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Articles 1 - 30 of 82
Full-Text Articles in Physical Sciences and Mathematics
Low-Resource Machine Learning Techniques For The Analysis Of Online Social Media Textual Data, Toktam Amanzadeh Oghaz
Low-Resource Machine Learning Techniques For The Analysis Of Online Social Media Textual Data, Toktam Amanzadeh Oghaz
Electronic Theses and Dissertations, 2020-
Low-resource and label-efficient machine learning methods can be described as the family of statistical and machine learning techniques that can achieve high performance without needing a substantial amount of labeled data. These methods include both unsupervised learning techniques, such as LDA, and supervised methods, such as active learning, each providing different benefits. Thus, this dissertation is devoted to the design and analysis of unsupervised and supervised techniques to provide solutions for the following problems: Unsupervised narrative summary extraction for social media content, Social media text classification with Active Learning (AL), Investigating restrictions and benefits of using Curriculum Learning (CL) for …
Observation Of Novel Phases Of Quantum Matter Beyond Topological Insulator, Sabin Regmi
Observation Of Novel Phases Of Quantum Matter Beyond Topological Insulator, Sabin Regmi
Electronic Theses and Dissertations, 2020-
Because of the unique electronic properties, intriguing novel phenomena, and potentiality in quantum device applications, the quantum materials with non-trivial band structures have enticed a bulk of research works over the last two decades. The experimental discovery of the three-dimensional topological insulators (TIs) - bulk insulators with surface conduction via spin-polarized electrons - kicked off the flurry of research interests towards such materials, which resulted in the experimental discovery of new topological phases of matter beyond TIs. The topological semimetallic phase in Dirac, Weyl, and nodal-line semimetals is an example, where the classification depends on the dimensionality, degeneracy, and symmetry …
Torward Real-World Cross-View Image Geo-Localization, Sijie Zhu
Torward Real-World Cross-View Image Geo-Localization, Sijie Zhu
Electronic Theses and Dissertations, 2020-
Cross-view image geo-localization aims to determine the locations of street-view query images by searching in a GPS-tagged reference image database from aerial view. One fundamental challenge is the dramatic view-point/domain difference between the street-view query images and aerial-view reference images. Recent works have made great progress on bridging the domain gap with advanced deep learning techniques and geometric prior knowledge, i.e. the query is aligned at the center of one aerial-view reference image (spatial alignment) and the orientation relationship between the two views is known (orientation alignment). However, such prior knowledge of the geometry correspondence of the two views is …
Multi-Plane Light Conversion: Devices And Applications, Yuanhang Zhang
Multi-Plane Light Conversion: Devices And Applications, Yuanhang Zhang
Electronic Theses and Dissertations, 2020-
Multi-plane light conversion (MPLC) has recently been developed as a versatile tool for manipulating spatial distributions of the optical field through repeated phase modulations. An MPLC Device consists of a series of phase masks separated by free-space propagation. It can convert one orthogonal set of beams into another orthogonal set through unitary transformation, which is useful for a number of applications. In telecommunication, for example, mode-division multiplexing (MDM) is a promising technology that will enable continued scaling of capacity by employing spatial modes of a single fiber. MPLC has shown great potential in MDM devices with ultra-wide bandwidth, low insertion …
Advanced Deep Learning Methodologies For Deepfake Detection, Aminollah Khormali
Advanced Deep Learning Methodologies For Deepfake Detection, Aminollah Khormali
Electronic Theses and Dissertations, 2020-
The recent advances in the field of Artificial Intelligence (AI), particularly Generative Adversarial Networks (GANs) and an abundance of training samples along with robust computational resources have significantly propelled the field of AI-generated fake information in all kinds, e.g., deepfakes. Deepfakes are among the most sinister types of misinformation, posing large-scale and severe security and privacy risks targeting critical governmental institutions and ordinary people across the world. The fact that deepfakes are AI-generated digital content and not actual events captured by a camera implies that they still can be detected using advanced AI models. Although the deepfake detection task has …
