Physical Sciences and Mathematics Commons^™

Baseball Decision-Making: Optimizing At-Bat Simulations, Varun Gopal, Krithika Kondakindi, Nibhrat Lohia, Morgan Williams

SMU Data Science Review

Pitch selection in baseball plays a crucial role, involving pitchers, catchers, and batters working together. This practice, dating back to early baseball, has seen teams try various methods to gain an advantage. This research aims to use reinforcement learning and pitch-by-pitch Statcast data to improve batting strategies. It also builds on previous statistical work (sabermetrics) to make better choices in pitch selection and plate discipline. The dataset used, including over 700,000 pitches for each full season and 200,000 pitches for the COVID-shortened 2020 season, encompasses a wealth of crucial metrics including pitch release point, velocity, and launch angle. This study …

Reevaluating Texas Energy Market Forecasts In The Wake Of Recent Extreme Weather Events, Robert A. Derner, Richard W. Butler Ii, Alexandria Neff, Adam R. Ruthford

SMU Data Science Review

This paper provides updated forecasts of energy demand in Texas and recognizes the impact of sustainable energy. It is important that the forecasts of the adoption of sustainable energy are reexamined after Winter Storm Uri crippled the Texas power grid and left many without power. This storm highlighted the issues the Texas power grid had and has continued to struggle with in supplying the state with energy. This paper will offer an overview of the relevant literature on the adoption of sustainable energy and relevant events that have occurred in the state of Texas that will give the reader the …

Multi-Class Emotion Classification With Xgboost Model Using Wearable Eeg Headband Data, James Khamthung, Nibhrat Lohia, Seement Srivastava

SMU Data Science Review

Electroencephalography (EEG) or brainwave signals serve as a valuable source for discerning human activities, thoughts, and emotions. This study explores the efficacy of EXtreme Gradient Boosting (XGBoost) models in sentiment classification using EEG signals, specifically those captured by the MUSE EEG headband. The MUSE device, equipped with four EEG electrodes (TP9, AF7, AF8, TP10), offers a cost-effective alternative to traditional EEG setups, which often utilize over 60 channels in laboratory-grade settings. Leveraging a dataset from previous MUSE research (Bird, J. et al., 2019), emotional states (positive, neutral, and negative) were observed in a male and a female participant, each for …

Building Effective Large Language Model Agents, Sydney Holder, Shreyash Taywade

SMU Data Science Review

The advancement of large language models (LLMs) has significantly expanded the influence of artificial intelligence across various sectors. This paper explores building LLM agents to power applications and examines what is necessary to build an efficient and helpful AI assistant. The research investigates the core components necessary to create specialized agents, facilitate collaboration in problem-solving, and improve human task performance. The development and application of tools designed to augment the capabilities of LLM agents are also explored. The paper addresses the potential risks of the unknowns, such as hallucinations, which can compromise the success of agent-based solutions within LLM applications. …

Game Recommendation Analysis Using Steam Profiles And Reviews, Robert Blue, Luis Garcia, Jacob Turner

SMU Data Science Review

Smaller game studios are at a disadvantage when it comes to getting their product noticed by users. This study aims to provide insights on how recommendation engines work so that these smaller studios can have their games noticed on Steam. Steam is one of the largest video game distribution services and they have a recommendation engine which promotes games to its user base. This study utilized user information such as number of games played, the type of games, and the hours played and created recommendation engines to identify the qualities in the game that are driving recommendations.

