Physical Sciences and Mathematics Commons^™

Advances In Differentially Methylated Region Detection And Cure Survival Models, Daniel Ahmed Alhassan

Doctoral Dissertations

"This dissertation focuses on two areas of statistics: DNA methylation and survival analysis. The first part of the dissertation pertains to the detection of differentially methylated regions in the human genome. The varying distribution of gaps between succeeding genomic locations, which are represented on the microarray used to quantify methylation, makes it challenging to identify regions that have differential methylation. This emphasizes the need to properly account for the correlation in methylation shared by nearby locations within a specific genomic distance. In this work, a normalized kernel-weighted statistic is proposed to obtain an optimal amount of "information" from neighboring locations …

Essays On Conditional Heteroscedastic Time Series Models With Asymmetry, Long Memory, And Structural Changes, K C M R Anjana Bandara Yatawara

Doctoral Dissertations

"The volatility of asset returns is usually time-varying, necessitating the introduction of models with a conditional heteroskedastic variance structure. In this dissertation, several existing formulations, motivated by the Generalized Autoregressive Conditional Heteroskedastic (GARCH) type models, are further generalized to accommodate more dynamic features of asset returns such as asymmetry, long memory, and structural breaks. First, we introduce a hybrid structure that combines short-memory asymmetric Glosten, Jagannathan, and Runkle (GJR) formulation and the long-memory fractionally integrated GARCH (FIGARCH) process for modeling financial volatility. This formulation not only can model volatility clusters and capture asymmetry but also considers the characteristic of long …

Recurrent Event Data Analysis With Mismeasured Covariates, Ravinath Alahakoon Mudiyanselage

Doctoral Dissertations

"Consider a study with n units wherein every unit is monitored for the occurrence of an event that can recur with random end of monitoring. At each recurrence, p concomitant variables associated to the event recurrence are recorded with q (q ≤ p) collected with errors. Of interest in this dissertation is the estimation of the regression parameters of event time regression models accounting for the covariates. To circumvent the problem of bias and consistency associated with model's parameter estimation in the presence of measurement errors, we propose inference for corrected estimating functions with well-behaved roots under additive measurement errors …

Efficient High Order Ensemble For Fluid Flow, John Carter

Doctoral Dissertations

"This thesis proposes efficient ensemble-based algorithms for solving the full and reduced Magnetohydrodynamics (MHD) equations. The proposed ensemble methods require solving only one linear system with multiple right-hand sides for different realizations, reducing computational cost and simulation time. Four algorithms utilize a Generalized Positive Auxiliary Variable (GPAV) approach and are demonstrated to be second-order accurate and unconditionally stable with respect to the system energy through comprehensive stability analyses and error tests. Two algorithms make use of Artificial Compressibility (AC) to update pressure and a solenoidal constraint for the magnetic field. Numerical simulations are provided to illustrate theoretical results and demonstrate …

Editorial: The Digitalization Of Neurology, Daniel B. Hier, Michael D. Carrithers, Jorge M. Rodríguez-Fernández, Benjamin Kummer

Chemistry Faculty Research & Creative Works

No abstract provided.

Quantum Electrodynamics Of Dicke States: Resonant One-Photon Exchange Energy And Entangled Decay Rate, Ulrich D. Jentschura, Chandra M. Adhikari

Physics Faculty Research & Creative Works

We calculate the fully retarded one-photon exchange interaction potential between electrically neutral, identical atoms, one of which is assumed to be in an excited state, by matching the scattering matrix (S matrix) element with the effective Hamiltonian. Based on the Feynman prescription, we obtain the imaginary part of the interaction energy. Our results lead to precise formulas for the distance-dependent enhancement and suppression of the decay rates of entangled superradiant and subradiant Dicke states (Bell states), as a function of the interatomic distance. The formulas include a long-range tail due to entanglement. We apply the result to an example calculation …

Stability And Dynamics Across Magnetic Phases Of Vortex-Bright Type Excitations In Spinor Bose-Einstein Condensates, Garyfallia C. Katsimiga, Simeon I. Mistakidis, K. Mukherjee, P. G. Kevrekidis, P. Schmelcher

