VictorJuiz/thesis huggingface dataset:
Deformation in Robotics: Applications in Tactile Sensing and Grasp Planning

If robots are to function in unconstrained, dynamic, and real-world environments, it is critical that are able to interact with deformable materials. Deformable materials have historically been overlooked in traditional robotics due to prevailing assumption of robot and object rigidty. These assumptions, though perfectly appropriate for constrained environments such as factory settings, are often broken in the real-world. At the same time, deformable interactions are uniquely challenging due to the infinite-dimensional, non-linear nature of the deformable materials involved. To address the challenges of introducing and implementing deformation in robotics, we present this thesis work in two parts. First, we study deformation on the robot side, where we design a soft tactile sensor and demonstrate its use in human-robot interaction and automation. Second, we also explore deformation on the object side, where in particular we focus on optimizing grasp strategies over 3D field quantities./nIn our first group of work, we propose and fabricate a novel soft tactile device that utilizes an embedded 3D depth-sensing camera to produce interpretable signals for geometry and force sensing. We demonstrate that this sensor is inherently safe and functional for applications including physical upper limb assistance for humans, contour following for domestic wiping tasks, as well as geometry-dependent learning from demonstration tasks for general contact-rich manipulation tasks. In the second part of the thesis, we develop grasp planners for 3D deformable objects (e.g., fruits, internal organs, containers) for applications in food processing, robotic surgery, and household automation. In particular, we optimize for field quantities that are not only inaccessible in the real world, but have also been, until recent years, computationally intractable to model. We create DefGraspSim, a finite element method-based physics simulator of arbitrary grasps on arbitrary 3D meshes over a wide range of material parameters. We also create DefGraspNets, a graph neural network-based forward dynamics model that is not only up to 1500x faster than DefGraspSim, but also enables gradient-based grasp optimization. For both methods, we demonstrate generalized performance across multiple test sets, including on real-world experiments.

Advisor: Bajcsy, Ruzena
Searching for Sub-GeV Dark Matter with Liquid Xenon and Superfluid Helium

Astrophysical evidence for dark matter is abundant; one of the great open problems in physics is its particle identity. In this dissertation we discuss two different detectors, LZ and HeRALD, designed to detect dark matter interactions. LZ is a recently assembled 10 tonne two-phase xenon time projection chamber. We discuss the first search for Weakly Interacting Massive Particles (WIMPs) from LZ, which set world-leading limits on the WIMP-nucleon interaction cross section. We present an extension of this search to lower masses via the Migdal effect, and suggest an alternative path for calculating the Migdal signal model for smaller recoil energies. We also show the design of a low energy photoneutron calibration source for LZ and data from its first deployment. The HeRALD detector is in active development, with R&D on different aspects of the superfluid helium-based design. We present an overview of this design and signal modeling work underlying projections of its sensitivity to low mass dark matter. We also discuss two experiments to probe this signal model by measuring helium scintillation from neutron and gamma ray interactions. Finally, we return to the photoneutron concept by presenting a source design that will serve as a path forward for even lower energy nuclear recoil calibrations important for further development of HeRALD.

Advisor: McKinsey, Daniel N
Graphic Impulses: Drawing, Sexuality, and Science in Germany, 1870-1933

This dissertation examines the roles played by drawing and the graphic arts in the conceptual emergence of the queer male subject in Germany over the course of the nineteenth and early twentieth centuries. A central premise, which provides the theoretical framework for the investigation, holds that early-nineteenth-century scientists and aesthetic theorists co-produced new conceptions of “healthy” and “degenerate” artistic expression that were intrinsically bound to a developing discourse on normative and non-normative sexual desire. This conception evaluated both human and artistic development according to a telic, hierarchical rubric that twinned creation and procreation, positing a healthy, disciplined sexual fantasy as a prerequisite for the development of artistic talent. The dissertation argues that within such a system, a persistent affinity developed between queer men and undisciplined drawing practices that stemmed from the perceived unnaturalness of both./nThe project is comprised of four case studies spanning roughly 1830 to 1930, each of which explores how drawing and its attendant subgenres served crucial functions for both queer men (who came to rely on their pens and pencils to visualize their sexual identity) and scientists (who used these drawings to map the contours of the new scientific category of “the homosexual”). In analyzing four diverse object types and genres of drawing—private sketching, academic nude studies, tattoos, and book illustration—the dissertation argues for an understanding of drawing as an inherently queer medium. Furthermore, it argues for the central significance of graphic expression as a key site of queer male identity formation, a practice pursued against scientific discourses aimed at pathologizing homosexual subjectivity.