Leveraging Transformer Models For Genre Classification, Andreea C. Craus, Ben Berger, Yves Hughes, Hayley Horn

SMU Data Science Review

As the digital music landscape continues to expand, the need for effective methods to understand and contextualize the diverse genres of lyrical content becomes increasingly critical. This research focuses on the application of transformer models in the domain of music analysis, specifically in the task of lyric genre classification. By leveraging the advanced capabilities of transformer architectures, this project aims to capture intricate linguistic nuances within song lyrics, thereby enhancing the accuracy and efficiency of genre classification. The relevance of this project lies in its potential to contribute to the development of automated systems for music recommendation and genre-based playlist …

Investigating Bias In Mortgage-Rate Machine Learning Models, Will Kalikman

Computer Science Senior Theses

Banks and fintech lenders increasingly rely on computer-aided models in lending decisions. Traditional models were interpretable: decisions were based on observable factors, such as whether a borrower's credit score was above a threshold value, and explainable in terms of combinations of these factors. In contrast, modern machine learning models are opaque and non-interpretable. Their opaqueness and reliance on historical data that is the artifact of past racial discrimination means these new models risk embedding and exacerbating such discrimination, even if lenders do not intend to discriminate. We calibrate two random forest classifiers using publicly available HMDA loan data and publicly …

Welfare Maximization In The Airplane Problem, Alina Chadwick

Computer Science Senior Theses

Given a set of passengers and a set of airplane seats, the goal of the airplane problem is to sit passengers in seats in a way that maximizes the sum of their total welfare, that is, the total happiness of the passengers in the plane. We aim to maximize their welfare subject to three constraints and how much they care about each constraint being satisfied: a group constraint (where passengers may want to sit together), a constraint on where in a row passengers want to sit (i.e. a window seat, a middle seat, or an aisle seat), and finally a …

Open Source Supply Chain Security: A Cost-Benefit Analysis Of Achieving Various Security Thresholds In Build Environments, Carly Retterer

Computer Science Senior Theses

Open source software has become a cornerstone of modern software development, offering unparalleled opportunities for innovation and collaboration. However, its widespread adoption has also introduced a host of security vulnerabilities, particularly in the software supply chain. This paper provides a comprehensive cost-benefit analysis of achieving various security thresholds to harden the build environment, focusing on isolated, hermetic, reproducible, and bootstrappable builds. For each build type, we provide a clear definition and outline the steps required for implementation. We then evaluate the associated costs and benefits of each build, emphasizing their roles in strengthening the build environment and enhancing supply chain …

Detecting Drifts In Data Streams Using Kullback-Leibler (Kl) Divergence Measure For Data Engineering Applications, Jeomoan Francis Kurian, Mohamed Allali

Engineering Faculty Articles and Research

The exponential growth of data coupled with the widespread application of artificial intelligence(AI) presents organizations with challenges in upholding data accuracy, especially within data engineering functions. While the Extraction, Transformation, and Loading process addresses error-free data ingestion, validating the content within data streams remains a challenge. Prompt detection and remediation of data issues are crucial, especially in automated analytical environments driven by AI. To address these issues, this study focuses on detecting drifts in data distributions and divergence within data fields processed from different sample populations. Using a hypothetical banking scenario, we illustrate the impact of data drift on automated …

Collisional Damping In Plasmonic Wakefield Accelerators, Maxime Pindrys

Honors Scholar Theses

We investigate the previously proposed role of collisional damping in plasmonic wakefield accelerators. Wakefields driven in a doped semi-conductor will exist in differing regimes dependent on the driving beam’s intensity. At low intensities, the mobility of free carrier electrons is limited. Here, wakefields will be small if the mean free path of an electron is short compared to the quiver amplitude of a free electron. After reaching a threshold intensity, conduction electrons in the semiconductor will be driven to such high speeds that their coulomb cross section will drop sharply. Here the resulting wakes will resemble those in a hollow …

Engineering Thermodynamics, Paul J. Marchese

Open Educational Resources

This collection of assignments is designed for an introductory thermodynamics class. It includes comprehensive readings that cover the fundamental concepts, problem sets to reinforce learning through practical application, and YouTube videos that provide detailed explanations and visual demonstrations of the material. These resources can be utilized in a regular classroom setting or for independent study, offering flexibility to accommodate different learning environments and preferences.

