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Full-Text Articles in Physical Sciences and Mathematics

Metareasoning For Planning And Execution In Autonomous Systems, Justin Svegliato Mar 2022

Metareasoning For Planning And Execution In Autonomous Systems, Justin Svegliato

Doctoral Dissertations

Metareasoning is the process by which an autonomous system optimizes, specifically monitors and controls, its own planning and execution processes in order to operate more effectively in its environment. As autonomous systems rapidly grow in sophistication and autonomy, the need for metareasoning has become critical for efficient and reliable operation in noisy, stochastic, unstructured domains for long periods of time. This is due to the uncertainty over the limitations of their reasoning capabilities and the range of their potential circumstances. However, despite considerable progress in metareasoning as a whole over the last thirty years, work on metareasoning for planning relies ...

Anticanonical Models Of Smoothings Of Cyclic Quotient Singularities, Arie A. Stern Gonzalez Mar 2022

Anticanonical Models Of Smoothings Of Cyclic Quotient Singularities, Arie A. Stern Gonzalez

Doctoral Dissertations

In this thesis we study anticanonical models of smoothings of cyclic quotient singularities. Given a surface cyclic quotient singularity $Q\in Y$, it is an open problem to determine all smoothings of $Y$ that admit an anticanonical model and to compute it. In \cite{HTU}, Hacking, Tevelev and Urz\'ua studied certain irreducible components of the versal deformation space of $Y$, and within these components, they found one parameter smoothings $\Y \to \A^1$ that admit an anticanonical model and proved that they have canonical singularities. Moreover, they compute explicitly the anticanonical models that have terminal singularities using Mori's ...

Reliable Decision-Making With Imprecise Models, Sandhya Saisubramanian Mar 2022

Reliable Decision-Making With Imprecise Models, Sandhya Saisubramanian

Doctoral Dissertations

The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial intelligence and machine learning, it is challenging to assure that autonomous systems will operate reliably in the open world. One of the causes of unreliable behavior is the impreciseness of the model used for decision-making. Due to the practical challenges in data collection and precise model specification, autonomous systems often operate based on models that do not represent all the details in the environment. Even if ...

Modeling Chain Packing In Complex Phases Of Self-Assembled Block Copolymers, Anugu Abhiram Reddy Mar 2022

Modeling Chain Packing In Complex Phases Of Self-Assembled Block Copolymers, Anugu Abhiram Reddy

Doctoral Dissertations

Block copolymer (BCP) melts undergo microphase seperation and form ordered soft matter crystals with varying domain shapes and symmetries. We study the con- nection between diblock copolymer molecular designs and thermodynamic selection of ordered crystals by modeling features of variable sub-domain geometry filled with individual blocks within non-canonical sphere-like and network phases that together with layered, cylindrical and canonical spherical phases forms “natural forms” of self- assembled amphiphilic soft matter at large. First, we present a model to revise our understanding of optimal Frank-Kasper sphere-like morphologies by advancing the- ory to account for varying domain volumes. We then develop generic ...

Decision Making With Limited Data, Kieu My Phan Mar 2022

Decision Making With Limited Data, Kieu My Phan

Doctoral Dissertations

This thesis studies different approaches to decision making with limited data.

First, we study the effects of approximate inference on Thompson sampling in the k-armed bandit problems. Thompson sampling is a successful algorithm but requires posterior inference, which often must be approximated in practice. We show that even small constant inference error (in alpha-divergence) can lead to poor performance (linear regret) due to under-exploration (for alpha < 1) or over-exploration (for alpha > 0) by the approximation. While for alpha > 0 this is unavoidable, for alpha <= 0 the regret can be improved by adding a small amount of forced exploration.

Second, we consider the problem of designing a randomized experiment on a source population to estimate the Average Treatment Effect (ATE ...

Moving Polygon Methods For Incompressible Fluid Dynamics, Chris Chartrand Mar 2022

Moving Polygon Methods For Incompressible Fluid Dynamics, Chris Chartrand

Doctoral Dissertations

Hybrid particle-mesh numerical approaches are proposed to solve incompressible fluid flows. The methods discussed in this work consist of a collection of particles each wrapped in their own polygon mesh cell, which then move through the domain as the flow evolves. Variables such as pressure, velocity, mass, and momentum are located either on the mesh or on the particles themselves, depending on the specific algorithm described, and each will be shown to have its own advantages and disadvantages. This work explores what is required to obtain local conservation of mass, momentum, and convergence for the velocity and pressure in a ...

