Physical Sciences and Mathematics Commons^™

Parameter Estimation And Inference Of Spatial Autoregressive Model By Stochastic Gradient Descent, Gan Luan

Dissertations

Stochastic gradient descent (SGD) is a popular iterative method for model parameter estimation in large-scale data and online learning settings since it goes through the data in only one pass. While SGD has been well studied for independent data, its application to spatially-correlated data largely remains unexplored. This dissertation develops SGD-based parameter estimation and statistical inference algorithms for the spatial autoregressive (SAR) model, a common model for spatial lattice data.

This research contains three parts. (I) The first part concerns SGD estimation and inference for the SAR mean regression model. A new SGD algorithm based on maximum likelihood estimator (MLE) …

Machine Learning And Computer Vision In Solar Physics, Haodi Jiang

Dissertations

In the recent decades, the difficult task of understanding and predicting violent solar eruptions and their terrestrial impacts has become a strategic national priority, as it affects the life of human beings, including communication, transportation, the power grid, national defense, space travel, and more. This dissertation explores new machine learning and computer vision techniques to tackle this difficult task. Specifically, the dissertation addresses four interrelated problems in solar physics: magnetic flux tracking, fibril tracing, Stokes inversion and vector magnetogram generation.

First, the dissertation presents a new deep learning method, named SolarUnet, to identify and track solar magnetic flux elements in …

Dependent Censoring In Survival Analysis, Zhongcheng Lin

Dissertations

This dissertation mainly consists of two parts. In the first part, some properties of bivariate Archimedean Copulas formed by two time-to-event random variables are discussed under the setting of left censoring, where these two variables are subject to one left-censored independent variable respectively. Some distributional results for their joint cdf under different censoring patterns are presented. Those results are expected to be useful in both model fitting and checking procedures for Archimedean copula models with bivariate left-censored data. As an application of the theoretical results that are obtained, a moment estimator of the dependence parameter in Archimedean copula models is …

Electric-Field-Driven Processes In Multiphase Fluid Systems, Qian Lei

Dissertations

Advantages of using electric fields in miniaturized apparatuses for a wide range of applications are revealed by numerous experimental and theoretical studies over the last several decades as it offers a simple and efficient method for manipulation of multiphase fluid systems. This approach is considered to be especially beneficial for control of boiling processes and colloidal suspensions considered in the presented work.

Boiling. Today's trends for enhancing boiling heat transfer in terrestrial and space applications focus on removal of bubbles to prevent formation of a vapor layer over the surface at a high overheat. In contrast, this dissertation presents a …

The Role Of Microorganisms In Rare Earth Elements Bioaccumulation, Maedeh Soleimanifar

Dissertations

No abstract provided.

Analyzing And Detecting Android Malware And Deepfake, Md Shohel Rana

Dissertations

Rapid advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) over the past several decades have produced a variety of technologies and tools that, among numerous cybersecurity issues, have enticed cybercriminals and hackers to design malware for the Android operating systems and/or manipulate multimedia. For example, high-quality and realistic fake videos, images, or audios have been created to spread misinformation and propaganda, foment political discord and hate, or even harass and blackmail people; these manipulated, high-quality and realistic videos became known recently as Deepfake. There has been much work done in recent years on malware analysis and …

Mechanisms And Applications Of Improved Protein Analysis By Desorption Electrospray Ionization Mass Spectrometry (Desi-Ms), Roshan Javanshad

Dissertations

Electrospray ionization mass spectrometry (ESI-MS) is a soft ionization technique that allows detection of macromolecules, such as intact proteins, by the formation of multiply charged ions from solutions. Desorption electrospray ionization mass spectrometry (DESI-MS) is an ambient ionization technique that directly samples analyte from a surface during ESI-MS analysis. Although DESI-MS is highly accomplished at the analyses of metabolites, lipids, and other small molecules, it is far more limited when it comes to protein analysis. While most of the field in ambient ionization MS has moved towards primarily applications, our approach has been to explore the use of DESI-MS and …

Lxr Acts As A Differentiator In The Regulation Of Fas And G6pdh Gene Expression Under Insulin Resistant Conditions, Jaafar Hachem

Dissertations

Diabetes is a chronic disease that effects 10 percent of the world’s population and causes more than 1.5 million deaths a year and billions of dollars in associated health care cost. It can lead to very serious complications such as renal failure, liver cirrhosis, heart attack, and vision loss. The most common type of diabetes is type 2 diabetes. Type 2 diabetes arises when blood glucose levels remain chronically high due to insulin resistance. The reason for this elevation is due to the failure of insulin to allow tissues to uptake glucose causing problems in subsequent metabolic pathways. Over the …

Synthesis Of Molecular Probes For The Detection Of Toxic Analytes, Rashid Mia

Dissertations

Two different types of molecular probes have been synthesized. The first family of probes is the coumarin class of compounds. These chemodosimters are referred to as Low Molecular Weight Fluorescent probes (LMFP). The other type of molecular probe is a macrocycle known as a pillar[5]arene receptor.