The Facile Preparation Of Bis(Imino)Pyridine Complexes Via Mechanochemistry And Inorganic Synthons Via Benign Reductants, Thomas Shaw
Electronic Theses and Dissertations, 2020-
This thesis is comprised of two key themes: (i) leveraging mechanochemical synthetic methods for the green preparation of bis(imino)pyridine ligands and associated first row transition metal coordination complexes, and (ii) the preparation and characterization of both historic and novel mid-valent inorganic synthons via the use of dimethylphenylsilane as a stoichiometric reducing agent. The first portion of this thesis highlights the utilization of solvent-free vibratory ball-milling in lieu of traditional, solvent-based refluxes. A variety of both bis(imino)pyridine ligands and complexes were formed in drastically reduced timeframes with solvent-economical work-up procedures. This methodology was also leveraged for the isolation of acetyl(imino)pyridine ligands …
Graph Neural Networks For Improved Interpretability And Efficiency, Patrick Pho
Graph Neural Networks For Improved Interpretability And Efficiency, Patrick Pho
Electronic Theses and Dissertations, 2020-
Attributed graph is a powerful tool to model real-life systems which exist in many domains such as social science, biology, e-commerce, etc. The behaviors of those systems are mostly defined by or dependent on their corresponding network structures. Graph analysis has become an important line of research due to the rapid integration of such systems into every aspect of human life and the profound impact they have on human behaviors. Graph structured data contains a rich amount of information from the network connectivity and the supplementary input features of nodes. Machine learning algorithms or traditional network science tools have limitation …
Synergistic Impacts Of Climate Change And Human Induced Stressors On The Apalachicola Bay Food Web, Kira Allen
Synergistic Impacts Of Climate Change And Human Induced Stressors On The Apalachicola Bay Food Web, Kira Allen
Electronic Theses and Dissertations, 2020-
Apalachicola Bay, an estuary located in northwest Florida, is likely to experience an increase in climate change and human-induced stressors, such as sea level rise and changes in freshwater inflow, in the future. A coupled hydrodynamic and food web modeling approach was used to simulate future scenarios of low and high river flow and sea level rise in Apalachicola Bay from 2020 to 2049 and demonstrate the range of temporal and spatial changes in water temperature, salinity, fisheries species populations and the broader food web. Concurrent with model development, a survey of Apalachicola Bay stakeholders was conducted to assess stakeholder …
Bayesian Spatiotemporal Modeling With Gaussian Processes, Qing He
Bayesian Spatiotemporal Modeling With Gaussian Processes, Qing He
Electronic Theses and Dissertations, 2020-
Bayesian spatiotemporal models have been successfully applied to various fields of science, such as ecology and epidemiology. The complicated nature of spatiotemporal patterns can be well represented through priors such as Gaussian processes. This dissertation is focused on two applications of Bayesian spatiotemporal models: a) anomaly detection for spatiotemporal data with missingness and b) zero-inflated spatiotemporal count data analysis. Missingness in spatiotemporal data prohibits anomaly detection algorithms from learning characteristic rules and patterns due to the lack of most data. This project is motivated by a challenge provided by the National Science Foundation (NSF) and the National Geospatial-Intelligence Agency (NGA). …
Imaging Based Beam Steering For Optical Communication And Lidar Applications, Sajad Saghaye Polkoo
Imaging Based Beam Steering For Optical Communication And Lidar Applications, Sajad Saghaye Polkoo
Electronic Theses and Dissertations, 2020-
Optical beam steering is a key component in any application that requires dynamic (i.e. realtime control) of beam propagation through free-space. Example applications include remote sensing, spectroscopy, laser machining, targeting, Lidar, optical wireless communications (OWC) and more. The pointing control requirements for many of these applications can be met by traditional mechanical steering techniques; however, these solutions tend to be bulky, slow, expensive, power hungry and prone to mechanical failures leading to short component lifetimes. Two emerging applications, Lidar imaging and OWC, truly need improved beam-steering capabilities to flourish and support the advancement of self-driving cars or relieve the congestion …
Raman Excitation Laser Effects On Peak Parameters And Peak Metamorphic Temperatures Of Primitive Carbonaceous Chondrites, Amy Lebleu-Debartola
