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Use Of Unoccupied Aerial Vehicle (Drones) Based Remote Sensing To Model Platform Topography And Identify Human-Made Earthen Barriers In Salt Marshes, Joshua J. Ward
Masters Theses
Elevation is a foundational driver of salt marsh morphology. Elevation governs inundation and hydrological patterns, vegetation distribution, and soil health. Anthropogenic impacts at grand scales (e.g., rising sea levels) and local scales (e.g., infrastructure) have altered the elevation of the salt marsh surface, changing the topography and morphology of these ecosystems. This study establishes and assesses means to document and analyze these impacts using Unoccupied Aerial Vehicle (UAV) based remote sensing to model platform topography. This thesis’s first and primary study presents and compares methods of producing high-resolution digital terrain models (DTMs) with UAV-based Digital Aerial Photogrammetry (DAP) and Light …
Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim
Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim
Masters Theses
Due to significant investment, research, and development efforts over the past decade, deep neural networks (DNNs) have achieved notable advancements in classification and regression domains. As a result, DNNs are considered valuable intellectual property for artificial intelligence providers. Prior work has demonstrated highly effective model extraction attacks which steal a DNN, dismantling the provider’s business model and paving the way for unethical or malicious activities, such as misuse of personal data, safety risks in critical systems, or spreading misinformation. This thesis explores the feasibility of model extraction attacks on mobile devices using aggregated runtime profiles as a side-channel to leak …
Multi-Scale Simulations Of Dynamic Protein Structures And Interactions, Yumeng Zhang
Multi-Scale Simulations Of Dynamic Protein Structures And Interactions, Yumeng Zhang
Doctoral Dissertations
Intrinsically disordered proteins (IDPs) are functional proteins that lack stable tertiary structures in the unbound state. They frequently remain dynamic even within specific complexes and assemblies. IDPs are major components of cellular regulatory networks and have been associated with cancers, diabetes, neurodegenerative diseases, and other human diseases. Computer simulations are essential for deriving a molecular description of the disordered protein ensembles and dynamic interactions for mechanistic understanding of IDPs in biology, diseases, and therapeutics. However, accurate simulation of the heterogeneous ensembles and dynamic interactions of IDPs is extremely challenging because of both the prohibitive computational cost and demanding force field …
An Efficient Privacy-Preserving Framework For Video Analytics, Tian Zhou
An Efficient Privacy-Preserving Framework For Video Analytics, Tian Zhou
Doctoral Dissertations
With the proliferation of video content from surveillance cameras, social media, and live streaming services, the need for efficient video analytics has grown immensely. In recent years, machine learning based computer vision algorithms have shown great success in various video analytic tasks. Specifically, neural network models have dominated in visual tasks such as image and video classification, object recognition, object detection, and object tracking. However, compared with classic computer vision algorithms, machine learning based methods are usually much more compute-intensive. Powerful servers are required by many state-of-the-art machine learning models. With the development of cloud computing infrastructures, people are able …
Automated Identification And Mapping Of Interesting Mineral Spectra In Crism Images, Arun M. Saranathan
Automated Identification And Mapping Of Interesting Mineral Spectra In Crism Images, Arun M. Saranathan
Doctoral Dissertations
The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has proven to be an invaluable tool for the mineralogical analysis of the Martian surface. It has been crucial in identifying and mapping the spatial extents of various minerals. Primarily, the identification and mapping of these mineral spectral-shapes have been performed manually. Given the size of the CRISM image dataset, manual analysis of the full dataset would be arduous/infeasible. This dissertation attempts to address this issue by describing an (machine learning based) automated processing pipeline for CRISM data that can be used to identify and map the unique mineral signatures present in …
