Physical Sciences and Mathematics Commons^™

Improving The Scalability Of Neural Network Surface Code Decoders, Kevin Wu

Undergraduate Honors Theses

Quantum computers have recently gained significant recognition due to their ability to solve problems intractable to classical computers. However, due to difficulties in building actual quantum computers, they have large error rates. Thus, advancements in quantum error correction are urgently needed to improve both their reliability and scalability. Here, we first present a type of topological quantum error correction code called the surface code, and we discuss recent developments and challenges of creating neural network decoders for surface codes. In particular, the amount of training data needed to reach the performance of algorithmic decoders grows exponentially with the size of …

Code Syntax Understanding In Large Language Models, Cole Granger

Undergraduate Honors Theses

In recent years, tasks for automated software engineering have been achieved using Large Language Models trained on source code, such as Seq2Seq, LSTM, GPT, T5, BART and BERT. The inherent textual nature of source code allows it to be represented as a sequence of sub-words (or tokens), drawing parallels to prior work in NLP. Although these models have shown promising results according to established metrics (e.g., BLEU, CODEBLEU), there remains a deeper question about the extent of syntax knowledge they truly grasp when trained and fine-tuned for specific tasks.

To address this question, this thesis introduces a taxonomy of syntax …

Dimensionlessly Comparing Hydrogen And Helium Plasmas At Lapd, Lela Creamer

Undergraduate Honors Theses

This project compares the hydrogen and helium gas puff plasmas created at the Large Plasma Device (LAPD) using dimensionless numbers to determine the extent to which the turbulence pattern can be explained by plasma physics. Since turbu- lence tends to dissipate energy and particles in a plasma, it can cause problems for fusion reactors by reducing their efficiency. With a better understanding of turbu- lence’s causes and behavior, some of this energy loss could potentially be avoided. In recent experiments at LAPD, an unexpectedly high amount of turbulence was de- tected when helium was used to create the plasma, which …

Agricultural Groundcover Update March 2024, Justin Laycock

Natural resources published reports

• In March, over 10% (1,577,000 ha) of the arable farmland in the south-west of Western Australia had less than 50% vegetative groundcover, which is inadequate to prevent wind erosion.

• The northern grainbelt had the highest risk of wind erosion and over 20% of this farmland had inadequate groundcover.

• About 1.3% (191,000 ha) of arable land had a high to very high risk of wind erosion because groundcover was less than 30%.

Comparative Predictive Analysis Of Stock Performance In The Tech Sector, Asaad Sendi

University of New Orleans Theses and Dissertations

This study compares the performance of deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, in predicting stock prices across five companies (AAPL, CSCO, META, MSFT, and TSLA) from July 2019 to July 2023. Key findings reveal that GRU models generally exhibit the lowest Mean Absolute Error (MAE), indicating higher precision, particularly notable for CSCO with a remarkably low MAE. While LSTM models often show slightly higher MAE values, they outperform Transformer models in capturing broader trends and variance in stock prices, as evidenced by higher R-squared (R2) values. Transformer models generally exhibit higher MAE …

Choreographing The Rhythms Of Observation: Dynamics For Ranged Observer Bipartite-Unipartite Spatiotemporal (Robust) Networks, Edward A. Holmberg Iv

University of New Orleans Theses and Dissertations

Existing network analysis methods struggle to optimize observer placements in dynamic environments with limited visibility. This dissertation introduces the novel ROBUST (Ranged Observer Bipartite-Unipartite SpatioTemporal) framework, offering a significant advancement in modeling, analyzing, and optimizing observer networks within complex spatiotemporal domains. ROBUST leverages a unique bipartite-unipartite approach, distinguishing between observer and observable entities while incorporating spatial constraints and temporal dynamics.

