Physical Sciences and Mathematics Commons^™

On Performance Optimization And Prediction Of Parallel Computing Frameworks In Big Data Systems, Haifa Alquwaiee

Dissertations

A wide spectrum of big data applications in science, engineering, and industry generate large datasets, which must be managed and processed in a timely and reliable manner for knowledge discovery. These tasks are now commonly executed in big data computing systems exemplified by Hadoop based on parallel processing and distributed storage and management. For example, many companies and research institutions have developed and deployed big data systems on top of NoSQL databases such as HBase and MongoDB, and parallel computing frameworks such as MapReduce and Spark, to ensure timely data analyses and efficient result delivery for decision making and business …

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 …

Long Term Predictive Modeling On Big Spatio-Temporal Data, Yong Zhuang

Graduate Doctoral Dissertations

In the era of massive data, one of the most promising research fields involves the analysis of large-scale Spatio-temporal databases to discover exciting and previously unknown but potentially useful patterns from data collected over time and space. A modeling process in this domain must take temporal and spatial correlations into account, but with the dimensionality of the time and space measurements increasing, the number of elements potentially contributing to a target sharply grows, making the target's long-term behavior highly complex, chaotic, highly dynamic, and hard to predict. Therefore, two different considerations are taken into account in this work: one is …

On Predicting Omnidirectional Honey Bee Traffic Using Weather And Electromagnetic Radiation, Daniel G. Hornberger

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Honey bees are responsible for pollinating many important crops in the United States. However, honey bee populations have declined significantly since 1961. While some causes of this decline are known, others are not. By utilizing electronic bee hive monitoring (EBM) systems, bee keepers and researchers have an added resource in determining the causes of these declines so that the issues can be remedied. For nearly five months (May through October) during the 2020 honey bee foraging season in Logan, Utah, USA, we collected on-site weather and electromagnetic radiation (EMR) readings and videos of the hive entrances of six bee hives …

Intelligent Resource Prediction For Hpc And Scientific Workflows, Benjamin Shealy

All Dissertations

Scientific workflows and high-performance computing (HPC) platforms are critically important to modern scientific research. In order to perform scientific experiments at scale, domain scientists must have knowledge and expertise in software and hardware systems that are highly complex and rapidly evolving. While computational expertise will be essential for domain scientists going forward, any tools or practices that reduce this burden for domain scientists will greatly increase the rate of scientific discoveries. One challenge that exists for domain scientists today is knowing the resource usage patterns of an application for the purpose of resource provisioning. A tool that accurately estimates these …

Quantum State Estimation And Tracking For Superconducting Processors Using Machine Learning, Shiva Lotfallahzadeh Barzili

Computational and Data Sciences (PhD) Dissertations

Quantum technology has been rapidly growing; in particular, the experiments that have been performed with superconducting qubits and circuit QED have allowed us to explore the light-matter interaction at its most fundamental level. The study of coherent dynamics between two-level systems and resonator modes can provide insight into fundamental aspects of quantum physics, such as how the state of a system evolves while being continuously observed. To study such an evolving quantum system, experimenters need to verify the accuracy of state preparation and control since quantum systems are very fragile and sensitive to environmental disturbance. In this thesis, I look …

Determining States Of Movement In Humans Using Minimally Processed Eeg Signals And Various Classification Methods, Maurice Barnett

All Theses

Electroencephalography (EEG) is a non-invasive technique used in both clinical and research settings to record neuronal signaling in the brain. The location of an EEG signal as well as the frequencies at which its neuronal constituents fire correlate with behavioral tasks, including discrete states of motor activity. Due to the number of channels and fine temporal resolution of EEG, a dense, high-dimensional dataset is collected. Transcranial direct current stimulation (tDCS) is a treatment that has been suggested to improve motor functions of Parkinson’s disease and chronic stroke patients when stimulation occurs during a motor task. tDCS is commonly administered without …

Factors Influencing Intent To Take A Covid-19 Test In The United States, Sheila Rutto

Theses and Dissertations

In 2020, COVID-19 became the first pandemic in the world’s history that brought the entire world to an abrupt and unexpected halt. Since the first reported case of the disease to date, the novel coronavirus has been able to wreak havoc in literary every corner of the globe and left an ever-growing number of unprecedented fatalities. The normal way of life has been disrupted, and the level of uncertainty about the end of this pandemic continues to manifest to many. Due to the urgency to bring this pandemic under control, medical officers have been able to recommend actions that people …

From Mdp To Alphazero, David Robert Sewell

Dissertations and Theses

In this paper I will explain the AlphaGo family of algorithms starting from first principles and requiring little previous knowledge from the reader. The focus will be upon one of the more recent versions AlphaZero but I hope to explain the core principles that allowed these algorithms to be so successful. I will generally refer to AlphaZero as theses [sic] core set of principles and will make it clear when I am referring to a specific algorithm of the AlphaGo family. AlphaZero in short combines Monte Carlo Tree Search (MCTS) with Deep learning and self-play. We will see how these …

