VictorJuiz/thesis huggingface dataset:
Convexity In Contact Geometry And Reeb Dynamics

Reeb flows are a rich, ubiquitous class of dynamical systems arising in symplectic geometry, which include billiard systems, many-body orbital systems, geodesic flows and many Hamiltonian flows. Convexity hypotheses play an important, albeit mysterious, role in the study of these flows. In this thesis, we discuss several new results in the study of convexity in symplectic geometry and Reeb dynamics./nIn Chapter 1, we resolve a longstanding open problem on the intrinsic characterization of Reeb flows arising from Hamiltonian flows on the convex boundaries. Namely, we prove that dynamically convex Reeb flows, introduced by Hofer-Wysocki-Zehnder, are not all convex. Our proof uses a novel relation between Riemannian geometry and Reeb dynamics, and uses constructions of Abbondandolo-Bramham-Hryniewicz-Salomao./nIn Chapter 2, we describe a powerful new framework for computationally modelling Reeb dynamics on the boundaries of convex polytopes. We apply this framework to provide new evidence and examples relating to the Viterbo conjecture, a major open problem in Reeb dynamics and quantitative symplectic geometry./nIn Chapter 3, we study convex toric domains and toric surfaces. A longstanding conjecture in toric geometry states that the Gromov width is monotonic under inclusion of moment polytopes of closed toric varieties. We use methods from toric geometry and ECH to prove a generalization of this conjecture in dimension 4.

Advisor: Hutchings, Michael L
Continuous Feedback on Quantum Superconducting Circuits

Quantum measurement theory describes the dynamics of a quantum system when it transfers information to an observer. Within the framework of continuous measurement, this outflow of information and resultant back-action occur at a finite rate. Continuously measured systems do not instantaneously collapse to eigenstates, but rather undergo stochastic evolution highly correlated with the observer’s received information. When the measurement rate is on the same timescale as the processing speed of classical control electronics, we can perform feedback on a quantum system during its collapse process. We experimentally realize such a protocol by using an adaptively controlled quantum amplifier to implement a canonical phase measurement on a flying photon. Continuous measurements can also be used as a tool in quantum error correction. Error correction typically requires discrete rounds of measurement, often using entangling gates and ancilla qubits. In contrast, continuous error correction is implemented via always-on measurements of parity syndromes. Using direct parity measurements of pairs of qubits in a three qubit system along with a custom classical controller, we implement the first continuous quantum error correction code.

Advisor: Siddiqi, Irfan
A Material and Anti-Material History of the Ancien Régime Harpsichord: Enlightenment Economies of Artisanal Knowledge

Throughout the seventeenth and eighteenth centuries, Flemish harpsichords by the Ruckers and Couchet families were exported widely across the globe. These instruments were so highly valued in France, that, beginning in the 1680s, French harpsichord makers began updating them for modern use. The process, described as ravalement, could involve rebuilding the keyboards and action, redecorating, and sometimes even enlarging the case. They also began copying and forging these instruments, creating an extremely lucrative luxury market: a Flemish ravalement could cost up to ten times as much as a new French-made harpsichord. The harpsichord in Ancien Régime Paris was thus not only a musical instrument and vehicle for artistic expression, but also a decorative objet d’art authored by numerous craftspeople in a vast collaboration sometimes spanning centuries.This dissertation positions the harpsichord as an actor within the polychromatic environment of Paris, investigating how it united communautés of professional musicians, artisans who mediated knowledge production in the arts and sciences, and the flourishing milieu of the salon. As I argue, the harpsichord, molded by generations of changing artistic and philosophical ideals, also came to shape these very ideals, actively transforming contemporary notions of history, communication, life, and human consciousness.

Advisor: Mathew, Nicholas
Canguilhem, Philippe
Visual Content Creation by Generative Adversarial Networks

