Physical Sciences and Mathematics Commons^™

Design And Evaluation Of A Code-Switching-Aware Multilingual Conversational Ai System Using Advanced Rag Architectures, Ashutosh Juvale

Master’s Dissertations

Conversational artificial intelligence has become the primary interface through which hundreds of millions of users in India seek information and customer support. Yet the way these users actually write and speak is fundamentally at odds with the monolingual assumptions baked into most retrieval and generation systems: they code-switch, fluidly mixing one or more of the twenty-two scheduled languages of India with English, frequently typing Indic words in the Roman script ("mera refund kab tak aayega"). Standard Retrieval-Augmented Generation (RAG) pipelines silently fail on such input — the retriever returns off-topic passages because the query and the knowledge base live in …

An Efficient Hierarchical Deployment Of Sensors For K-Coverage In Planner Wireless Sensor Network, Abhay Raj Singh

Master’s Dissertations

Ensuring reliable sensing coverage is a fundamental challenge in wireless sensor networks (WSNs), particularly when multiple sensors are monitoring each location to provide robustness against node failures. In this work, we address the problem of deterministic k-coverage in planar WSN by proposing a hierarchical triangular lattice-based deployment strategy that organizes sensor locations across di↵erent refinement levels and guarantees coverage of every point in the sensing domain by at least k sensors. Each lattice is three-colorable, and selective activation of color classes ensures adjustable coverage guarantees. We prove that activating a single color class at refinement level t guarantees at least …

Telluric Correction Of M-Dwarf Stars Using Machine Learning, Sayak Rana

Master’s Dissertations

The study of M-dwarf stars is of prime scientific interest to us because of their closer habitable zones and the favorable conditions they offer for exoplanet detection. However, telluric contamination of the ground-based spectra results in sharp absorption lines, which makes their study cumbersome. Removing this contamination is necessary for estimating key stellar parameters. The central contribution is a one-dimensional Convolutional Neural Network (CNN) that retrieves the four atmospheric parameters governing telluric absorption: pressure, temperature, humidity, and airmass. These predicted parameters are passed to Telfit which produces an estimated telluric spectrum. The observed spectrum is then divided by this estimated …

Efficiency Improvement Of Rag Based Slm For Edge Devices, Pavan Prashanth Avanigadda

Master’s Dissertations

The increasing need to deploy language models on constrained devices has given rise to efficiency issues in retrieval-augmented generation (RAG) approaches. Although RAGs boost answers’ quality by retrieving knowledge from external sources, current methods utilize static retrieval mechanisms, resulting in unnecessary computation, higher latencies, and inefficiency in resource usage. In this work, an efficient RAG approach based on small language models (SLMs) is presented, which uses a efficient and adaptive retrieval scheme. This method dynamically changes the retrieval depth and context constrution based on the complexity of the query, using a trained MLP router whose routing decisions are learned from …

Developing A Model To Generate More Digital Data Of Indian Languages For Multilingual Applications, Arya Bagde

Master’s Dissertations

Most of India’s scheduled languages remain critically under-served by language technology because parallel (translated) text — the raw material that modern multilingual systems depend on — is extremely scarce. Back-translation can synthesise such data automatically, but its quality varies enormously, and unfiltered synthetic data can be worse than no data at all. This dissertation develops a framework that generates synthetic parallel data for four low-resource Indian languages spanning three language families and four scripts — Assamese (Indo-Aryan, Bengali script), Bodo (Tibeto-Burman, Devanagari), Manipuri (Tibeto-Burman, Bengali script) and Santali (Austroasiatic, Ol Chiki)—and introduces CASCADE, a learned multi-signal quality gate that scores …

Dynamic Property Ordering For Efficient Multi-Property Bounded Model Checking, Vivek Kumar

Master’s Dissertations

Formal verification plays a critical role in ensuring the correctness of modern hardware designs. As the complexity of digital systems increases, designs are often associated with a large number of verification properties that must be analyzed within limited computational resources. In conventional multi-property bounded model checking (BMC), all properties are verified simultaneously. While this approach enables parallel analysis, difficult properties can consume a disproportionate amount of resources, causing simpler properties to be delayed and reducing the overall efficiency of bug detection. This thesis presents dynamic property ordering techniques for efficient multi-property verification using SAT-based bounded model checking in the ABC …

Multi-Frequency Associative Memory For Continual Graph Learning Through Nested Optimization, Shuvam Kundu