Mixture Models In Machine Learning, Soumyabrata Pal Mar 2022

Mixture Models In Machine Learning, Soumyabrata Pal

Doctoral Dissertations

Modeling with mixtures is a powerful method in the statistical toolkit that can be used for representing the presence of sub-populations within an overall population. In many applications ranging from financial models to genetics, a mixture model is used to fit the data. The primary difficulty in learning mixture models is that the observed data set does not identify the sub-population to which an individual observation belongs. Despite being studied for more than a century, the theoretical guarantees of mixture models remain unknown for several important settings.

In this thesis, we look at three groups of problems. The first part ...

Mar 2022

Decision-Analytic Models Using Reinforcement Learning To Inform Dynamic Sequential Decisions In Public Policy, Seyedeh Nazanin Khatami

Doctoral Dissertations

We developed decision-analytic models specifically suited for long-term sequential decision-making in the context of large-scale dynamic stochastic systems, focusing on public policy investment decisions. We found that while machine learning and artificial intelligence algorithms provide the most suitable frameworks for such analyses, multiple challenges arise in its successful adaptation. We address three specific challenges in two public sectors, public health and climate policy, through the following three essays.

In Essay I, we developed a reinforcement learning (RL) model to identify optimal sequence of testing and retention-in-care interventions to inform the national strategic plan “Ending the HIV Epidemic in the US ...

Incremental Non-Greedy Clustering At Scale, Nicholas Monath Mar 2022

Incremental Non-Greedy Clustering At Scale, Nicholas Monath

Doctoral Dissertations

Clustering is the task of organizing data into meaningful groups. Modern clustering applications such as entity resolution put several demands on clustering algorithms: (1) scalability to massive numbers of points as well as clusters, (2) incremental additions of data, (3) support for any user-specified similarity functions.

Hierarchical clusterings are often desired as they represent multiple alternative flat clusterings (e.g., at different granularity levels). These tree-structured clusterings provide for both fine-grained clusters as well as uncertainty in the presence of newly arriving data. Previous work on hierarchical clustering does not fully address all three of the aforementioned desiderata. Work on ...

Mar 2022

Few-Shot Natural Language Processing By Meta-Learning Without Labeled Data, Trapit Bansal

Doctoral Dissertations

Humans show a remarkable capability to accurately solve a wide range of problems efficiently -- utilizing a limited amount of computation and experience. Deep learning models, by stark contrast, can be trained to be highly accurate on a narrow task while being highly inefficient in terms of the amount of compute and data required to reach that accuracy. Within natural language processing (NLP), recent breakthroughs in unsupervised pretraining have enabled reusable models that can be applied to many NLP tasks, however, learning of new tasks is still inefficient. This has led to research on few-shot learning, where the goal is to ...

The Thermoelectric, Thermoresistive, And Hygroresistive Properties And Applications Of Vapor Printed Pedot-Cl, Linden K. Allison

Doctoral Dissertations

Wearable electronics are a valuable tool to increase consumer access to real-time and long-term health care monitoring. The development of these technologies can also lead to major advancements in the field, such as self-charging systems that are completely removed from the electrical grid. However, much of the wearable technology available commercially contain rigid components, use unsustainable synthetic methods, or undesirable materials. The field has thus been moving towards wearables that mimic textiles or use textiles as a substrate. Herein, we discuss the use of oxidative chemical vapor deposition (oCVD) to produce textiles coated with poly(3,4-ethylenedioxythiophene) known as PEDOT-Cl ...

Improving The Hydrological Analysis Of Groundwater Flow Paths By Integrating Geochemical And Physical Characteristics Of A Highly Fractured Aquifer System To Create Sustainable Use Of Groundwater In A Climate With Projected Drying Trends., Marsha K. Allen

Doctoral Dissertations

Improving the hydrological analysis of groundwater flow paths by integrating geochemical and physical characteristics of a highly fractured aquifer system to create sustainable use of groundwater in a climate with projected drying trends.

Precipitation over Caribbean islands has decreased steadily since the 1950's, which has led to severe drought conditions. The most recent Pan-Caribbean Drought occurred from 2013 to 2016. Climate models predict that drying trends are expected to continue and become more severe over time as precipitation decreases and temperatures rise. In addition, evaporation rates on these islands are expected to increase by ~15-17%, contributing to the drought ...