We live in a world made up of different objects, people, and environments interacting with each other: people who work, write, eat, and drink; vehicles that move on land, water, or in the air; rooms that are furnished with chairs, tables, and carpets. This vast amount of information can be easily collected from the recorded videos and photographs shared online. However, it still remains a challenge to teach an intelligent machinery agent to reliably analyze and understand this extensive collection of data. Generative models are one of the most compelling methods towards modeling visual realism from the large corpus of available images, which operate by teaching a machine to create new contents. These models are not only beneficial in understanding the visual world, but more deeply in visual synthesis and content creation. They can assist human users in manipulating and editing an existing visual content. In the last few years, Generative Adversarial Networks (GANs) as an important type of generative models have made remarkable enhancements in learning complex data manifolds by generating data points from scratch. The GAN training procedure pits two neural networks against each other, a generator and a discriminator. The discriminator is trained to distinguish between the real samples and the generated ones. The generator is trained to fool the discriminator into thinking its outputs are real. The network learns the real-world distribution while generating high-quality images, translating a text phrase into an image, or transforming images from one domain to another. This dissertation investigates algorithms to improve the performance of such models in creating new visual content specifically in structural and compositional domains in a wide range from hand-designed fonts to natural complex scenes. In Chapter 2, we consider text as a visual element and propose tools to synthesize new glyphs in a font domain and transfer the style of the seen characters to the generated ones. From Chapter 3, we focus on the domain of natural images and propose GAN models capable of synthesizing complex scene images with lots of variations in the number of objects, their locations, shapes, etc. In Chapter 4, we explore the role of compositionality in the GAN frameworks and propose a new method to learn a function that maps images of different objects sampled from their marginal distributions into a combined sample that captures the joint distribution of object pairs. Despite all the improvements in training GANs, it still remains a challenge to fully optimize the GAN generator in a two-player adversarial game, resulting in samples that do not always follow the target distribution. In Chapter 5, instead of trying to improve the training procedure, we propose an approach to improve the quality of the trained generator by post-processing its generated samples using information from the optimized discriminator.

Advisor: Darrell, Trevor
The Identification of Sources and Genomic Characteristics of Pandemic Extraintestinal Pathogenic Escherichia coli

This dissertation investigates the epidemiologic and microbiologic features of extraintestinal pathogenic Escherichia coli (ExPEC) organisms, with a particular emphasis on antimicrobial drug resistance (AMR) associated with these ExPECs. The first section and the second section (Chapter 2 and Chapter 3) describes the risk factors for healthy adults to carry drug-resistant E. coli in their gut. Chapter 2 presents an overview of what has been described in the literature on risk factors for fecal carriage of drug-resistant E. coli. Chapter 3 focuses on a study we conducted on campus here at the University of California, Berkeley to examine risk factors for carriage of drug-resistant E. coli using a penalized regression model. Chapter 4 describes findings based on molecular epidemiologic and computational biologic methods to investigate genetic features associated with the lack of carriage of drug-resistance genes in a major pandemic lineage of ExPEC, ST95. The focus on a lineage that lacks drug-resistance genes was an attempt to characterize counterfactual genetic factors associated with AMR ExPECs that may help to better understand why the prevalence of AMR infections are caused by a limited set of ExPEC lineages. We were able to determine that certain dietary behavior and travel destinations can contribute to colonization with drug-resistant E. coli. Such knowledge could be potentially used to devise prevention of acquisition of drug-resistant E. coli. We also found potential bacterial mechanisms to resist acquiring drug resistant genes. Such knowledge could be exploited to devise biologic interventions to interrupt E. coli from gaining drug-resistance genes. In conclusion, this dissertation work highlights the importance of global bacterial drug resistant infectious disease problem and opened a path towards further studies that may ultimately lead to the creation of new prevention strategies against AMR infections.

Advisor: Riley, Lee W
Neural Guidance in Constraint Solvers

Boolean Constraint Satisfaction Problems naturally arise in a variety of fields in Formal Methods and Artificial Intelligence. Constraint Solvers, the specialized software tools that solve them, are therefore a core enabling technology in industry and research. They are normally used as black-box components, applied to practical problems such as hardware and software verification, test generation, planning, synthesis and more. Based on classical algorithms that have been optimized over decades by researchers, there is a noticeable gap between Constraint Solvers and the technology of Deep Learning, which over the last decade found its way into countless domains, outperforming established domain-specific algorithms./nThis thesis aims to narrow this gap, and by using Deep Neural Networks, teach classical Constraint Solvers to ``learn from experience''. The research I present in this thesis starts by addressing the challenge of representation, using a Graph Neural Networks based architecture to process propositional formulas as graphs. I will then show how to automatically learn a solver's branching heuristic by mapping it to a Markov Decision Process and training it using Deep Reinforcement Learning. I will present an implementation based on different two competitive solvers, and experiments showing automatically learned heuristics to outperform the state of the art in the two domains.

Advisor: Lee, Edward A.
Seshia, Sanjit A.
Representation Learning for Perception and Control

