffic in fifth-generation (5G) and beyond wireless networks. However, due to the substantial propagation and penetration losses of mmWave, a direct line of sight (LoS) link between a pair of proximity devices is required for effective communication. Static obstacles like trees and buildings can easily obstruct the direct LoS connectivity between a device pair. In such cases, RISs help to establish an indirect LoS link between an obstructed device pair by reflecting the signals …

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Vibrations Of Tapered Beam Via The Exterior Matrix Method, Simranjit Kaur Jun 2026

Vibrations Of Tapered Beam Via The Exterior Matrix Method, Simranjit Kaur

Student Theses and Dissertations

Cell phone towers, utility poles, and traffic signal poles all use hollow, tapered beams as their main structural element. Because these structures are tall and exposed to wind forces, understanding their vibration behavior is important for ensuring stability and safety. We will use the Exterior Matrix Method to analyse a single beam, which can be used to analyse compound structures involving tapered beams. First, we find the system of four equations satisfied by the tapered beam, which can be converted to a 4 x 4 matrix. Then we find the exterior matrix, which is a 6 x 6 matrix, corresponding …

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The Ecotoxicological Implications Of Switching From Fluorescent To Light Emitting Diode Lighting For Zooplankton Culturing And Whole Effluent Toxicity Testing, Orithea Z. Regn Jun 2026

The Ecotoxicological Implications Of Switching From Fluorescent To Light Emitting Diode Lighting For Zooplankton Culturing And Whole Effluent Toxicity Testing, Orithea Z. Regn

Student Theses and Dissertations

In the global transition toward light emitting diode (LED) lighting, an understudied area is the impact on the culturing and testing of Whole Effluent Toxicity (WET) testing organisms, more specifically, the zooplankton species. Without comparison to the current lighting used for culturing and testing, it is unknown if there will be an impact on performance, which could affect effluent, regulatory, and product safety decisions. This dissertation investigated if culturing and reference toxicity testing using sodium chloride for Ceriodaphnia dubia, Daphnia magna, and D. pulex under LED lighting was comparable to fluorescent. Comparisons were made between two laboratories and by season, …

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American Sign Language Recognition And Analysis Using Deep Learning, Saurabh Kumar Soni Jun 2026

American Sign Language Recognition And Analysis Using Deep Learning, Saurabh Kumar Soni

Master’s Dissertations

In this work I build a system that recognizes isolated American Sign Language (ASL) words, and I use it to ask one fairly direct question: when training data is scarce, is it better to look at the video pixels or at the geometry of the signer’s body? To find out, I train two very different models on exactly the same clips. The first is appearance-based. Every frame is run through standard preprocessing and a ResNet50 backbone pre-trained on ImageNet, which turns it into a 2048-dimensional feature vector, and a Bidirectional LSTM then reads that sequence over time. The second model …

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Enhanced Embedding For Multimodal Medical Visual Question And Answering, Akash Suna Jun 2026

Enhanced Embedding For Multimodal Medical Visual Question And Answering, Akash Suna

Master’s Dissertations

Visual Answering of questions in the field of Medical which is called as (VqA) has grown as a dominant area of research that fuse processing of natural language and vision of computer often known as CV or NLP to assist in medical decision-making. However, effective multimodal fusion between medical images and clinical questions remains a significant challenge. This thesis examines the application of the Perceiver IO architecture as an efficient multimodal aggregator for medical VQA. The work has been carried out in multiple directions. First, a classification-based framework is developed by combining Vision Transformer (ViT) and ClinicalBERT alongside a Perceiver …

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An Empirical Study Of Rlvr Fine-Tuning For Mathematical Problem Solving In Llms, Rashmi Konnur Jun 2026

An Empirical Study Of Rlvr Fine-Tuning For Mathematical Problem Solving In Llms, Rashmi Konnur

Master’s Dissertations

Large language models have shown immense improvement in coding and math performances thanks to reinforcement learning boosted algorithms. However, its true impact on broadening the reasoning and analytical capacities of an LLM is still contended. In this dissertation, we outline the foundations of Large Language Models, and delve into Reinforcement Learning with Verifiable Rewards (RLVR). We discuss various strategies to efficiently manipulate memory during a fine tuning update. We finally perform RLVR fine-tuning techniques on different models with varied use cases and compare their performances, which corroborate the efficiency of RLVR.

