Physical Sciences and Mathematics Commons^™

Visual Loop: Bridging The Cognitive Gap In Software Development Through Visual-Ai Collaboration, Luis Filipe Fernandes Gomes, Xin Zhou, David Lo, Rui Abreu

Research Collection School Of Computing and Information Systems

Software development remains predominantly text-centric, despite decades of evidence showing that developers think and communicate visually. While sketches and diagrams externalize developers’ mental models, they remain disconnected from source code and quickly become outdated. Recent advances in foundation models, capable of both code and visual reasoning, create an opportunity to unify these representations. In this vision paper, we introduce Visual Loop, a continuous visual development environment that keeps code and informal sketches in bidirectional synchronization. Our prototype connects a code editor with a tablet-based visualization workspace, allowing developers to explore, annotate, and modify systems through freehand sketches interpreted by multimodal …

Understanding Codebase Like A Professional! Human-Ai Collaboration For Code Comprehension, Jie Gao, Yue Xue, Xiaofei Xie, Junming Cao, Soemin Thant, Erika Lee, Bowen Xu

Research Collection School Of Computing and Information Systems

Understanding an unfamiliar codebase is an essential task for developers in various scenarios, such as during the onboarding process. Especially when the codebase is large and time is limited, achieving a decent level of comprehension remains challenging for both experienced and novice developers, even with the assistance of large language models (LLMs). Existing studies have shown that LLMs often fail to support users in understanding code structures or to provide user-centered, adaptive, and dynamic assistance in real-world settings.To address this, we propose learning from the perspective of a unique role, code auditors, whose work often requires them to quickly familiarize …

Autobackdoor: Automating Backdoor Attacks Via Llm Agents, Yige Li, Zhe Li, Wei Zhao, Nay Myat Min, Hanxun Huang, Xingjun Ma, Jun Sun

Research Collection School Of Computing and Information Systems

Backdoor attacks pose a serious threat to the secure deployment of large language models (LLMs), enabling adversaries to implant hidden behaviors triggered by specific inputs. However, existing methods often rely on manually crafted triggers and static data pipelines, which are rigid, labor-intensive, and inadequate for systematically evaluating modern defense robustness. As AI agents become increasingly capable, there is a growing need for more rigorous, diverse, and scalable \textit{red-teaming frameworks} that can realistically simulate backdoor threats and assess model resilience under adversarial conditions. In this work, we introduce \textsc{AutoBackdoor}, a general framework for automating backdoor injection, encompassing trigger generation, poisoned data …

Ultra-High Vacuum Outgassing Characterization Of Thermally Processed Low-Carbon Steel For Advanced Particle Accelerator And Gravitational Wave Detector Applications, Aiman H. Al-Allaq

Mechanical & Aerospace Engineering Theses & Dissertations

This dissertation investigated AISI 1020 low-carbon steel as an alternative vacuum chamber material to conventional stainless steel for ultra-high vacuum (UHV) and extreme-high vacuum (XHV) applications. After a 400 °C/48 h bake, AISI 1020 tube chambers achieved a hydrogen outgassing rate of 2.4 × 10⁻¹⁶ Torr·L·s⁻¹·cm⁻², approximately 2,300 times lower than the prebaked 316L stainless-steel comparator, among the lowest hydrogen outgassing rates ever reported for an uncoated metallic vacuum chamber. Bare and magnetite-coated AISI 1020 chambers were then compared using throughput and rate-of-rise methods. The magnetite coating yielded 5× lower water outgassing at room temperature, but this advantage disappeared after …

Dissecting The Etiology Of Alcohol Use Disorder By An Integrative Heritable Component Approach, Ivy Garrenton

Computer Science Theses & Dissertations

Alcohol Use Disorder (AUD) is a pervasive condition characterized by complex interplay among genetic, phenotypic, and environmental factors. Although previous studies have identi fied genetic loci associated with alcohol consumption, these efforts have not captured the genetic heterogeneity and gene-environment interactions underlying AUD pathogenesis. To address this critical gap, we developed a novel statistical methodology that integrates phenotypic, genotypic, and environmental data through an environmentally modified Genetic Relationship Matrix (GRM) to derive AUD-related traits with enhanced heritability.