This research extends spatiotemporal network theory by introducing novel graph-based measures, including myopic degree, spatial closeness centrality, and edge length proportion. These measures, coupled with advanced clustering techniques like Proximal Recurrence, provide insights into network structure, resilience, and the effectiveness …

Modeling Prices In Limit Order Book Using Univariate Hawkes Point Process, Wenqing Jiang

University of New Orleans Theses and Dissertations

This thesis presents a time-changed geometric Brownian price model with the univariate Hawkes processes to trace the price changes in a limit order book. Limit order books are the core mechanism for trading in modern financial markets, continuously collecting outstanding buy and sell orders from market participants. The arrival of orders causes fluctuations in prices over time. A Hawkes process is a type of point process that exhibits self-exciting behavior, where the occurrence of one event increases the probability of other events happening in the near future. This makes Hawkes processes well-suited for capturing the clustered arrival patterns of orders …

The Pawn System: How Procedurally Adaptive Webbed Narratives Create Stories, Steven T. Bordelon

University of New Orleans Theses and Dissertations

This thesis describes the design, implementation, and testing of a novel procedural narrative system called the Procedurally Adaptive Webbed Narrative (PAWN) system. PAWN procedurally generates characters and, responding to choices made by the player, produces more responsive characters and relationships involving the player and these narrative agents. Initially, this thesis discusses other interactive narrative types that exist, such as emergent or event-driven narratives, along with their strengths and weaknesses. It then examines each aspect of PAWN, starting with initial actor generation, then moving to the capturing of game events and translating them into logical objects called Occurrences. These Occurrences are …

Classification Of Remote Sensing Image Data Using Rsscn-7 Dataset, Satya Priya Challa

Electronic Theses, Projects, and Dissertations

A novel technique for remote sensing image scene classification is employed using the Compact Vision Transformer (CVT) architecture. This model strengthens the power of deep learning and self-attention algorithms to significantly intensify the accuracy and efficiency of scene classification in remote sensing imagery. Through extensive training and evaluation of the RSSCNN7 dataset, our CVT-based model has achieved an impressive accuracy rate of 87.46% on the original dataset. This remarkable result underscores the prospect of CVT models in the domain of remote sensing and underscores their applicability in real-world scenarios. Our report furnishes an elaborate account of the model's architecture, training …

An Empirical Study On The Efficacy Of Llm-Powered Chatbots In Basic Information Retrieval Tasks, Naja Faysal

Electronic Theses, Projects, and Dissertations

The rise of conversational user interfaces (CUIs) powered by large language models (LLMs) is transforming human-computer interaction. This study evaluates the efficacy of LLM-powered chatbots, trained on website data, compared to browsing websites for finding information about organizations across diverse sectors. A within-subjects experiment with 165 participants was conducted, involving similar information retrieval (IR) tasks using both websites (GUIs) and chatbots (CUIs). The research questions are: (Q1) Which interface helps users find information faster: LLM chatbots or websites? (Q2) Which interface helps users find more accurate information: LLM chatbots or websites?. The findings are: (Q1) Participants found information significantly faster …

Traffic Analysis Of Cities In San Bernardino County, Sai Kalyan Ayyagari

Electronic Theses, Projects, and Dissertations

This research offers an in-depth analysis of vehicular traffic within San Bernardino County, California, aiming to spotlight congestion areas and suggest improvements for more efficient and sustainable transportation. Leveraging 2021 data from StreetLight Data, traffic patterns in 15 key cities were examined based on their population sizes, covering various vehicle types to dissect dynamics and flow. The methodology focused on analyzing trip purposes and metrics to calculate Vehicle Miles Traveled (VMT) and its influence on congestion and environmental factors.

Findings indicate considerable disparities in traffic volume, purposes, and timings across different urban areas, with population density and intercity connections significantly …

Truck Traffic Analysis In The Inland Empire, Bhavik Khatri

Electronic Theses, Projects, and Dissertations

This study undertakes a meticulous examination of truck traffic within the Inland Empire, focusing on the distribution and dynamics of medium and heavy-duty vehicles, to advocate for the region's transition to electric trucks. Utilizing advanced spatial analysis and data from Streetlight Data, it segments the region into six subregions, revealing distinct traffic patterns and environmental impacts. Notably, the research uncovers that the North Center and West zones, integral to the logistics and warehousing sectors, exhibit the highest traffic volumes, significantly influencing air quality and infrastructure.

Quantitative results from 2021 illustrate a pronounced disparity in truck activity: medium-weight vehicles accounted for …

Year-Round Co2 Emissions From The Drawdown Area Of A Tropical Reservoir: Strong Seasonal And Spatial Variation, Ícaro Barbosa, José R. Paranaíba, Sebastian Sobek, Sarian Kosten, Rafael M. Almeida, Vitor Duque, Natália Mendonça, Nathan Barros, Raquel Mendonça

School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations

Highlights

• Drawdown areas emitted 80% of reservoir CO2 with just 1/5 area.

