Digital Commons Network^™

Large Language Models And International Law, Ashley Deeks, Duncan Hollis

Chicago Journal of International Law

Large Language Models (LLMs) have the potential to transform public international lawyering in at least five ways: (i) helping to identify the contents of international law; (ii) interpreting existing international law; (iii) formulating and drafting proposals for new legal instruments or negotiating positions; (iv) assessing the international legality of specific acts; and (v) collating and distilling large datasets for international courts, tribunals, and treaty bodies.

This Article uses two case studies to show how LLMs may work in international legal practice. First, it uses LLMs to identify whether particular behavioral expectations rise to the level of customary international law. In …

Two Terribles: A Day Without Space And Ai Enabled Synthetic Biological Weapons A Warning Order, James E. Baker

Chicago Journal of International Law

A day without space, a term used to describe the loss or destruction of America’s space assets, and the potential generation of novel biological threats using AI and synthetic biology present catastrophic and potentially existential threats to U.S. security in a way that nuclear weapons did before and continue to do so today. Yet they have not received the level of attention from national security lawyers or commentators they warrant. This article describes the threats. It describes the current and inchoate nature of the law to address these threats. And it makes initial recommendations to policymakers and lawyers about how …

Ideal Query Expansion Using Reinforcement Learning, Madhuchchhanda Das

Master’s Dissertations

Information retrieval (IR) systems often struggle with short, ambiguous, or underspecified queries, leading to suboptimal document retrieval. Traditional query reformulation methods, such as those based on the Rocchio algorithm, rely on heuristic term selection and relevance feedback but typically apply fixed or manually tuned weights to expanded terms. This limits their adaptability and generalization across diverse query-document contexts. In this thesis, we propose a novel reinforcement learning (RL)-based framework to dynamically optimize term weighting in reformulated queries. We model the problem as a Markov Decision Process (MDP), where each state represents a query as a vector of term weights. An …

Glacier Velocity Estimation Using Adaptive Search Window And Patch Size, Pratik Patil

Master’s Dissertations

Synthetic Aperture Radar (SAR) technology offers a robust solution for monitoring glacier surface motion, particularly in regions with challenging environmental conditions, since it do not dependence on time of day and weather. This paper presents an enhanced glacier motion monitoring approach based on a Deep Matching Network (DMN), which learns patch-pair correspondences in an end-to-end manner. Unlike traditional shallow feature tracking methods, DMN utilize deep feature similarity through a Siamese network architecture with dense connection blocks to maximize feature reuse and improve training efficiency. To further improve precision and reduce computational cost, the proposed method uses a variable search window …

On The Deployment Of Ris-Mounted Uav Networks, Anupam Mondal

Master’s Dissertations

Reconfigurable intelligent surfaces (RIS) enable smart wireless environments by dynamically controlling signal propagation to enhance communication and localization. Unmanned aerial vehicles (UAVs) can act as flying base stations and thus, improve system performance by avoiding signal blockages. In this paper, we propose a gradient ascent and coordinate search based method to determine the optimal location for a system that consists of a UAV and a RIS, where the UAV serves cellular users (CUs) and the RIS serves device-to-device (D2D) pairs. In particular, by optimizing the net throughput for both the D2D pairs and the CUs, the suggested method establishes the …

Modeling And Verification Of Sigma Delta Neural Networks, Sirshendu Das

Master’s Dissertations

In the context of modern day embedded safety-critical systems and low-resource edge devices in particular, Sigma-Delta Neural Networks (SDNNs) offer a promising alternative to traditional Artificial Neural Networks (ANNs) by leveraging eventdriven, sparse computations inspired by biological neural processing. This energyefficient paradigm makes SDNNs well-suited for neuromorphic hardware and realtime applications, particularly in scenarios with temporal redundancy, such as video processing. However, as neural networks become integral to safety-critical systems, ensuring their robustness against adversarial perturbations is an absolute necessity. In this work, we propose an end-to-end framework for formal modeling and verification of SDNNs using Satisfiability Modulo Theory (SMT). …

