Digital Commons Network^™

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. …

Insecticide-Treated Net Use And Elimination Of Malaria In Sub-Saharan African Countries: Assessing The Global Technical Strategy Using An Evolutionary Game Approach, Laxmi, Tamer Oraby, Michael G. Tyshenko, Ina Danquah, Samit Bhattacharyya

School of Mathematical & Statistical Sciences Faculty Publications

Background: Malaria continues to be a major public health challenge in Sub-Saharan Africa (SSA), where the majority of the countries have not met the World Health Assembly's endorsed Global Technical Strategy (GTS) milestones in 2020 for malaria reduction. Insecticide-treated net (ITN) usage is a well-established and effective intervention, often outperforming other measures such as indoor residual spraying (IRS). However, multiple survey studies have reported improper use of ITNs across various SSA countries. This misuse likely poses an important barrier to the intervention's success, although it remains a largely untested hypothesis.

Methods: We developed a behaviour-incidence model and statistical analysis of …

Quantization For The Mixtures Of Overlap Probability Distributions, Asha Barua, Angelina Chavera, Ivan Djordjevic, Valerie Manzano, Sergio Soto Quintero, Mrinal Kanti Roychowdhury, Hilda Tejeda

School of Mathematical & Statistical Sciences Faculty Publications

Optimal quantization for mixed distributions has emerged as a compelling area of study. In this work, we have focused on a mixed distribution formed from two uniform distributions with partially overlapping supports. For this class of distributions, we have examined the structure of optimal sets of n-means and the corresponding nth quantization errors for all positive integers n. Initially, we explicitly determined the optimal sets and quantization errors for 1 < = n < = 6. Subsequently, we established several key lemmas and propositions and proposed an algorithm that facilitates the computation of optimal n-means and quantization errors for all n >= 5. Numerical results are also presented to illustrate the application of the algorithm in deriving these quantities. The findings of this study offer valuable insight and serve as a …

Variants Of Conway Checkers And K-Nacci Jumping, Glenn Bruda, Joseph Cooper, Kareem Jaber, Raul Marquez, Steven J. Miller

School of Mathematical & Statistical Sciences Faculty Publications

Conway Checkers is a game played with a checker placed in each square of the lower half of an infinite checkerboard. Pieces move by jumping over an adjacent checker, removing the checker jumped over. Conway showed that it is not possible to reach row 5 in finitely many moves by weighting each cell in the board by powers of the golden ratio such that no move increases the total weight.

Other authors have considered the game played on many different boards, including generalizing the standard game to higher dimensions. We work on a board of arbitrary dimension, where we allow …

Semi-Hyponormality Of Commuting Pairs Of Hilbert Space Operators, Raul E. Curto, Jasang Yoon

School of Mathematical & Statistical Sciences Faculty Publications

We first find an explicit formula for the square root of positive 2 ×2 operator matrices with commuting entries, and then use it to define and study semi-hyponormality for commuting pairs of Hilbert space operators. For the well-known 3–parameter family 𝑊(𝛼,𝛽)⁡(𝑎,𝑥,𝑦) of 2–variable weighted shifts, we completely identify the parametric regions in the open unit cube where 𝑊(𝛼,𝛽)⁡(𝑎,𝑥,𝑦) is subnormal, hyponormal, semi-hyponormal, and weakly hyponormal. As a result, we describe in detail concrete sub-regions where each property holds. For instance, we identify the specific sub-region where weak hyponormality holds but semi-hyponormality does not hold, and vice versa. To accomplish this, …

Precision-Weighted Federated Learning, Jonatan Reyes, Lisa Di Jorio, Cecile Low-Kam, Marta Kersten-Oertel

Computer Science Faculty Publications

Federated learning (FL) using the federated averaging (FedAvg) algorithm has shown great advantages for large-scale applications that rely on collaborative learning, especially when the training data is either unbalanced or inaccessible due to privacy constraints. We hypothesize that FedAvg underestimates the full extent of heterogeneity of data when the aggregation is performed. We propose Precision-Weighted Federated Learning (PW) a novel algorithm that takes into account the second raw moment (uncentered variance) of the stochastic gradient when computing the weighted average of the parameters of independent models trained in a FL setting. With PW, we address the communication and statistical challenges …

