Grokking Applied To Chaotic Iterates Of The Logistic Map,
2025
East Tennessee State University
Grokking Applied To Chaotic Iterates Of The Logistic Map, Felix Donkoh
Electronic Theses and Dissertations
This thesis investigates grokking, the delayed transition from memorization to generalization in neural networks trained on deterministic chaotic data. Using an integer–arithmetic discretization of the logistic map, yn+1 =( a yn(p − yn))/ p 2 , bounded aperiodic sequences were generated across control parameters α ranging from 3.0 to 4.0. Transformer-based models displayed characteristic grokking curves. In periodic and chaotic regimes, validation accuracy rose suddenly after long plateaus, while at the Feigenbaum boundary (α ≈ 3.57) generalization failed completely. Increasing data diversity restored learning in chaotic domains, and explicit α–conditioning enabled a single network to generalize across all regimes. A …
An Income Subsystem As A Discrete Stochastic Leslie System: A Simulation-Based Approach,
2025
East Tennessee State University
An Income Subsystem As A Discrete Stochastic Leslie System: A Simulation-Based Approach, Fahd Nii Okantah Cobblah
Electronic Theses and Dissertations
This thesis formulates the household-income engine of an integrated population sim- ulator as a Discrete Stochastic Leslie System (DSLS). The nonnegative state vector nt ∈ Rk + aggregates income, savings, debt, employment, and transfers. (Here, the subscript + denotes the positive cone, i.e., vectors with nonnegative components). Annual evolution is linear in state, stochastic in coefficients: nt+1 = Ttnt + εt, with Tt : Rk + → Rk + cone-preserving. Exogenous macro drivers (inflation, employment, tax, salary inflation, mortgage) are forecast via ARIMA; forecasts multiply entries of Tt, preserving linearity in expectation while introducing realistic temporal correlation. The discrete-event implemented …
Feedback Strategies In The Market With Uncertainties,
2025
University of South Alabama
Feedback Strategies In The Market With Uncertainties, Mustapha Nyenye Issah
Graduate Theses and Dissertations (2019 - present)
This paper explores how established firms use strategic advertising to deter new competitors in uncertain markets. Specifically, it models a situation where market demand evolves unpredictably - captured by the CKLS stochastic process, and the incumbent firm may be either strong or weak, a fact hidden from potential entrants. For a company already in the market, advertising is not just about driving immediate sales, it is a strategic tool to project an image of strength and deter potential new competitors. On the other side, a business thinking about entering that market faces a high-stakes, irreversible decision. It will typically hold …
On The Scalability Of Anisotropic Mesh Adaptation On Distributed And Shared Memory Architectures For Numerical Approximations,
2025
Old Dominion University
On The Scalability Of Anisotropic Mesh Adaptation On Distributed And Shared Memory Architectures For Numerical Approximations, Kevin Mark Garner Jr.