Master’s Dissertations

Graph Neural Networks struggle to learn new tasks without forgetting old ones a problem known as catastrophic forgetting. In graph domains, this is compounded by structural shift, where newly added edges corrupt the learned representations of historical nodes even when model weights remain unchanged. We present CAM-Titans, a continual graph learning framework built around a two-buffer associative memory to address both parametric and structural forgetting. Our architecture operates across three timescales of adaptation: a slow base memory updated via ordinary gradient descent, an intermediate task buffer re-encoded after every task using the delta-rule, and a transient in-context state for rapid …

A Study Of Prompt Tuning On Small Language Models(Slms): A Controlled Benchmark And A Lightweight Instance-Aware Method, Narkadamilli Sahith

Master’s Dissertations

Parameter-efficient fine-tuning (PEFT) adapts a frozen pre-trained language model by training only a small number of additional parameters. Among PEFT approaches, prompt tuning prepends trainable continuous vectors (soft prompts) to the input. A recurring finding in the literature is that prompt tuning is strongly scale dependent: it rivals full fine-tuning on very large models but lags on smaller ones. This dissertation studies prompt tuning specifically in the small-language-model (SLM) regime. We (i) re-implement a representative set of prompt-tuning methods—Prompt Tuning, P-Tuning v2, LoPT, DPT, DePT, ACCEPT, Residual Prompt Tuning, and PARA—within a single controlled harness, enabling a fair head-to-head comparison …

Reproducing And Analyzing The “Lost In The Middle” And “The Power Of Noise” Phenomenon In Retrieval-Augmented Generation, Kousik Samanta

Master’s Dissertations

Retrieval-Augmented Generation has become the way to improve Large Language Models. They help with problems like knowledge and hallucinations. Recent studies show that these models still have limitations. One big problem is the “Lost in the Middle” phenomenon. Models can’t access information in the middle of contexts properly. Another counterintuitive observation is the “Power of Noise” paradigm, which suggests adding unrelated documents can actually make the generation better. We know these happen in extractive QA tasks, but we don’t know if they happen in tasks that need complex reasoning. This dissertation looks into how position and noise affect Long-Form Question …

Learning To See Lesions, Not Skin Tone: Counterfactual Multimodal Learning For Fair, Trustworthy, And Text-Free Dermatology Ai, Shivam Jangid

Master’s Dissertations

Recent advances in deep learning have significantly improved the performance of automated skin lesion classification systems, enabling accurate detection of various dermatological conditions from medical images. Despite these achievements, concerns regarding fairness and generalization remain a major challenge for the deployment of such systems in real-world clinical settings. A key factor contributing to these challenges is the presence of bias in training datasets, particularly with respect to skin-tone representation. Most publicly available skin lesion datasets contain a disproportionate number of samples from individuals with lighter skin tones. As a result, deep learning models trained on these datasets often learn representations …

Adaptive Spectral Trust Gate For Physics- Constrained Operator Learning, Soham Chakraborty

Master’s Dissertations

Physics-informed machine learning improves the plausibility, data-efficiency and generalization of surrogate models by injecting prior physical knowledge into the learning process. The current approaches can be broadly divided into two main categories: soft constraints, which add a physics residual to the training loss but guarantee nothing at inference time, and hard constraints, which project the model output onto the constraint set exactly but apply the projection uniformly to every part of the signal — including parts that are dominated by noise, discretization error, or model mismatch, where the idealized physics is not actually trustworthy. This dissertation proposes the Adaptive Spectral …

Deep Reinforcement Learning With Directed Asymmetry And Kolmogorov-Arnold Networks For Dismantling Interdependent Multiplex Networks, Soumyajit Dev

Master’s Dissertations

Identifying the minimum-cost node-removal sequence that fragments a complex network - the network dismantling problem is NP-hard and central to infrastructure resilience. In interdependent multiplex networks, this difficulty is compounded by cascading cross-layer failures. While deep reinforcement learning (DRL) agents utilizing graph neural network (GNN) encoders achieve near-optimal dismantling, current state-of-the-art architectures suffer from two critical limitations. Topologically, existing agents strictly assume undirected edges, rendering them inapplicable to directed systems - such as supply chains or gene regulatory cascades - where failure propagation is fundamentally asymmetric. To resolve this, we propose Disassembling Directed Interdependent Networks (DDIN). DDIN introduces an asymmetric …

Adding 3,4-Methylenedioxy-Α-Cyclohexylaminopropiophenone To A Standard Operating Procedure (Sop) For Identifying Synthetic Cathinones Using Gas Chromatography-Mass Spectrometry, Napassawon Timpratoom