Advisor: Davis, Whitney
Instance-dependent Optimality in Statistical Decision-making

Data-driven and learning-based methodologies have been very popular in modern decision-making systems. In order to make optimal use of data and computational resources, these problems require theoretically sound procedures for choosing between estimators, tuning their parameters, and understanding bias/variance trade-offs. In many settings, asymptotic and/or worst-case theory fails to provide the relevant guidance./nIn this dissertation, I present some recent advances that involve a more refined approach, one that leads to non-asymptotic and instance-optimal guarantees. Focusing on function approximation methods for policy evaluation in reinforcement learning, in Part I, I describe a novel class of optimal oracle inequalities for projected Bellman equations, as well as computationally efficient algorithms achieving them. In contrast to corresponding results for ordinary regression, the approximation pre-factor depends on the geometry of the problem, and can be much larger than unity. In Part II, I discuss optimal procedures for estimating linear functionals from observational data. Our theory reveals a rich spectrum of behavior beyond the asymptotic semi-parametric efficiency bound. It also highlights the fundamental roles of geometry, and provides concrete guidance on practical procedures and parameter choices.

Advisor: Wainwright, Martin
Bartlett, Peter
Modulating Condensed Phase Thermophysical and Thermochemical Transition Temperatures Using Chemical and Electrochemical Methods

Refrigeration, heat pumping, and thermal energy storage will play a critical role in decarbonizing the residential and industrial sectors. Refrigeration and heat pumping are abundant but employ refrigerants with high global warming potential. Thermal energy storage can provide low cost solutions for load-shifting and demand side flexibility to enable a more renewable grid, but implementation in the real world has been slowed by economic barriers. Thus, shifting to new heating and cooling technologies with zero global warming potential and connecting those technologies to affordable thermal storage solutions is an important step toward carbon neutrality. This thesis takes a critical look at the fundamental principles governing thermal energy storage from the material level up to the system level, specifically focusing on the solid to liquid phase transition. Both equilibrium material properties and non-equilibrium material behavior are treated in relation to their applications in thermal energy storage systems. In addition, we analyze up and coming thermal storage technologies, such as thermochemical reaction-based storage, wherein we demonstrate design rules, equilibrium, and non-equilibrium stability criteria for hydration/dehydration based storage, along with a novel liquid-solubilized reactant storage. Finally, we show how fundamental understanding of the solid to liquid transition leads to insight on phase stabilization, from which we develop the ionocaloric effect. We then show how we can embed the ionocaloric effect in various thermodynamic cycles, and demonstrate how a novel, condensed-phase and zero global warming potential heat pumping cycle emerges.

Advisor: Prasher, Ravi
Wildlife ecology in a human-dominated world: Insights from migratory animals, large carnivores, and ungulate prey

The ecological consequences of animal behaviors range from the fitness of individuals to the functioning of ecosystems. As historic patterns of animal behaviors change in response to changes in climate, land use, and human presence, there is a growing need to better understand the mechanisms by which wildlife perceive and respond to anthropogenic and environmental influences. Understanding how and why animals alter their behavior in anthropogenic spaces requires moving beyond general descriptions of behavioral patterns to investigate the biological mechanisms underpinning these behavioral responses. Such investigations would fill gaps in current theories of animal cognition and behavioral ecology while improving effectiveness of management and conservation efforts. In this dissertation, I aim to help fill these knowledge gaps by investigating the behavioral ecology of large carnivores, their ungulate prey, and migratory wildlife moving through areas where people live, work, and play. In chapter 1, I provide a brief introduction to key themes of wildlife behavioral ecology in the context of anthropogenic influence. In chapter 2, I work with collaborators to synthesize key insights from migration restoration efforts spanning diverse taxa and ecosystems. We use lessons learned to draw broad inference about the conditions under which lost migratory behaviors can be restored. In chapter 3, I use new data collected from wolf kill sites to investigate how humans change natural patterns of predation on native ungulates. Building on this work, chapter 4 investigates how ungulates perceive and respond to these human-influenced patterns of wolf predation risk. I conclude by discussing the considerable opportunities that exist to restore and facilitate the wildlife behaviors on which ecosystems are built. I urge ecologists, natural resource professionals, and policymakers to expand their concept of conservation beyond physical landscapes and species to include the behavior of wild animals moving through an increasingly human-dominated world.