Computer Science Theses & Dissertations
Mesh generation is a critical component in numerical approximations of Partial Differential Equations (PDEs). One such example includes Computational Fluid Dynamics (CFD), as CFD simulations in turn are crucial for applications in many industries, such as personalized healthcare and the design of aerospace vehicles. Generating high quality meshes for large-scale CFD problems presents a significant bottleneck in the CFD workflow. This dissertation proposes “fast,” parallel 3D mesh generation methodologies that are designed to leverage the concurrency offered by emerging High-Performance Computing (HPC) architectures. First, a distributed memory method is presented that integrates a sequential state-of-the-art isotropic, advancing front local reconnection-based …
Estimation Of 3d Facial Dynamics With Nonlinear Filters For Position Tracking,
2025
The University of Texas Rio Grande Valley
Estimation Of 3d Facial Dynamics With Nonlinear Filters For Position Tracking, Thoa Thieu, Roderick Melnik
School of Mathematical & Statistical Sciences Faculty Publications
This study presents a comparative evaluation of three nonlinear state estimation filters, the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF), for the task of 3D facial landmark tracking. Using a publicly available dataset, we assess each filter's performance under both deterministic (noise-free) and stochastic (noisy) conditions. Metrics such as mean squared error (MSE), convergence rates of state and covariance estimates, and consistency over time are used to quantify tracking performance. Results show that the EKF consistently outperforms the UKF and PF, achieving faster convergence and lower estimation error, particularly in scenarios characterized by mild nonlinearity. …
Ai-Driven Optimization Of Wind Energy Distribution In Texas Using Multi-Agent Reinforcement Learning,
2025
Southern Methodist University
Ai-Driven Optimization Of Wind Energy Distribution In Texas Using Multi-Agent Reinforcement Learning, Waleed Amer, Owolabi Oluwadamilola, Bassey Ogbonnaya
SMU Data Science Review
Abstract. The integration of large-scale wind power into modern electrical grids presents persistent challenges due to variability, curtailment, and compliance with operational constraints. This study proposes a multi-agent reinforcement learning (MARL) framework for optimizing wind energy distribution within the Texas power grid. The system employs three specialized agents—managing wind curtailment, storage utilization, and load adjustments—to collaboratively balance supply and demand under dynamic grid conditions. Using historical operational data from the Electric Reliability Council of Texas (ERCOT), the framework was trained and evaluated on a range of scenarios encompassing both typical and extreme operating conditions. Results demonstrate substantial performance improvements compared …
The Odds Don’T Lie: Mathematical Reasoning And Societal Ignorance In Don’T Look Up,
2025
West Morris Central High School
The Odds Don’T Lie: Mathematical Reasoning And Societal Ignorance In Don’T Look Up, Nysa Vedwan, Shane Carey
LASER Journal
In Adam McKay’s 2021 satirical sci-fi movie Don’t Look Up, two astronomers discover a comet heading directly toward Earth. Despite overwhelming evidence and near-certainty of global extinction, their warnings are ignored and ridiculed. This paper discusses the mathematical and scientific foundations of the movie’s social and political reception, and specifically focuses on orbital prediction and probabilistic modeling as they relate to public understanding of risk. This paper shows how data is often undermined by political and social dynamics, by connecting the fictional events of the movie with real-world crises like the COVID-19 pandemic and the climate emergency. In Don’t Look …
An Exploration Of Image Segmentation Techniques For Real-Time Product Detection,
2025
San Jose State University
An Exploration Of Image Segmentation Techniques For Real-Time Product Detection, Andrew C. Dunton
Master's Theses
Recent progress in LLMs enables advanced multimodal understanding, but their high computational cost necessitates monetization strategies like interactive advertising. While bounding boxes show promise for this concept, they can lack precision and visual appeal. Image segmentation offers a superior solution but faces a dual problem: traditional models demand scarce, costly training data, and open-vocabulary segmentation models like SAM are class-agnostic, unable to semantically identify a "consumer product" object class. In this research, we address these limitations by: 1) developing the Prompt-Guided Inpainting Framework (PGIF), which injects negative prompts to generate robustly annotated synthetic segmented product images; 2) investigating the Class-Agnostic …
Towards Robust Autonomous Systems: Handling Multi-Modal Uncertainties In Gps-Denied Environments,
2025
Louisiana Tech University
Towards Robust Autonomous Systems: Handling Multi-Modal Uncertainties In Gps-Denied Environments, Vivya Kalidindi
Doctoral Dissertations