ETDs from 2020-2029

Synthetic cathinones were identified as new psychoactive substances (NPS) in the early warning advisory system by the United Nations Office on Drugs and Crime (UNODC). Synthetic cathinones have been commonly referred to as bath salts, which offered “legal highs” because they were not explicitly scheduled. These compounds were detected dur-ing the screening process in casework and required internal validation for confirmation. 3,4-methylenedioxy-α-cyclohexylaminopropiophenone was the target analyte for this study. It was alternatively named N-cyclohexyl methylone and “cyputylone." The aim of the study was to validate 3,4-methylenedioxy-α-cyclohexylaminopropiophenone into a Standard Operating Procedure (SOP) for identifying synthetic cathinones using gas chromatography/mass spectrometry …

Spectral Unmixing Using Machine Learning, Debashis Dhar

Master’s Dissertations

Spectral Unmixing is an important field of study nowadays which focuses on gener ating fractional abundance of each pixel into constituent materials .In this thesis we have tried to unmix each pixel into three end members namely glacial lake,debris and others with primarily focusing on glacial lake.We have performed various meth ods of linear spectral unmixing and non linear spectral unmixing. These methods are applied on the collected LandSat Data of east Himalayan terrain .Experimental results demonstrate the effectiveness of the proposed approach in achieving high accuracy and efficiency in glacier lake tracking on LandSat data.

A Switch-Point-Aware Contrastive Approach To Sentiment Analysis Of Hinglish Code-Mixed Text, Prasant Kumar Sahoo

Master’s Dissertations

With the increasing use of social media in non-English-speaking regions, especially in India, people often use Romanized Hindi and English together in their online communication. In a single sentence, they frequently mix Romanized Hindi and English, creating code-mixed text. However, most multilingual transformer models are pre-trained primarily on monolingual data. As a result, NLP systems face challenges when processing code-mixed text, as a single word may be fragmented into meaningless subword pieces, making it difficult for the model to capture its semantic meaning accurately. In this dissertation, we propose a parameter efficient neural architecture consisting of three main components to …

Tuning Ion Mobility And Molecular Confinement In High-Performance Polymer Electrolytes For Energy Storage, Ezzeldien Yousef Muhammed Yousef

Theses and Dissertations

This research addresses the critical energy density limitations of aqueous supercapacitors, which are traditionally constrained by the narrow electrochemical stability window (ESW) of water 1.23 V. By employing two distinct molecular engineering strategies, this study developed high-performance electrolyte systems that significantly extend voltage stability and thermal resilience.

The first system, CsBr@PAM/HA, utilizes a polyacrylamide and hyaluronic acid hydrogel matrix. This system exploits the chaotropic nature of Cs⁺ ions to disrupt the aqueous hydrogen-bonding network, enhancing ionic conductivity to 104 mS cm^-1. Through systematic salt screening, CsBr was identified as the optimal electrolyte, enabling a stable 2.0 V …

Extending Unibreak: Semantic Retrieval And Harmful-Intent Direction Suppression For Token-Level Llm Jailbreaking, Sanket Saha

Master’s Dissertations

Token-level adversarial perturbations remain one of the most efficient known attacks against the safety alignment of instruction-tuned large language models (LLMs). Among recent works, the UniBreak framework (You et al., 2026) stands out for unifying gradient-based optimization with an evolutionary perturbation repository. However, its repository relies solely on accumulated success frequency without utilizing query content, and its fitness function implicitly assumes that suppressing refusal tokens is sufficient to elicit harmful responses. In this dissertation, we extend UniBreak along both axes and re-evaluates the framework under stricter generalization and judgment protocols. Specifically, we introduce a semantic perturbation repository that replaces frequency-only …

Simultaneous Tumor Delineation And Report Generation From Brain Mr Images, Adish Mallik

Master’s Dissertations

Brain tumor analysis is an important application of medical image computing, where accurate segmentation and interpretation of tumor regions can support diagnosis and treatment planning. However, existing methods often address tumor segmentation and radiology report generation as separate tasks. Moreover, one of the major challenges in report generation tasks from MRI is accurate tumor localization. Most models fail to locate the lobe and hemisphere in which the tumor is located, causing incorrect report generation. In this regard, a unified 3D vision-language framework is proposed for simultaneous brain tumor segmentation and report generation from multi-modal MRI. Given T1, T2, T1C, and …

(Re)Claiming Homeplace: Dance As Embodied Resistance & Healing For Health & Spatial Justice, Preeya G. Kannan

University Honors Theses

This thesis argues that dance functions as both an upstream public health intervention and a form of radical spatial resistance; creating healing, agency, and belonging among historically marginalized communities. Drawing on bell hooks' concept of homeplace, Jasbir Puar's assemblage theory, Nancy Krieger's eco-social theory, and Katherine McKittrick's Black geographies, I position the body as both an ecological and political landscape shaped by histories of colonialism, racial capitalism, displacement, and resilience.