Data To Science With Ai And Human-In-The-Loop, Gustavo Perez Sarabia
Data To Science With Ai And Human-In-The-Loop, Gustavo Perez Sarabia
Doctoral Dissertations
AI has the potential to accelerate scientific discovery by enabling scientists to analyze vast datasets more efficiently than traditional methods. For example, this thesis considers the detection of star clusters in high-resolution images of galaxies taken from space telescopes, as well as studying bird migration from RADAR images. In these applications, the goal is to make measurements to answer scientific questions, such as how the star formation rate is affected by mass, or how the phenology of bird migration is influenced by climate change. However, current computer vision systems are far from perfect for conducting these measurements directly. They may …
Policy Gradient Methods: Analysis, Misconceptions, And Improvements, Christopher P. Nota
Policy Gradient Methods: Analysis, Misconceptions, And Improvements, Christopher P. Nota
Doctoral Dissertations
Policy gradient methods are a class of reinforcement learning algorithms that optimize a parametric policy by maximizing an objective function that directly measures the performance of the policy. Despite being used in many high-profile applications of reinforcement learning, the conventional use of policy gradient methods in practice deviates from existing theory. This thesis presents a comprehensive mathematical analysis of policy gradient methods, uncovering misconceptions and suggesting novel solutions to improve their performance. We first demonstrate that the update rule used by most policy gradient methods does not correspond to the gradient of any objective function due to the way the …
Multi-Slam Systems For Fault-Tolerant Simultaneous Localization And Mapping, Samer Nashed
Multi-Slam Systems For Fault-Tolerant Simultaneous Localization And Mapping, Samer Nashed
Doctoral Dissertations
Mobile robots need accurate, high fidelity models of their operating environments in order to complete their tasks safely and efficiently. Generating these models is most often done via Simultaneous Localization and Mapping (SLAM), a paradigm where the robot alternatively estimates the most up-to-date model of the environment and its position relative to this model as it acquires new information from its sensors over time. Because robots operate in many different environments with different compute, memory, sensing, and form constraints, the nature and quality of information available to individual instances of different SLAM systems varies substantially. `One-size-fits-all' solutions are thus exceedingly …
Quantum Chaos, Integrability, And Hydrodynamics In Nonequilibrium Quantum Matter, Javier Lopez Piqueres
Quantum Chaos, Integrability, And Hydrodynamics In Nonequilibrium Quantum Matter, Javier Lopez Piqueres
Doctoral Dissertations
It is well-known that the Hilbert space of a quantum many-body system grows exponentially with the number of particles in the system. Drive the system out of equilibrium so that the degrees of freedom are now dynamic and the result is an extremely complicated problem. With that comes a vast landscape of new physics, which we are just recently starting to explore. In this proposal, we study the dynam- ics of two paradigmatic classes of quantum many-body systems: quantum chaotic and integrable systems. We leverage certain tools commonly employed in equilibrium many-body physics, as well as others tailored to the …
High Resolution Mass Spectrometry As A Platform For The Analysis Of Polyoxometalates, Their Solution Phase Dynamics, And Their Biological Interactions., Daniel T. Favre
High Resolution Mass Spectrometry As A Platform For The Analysis Of Polyoxometalates, Their Solution Phase Dynamics, And Their Biological Interactions., Daniel T. Favre
Doctoral Dissertations
Polyoxometalates (POMs) are a class of inorganic molecule of increasing interest to the inorganic, bioinorganic and catalytic communities among many others. While their prevalence in research has increased, tools and methodologies for the analysis of their fundamental characteristics still need further development. Decavanadate (V10) specifically has been postulated to have several unique properties that have not been confirmed independently. Mass spectrometry (MS) and its ability to determine the composition of solution phase species by both mass and charge is uniquely well suited to the analysis of POMs. In this work we utilized high-resolution mass spectrometry to characterize V10 in aqueous …
