Physical Sciences and Mathematics Commons^™

Optimization Opportunities In Human In The Loop Computational Paradigm, Dong Wei

Dissertations

An emerging trend is to leverage human capabilities in the computational loop at different capacities, ranging from tapping knowledge from a richly heterogeneous pool of knowledge resident in the general population to soliciting expert opinions. These practices are, in general, termed human-in-the-loop (HITL) computations.

A HITL process requires holistic treatment and optimization from multiple standpoints considering all stakeholders: a. applications, b. platforms, c. humans. In application-centric optimization, the factors of interest usually are latency (how long it takes for a set of tasks to finish), cost (the monetary or computational expenses incurred in the process), and quality of the completed …

Towards Practicalization Of Blockchain-Based Decentralized Applications, Songlin He

Dissertations

Blockchain can be defined as an immutable ledger for recording transactions, maintained in a distributed network of mutually untrusting peers. Blockchain technology has been widely applied to various fields beyond its initial usage of cryptocurrency. However, blockchain itself is insufficient to meet all the desired security or efficiency requirements for diversified application scenarios. This dissertation focuses on two core functionalities that blockchain provides, i.e., robust storage and reliable computation. Three concrete application scenarios including Internet of Things (IoT), cybersecurity management (CSM), and peer-to-peer (P2P) content delivery network (CDN) are utilized to elaborate the general design principles for these two main …

Representation Learning In Finance, Ajim Uddin

Dissertations

Finance studies often employ heterogeneous datasets from different sources with different structures and frequencies. Some data are noisy, sparse, and unbalanced with missing values; some are unstructured, containing text or networks. Traditional techniques often struggle to combine and effectively extract information from these datasets. This work explores representation learning as a proven machine learning technique in learning informative embedding from complex, noisy, and dynamic financial data. This dissertation proposes novel factorization algorithms and network modeling techniques to learn the local and global representation of data in two specific financial applications: analysts’ earnings forecasts and asset pricing.

Financial analysts’ earnings forecast …

Periodic Fast Multipole Method, Ruqi Pei

Dissertations

Applications in electrostatics, magnetostatics, fluid mechanics, and elasticity often involve sources contained in a unit cell C, centered at the origin, on which periodic boundary condition are imposed. The free-space Green’s functions for many classical partial differential equations (PDE), such as the modified Helmholtz equation, are well-known. Among the existing schemes for imposing the periodicity, three common approaches are: direct discretization of the governing PDE including boundary conditions to yield a large sparse linear system of equations, spectral methods which solve the governing PDE using Fourier analysis, and the method of images based on tiling the plane with copies of …

Nystrom Methods For High-Order Cq Solutions Of The Wave Equation In Two Dimensions, Erli Wind-Andersen

Dissertations

An investigation of high order Convolution Quadratures (CQ) methods for the solution of the wave equation in unbounded domains in two dimensions is presented. These rely on Nystrom discretizations for the solution of the ensemble of associated Laplace domain modified Helmholtz problems. Two classes of CQ discretizations are considered: one based on linear multistep methods and the other based on Runge-Kutta methods. Both are used in conjunction with Nystrom discretizations based on Alpert and QBX quadratures of Boundary Integral Equation (BIE) formulations of the Laplace domain Helmholtz problems with complex wavenumbers. CQ in conjunction with BIE is an excellent candidate …

Atmospheric Mercury Chemistry: Detection, Kinetics, And Mechanism, Na Mao

Dissertations

The presence of mercury in the environment is of global concern due to its toxicity. The atmosphere is an important transient reservoir for mercury released by human activities and natural sources. The knowledge of atmospheric mercury chemistry is critical for understanding the global biogeochemical cycle. In the atmosphere, mercury primarily exists in three forms: gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particulate-bound mercury (PBM). Over the last decade, the existing knowledge of mercury cycle has dramatically changed: (1) There has been increasing evidence that current detection methods do not accurately quantify gaseous oxidized mercury and a technique which …