Through this community-based participatory research study, Homeplace, I explore dance as a form of social prescription that centers liberation rather than pathology. Movement is often reduced in public health discourse …

Engineering Nanoelectrocatalytic Systems For Co2 And Nitrate Conversion Into Value-Added Chemicals, Abdelrahman Mohamed Abdelmohsen

Theses and Dissertations

The thesis addresses two major related environmental crises: nitrate pollution of water systems and the ever-increasing levels of atmospheric carbon dioxide. Both challenges are closely linked to anthropogenic disturbance of nitrogen and carbon cycles and require sustainable, energy efficient mitigation techniques. In this study, we investigate electrochemical conversion routes as a unified approach to convert these pollutants into value-added compounds: ammonia (NH3) via nitrate reduction (NO3-RR) and ethylene (C2H4) via carbon dioxide reduction (CO2RR). The first half of this work deals with the design and engineering of Cu-Zn alloy electrocatalysts for efficient NO3-RR. Tuning the alloy composition and surface nanostructure …

Automatic Glossing In Under-Resourced Languages: Case Studies In Bribri And Cook Islands Māori, Carter D. Anderson

Linguistics Undergraduate Senior Theses

Interlinear glossing is a major task in Indigenous language documentation. In this paper, I explore how effectively two Large Language Models, ByT5 and Gemini 2.5 Flash, can produce interlinear glossed text. I also examine how prompting an LLM with different types of information (dictionary entries, other training samples, and translations) can augment model performance. I apply these models to two under-resourced Indigenous languages: Bribri, which is morphologically complex from Costa Rica, and Cook Islands Māori, which has a simpler morphology and is from the Cook Islands in the Pacific Ocean. ByT5 exhibits much better performance when glossing Cook Islands Māori …

Examining The Impact Of The Palisades And Eaton Fires On Air Quality In Los Angeles, Ryan Edward Glenn

Earth Sciences Undergraduate Senior Theses

Wildland-urban-interface (WUI) fires in the US are increasing in frequency and intensity with disproportionately large impacts on air quality and human health. In January 2025, the Palisades and Eaton wildfires swept across the Los Angeles Basin, burning residential areas and destroying vegetation. Despite their significance, WUI fires remain understudied compared to wildland fires, especially in regard to emissions composition. Here, I utilize a combination of field samples, modeling, and remotely-sensed datasets to survey the emissions profiles and variations during the 2025 Palisades and Eaton wildfires. I find that 75.1% of Los Angeles County was exposed to surface PM2.5 rated ‘unhealthy’ …

Assessing The Origin Of The Talus Deposit In King Ravine In The Presidential Range Of New Hampshire, Margaret M. Keating

Earth Sciences Undergraduate Senior Theses

The timing and mechanisms driving the retreat of the Laurentide Ice Sheet (LIS) after the Last Glacial Maximum (LGM; 26-19 ka) in the Presidential Range of the White Mountains, New Hampshire, remains inconsistently interpreted. King Ravine, located within the Presidential Range, contains a landform that preserves geomorphic evidence relevant to this debate. Here, I investigate the origin of this landform by assessing its age, surficial morphology, and internal structure. I present a topographic analysis of King Ravine, including a detailed new geomorphic map of the drainage basin and deposit. To constrain the timing of landform formation and activity, I determined …

Exploring Paleoproterozoic Marine Redox States In A Young Banded Iron Formation From The Needle Mountains, Jaya Sharma

Earth Sciences Undergraduate Senior Theses

Banded iron formations (BIF) are redox-dependent chemical sedimentary rocks that have served as proxies for marine and atmospheric oxygen and iron conditions throughout Earth's history. While abundant in the Archean and early Proterozoic, the widespread deposition of BIFs declined following the Great Oxidation Event, as oxygenated surface waters prevented the accumulation of ferrous iron. However, the Needle Mountains, located in southwestern Colorado, house an anomalously young BIF deposited between 1786±10 Ma - 1801±6 Ma given existing geochronology. Here, we use rare earth element (REE) systematics, bulk chemistry, and petrographic analysis to determine the depositional conditions under which this BIF was …