• Emissions from reservoir shorelines near forests were greater than from areas near grassland.

• CO2 emissions increase with the distance from the water.

• Estimates of reservoir drawdown CO2 flux vary by ∼ 300 % across the seasons examined here.

• There was no discernible difference in CO2 emissions between day and night-time.

• A significant increase in CO2 fluxes was observed 30 min after a rewetting event.

Abstract

A growing body of literature points to drawdown areas as important sources of atmospheric CO2 within reservoirs. Yet seasonal and temporal patterns of …

Rapid Parameter Estimation Of Compact Binary Coalescences With Gravitational Waves, Caitlin Rose

Theses and Dissertations

In the age of multi-messenger astrophysics, fast, reliable information about gravitational-wave candidates is crucial for electromagnetic follow-up observations. While sky localization tells astronomers where to observe an event, source classification estimates the probability that the event might have an electromagnetic counterpart. Furthermore, astronomers need to have enough time to point their telescopes towards the fading light. Rapid PE is a low-latency parameter estimation scheme which parallelizes Bayesian inference by fixing the intrinsic parameters to a grid, and marginalizing over the extrinsic parameters at each grid point via Monte Carlo sampling. The gravitational-wave search pipelines identify the highest signal-to-noise ratio (SNR) …

Electrocatalytic Degradation Of Bisphenol A Using Nickel Sulfide Supported On Graphene Oxide And Cobalt Sulfide Supported On Graphene Oxide Composite Materials, Katherine Ellynn Wright

Theses and Dissertations

The degradation of bisphenol A (BPA) by nickel sulfide supported on graphene oxide (NiS/GO) and cobalt sulfide supported on graphene oxide (CoS/GO) composite materials was investigated. The catalysts were studied to determine the optimal conditions for BPA degradation using electrocatalysis. Electrocatalytic trials were performed using varying parameters, which included electrolyte concentrations of 1.0 g/L, 2.0 g/L, and 4.0 g/L Na2SO4, pHs of 2, 4, and 6, and constant currents of either 0.100 A or 0.200 A. During the reaction samples were taken before the current was applied and 30 minutes and 60 minute intervals to observe the degradation of …

Care-Teach: Proposing An Open-Source Approach To Personalized Learning, Jaime Augusto Alvarez Perez

Theses and Dissertations

Care-Teach is an algorithmic educational model that provides teachers and educators with the tools to create interactive, text-based lessons that address a student’s need for continuity and reinforcement. Care-Teach is built around two core components: a Student Behavior Profile and the Skill Tree. These models work together to give each student a personalized learning experience that reinforces their pre-existing strengths and inclinations. The Student Behavior Profile model keeps track of a learner’s inclinations and mood, utilizing metrics such as average response time and accuracy to categorize opportunities for educator involvement. The Skill Tree is an organizational …

Conditional Constrained And Unconstrained Quantization For A Uniform Distribution On A Hexagon, Christina Hamilton

Theses and Dissertations

In this thesis, we have considered a uniform distribution on a regular hexagon and the set of all its six vertices as a conditional set. For the uniform distribution under the conditional set first, for all positive integers n ≥ 6, we obtain the conditional optimal sets of n-points and the nth conditional quantization errors, and then we calculate the conditional quantization dimension and the conditional quantization coefficient in the unconstrained scenario. Then, for the uniform distribution on the hexagon taking the same conditional set, we investigate the conditional constrained optimal sets of n-points and the conditional constrained quantization errors …

A Multivariate Analysis Of The Gravitational Wave Signal Landscape From Core Collapse Supernovae, Raul Alberto Espinosa Perez

Theses and Dissertations

Core collapse supernovae (CCSN) are highly anticipated sources of gravitational waves (GW) during the on-going fourth observation run (O4) of GW detectors like LIGO and the future observation runs. The GW signal from the CCSN cannot be modeled mathematically. Several groups around the world have engaged in simulation of the predicted GW signals from CCSN sources. These simulations are carried out in supercomputers, and they incorporate general relativity, hydrodynamics, neutrino physics, mass and angular momentum of the stellar progenitor and nuclear equations of state (EoS). The output consists of simulated signals with varying duration, peak frequency, GW energy and time-frequency …

