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Reach Avoid Games With Output Observer Mixed Monotone Reachable Set Estimation
No full textWe consider the problem of overestimating the reachable set of a partially observable dynamical agent with unknown disturbance dynamics. We use the framework of mixed-monotonicity to obtain a conservative estimate of the reachable set with respect to bounds on the uncertain dynamics. High-gain observers are used to produce states of the estimates and collapse the reachable set estimate error. Analysis shows that the reachable set estimate can be bounded through careful gain design. The results are validated in simulation and on a real ballistic dynamical systems. The results are used to show capture can be achieved by a team of slow pursuers against a fast evader in a reach-avoid game.https://doi.org/10.2514/6.2025-227
Rydberg Atoms, Photonic Devices, and Graduate Student Mental Health: Embracing Complexity and Care in Physics
No full textThis dissertation combines work from several disparate fields of research in order to explore the epistemic value of complexity and care in physics. Complexity means intentionally constructing models that account for multiple interacting effects, yielding deeper insight and new research directions. Care means viewing physics as a fundamentally human endeavor and recognizing that the human aspects are as important as the technical aspects for rigorous and sustainable progress in physics.
The dissertation begins with experimental work involving Rydberg atoms. Rydberg atoms, with their long lifetimes, sensitivity to electric fields, and strong interactions, have found use across atomic physics, from fundamental physics investigations to emerging quantum technologies. In this dissertation I describe efforts to optimize a Rydberg-ensemble single-photon source, which resulted in an accurate model of Rydberg spin-wave dephasing. I then show evidence of enhanced Rydberg interactions using microwave dressing. This is a first step toward many possible applications, including fully tunable interactions, the exploration of non-equilibrium phenomena, the generation of nonclassical states of light, and improved atom-photon entanglement for quantum networking.
The second part of this dissertation explores two photonic devices critical to quantum science and technology. First, we describe an algorithm for reconstructing the photon number distribution of light pulses using a single single-photon avalanche detector. We achieve high-fidelity reconstruction of both coherent and anti-bunched pulses whose duration and correlation timescales are both at least a few detector dead times in duration, and we explore the limits of the algorithm at high input photon rates. Second, we describe an ab initio model of the random and time-varying birefringences of optical fibers. Environmentally induced drifts make this a serious challenge for polarization-encoded quantum networks, which is becoming crucial to understand as a new generation of quantum networks comes online. A first-principles model of the birefringences in fibers will allow for determinations about the suitability of installed fibers for quantum networking applications and for development of mitigation methods and compensation strategies.
In the final part of this dissertation, I describe the development and results of a national-scale study of physics and astronomy graduate student mental health. Mental health challenges in academia are well-documented, but the mental health of graduate students remains understudied, especially in physics and astronomy. Our sample reports clinical levels of anxiety and depression at 5-7 times the rate of the general population. Female and non-binary students in our sample report more symptoms of anxiety, depression, and impostor phenomenon than male students. Using structural equation modeling, we find that loneliness is an especially strong predictor of these mental health issues in our sample, and we clarify pathways by which advisors support their graduate students. These results suggest specific ways that peers, faculty, and departments can support graduate student mental health
THE REGULATION OF EXCITATORY SYNAPSES ONTO PARVALBUMIN-POSITIVE INTERNEURONS BY BRIEF MONOCULAR DEPRIVATION IN THE JUVENILE VISUAL CORTEX
No full textAmblyopia, a visual system disorder that affects about two percent of the world's population, is characterized by the asymmetric reception of visual input across the two eyes. It leads to visual deficits in acuity detection and depth perception in the amblyopic eye and causes a shift in preference of visual input towards the uncompromised eye. The mechanisms that underlie synaptic deficits in amblyopia are partially understood. Therefore, identifying factors contributing to amblyopia during the developmental critical period remains a priority.
Amblyopia therapies become less effective once humans reach eight years old due to the maturation of parvalbumin-positive interneurons (PV+ INs) in the visual circuit. However, altering excitatory inputs onto PV+ INs can induce layer-specific synaptic plasticity following monocular deprivation (MD), a treatment that mimics amblyopia. Cleavage of the structural barriers around PV+ INs can also reactivate plasticity, suggesting that enzymatic activity within the visual cortex could be used in amblyopia therapy.