Adaptive Von Mises–Fisher Likelihood Loss For Supervised Deep Time Series Hashing, Juan Manuel Perez, Kevin Garcia, Brooklyn Berry, Dongjin Song, Yifeng Gao

Computer Science Faculty Publications

Indexing time series by creating compact binary representations is a fundamental task in time series data mining. Recently, deep learning-based hashing methods have proven effective for indexing time series based on semantic meaning rather than just raw similarity. The purpose of deep hashing is to map samples with the same semantic meaning to identical binary hash codes, enabling more efficient search and retrieval. Unlike other supervised representation learning methods, supervised deep hashing requires a discretization step to convert real-valued representations into binary codes, but this can induce significant information loss. In this paper, we propose a von Mises–Fisher (vMF) hashing …

Adsorption-Photodegradation Performance Of Silver Titanate For Methylene Blue Removal: Kinetics, Thermodynamics And Isotherm Studies, Nawrin Rahman Shefa, Most. Afroza Khatun, Ahmed Hasnain Jalal, M. Jasim Uddin, Md. Wasikur Rahman

Physics & Astronomy Faculty Publications

Silver titanate (AgTO) was synthesized via an ion-exchange reaction between sodium titanate and silver nitrate. This study investigates the efficiency of AgTO in the combined adsorption-photodegradation process for removing Methylene Blue (MB). The synthesized material was characterized using FTIR, XRD, SEM, and EDX techniques. Batch adsorption experiments were conducted to assess the effects of AgTO dosage (0.1–1.0 g/L), initial MB concentration (5–20 mg/L), pH (3–11), and temperature (313–333 K). The highest MB removal efficiency (90 %) was achieved at 313 K, pH 3, and an initial MB concentration of 5 mg/L. The photocatalytic performance of AgTO was further evaluated under …

The Dissemination Potential Of Microsporidia Mb In Anopheles Arabiensis Mosquitoes Is Modulated By Temperature, Fidel Gabriel Otieno, Priscille Barreaux, Affognon Steeven Belvinos, Edward Edmond Makhulu, Thomas Ogao Onchuru, Anne Wambui Wairimu, Stancy Mandere Omboye, Cynthia Nyambura King’Ori, Bonoukpoè Mawuko Sokame, Anthony Kebira Nyamache, Jeremy Keith Herren

All Peer-Reviewed Publications

Microsporidia MB, a vertically transmitted endosymbiont of Anopheles mosquitoes, shows strong potential as a malaria control agent due to its ability to inhibit Plasmodium development within the mosquito host. To support its deployment in malaria transmission reduction strategies, it is critical to understand how environmental factors, particularly temperature, influence its infection dynamics. In this study, we investigated the impact of four temperature regimes (22 °C, 27 °C, 32 °C, and 37 °C) on Microsporidia MB prevalence and infection intensity by rearing mosquito larvae under controlled laboratory conditions. Our results demonstrate that elevated temperatures, especially 32 °C, significantly enhance both larval …

Predicting Cryptocurrency Prices Using Stochastic Modeling, Reem Hani Al Omari

Theses

Cryptocurrencies are digital currencies that operate independently of central banks and governments. They were designed to overcome the limitations of traditional financial systems through a decentralized, peer-to-peer electronic cash mechanism. Trading in cryptocurrencies offers several advantages, including decentralized and efficient transactions, reduced costs through the elimination of intermediaries, investment opportunities across exchanges, and seamless cross-border remittances. Modeling cryptocurrency prices is therefore essential, not only due to these advantages but also because of the substantial market capitalization of cryptocurrencies, estimated to exceed 900 billion dollars according to CoinMarketCap [6]. The main objective of this thesis is to propose a predictive framework …

Learning-Assisted Schedulability Analysis: Opportunities And Limitations, Sanjoy Baruah, Pontus Ekberg, Marion Sudvarg