Towards A Cross-Layer Coupled Design Framework For Big Data Workflows, Qianwen Ye

Dissertations

The processing and analysis of big data increasingly rely on workflow technologies for knowledge discovery and scientific innovation. The execution of such workflows goes far beyond the capability and capacity of single computers and is now commonly supported on reliable and scalable data storage and analysis platforms in distributed environments, such as the Hadoop ecosystem. Workflow performance largely depends on how big data systems are configured and used. For example, the makespan of a big data workflow is affected by multiple layers of big data systems, including the parallel computing engine it runs on, the resource manager that orchestrates various …

Graph Enabled Cross-Domain Knowledge Transfer, Shibo Yao

Dissertations

The world has never been more connected, led by the information technology revolution in the past decades that has fundamentally changed the way people interact with each other using social networks. Consequently, enormous human activity data are collected from the business world and machine learning techniques are widely adopted to aid our decision processes. Despite of the success of machine learning in various application scenarios, there are still many questions that need to be well answered, such as optimizing machine learning outcomes when desired knowledge cannot be extracted from the available data. This naturally drives us to ponder if one …

Reactive Iron Mineral Coatings In Redox Transition Zones Of A Site With Historical Contamination: Abiotic Attenuation, Xin Yin

Dissertations

Reactive iron mineral coatings are found throughout reduction-oxidation (redox) transition zones and play a significant role in contaminant transformation processes. In this study, an 18.3-meter core is collected, subsampled, and preserved under anoxic conditions to maintain its original redox state. Screening analyses are conducted at sampling increments of 5.08 cm in depth for the following: elemental concentrations with X-ray fluorescence (XRF), sediment pH, sediment oxidation-reduction potential (ORP), total volatile organic carbon (TVOC) in the sample headspace, and abundant bacteria (16S rRNA sequencing). Using the Fe and S gradients correlated with microbial data, five RTZs are delineated. To characterize iron mineral …

Development Of Novel Mass Spectrometric Methods For Reaction Screening, Oligosaccharide Detection, And Nitrosamine Quantitation, Qi Wang

Dissertations

Benefitting from its high detection sensitivity and specificity, mass spectrometry (MS) has become a powerful technique in academia and industry. The aim of this dissertation study is to develop new mass spectrometric methods for organic reaction screening, detection of oligosaccharide/glycan in complex matrices, and nitrosamine absolute quantitation.

First, an electrochemistry/mass spectrometry (EC/MS) platform is built to generate an N-cyclopropylaniline radical cation electrochemically and to monitor its reactivity toward alkenes, which leads to the discovery of a new redox neutral reaction of intermolecular [3 + 2] annulation of N-cyclopropylanilines and alkenes. Net redox neutral electrosynthesis is quite rare in synthetic organic …

Adversarially Robust And Accurate Machine Learning For Image Classification, Yanan Yang

Dissertations

Machine learning techniques in medical imaging systems are accurate, but minor perturbations in the data known as adversarial attacks can fool them. These attacks make the systems vulnerable to fraud and deception, and thus a significant challenge has been posed in practice. This dissertation presents the gradient-free trained sign activation networks to detect and deter adversarial attacks on medical imaging AI (Artificial Intelligence) systems. Experimental results show a higher distortion value is required to attack the proposed model than other state-of-the-art models on brain MRI (Magnetic resonance imaging), Chest X-ray, and histopathology image datasets. Moreover, the proposed models outperform the …

Private Information Retrieval And Function Computation For Noncolluding Coded Databases, Sarah A. Obead

Dissertations

The rapid development of information and communication technologies has motivated many data-centric paradigms such as big data and cloud computing. The resulting paradigmatic shift to cloud/network-centric applications and the accessibility of information over public networking platforms has brought information privacy to the focal point of current research challenges. Motivated by the emerging privacy concerns, the problem of private information retrieval (PIR), a standard problem of information privacy that originated in theoretical computer science, has recently attracted much attention in the information theory and coding communities. The goal of PIR is to allow a user to download a message from a …