Raman Excitation Laser Effects On Peak Parameters And Peak Metamorphic Temperatures Of Primitive Carbonaceous Chondrites, Amy Lebleu-Debartola
Electronic Theses and Dissertations, 2020-
MicroRaman (µRaman) spectroscopy is often regarded as a non-destructive technique, utilized to analyze limited materials, both terrestrial and extraterrestrial. Carbonaceous chondrite meteorites are of particular interest but they are dark (low albedo) materials, and thus absorb the majority of incident visible light. Raman excitation lasers can induce considerable localized heating, even when low laser powers are used. It has been previously suggested to utilize low power lasers of =0.4 mW to minimize damaging carbonaceous samples in several fields, including Meteoritics, Geology, Chemistry, and Paleontology. Peak Metamorphic Temperatures (PMT) experienced by the meteorite can be estimated from Raman fitting parameters related …
Exploring The Privacy Dimension Of Wearables Through Machine Learning-Enabled Inference, Ulku Meteriz Yildiran
Exploring The Privacy Dimension Of Wearables Through Machine Learning-Enabled Inference, Ulku Meteriz Yildiran
Electronic Theses and Dissertations, 2020-
Today's hyper-connected consumers demand convenient ways to tune into information without switching between devices, which led the industry leaders to the wearables. Wearables such as smartwatches, fitness trackers, and augmented reality (AR) glasses can be comfortably worn on the body. In addition, they offer limitless features, including activity tracking, authentication, navigation, and entertainment. Wearables that provide digestible information stimulate even higher consumer demand. However, to keep up with the ever-growing user expectations, developers keep adding new features and interaction methods to augment the use cases without considering their privacy impacts. In this dissertation, we explore the privacy dimension of wearables …
Light Trapping Transparent Electrodes, Mengdi Sun
Light Trapping Transparent Electrodes, Mengdi Sun
Electronic Theses and Dissertations, 2020-
Transparent electrodes represent a critical component in a wide range of optoelectronic devices such as high-speed photodetectors and solar cells. Fundamentally, the presence of any conductive structures in the optical path leads to dissipation and reflection, which adversely affects device performance. Many different approaches have been attempted to minimize such shadowing losses, including the use of transparent conductive oxides (TCOs), metallic nanowire mesh grids, graphene-based contacts, and high-aspect ratio metallic wire arrays. In this dissertation I discuss a conceptually different approach to achieve transparent electrodes, which involves recapturing photons initially reflected by highly conductive electrode lines. To achieve this, light-redirecting …
Electronic And Optoelectronic Properties Of Two-Dimensional Heterostructures For Next-Generation Device Technologies, Jesse Thompson
Electronic And Optoelectronic Properties Of Two-Dimensional Heterostructures For Next-Generation Device Technologies, Jesse Thompson
Electronic Theses and Dissertations, 2020-
Since monolayer graphene was isolated in 2004, there has been significant interest in integrating layered materials into innovative device designs and hybrid materials to help solve pressing technological challenges. This is partially because they can typically be thinned to a two-dimensional (2D) form without suffering from roughness-induced scattering and can exhibit thickness-dependent variations in properties such as their energy band gap. This dissertation reports on investigations of electronic and optoelectronic device physics in 2D material heterostructures. The investigation of electronic device physics focuses on the interface between 2D molybdenum disulfide (MoS2) and gold (Au), which behaves as a resistive switching …
Surface Engineering For Controlled Growth And Deposition Of Nanomaterials - Assembly And Design At The Nano-Microscale, David Fox
Electronic Theses and Dissertations, 2020-
Materials with nanoscale dimensions offer several important benefits over bulk materials (e.g. increased surface area, low-cost, deviation from bulk properties, etc.). Such materials are critical components for next-generation energy storage materials, optoelectronic devices, and catalyst systems. However, these materials are often processed in liquid media, and their diminutive structures are fragile in the presence of capillary forces. As such, preparing uniform and stable nanomaterial coatings is a significant challenge. Herein, we discuss an approach where the substrate itself is factored into the assembly and growth of these materials. First, nanoporous surfaces were utilized to achieve a uniform deposition of one-dimensional …