This approach demonstrated superior performance in both simulated and real-world datasets. Traits derived using the environmentally modified GRM exhibited significantly higher estimated heritability …

Discovery Of A New Cataclysmic Variable, Galex J211240.9–422959, Via The Exoasteroids Citizen Science Project, Mei Lin, Aaron Meisner, Siyi Xu, Arttu Sainio, Liliana E. Rivera Sandoval, Paula Szkody, Christopher Tanner, Laura K. Rogers, Sarah Casewell, Austin Humphreys

Physics & Astronomy Faculty Publications

We report the discovery and spectroscopic classification of a new cataclysmic variable (CV), GALEX J211240.9–422959 (Gaia DR3 source ID 6580244356829724928). This object was initially identified as a source exhibiting strong mid-infrared variability in Wide-field Infrared Survey Explorer (WISE) time-series data by a volunteer participating in the Exoasteroids citizen science project. Prompted by this strong variability at 3–5 μm, we obtained a follow-up optical spectrum using the Gemini Multi-Object Spectrograph at the Gemini South Observatory. The resulting spectrum displays a blue continuum dominated by strong, broad emission lines of the hydrogen Balmer series and neutral helium, spectral signatures characteristic of …

A Search For Magnetically Active Binary Counterparts To Chandra X-Ray Sources In 47 Tucanae With Hst Imaging, Haldan N. Cohn, Phyllis M. Lugger, Craig O. Heinke, Maureen Van Den Berg, Liliana Rivera Sandoval, Jay Anderson

Physics & Astronomy Faculty Publications

Using Hubble Space Telescope (HST) imaging, we searched for candidate magnetically active binary (AB) counterparts to low-luminosity Chandra X-ray sources in the globular cluster 47 Tucanae (NGC 104). We consider the catalog of 300 Chandra sources within the half-light radius of 47 Tuc compiled by C. O. Heinke et al. (2005). The 10-band HST imaging used to identify these sources spans the range from 275–658 nm. We generated a cross-matched catalog of objects detected by photometric reductions. We used this catalog to search for counterparts within Chandra source error circles using color–magnitude diagrams and a color–color plot. We …

Development And Evaluation Of An Mpas-Based Urban Modeling System For The New York City Region, Marco S. Yu, Kristian Rice

Publications and Research

Complex geometry and varying land cover make urban environments difficult to simulate accurately. This research presents a performance evaluation of the newly developed urban modeling system in the Model for Prediction Across Scales (MPAS). Weather over the New York metropolitan area during June 2018 was simulated using supercomputing resources and the new urban modeling system. Modeled 2-m air temperature was compared with historical hourly observations from five NYC Mesonet stations, one located in each of the five boroughs. Comparison showed that the model follows the timing of warming and cooling well, but overestimates the magnitude of temperature swings at peaks …

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

Faculty Work Comprehensive List

Background

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

Objectives

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

Methods

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

Aiw26s: Machine Learning Of Structured Data, Moumita Saha

Paul English Applied Artificial Intelligence (AI) Institute Publications

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

Weather And The Question Of Human Agency In Medieval Texts, Kaleigh Bennett

Undergraduate Theses and Capstone Projects

During the Middle Ages, humans and the weather were believed to have an inherently adversarial relationship. Understanding how people operate within the natural world is a difficult topic, and this especially holds true for a time that lacked modern scientific knowledge. While there is greater appreciation for how the weather is formed today, understanding of the weather during the medieval period was largely superstitious, especially during the early medieval period when pagan and Christian beliefs towards the environment began to overlap. According to Gillian Rudd’s essay “Being Green In Late Medieval English Literature” found within The Oxford Handbook of Ecocriticism …

Radiation Protection Factor Research: The Keystone For Conventional-Nuclear Integration (Cni), Andrew W. Decker

Faculty Publications

This article briefly describes Defense Threat Reduction Agency (DTRA) vehicle Radiation Protection Factor (RPF) research and explains its critical role in enabling future Operations in a Nuclear Environment (ONE) and Conventional-Nuclear Integration (CNI) on behalf of the US Department of War (DoW).1 As such, the background and practical utility of RPF values is discussed, as well as the justification for renewed Army and DoW investment into RPF research to enhance Joint military planning, survivability, and lethality on tomorrow’s nuclear battlefields. Advances in DTRA RPF research directly strengthen US strategic and extended deterrence efforts and support all Agencies and Departments responsible …

Antimicrobial Performance Of Amorphous Vs. Crystalline La1-Xsrxcoo3 Perovskites, S. M. Au Shohag, Fahmida Akhter, Ahmed Touhami, Swati Mohan, Md. Wasikur Rahman, Mohammed Jasim Uddin