Bodipy-Based Light-Responsive Materials, Zhaoxiong Wan

Dissertations

The energy required for life on our planet is provided by light. In science, light is an important and powerful tool for measuring and modifying substances. For example, under the effect of illumination, light-responsive materials can change their physical and chemical properties. Photochemistry is a branch of research to study the effects of light irradiation on chemical structures and reactions. Some photochemical changes could lead to chemical bond dissociation (such as in photosynthesis) and structure isomerization (such as in the retina). Photochemistry has a strong relationship with human lives and it is essential in many ways. Among photophysics, fluorescence emission …

Type I Error Rate Controlling Procedures For Multiple Hypotheses Testing, Beibei Li

Dissertations

This dissertation addresses several different but related topics arising in the field of multiple testing, including weighted procedures and graphical approaches for controlling the familywise error rate (FWER), and stepwise procedures with control of the false discovery rate (FDR) for discrete data. It consists of three major parts.

The first part investigates weighted procedures for controlling the FWER. In many statistical applications, hypotheses may be differentially weighted according to their different importance. Many weighted multiple testing procedures (wMTPs) have been developed for controlling the FWER. Among these procedures, two weighted Holm procedures are commonly used in practice: one is based …

Experimental And Theoretical Studies Of Flows Driven By Soluto- And Thermo-Capillary Forces, Islam Benouaguef

Dissertations

This dissertation aims to study the flow induced in a water body due to the presence of a solute or heat source on its surface. The flow arises because of a change in the local solute concentration or the temperature, which modify the local surface tension, giving rise to a surface tension gradient that drives the flow. These surface tension-driven flows are referred to as Marangoni flows. The following three induced-flows are investigated experimentally and theoretically: (i) Flow induced by a solute source present on a waterbody; (ii) Flow induced due to the impingement of a freshwater drop on a …

Synthesis Of Polynitrogen With Radio Frequency Plasma, Haizheng Zhuang

Dissertations

Cubic gauche polynitrogen (cgPN) has been very attractive due to its high energy density that is 2.5 times higher than that of TNT. In the literature, cgPN has been investigated theoretically in detail, but few experimental works have been reported. In 2004, cgPN was first synthesized from nitrogen gas under extremely high temperature and high pressure (HTHP) conditions and the trace amount of cgPN in the high-pressure vessel decomposed once the pressure was released. Until recently, our group was the first to synthesize cgPN from NaN₃ precursor under ambient conditions with radio frequency (RF) plasma. In this study, the …

Integration Of Customer Preference To Smart Locker Location Planning For Last Mile Delivery Optimization, Colette Malyack

Dissertations

Understanding customer preference associated with delivery solutions, such as the use of smart lockers, is an area of increasing interest for package delivery companies. Applications of this data could result in cost savings through vehicle route decisions, cross-docking and fleet size optimization, and increased placement of smart locker technology for stop reduction. However, there has been little effort applied to gathering information related to public preference solutions to last mile package delivery. Therefore, preference data is gathered and analyzed related to smart lockers, and apply the results to delivery route and cross-docking optimization through package locker location planning within the …

A New Model For Predicting The Drag And Lift Forces Of Turbulent Newtonian Flow On Arbitrarily Shaped Shells On The Seafloor, Carley R. Walker, James V. Lambers, Julian Simeonov

Dissertations

Currently, all forecasts of currents, waves, and seafloor evolution are limited by a lack of fundamental knowledge and the parameterization of small-scale processes at the seafloor-ocean interface. Commonly used Euler-Lagrange models for sediment transport require parameterizations of the drag and lift forces acting on the particles. However, current parameterizations for these forces only work for spherical particles. In this dissertation we propose a new method for predicting the drag and lift forces on arbitrarily shaped objects at arbitrary orientations with respect to the direction of flow that will ultimately provide models for predicting the sediment sorting processes that lead to …

Bis(Tryptophan) Amphiphiles: Design, Synthesis And Efficacy As Antimicrobial Agents, Michael Mckeever

Dissertations

Amphiphiles play important roles in nature. These molecules contain both hydrophilic and hydrophobic regions, leading to some astonishing properties. The lipid bilayer of the cell membrane is a fascinating organization of amphiphilic phospholipids. Natural and synthetic amphiphiles, such as antimicrobial peptides, interact with the cell membrane. Such interactions can impact transport of molecules across the cell membrane, disrupting cell functions. In this work, a library of tryptophan-containing amphiphiles was synthesized and their antimicrobial properties were explored.