Physics Faculty Research & Creative Works

The Static Properties, I.e., Existence And Stability, As Well As The Quench-Induced Dynamics Of Vortex-Bright Type Excitations In Two-Dimensional Harmonically Confined Spin-1 Bose-Einstein Condensates Are Investigated. Linearly Stable Vortex-Bright-Vortex And Bright-Vortex-Bright Solutions Arise In Both Antiferromagnetic And Ferromagnetic Spinor Gases Upon Quadratic Zeeman Energy Shift Variations. Their Deformations Across The Relevant Transitions Are Exposed And Discussed In Detail, Evincing Also That Emergent Instabilities Can Lead To Pattern Formation. Spatial Elongations, Precessional Motion, And Spiraling Of The Nonlinear Excitations When Exposed To Finite Temperatures And Upon Crossing The Distinct Phase Boundaries, Via Quenching Of The Quadratic Zeeman Coefficient, Are Unveiled. Spin-Mixing …

Continual Optimal Adaptive Tracking Of Uncertain Nonlinear Continuous-Time Systems Using Multilayer Neural Networks, Irfan Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This study provides a lifelong integral reinforcement learning (LIRL)-based optimal tracking scheme for uncertain nonlinear continuous-time (CT) systems using multilayer neural network (MNN). In this LIRL framework, the optimal control policies are generated by using both the critic neural network (NN) weights and single-layer NN identifier. The critic MNN weight tuning is accomplished using an improved singular value decomposition (SVD) of its activation function gradient. The NN identifier, on the other hand, provides the control coefficient matrix for computing the control policies. An online weight velocity attenuation (WVA)-based consolidation scheme is proposed wherein the significance of weights is derived by …

Lifelong Learning Control Of Nonlinear Systems With Constraints Using Multilayer Neural Networks With Application To Mobile Robot Tracking, Irfan Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This Paper Presents a Novel Lifelong Multilayer Neural Network (MNN) Tracking Approach for an Uncertain Nonlinear Continuous-Time Strict Feedback System that is Subject to Time-Varying State Constraints. the Proposed Method Uses a Time-Varying Barrier Function to Accommodate the Constraints Leading to the Development of an Efficient Control Scheme. the Unknown Dynamics Are Approximated using a MNN, with Weights Tuned using a Singular Value Decomposition (SVD)-Based Technique. an Online Lifelong Learning (LL) based Elastic Weight Consolidation (EWC) Scheme is Also Incorporated to Alleviate the Issue of Catastrophic Forgetting. the Stability of the overall Closed-Loop System is Analyzed using Lyapunov Analysis. the …

Sigmoid Activation-Based Long Short-Term Memory For Time Series Data Classification, Sajal Das

Computer Science Faculty Research & Creative Works

With the enhanced usage of Artificial Intelligence (AI) driven applications, the researchers often face challenges in improving the accuracy of the data classification models, while trading off the complexity. In this paper, we address the classification of time series data using the Long Short-Term Memory (LSTM) network while focusing on the activation functions. While the existing activation functions such as sigmoid and tanh are used as LSTM internal activations, the customizability of these activations stays limited. This motivates us to propose a new family of activation functions, called log-sigmoid, inside the LSTM cell for time series data classification, and analyze …

Unifying Threats Against Information Integrity In Participatory Crowd Sensing, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

This article proposes a unified threat landscape for participatory crowd sensing (P-CS) systems. Specifically, it focuses on attacks from organized malicious actors that may use the knowledge of P-CS platform's operations and exploit algorithmic weaknesses in AI-based methods of event trust, user reputation, decision-making, or recommendation models deployed to preserve information integrity in P-CS. We emphasize on intent driven malicious behaviors by advanced adversaries and how attacks are crafted to achieve those attack impacts. Three directions of the threat model are introduced, such as attack goals, types, and strategies. We expand on how various strategies are linked with different attack …

Preserving Privacy In Image Database Through Bit-Planes Obfuscation, Vishesh K. Tanwar, Ashish Gupta, Sanjay Kumar Madria, Sajal K. Das

Computer Science Faculty Research & Creative Works

The recent surge in computer vision applications has caused visual privacy concerns to people who are either users or exposed to an underlying surveillance system. To preserve their privacy, image obfuscation lays out a strong road through which the usability of images can also be maintained without revealing any visual private information. However, prior solutions are susceptible to reconstruction attacks or produce non-trainable images even by leveraging the obfuscation ways. This paper proposes a novel bit-planes-based image obfuscation scheme, called Bimof, to protect the visual privacy of the user in the images that are input into a recognition-based system. By …

Bits 2023 Welcome Message From General Chairs And Tpc Chairs, Sajal K. Das, Keiichi Yasumoto, Hayato Yamana, Shameek Bhattacharjee

Computer Science Faculty Research & Creative Works

No abstract provided.