Physics & Astronomy Faculty Publications

Perovskite materials, with their unique crystal structure and chemical properties, are emerging as promising antimicrobial agents for potential biomedical applications. In this research, different weight percentages of Sr2 + doped amorphous and crystalline Lanthanum Cobaltite (La1-xSrxCoO3 defined as LSCO) (x = 0, 0.025, 0.05, 0.1, and 0.15) perovskite was synthesized by using a simple combination of sol-gel and molten-salt processes and characterized by X-ray diffraction (XRD), and scanning electron microscopy (SEM) ensures the uniform morphology of the materials. During antimicrobial study using the Kirby Bauer method, amorphous LSCO showed much better activity than crystalline LSCO against Escherichia coli, Vibrio …

Fear In The Forecast: Do You Believe The Government Controls The Weather?, Gabriella Bartsch

Honors Papers and Posters

Weather manipulation by the government is among the most widely believed conspiracy theories in America, with over 49 percent of Americans subscribing to this belief. As belief in weather-related conspiracies continues to gain traction, understanding the political and social factors that drive these beliefs becomes increasingly essential. In this study, data from the Chapman Survey of American Fears, a representative national sample of U.S. adults, as well as a qualitative content analysis of X/Twitter posts from 2020 to 2026, are used. This research examines how political partisanship and conspiratorial predisposition are reflected in media consumption and levels of institutional trust, …

Short-Term Electricity Price Forecasting With Constrained Regressors, Mucun Sun, Li Zhang, Yifeng Gao

Computer Science Faculty Publications

The volatility of electricity price presents a challenge to market participants as their decision-making process are highly depend on the accuracy of price forecasts. However, there is growing empirical evidence of increasing price volatility and price spikes in electricity markets as a result of variable renewable energy generation, extreme weather events, and other factors. The distribution shift caused by spikes in electricity price data differentiates the forecasting tasks from other renewable energy sources. Moreover, the observations may be compromised by cyberattacks and thus not available in the testing phase. To this end, we propose a Similarity-Enhanced Electricity Decomposition Forecasting model …

Algorithm Performance In The Search For Hamiltonian Cycles, Chance Davis

Honors Theses

The Hamiltonian cycle problem is ubiquitous in both computer science and graph theory: Given a connected graph, a solution would either confirm the existence of a cycle which visits each vertex only once or its nonexistence. The importance of this problem, as well as its difficulty, is described in the Clay Mathematics Institute’s Millenium Prize Problems and Karp’s 21 NP-complete problems. Despite its “hardness,” solutions to the Hamiltonian cycle problem are desired in logistics, electronic circuit design, and network routing, among other fields. In this work, we benchmark a promising exhaustive enumeration algorithm on various graphs, including ones derived from …

Rethinking News Classification Through A Multi-Dimensional Framework, Luana De Jesus Ferreira

Honors Theses

This thesis proposes a multi-dimensional framework for news classification that evaluates articles across three independent dimensions: headline accuracy, language neutrality, and content reliability. These dimensions produce both a continuous reliability score and a five-tier interpretive scale, while additionally classifying articles by genre and topic. To operationalize this framework, a structured annotation protocol was developed and applied to a dataset of 373 news articles drawn from 79 outlets spanning a wide range of contemporary media ecosystem. A binary Logistic Regression classifier trained on the ISOT Fake News Dataset was then evaluated against this dataset to examine how a model trained on …

Software Engineering In The Age Of Coding Agents: Failure Modes And Rejection Patterns, Mahd Mohd Hindi

Thesis/ Dissertation Defenses

This thesis investigates the real-world behavior of LLM-driven coding agents that generate code changes and submit pull requests (PRs) to public software repositories. As these tools evolve from autocomplete-style assistants into more autonomous agents, their contributions increasingly interact with socio-technical review processes (human reviewers, bots, CI/CD gates, and project norms). This thesis focuses on understanding why agent-generated PRs are accepted or rejected and what these outcomes reveal about current agent limitations in practical development workflows. The main objective of this thesis is to systematically characterize rejection patterns and failure modes of agent-generated pull requests in real repositories. Specifically, the thesis …

Software Engineering In The Age Of Coding Agents: Failure Modes And Rejection Patterns, Mahd Mohd Taysir Hindi