Toltec: A New Multichroic Imaging Polarimeter For The Large Millimeter Telescope, Nat S. Denigris
Toltec: A New Multichroic Imaging Polarimeter For The Large Millimeter Telescope, Nat S. Denigris
Doctoral Dissertations
The TolTEC camera is a new millimeter-wave imaging polarimeter designed to fill the focal plane of the 50-m diameter Large Millimeter Telescope (LMT). Combined with the LMT, TolTEC offers high angular resolution (5", 6.3", 9.5") for simultaneous, polarization-sensitive observations in its three wavelength bands: 1.1, 1.4, and 2.0 mm. Additionally, TolTEC is designed to reach groundbreaking mapping speeds in excess of 1 deg2/mJy2/hr, which will enable the completion of deep surveys of large-scale structure, galaxy evolution, and star formation that are currently limited when considering practical observation times for other ground-based observatories. This thesis covers the …
Development Of A Decision Support System Webtool For Historic And Future Low Flow Estimation In The Northeast United States With Applications Of Machine Learning For Advancing Physical And Statistical Methodologies, Andrew F. Delsanto
Doctoral Dissertations
Droughts are a global challenge and anthropogenic climate change is expected to increase the frequency and severity of extreme low flow events. A major challenge for resource managers is how best to incorporate future climate change projections into low flow event estimations, especially in ungaged basins. Using both physically based hydrology models and statistical models, this dissertation contributes novel methodologies to three key challenges associated with 7-day, 10-year low flow (7Q10) estimation in the northeast United States. Chapter 2 builds upon statistically based 7Q10 estimation in ungaged basins by comparing multiple machine learning algorithms to classical statistical methodologies. This chapter’s …
Migmatite Formation, Geochronometer Petrogeneisis, And Rare Earth Element Mineralization In The Adirondack Mountains, Ny, Kaitlyn Suarez
Migmatite Formation, Geochronometer Petrogeneisis, And Rare Earth Element Mineralization In The Adirondack Mountains, Ny, Kaitlyn Suarez
Doctoral Dissertations
The Adirondack Mountains in upstate New York contain exposures of complex partially melted rocks, in addition to iron oxide-apatite (IOA) deposits with variable rare earth element (REE) concentrations. Previous workers have suggested that melting occurred during the ca. 1150 Ma Shawinigan and the ca. 1050 Ma Ottawan orogenies. However, there are challenges in determining the timing of melting and the number of partial melt events. Further, tectonic models must be developed to describe the petrogenesis of IOA and REE mineralization. Migmatites are present along Rt. 4/22 near Whitehall, NY. In chapter two, all layers of a single migmatitic rock were …
Polymer-Based Nanotherapeutics To Combat Difficult-To-Treat Bacterial Infections, Jessa Marie V. Makabenta
Polymer-Based Nanotherapeutics To Combat Difficult-To-Treat Bacterial Infections, Jessa Marie V. Makabenta
Doctoral Dissertations
The continuous emergence and spread of antibiotic-resistant bacteria are a global health emergency, debilitating the capability to prevent and cure various infectious diseases that were once treatable. Antibiotic therapy is further rendered ineffective due to biofilm formation and the ability of bacteria to thrive and colonize inside mammalian cells. Given the diminishing efficacy of available antibiotics combined with the scarcity of new therapeutics entering the antibiotic pipeline, innovative treatment strategies are urgently in demand. Nanomaterial-based strategies offer ‘outside of the box’ approach for the treatment of antibiotic-resistant bacterial infections. Nanomaterials feature tunable physicochemical properties that can be carefully modified to …
Intersection Cohomology Of Rank One Local Systems For Arrangement Schubert Varieties, Shuo Lin
Intersection Cohomology Of Rank One Local Systems For Arrangement Schubert Varieties, Shuo Lin
Doctoral Dissertations
In this thesis we study the intersection cohomology of arrangement Schubert varieties with coefficients in a rank one local system on a hyperplane arrangement complement. We prove that the intersection cohomology can be computed recursively in terms of certain polynomials, if a local system has only $\pm 1$ monodromies. In the case where the hyperplane arrangement is generic central or equivalently the associated matroid is uniform and the local system has only $\pm 1$ monodromies, we prove that the intersection cohomology is a combinatorial invariant. In particular when the hyperplane arrangement is associated to the uniform matroid of rank $n-1$ …