Carbon Dioxide And Methane Greenhouse Gas Fluxes In Coastal Salt Marshes Of Cape Cod, Massachusetts, Hannah C. Clark

Earth Sciences Undergraduate Senior Theses

Preserving “Blue Carbon”, or the carbon buried in suboxic or anoxic soil in coastal environments, is emerging as a potential natural climate solution (Fargione et al., 2018; Mcleod et al., 2011). Tidal wetlands act as both carbon sinks through uptake by vegetation and soil storage and serve as carbon sources via lateral transport of carbon through tidal flushing and emission of greenhouse gases (GHGs), including carbon dioxide (CO₂) and methane (CH₄) (Bansal et al., 2023). The C-NEWTS (“Carbon New England Wetland Time Series”) project, an NSF-funded collaboration between the USGS Woods Hole Coastal and Marine Science Center, Woods Hole Oceanographic …

Custom Sbc Gps Tracking And Geocaching Carputer Software Development And Implementation, Joshua A. Davis

University Honors Theses

This thesis argues that hardware-integrated capstone projects develop software engineering skills that traditional coursework cannot replicate. A team of eight developers built a GPS tracking system on a Raspberry Pi 4 over two academic terms, integrating real-time position streaming, the APRS amateur radio protocol for network-independent location sharing, and PostGIS spatial queries for "new road" detection. The system implements SmartBeaconing for adaptive GPS data reduction, achieving approximately 80% storage savings while preserving route fidelity. The project exposed challenges absent from classroom assignments: hardware debugging without stack traces, cross-layer integration failures, and coordination overhead when deploying to unfamiliar architecture; demonstrating that …

Beyond The Best Prompt: A Coverage View Of Multilingual Reasoning, Harshiv Mistry

University Honors Theses

Multilingual LLMs reason more accurately in English than in other languages, and recent work links part of this gap to reasoning behavior: native-language traces contain fewer cognitive behaviors (verification, backtracking, subgoal setting, backward chaining) that support effective problem solving. We test whether prompting for these behaviors at inference time narrows the gap, across seven conditions varying chain-of-thought, instruction and reasoning language, and cognitive-behavior descriptions, on two models, three languages. We find that English-scaffolded reasoning is the strongest single strategy on both models, closing the Hindi gap on Qwen, though the explicit scaffold's value over plain chain-of-thought is model-dependent. Beyond aggregate …

Effects Of Changing Fire Regimes On Black Huckleberry Habitat Suitability, Emily Labarge

University Honors Theses

Black huckleberries are a significant cultural, ecological, and economic species in the Pacific Northwest. Historically, black huckleberries were tended with cultural fire by Indigenous peoples, and now also hold importance as a resource in the non-timber forest product economy. Research has begun on the impacts of climate change on suitable black huckleberry habitat and on changing fire regimes, but not in relation to each other. To address this gap, I examined the area of overlap between suitable black huckleberry habitat and fire burn probability into mid-century climate projections. I determined the current ownership entities with management jurisdiction in areas with …

Chondrites From Northwestern Nevada: The Jungo Dense Collection Area, Nico Morrelli

University Honors Theses

Dense collection areas are regions where meteorites are more frequently found, and can help researchers study the meteorite flux to Earth. The Jungo dry lake bed is a dense collection area (DCA) in Humboldt County, Nevada and is currently the recovery location of eleven officially classified meteorites. Five of these samples were classified as part of this work. All five are ordinary chondrites, two of which are likely paired with previously classified Jungo samples. Jungo 021 is likely paired with Jungo 005, and Jungo 017 is likely part of the previously established pairing group of Jungo 002, 003, and 004. …

Effects Of Fine Sediment On The Productivity Of Stream Ecosystems In The Pacific Northwest, Veena I. Davis

University Honors Theses

Salmon are important to the culture, ecosystem and economy of the Pacific Northwest. However, populations have declined significantly since European colonization. Salmon require specific conditions for each life stage and are highly sensitive to environmental stressors. Excess fine sediment is a leading form of pollution as it degrades salmon spawning habitat and the benthos upon which their main food source, stream insects, live. To evaluate the effect of sediment pollution on food availability for salmon smolt, we conducted an experiment  estimating the amount of stream insect biomass and calories lost due to sediment pollution. We counted insects on substrate samples …