Advisor: Middleton, Arthur D
Offline Data-Driven Optimization: Benchmarks, Algorithms and Applications

Black-box model-based optimization problems, where the goal is to find a design input that maximizes an unknown objective function, are ubiquitous in a wide range of domains, such as the design of proteins, DNA sequences, aircraft, and robots. Solving model-based optimization problems typically requires actively querying the unknown objective function on design proposals, which means physically building the candidate molecule, aircraft, or robot, testing it to obtain the result. This process can be expensive and time consuming, and one might instead prefer to optimize for the best design using only the data one already has. This setting, called offline model-based optimization (MBO), poses substantial and different algorithmic challenges than more commonly studied online techniques. In this thesis, I will cover how to build benchmarks and algorithms to tackle these challenges. In particular, I will first define the offline MBO problem formally, and identify the common challenging properties associated with real-world offline MBO problems. I will then present Design-Bench, a benchmark for evaluating offline MBO methods with a suite of diverse and realistic tasks derived from real-world optimization problems. With the benchmark set up, I will describe conservative objective models (COMs), a surprisingly simple but effective method for tackling offline MBO problems. Finally, I will cover applications of offline MBO in computational chemistry and synthetic biology to demonstrate how variants of COMs can be applied to solve real-world scientific problems.

Advisor: Levin, Sergey
Theoretical Advances in Gravitational Microlensing Guided by Artificial Intelligence

Three decades have passed since the technique of gravitational microlensing was proposed as a means for exoplanet detection, and nearly 200 microlensing exoplanets have been discovered to date.Previously, theoretical studies of the two-body point-mass gravitational lens have primarily focused on the properties of caustics, which are the singularities in the magnification map. The invariances and symmetries of microlensing caustics have led to the identification of physical degeneracies that cause distinctly different lens configurations to give rise to nearly identical observations. Nevertheless, inconsistencies in the application of existing degeneracy theories to observed events indicate that our current theoretical understanding of binary-lens gravitational microlensing, which was largely laid out in the late twentieth century, may in fact be incomplete./nThis thesis introduces a novel approach to utilizing Artificial Intelligence as a driver for theoretical explorations, which represents a departure from its traditional role in accelerating empirical discoveries.First, a scalable inference framework is developed for binary-lens microlensing using the technique of Neural Posterior Estimation, which is then applied to model hundreds of simulated microlensing light-curve observations. By examining the large numbers of multi-modal modeling solutions, I propose and subsequently prove the offset degeneracy, which is shown to be ubiquitous in the interpretation of planetary microlensing observations, and unifies two leading types of caustic degeneracies as limiting cases. Motivated by properties of the offset degeneracy, I subsequently propose the generalized perturbative picture for planetary microlensing, which states that the planet can be considered to act as a variable-shear Chang-Refsdal lens on one of the images produced by the host star, leaving the other image largely unaffected. The analytic nature of the Chang-Refsdal lens indicates that the proposed formalism would allow for full magnification maps of the planetary lens to be derived analytically, thereby facilitating the accelerated modeling of observed events. The methodologies and results presented in this thesis may substantially benefit the analysis of the deluge of data expected from the first space-based microlensing survey of the Roman Space Telescope.

Advisor: Bloom, Joshua S
Impactful Design: from Microfluidic Devices for Modeling the Spine to Equipment for Pandemic Relief

