VictorJuiz/thesis huggingface dataset:
Dual Processing Approaches to Fabricate Nacre-like Alumina with a Metallic Compliant Phase for Damage Tolerance

Ceramics have great properties, such as low density, high creep resistance, and high corrosion resistance, but it fractures in a very brittle fashion. This limits the applications of ceramic materials for structural applications due to the potential for catastrophic failure. Many scientists have studied nacre, or the mother-of-pearl portion of abalone shells as an inspiration for creating a tough and strong ceramic material. It has garnered a lot of interest because its fracture toughness is 40x higher (3000x higher in energy terms) than its constituent parts, which is 95 vol% calcium carbonate and 5 vol% biopolymers arranged in a brick-and-mortar structure./nThe polymer mortar phase prevents viable applications of this ceramic at higher temperatures, which is critical for applications in aerospace, nuclear materials, and transportation. In lieu of a polymeric mortar, a metallic mortar can improve the high temperature resistance of the material. Unfortunately, capillary forces prevent infiltration of metals into porous ceramics. The thesis presents a “top-down” processing approach and a “bottom-up” processing approach to overcome these capillary forces./nThe “top-down” approach uses reactive wetting to infiltrate alumina scaffolds with bulk metallic glass without any added pressure. The results illustrate the effects of infiltration temperatures on the mechanical properties of the materials. The infiltration temperature affects the mechanical properties of the brick-and-mortar materials by changing the interfacial properties of the materials. At higher infiltration temperatures, the interface fails to allow for extrinsic toughening mechanisms, but it leads to significantly lower flexural strength. At lower infiltration temperatures, the material is very brittle and shows will no crack deflection or extrinsic toughening, but the flexural strength is higher./nThe alternative (“bottom-up”) approach to synthesize “nacre-like” structures ceramics containing a metallic nickel compliant phase. These materials were fabricated using core-shell alumina/nickel platelets that are aligned using slip casting and rapidly sintered using spark-plasma sintering (SPS). With this process, nacre-like ceramic/metal nanostructures have been fabricated with various microstructural features. By casting NiO-coated platelets along with Ni-coated platelets and very rapid SPS processing, there was limited dewetting of the nickel mortar. As a result, we have produced a “nacre-like” alumina ceramic displaying a resistance-curve toughness up to ~16 MPa·m½ with a flexural strength of ~300 MPa.

Advisor: Ritchie, Robert O
Machine Learning for Automatic Resource Management in the Datacenter and the Cloud

Traditional resource management techniques that rely on simple heuristics often fail to achieve predictable performance in contemporary complex systems that span physical servers, virtual servers, private and/or public clouds. My research aims to bring the benefits of data-driven models to resource management of such complex systems. In my dissertation, I argue that the advancements in machine learning can be leveraged to manage and optimize today's systems by deriving actionable insights from the performance and utilization data these systems generate. To realize this vision of model-based resource management, we need to deal with the key challenges data-driven models raise: uncertainty in predictions, cost of training, generalizability from benchmark datasets to real-world systems datasets, and interpretability of the models./nIn this dissertation, to demonstrate how to handle these challenges, we chose two main problem domains: (I) Scheduling in parallel data intensive computational frameworks for improved tail latencies, and (II) Performance-aware resource allocation in the public cloud environments for meeting user-specified performance and cost goals./nWe begin by presenting Wrangler, a system that predicts when stragglers (slow-running tasks) are going to occur based on cluster resource utilization counters and makes scheduling decisions to avoid such situations. Wrangler introduces a notion of a confidence measure with these predictions to overcome modeling uncertainty. We then describe our Multi-Task Learning formulations that share information between the various models, allowing us to significantly reduce the cost of training. To capture the challenges of resource allocation in the public cloud environments, we present key observations from our empirical analysis based on performance profiles of workloads executing across different public cloud environments. Finally, we describe PARIS, a Performance-Aware Resource Inference System, that we built to enable cloud users to select the best VM (virtual machine) for their applications in the public cloud environments so as to satisfy any performance and cost constraints.

Advisor: Katz, Randy H.
Gonzalez, Joseph E.
Multi-spatial-scale observational studies of the Sierra Nevada snowpack using wireless-sensor networks and multi-platform remote-sensing data

The Sierra Nevada winter snowpack is the major water resource for the state of California. To better quantify the input of the water system, we deployed wireless-sensor networks across several basins in the Sierra Nevada. Together with operational and scientific research agencies, we also collected numerous scans of snow-on and snow-off lidar data over several basins in the high Sierra. We mined the lidar data and found how spatial patterns of snow depth are affected by topography and vegetation while elevation is the primary variable, other lidar-derived attributes slope, aspect, northness, canopy penetration fraction explained much of the remaining variance. By segmenting the vegetation into individual trees using lidar point clouds, we were able to extract tree wells from the high resolution snow-depth maps and we found the spatial snow distribution to be affected by the interactions of terrain and canopies. The snowpack is deeper at the downslope direction from the tree bole, however the snowpack at upslope direction being deeper. On sub-meter to meter scales, non-parametric machine-learning models, such as the extra-gradient boosting and the random-forest model, were found to be effective in predicting snow depth in both open and under-canopy areas. At spatial scales that are larger than 100 × 100 m2, we developed a novel approach of using the k-NN algorithm to combine the real-time wireless-sensor-network data with historical spatial products to estimate snow water equivalent spatially. The results suggest only a few historical snow-water-equivalent maps are needed if the historical maps can accurately represent the spatial distribution of snow water equivalent. The residual from the k-NN estimates can be distributed spatially using a Gaussian-process regression model. The entire estimation process can explain 90% of the variability of the spatial SWE.