Message From The Ieee Mdm 2023 Test-Of-Time Committee, Christian S. Jensen, Sanjay Kumar Madria, Timos Sellis

Computer Science Faculty Research & Creative Works

No abstract provided.

Effects Of Molecular Size And Orientation On The Interfacial Properties And Wetting Behavior Of Water/ N -Alkane Systems: A Molecular-Dynamics Study, Fawaz Hrahsheh, Gerald Wilemski

Physics Faculty Research & Creative Works

Molecular Dynamics Simulations (MD) Are Performed to Study the Interfacial Structure/tension and Wetting Behavior of Water/n-Alkane Systems (Water/nC5 to Water/nC16 Where nCx = CxH(2x + 2)). in Particular, We Study Complete-To-Partial Wetting Transitions by Changing the N-Alkane Chain Length (N_C) at a Constant Temperature, T = 295 K. Simulations Are Carried Out with a United-Atom TraPPE Model for N-Alkanes and the TIP4P-2005 Model of Water. Simulation Results Are in Excellent Agreement with the Initial Spreading Coefficients and Contact Angles Calculated using Experimental Values of the Surface and Interfacial Tensions. in Addition, It Has Been Determined that Water/(nC5-nC7) and …

A Distributed Algorithm For Identifying Strongly Connected Components On Incremental Graphs, S. Srinivasan, A. Khanda, S. Srinivasan, A. Pandey, S. (Sajal) K. Das, S. Bhowmick, B. Norris

Computer Science Faculty Research & Creative Works

Incremental graphs that change over time capture the changing relationships of different entities. Given that many real-world networks are extremely large, it is often necessary to partition the network over many distributed systems and solve a complex graph problem over the partitioned network. This paper presents a distributed algorithm for identifying strongly connected components (SCC) on incremental graphs. We propose a two-phase asynchronous algorithm that involves storing the intermediate results between each iteration of dynamic updates in a novel meta-graph storage format for efficient recomputation of the SCC for successive iterations. To the best of our knowledge, this is the …

Programmable Software-Defined Testbed For Visible Light Uav Networks: Architecture Design And Implementation, Yue Zhang, Nan Cen

Computer Science Faculty Research & Creative Works

As of Today, There Has Been Increasing Research on Designing Optimization Algorithms and Intelligent Network Control Methods for Visible Light Unmanned Aerial Vehicles (UAV) Networks to Provide Pervasive and Broadband Connections. for Those Theoretical Analysis based Algorithms, there is an Urgent Need to Have a Visible Light UAV Network Platform that Can Help Evaluate the Proposed Algorithms in Real-World Scenarios. However, to the Best of Our Knowledge, there is Currently No Dedicated High Data Rate and Flexible Visible Light UAV Networking Prototype. to Bridge This Gap, in This Paper, We First Design a Novel Programmable Software-Defined Architecture for Visible Light …

One-Shot Federated Learning For Leo Constellations That Reduces Convergence Time From Days To 90 Minutes, Mohamed Elmahallawy, Tie (Tony) Tie Luo

Computer Science Faculty Research & Creative Works

A Low Earth orbit (LEO) satellite constellation consists of a large number of small satellites traveling in space with high mobility and collecting vast amounts of mobility data such as cloud movement for weather forecast, large herds of animals migrating across geo-regions, spreading of forest fires, and aircraft tracking. Machine learning can be utilized to analyze these mobility data to address global challenges, and Federated Learning (FL) is a promising approach because it eliminates the need for transmitting raw data and hence is both bandwidth and privacy friendly. However, FL requires many communication rounds between clients (satellites) and the parameter …

Lightesd: Fully-Automated And Lightweight Anomaly Detection Framework For Edge Computing, Ronit Das, Tie (Tony) T. Luo