Distance Perception Through Head-Mounted Displays, Sina Masnadi
Distance Perception Through Head-Mounted Displays, Sina Masnadi
Electronic Theses and Dissertations, 2020-
It has been shown in numerous research studies that people tend to underestimate distances while wearing head-mounted displays (HMDs). We investigated various possible factors affecting the perception of distance is HMDs through multiple studies. Many contributing factors has been identified by researchers in the past decades, however, further investigation is required to provide a better understanding of this problem. In order to find a baseline for distance underestimation, we performed a study to compare the distance perception in real world versus a fake headset versus a see-through HMD. Users underestimated distances while wearing the fake headset or the see-through HMD. …
Methods For Defending Neural Networks Against Adversarial Attacks, Sharvil Shah
Methods For Defending Neural Networks Against Adversarial Attacks, Sharvil Shah
Electronic Theses and Dissertations, 2020-
Convolutional Neural Networks (CNNs) have been at the frontier of the revolution within the field of computer vision. Since the advent of AlexNet in 2012, neural networks with CNN architectures have surpassed human-level capabilities for many cognitive tasks. As the neural networks are integrated in many safety critical applications such as autonomous vehicles, it is critical that they are robust and resilient to errors. Unfortunately, it has recently been observed that deep neural network models are susceptible to adversarial perturbations which are imperceptible to human vision. In this thesis, we propose a solution to defend neural networks against white box …
Various Dynamical Regimes, And Transitions From Homogeneous To Inhomogeneous Steady States In Nonlinear Systems With Delays And Diverse Couplings, Ryan Roopnarain
Various Dynamical Regimes, And Transitions From Homogeneous To Inhomogeneous Steady States In Nonlinear Systems With Delays And Diverse Couplings, Ryan Roopnarain
Electronic Theses and Dissertations, 2020-
This dissertation focuses on the effects of distributed delays modeled by 'weak generic kernels' on the collective behavior of coupled nonlinear systems. These distributed delays are introduced into several well-known periodic oscillators such as coupled Landau-Stuart and Van der Pol systems, as well as coupled chaotic Van der Pol-Rayleigh and Sprott systems, for a variety of couplings including diffusive, cyclic, or dynamic ones. The resulting system is then closed via the 'linear chain trick' and the linear stability analysis of the system and conditions for Hopf bifurcations that initiate oscillations are investigated. A variety of dynamical regimes and transitions between …
Static Analysis Of The Build System To Accelerate Continuous Testing Of Highly Configurable Software, Necip Fazil Yildiran
Static Analysis Of The Build System To Accelerate Continuous Testing Of Highly Configurable Software, Necip Fazil Yildiran
Electronic Theses and Dissertations, 2020-
Continuous testing is widely used for facilitating fast and reliable software delivery. However, build-time configurability makes such testing harder for configurable software. As configurable software forms the basis of much of our computing infrastructure, there is even more need for better continuous testing for configurable software. In this dissertation, our goal is to improve the quality of configurable software. To this end, we tackle two, previously unsolved problems. The build system of configurable software is one of the biggest reasons why testing configurable software is hard. Therefore, in our solutions, we deal with the build system by using a comprehensive …
Towards Leveraging Sparse Infrared Datasets For Multiple View Synthesis, Few Shot Learning And Background Invariant Recognition, Maliha Arif
Electronic Theses and Dissertations, 2020-
This dissertation presents a study of various machine learning techniques for recognizing vehicular objects in infrared images. State of the art methods for computer vision have not been widely explored for this part of the electromagnetic spectrum (EM). Challenges that arise due to the dearth of infrared training images, terrain clutter, and thermal phenomenology have not been fully addressed. Infrared dataset collection and annotation is both difficult and expensive. What if there is a way we can generate infrared images and diminish the need for collecting data out in the field? Our first research study encompasses an encoder-decoder model that …
Uncooled Microbolometer Imaging Systems For Machine Vision, Robert Grimming
Uncooled Microbolometer Imaging Systems For Machine Vision, Robert Grimming
Electronic Theses and Dissertations, 2020-