When To Hold And When To Fold: Studies On The Topology Of Origami And Linkages, Mary Elizabeth Lee
When To Hold And When To Fold: Studies On The Topology Of Origami And Linkages, Mary Elizabeth Lee
Doctoral Dissertations
Linkages and mechanisms are pervasive in physics and engineering as models for a
variety of structures and systems, from jamming to biomechanics. With the increase
in physical realizations of discrete shape-changing materials, such as metamaterials,
programmable materials, and self-actuating structures, an increased understanding
of mechanisms and how they can be designed is crucial. At a basic level, linkages
or mechanisms can be understood to be rigid bars connected at pivots around which
they can rotate freely. We will have a particular focus on origami-like materials, an
extension to linkages with the added constraint of faces. Self-actuated versions typ-
ically start …
Reactive Chemistries For Protein Labeling, Degradation, And Stimuli Responsive Delivery, Myrat Kurbanov
Reactive Chemistries For Protein Labeling, Degradation, And Stimuli Responsive Delivery, Myrat Kurbanov
Doctoral Dissertations
Reactive chemistries for protein chemical modification play an instrumental role in chemical biology, proteomics, and therapeutics. Depending on the application, the selectivity of these modifications can range from precise modification of an amino acid sequence by genetic manipulation of protein expression machinery to a stochastic modification of lysine residues on the protein surface. Ligand-Directed (LD) chemistry is one of the few methods for targeted modification of endogenous proteins without genetic engineering. However, current LD strategies are limited by stringent amino acid selectivity. To bridge this gap, this thesis focuses on the development of highly reactive LD Triggerable Michael Acceptors (LD-TMAcs) …
Towards Robust Long-Form Text Generation Systems, Kalpesh Krishna
Towards Robust Long-Form Text Generation Systems, Kalpesh Krishna
Doctoral Dissertations
Text generation is an important emerging AI technology that has seen significant research advances in recent years. Due to its closeness to how humans communicate, mastering text generation technology can unlock several important applications such as intelligent chat-bots, creative writing assistance, or newer applications like task-agnostic few-shot learning. Most recently, the rapid scaling of large language models (LLMs) has resulted in systems like ChatGPT, capable of generating fluent, coherent and human-like text. However, despite their remarkable capabilities, LLMs still suffer from several limitations, particularly when generating long-form text. In particular, (1) long-form generated text is filled with factual inconsistencies to …
Human-Centered Technologies For Inclusive Collection And Analysis Of Public-Generated Data, Mahmood Jasim
Human-Centered Technologies For Inclusive Collection And Analysis Of Public-Generated Data, Mahmood Jasim
Doctoral Dissertations
The meteoric rise in the popularity of public engagement platforms such as social media, customer review websites, and public input solicitation efforts strives for establishing an inclusive environment for the public to share their thoughts, ideas, opinions, and experiences. Many decisions made at a personal, local, or national scale are often fueled by data generated by the public. As such, inclusive collection, analysis, sensemaking, and utilization of pubic-generated data are crucial to support the exercise of successful decision-making processes. However, people often struggle to engage, participate, and share their opinions due to inaccessibility, the rigidity of traditional public engagement methods, …
Quantifying And Enhancing The Security Of Federated Learning, Virat Vishnu Shejwalkar
Quantifying And Enhancing The Security Of Federated Learning, Virat Vishnu Shejwalkar
Doctoral Dissertations
Federated learning is an emerging distributed learning paradigm that allows multiple users to collaboratively train a joint machine learning model without having to share their private data with any third party. Due to many of its attractive properties, federated learning has received significant attention from academia as well as industry and now powers major applications, e.g., Google's Gboard and Assistant, Apple's Siri, Owkin's health diagnostics, etc. However, federated learning is yet to see widespread adoption due to a number of challenges. One such challenge is its susceptibility to poisoning by malicious users who aim to manipulate the joint machine learning …