Molecular-Level Studies Of Nanopatterned Biomolecules With Atomic Force Microscopy, Ashley R. Walker

LSU Doctoral Dissertations

Atomic force microscopy (AFM) is an analytical technique in which a tipped probe is gently scanned across the surface in a raster pattern to generate digital images of a sample at the nanoscale. The AFM instrument has three general operational modes, which are contact, non-contact and tapping-mode, that can be used to examine materials at the atomic level. Single-molecular details of biological molecules and other soft organic materials can be captured with minimal denaturation in either ambient or liquid environments when using tapping-mode AFM. In tapping-mode, the probe is driven to oscillate vertically while the tip is scanned across the …

Connection-Saving Gate Assignment: A Computational Approach, Rob Mailley

Computer Science Senior Theses

The growth of the commercial aviation industry has yielded many interesting problems in the field of Operations Research, many of which are now able to be solved as both technology and mathematical optimization improve. A particularly interesting problem in airport operations re- search is the Aircraft Gate Assignment Problem (AGAP), which seeks to create a feasible match- ing between planes and flights at an airport. This problem is well-suited to modeling with Integer Programming, and has attracted research since the 1970s. Researchers of the AGAP have considered many different objectives, ranging from airline-focused objectives to more passenger-focused objective functions. In …

Space Bounds For Estimating Minimum Norm Of Solutions In Underconstrained Systems, Jeffrey Jiang

Computer Science Senior Theses

In this work, we wish to investigate the following situation: suppose we are in an underconstrained linear system where observations are constant but predictors are streaming in. That is, the number of predictors—and therefore the dimensionality of our solution—is changing. How hard is it for a streaming algorithm to maintain the ”size” or norm of the solution if we are constrained in space? More informally, can we keep track of the norm of the solution as new data is streaming in without naively memorizing all data and computing the solution directly? We first show a lower bound that any streaming …

Data-Driven Computing Methods For Nonlinear Physics Systems With Geometric Constraints, Yunjin Tong

Computer Science Senior Theses

In a landscape where scientific discovery is increasingly driven by data, the integration of machine learning (ML) with traditional scientific methodologies has emerged as a transformative approach. This paper introduces a novel, data-driven framework that synergizes physics-based priors with advanced ML techniques to address the computational and practical limitations inherent in first-principle-based methods and brute-force machine learning methods. Our framework showcases four algorithms, each embedding a specific physics-based prior tailored to a particular class of nonlinear systems, including separable and nonseparable Hamiltonian systems, hyperbolic partial differential equations, and incompressible fluid dynamics. The intrinsic incorporation of physical laws preserves the system's …

Explorando Las Ciencias Exactas: Teoría Y Aplicaciones En El Mundo De Los Números, Fabrício Moraes De Almeida, Daniel Méndez De La Cruz, Patricia Del Carmen Gerónimo Ramos, Josué Ojeda Montejo, Cristo Leon

STEM for Success Resources

Las ciencias exactas, compuestas por las matemáticas, la física y otras, nos ofrecen un marco fundamental para comprender el universo y su región fronteriza. En este fascinante viaje nos adentraremos en el mundo de los números, explorando tanto sus fundamentos teóricos como sus aplicaciones prácticas en diferentes áreas de un amplio espectro. Por lo tanto, el libro presenta los conceptos teórico-prácticos en los resultados obtenidos por los distintos autores y coautores en la producción de cada capítulo. Por encima de todo, Atena Editora ofrece divulgación científica con calidad y excelencia, esenciales para asegurar protagonismo entre las mejores editoriales de Brasil …

Math, Chatgpt, And You: The Problem With Mathematical Accuracy In Large Language Models, Alexandre M. Hamel

Computer Science Senior Theses

ChatGPT and other Large Language Models (LLMs) currently do a good job at generating novel text across many domains, but math remains a consistent issue when it comes to the accuracy of answers generated by these models. My research into various ways to manipulate the model have led me to the conclusion that a general closed form solution to help LLMs with math is both unrealistic and likely impossible. LLMs can be trained more successfully as you narrow the problem space, but consideration must be taken on the part of human user to recognize when an LLM is detrimental to …