Multi-Object Localization In Robotic Hand, Tsing Tsow

USF Tampa Graduate Theses and Dissertations

We have developed a machine learning approach to localized objects inside a robotic hand using only images from 2D cameras. Specifically, we used deep learning method (You Only Look Once, YOLO) and Iterative closest Point (ICP) to estimate the 3D coordinates of the objects in a robotic hand. This method will also output the number of objects inside the robotic hand in addition to the coordinates of the objects. We have demonstrated the performance with simulation and obtained typical accuracy within a few pixels (couple mm) and counting accuracy of about 76%. We have also applied it to real images, …

High-Dimensional Feature Selection And Multi-Level Causal Mediation Analysis With Applications To Human Aging And Cluster-Based Intervention Studies, Hachem Saddiki

Doctoral Dissertations

Many questions in public health and medicine are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome of interest. As a result, causal inference frameworks and methodologies have gained interest as a promising tool to reliably answer scientific questions. However, the tasks of identifying and efficiently estimating causal effects from observed data still pose significant challenges under complex data generating scenarios. We focus on (1) high-dimensional settings where the number of variables is orders of magnitude higher than the number of observations; and (2) multi-level settings, where study participants …

3d Shape Understanding And Generation, Matheus Gadelha

Doctoral Dissertations

In recent years, Machine Learning techniques have revolutionized solutions to longstanding image-based problems, like image classification, generation, semantic segmentation, object detection and many others. However, if we want to be able to build agents that can successfully interact with the real world, those techniques need to be capable of reasoning about the world as it truly is: a tridimensional space. There are two main challenges while handling 3D information in machine learning models. First, it is not clear what is the best 3D representation. For images, convolutional neural networks (CNNs) operating on raster images yield the best results in virtually …

Deep Learning Applications In Medical Bioinformatics, Ziad Omar

Electronic Theses and Dissertations

After a patient’s breast cancer diagnosis, identifying breast cancer lymph node metastases is one of the most important and critical factor that is directly related to the patient’s survival. The traditional way to examine the existence of cancer cells in the breast lymph nodes is through a lymph node procedure, biopsy. The procedure process is time-consuming for the patient and the provider, costly, and lacks accuracy as not every lymph node is examined. The intent of this study is to develop an artificial neural network (ANNs) that would map genetic biomarkers to breast lymph node classes using ANNs. The neural …

Comparative Study Of Reinforcement Learning Methods In Path Planning, Daniel Obawole

Electronic Theses and Dissertations

In order to perform a large variety of tasks and achieve human-level performance in complex real-world environments, an intelligent agent must be able to learn from its dynamically changing environment. Generally speaking, agents have limitations in obtaining an accurate description of the environment from what they perceive because they may not have all the information about the environment. The present research is focused on reinforcement learning algorithms that represent a defined category in the field of machine learning because of their unique approach based on a trial-error basis. Reinforcement learning is used to solve control problems based on received rewards. …

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 …

Exploiting Group Structures To Infer Social Interactions From Videos, Maksim Bolonkin

Dartmouth College Ph.D Dissertations

In this thesis, we consider the task of inferring the social interactions between humans by analyzing multi-modal data. Specifically, we attempt to solve some of the problems in interaction analysis, such as long-term deception detection, political deception detection, and impression prediction. In this work, we emphasize the importance of using knowledge about the group structure of the analyzed interactions. Previous works on the matter mostly neglected this aspect and analyzed a single subject at a time. Using the new Resistance dataset, collected by our collaborators, we approach the problem of long-term deception detection by designing a class of histogram-based features …

Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling, Benjamin O. Morris

Theses and Dissertations

This dissertation studies the underlying optimization problem encountered during the early-learning stages of convolutional neural networks and introduces a training algorithm competitive with existing state-of-the-art methods. First, a Design of Experiments method is introduced to systematically measure empirical second-order Lipschitz upper bound and region size estimates for local regions of convolutional neural network loss surfaces experienced during the early-learning stages. This method demonstrates that architecture choices can significantly impact the local loss surfaces traversed during training. Next, a Design of Experiments method is used to study the effects convolutional neural network architecture hyperparameters have on different optimization routines' abilities to …

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 …

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 …

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 …

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 …

Turn Of Phrase: Contrastive Pre-Training For Discourse-Aware Conversation Models, Roland Laboulaye