Sources And Controls Of Carbon Dioxide In Inland Waters At Watershed, Regional, And Continental Scales, Brian Saccardi
Sources And Controls Of Carbon Dioxide In Inland Waters At Watershed, Regional, And Continental Scales, Brian Saccardi
Doctoral Dissertations
Inland waters are significant sources of carbon dioxide to the atmosphere, and estimates of emissions are similar in magnitude to those of the carbon dioxide sequestered by the net terrestrial sink. Currently, methods of estimating carbon dioxide emissions are based on statistical approaches and often do not consider landscape attributes such as human development, agriculture, or the hydrologic connectivity of the stream network. The following research addresses these issues in chapter 1 by developing and validating a reactive transport model at the watershed scale, then in chapter 2 by applying the reactive transport model at the continental scale across US …
Search For Exotic Higgs Boson Decay To Multiple B-Quarks With The Atlas Detector At Lhc Using Machine Learning Methods, Yuan-Tang Chou
Search For Exotic Higgs Boson Decay To Multiple B-Quarks With The Atlas Detector At Lhc Using Machine Learning Methods, Yuan-Tang Chou
Doctoral Dissertations
The discovery of the Higgs boson has opened up new possibilities for investigating physics beyond the Standard Model (SM). New particles may interact with the SM through the Higgs boson, and deviations from SM predictions can indicate the presence of new physics. This dissertation focuses on the search for exotic Higgs decay, $H\rightarrow aa \rightarrow (b\bbar)(b\bbar)$ where a is a new scalar boson and focuses on the case where the Higgs boson is produced in association with a Z boson. The data were collected by the ATLAS detector at the Large Hadron Collider at center-of-mass energy $\sqrt{s} =~13~\TeV$ from 2015 …
Learning To See With Minimal Human Supervision, Zezhou Cheng
Learning To See With Minimal Human Supervision, Zezhou Cheng
Doctoral Dissertations
Deep learning has significantly advanced computer vision in the past decade, paving the way for practical applications such as facial recognition and autonomous driving. However, current techniques depend heavily on human supervision, limiting their broader deployment. This dissertation tackles this problem by introducing algorithms and theories to minimize human supervision in three key areas: data, annotations, and neural network architectures, in the context of various visual understanding tasks such as object detection, image restoration, and 3D generation. First, we present self-supervised learning algorithms to handle in-the-wild images and videos that traditionally require time-consuming manual curation and labeling. We demonstrate that …
Foundations Of Node Representation Learning, Sudhanshu Chanpuriya
Foundations Of Node Representation Learning, Sudhanshu Chanpuriya
Doctoral Dissertations
Low-dimensional node representations, also called node embeddings, are a cornerstone in the modeling and analysis of complex networks. In recent years, advances in deep learning have spurred development of novel neural network-inspired methods for learning node representations which have largely surpassed classical 'spectral' embeddings in performance. Yet little work asks the central questions of this thesis: Why do these novel deep methods outperform their classical predecessors, and what are their limitations? We pursue several paths to answering these questions. To further our understanding of deep embedding methods, we explore their relationship with spectral methods, which are better understood, and show …
Thermodynamic Laws Of Billiards-Like Microscopic Heat Conduction Models, Ling-Chen Bu
Thermodynamic Laws Of Billiards-Like Microscopic Heat Conduction Models, Ling-Chen Bu
Doctoral Dissertations
In this thesis, we study the mathematical model of one-dimensional microscopic heat conduction of gas particles, applying both both analytical and numerical approaches. The macroscopic law of heat conduction is the renowned Fourier’s law J = −k∇T, where J is the local heat flux density, T(x, t) is the temperature gradient, and k is the thermal conductivity coefficient that characterizes the material’s ability to conduct heat. Though Fouriers’s law has been discovered since 1822, the thorough understanding of its microscopic mechanisms remains challenging [3] (2000). We assume that the microscopic model of heat conduction is a hard ball system. The …