Advisor: Bales, Roger C
Glaser, Steven D
Deformations of overconvergent isocrystals on the projective line

Let k be an algebraically closed field and Z an effective Cartier divisor in the projective over k with complement U. When k = C, a local system on the analytification of U is said to be physically rigid when it is determined by the conjugacy classes of its monodromy operators around the points of Z. Katz proves a convenient cohomological characterization of irreducible physically rigid local systems. Roughly, it arises from the observation that irreducible physically rigid local systems are smooth isolated points in the moduli of local systems on U with fixed local monodromy data along Z./nIn this dissertation, we consider the situation where char(k) > 0 and local systems are replaced with overconvergent isocrystals on U. The "moduli of overconvergent isocrystals" is an elusive object, but we establish some results about the formal deformation theory of overconvergent isocrystals with fixed "local monodromy" along Z. These results bear strong resemblances to facts about the infinitesimal structure of the moduli of local systems with fixed monodromy./nEn route, we establish a general result which shows that a Hochschild cochain complex governs deformations of a module over an arbitrary associate algebra. We also relate this Hochschild cochain complex to a de Rham complex in order to understand the deformations of a differential module over a differential ring.

Advisor: Nadler, David
Image Synthesis for Self-Supervised Visual Representation Learning

Deep networks are extremely adept at mapping a noisy, high-dimensional signal to a clean, low-dimensional target output (e.g., image classification). By solving this heavy compression task, the network also learns about natural image priors. However, this process requires the curation of large, labeled datasets. Meanwhile, the world provides massive amounts of raw, unlabeled pixels for free. This thesis investigates learning representations of high-dimensional input signals by mapping them to \textit{high-dimensional} output targets. While more difficult, it is not only possible to learn a strong feature representation, but also to synthesize realistic images./nPart I describes the use of deep networks for conditional image synthesis. The section begins by exploring the problem of image colorization, proposing both automatic and user-guided approaches. This section then proposes a system for general image-to-image translation problems, BicycleGAN, with the specific aim of capturing the multimodal nature of the output space./nPart II explores the visual representations learned within deep networks. Colorization, as well as cross-channel prediction in general, is a simple but powerful pretext task for self-supervised learning. The representations from cross-channel prediction networks transfer strongly to high-level semantic tasks, such as image classification, and to low-level human perceptual similarity judgments. For the latter, a large-scale dataset of human perceptual similarity judgments is collected. The proposed cross-channel network method outperforms traditional metrics such as PSNR and SSIM. In fact, many unsupervised and self-supervised methods transfer strongly, even comparably to fully-supervised methods.

Advisor: Efros, Alexei A.
Dynamic Analysis for JavaScript Code

The effectiveness of the widely adopted static analysis tools is often limited by JavaScript’s dynamic nature and the need to over-approximate runtime behaviors. To tackle this challenge, we research robust dynamic analysis techniques for real-world JavaScript code./nTo analyze front-end web applications, we first extend Jalangi which is a dynamic analysis framework based on source code instrumentation. Our extension of Jalangi intercepts and rewrites JavaScript code during network transmission. We also develop NodeSec, which is a dynamic instrumentation framework that traces and sandboxes the interactions between a Node.js program and the operating system. Based on the two frameworks, we research dynamic analysis techniques to detect correctness, performance, and security issues in JavaScript code./nFirst, we present DLint, a dynamic analysis approach to check code quality rules in JavaScript. DLint consists of a generic framework and an extensible set of checkers that each addresses a particular rule. We formally describe and implement 28 checkers that address problems missed by state-of-the-art static approaches. Applying the approach in an empirical study on over 200 popular websites shows that static and dynamic checking complement each other. On average per website, DLint detects 49 problems that are missed statically, including visible bugs on the websites of IKEA, Hilton, eBay, and CNBC./nSecond, we present JITProf, a profiling framework to dynamically identify JIT-unfriendly code, which prohibits profitable JIT optimizations. The key idea is to associate meta-information with JavaScript objects and code locations, to update this information whenever particular runtime events occur, and to use the meta-information to identify JIT-unfriendly operations. We use JITProf to analyze widely used JavaScript web applications and show that JIT-unfriendly code is prevalent in practice. We show that refactoring JIT-unfriendly code identified by JITProf leads to statistically significant performance improvements of up to 26.3% in 15 popular benchmarks./nFinally, we conduct the first large-scale empirical study of security issues on over 330,000 npm packages. We adopted an iterative approach to dynamically analyze those packages and identified 360 previously unknown malicious or vulnerable packages, 315 of which have been validated by the community so far; 258 of those issues are considered as highly severe. All those packages with security issues in aggregate have 2,138 downloads per day, stressing the risks for the Node.js ecosystem.