Electroweak Interactions And Fundamental Symmetries In Light Nuclei With Short-Range Effective Field Theories, Zichao Yang

Doctoral Dissertations

Effective field theories(EFTs) are powerful tools to study nuclear systems that display separation of scales. In this dissertation, we present halo EFT results for the $\beta$-delayed proton emission from $^{11}$Be, and pionless EFT results for three-nucleon systems. Halo nuclei are simply described by a tightly bound core and loosely bound valence nucleons. Using the halo EFT, we calculate the rate of the rare decay $^{11}$Be, which is a well-known halo nucleus, into $^{10}\text{Be} + p +e^- + \bar{\nu}_e$. We assume a shallow $1/2 ^+$ resonance in the $^{10}$Be$-p$ system with an energy ...

Production And Adsorption Of Volatile Tellurium Hexafluoride, Stephanie H. Bruffey Dec 2021

Production And Adsorption Of Volatile Tellurium Hexafluoride, Stephanie H. Bruffey

Doctoral Dissertations

Research and development supporting the management of off-gases from nuclear fuel reprocessing has historically been focused on the off-gas streams that arise from aqueous reprocessing technology. With the advent of advanced reactor designs off-gas streams arising from advanced reprocessing methodology, such as that of FV [fluoride volatility] processing, also merit consideration. This work focuses on TeF6 [tellurium hexafluoride], one of the most volatile radioactive compounds produced during FV, and investigates TeF6 production, measurement, and abatement technologies.

To assist in on-line monitoring of TeF6 by Fourier-transformed infrared spectroscopy, this work systematically used the ideal gas law and Beer ...

Auto-Curation Of Large Evolving Image Datasets, Sara Mousavicheshmehkaboodi Dec 2021

Auto-Curation Of Large Evolving Image Datasets, Sara Mousavicheshmehkaboodi

Doctoral Dissertations

Large image collections are becoming common in many fields and offer tantalizing opportunities to transform how research, work, and education are conducted if the information and associated insights could be extracted from them. However, major obstacles to this vision exist. First, image datasets with associated metadata contain errors and need to be cleaned and organized to be easily explored and utilized. Second, such collections typically lack the necessary context or may have missing attributes that need to be recovered. Third, such datasets are domain-specific and require human expert involvement to make the right interpretation of the image content. Fourth, the ...

Dec 2021

Total Internal Reflection: Applications In Nonlinear Microscopy And Fluorescence Anisotropy, Brandon Colon

Doctoral Dissertations

As technology advances to harness new energies and to create new cures, the sophistication of analysis grows not only in depth but in efficiency. Total internal reflection (TIR) has been coupled to microscopy leveraging its unique optical phenomenon on a breadth of topics. In this dissertation, the work presented will show how TIR was applied in two different instrumental analyses to evaluate two unique and complex systems. The first project features TIR paired with the transient absorption microscopy (TAM), a nonlinear optical technique, to gauge solvent mixing and diffusion in microreactors. Microreactors gained acclaim for their ability to produce high ...

A Connectivity Framework To Explore The Role Of Anthropogenic Activity And Climate On The Propagation Of Water And Sediment At The Catchment Scale, Christos Giannopoulos

Doctoral Dissertations

Anthropogenic disturbance in intensively managed landscapes (IMLs) has dramatically altered critical zone processes, resulting in fundamental changes in material fluxes. Mitigating the negative effects of anthropogenic disturbance and making informed decisions for optimal placement and assessment of best management practices (BMPs) requires fundamental understanding of how different practices affect the connectivity or lack thereof of governing transport processes and resulting material fluxes across different landscape compartments within the hillslope-channel continuum of IMLs. However, there are no models operating at the event timescale that can accurately predict material flux transport from the hillslope to the catchment scale capturing the spatial and ...

Ultrasound-Driven Fabrication Of Nanosized High-Entropy Materials For Heterogeneous Catalysis, Francis Uchenna Okejiri Dec 2021

Ultrasound-Driven Fabrication Of Nanosized High-Entropy Materials For Heterogeneous Catalysis, Francis Uchenna Okejiri

Doctoral Dissertations

High-entropy materials (HEMs) have emerged as a new class of multi-principal-element materials with great technological prospects. As a unique class of concentrated solid-solution materials, HEMs, formed on the premise of incorporating five or more principal elements into a single crystalline phase, provide an excellent opportunity to access superior catalytic materials ‘hiding’ in the unexplored central regions of a multicomponent phase space of higher orders.