The chemodosimters (2.12a-c and 3.12a) were synthesized in four to five steps. The photophysical properties were extensively studied in various solvent systems (DMSO, CH₃CN, DMF, MeOH, EtOH, Me₂CO, MeCO₂Et, CHCl₃, C₆H₅Me, and C₆H₆). Dimethyl sulfoxide (DMSO) …

New Methods For The Synthesis, Activation, And Application Of Thioglycosides, Samira Escopy

Dissertations

From their ubiquitous presence in Nature to their vital roles in biology and medicine, carbohydrates (sugars or glycans) are essential molecules of life, which are made and/or utilized by every living organism. Our cells are coated with sugars that are involved in almost every biological process and defensive mechanism in our body. To mention some of their crucial biological functions, carbohydrates are essential source of energy, they participate in blood coagulation, immune defense, cell growth, cell-cell interaction, and anti-inflammatory processes. Understanding of glycan functions and structure is crucial for the development of vaccines and therapeutics. Producing complex carbohydrates in sufficient …

Development Of Sensor, Sensory System And Signal Processing Algorithm For Intelligent Sensing Applications, Xingzhe Zhang

Dissertations

Sensors have been receiving significant attention in the last decade and the demand for sensory systems has increased in recent years due to the rapid growth in the field of artificial intelligence (AI). Sensors can improve people’s awareness by providing them with real-time information on the environment and their immediate health conditions. This dissertation presents the fulfilment of three main projects and focuses on the development of a sensor, a sensory system, and a sensor signal recognition system for AI applications by employing printed electronics, analog circuit design, and digital signal processing techniques.

In the first project, a multi-channel stethograph …

Semi-Empirical Modeling Of Liquid Carbon's Containerless Solidification, Philip Chrostoski

Dissertations

Elemental carbon has important structural diversity, ranging from nanotubes through graphite to diamond. Previous studies of micron-size core/rim carbon spheres extracted from primitive meteorites suggest they formed around such stars via the solidification of condensed carbon-vapor droplets, followed by gas-to-solid carbon coating to form the graphite rims. Similar core/rim particles result from the slow cooling of carbon vapor in the lab. The long-range carbon bond-order potential was used to computationally study liquid-like carbon in (1.8 g/𝐜𝐦𝟑) periodic boundary (tiled-cube supercell) and containerless (isolated cluster) settings. Relaxations via conjugate-gradient and simulatedannealing nucleation and growth simulations using molecular dynamics were done to …

Estimation Of Odds Ratio In 2 X 2 Contingency Tables With Small Cell Counts, Guohao Zhu

Dissertations

This study is focusing on properties of estimators of odds ratio or its logarithm in case of 2x2 tables with small counts. The odds ratio represents the odds that an outcome of interest will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. Both parameters are often used to quantify the strength of association of two binary variables and are common measurements reported in case-control, cohort, and cross-sectional studies.

Because of their wide applicability, both parameters, odds ratio, and its logarithm, have been intensively studied in the literature. However, most …

Soliton Based All-Optical Data Processing In Waveguides, Amaria Javed

Dissertations

The growing demand for higher data processing speed and capacity motivates the replacement of the current electronic data processing by optical data processing in analogy with the successful replacement of electronic data communication by optical data communication. In a quest to achieve comprehensive optical data processing we aim at using solitons in waveguide arrays to perform all-optical data processing operations. Solitons are special nonlinear waves appreciated for their ability to conserve their shape and velocity before and after scattering. They are observed naturally in diverse fields of science, namely, nonlinear physics, mathematics, hydrodynamics, biophysics, and quantum field theory, etc. with …

Controlling Degradation With Force And Light, Brad Davis

Dissertations

Stimuli-responsive polymers respond to changes in their environment by altering their physical and chemical properties. Their responsiveness allows them to be used as sensors, mechanical actuators, delivery systems, and can yield either elongated lifetimes through healing mechanisms or shortened lifetimes through triggered degradation. Still a growing field in polymer science, researchers seek to expand the capabilities of these materials by improving their specificity, range, and mechanisms of both the stimuli and the response. The work presented explores stimuli-responsive materials, focusing on mechanical and light stimuli, and how to gain control of the response by specific changes in the polymeric material. …