Computer Science Faculty Research & Creative Works

We present the first (to our knowledge) Deep-Learning based framework for real-time schedulability-analysis that guarantees to never incorrectly mis-classify an unschedulable system as being schedulable, and is hence suitable for use in safety-critical scenarios. We relate applicability of this framework to well-understood concepts in computational complexity theory: membership in the complexity class NP. We apply the framework upon the widely-studied schedulability analysis problems of determining whether a given constrained-deadline sporadic task system is schedulable on a preemptive uniprocessor under both Deadline-Monotonic and EDF scheduling. As a proof-of-concept, we implement our framework for Deadline-Monotonic scheduling, and demonstrate that it has a …

Enhancing Smart Contract Security Using A Code Representation And Gan Based Methodology, Dileep Kumar Murala, Samia Loucif, K. Vara Prasada Rao, Habib Hamam

All Works

Smart contracts are changing many business areas with blockchain technology, but they still have vulnerabilities that can cause major financial losses. Because deployed smart contracts (SCs) are irreversible once deployed, fixing these vulnerabilities before deployment is critical. This research introduces a new method that combines code embedding with Generative Adversarial Networks (GANs) to find integer overflow vulnerabilities in smart contracts. Using Abstract Syntax Trees, we can vectorize the source code of smart contracts while keeping all of the important contract characteristics and going beyond what can be achieved with conventional textual or structural analysis. Synthesizing contract vector data using GANs …

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 …

Cnn Based Deep Learning Modeling With Explainability Analysis For Detecting Fraudulent Blockchain Transactions, Mohammad Hasan, Mohammad Shahriar Rahman, Mohammad Jabed Morshed Chowdhury, Iqbal H. Sarker

Research outputs 2022 to 2026

In the era of growing cryptocurrency adoption, Blockchain has emerged as a leading player in the digital payment landscape. However, this widespread popularity also brings forth various security challenges, including the need to safeguard against fraudulent activities. One of the paramount challenges in this regard is the detection of fraudulent transactions within the realm of Bitcoin data. This task significantly influences the trust and security of digital payments. Yet, it's a formidable challenge given the relatively low occurrence of fraudulent Bitcoin transactions. While deep learning techniques have demonstrated their prowess in fraud detection, there remains a scarcity of studies exploring …

Zero Day Malware Detection With Alpha: Fast Dbi With Transformer Models For Real World Application, Matthew Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

The effectiveness of an AI model in accurately classifying novel malware hinges on the quality of the features it is trained on, which in turn depends on the effectiveness of the analysis tool used. Peekaboo, a Dynamic Binary Instrumentation (DBI) tool, defeats malware evasion techniques to capture authentic behavior at the Assembly (ASM) instruction level. This behavior exhibits patterns consistent with Zipf's law, a distribution commonly seen in natural languages, making Transformer models particularly effective for binary classification tasks. We introduce Alpha, a framework for zero-day malware detection that leverages Transformer models, Support Vector Machines (SVMs) and ASM language features. …

Defeating Evasive Malware With Peekaboo: Extracting Authentic Malware Behavior With Dynamic Binary Instrumentation, Matthew Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

The accuracy of Artificial Intelligence (AI) in malware detection is dependent on the features it is trained with, where the quality and authenticity of these features is dependent on the dataset and the analysis tool. Evasive malware, that alters its behavior in analysis environments, is challenging to extract authentic features from where widely used static and dynamic analysis tools have several limitations. However, Dynamic Binary Instrumentation (DBI) allows deep and precise control of the malware sample, thereby facilitating the extraction of authentic behavior from evasive malware. Considering the limitations of malware analysis for use with AI, this research had two …

Cosmetic Surgery And Physiological Disorder: You Should Talk To Someone About Your Unwinding Anxiety, Mona Muzammil, Washain Muzammil

School of Integrative Biological & Chemical Sciences (Formerly Dept. of Chemistry)

Body dysmorphic disorder is a mental health condition in which you can't stop thinking about one or more perceived defects or flaws in your appearance — a flaw that appears minor or can't be seen by others. But you may feel so embarrassed, ashamed and anxious that you may avoid many social situations.