Theses and Dissertations

Understanding long conversations requires recognizing a discourse flow unique to conversation. Recent advances in unsupervised representation learning of text have been attained primarily through language modeling, which models discourse only implicitly and within a small window. These representations are in turn evaluated chiefly on sentence pair or paragraph-question pair benchmarks, which measure only local discourse coherence. In order to improve performance on discourse-reliant, long conversation tasks, we propose Turn-of-Phrase pre-training, an objective designed to encode long conversation discourse flow. We leverage tree-structured Reddit conversations in English to, relative to a chosen conversation path through the tree, select paths of varying …

Machine Learning For Analog/Mixed-Signal Integrated Circuit Design Automation, Weidong Cao

McKelvey School of Engineering Theses & Dissertations

Analog/mixed-signal (AMS) integrated circuits (ICs) play an essential role in electronic systems by processing analog signals and performing data conversion to bridge the analog physical world and our digital information world.Their ubiquitousness powers diverse applications ranging from smart devices and autonomous cars to crucial infrastructures. Despite such critical importance, conventional design strategies of AMS circuits still follow an expensive and time-consuming manual process and are unable to meet the exponentially-growing productivity demands from industry and satisfy the rapidly-changing design specifications from many emerging applications. Design automation of AMS IC is thus the key to tackling these challenges and has been …

Exploratory Search With Archetype-Based Language Models, Brent D. Davis

Electronic Thesis and Dissertation Repository

This dissertation explores how machine learning, natural language processing and information retrieval may assist the exploratory search task. Exploratory search is a search where the ideal outcome of the search is unknown, and thus the ideal language to use in a retrieval query to match it is unavailable. Three algorithms represent the contribution of this work. Archetype-based Modeling and Search provides a way to use previously identified archetypal documents relevant to an archetype to form a notion of similarity and find related documents that match the defined archetype. This is beneficial for exploratory search as it can generalize beyond standard …

Comparative Study Of Machine Learning Models On Solar Flare Prediction Problem, Nikhil Sai Kurivella

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Solar flare events are explosions of energy and radiation from the Sun’s surface. These events occur due to the tangling and twisting of magnetic fields associated with sunspots. When Coronal Mass ejections accompany solar flares, solar storms could travel towards earth at very high speeds, disrupting all earthly technologies and posing radiation hazards to astronauts. For this reason, the prediction of solar flares has become a crucial aspect of forecasting space weather. Our thesis utilized the time-series data consisting of active solar region magnetic field parameters acquired from SDO that span more than eight years. The classification models take AR …

Essays On Fake Review Detection, Managerial Response, And Consumer Perceptions, Long Chen

Theses and Dissertations

This dissertation investigates how online reviews and managerial responses jointly affect consumer perceptions. I first examine and compare the outcomes of multiple fake review classifiers using various algorithms, including traditional machine learning methods and recently developed deep learning methods (essay I). Then, based on the findings of the first essay, I examine the interrelationship between fake review detection, managerial response, and hotel ratings and ratings’ growths (essay II).The first essay is a comparative study on the methodology of identifying fake reviews. Although online reviews have attracted much attention from academia and industry for over fifteen years, how to identify fake …

Applying Deep Learning To The Ice Cream Vendor Problem: An Extension Of The Newsvendor Problem, Gaffar Solihu

Electronic Theses and Dissertations

The Newsvendor problem is a classical supply chain problem used to develop strategies for inventory optimization. The goal of the newsvendor problem is to predict the optimal order quantity of a product to meet an uncertain demand in the future, given that the demand distribution itself is known. The Ice Cream Vendor Problem extends the classical newsvendor problem to an uncertain demand with unknown distribution, albeit a distribution that is known to depend on exogenous features. The goal is thus to estimate the order quantity that minimizes the total cost when demand does not follow any known statistical distribution. The …

Modeling Real And Fake News Sharing In Social Networks, Abishai Joy

Boise State University Theses and Dissertations

Online media is changing the traditional news industry and diminishing the role of journalists, newspapers, and even news channels. This in turn is enhancing the ability of fake news to influence public opinion on important topics. The threat of fake news is quite imminent, as it allows malicious users to share their agenda with a larger audience. Major social media platforms like Twitter, Facebook, etc., are making it easy to spread fake news due to the minimal moderation/ fact-checking on these platforms.

This work aims at predicting fake and real news sharing in social media. Specifically, we employ a multi-level …

Modeling And Analyzing Users' Privacy Disclosure Behavior To Generate Personalized Privacy Policies, A.K.M. Nuhil Mehdy

Boise State University Theses and Dissertations

Privacy and its importance to society have been studied for centuries. While our understanding and continued theory building to hypothesize how users make privacy disclosure decisions has increased over time, the struggle to find a one-size solution that satisfies the requirements of each individual remains unsolved. Depending on culture, gender, age, and other situational factors, the concept of privacy and users' expectations of how their privacy should be protected varies from person to person. The goal of this dissertation is to design and develop tools and algorithms to support personal privacy management for end-users. The foundation of this research is …