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A Modular Saponin Adjuvant Platform: Development, Structure–Activity Relationships, And Applications, Di Bai Jun 2026

A Modular Saponin Adjuvant Platform: Development, Structure–Activity Relationships, And Applications, Di Bai

ETDs from 2020-2029

ABSTRACT Saponin-based vaccine adjuvants have attracted considerable attention owing to the clinical success of QS-21, a potent immunostimulant capable of inducing balanced humoral and cellular immune responses, yet its widespread application is limited by supply constraints, chemical instability, and dose-dependent toxicity. To address these challenges, we previously developed two semisynthetic saponin adjuvants, VSA-1 and VSA-2, derived from abundant Momordica saponins through a single-step modification, which exhibit QS-21–comparable immunopotentiating activity with improved accessibility and stability. Here, we first performed a systematic structure–activity relationship (SAR) study of the VSA series using MS I as the lead scaffold. Modulation of the side-chain length …

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Investigation Of Air Emissions With A Local Composites Industry, Chris White Jun 2026

Investigation Of Air Emissions With A Local Composites Industry, Chris White

ETDs from 2020-2029

Workers at an office space raised complaints about a sweet-smelling odor directly outside their building. After an initial assessment of the surrounding businesses and their processes, it was determined that the workers were most likely exposed to styrene emissions from a nearby composites company. This investigation utilizes multiple weeks of direct readings with a photoionization device and active environmental sampling with a charcoal tube and pump to confirm the styrene exposure hypothesis. Weather trends were also recorded to determine correlation between weather and exposure to the sweet-smelling chemical. The direct readings found an exposure maximum of less than 2 ppm …

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Statistical Methods For Mendelian Randomization Under Nonlinearity And Non-Normality, Mary Appah Jun 2026

Statistical Methods For Mendelian Randomization Under Nonlinearity And Non-Normality, Mary Appah

ETDs from 2020-2029

Instrumental variable (IV) methods are widely used for estimating causal effects in observational studies where unmeasured confounding may bias traditional regression estimates. The core idea is to use a variable referred to as an instrument that is associated with the exposure of interest, independent of unmeasured confounders, and influences the outcome only through the exposure. While originally developed in econometrics, IV methods have been increasingly adopted in epidemiology and genetic research under the framework of Mendelian Randomization (MR), where genetic variants, most commonly single nucleotide polymorphisms (SNPs), serve as instruments. MR provides a powerful tool for investigating causal relationships between …

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Efficient Multimodal Foundation Model Tuning For Hallucination Mitigation, Fei Zhao Jun 2026

Efficient Multimodal Foundation Model Tuning For Hallucination Mitigation, Fei Zhao

ETDs from 2020-2029

Over the past few years, multimodal foundation models have achieved remarkable progress in perception and understanding. However, two challenges limit their reliability: (1) dependence on offline training, which in most real-world settings requires large volumes of labeled data and, as a result, hinders the model’s ability to adapt to new data or domains; (2) weak cross-modal grounding, which often leads to hallucinated content generation, producing descriptions that are linguistically fluent but inconsistent with the input visual evidence. This dissertation frames hallucination mitigation as an outcome of transitioning from fixed learning (static, offline fine-tuning) to adaptive, feedback-driven lifelong learning. By incorporating …

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Learnable Structured Attention For Student-Aware Knowledge Distillation In Dense Prediction, Chen Liu Jun 2026

Learnable Structured Attention For Student-Aware Knowledge Distillation In Dense Prediction, Chen Liu

ETDs from 2020-2029

Dense prediction tasks, including object detection and semantic segmentation, require models to produce structured predictions and are widely used in real-world vision applications. Although deep networks have achieved strong performance on these tasks, their high computational cost limits deployment on resource-constrained systems such as autonomous-driving vehicles. Knowledge distillation (KD) addresses this issue by transferring knowledge from a large teacher model to a compact student model. However, existing distillation methods for dense prediction face greater challenges than those for classification due to the more complex task requirements. To overcome the challenges, this thesis presents a unified study of adaptive distillation for …

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Kernelizing Protein Interaction Languages: Spectral Approximations And Random Fourier Features, Aishik Ghosh Jun 2026

Kernelizing Protein Interaction Languages: Spectral Approximations And Random Fourier Features, Aishik Ghosh

Master’s Dissertations

Protein-peptide interactions play an important role in many biological phenomena, spanning adaptive immunity to disease pathology. In the Sliding Window Interaction Grammar (SWING) framework, interactions are represented as sequences of biochemical tokens embedded using Doc2Vec, allowing robust generalisation to unobserved MHC alleles. However, classification remains limited to a single Euclidean feature space that is incapable of resolving binding landscapes. This dissertation develops SWING for four distinct kernel types: Gaussian, Laplacian, anisotropic (ARD), and the Spectral Mixture (SM) kernel, each approximated using scalable Random Fourier Features. The SM kernel incorporates prior knowledge about secondary structure into its spectral density as biological …

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Learning In Infants Using Intrinsically Motivated Goal Conditioned Reinforcement Learning, T I Darsan Jun 2026

Learning In Infants Using Intrinsically Motivated Goal Conditioned Reinforcement Learning, T I Darsan

Master’s Dissertations

Traditional artificial intelligence models learn by passively digesting large datasets. In contrast, human infants discover skills by actively interacting with their bodies and environments without explicit external rewards. This thesis introduces the Composer Architecture, a machine learning framework designed to mimic this autonomous, open-ended development. The Composer architecture operates in a multi-stage loop, the latent model using Contrastive Learning Through Time (CLTT) to compress high-dimensional raw data from visual, proprioceptive, and touch sensors into a low-dimensional space. To preserve data relationships and prevent topological collapse, a Softmax activation forces these latent representations to lie smoothly on a probability simplex. A …

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