Here, I investigate the role of tumor necrosis factor alpha converting enzyme (TACE) in the juvenile visual cortex following MD. I hypothesize that reduced excitatory synaptic contacts onto PV+ INs in the deprived hemisphere is governed by upregulated TACE activity. FRET-based fluorescence was utilized to determine the spatial distribution and localization of TACE activity within the juvenile cortex. First, I demonstrate that TACE activation decreases in the compromised hemisphere following MD, independent of presynaptic excitatory biomarkers. Second, I show that TACE activity within Layer 2/3 of the visual cortex was time-dependent and unilaterally disrupted by MD. Lastly, I investigated the prevalence of TACE substrate neuronal pentraxin receptor (NPR) and found that NPR fluorescence increased in the deprived hemisphere following MD, independent of changes in excitatory synaptic contacts. Although the involvement of TACE within the excitatory disconnection pathway was disproven, the in vivo application of FRET-based biomarkers provides new insight into enzyme-specific therapeutics for amblyopia and other visual system disorders
ASSESSING THE DYNAMICS OF SALMONELLA NEWPORT IN TOMATO PRE-HARVEST ECOSYSTEMS: INSIGHTS FROM ENVIRONMENTAL SURVEYS AND PHENOTYPIC INVESTIGATIONS
No full textIllnesses caused by Salmonella have been associated with a variety of food commodities, such as nuts, spices, fresh produce, low moisture packaged foods, seafood, poultry, and other meat. This diversity highlights the adaptability of this pathogen to persist in various environments and demonstrates the genetic diversity of this microbe. Epidemiological trends reveal that infections attributed to this pathogen exhibit peak incidences during the summer months, a period that coincides with the peak harvest of fresh produce in agricultural regions across the United States. Investigation and trace-back of many fresh produce-related foodborne outbreaks often implicate the growing environment or surrounding areas as the source of contamination. Salmonella is capable of surviving for prolonged periods in soil, water and sediment while maintaining the ability to contaminate produce plants and cause human illness. To gain a better understanding of the ecology, distribution and persistence of Salmonella in the Atlantic region environmental sampling of surface waters and sediments were conducted. Collectively, over 1,600 samples were tested yielding 1,420 Salmonella isolates. Whole genome sequencing (WGS) identified the most common serovar as S. Newport sequence type (ST) 118 and suggests that Newport(JJPX01.0061) may be geographically sequestered to the Virginia Eastern Shore (VES), a major producer of tomatoes and where outbreaks linked to the consumption of them has occurred.
Using five distinct S. Newport isolates, characterized by varying pulsed-field gel electrophoresis (PFGE) patterns, viability assays using the nematode Caenorhabditis elegans were conducted to investigate differences in the virulence potential of environmentally acquired isolates. Phylogenetic analysis revealed the related nature of ST118 Newports and provided a comprehensive diversity profile of environmental isolates found on the VES. After closing the genomes of Newport isolates with PFGE patterns significant to foods, we focused on the nearly genetically identical Newport-61 and the Newport-1015 isolates which differ by a ~1.7 Mbp genomic inversion. Viability assays with these isolates demonstrated that C. elegans exposed to Newport-61 had a significantly shortened lifespan compared to other strains tested said for one of the Newport-1015 which caused greater mortality. These findings enhance our understanding of the pathogenic potential of environmental S. Newport and highlighted the need to understand the regulatory mechanisms that contribute to virulence capacity.
To clarify the potential mechanisms behind Newport's persistence and colonization within the tomato plant and its pre-harvest environment, a comparative analysis including both Newport and non-Newport strains was conducted using a phenotypic microarray approach. Under controlled conditions the difference in utilization capacity of various carbon, nitrogen, phosphorus and sulfur sources as well as nutrient supplements, osmolytes and various pHs were assessed. The results demonstrated that S. Newport exhibited distinct phenotypic traits in the presence of certain metabolites that may enhance their survival in agricultural settings.
To understand the specific genetic and regulatory mechanisms that facilitate Salmonella’s adaptation to the agricultural environment, transposon mutant libraires were constructed. The transposon library will be used, as independent isolates, in tomato plants to identify the genes necessary for colonization and proliferation of this organism. Additionally, RNA expression studies in tomato intracellular fluid will highlight secondary regulation mechanisms that may also be contributing to the adaptive phenotype.
This collective body of research demonstrates Salmonella’s pervasiveness throughout the pre-harvest environment as well as evidence to suggest geographical confinement of particular serovars, such as serovar Newport (JJPX01.0061). It further presents evidence that though serovars may be highly related, their phenotypic profiles and virulence capacity may not be predictable by their genetic sequence alone. Future research into the expression profile of these highly related strains in various environments as well as genes necessary for adaptation within them, will contribute to the understanding of Salmonella ecology in the tomato production environment, and highlight potential pathways for reducing the risk of foodborne outbreaks associated with contaminated produce
METHODS FOR EFFICIENT PROCESSING AND UNCERTAINTY-AWARE ANALYSIS OF HIGH THROUGHPUT GENOMICS AND TRANSCRIPTOMICS DATA
No full textThe advent of modern high-throughput sequencing technologies has empowered researchers to rapidly and cost-effectively sequence large amounts of DNA/RNA fragments from biological samples. A crucial step following sequencing is the estimation of feature abundances, which requires mapping/aligning reads to a reference that far surpasses the length of the individual reads. We thus require efficient algorithms for read mapping, especially considering that many datasets comprise millions of reads. However, even with efficient mapping, overlapping sequences among features introduce uncertainty in determining the true locus of origin for a read. This dissertation seeks to address these challenges in the context of bulk RNA-Seq and single-cell ATAC-Seq data, with applications in transcriptomics and genomics.