However, the fabrication of HEMs is energy-intensive, typically requiring extreme temperatures and/or pressures under which agglomeration of particles occurs with a commensurate decrease in surface area. For most catalytic applications, non-agglomerated particles with high surface ...

Dec 2021

Application Of Single-Ion Conducting Polymer Electrolytes (Sicpes), Sheng Zhao

Doctoral Dissertations

Polymer electrolytes have been widely studied as a potential candidate for next generation batterie with improved safety and higher energy density. Especially, single-ion conducting polymer electrolytes (SICPEs) have attracted significant attention due to their almost unity lithium-ion transport number, which is believed to help suppress lithium dendrite growth and extend battery cycle life. However, there is still a long way to go before they can be practically applied in batteries, due to their relatively low ionic conductivity at ambient temperature. Therefore, the main goal of this work is to explore various methods that can improve the ionic conductivity of SICPEs ...

Dec 2021

Analytical Considerations And Methods For Comprehensive Analysis Of Bacterial Phospholipidomics Using Hilic-Ms/Ms, David Thomas Reeves

Doctoral Dissertations

Omics technologies have rapidly evolved over the last half century through vast improvements in efficient extraction methodologies, advances in instrumentation for data collection, and a wide assortment of informatics tools to help deconvolute sample data sets. However, there are still untapped pools of molecules that warrant further analytical attention. As the frontline defense of the cell against exterior influences, the phospholipid membrane is key in structure, defense, and signaling, but current omics studies are only just now catching up to the potential hidden within cellular lipid profiles. Examination of shifts in phospholipid speciation and character could provide researchers with a ...

Instrument Development For High Sensitivity Size Characterization Of Lipid Vesicles And Other Biological Macromolecules Via Taylor Dispersion Analysis, Meagan Moser

Doctoral Dissertations

Just as humans communicate with other humans, the cells in our bodies communicate with each other through various, often complex, mechanisms. Cell-to-cell transmission of small molecules, lipids, proteins, peptides, or nucleic acids can be mediated by extracellular lipid vesicles called exosomes. Exosomes have been found to play a role in the delivery of regulatory molecules from one cell to another, serving as a universal communication mechanism. Currently, there is an emerging focus on characterizing exosome communication dynamics. Understanding exosome mechanisms of cell-to-cell communication requires accurate measurements of the spatiotemporal and chemical dynamics of exosome secretion. No current analytical approach offers ...

Search For New Physics In Rare Higgs Boson Decays With The Cms Detector At The Large Hadron Collider, Himal Acharya

Doctoral Dissertations

A new boson with a mass of 125 GeV was discovered at the large hadron collider (LHC) in July 2012. The properties of this particle are so far consistent with the standard model (SM) expectation. Differences in the Higgs boson decay rates and predicted by the SM might indicate the presence of new particles and forces between them. Particularly, rare exclusive decays of the Higgs boson are a promising laboratory to study physics beyond the standard model. Searches for decays of the Higgs boson into a Z boson and a J/ψ meson or into pairs of J/ψ or ...

Interfaces And Dynamics In Polymeric 3d Printing And Crystalline Polymer Blends, Stevenson C. Perryman Dec 2021

Interfaces And Dynamics In Polymeric 3d Printing And Crystalline Polymer Blends, Stevenson C. Perryman

Doctoral Dissertations

This dissertation presents experimental work that provide a foundation to rationally improve fused filament fabrication (FFF) and immiscible blend compatibilization. Objects generated from additive manufacturing processes, such as FFF, have intrinsic structural weaknesses which include two project specific examples: structural anisotropy and irreversible thermal strain. Due to low adhesion between individual print layers that results in macroscopic defects, the mechanical strength of printed objects when force is applied perpendicular to the build orientation is drastically reduced. In the first dissertation chapter, we present a protocol to produce interlayer covalent bonds by depositing multi-amine additives between individual layers of a print ...