Computer Science Faculty Research & Creative Works

Anomaly Detection is Widely Used in a Broad Range of Domains from Cybersecurity to Manufacturing, Finance, and So On. Deep Learning based Anomaly Detection Has Recently Drawn Much Attention Because of its Superior Capability of Recognizing Complex Data Patterns and Identifying Outliers Accurately. However, Deep Learning Models Are Typically Iteratively Optimized in a Central Server with Input Data Gathered from Edge Devices, and Such Data Transfer between Edge Devices and the Central Server Impose Substantial overhead on the Network and Incur Additional Latency and Energy Consumption. to overcome This Problem, We Propose a Fully Automated, Lightweight, Statistical Learning based Anomaly …

Transport And Plugging Performance Evaluation Of A Novel Re-Crosslinkable Microgel Used For Conformance Control In Mature Oilfields With Super-Permeable Channels, Adel Alotibi, T. Song, Baojun Bai, Thomas P. Schuman

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Preformed particle gels (PPG) have been widely applied in oilfields to control excessive water production. However, PPG has limited success in treating opening features because the particles can be flushed readily during post-water flooding. We have developed a novel micro-sized Re-crosslinkable PPG (micro-RPPG) to solve the problem. The microgel can re-crosslink to form a bulk gel, avoiding being washed out easily. This paper evaluates the novel microgels' transport and plugging performance through super-permeable channels. Micro-RPPG was synthesized and evaluated for this study. Its storage moduli after fully swelling are approximately 82 Pa. The microgel characterization, self-healing process, transportation behavior, and …

Novel Quantum Materials For Spintronic And Opto-Electronic Applications, Ali Sarikhani

Doctoral Dissertations

"Multi-functional quantum materials play a crucial role in the development of spintronics and opto-electronics, as their properties can greatly influence device performance. For instance, in spintronics, materials such as ferromagnetic half-metals, Giant Magnetoresistants (GMR), and magnetic semiconductors have been extensively studied due to their ability to manipulate the spin of electrons for applications in magnetic storage. In opto-electronics, materials such as Diluted Magnetic Semiconductors (DMS) and non-oxide Transparent Conductors (TC) offer advantages such as tunable bandgap and high absorption coefficients, which enable improved device performance.

For this purpose, we have experimentally investigated the compounds that have shown theoretically interesting physical …

Investigating Pore Body, Pore Throat, Nano-Pore Wettability Preference In Several Unconventional Kuwaiti Carbonate Reservoirs, Saleh Al-Sayegh, Ralph E. Flori, Hussain Alajaj, Waleed Hussien Al-Bazzaz

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This study will investigate measuring the wettability contact angles of native unconventional tight carbonate as well as other unconventional pore system reservoir samples that hosts varied pore shapes and subsequent wettability contact angle distributions in both reservoir matrix and possible natural fractures. Also, the investigation will include validation of the grain/ pore-wall wettability regions and classify the natural wettability preference available inside pores of the rock and their overall wettability performance and recovery efficiency contributions. Further investigation will include modeling pore throat contact angle wettability, and to understand their new physics that will advance reservoir characterization and oil recovery improvement.

Kuwaiti Carbonate Reservoir Oil Recovery Prediction Through Static Wettability Contact Angle Using Machine Learning Modeling, Saleh Al-Sayegh, Ralph E. Flori, Waleed Hussien Al-Bazzaz, Hasan Al-Saedi, Mostafa Al-Kaouri, Ali Qubian

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The objective of this study is to predict EOR efficiencies through static wettability contact angle measurement by Machine Learning (ML) modeling. Unlike conventional methods of measuring static wettability contact angle, the unconventional digital static wettability contact angle is captured and measured, then (ML) modeled in order to forecast the recovery based on wettability distribution phenomenon. Due to success in big data collection from reservoir imaging samples, this study applies data science lifecycle logic and utilizes Machine Learning (ML) models that can predict the recovery through wettability contact angles and thus identify the treatment of oil recovery for a candidate reservoir. …

Meta-Analysis Of Mesenchymal Stem Cell Gene Expression Data From Obese And Non-Obese Patients, Dakota William Shields

Masters Theses

"The prevalence of gene expression microarray datasets in public repositories gives opportunity to analyze biologically interesting datasets without running the laboratory aspect in house. Such experimentation is expensive in terms of finances, time, and expertise, which often results in low numbers of replicates. Meta-analysis techniques attempt to overcome issues due to few biological or technical replicates by combining separate experiments together to increase statistical power. Proper statistical considerations help to offset issues like simultaneous testing of thousands of genes, unintended hybridization, and other noises.