Over the last 20 years, the cost of uncooled microbolometer-based imaging systems has drastically decreased while performance has increased. In the simplest terms, the figure of merit for these types of thermal detectors is given in terms of the τ-NETD product, the combination of the thermal time constant and the noise equivalent temperature difference. Considering these factors, optimal system design parameters are investigated to maximize visual information content. This dissertation focuses on improving scene information in the longwave infrared (LWIR) spectrum that has had its validity and quality degraded by noise, blur, and reflected radiance. Taken together, noise and blur …
Algorithms For The Detection Of Resolved And Unresolved Targets In The Infrared Bands, Bruce Mcintosh
Algorithms For The Detection Of Resolved And Unresolved Targets In The Infrared Bands, Bruce Mcintosh
Electronic Theses and Dissertations, 2020-
This dissertation proposes algorithms for the detection of both resolved and unresolved targets in the infrared bands. Recent breakthroughs in deep learning have spurred major advancements in computer vision, but most of the attention and progress has been focused on RGB imagery from the visual band. The infrared bands such as Long Wave Infrared (LWIR), Medium Wave Infrared (MWIR), Short Wave Infrared (SWIR) and Near Infrared (NIR) each respond differently to physical phenomena, providing information that can be used to better understand the environment. The first task addressed is that of detecting vehicles in heavy clutter in MWIR imagery. A …
Applications For Machine Learning On Readily Available Data From Virtual Reality Training Experiences, Alec Moore
Applications For Machine Learning On Readily Available Data From Virtual Reality Training Experiences, Alec Moore
Electronic Theses and Dissertations, 2020-
The purpose of the research presented in this dissertation is to improve virtual reality (VR) training systems by enhancing their understanding of users. While the field of intelligent tutoring systems (ITS) has seen value in this approach, much research into making use of biometrics to improve user understanding and subsequently training, relies on specialized hardware. Through the presented research, I show that with machine learning (ML), the VR system itself can serve as that specialized hardware for VR training systems. I begin by discussing my explorations into using an ecologically valid, specialized training simulation as a testbed to predict knowledge …
Addressing Human-Centered Artificial Intelligence: Fair Data Generation And Classification And Analyzing Algorithmic Curation In Social Media, Amirarsalan Rajabi
Addressing Human-Centered Artificial Intelligence: Fair Data Generation And Classification And Analyzing Algorithmic Curation In Social Media, Amirarsalan Rajabi
Electronic Theses and Dissertations, 2020-
With the growing impact of artificial intelligence, the topic of fairness in AI has received increasing attention. Artificial intelligence is observed to have caused unanticipated negative consequences. In this dissertation, we address two critical aspects regarding human-centered artificial intelligence (HCAI), a new paradigm for developing artificial intelligence that is ethical, fair, and helps to improve the human condition. In the first part of this dissertation, we investigate the effect that AI curation of contents by social media platforms has on an online discussions, by studying a polarized discussion in the Twitter network. We then develop a network communication model that …
Methodology Of Augmented Reality Chinese Language Articulatory Pronunciation Practice: Game And Study Design, Daria Sinyagovskaya
Methodology Of Augmented Reality Chinese Language Articulatory Pronunciation Practice: Game And Study Design, Daria Sinyagovskaya
Electronic Theses and Dissertations, 2020-
Learning a language can be hard. Learning a language that contains tones to convey meaning is even harder. This dissertation presents a novel methodology for creating a language practice using augmented reality that has never been used before. The design of a new app in AR and non-AR versions can evaluate the same practice methodology. This methodology was applied to new software and was examined in regard to the importance of this software. Although the study results are inconclusive, progress has been made in answering research questions on the effectiveness of AR versus non-AR and the reliability of peer assessment. …
Testing The Influence Of Water Depth In Design Of Created Oyster Reef For Living Shoreline Applications, Peter Vien
Testing The Influence Of Water Depth In Design Of Created Oyster Reef For Living Shoreline Applications, Peter Vien
Electronic Theses and Dissertations, 2020-