Cutting Through The Infodemic Efficiently: News Claims Surveillance And Llm-Based Lightweight Fact Verification, Xuan Zhang

Dissertations and Theses Collection (Open Access)

In the context of the current infodemic, the rapid spread of misinformation poses a severe threat to social stability and public health. Recently, the rise of deep learning technologies has offered the potential for accelerating the development of automated misinformation detection and verification. However, current technological capabilities and computational resources often prove inadequate for the exhaustive scrutiny required, rendering the enhancement of processing efficiency a critical imperative. Given the vast amount of data on the internet, current technology and computational power often fall short in timely and accurate scrutiny of each piece of information, making the improvement of processing efficiency …

Resilience In Stem: How Gender-Professional Identity Integration (G-Pii) And Self-Compassion Empower Women To Thrive, Isabelle Chiew

Dissertations and Theses Collection (Open Access)

Fostering resilience is crucial for the retention of women in male-dominated fields such as STEM. This paper investigates the interplay between gender-professional identity integration (G-PII), resilience, and self-compassion through two complementary studies. It is hypothesised that G-PII positively predicts resilience, with higher levels of integration associated with greater resilience. Furthermore, self-compassion is expected to moderate this relationship. Study 1 employs a cross sectional survey design to explore the relationships between G-PII, self-compassion, and resilience, while Study 2 uses an experimental design to assess the causal impact of G-PII on resilience. Study 1 found that G-PII was significantly and positively associated …

Learning And Optimization Under Human-Centric Considerations, Qian Shao

Dissertations and Theses Collection (Open Access)

This dissertation investigates learning and optimization problems shaped by humancentric considerations, such as preferences, demonstrations, behavioral patterns, and resource constraints. As real-world decision-making increasingly involves interaction with human agents, data, and limitations, modeling these factors becomes critical for building practical, adaptive, and robust systems.

The research spans four domains. First, we study preference-aware delivery routing by learning implicit practitioner preferences and incorporating them into a hierarchical route optimization framework. Second, we develop imitation learning methods for cost-constrained settings, enabling agents to mimic expert behavior while respecting safety and resource limitations. Third,we explore early rumor detection in data-limited environments, integrating large …

Using Multiculturalism To Combat The Negative Creativity Effect Of Cultural Tightness: How It Is Done Matters, Jung Kiu Choi

Dissertations and Theses Collection (Open Access)

This study investigates the multifaceted influence of cultural tightness and multiculturalism on the global commercial success of pop music. Addressing an under-explored moderating effect of multicultural factors on creative output under tight culture, this research analyzes Billboard Global 200 and Spotify Top 50 chart data from 2013-2021. The empirical analysis reveals that cultural tightness negatively impacts pop song’s success. Conversely, multiculturalism, specifically through diverse languages and performance teams, generally enhances global success. A key finding is that while multicultural production teams do not directly correlate with success, they critically moderate the detrimental effects of cultural tightness, a buffering effect unique …

Carolyn Parker's Electronic Frequencies, Charnell Chasten Long

Faculty Publications

The chapter illustrates what it meant for Carolyn Beatrice Parker (1917–1966) to be a Black woman physicist in the US during the Jim Crow era. Her father, a physician, and her mother, a teacher, shepherded her into Fisk University, an historically Black college. As a physics major she studied infrared spectroscopy with the Black physicist Elmer Imes, graduating with a BS in 1938. She later attended the University of Michigan, obtaining an MA in physics in 1941. But like many Black women, she spent time before and after graduate school teaching in the K–12 system. In 1943, she became a …

Unraveling The Decline: A Phenomenological Narrative Exploration Of Chinese Students' Experiences And Decisions In Selecting U.S. Higher Education, Lorne K. Brandt