Thesis/ Dissertation Defenses

This thesis investigates the real-world behavior of LLM-driven coding agents that generate code changes and submit pull requests (PRs) to public software repositories. As these tools evolve from autocomplete-style assistants into more autonomous agents, their contributions increasingly interact with socio-technical review processes (human reviewers, bots, CI/CD gates, and project norms). This thesis focuses on understanding why agent-generated PRs are accepted or rejected and what these outcomes reveal about current agent limitations in practical development workflows. The main objective of this thesis is to systematically characterize rejection patterns and failure modes of agent-generated pull requests in real repositories. Specifically, the thesis …

On The Extreme Complexity Of Certain Nearly Regular Graphs, Gregory P. Constantine, Gregory C. Magda

Theory & Applications of Graphs

The complexity of a graph is the number of its labeled spanning trees. It is demonstrated that the seven known triangle-free strongly regular graphs are graphs of maximal complexity among all graphs of the same order and degree; their complements are shown to be of minimal complexity. A generalization to nearly regular graphs with two distinct eigenvalues of the Laplacian is presented. Conjectures and applications of these results to biological problems on neuronal activity are described.

Cycle-Based Characterizations Of The Cycle Completable Graphs, Terry A. Mckee

Theory & Applications of Graphs

Cycle completable graphs were originally defined to answer matrix completion problems and have since received diverse graph-theoretic descriptions, in spite of not directly mentioning cycles. This paper characterizes such graphs by their chordless cycles never having ``bridges'' (as defined in H.-J. Voss's 1991 monograph {\em Cycles and Bridges in Graphs\/}) that have more than two vertices of attachment in the cycle. This approach is then related to the well-studied, yet seemingly quite distinct, classes of chordal graphs and series-parallel graphs. This is done by allowing chords to be ``bridges'' that have exactly two vertices of attachment.

Observational Diagnostics And Spectroscopic Detection Of Tropospheric Trace Gases And Oxidation Processes, Callum E. Flowerday

Theses and Dissertations

The troposphere is governed by photochemical and radical-mediated processes that control atmospheric oxidation capacity, secondary pollutant formation, and the chemical lifetime of gases. Quantitative understanding of these processes requires both robust observational frameworks capable of resolving complex precursor–product relationships and instrumentation with sufficient sensitivity and selectivity to detect reactive and low-concentration species. This dissertation advances atmospheric chemistry through the parallel development of quantitative observational methodologies and refined spectroscopic measurement techniques. The first component of this work evaluates atmospheric oxidation systems using long-term monitoring data and empirical diagnostics. Multi-year analyses of ozone and particulate matter trends were conducted to characterize variability …

Decorrelation, Diversity, And Emergent Intelligence: The Isomorphism Between Social Insect Colonies And Ensemble Machine Learning, Ernest Fokoue, Gregory Babbitt, Yuval Levental

Articles

Social insect colonies and ensemble machine learning methods represent two of the most successful examples of decentralized information processing in nature and computation respectively. Here we develop a rigorous mathematical framework demonstrating that ant colony decision-making and random forest learning are isomorphic under a common formalism of stochastic ensemble intelligence. We show that the mechanisms by which genetically identical ants achieve functional differentiation— through stochastic response to local cues and positive feedback—map precisely onto the bootstrap aggregation and random feature subsampling that decorrelate decision trees. Using tools from Bayesian inference, multi-armed bandit theory, and statistical learning theory, we prove that …

A General Weighting Theory For Ensemble Learning: Beyond Variance Reduction Via Spectral And Geometric Structure, Ernest Fokoue

Articles

Ensemble learning is traditionally justified as a variance-reduction strategy, explaining its strong performance for unstable predictors such as decision trees. This explanation, however, does not account for ensembles constructed from intrinsically stable estimators-including smoothing splines, kernel ridge regression, Gaussian process regression, and other regularized reproducing kernel Hilbert space (RKHS) methods whose variance is already tightly controlled by regularization and spectral shrinkage. This paper develops a general weighting theory for ensemble learning that moves beyond classical variance-reduction arguments. We formalize ensembles as linear operators acting on a hypothesis space and endow the space of weighting sequences with geometric and spectral constraints. …

Employing Effective Solution Methods For Caputo-Based Sequential Fractional Models, Eman A. A. Ziada, Mohamed F. Abouelenein, Hijaz Ahmad, Monica Botros