Minimal Specialization: The Coevolution Of Network Structure And Dynamics, Annika King

Theses and Dissertations

The changing topology of a network is driven by the need to maintain or optimize network function. As this function is often related to moving quantities such as traffic, information, etc., efficiently through the network, the structure of the network and the dynamics on the network directly depend on the other. To model this interplay of network structure and dynamics we use the dynamics on the network, or the dynamical processes the network models, to influence the dynamics of the network structure, i.e., to determine where and when to modify the network structure. We model the dynamics on the network …

Automatic Measurement Of Dialogue Engagingness In Multilingual Settings, Amila Ferron

Dissertations and Theses

Expansive use of large language models (LLMs) as dialogue systems brings increased importance to the evaluation of the responses they generate. Although evaluation of qualities such as coherence and fluency are readily possible with well-established automatic metrics, engagingness is often measured with human evaluation -- a process that can be costly and slows the pace of development. Existing automatic metrics for engagingness have low to moderate correlation with human annotations, evaluate the response without the conversation history, are complicated to implement, or are designed for a specific dataset. Moreover, they have been tested exclusively on English conversations. Given that dialogue …

Investigating Co2 Adsorption Behavior In Metal-Organic Frameworks By Solid-State Nmr, Yan Ham Ng

Electronic Thesis and Dissertation Repository

With increasing demands in controlling carbon dioxide emissions, metal-organic frameworks (MOFs) are considered as promising candidates for CO₂ capture due to their large CO₂ adsorption capacity. In this study, the adsorption behavior of CO₂ molecules in [Zn₂(TRZ)₂(NH₂-BDC)] (TRZ=1,2,4-triazolate, NH₂-BDC=2-amino-1,4-benzenedicarboxylic acid), and CALF-20 are studied. Moreover, water is ubiquitous in the atmosphere and could have a negative impact on the MOF’s gas adsorption. Therefore, understanding the behavior and dynamics of water and CO₂ in MOFs is of fundamental importance. Solid-state nuclear magnetic resonance (SSNMR) is a powerful technique which …

Memories Of Recipes In Twentieth-Century Irish Cookbooks, Gary Thompson

Dublin Gastronomy Symposium

This paper analyses and categorises the ways in which authors and their publishers have chosen to include the author’s culinary, food and personal memories within the texts of twenty twentieth century Irish Cookbooks. Cookbooks are subjects of culinary nostalgia with the reading of a recipe capable of triggering in the reader a memory of a meal enjoyed, a dish cooked in times past by a loved one, or recollections of the disgust felt for a food hated in childhood. Independent from the reader, the culinary memories of the author can be captured at the time of publication in the text …

Academic Search And Discovery Tools In The Age Of Ai And Large Language Models: An Overview Of The Space, Aaron Tay

AI for Research Week

In the ever-evolving landscape of academic research, “AI tools” for literature search and synthesis are currently getting a lot of attention. These tools promise to ramp up productivity, enabling us to accomplish more in less time or absorb more knowledge without drowning in endless reading. With the sheer number of these systems increasing daily, it's natural to wonder: are they really worth our time and money? And if they are, how should we go about picking the right one from the multitude of options?

In this talk, I will share my views on how the space has developed over two …

Crisis Management: Unveiling Information And Communication Technologies’ Revamped Role Through The Lens Of Sub-Saharan African Countries During Covid-19, Ruthbetha Kateule, Egidius Kamanyi, Mahadia Tunga

The African Journal of Information Systems

The management of COVID-19 pandemic has revealed inefficiencies in coordinating global response, particularly in African countries. Therefore, creating an urgent need to examine the literature on Information and Communication Technologies (ICT) in crisis management to appreciate its contextual role. Employing a systematic review, using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), this paper critically assessed the extent of the use of ICT in crisis management in Africa’s response to COVID-19 to reconstruct its resilience against future crises. Findings indicate that while countries with limited ICT infrastructure faced considerable challenges in utilizing ICT solutions in COVID-19 management, countries …