The goal of extracting reusable and rich representations that capture what you care about for downstream tasks remains challenging even though the field of deep learning has made tremendous progress in this direction. This thesis presents a few promising contributions to further that goal. The two axes of contributions are: (1) self-supervised (or unsupervised) representation learning; (2) deep neural network architectures powered by self-attention. Progress in architectures and the ability to leverage massive amounts of unlabeled data have been responsible for major advances in NLP such as GPT-x and BERT. This thesis presents small steps towards realizing such progress for perceptual and reinforcement learning tasks. This is a thesis by articles containing four articles, two focused on computer vision benchmarks, with the other two focused on reinforcement learning./nWith respect to the first axis, the thesis presents three articles: (1) Data-Efficient Image Recognition using Contrastive Predictive Coding (CPCv2); (2) Contrastive Unsupervised Representations for Reinforcement Learning (CURL); (3) Reinforcement Learning with Augmented Data (RAD). The first two articles explore a form of unsupervised learning called contrastive learning, a technique better suited for raw inputs such as images compared to generative pre-training that is popular for language. The first article presents results for label-efficient image recognition. The second article presents the benefits of contrastive learning for sample-efficient reinforcement learning from pixels. Contrastive learning in practice is heavily dependent on data augmentations, and the third article presents a detailed investigation and discussion of its role./nAs for the second axis, the thesis presents a thorough empirical investigation of the benefits of self-attention and Transformer-like architectures for computer vision through the article: Bottleneck Transformers for Visual Recognition. Self-attention has revolutionized language processing but computer vision presents a challenge to vanilla Transformers through high resolution inputs that challenge the quadratic memory and computational complexity of the primitive. The article presents the empirical effectiveness of a straightforward hybrid composed of convolutions and self-attention and unifies the ResNet and Transformer based architecture design for computer vision.

Advisor: Abbeel, Pieter
Assessing the effects of combinations of genetic and environmental factors on multiple sclerosis using genomic and computational approaches

Multiple sclerosis (MS) is a chronic, often disabling disease characterized by neurodegeneration and inflammation. It is the most common non-traumatic neurological disorder among young adults. What triggers MS pathology and symptoms over time remains largely unknown. Evidence suggests genetic and environmental factors contribute to risk of MS and there are at least 230 known genetic risk variants. However, much less is known about the effect of multiple environmental and/or genetic risk factors, which can co-occur in the same individual at the same or different times, on MS risk and clinical manifestations. It is important to consider combinations of risk factors because their joint effects may differ from individual effects. Additionally, it might not be possible to tease apart individual effects from highly inter-related variables, so clustering or other methods should be considered. In this dissertation, I utilize computational, statistical, genomic, and epidemiologic approaches to study the role of combinations of genetic and environmental/behavioral risk factors on MS risk and clinical outcomes in humans. Chapter one introduces MS and background relevant for proceeding chapters. Chapter two shows that individual and clusters of co-occurring gut microbes are associated with new brain lesions on MRI and relapses among individuals with pediatric-onset MS. Chapter three shows that adverse childhood experiences, assessed as individual events and combinations of events, are not associated with MS risk or clinical outcomes in our data. Chapter four suggests that MS and migraine (a common comorbidity of MS) may co-occur because they share several genetic variants. Altogether, this dissertation advances our knowledge of risk factors for MS onset and clinical features of disease and will inform future work of gut microbe, comorbidities, and stress research.

Advisor: Barcellos, Lisa F
Development of Simons Array Optics for Cosmic Microwave Background Polarimetry

Precision measurements of the cosmic microwave background (CMB) polarization anisotropies provide a powerful probe into the history and contents of the universe. Detection of degree-scale, odd parity B-modes polarization signal imparted from a stochastic gravitational wave background of the primordial universe would strongly confirm the theory of cosmic inflation. This requires a telescope which observes the sky with high sensitivity and optical fidelity over a broad frequency spectrum for foreground contamination removal. Simons Array is a new telescope array in the Atacama Desert of Chile, designed for simultaneous sensitivity to the large-scale primordial B-mode signal and the small-scale B-mode signal from gravitational lensing of the CMB by large-scale structure./nThis thesis presents the development of the cryogenic optics of the Simons Array cameras and a new anti-reflection (AR) coating technology for precision CMB polarimetry. An overview of Big Bang cosmology and the CMB is first given as scientific motivation. Then a brief description of the basic principles of telescope optics is given as a background to the following discussion on Simons Array optics and AR coatings. Next, the development and validation of the Simons Array optics tubes, the sub-component of the camera which houses the optical components, are described. Finally, the developments and fabrication processes of AR coating technologies utilized by Simons Array and other experiments for CMB observation are discussed.

Advisor: Lee, Adrian
Dark Matter Detection Phenomenology

In the last century, cosmological and astrophysical observations indicated that ~ 85% of the matter in the universe is inert, non-baryonic and non-luminous dark matter. The existence of particle dark matter is well motivated, however, all that is known about its nature is indirect information coming from large-scale astrophysical/cosmological observations or laboratory bounds. The nature of this elusive yet abundant dark matter naturally became one of the greatest conundrums in modern science. A tremendous effort was spent on exploring the theory space and building detection experiments. Still, the dark matter remained elusive. This elusiveness emphasized the importance of a more comprehensive approach to the dark matter hunt: The standard minimalist scenarios may not be sufficient; wider mass ranges and richer ``dark sectors'' may need to be considered. The dark sector theories' potential to address the dark matter problem along with other problems in the Standard Model, and the independent motivation for them coming from various top-down models, caused them to get significant traction. In addition to exploring a broad range of theoretical possibilities, another important aspect of dark matter detection is background reduction. Since the dark matter particles are competing with the Standard Model particles for interacting with the experiments, the methods distinguishing the dark matter events are crucial to improve the signal-to-noise ratio. In this dissertation, we have embraced a comprehensive approach to dark matter detection by considering a wide range of dark matter candidates (with large freedom in mass, and considering nonminimal dark sectors), interactions (including nuclear recoils, collective excitations, electron excitations), and targets (including superfluids, superconductors, Dirac materials, polar crystals.) In addition to overall reach constraints, we also focused on the anisotropies and the daily modulation prospects to explore the potential to distinguish dark matter signals from the background noise effectively.