Theses and Dissertations from DePaul University

This study examined the declining trend of Chinese student enrollment in U.S. higher education by incorporating recent enrollment data and findings from the 2024 Open Doors Report. The researcher employed a phenomenological narrative inquiry approach and conducted semi-structured interviews to explore the lived experiences of Chinese international students through the lens of the push-pull theoretical framework. The analysis confirmed several well-established push-pull factors, including the influence of exchange programs and parental guidance. One counter theme emerged as most participants described a desire to avoid China’s high-stakes exam system. The study identified six additional themes: flexible admissions, desire for independence, merit-based …

Revised Draft Final Bpsou Unreclaimed Sites: Ur-05 Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Revised Draft Final Butte Priority Soils Operable Unit (Bpsou) Unreclaimed Sites: Ur-03 Site Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Deeppanorf: Deep Prior For Neural 3d Reconstruction From Sparse Panoramas, Edward Du

Master's Theses

Advances in neural field representations have led to a significant improvement in view synthesis quality. However, many current novel view synthesis methods rely on a dense set of input views, which can be impractical and inefficient in real-world applications. We propose DeepPanoRF, a novel method for 360◦ scene reconstruction from a sparse set of input equirectangular panoramas. Built upon K-Planes, a radiance field representation that encodes explicit features on orthogonal feature planes, our method does not directly learn feature grids. Instead, we parameterize the feature grids to enable sparse view reconstruction without pretraining or additional regularization. We implement a custom …

Compact, Low-Power Underwater Acoustic Modem Characterization And Sensing Network Node Implementation, Lucas W. Lucia

Master's Theses

There is a growing market for compact, power-efficient underwater communication technology. Due to the limited propagation of electromagnetic waves through water, modulated acoustic waves have emerged as an effective means of wireless communication underwater. In decades past, many acoustic modems had been developed primarily for defense customers in larger systems functioning at high power. In recent years, smaller, relatively inexpensive acoustic modems have become available on the market—but data on their performance in a real-world environment is scarce. Companies such as OWL Integrations are interested in employing such acoustic modems as part of sensing networks across multiple mediums. For such …

Spos: An Attestation Solution For The Detection And Mitigation Of Point Of Sale Malware, Damian Singh Dhesi

Master's Theses

Securing 95% of card present transactions, accounting for billions of transactions a year, has made EMV the premier protocol for card-based payment. Created by and named after Europay, Mastercard, and Visa, the EMV protocol provides multiple solutions to resolve security concerns with the outdated, swipe-based, magnetic stripe payment. Such solutions are Chip and PIN which provides a more secure transaction at a significant time cost and EMV contactless which provides improved security to Chip and PIN at greater ease of use with its quick, tap-to-pay based payment. However, regardless of how secure the EMV protocol makes the card side of …

Cislunar Orbital Debris Removal Feasibility Analysis Using Nrho To Dro Transfers At Varying Solar Angles, Peter C. Lie

Master's Theses

The resurgence of lunar exploration necessitates the expansion of space situational awareness into cislunar space. The current tracking technologies for cislunar objects are insufficient to determine vacant orbital regimes for human spaceflight. Orbital debris generated in the cislunar environment often evolves along complex and chaotic trajectories due to the influence of multi-body dynamics. Current research into methods of removing orbital debris from space is limited to debris in Earth orbits. For cislunar orbits, debris disposal options are slowly gaining research, mostly centered on hypothetical solutions for active missions. Lunar impact and escape to heliocentric space are low-cost options, but serve …

Analog Hardware Implementation Of A Linearly Constrained Quadratic Program Real-Time Solver, Claire E. Tylutki, Claire Tylutki