First, we introduce TreeTerminus, a tree-based framework designed to incorporate uncertainty in abundance estimates for RNA-seq data. TreeTerminus constructs hierarchical trees from the samples in an RNA-Seq experiment, where the leaf nodes represent the individual transcripts, and the inner nodes represent aggregated transcript groups. The uncertainty decreases as one ascends the tree. The tree provides the flexibility to analyze data at nodes that are at different levels ofresolution in the tree, adjustable based on the analysis of interest.
Next, we present mehenDi, a method for uncertainty-aware differential analysis that leverages the tree generated from TreeTerminus. mehenDi identifies differential candidate nodes that can include both transcripts and inner nodes, maximizing signal detection while controlling for uncertainty in inference in a data-driven manner. Applying mehenDi to both simulated and experimental datasets, we discovered inner nodes with a strong differential signal that would have been overlooked when analyzing the individual transcripts alone.
Finally, we introduce alevin-fry-atac, a method for processing and mapping single-cell ATAC-Seq data. We propose a novel pseudoalignment algorithm and a caching scheme that enables fast and memory-efficient read mapping to the genome utilizing the piscem index. Alevin-fry-atac is currently three times faster while using three times less memory compared to Chromap - the only fully open-source alternative—which itself was significantly faster than Cell-Ranger ATAC, a method developed by 10X Genomics. With the introduction of alevin-fry-atac, we establish the first fully open-source ecosystem capable of processing both single-cell RNA-Seq and ATAC-Seq data, facilitating seamless multi-omic analysis
Topological Quantum Matter: Bridging Theory and Experiment
No full textQuantum many-body systems host a variety of exotic phases which can be described as the deconfined phase of an emergent gauge theory. Such phases in the context of spin systems go by the name Quantum Spin Liquids (QSLs). Often, the same features that make them interesting also make them hard to detect experimentally. This thesis is a collection of works aimed at connecting the defining theoretical properties of such phases to experimentally accessible observables, both in the setting of solid state materials and quantum devices.
The main theme of the first part of the thesis is magnetic monopoles of emergent compact U(1) gauge theories that describe certain QSLs, namely Quantum Spin Ice and Dirac Spin Liquid in three and two spatial dimensions respectively. The condensation of monopoles drives a deconfinement-confinement phase transition in the gauge theory, and in the context of spin systems, drives transitions from QSL to ordered phases. We exploit this understanding to propose a ``Monopole Josephson Junction" scheme to test if a candidate material is a Dirac Spin Liquid. A key component of our detection scheme is Raman Scattering. Next, we provide a proposal to prepare and diagnose Quantum Spin Ice (deconfined phase of U(1) gauge theory in three spatial dimensions) in Rydberg atom arrays.
In the second part of the thesis, we explore quantum optics techniques to probe correlated quantum materials. In optical experiments, the photonic observable measured is usually the intensity or photon number operator of inelastically scattered light. We ask a general question -- what can we learn about a material, given access to other photonic observables like quadrature and correlation between pairs of photons (G2)? We develop a general formalism to map such photonic correlation functions to electronic ones. Focusing on the Hubbard model at half-filling, we show that such correlators can be used to probe spin-charge correlations, and to detect QSLs by detecting spin chirality and existence of fractional statistics
Pruning for Efficient Deep Learning: From CNNs to Generative Models
No full textDeep learning models have shown remarkable success in visual recognition and generative modeling tasks in computer vision in the last decade. A general trend is that their performance improves with an increase in the size of their training data, model capacity, and training iterations on modern hardware. However, the increase in model size naturally
leads to higher computational complexity and memory footprint, thereby necessitating high-end hardware for their deployment. This trade-off prevents the deployment of deep learning models in resource-constrained environments such as robotic applications, mobile phones, and edge devices employed in the Artificial Internet of Things (AIoT). In addition, private companies and organizations have to spend significant resources on cloud services to serve deep models for their customers. In this dissertation, we develop model pruning and Neural Architecture Search (NAS) methods to improve the inference efficiency of deep learning models for visual recognition and generative modeling applications. We design our methods to be tailored to the unique characteristics of each model and its task.
In the first part, we present model pruning and efficient NAS methods for Convolutional Neural Network (CNN) classifiers. We start by proposing a pruning method that leverages interpretations of a pretrained model's decisions to prune its redundant structures. Then, we provide an efficient NAS method to learn kernel sizes of a CNN model using their training dataset and given a parameter budget for the model, enabling designing efficient CNNs customized for their target application. Finally, we develop a framework for simultaneous pretraining and pruning of CNNs, which combines the first two stage of the pretrain-prune-finetune pipeline commonly used in model pruning and reduces its complexity.