Critical Behavior In Evolutionary And Population Dynamics, Stephen Ordway

Dissertations

This study is an exploration of phase transition behavior in evolutionary and population dynamics, and techniques for predicting population changes, across the disciplines of physics, biology, and computer science. Under the looming threat of climate change, it is imperative to understand the dynamics of populations under environmental stress and to identify early warning signals of population decline. These issues are explored here in (1) a computational model of evolutionary dynamics, (2) an experimental system of decaying populations under environmental stress, and (3) a machine learning approach to predict population changes based on environmental factors. Through the lens of critical phase …

Colloidal Quantum Dot (Cqd) Based Mid-Wavelength Infrared Optoelectronics, Shihab Bin Hafiz

Dissertations

Colloidal quantum dot (CQD) photodetectors are a rapidly emerging technology with a potential to significantly impact today’s infrared sensing and imaging technologies. To date, CQD photodetector research is primarily focused on lead-chalcogenide semiconductor CQDs which have spectral response fundamentally limited by the bulk bandgap of the constituent material, confining their applications to near-infrared (NIR, 0.7-1.0 um) and short-wavelength infrared (SWIR, 1-2.5 um) spectral regions. The overall goal of this dissertation is to investigate a new generation of CQD materials and devices that advances the current CQD photodetector research toward the technologically important thermal infrared region of 3-5 ?m, known as …

On Non-Linear Network Embedding Methods, Huong Yen Le

Dissertations

As a linear method, spectral clustering is the only network embedding algorithm that offers both a provably fast computation and an advanced theoretical understanding. The accuracy of spectral clustering depends on the Cheeger ratio defined as the ratio between the graph conductance and the 2nd smallest eigenvalue of its normalizedLaplacian. In several graph families whose Cheeger ratio reaches its upper bound of Theta(n), the approximation power of spectral clustering is proven to perform poorly. Moreover, recent non-linear network embedding methods have surpassed spectral clustering by state-of-the-art performance with little to no theoretical understanding to back them.

The dissertation includes work …

Advances In Modeling Gas Adsorption In Porous Materials For The Characterization Applications, Max A. Maximov

Dissertations

The dissertation studies methods for mesoporous materials characterization using adsorption at various levels of scale and complexity. It starts with the topic introduction, necessary notations and definitions, recognized standards, and a literature review.

Synthesis of novel materials requires tailoring of the characterization methods and their thorough testing. The second chapter presents a nitrogen adsorption characterization study for silica colloidal crystals (synthetic opals). These materials have cage-like pores in the range of tens of nanometers. The adsorption model can be described within a macroscopic approach, based on the Derjaguin-Broekhoff-de Boer (DBdB) theory of capillary condensation. A kernel of theoretical isotherms is …

Novel Statistical Modeling Methods For Traffic Video Analysis, Hang Shi

Dissertations

Video analysis is an active and rapidly expanding research area in computer vision and artificial intelligence due to its broad applications in modern society. Many methods have been proposed to analyze the videos, but many challenging factors remain untackled. In this dissertation, four statistical modeling methods are proposed to address some challenging traffic video analysis problems under adverse illumination and weather conditions.

First, a new foreground detection method is presented to detect the foreground objects in videos. A novel Global Foreground Modeling (GFM) method, which estimates a global probability density function for the foreground and applies the Bayes decision rule …

Exploring, Understanding, Then Designing: Twitter Users’ Sharing Behavior For Minor Safety Incidents, Mashael Yousef Almoqbel

Dissertations

Social media has become an integral part of human lives. Social media users resort to these platforms for various reasons. Users of these platforms spend a lot of time creating, reading, and sharing content, therefore, providing a wealth of available information for everyone to use. The research community has taken advantage of this and produced many publications that allow us to better understand human behavior. An important subject that is sometimes discussed and shared on social media is public safety. In the past, Twitter users have used the platform to share incidents, share information about incidents, victims and perpetrators, and …

Gradient Free Sign Activation Zero One Loss Neural Networks For Adversarially Robust Classification, Yunzhe Xue

Dissertations

The zero-one loss function is less sensitive to outliers than convex surrogate losses such as hinge and cross-entropy. However, as a non-convex function, it has a large number of local minima, andits undifferentiable attribute makes it impossible to use backpropagation, a method widely used in training current state-of-the-art neural networks. When zero-one loss is applied to deep neural networks, the entire training process becomes challenging. On the other hand, a massive non-unique solution probably also brings different decision boundaries when optimizing zero-one loss, making it possible to fight against transferable adversarial examples, which is a common weakness in deep learning …

Solar Flares As Observed In The Low Frequency Microwave Gyrosynchrotron Emission, Shaheda Begum Shaik

Dissertations

Solar flares involve the sudden catastrophic release of magnetic energy stored in the Sun’s corona. This dissertation focuses on investigating the low frequency, optically-thick gyrosynchrotron emission during solar flares for its spatial and spectral dynamics, characteristics, and role in the flare process.