Master's Theses

This thesis presents the design, implementation, and analysis of a hardware system for solving Linearly Constrained Quadratic Programs (LCQPs) in real time. The architecture follows a generalized feedback structure composed of three key elements: gradient descent on the quadratic cost function, saturation-based nonlinearity to enforce inequality constraints, and an integral controller with an anti-windup mechanism to regulate dynamic behavior and determine steady-state error. This majority analog system converges with equilibria that satisfy the Karush-Kuhn-Tucker (KKT) optimality conditions. Using a representative LCQP, this work presents simulation of the circuit in PLECS and LT Spice to confirm the feasibility of the novel …

Adversarial Deep Reinforcement Learning For Tank Duel Simulation Using Lidar-Based Observations, Braedan Kennedy

Master's Theses

Previous research has demonstrated that reinforcement learning agents can learn to steer differential-drive robots around obstacles using 2D lidar scans as observations. However, these studies typically treat all range returns as undifferentiated obstacles—objects to avoid—without distinguishing between different object types. This thesis builds upon previous research by introducing an adversarial task in which an agent must interpret raw range readings to both avoid static obstacles and identify, pursue, and engage a hostile target.

To investigate this problem, this thesis introduces TankGame, a novel, lightweight 2D tank duel simulator. Each agent receives a 360° lidar scan, controls its motion via tread …

Flair: A Fog-Native Workload Placement Framework For Qos-Aware Scheduling In Smart Manufacturing, Daiwik N. Swaminathan

Master's Theses

Smart manufacturing environments utilize a variety of heterogeneous IoT sensors that continuously stream large volumes of data. To detect and mitigate critical events such as fires or accidents in real time, this data must be promptly analyzed by machine learning (ML) image classifiers. These classifiers are typically deployed on fog computing nodes located within the manufacturing warehouse facilities to ensure low-latency responses and to protect data privacy. Although fog nodes offer a better alternative to remote cloud data centers for such latency-sensitive tasks, they are often resource-constrained and highly sensitive to the composition of workloads, particularly in heterogeneous environments where …

Opening The Black Box With Regal: A Novel Explainable Ai Approach To Uncover Key Predictors In Search And Rescue Success, Brandon Hyunjun Kim

Master's Theses

The outcome of a search and rescue (SAR) operation is influenced by a complex, non-linear interplay among numerous factors, including geographic context, subject-specific characteristics, and environmental conditions. The high dimensionality and intricate dependencies among these variables pose significant challenges to traditional exploratory modeling approaches, limiting their ability to uncover meaningful patterns and relationships associated with mission success. This study introduces Rules Based Explanations for Generated neighborhoods Around Localized cases (REGAL), a novel adaptation of the Local Interpretable Model-agnostic Explanations (LIME) framework to explain deep multimodal neural networks and what key features it assesses to determine search and rescue success. REGAL …

Fault Tolerant Dynamic Task Allocation For Heterogeneous Multi-Robot Systems, Jack R. Cline

Master's Theses

This research presents a novel approach to dynamic task allocation in heterogeneous multi-robot systems with integrated fault detection capabilities. As multiple industries are becoming more reliant on multi-robot systems for tasks, maintaining operational efficiency despite robot failures becomes critical. We propose a framework that combines optimization-based task allocation with a Kalman filter that estimates task progress for anomaly detection to identify unreliable agents and dynamically redistribute tasks. Observing values such as the normalized innovation squared (NIS), covariance, and progress rate, the algorithm can designate a robot as faulty. Embedding information about which robots are faulty in the task algorithm allows …

Sentry V3: Extending Context Switches On A Trusted Secure Coprocessor, Mark Kong

Master's Theses

Software correctness and integrity is only ensured through trust in the un- derlying hardware. However, modern computer systems are complex to design and secure. Thus, given the choice between performance and security, companies will often prioritize performance, resulting in vulnerable systems. This creates exploitable systems that must be patched retroactively because business value performance over security. One approach to this issue is to separate the root of security from the rest of the system to create a minimal trusted computing base. Trustguard is one instance of this. Trustguard implements a Containment Architecture with Ver- ified Output (CAVO) model which shows …

Data-Driven Product Recommendations: A Decision Support Framework Utilizing Customer Reviews, Tyler A. Lopez