In the second part, we propose model pruning methods for visual generative models. First, we present a pruning method for conditional Generative Adversarial Networks (GANs) in which we prune the generator and discriminator models in a collaborative manner. We then address the inference efficiency of diffusion models by proposing a method that prunes a pretrained diffusion model into a mixture of efficient experts, each handling a separate part of the denoising process. Finally,
we develop an adaptive prompt-tailored pruning method for modern text-to-image diffusion models. It prunes a pretrained model like Stable Diffusion into a mixture of efficient experts such that each expert specializes in certain type of input prompts
OUTLAST: INTERACTIVE ART INSTALLATION ON DIGITAL PRESENCE EXPLORING INTERACTIVE ART AS REFLECTIVE PRACTICE
No full textThis thesis explores the creation of an interactive art installation designed to engage viewers with the accumulation of digital artifacts, digital decay, and the act of revisiting one’s digital possessions. The primary research question that this thesis aims to answer is: “How can user responses to the art installation object improve the object itself, which was designed to represent processes of digital accumulation, digital decay, and prompts for revisitation?” The methodology involved an iterative design process through experience prototyping and was guided by reflection-in-action. The author conducted a qualitative user study involving 19 interviews with individuals who had experience with archives, and observed their interactions with the installation. Thematic analysis was used to analyze the collected data
NOVEL BACTERIA ASSOCIATED WITH TWO MICROALGAE OPTIMIZED FOR CARBON DIOXIDE SEQUESTRATION FROM FLUE GAS
No full textThe combustion of fossil fuels pollutes the atmosphere with carbon dioxide (CO2) and contributes to global warming. Microalgae are significant players in the capture and transformation of CO2 by way of their natural photosynthetic pathways. Moreover, the bacterial symbionts of microalgae can promote algal growth and resilience, thereby leading to additional carbon capture by microalgae. The significance of bacteria within microalgal systems is a significantly undervalued factor in microalgal research even though microalgae and bacteria are known to synergistically affect each other's growth and physiology. This dissertation focuses on the crucial and often unstudied contributions of bacteria in promoting microalgal growth. This work focuses on the role of bacteria in both small-scale (1 liter) and large-scale (500 liter, 6,800 liter) cultures of two microalgae, Tetradesmus obliquus strain HTB1 and Nannochloropsis oceanica strain IMET1 bubbled with simulated flue gas at 5% and 10% CO2. Bacterial community analysis by using 16S rRNA gene sequencing of themost productive algal cultures repeatedly revealed two dominant and novel bacteria with no taxonomic classification beyond the class level (Paceibacteria). Long read metagenomic sequencing yielded seven high quality metagenome assemble genomes (MAGs) of interest, including these two enigmatic unclassified bacteria. Two novel genera and species were taxonomically classified under the Seqcode (seqco.de/r:ywe1blo2): Phycocordibacter aenigmaticus gen. nov. sp. nov. and Minusculum obligatum gen. nov. sp. nov. The genus Phycocordibacter gen. nov. was proposed as the nomenclatural type of the family Phycocordibacteraceae fam. nov. and the order Phycocordibacterales ord. nov. In addition, functional analysis of plant growth promotion genes and metabolic reconstruction were conducted to elucidate how these bacterial symbionts promote microalgal growth and carbon fixation capacity. Epifluorescent microscopy and scanning electron microscopy (SEM) were conducted on both N. oceanica and T. obliquus cultures to understand the spatial relationship between each microalga and their respective bacterial symbionts. Digital PCR (dPCR) was conducted to quantify the ratio of microalgal cells to bacterial cells within cultures. To further improve CO2 capture strategies using microalgae, understanding the taxonomy and functional impact of bacterial symbionts is vital
Sowing Seeds of Sustainability at the Deale Community Library
No full textPublic libraries provide more than books: they are vital community spaces that provide access to education, connection, and opportunity. In rural areas where resources are already scarce, libraries hold their ground as resources and community hubs. This thesis will explore how landscape architecture can expand the role of libraries beyond the building by transforming outdoor spaces into places for environmental education, mental well-being, and community connection. The design reimagines the landscape surrounding the Deale Community Library to include native display gardens, an outdoor classroom, stormwater management, and spaces to encourage stewardship.By integrating site analysis, stakeholder interviews, and precedent studies, this research presents a practical and aesthetically engaging design that balances the needs of the library staff and community. Design features, including signage, accessible seating, and educational gardens, demonstrate how sustainable design can support both people and the environment and inspire the community. This project aims to reinforce the idea that libraries are not simply buildings but
evolving community anchors, where their outdoor spaces can inspire stewardship, learning, and well-being for future generations