Living shoreline stabilization has become a popular practice in shoreline restoration and bank protection; however, there are still many uncertainties regarding effective site design using living materials. For example, natural wave-breaks may be formed of created reefs, but the optimum water depth for hydrodynamic influence may differ from the preferred depth to ensure organism recruitment. The objective of this research is to understand how water depth relative to the crest of submerged artificial oyster reef structures influences nearshore hydrodynamic processes and sediment transport or retention in nearshore areas. A field study, sited in a microtidal estuary on the Atlantic coast …
A Human-Centered Approach To Improving Adolescent Online Sexual Risk Detection Algorithms, Afsaneh Razi
A Human-Centered Approach To Improving Adolescent Online Sexual Risk Detection Algorithms, Afsaneh Razi
Electronic Theses and Dissertations, 2020-
Computational risk detection has the potential to protect especially vulnerable populations from online victimization. Conducting a comprehensive literature review on computational approaches for online sexual risk detection led to the identification that the majority of this work has focused on identifying sexual predators after-the-fact. Also, many studies rely on public datasets and third-party annotators to establish ground truth and train their algorithms, which do not accurately represent young social media users and their perspectives to prevent victimization. To address these gaps, this dissertation integrated human-centered approaches to both creating representative datasets and developing sexual risk detection machine learning models to …
Development Of A Computational Application To Aid With Chemometric And Forensic Analysis Of Fire Debris Samples, Michelle Corbally
Development Of A Computational Application To Aid With Chemometric And Forensic Analysis Of Fire Debris Samples, Michelle Corbally
Electronic Theses and Dissertations, 2020-
Fire debris analysis is a forensic science discipline that determines if an ignitable liquid residue is present or absent in a fire debris sample. Currently, fire debris analysis results in categorical statements based on qualitative data, not the quantitative evidentiary value of data. The purpose of this research was to develop a novel software application to aid fire debris analysts in the identification and classification of ignitable liquid residues that are found in fire debris samples. The developed application uses target factor analysis (TFA) and Pearson correlation for compound identification in gas chromatograms using mass spectral comparison and allows for …
Extensions Of The General Solution To The Inverse Problem Of The Calculus Of Variations, And Variational, Perturbative And Reversible Systems Approaches To Regular And Embedded Solitary Waves, Ranses Alfonso Rodriguez
Extensions Of The General Solution To The Inverse Problem Of The Calculus Of Variations, And Variational, Perturbative And Reversible Systems Approaches To Regular And Embedded Solitary Waves, Ranses Alfonso Rodriguez
Electronic Theses and Dissertations, 2020-
In the first part of this Dissertation, hierarchies of Lagrangians of degree two, three or four, each only partly determined by the choice of leading terms and with some coefficients remaining free, are derived. These have significantly greater freedom than the most general differential geometric criterion currently known for the existence of a Lagrangian and variational formulation since our existence conditions are for individual coefficients in the Lagrangian. For different choices of leading coefficients, the resulting variational equations could also represent traveling waves of various nonlinear evolution equations. Families of regular and embedded solitary waves are derived for some of …
Function Approximation Guarantees For A Shallow Neural Network Trained By Gradient Flow, Russell Gentile
Function Approximation Guarantees For A Shallow Neural Network Trained By Gradient Flow, Russell Gentile
Electronic Theses and Dissertations, 2020-
This work features an original result linking approximation and optimization theory for deep learning. Several examples from recent literature show that, given the same number of learnable parameters, deep neural networks can approximate richer classes of functions, with better accuracy than classical methods. The bulk of approximation theory results though, are only concerned with the infimum error for all possible parameterizations of a given network size. Their proofs often rely on hand-crafted networks, where the weights and biases are carefully selected. Optimization theory indicates that such models would be difficult or impossible to realize with standard gradient-based training methods. The …