First, a library of bis(tryptophan) amphiphiles was synthesized. Preparation included a coupling reaction of a diamine with tryptophan residues, via their carboxy-termini, at …

Hydrolytically Degradable Thermosets With Tunable Degradation Profiles Via Ketal-Based Crosslinks, Benjamin Alameda

Dissertations

Thermoset polymer networks are ubiquitous in the construction of high-performance materials due to their excellent mechanical properties, solvent resistance, and thermomechanical performance. However, the crosslinked structure that instills these materials with favorable performance also makes them incredibly resistant to degradation and are nearly impossible to recycle – adding to the ever-growing problem of plastic pollution. Hydrolytically degradable thermosets have emerged as a potentially sustainable alternative to traditional thermosets by affording networks that are inherently degradable in aqueous environments. This dissertation focuses on the development of hydrolytically degradable thermoset networks with tunable degradation behavior through the implementation of ketal-based crosslinks. Given …

Vertex Identification In Graphs, Yuya Kono

Dissertations

In recent decades, there has been increased interest in studying ways touniquely identify the vertices of a given graph. Two of best-known vertex identification methods are referred to as the metric dimension introduced by Slater and by Harary, and Melter in the1970s, and the partition dimension introduced by Chartrand in 2000. The first method deals with locating an ordered set of vertices in a connected graph G and uniquely identifying each vertex of G by means of the distance between this vertex and the vertices in the ordered set. The second method deals with partitioning the vertex set of a …

Irregular Orbital Domination In Graphs, Peter E. Broe

Dissertations

In recent decades, domination in graphs has become a popular area of study due in large degree to its applications to modern society and the mathematical beauty of the topic. While this area evidently began with the work of Claude Berge in 1958 and of Oystein Ore in 1962, domination did not become an active area of research until 1977 with the appearance of a survey paper by Ernest Cockayne and Stephen Hedetniemi. Since then a large number of variations of domination have surfaced and provided numerous applications to different areas of science and real-life problems. Among these variations are …

A Remote Sensing And Machine Learning-Based Approach To Forecast The Onset Of Harmful Algal Bloom (Red Tides), Moein Izadi

Dissertations

In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of the most detrimental natural phenomena all around the world especially in Florida’s coastal areas due to local environmental factors and global warming in a larger scale. Karenia brevis produces toxins that have harmful effects on humans, fisheries, and ecosystems. In this study, I developed and compared the efficiency of state-of-the-art machine learning models (e.g., XGBoost, Random Forest, and Support Vector Machine) in predicting the occurrence of HABs. In the proposed models, the K. brevis abundance is used as the target, and 10 …

Experimental Insights Into The Origin Of Microcrystalline Calcites, Mohammed Hashim

Dissertations

A significant proportion of modern marine calcium carbonate sediments is dominated by metastable aragonite and high Mg calcite that either dissolves or stabilizes to low-Mg-calcite (calcite) or dolomite during diagenesis. Sediment dissolution and stabilization have implications for the CaCO3 budget in the ocean and carbon burial rates. Yet, the diagenetic conditions that promote each process and their relative importance are poorly understood. Further, stabilization most commonly produces calcite microcrystals that exhibit various textures and host micropores. Despite their ubiquity in the rock record, the controls on microcrystal textures remain unclear. Here, laboratory experiments were used to investigate aragonite-to-calcite stabilization as …

