Generalizing Threshold-Based Multiparty Computation To Ramp Schemes,
2025
Clemson University
Generalizing Threshold-Based Multiparty Computation To Ramp Schemes, Christian Tucker
All Theses
Secure multiparty computation (MPC) enables multiple participants to jointly compute functions over their private inputs without revealing them. Classical threshold based protocols, such as the BGW protocol, perform computations on scalar values using (k,n)-threshold secret sharing. While these protocols provide strong security guarantees, they become computationally expensive when applied to large matrices or multiple secret values. In this work, we investigate the use of ramp schemes, secret sharing schemes that encode sets of secrets with a trade-off between privacy and efficiency, to generalize BGW computations. We show that the linear operations performed on shares (k,n)-threshold schemes in BGW can be …
Instance-Adaptive Gated Fusion Of Multi-Transform Image Representations,
2025
University of Texas at El Paso
Instance-Adaptive Gated Fusion Of Multi-Transform Image Representations, Prince Appiah
Open Access Theses & Dissertations
This dissertation proposes the Instance-Adaptive Gated Fusion (IAGF) framework, a novel deep learning architecture for adaptive and interpretable fusion of multiple time–series image transformations. While existing methods rely on static concatenation or dataset-level optimization, IAGF introduces a learnable gating mechanism that dynamically assigns per-instance weights to Recurrence Plots (RP), Gramian Angular Summation Fields (GASF), and Gramian Angular Difference Fields (GADF). The gating layer performs a convex fusion of transformation-specific embeddings under a softmax constraint, ensuring mathematical stability and interpretability. An entropy-regularized objective prevents dominance collapse and promotes balanced exploration of transformations during training. Comprehensive experiments across eighteen benchmark datasets, spanning …
Stochastic Functional Data-Driven Models For Real-Time Battery Health Forecasting Under Dynamic Operating Conditions,
2025
East Tennessee State University
Stochastic Functional Data-Driven Models For Real-Time Battery Health Forecasting Under Dynamic Operating Conditions, Joshua Owusu
Electronic Theses and Dissertations
This thesis provides an effective statistical model to predict the real-time state of lithium-ion batteries for reliable Battery Management Systems (BMS). It highlights battery data (voltage, current, temperature) as smooth functional curves. The principal method demonstrates diminishing trends to health outcomes like State of Health (SoH) and Remaining Useful Life (RUL) by employing Functional Principal Component Analysis (FPCA) and Bayesian Functional Linear Models (FLMs). The primary objective is to figure out how uncertain forecasts are. Simulations demonstrate that the highest accuracy (lowest MSE) is achieved through low noise levels along with large sample sizes. The final system provides a highly …
The Food Truck: A Multi-Product Newsvendor With Expectile Risk,
2025
East Tennessee State University
The Food Truck: A Multi-Product Newsvendor With Expectile Risk, Sekyiwaah Nuamah
Electronic Theses and Dissertations
The Newsvendor Problem is a fundamental model in Operation Research and Supply Chain Management used to determine the optimal order quantity under uncertain demand to minimize expected costs.
This research extends the classical Newsvendor problem to a multi--product setting, addressing the risk of asymmetric cost structures faced by a food truck. This thesis introduces expectile risk measures to quantify and manage uncertainty in demand, moving beyond traditional risk metrics. To evaluate the impact of expectile-based decision-making, we analyze three types of demand distributions--simple, symmetric, and skewed. For skewed distributions, we apply linear spline inverse interpolation to derive expectile values from …
Deep Learning With Kalman Filter,
2025
East Tennessee State University
Deep Learning With Kalman Filter, Rexford Julius Quaye
Electronic Theses and Dissertations
This thesis presents an extension of the Kalman filter to handle nonlinear and non-Gaussian systems. The standard Kalman filter is optimal under Gaussian assumptions but struggles with more complex noise models. This work introduces a novel loss function based on the Mahalanobis distance, which incorporates the covariance structure of measurement errors, enabling the filter to adapt to non-Gaussian scenarios. The neural network framework is applied to predict the system’s process model, while retaining the classical Kalman measurement update. The proposed methodology is demonstrated through examples of car position and rocket altitude tracking. The results show that the new approach performs …
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 …
A Leslie System For A Demographic Simulation: From An Actuarial Point Of View,
2025
East Tennessee State University
A Leslie System For A Demographic Simulation: From An Actuarial Point Of View, David Kings
Electronic Theses and Dissertations
This thesis develops a discrete stochastic linear systems interpretation of age–stage demographic evolution grounded in Leslie operators and realized in a discrete-event simulation implemented with salabim. The central claim is that one annual cycle of the simulation constitutes a cone-preserving, stochastic affine transformation on a high- dimensional population state vector indexed by age, sex, marital status, household type, employment, and education, and that the composition of yearly operators yields a random matrix product whose top Lyapunov exponent is the stochastic counterpart of the Perron–Frobenius growth rate (Caswell, 2001; Tuljapurkar, 1997)[1, 2]. The actuarial bridge is constructed by mapping simulated survival …
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.
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.