Oct 2021

Understanding Of Visual Domains Via The Lens Of Natural Language, Chenyun Wu

Doctoral Dissertations

A joint understanding of vision and language can enable intelligent systems to perceive, act, and communicate with humans for a wide range of applications. For example, they can assist a human to navigate in an environment, edit the content of an image through natural language commands, or search through image collections using natural language queries. In this thesis, we aim to improve our understanding of visual domains through the lens of natural language. We specifically look into (1) images of categories within a fine-grained taxonomy such as species of birds or variants of aircraft, (2) images of textures that describe ...

Oct 2021

Human Mobility Monitoring Using Wifi: Analysis, Modeling, And Applications, Amee Trivedi

Doctoral Dissertations

Understanding and modeling humans and device mobility has fundamental importance in mobile computing, with implications ranging from network design and location-aware technologies to urban infrastructure planning. Today's users carry a plethora of devices such as smartphones, laptops, tablets, and smartwatches, with each device offering a different set of services resulting in different usage and mobility leading to the research question of understanding and modeling multiple user device trajectories. Additionally, prior research on mobility focuses on outdoor mobility when it is known that users spend 80% of their time indoors resulting in wide gaps in knowledge in the area of ...

Windows In Algebraic Geometry And Applications To Moduli, Sebastian Torres Oct 2021

Windows In Algebraic Geometry And Applications To Moduli, Sebastian Torres

Doctoral Dissertations

We apply the theory of windows, as developed by Halpern-Leistner and by Ballard, Favero and Katzarkov, to study certain moduli spaces and their derived categories. Using quantization and other techniques we show that stable GIT quotients of $(\mathbb{P}^1)^n$ by $PGL_2$ over an algebraically closed field of characteristic zero satisfy a rare property called Bott vanishing, which states that $\Omega^j_Y \otimes L$ has no higher cohomology for every j and every ample line bundle L. Similar techniques are used to reprove the well known fact that toric varieties satisfy Bott vanishing. We also use windows to explore ...

Oct 2021

Learning From Limited Labeled Data For Visual Recognition, Jong-Chyi Su

Doctoral Dissertations

Recent advances in computer vision are in part due to the widespread use of deep neural networks. However, training deep networks require enormous amounts of labeled data which can be a bottleneck. In this thesis, we propose several approaches to mitigate this in the context of modern deep networks and computer vision tasks.

While transfer learning is an effective strategy for natural image tasks where large labeled datasets such as ImageNet are available, it is less effective for distant domains such as medical images and 3D shapes. Chapter 2 focuses on transfer learning from natural image representations to other modalities ...

United States Household Carbon Footprints: Quantifying The Relationship Between Household-Level Income Inequality And Greenhouse Gas Emissions (1996-2015), Jared Starr

Doctoral Dissertations

As long as humanity has existed, we have altered our environment to provide goods, services, and (more recently) wealth to people. Over the last several centuries, the scope and pace of this transformation has accelerated with the onset of technological innovation, social and economic reorganization, and an ensuing population boom. Today, humanity’s demands on nature have become the dominant force shaping the critical earth systems upon which all life depends. From local land-use change to the global climate many of these anthropogenic pressures pose an existential threat to nature and the dependent social systems that rely on them. Yet ...

Oct 2021

Mathematical Model For Osteosarcoma Progression And Treatments, Trang M. Le

Doctoral Dissertations

Cancer is a complex disease where every tumor has its own characteristics, and thus different tumors may respond differently to the same treatments. Osteosarcoma, which is a rare type of cancer with poor prognosis, is especially characterized by its high heteogeneity. Therefore, it is important to study the progression of osteosarcoma tumors in different groups of patients with distinct characteristics. The immune system has been reported to play an important role in the development of various cancers with some immune cells having anti-tumor effects and others having pro-tumor effects. With recent advances in digital cytometry methods, which are techniques to ...

Equivariant Smoothings Of Cusp Singularities, Angelica Simonetti Oct 2021

Equivariant Smoothings Of Cusp Singularities, Angelica Simonetti

Doctoral Dissertations

Let $p \in X$ be the germ of a cusp singularity and let $\iota$ be an antisymplectic involution, that is an involution free on $X\setminus \{p\}$ and such that there exists a nowhere vanishing holomorphic 2-form $\Omega$ on $X\setminus \{p\}$ for which $\iota^*(\Omega)=-\Omega$. We prove that a sufficient condiition for such a singularity equipped with an antisymplectic involution to be equivariantly smoothable is the existence of a Looijenga (or anticanonical) pair $(Y,D)$ that admits an involution free on $Y\setminus D$ and that reverses the orientation of $D$.