Mathematical Modelling and Numerical Simulation with Applications

This paper investigates a class of nonlinear sequential singular fractional differential equations (FDEs) involving Caputo derivatives. This type of equation has several key advantages that enhance its value, such as capturing memory and hereditary effects. Viscoelastic materials and anomalous diffusion, as well as biological systems, can take advantage of this feature. In addition, fractional derivatives possess a sequential structure that enables the implementation of multiscale processes and hierarchical memory responses. Moreover, it provides an effective and flexible framework for solving differential equations compared to classical differential equations. It can therefore be used to model complex systems in physics, biology, and …

Optimal Control Strategies For Infectious Diseases: A Numerical Simulation Approach, M. Abu Salek, Jannatun Nayeem, Romana Yesmin, M. Haider Ali Biswas, M. Humayun Kabir

Mathematical Modelling and Numerical Simulation with Applications

Various infectious diseases, such as Tuberculosis and Hepatitis B virus (HBV), caused by Mycobacterium bacteria and hepatitis B virus, respectively, pose serious threats worldwide. This paper examines the spread of these diseases using a mathematical model that incorporates control measures, including vaccination and awareness programs. We use a system of differential equations with control variables and apply Pontryagin's principle to determine optimal control strategies. The stability of the Disease Free Equilibrium (DFE) has been analysed and demonstrated to be both locally and globally asymptotically stable when the basic reproduction number remains below unity, thereby ensuring disease elimination. Furthermore, the Endemic …

On The Scientific Stature Of Data Science: The Epistemological Unicorn, Ernest Fokoue

Articles

Data Science has ignited unprecedented academic, industrial, and pedagogical fervor, yet its status as a \textit{science} in the classical sense---comparable to physics or biology---remains profoundly unsettled. This article interrogates the epistemological foundations of Data Science by examining its hybrid theoretical lineage, from the Universal Approximation Theorem to the No-Free-Lunch Theorems, with special emphasis on the fundamental Bayesian optimality results for both regression and classification. We argue that Data Science is in a vigorous \textit{gestational period}, characterized not by an absence of principles but by a creative tension between empirical pragmatism and deep mathematical theory. The Cross-Validation score emerges as the …

Learning Ordinal Geometry: Semantic–Aware Kernels For Ordered Categorical Data, Ernest Fokoue

Articles

Ordinal data arise ubiquitously in survey research, psychology, medicine, economics, and recommender systems, yet kernel methods for such data typically rely on either nominal encodings or arbitrary numeric codings. The former discards order information; the lat- ter imposes a fictitious metric structure. This paper develops a principled framework for kernel design on ordinal scales and introduces a new class of Semantic–Aware Ordinal Ker- nels (SAOK) that simultaneously capture ordinal order and semantic proximity between categories. We begin by formalizing order–preserving embeddings of finite chains and characterizing a broad family of chain distances that are conditionally negative definite. Through Schoen- berg …

Saturated Hierarchical Atomic Incremental Learning (Shail): A Behavioral Learning Perspective On Staged Mastery And Saturation, Ernest Fokoue

Articles

We introduce Saturated Hierarchical Atomic Incremental Learning (sHAIL), a learning paradigm in which complex tasks are approached through a sequence of simpler atomic subtasks, each mastered to saturation before progression. The central mechanism is a saturation criterion that detects when learning dynamics enter a plateau region, triggering consolidation and subsequent ascent to a higher level of task complexity. We develop a theoretical framework for sHAIL and show that it naturally gives rise to \emph{staircased convergence}: alternating phases of rapid improvement and genuine plateau. Within each level, classical convergence guarantees apply under standard smoothness conditions, while the hierarchical transitions are driven …

No Intelligence Without Statistics: The Invisible Backbone Of Artificial Intelligence, Ernest Fokoue

Articles

The rapid ascent of artificial intelligence (AI) is often portrayed as a revolution born from computer science and engineering. This narrative, however, obscures a fundamental truth: the theoretical and methodological core of AI is, and has always been, statistical. This paper systematically argues that the field of statistics provides the indispensable foundation for machine learning and modern AI. We deconstruct AI into nine foundational pillars—Inference, Density Estimation, Sequential Learning, Generalization, Representation Learning, Interpretability, Causality, Optimization, and Unification—demonstrating that each is built upon century-old statistical principles. From the inferential frameworks of hypothesis testing and estimation that underpin model evaluation, to the …