Positive Factorizations Via Planar Mapping Classes And Braids, Richard E. Buckman
Positive Factorizations Via Planar Mapping Classes And Braids, Richard E. Buckman
Doctoral Dissertations
In this thesis we seek to better understand the planar mapping class group in
order to find factorizations of boundary multitwists, primarily to generate and study
symplectic Lefschetz pencils by lifting these factorizations. Traditionally this method
is applied to a disk or sphere with marked points, utilizing factorizations in the stan-
dard and spherical braid groups, whereas in our work we allow for multiple boundary components. Dehn twists along these boundaries give rise to exceptional sections of Lefschetz fibrations over the 2–sphere, equivalently, to Lefschetz pencils with base points. These methods are able to derive an array of known examples …
Experiments With Monopoles, Rings And Knots In Spinor Bose-Einstein Condensates, Alina A. Blinova
Experiments With Monopoles, Rings And Knots In Spinor Bose-Einstein Condensates, Alina A. Blinova
Doctoral Dissertations
Topological excitations are ubiquitous in nature, their charge being a naturally-quantized, conserved quantity that can exhibit particle-like behavior. Spinor Bose-Einstein condensates (BECs) are an exceptionally versatile system for the study and exploration of topological excitations. Between the spin-1 and spin-2 87Rb condensates there are seven possible broken-symmetry magnetic phases, with each one hosting unique opportunities for topological defects. We have created and observed several novel topological excitations in a spinor 87Rb BEC. In this dissertation I present and discuss three principal experimental findings: (1) The discovery of an Alice ring, or a half-quantum vortex ring, emerging from a …
Probing The Physical Mechanisms Responsible For Brown Dwarf And Giant Planet Formation, Sarah Betti
Probing The Physical Mechanisms Responsible For Brown Dwarf And Giant Planet Formation, Sarah Betti
Doctoral Dissertations
The disks that form around young stellar objects provide the essential material for their continued growth as well as the formation of planets, making them ideal laboratories to investigate the mechanisms and environments key for substellar and planetary formation. In this dissertation, I explore two main formation processes: the transportation of water necessary for giant planet formation, and the accretion and growth of young brown dwarfs. First, I study the water ice content in the circumstellar disk of AB Aurigae, a young Herbig Ae star. I detect and map icy grains on the disk surface using high contrast observations taken …
Nonparametric Derivative Estimation Using Penalized Splines: Theory And Application, Bright Antwi Boasiako
Nonparametric Derivative Estimation Using Penalized Splines: Theory And Application, Bright Antwi Boasiako
Doctoral Dissertations
This dissertation is in the field of Nonparametric Derivative Estimation using
Penalized Splines. It is conducted in two parts. In the first part, we study the L2
convergence rates of estimating derivatives of mean regression functions using penalized splines. In 1982, Stone provided the optimal rates of convergence for estimating derivatives of mean regression functions using nonparametric methods. Using these rates, Zhou et. al. in their 2000 paper showed that the MSE of derivative estimators based on regression splines approach zero at the optimal rate of convergence. Also, in 2019, Xiao showed that, under some general conditions, penalized spline estimators …
A Multi-Regional Assessment Of Eastern Whip-Poor-Will (Antrostomus Vociferus) Occupancy In Managed And Unmanaged Forests Using Autonomous Recording Units, Jeffery T. Larkin
A Multi-Regional Assessment Of Eastern Whip-Poor-Will (Antrostomus Vociferus) Occupancy In Managed And Unmanaged Forests Using Autonomous Recording Units, Jeffery T. Larkin
Masters Theses
State and federal agencies spend considerable time and resources to enhance and create habitat for wildlife. Understanding how target and non-target species respond to these efforts can help direct the allocation of limited conservation resources. However, monitoring species response to habitat management comes with several logistical challenges that are exacerbated as the area of geographic focus increases. I used autonomous recording units (ARUs) to mitigate these challenges when assessing Eastern Whip-poor-will (Antrostomus vociferus) response to forest management. I deployed 1,265 ARUs across managed and unmanaged public and private forests from western North Carolina to southern Maine. I then …