Bayesian Estimation Of Reproduction Numbers From Distributions Of Outbreaks Sizes: Branching Process Approach, Alberta Araba Johnson

Theses and Dissertations

The Generalized Poisson distribution is useful in modeling epidemiological processes as a branching stochastic processes problem. Our goal is to construct accurate and reliable estimators for the reproduction number (R0) (i.e., the number of secondary infections), particularly in the context of disease outbreaks modeled by a Galton-Watson process. Towards this goal, we construct the classical Bayes estimator, the Maximum Likelihood estimator, and the Empirical Bayes (EB) estimator under the Square Error Loss function in Chapter II. We prove that the Empirical Bayes estimator is asymptotically optimal and estimate the rate of convergence. We then proceed to monotonize the Empirical Bayes …

Mitigation Methods For Instrumental Artifacts In Gravitational Wave Data, Raghav Girgaonkar

Theses and Dissertations

Strain data from ground-based gravitational wave detectors are regularly affected by instrumental artifacts known as glitches. Such glitches form the background of false alarms in the detection of gravitational waves from compact binary coalescences, in-turn reducing search sensitivity. If left unaccounted, these glitches along with other non-stationarity may also contribute towards a corrupted representation of the statistical properties of detector noise, subsequently affecting parameter estimation for detected gravitational wave candidates. Therefore, effective data analysis methods to veto glitches and identify non-stationarities in detector data are crucial to enhancing the sensitivity of a gravitational wave search. In this thesis, we …

Conditional Quantization For Uniform Distributions On Line Segments And Regular Polygons, Tsianna Danielle Dominguez

Theses and Dissertations

Quantization for a Borel probability measure refers to the idea of estimating a given probability by a discrete probability with support containing a finite number of elements. If in the quantization some of the elements in the support are preselected, then the quantization is called a conditional quantization. In this thesis, we have investigated the conditional quantization for the uniform distributions defined on the unit line segments and m-sided regular polygons, where m ≥ 3, inscribed in a unit circle.

Variational Bias Sampling For Collaborative Filtering Recommender Systems, Prisca Stephens

Theses and Dissertations

Advancements in digitalization has yielded enormous growth of data on online platforms, overwhelming users with multitude of options to choose from. Recommender systems narrow down these options to a few relevant ones thereby facilitating the decision-making processes for users. This study presents a framework for integrating variational bias sampling into model-based collaborative filtering techniques for recommender systems. Variational bias sampling is a novel and unique way to account for random factors that affect explicit ratings in collaborative filtering recommender systems. A Gaussian distribution is used to model all the possible random factors that could affect ratings. Sampling user and item …

A Study Of Quantitative Reasoning Instructors’ Choices And Motivations When Teaching Quantitative Reasoning For The First Time, Trish Ann Harding

Theses and Dissertations

This qualitative study delves into the instructional decision-making processes of post-secondary instructors teaching quantitative reasoning (QR) courses for the first time. The study aims to address the gap in understanding how first-time QR instructors navigate the complexities of curriculum design and pedagogical strategies, and how these experiences contribute to their professional development. The research questions center on identifying the instructional decisions made by these instructors, exploring the factors influencing their decision-making, and understanding the impact of exercising agency has on their professional development. Through in-depth exploration, this study seeks to shed light on the challenges and opportunities faced by first-time …

How Mathematics Instructors Foster The Development Of Black Students' Mathematics Identity In Undergraduate Active Learning Mathematics Courses, Ashly J. Olusanya

Theses and Dissertations

Black students must overcome unique challenges to succeed in mathematics. Educators are tasked with identifying equitable teaching practices to support these students. Active learning (AL) is a teaching pedagogy that engages students in rigorous mathematical activities and encourages student participation. This research study will explore the professors’ beliefs about how students learn mathematics and why they use active learning in their collegiate mathematics courses. The study explores the connections between these beliefs and their reported use of instructional practices. The study also identifies the instructors’ beliefs about developing students’ mathematics identities, particularly their Black …