Advisor: Zurek, Kathryn M
Hall, Lawrence J
The Regulation of CD8+ T Cell Fate During Chronic Toxoplasma gondii Infection

Production of armed effector CD8+ T cells during persistent infection requires a stable pool of stem-like cells that can give rise to short-lived effector T cells via a proliferative intermediate population. In chronic infection models marked by T cell exhaustion, this process can be transiently induced by check-point blockade, but it occurs spontaneously and continuously in the spleens of mice chronically infected with the protozoan intracellular parasite, Toxoplasma gondii, providing a unique opportunity to examine the steps in the differentiation process. Using this model, we observed distinct locations and gene expression patterns for stem-like memory cells, proliferative intermediate cells, and terminally differentiated effector cells, implying a link between differentiation and discrete anatomical niches in the spleen. Loss of the chemokine receptor CXCR3 on parasite-specific T cells did not impair their white pulp to red pulp migration but reduced their interactions with CXCR3 ligand-producing type 2 conventional DCs in the bridging channels and impaired their transition from memory to intermediate states, leading to a build up of memory T cells localized to the red pulp. These results demonstrate a critical role for CXCR3 during chronic infection by increasing T cell exposure to differentiation-inducing signals during red pulp migration, providing a dynamic and flexible mechanism for modulating effector differentiation in response to environmental signals. While mice chronically infected with T. gondii are able to effectively establish long-term control of the pathogen, they do not establish sterilizing immunity. Although the parasite is primarily restricted to the brain during the chronic phase of infection, CD8+ T cells in the periphery experience continuous antigenic stimulation, resulting in ongoing proliferation and effector differentiation. We investigated the role of IFNγ in regulating CD8+ T cell fate during chronic T. gondii infection. We observed that neutralization of IFNγ during chronic infection led to dramatic changes in CD8+ T cell fate, promoting robust proliferation at the expense of terminal effector differentiation. These changes in CD8+ T cell fate were dependent on CXCR3 and linked to increases in antigen presentation in the spleen, which occurred in the absence of widespread parasite reactivation. Finally, IFNγ neutralization resulted in the emergence of clusters of CD11c+ cells in the red pulp of the spleen that were robust producers of CXCR3 ligands and supported a proliferation rich-environment. These results highlight an ongoing role for IFNγ in mediating the clearance of T. gondii antigens during chronic infection and contribute to our understanding of the factors regulating CD8+ T cell fate.

Advisor: Robey, Ellen
Enhancing Quadcopter Capabilities via Design and Control

In recent years, quadcopters have gained remarkable popularity in a wide range of industries. Given that the utility of quadcopters has already been extensively demonstrated, this dissertation explores changes to the design and control of conventional quadcopters which either enable operation in new environments, allow for novel tasks to be performed, or reduce computational hardware requirements./nTwo novel designs are presented in this dissertation. First, we explore the use of angular momentum to reduce the sensitivity of a vehicle to torque disturbances. We show both theoretically and experimentally how torque disturbance sensitivity monotonically decreases with increasing net angular momentum of the vehicle when using an appropriately designed controller, and discuss how this effect scales with vehicle size. Second, we consider the use of passive (i.e. unactuated) mechanisms to expand the types of tasks a quadcopter can perform. Specifically, we demonstrate how a vehicle with freely rotating arms can change shape mid-flight, allowing for traversal of narrow passageways, simple manipulation, and perching behaviors./nNext, two algorithms are presented which enable autonomous flight in known and unknown environments. Both algorithms are focused on improving the time required to check whether a given trajectory collides with the environment, reducing the computational power requirements of onboard computers used the fly the vehicle. The first method is used to quickly determine whether a given trajectory collides with a convex obstacle, enabling the avoidance of both static and dynamic obstacles. The second method allows for operation in previously unseen environments by using depth images from an onboard sensor to represent the environment. Rectangular pyramids are used to partition the free-space of the depth image,which enable highly efficient collision checking between trajectories and the environment./nEach proposed design and algorithm is demonstrated experimentally on custom hardware, and relevant code has been made publicly available where appropriate.

Advisor: Mueller, Mark W