The first part of this dissertation first addresses the spectral dynamics and characteristics of the source morphology. The high-resolution spectra of a set of microwave bursts observed by the Expanded Owens Valley Solar Array (EOVSA) during its commissioning phase in the 2.5-18 GHz frequency range with 1-s time resolution are presented here. Out of the 12 events analyzed …

Towards Adversarial Robustness With 01 Lossmodels, And Novel Convolutional Neural Netsystems For Ultrasound Images, Meiyan Xie

Dissertations

This dissertation investigates adversarial robustness with 01 loss models and a novel convolutional neural net systems for vascular ultrasound images.

In the first part, the dissertation presents stochastic coordinate descent for 01 loss and its sensitivity to adversarial attacks. The study here suggests that 01 loss may be more resilient to adversarial attacks than the hinge loss and further work is required.

In the second part, this dissertation proposes sign activation network with a novel gradient-free stochastic coordinate descent algorithm and its ensembling model. The study here finds that the ensembling model gives a high minimum distortion (as measured by …

Modeling Dewetting, Demixing, And Thermal Effects In Nanoscale Metal Films, Ryan Howard Allaire

Dissertations

Thin film dynamics, particularly on the nanoscale, is a topic of extensive interest. The process by which thin liquids evolve is far from trivial and can lead to dewetting and drop formation. Understanding this process involves not only resolving the fluid mechanical aspects of the problem, but also requires the coupling of other physical processes, including liquid-solid interactions, thermal transport, and dependence of material parameters on temperature and material composition. The focus of this dissertation is on the mathematical modeling and simulation of nanoscale liquid metal films, which are deposited on thermally conductive substrates, liquefied by laser heating, and subsequently …

Modeling And Design Optimization For Membrane Filters, Yixuan Sun

Dissertations

Membrane filtration is widely used in many applications, ranging from industrial processes to everyday living activities. With growing interest from both industrial and academic sectors in understanding the various types of filtration processes in use, and in improving filter performance, the past few decades have seen significant research activity in this area. Experimental studies can be very valuable, but are expensive and time-consuming, therefore theoretical studies offer potential as a cost-effective and predictive way to improve on current filter designs. In this work, mathematical models, derived from first principles and simplified using asymptotic analysis, are proposed for: (1) pleated membrane …

Shale Softening Based On Pore Network And Laboratory Investigations, Di Zhang

Dissertations

This dissertation consists of two major parts: Firstly, experimental investigation of four major shale softening mechanisms and quantifications of structural parameters. Secondly, numerical simulations of nano-scale flow behaviors using the previous experiments determined parameters based on modified pore network modeling.

Hydraulic fracturing is widely applied to economical gas production from shale reservoirs. Still, the gradual swelling of the clay micro/nano-pores due to retained fluid from hydraulic fracturing causes a gradual reduction of gas production. Four different gas-bearing shale samples are investigated to quantify the expected shale swelling due to hydraulic fracturing. These shale samples are subject to heated deionized (DI) …

Data-Driven Learning For Robot Physical Intelligence, Leidi Zhao

Dissertations

The physical intelligence, which emphasizes physical capabilities such as dexterous manipulation and dynamic mobility, is essential for robots to physically coexist with humans. Much research on robot physical intelligence has achieved success on hyper robot motor capabilities, but mostly through heavily case-specific engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous manner, robot learning from human demonstration (LfD) has achieved great progress, but still has limitations handling dynamic skills and compound actions. In this dissertation, a composite learning scheme which goes beyond LfD and integrates robot learning from human definition, demonstration, and evaluation is proposed. This method tackles …

Participatory Learning: Measuring Learning And Educational Technology Acceptance, Erick Sanchez Suasnabar

Dissertations

Participatory Learning (PL) integrates several learning approaches, engaging students throughout the entire assignment process for both online and face-to-face courses. Beyond simply providing a solution, students also craft a problem (problem-based learning), grade each other (peer assessment and feedback), evaluate themselves (self-assessment), and can view others’ work (learning by example). This dissertation research explores the resulting learning effects. Contributions to both educational and Information Systems research include extending an early PL model and experiments that applied the PL approach to examinations, by validating and testing new constructs based on user activity and critical thinking. In addition, the study explores a …

Reserve Price Optimization In Display Advertising, Achir Kalra

Dissertations

Display advertising is the main type of online advertising, and it comes in the form of banner ads and rich media on publishers' websites. Publishers sell ad impressions, where an impression is one display of an ad in a web page. A common way to sell ad impressions is through real-time bidding (RTB). In 2019, advertisers in the United States spent nearly 60 billion U.S. dollars on programmatic digital display advertising. By 2022, expenditures are expected to increase to nearly 95 billion U.S. dollars. In general, the remaining impressions are sold directly by the publishers. The only way for publishers …