A Study On A Vector Complex Modified Korteweg-De Vries Equation, Changyan Shi

Theses and Dissertations

In this thesis, we systematically study a vector complex modified Kordeweg-de Vries equation by combining Hirota's bilinear method and the the Kadomtsev–Petviashvili (KP) reduction method. This vector nonlinear equation is a multi-component generalization of the well-known modified Kordeweg-de Vries (mKdV) equation and can be reduced to the known Hirota equation, Sasa-Satsuma (SS) equation, Sasa-Satsuma-mKdV equation as well as coupled Sasa-Satsuma equation. First, we bilinearize the vector complex mKdV equation under both the zero and nonzero boundary conditions by introducing auxiliary tau functions. Then, starting from two sets of bilinear equations of multi-component KP hierarchy and single-component KP-Toda …

Soil Carbon Sequestration Efforts In Arid And Semi-Arid Climates Under Conservation Agriculture And Reforestation, Samantha Lynn Colunga

Theses and Dissertations

Soil organic carbon (SOC) plays a vital role in the global carbon cycle and aids in climate change mitigation. However, deforestation and intensive agricultural practices threaten topsoil and carbon (C) pools through soil disturbances releasing vast amounts of carbon dioxide (CO2) by enhancing the decomposition of organic materials. Degraded soils typically lack stable aggregates, nutrient and water retention, and overall low fertility. In arid and semi-arid regions, soils are in an even more dire situation, as low precipitation and high temperatures slow down the accumulation of SOC. This thesis has two objectives: 1) to evaluate whether conservation tillage practices increase …

Pedestrian Pathing Prediction Using Complex Contextual Behavioral Data In High Foot Traffic Settings, Laurel Bingham

All Graduate Theses and Dissertations, Fall 2023 to Present

Ensuring the safe integration of autonomous vehicles into real-world environments requires a comprehensive understanding of pedestrian behavior. This study addresses the challenge of predicting the movement and crossing intentions of pedestrians, a crucial aspect in the development of fully autonomous vehicles.

The research focuses on leveraging Honda's TITAN dataset, comprising 700 unique clips captured by moving vehicles in high-foot-traffic areas of Tokyo, Japan. Each clip provides detailed contextual information, including human-labeled tags for individuals and vehicles, encompassing attributes such as age, motion status, and communicative actions. Long Short-Term Memory (LSTM) networks were employed and trained on various combinations of contextual …

Generative Ai In Education From The Perspective Of Students, Educators, And Administrators, Aashish Ghimire

All Graduate Theses and Dissertations, Fall 2023 to Present

This research explores how advanced artificial intelligence (AI), like the technology that powers tools such as ChatGPT, is changing the way we teach and learn in schools and universities. Imagine AI helping to summarize thick legal documents into something you can read over a coffee break or helping students learn how to code by offering personalized guidance. We looked into how teachers feel about using these AI tools in their classrooms, what kind of rules schools have about them, and how they can make learning programming easier for students. We found that most teachers are excited about the possibilities but …

A Review Of Student Attitudes Towards Keystroke Logging And Plagiarism Detection In Introductory Computer Science Courses, Caleb Syndergaard

All Graduate Theses and Dissertations, Fall 2023 to Present

The following paper addresses student attitudes towards keystroke logging and plagiarism prevention measures. Specifically, the paper concerns itself with changes made to the “ShowYourWork” plugin, which was implemented to log the keystrokes of students in Utah State University’s introductory Computer Science course, CS1400. Recent work performed by the Edwards Lab provided insights into students’ feelings towards keystroke logging as a measure of deterring plagiarism. As a result of that research, we have concluded that measures need to be taken to enable students to have more control over their data and assist students to feel more comfortable with keystroke logging. This …

Advancing Game Development And Ai Integration: An Extensible Game Engine With Integrated Ai Support For Real-World Deployment And Efficient Model Development, Ryan Anderson

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis introduces Acacia, a game engine with built-in artificial intelligence (AI) capabilities. Acacia allows game developers to effortlessly incorporate Reinforcement Learning (RL) algorithms into their creations. By tagging game elements to convey information about the game state or rewards, developers gain precise control over how RL algorithms interact with their games, mirroring real player behavior or providing full knowledge of the game world.

To showcase Acacia’s versatility, the thesis presents three games across different genres, each demonstrating the engine’s AI plugin. The goal is to establish Acacia as a preferred resource for creating 2D games with RL support without …