When you have body dysmorphic disorder, you intensely focus on your appearance and body image, repeatedly checking the mirror, grooming or seeking reassurance, sometimes for many hours each day. Your perceived flaw and the repetitive behaviors cause you significant distress and impact your ability to function in …

Tuning A Magnetic Energy Scale With Pressure And Field In Ute2, Hyunsoo Kim, I. Lin Liu, Wen Chen Lin, Yun Suk Eo, Sheng Ran, Nicholas P. Butch, Johnpierre Paglione

Physics Faculty Research & Creative Works

When a fragile ordered state is suppressed to zero temperature, a quantum phase transition occurs, which is often marked by the appearance of unconventional superconductivity. While the quantum critical point can be hidden, the influence of the quantum criticality extends to fairly high temperatures, manifesting non-Fermi liquid behavior in a wide range of the field-temperature-pressure phase space. Here, we report the tuning of a magnetic energy scale in the heavy-fermion superconductor UTe₂, previously identified with a peak in the c-axis electrical transport temperature dependence, using applied hydrostatic pressures and a-axis-oriented magnetic fields as complementary (and opposing) tuning parameters: …

Applications Of Machine Learning In Gravitational-Wave Research With Current Interferometric Detectors, Elena Cuoco, Marco Cavaglià, Ik Siong Heng, David Keitel, Christopher Messenger

Physics Faculty Research & Creative Works

This article provides an overview of the current state of machine learning in gravitational-wave research with interferometric detectors. Such applications are often still in their early days but have reached sufficient popularity to warrant an assessment of their impact across various domains, including detector studies, noise and signal simulations, and the detection and interpretation of astrophysical signals. In detector studies, machine learning could be useful to optimize instruments like LIGO, Virgo, KAGRA, and future detectors. Algorithms could predict and help in mitigating environmental disturbances in real time, ensuring detectors operate at peak performance. Furthermore, machine-learning tools for characterizing and cleaning …

Time-Resolved 3d Momentum Spectroscopy In Continuous Wave Atomic Photoionization Experiments, K. L. Romans, B. P. Acharya, A. H.N.C.De Silva, K. Foster, O. Russ, Daniel Fischer

Physics Faculty Research & Creative Works

An experimental continuous-wave (cw) pump-probe scheme is demonstrated by investigating the population and photoionization dynamics of an atomic system. In particular, 6Li atoms are initially prepared in optically pumped 22S1/2 and 22P3/2 states before being excited via multi-photon absorption from a tunable femtosecond laser. The subsequent cascade back to the ground state is analyzed by ionizing the atoms in the field of a cw optical dipole trap laser. Conventional spectroscopic methods, such as standard cold-target recoil ion momentum spectroscopy or velocity map imaging, cannot provide simultaneous momentum and time-resolved information on an event-by-event basis for the system investigated here. The …

Scalable And Durable Superhydrophobic-Superoleophilic Nanostructured Zinc-Coated Steel Wool For Efficient Oil-Water Separation, Nawzat Saadi, Laylan B. Hassan, Tansel Karabacak

Faculty Scholarship

We present a scalable, low-cost method to fabricate durable, superhydrophobic-superoleophilic steel wool for oil- water separation. The process involves zinc electroplating, hot water treatment (75◦C) to grow ZnO nanowires, and functionalization with 1-dodecanethiol. The resulting material exhibits a water contact angle of ~160◦ and an oil contact angle of ~0◦. It achieves a high oil absorption capacity (~18 times its weight) and separation efficiencies of ~99–100 % for cyclohexane (within 60 s) and crude oil (in under 3 min). The material demon- strates exceptional corrosion resistance in saline environments (14 wt% NaCl), retains its nanostructural integrity after prolonged exposure, and …

Machine Learning For Anomaly Detection In Neural Network Security And Srf Cavities, Hal Ferguson

Electrical & Computer Engineering Theses & Dissertations

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications.