Lower back pain is the leading cause of physical disability worldwide. In the United States, annual costs associated with lower back pain exceed $100 billion, as 70%-80% of Americans experience debilitating back pain at least once in their lifespan, limiting quality of life and ability to work. Disc degeneration is a major incurable cause of back pain often brought on or exacerbated by excessive loading of the spine. To enable the development of treatments for disc degeneration, more physiological relevant models which include the harsh mechanical environment of the human disc are needed. The dissertation research focused on the Annulus-on-a-Chip (AoC) design, fabrication, and testing of primary cells experimentally and computationally to replicate harsh strains in the annulus fibrosis of the human disc. Strains proved to be controllable, multidirectional, and uniform with magnitudes ranging from -10% in compression to 12% in tension in the axial, radial, and circumferential directions. The dissertation is the first of its kind to replicate complex mechanical loading on a microscale. The Annulus-on-a-Chip was designed to be reconfigurable for other uses such as the modeling of cardiovascular vessels, lymphatic vessels, and the cervix./nDue to the COVID-19 pandemic, the dissertation includes design for pandemic response to help rapidly address the inadequate supply of personal protective equipment (PPE). During the pandemic, shortages of PPE in rural and low-income healthcare settings were thrust into an international bidding war for N95 respirators due to global supply chain disruptions. To extend stockpiles of N95 respirators, benchtop devices for treating N95 respirators with Ultraviolet-C (UVC) germicidal light uniformly, rapidly, and inexpensively were developed and tested. Design iterations improved user safety, efficacy, and process flow, resulting in a set of open-source designs./nFinally, the lessons learned while targeting improvements to societal healthcare through engineering design of the AoC and pandemic response devices led to the ideating, proposing, and implementing of the College of Engineering Business Minor for Engineering PhDs. Intended to complement the strengths specific to PhDs at UC Berkeley, the Engineering Business Minor was designed to significantly support the many students looking to make an impact with their research.

Advisor: O'Connell, Grace D
Computational and Experimental Studies of Field-Driven Additive Manufacturing Processes

Industrial coating and painting operations have used electric fields as a driving force for decades to help more of the particles reach and stick to the intended part. More recently, this concept has been explored for use in additive manufacturing (AM) processes as a way to control the microstructure and geometry of particle deposits through processes like Electrospray Deposition and Electrophoretic Deposition (EPD). These AM processes are increasingly used to make components that require specific structures such as colloidal crystals made of micro- and nano- particles, which are then used in applications in microelectronics, sensors, and superlattice materials. So far, most development of the EPD process for colloidal crystals has been performed via experiments, with many measurements taken ex-situ to weigh deposits over time. However these experiments do not directly observe how the particles integrate into the crystal, so the kinetics of the deposit formation are not yet well understood. This work aims to contribute towards EPD and other field-driven AM process development in two ways: 1) by creating a computational framework to directly simulate the particles in these field-driven processes and 2) work towards an experimental platform for in-situ observations of these particles to better understand how they form the deposit./nIn this dissertation, I present my work towards a computational framework for simulating these field-driven processes and a preliminary experimental setup for in-situ process observation. The simulation model captured the essential physics of the processes, while remaining computationally tractable on limited resources, such as a laptop. I discuss the results from verification studies showing good agreement with analytical theory as the model is developed and then use illustrative examples to explore deposit behavior under different processing conditions. I then present preliminary results from experiments aimed at further expanding observations of EPD. The preliminary results from both the simulation and experimental work show that these methods can contribute to process development and have several avenues for future extensions.

Advisor: Zohdi, Tarek I
Geometry of Local-spectral Expanders

Expanders are well-connected graphs. They have numerous applications in constructions of error correcting codes, metric embedding, derandomization, sampling algorithms, etc. Local-spectral expanders (HDXes) are a generalization of expander graphs to hypergraphs. They have recently received more attention due to their applications to agreement tests [24], locally testable codes [28, 99, 75, 27], hardness of SoS refutation [25, 59], and connections with local sampling algorithms [5]. In comparison to expanders we have very limited understanding of HDXes: there are abundant random or explicit constructions of sparse expander graphs such as random d-regular graphs [50], algebraic expanders [92, 51], the zig-zag product [101], etc. In contrast, we know only two general constructions of sparse HDXes: the LSV complexes [90] and thecoset construction [66]. In this thesis, we take two approaches to tackle the construction problem. The first approach is taking graph products of sparse expander graphs. This is inspired by the zig-zag product. However, this construction fails to give good local-spectral expansion. The second approach is inspired by the following question: does any continuous space have the local-spectral expansion property? We show that the answer is affirmative for high-dimensional spheres. More precisely, we show that 3-uniform hypergraphs sampled randomly over high-dimensional spheres are (relatively sparse) local-spectral expanders. Furthermore, tight isoperimetric inequalities of local-spectral expanders have remained elusive. Intuitively, isoperimetric inequalities provide a lower bound on the probability that a random walk leaves a subset of vertices in the graph. A tight bound on this probability is crucial for applications to agreement testing. In this thesis, we explore this problem and give an improved bound for good local-spectral expanders.

Advisor: Chiesa, Alessandro