Examining Differences In Concept Representation Across Similarity Spaces Between Humans And Large Language Models, Krishnachandra Nair

Computer Science Senior Theses

The replication of human concept representation is a critical task for the pursuit of artificial general intelligence. With the recent influx of large language models that demonstrate text-generation capabilities nearly on par with humans, the question stands on whether these large language models can capture concepts within language. We examine this question by exploring differences in concept representation across similarity spaces between humans and LLMs. We find that, while concept representation within LLMs does partially mimic human concept representation, LLMs are greatly limited by their dependence on semantic information and cannot therefore develop an understanding of human social code or …

Ripl: Recursive Inference For Policy Learning, Kunal Jha, Jeremy R. Manning, Alberto Quattrini Li

Computer Science Senior Theses

Humans excel at understanding the thoughts and intentions of others (theory of mind) and leverage this ability to learn and adapt in social environments. However, replicating this capability in artificial agents remains a challenge. This paper explores the gap between fast, efficient learning often achieved by Reinforcement Learning (RL) algorithms and the interpretability and adaptability desired in agents interacting with humans. We propose a novel approach that integrates an inference network within existing RL frameworks. This allows agents to reason about the beliefs of others (nested reasoning) while learning optimal actions. Our method leverages approximate solutions to the I-POMDP framework, …

Investigation Of Volatility, Composition, And Gas-Particle Phase Partitioning Of Atmospheric Organic Compounds Through Novel Instrumentation And Techniques, Karolina Cysneiros De Carvalho

McKelvey School of Engineering Theses & Dissertations

Atmospheric aerosols are ubiquitous indoors and outdoors and their impact on human life on Earth is extensive. Aerosol particles scatter and absorb solar radiation, are key in the formation of clouds and precipitation, and can affect the abundance and distribution of greenhouse and atmospheric trace gases by physicochemical multiphase processes, thus they play an important role in regulating regional and global climate. On the other hand, poor indoor and outdoor air quality associated with high particulate matter (PM) levels is among the leading health risks worldwide, affecting life quality and expectancy by increasing the risk of cancer, cardiovascular and respiratory …

Dna-Templated Nanofabrication Of Metal-Semiconductor Heterojunctions And Their Electrical Characterization, Chao Pang

Theses and Dissertations

Bottom-up nanofabrication, although still in its early stages with formidable challenges, is considered a potential alternative method to address the limitations of traditional top-down techniques by offering benefits including process simplification, cost reduction, and environmental friendliness. DNA-templated nanofabrication, one of the most powerful bottom-up methods, presents an innovative way to create advanced nanoelectronics. In this approach, nanomaterials with specific electronic, photonic, or other functions are precisely and programmably positioned on DNA nanostructures from a disordered collection of smaller parts. These self-assembled structures offer significant potential for improving many fields such as biosensing, drug delivery and electronic device manufacturing. This dissertation …

Nickel-Catalyzed Α-Arylation Of Α-Cyanoacetates Enabled By Electrochemistry, Zi-Meng Li, Zhang-Jian Li, Anat Milo, Ping Fang, Tian-Sheng Mei

Journal of Electrochemistry

β-Amino acids have a wide range of applications in the field of pharmaceuticals. Utilizing a combination strategy of nickel catalysis and paired electrolysis, a catalytic α-arylation protocol of carbonyl compounds has been developed. This protocol affords various a-aryl-a-cyanoacetates, which can be reduced to high-value-added α-aryl-β-amino acids. The cross-coupling reaction of electron-deficient aryl bromides with a-cyanoacetates achieves the expected products with good yields and functional group compatibility under mild conditions. Excessive electron-richness in initial aryl bromides facilitates the self-coupling of desired products. DFT calculations confirm that the presence of electron-rich aryl substitutions decreases the …

Re: Silver Bow Creek Conservation Area Repository Data Gap Quality Assurance Project Plan (Qapp), Josh Bryson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.