This dissertation focuses on designing a robust and uncertainty-aware framework for autonomous systems operating in GPS-denied environments, such as indoor infrastructures, underground tunnels, and lunar surfaces. The proposed framework addresses the challenges posed by multi-modal uncertainties, including sensor noise, distributional shifts under adverse conditions, and conflicting decision-making preferences. These challenges compromise the reliability and adaptability of autonomous platforms. To overcome these challenges, the proposed framework adopts a layered architecture that integrates advanced methodologies across the sensing, perception, and decision-making layers. At the sensing layer, an Edge-Kalman Filter combined with a density ratio-based update mechanism is employed to reduce aleatoric uncertainty …
Comparing Machine Learning, Deep Learning, And Reinforcement Learning Performance In Culex Pipiens Predictive Modeling,
2025
The University of Texas Rio Grande Valley
Comparing Machine Learning, Deep Learning, And Reinforcement Learning Performance In Culex Pipiens Predictive Modeling, Wei Yin, Sanad H. Ragab, Michael G. Tyshenko, Teresa Patricia Feria-Arroyo, Tamer Oraby
School of Mathematical & Statistical Sciences Faculty Publications
Several machine learning (ML) and deep learning (DL) methods have been used to predict the presence of species in classification problems. Another set of methods, called reinforcement learning (RL), has been used in training agents to perform various tasks, but not in predicting species distribution. Culex pipiens (Diptera: Culicidae), commonly known as the common house mosquito, is a globally distributed species prevalent in temperate and subtropical regions. They serve as a primary vector for West Nile Virus (WNV), a mosquito-borne pathogen that affects humans and other animals. The study objective is to compare the performance of logistic regression, random forest …
Using Compartmental Systems Of Ordinary Differential Equations And Optimal Control Theory To Compute Ideal Quantities Of Mentors For Student Populations,
2025
George Mason University
Using Compartmental Systems Of Ordinary Differential Equations And Optimal Control Theory To Compute Ideal Quantities Of Mentors For Student Populations, Timofey B. Gafurov
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
[Kyda] Multi-Population Seir Modeling With Data Assimilation: Uncovering Covid-19 Disparities Beyond Aggregate Statistics,
2025
Illinois State University
[Kyda] Multi-Population Seir Modeling With Data Assimilation: Uncovering Covid-19 Disparities Beyond Aggregate Statistics, Emmanuel Fleurantin
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Data-Driven Modeling Of Dynamics And Adaptation In Mapk Pathway,
2025
George Mason University
Data-Driven Modeling Of Dynamics And Adaptation In Mapk Pathway, Elisha Erzoah, Maria Emelianenko, Mariaelena Pierobon
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Explainable Post-Operative Patients Recovery Prediction Following Elective Brain Tumor Resection: A Precision Medicine Approach,
2025
Johns Hopkins University
Explainable Post-Operative Patients Recovery Prediction Following Elective Brain Tumor Resection: A Precision Medicine Approach, Eleanor Belkin
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Multi-Resolution Graph Neural Networks For Spread Prediction,
2025
Illinois State University
Multi-Resolution Graph Neural Networks For Spread Prediction, Petr Kisselev
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
[Kyda] Biologically Grounded Surrogate-Driven Parameter Inference For Sparsely Observed Dynamical Systems,
2025
Johns Hopkins University
[Kyda] Biologically Grounded Surrogate-Driven Parameter Inference For Sparsely Observed Dynamical Systems, Joshua C. Macdonald
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Modeling And Analysis Of The Impacts Of The Rise In Ai Use On Data Center Growth, Resulting Workforce Displacement, And Environmental Impacts Through A Coupled System Of Ordinary Differential Equations Within A Feedback Framework,
2025
Freedom High School
Modeling And Analysis Of The Impacts Of The Rise In Ai Use On Data Center Growth, Resulting Workforce Displacement, And Environmental Impacts Through A Coupled System Of Ordinary Differential Equations Within A Feedback Framework, Divya Laddha, Alonso Gaberial Ogueda, Padmanabhan Seshaiyer
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Investigating The Basic Reproduction Number For An Avian Influenza Model,
2025
Virginia Polytechnic Institute and State University
Investigating The Basic Reproduction Number For An Avian Influenza Model, Omar Saucedo
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Code, Paper, Scissors: What To Cut In The Age Of Ai,
2025
University of Chicago
Code, Paper, Scissors: What To Cut In The Age Of Ai, Dmitry Kondrashov
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Computational Models For Pre-Lens Tear Film Drug Concentration Dynamics With Drug Supply From A Contact Lens And Drug Exchange During Blinking,
2025
George Mason University
Computational Models For Pre-Lens Tear Film Drug Concentration Dynamics With Drug Supply From A Contact Lens And Drug Exchange During Blinking, Mazen A. Althobaiti
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