Microarrays contain light intensities from tens of thousands of hybridized probes giving a measure of gene …

Computer Vision In Adverse Conditions: Small Objects, Low-Resoltuion Images, And Edge Deployment, Raja Sunkara

Masters Theses

"Computer vision based on deep learning is an essential field that plays a significant role in object detection, image classification, semantic segmentation, instance segmentation, and other applications. However, these models face significant challenges in adverse conditions, such as small objects, low-resolution images, and edge deployment. These challenges limit the accuracy and efficiency of computer vision algorithms, making it difficult to obtain reliable results.

The primary objective of this thesis is to assess the performance of deep learning- based computer vision models in challenging conditions and provide viable solutions to overcome the obstacles. The study will specifically address three key challenges, …

Mat: Genetic Algorithms Based Multi-Objective Adversarial Attack On Multi-Task Deep Neural Networks, Nikola Andric

Masters Theses

"Vulnerability to adversarial attacks is a recognized deficiency of not only deep neural networks (DNNs) but also multi-task deep neural networks (MT-DNNs) that attracted much attention in the past few years. To the best of our knowledge, all multi-task deep neural network adversarial attacks currently present in the literature are non-targeted attacks that use gradient descent to optimize a single loss function generated by aggregating all loss functions into one. On the contrary, targeted attacks are sometimes preferred since they give more control over the attack. Hence, this paper proposes a novel targeted multi-objective adversarial ATtack (MAT) based on genetic …

Dynamic Discounted Satisficing Based Driver Decision Prediction In Sequential Taxi Requests, Sree Pooja Akula

Masters Theses

"Ridesharing platforms rely on connecting available taxi drivers to potential passengers to maximize their revenue. However, predicting the stopping decision made by every driver, i.e., the final task performed during a given day, is crucial to achieving this goal. Unfortunately, little research has been done on predicting drivers’ stopping decisions, especially when they deviate from expected utility maximization behavior. This research proposes a Dynamic Discounted Satisficing (DDS) heuristic to model and learn the task at which human agents will stop working for that day, assuming that the human agents are taking sequential decisions based on their preference order. We apply …

Investigation Of Defect Production And Displacement Energies In Wurtzite Aluminum Nitride, Sean Anderson

Masters Theses

"Aluminum Nitride is an active element of sensors that monitor the performance and well-being of the nuclear reactors due to its piezoelectric properties. Yet, the variations of its properties under irradiation are largely unexplored. We report the results of the molecular dynamics simulations of the structural changes in AlN under irradiation via the knock-on atom technique. By creating and evolving the irradiation cascades due to energetic particle interaction with the atom of the crystalline lattice we determine the rate of the defect production as a function of the deposited energy. Further, we determine a displacement energy, a key characteristic that …

The Application Of Statistical Modeling To Identify Genetic Associations With Mild Traumatic Brain Injury Outcomes, Caroline Schott

Masters Theses

"Traumatic brain injury (TBI) is a growing health concern, with millions of TBI diagnoses in the United States each year. The vast majority of TBI diagnoses are mild traumatic brain injuries (mTBI), which can be challenging to manage due to variation in symptoms and outcomes. Most individuals with mTBI successfully recover quickly, but a small subset has a delayed recovery. Although the factors that contribute to this variation in recovery are not clearly understood, it is possible that genetic differences may play a role. Very few studies have investigated the association between single nucleotide polymorphisms (SNPs) with mTBI outcomes and …

Predicting Convection Configurations In Coupled Fluid-Porous Systems, Matthew Mccurdy, Nicholas J. Moore, Xiaoming Wang

Mathematics and Statistics Faculty Research & Creative Works

A ubiquitous arrangement in nature is a free-flowing fluid coupled to a porous medium, for example a river or lake lying above a porous bed. Depending on the environmental conditions, thermal convection can occur and may be confined to the clear fluid region, forming shallow convection cells, or it can penetrate into the porous medium, forming deep cells. Here, we combine three complementary approaches - linear stability analysis, fully nonlinear numerical simulations and a coarse-grained model - to determine the circumstances that lead to each configuration. the coarse-grained model yields an explicit formula for the transition between deep and shallow …