Master's Theses

With the considerable presence of e-commerce in society, vast number of purchasable goods, and increasing brand variety, consumers are faced with the challenge of buying products that they perceive to be of greatest value to them. To assist consumers with making better informed decisions, e-commerce websites allow individuals to post their own experiences and score the products that they purchase. Despite this information, the variety of experiences and feedback that consumers share do not always lead to clarity on whether a product is best suited for the purchaser. To help guide customers through a simplified purchasing process from the perspective …

Design And Development Of A Laboratory Star Tracker System, Kevin D. Iverson

Master's Theses

This thesis presents the design, calibration, and validation of a low-cost star tracker system and hardware-in-the-loop testbed intended for small satellite applications. The system is composed entirely of commercial off-the-shelf components and integrates both a custom star tracker and a screen-based star field simulator for controlled, repeatable testing. An analytical modeling approach was used in the early design phase to predict system accuracy based on key optical and geometric parameters, informing the selection and configuration of components and guiding the development of calibration routines.

A simulation environment was developed to validate the star tracker software and assess the impact of …

Using Hydrologic And Hydraulic Simulations To Predict Near-Term Effects Of Beaver Habitat Restoration On Flood Flows, Luke S. Stewart

Master's Theses

There is a growing need to predict the complex hydrologic and hydraulic effects of beaver-maintained wetlands. This study uses relatively simple inputs and a replicable methodology to predict flood flow impacts of beaver habitat restoration at the site design scale using the engineering industry’s customary hydrology and hydraulic modeling approach. This study focuses on Toro Creek, the primary drainage for a 15.2 square mile watershed on the California Central Coast.

A hydrologic model of Toro Creek watershed was developed in HEC-HMS to approximate saturated soil conditions typical of late-spring flooding events observed in the region. The hydrologic model was calibrated …

Vertical And Latitudinal Variability Of Marine Heatwaves In The California Current, Gavin M. Plume

Master's Theses

Marine heatwaves (MHWs) are increasing in frequency and intensity globally, with grave effects on marine ecosystems and their dependent industries. There are significant gaps in knowledge of the subsurface behavior and geographic distribution of MHWs, especially in eastern boundary upwelling systems like the California Current. Here, two decades of full water-column temperature observations from a shallow autonomous profiler in San Luis Obispo Bay (~10m) revealed that while MHWs have similar average durations and intensities across all depths, one-third of bottom MHWs occur without a concurrent surface signal. MHWs across the water column initiated during anomalously weak upwelling or downwelling conditions …

Identifying Interchromosomal Interactions Using A Graph-Based Computational Pipeline, Ethan Outangoun

Master's Theses

Current computational tools for analyzing chromatin organization are mainly focused on intrachromosomal interactions, despite growing evidence that suggests long-range interactions across chromosomes contribute to transcriptional regulation and disease development. This thesis aims to address this gap in interchromosomal genome analysis, presenting a robust computational pipeline that identifies a clique (i.e., a subgraph) of highly interacting trans-chromosomal regions anchored at a user-specified seed genomic locus. A weighted interaction network is constructed from an input Hi-C contact matrix, a widely used experimental assay for measuring genome-wide chromatin interactions. We model this input contact matrix as a graph and devise three different strategies …

Investigating And Evaluating Poison Unlearning Under Imperfect Detection, Mitashi D. Parikh

Master's Theses

Machine unlearning is an emerging field focused on removing the influence of specific data points from trained machine learning models. Early work in this field has primarily focused on privacy-oriented unlearning, often motivated by data protection regulations such as the GDPR. More recently, security-oriented unlearning has emerged as a response to data poisoning attacks, where malicious inputs are injected into training data to manipulate model behavior. In these security-oriented settings, unlearning methods aim to reverse the influence of malicious training data after a model has already been deployed. However, existing methods assume access to an accurately identified portion of poisoned …