Unsupervised Learning With Word Embeddings Captures Quiescent Knowledge From Covid-19 And Materials Science Literature, Tasnim H. Gharaibeh

Dissertations

Millions of scientific papers are produced each year and the scientific literature is continuing to grow at a head-spinning speed. Thus, massive scientific knowledge exists in solid text, but all these publications make it difficult, if not impossible, for researchers to keep in up to date with discoveries, even within a narrow scientific area. This massive amount of information also makes it difficult to find implicit and hidden connections, relationships, and dependencies within the information that may guide the direction of future research or lead to valuable new insights. So, there is a need for algorithms or models that can …

Management Of Data Brokers In Support Of Smart Community Applications, Shadha Tabatabai

Dissertations

The widespread use of smart devices has led to the Internet of Things (IoT) revolution. Big data generated by billions of devices must be analyzed to make better decisions. However, this introduces security, communication, and processing problems. To solve these problems, we develop algorithms to enhance the work of brokers. We focus our efforts on three problems.

In the first problem, brokers are used in the cloud along with Software Defined Network (SDN) switches. We formulate minimizing brokers’ load difference within a reconfiguration budget with the constraint of indivisible topics as an Integer Linear Programming (ILP) problem. We show that …

Bayesian Credible Subgroup For Excess Zeroes, Daniel Nii Kwatei Quartey

Dissertations

A goal of subgroup analysis is to identify which subgroups of the population benefit from a given treatment. Traditional and also many recently introduced approaches to subgroup analysis that include statistical hypothesis testing of treatment-covariate interactions, prediction of personalized treatment effects via machine learning tree splitting algorithms, etc. do not explicitly address multiplicity issues. In this article, we extend the two–step Bayesian credible subgroups approach to assessment of heterogeneity in treatment effects and multiplicity-adjusted benefiting subgroup identification for zero–inflated count data, which are often encountered in medical and public health studies. Our approach is to conceptualize a zero–inflated Poisson regression …

The Global Rise Of Online Devices, Cyber Crime And Cyber Defense: Enhancing Ethical Actions, Counter Measures, Cyber Strategy, And Approaches, Naresh Kshetri

Dissertations

The rise of online devices, online users, online shopping, online gaming, and online teaching has ultimately given rise to online attacks and online crimes. As cases of COVID-19 seem to increase day by day, so do online crimes and attacks (as many sectors and organizations went 100% online). Technological advancements and cyber warfare already generated many ethical issues, as internet users increasingly need ethical cyber defense strategies.

Individual internet users have challenges on their end; and on the other end, nation states (some secretly, some openly), are investing in robot weapons and autonomous weapons systems (AWS). New technologies have combined …

Measuring And Comparing Social Bias In Static And Contextual Word Embeddings, Alan Cueva Mora

Dissertations

Word embeddings have been considered one of the biggest breakthroughs of deep learning for natural language processing. They are learned numerical vector representations of words where similar words have similar representations. Contextual word embeddings are the promising second-generation of word embeddings assigning a representation to a word based on its context. This can result in different representations for the same word depending on the context (e.g. river bank and commercial bank). There is evidence of social bias (human-like implicit biases based on gender, race, and other social constructs) in word embeddings. While detecting bias in static (classical or non-contextual) word …

Rhodium-Catalyzed Decarbonylation Of Aroyl Chlorides, Wiktoria M. Koza

Dissertations

The development of efficient strategies for the synthesis of aryl–halogen bonds is highly desirable due to the prevalence of these moieties in pharmaceuticals, agrochemicals, and organic synthesis. Although there are numerous applications of aryl chlorides in chemistry, an efficient strategy for the preparation of these molecules is underdeveloped. Transition metal-catalyzed decarbonylation provides an efficient and selective approach for aryl–halogen bond formation. There has been significant progress in the development of new decarbonylation strategies, particularly involving aldehydes for the synthesis of new carbon–hydrogen (C–H) bonds or for cross-coupling reactions. However, transition metal-catalyzed decarbonylation methods for carbon–halogen (C–X) bond formation have been …