First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves …

Large Language Models (Llms) For Clinical Note Generation: International Classification Of Disease (Icd) Code, Knowledge Graph (Kg) And Prompt Evaluation, Ivan P. Makohon

Computer Science Theses & Dissertations

In the past decade, a surge in the amount of electronic health record (EHR) data in the United States occurred, driven by a favorable policy environment created by the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 and the 21st Century Cures Act of 2016. Clinical notes for patients’ assessments, diagnoses, and treatments are captured in these EHRs in free-form text by physicians, who spend a considerable amount of time entering them. Manually writing these notes is time-consuming, increasing patient waiting times and potentially delaying diagnoses. Large language models (LLMs), such as GPT-4o, possess the ability …

Re: Approval Letter For The Butte Priority Soils Operable Unit Clark Tailings Consolidated Waste Management Area 2019-2023 Data Summary Report And Response To Epa Comments (Dated September 24, 2025), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Opportunities For Expansion Of Push-Pull Technology As An Agroecological And Sustainable Intensification Approach In Africa, Gudeta W. Sileshi, Shem Kuyah, Meredith C. Schuman, Frank Chidawanyika, Beatrice W. Muriithi, Charles A.O. Midega, Adewole Olagoke, Michael H. Otim, Paramu L. Mafongoya, Emily A. Martin

All Peer-Reviewed Publications

The push-pull technology (PPT) has often been presented as a management strategy for stemborers and witchweed. However, its value as an agronomic practice and an agroecological approach remains largely underappreciated. This review aims to appraise the PPT used in eastern Africa, synthesize evidence for its ecological and economic benefits, and identify barriers to its adoption and opportunities for its expansion to other crops and farming systems in sub-Saharan Africa.

Copyright Ownership And Duration Of Ai-Authored Works, Cheng Lim Saw

Research Collection Yong Pung How School Of Law

On the assumption that Parliament has endorsed the notion of AI authorship and the prospect that copyright may well subsist in works created autonomously by the AI itself, this essay further explores allied issues surrounding the ownership and duration of copyright in AI-authored works.

Stableguard: Towards Unified Copyright Protection And Tamper Localization In Latent Diffusion Models, Haoxin Yang, Bangzhen Liu, Xuemiao Xu, Cheng Xu, Yuyang Yu, Zikai Huang, Yi Wang, Shengfeng He

Research Collection School Of Computing and Information Systems

The advancement of diffusion models has enhanced the realism of AI-generated content but also raised concerns about misuse, necessitating robust copyright protection and tampering localization. Although recent methods have made progress toward unified solutions, their reliance on post hoc processing introduces considerable application inconvenience and compromises forensic reliability. We propose StableGuard, a novel framework that seamlessly integrates a binary watermark into the diffusion generation process, ensuring copyright protection and tampering localization in Latent Diffusion Models through an end-to-end design. We develop a Multiplexing Watermark VAE (MPW-VAE) by equipping a pretrained Variational Autoencoder (VAE) with a lightweight latent residual-based adapter, enabling …

The Presentation Of Self In Everyday Digital Life: A Study Of Self- Disclosure And Work Environments, Mackenzie Michelle Skiff

Communication & Theatre Arts Theses

The digital age impacts individuals’ lives in many ways. One impact is how and where work is completed across many careers. The work-from-home strategy enables individuals to complete work that is not within a shared space, such as an office. With the absence of this shared space, communication practices within workplaces could be changing. Specifically, self-disclosure while working from home may differ from self-disclosure within the office or hybrid (both in office and remote) work environments. This thesis investigates whether there are differences in self-disclosure practices across three different types of contemporary work environments and offers a digital update to …

Private Sand Mining In The Mississippi River: Sediment Budget And Morphology Implications Between Baton Rouge And Belle Chasse, Louisiana, Brett Mcmann

LSU New Orleans Theses and Dissertations

This analysis examined how private sand mining within the Mississippi River between Baton Rouge, LA and Belle Chasse, LA affected its sand budget from 2004-2012.

Records assembled indicated that private mining removed an annual average of 2.8 million cubic yards of sand. A HEC-RAS one-dimensional sediment transport model was utilized to assess this practice. Three scenarios were simulated: a baseline without mining, conditions reflecting documented mining rates, and a hypothetical case with excessive extraction.

Results suggest that private mining constituted approximately 26 percent of the total sand deficit between Baton Rouge and Belle Chasse. Mining appeared to cause localized changes …

Re: Comment Letter For The Butte Priority Soils Operable Unit (Bpsou) Butte Treatment Lagoons (Btl) Groundwater Treatment System Quarterly Operation And Maintenance Report – Quarter 2 2025 (Dated September 30, 2025), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.