Advisor: Sen, Koushik
Idle Attentions: Modern Fiction and the Dismissal of Distraction

Idle Attentions challenges a long critical tradition that regards distraction as the default state of the modern subject. I argue that, far from leading inevitably to distraction, the culture of industrial capitalism trained individuals to treat their own attention as a scarce resource. Distraction, in this framework, was recast as a form of imprudent squandering. By bringing original analyses of the history of science and political theory to bear on four quintessentially distracted novels, I demonstrate that these texts’ unfocused form constitutes itself in opposition to an ever more ubiquitous demand for productive attention. In these novels, I reveal, distraction appears not as the necessary outgrowth of modern life, but as an increasingly marginalized cognitive mode./nContemporary discussions of distraction tend to follow a narrative first established by critics of the late nineteenth and early twentieth centuries, who saw it as a condition both endemic to and emblematic of their age. If reactionary writers like Max Nordau denounced distraction as a symptom of the modern individual’s weak willpower, Marxists like Theodor Adorno saw it as a lamentable consequence of the proliferation of the commodity form. Even leftist theorists who found value in distraction—arguing, like Siegfried Kracauer and Walter Benjamin, that it exposed the unsustainable tensions of modern life—understood it as the corollary of industrial modernity. Idle Attentions offers a different account. The novels I consider locate distraction not in the bustling centers of modern capitalism, but at its margins: for Charles Dickens, among abjected working-class subjects; for Bram Stoker and Joseph Conrad, at the colonial edges of the British Empire; for James Joyce, in private spaces set apart from civic life. By bringing to light distraction’s status as a marginalized phenomenon, I am able to give a new account of the novel’s evolution during this period, from the teleological narratives of serialized Victorian fiction to the associative forms of modernism. I argue that novelists develop radically experimental forms not in order to represent the new realities of modernity, but rather to reactivate a distractible, spontaneous mode of perception out of sync with the demands of the capitalist market. In historicizing the imperative to maintain control over one’s own attention, my project also challenges the value of willful concentration assumed in disciplines from art history to philosophy. I propose that distraction, by accommodating spontaneous impressions and unforeseen realizations, offers a vital means to move outside reified structures of thought and perception.

Advisor: Masiello, Francine
Old Struggles New Platforms: Black Athletes, Black Twitter and the Fight for Social Justice

Since the death of Michael Brown Jr. on August 9, 2014, the social media platform Twitter and its Black usership have become central actors in the production and dissemination of news, political action, and protests challenging anti-Blackness. This dissertation explores what I call the Black Twitter Turn as a pivot away from what I term selfsploitation as its primary identifiable cultural currency. The Black Twitter Turn has transformed Twitter into a place to pushback and challenge dominant white racial frames rooted in white supremacy. It also serves as a site for making Black voices and concerns audible and unavoidable as both a community and as a media force. Black Twitter has provided community for working-class folk, Black freedom fighters and also for those considered to be Black elites (athletes and celebrities), furnishing them all with a safe-space to publicly protest anti-Blackness, becoming a homeplace of resistance.

Advisor: Taylor, Ula Y
Dockworkers of the World Unite: Worker Power and Trade Union Strategy in a Global Economy

This dissertation examines contemporary transnational organization and strategy among dockworkers unions through a global organizational ethnography of the International Dockworkers Council (IDC), an independent global union organization. The research draws on 80 in-depth interviews and participant observation at a dozen international meetings of union activists, while the analysis relies on nested comparisons. In Part I, Chapter 2 examines the IDC’s regional-level organization in Europe, while chapter 3 examines three European country case studies (Portugal, Greece and England). In Part II, Chapter 4 examines the IDC’s regional-level organization in Latin America, while chapter 5 examines two Latin American country case studies (Chile and Colombia)./nOlin Wright (2000) and Silver’s (2003) theory of worker power would suggest that dockworkers in general have a high degree of ‘structural power’ – the power accruing to workers as a result of their position in the economic system -- because of their central role in the global circulation of commodities and capital. Nevertheless, I find that dockworkers’ ‘structural power’ is heavily conditioned by the state, suggesting that frameworks that partition the economic basis of worker power off from its political and social basis may be insufficient. Instead, comparative research at the national and regional levels demonstrates that dockworkers’ structural power varies considerably in different parts of the world as a result of differing political and social conditions, with significant consequences for sectoral labor internationalism./nWithin Europe, dockworkers’ power at the point of production is supported by strong industrial relations frameworks protecting the right to organize and take industrial action free from the threat of state-sanctioned or state-sponsored violence. Consequently, dockworkers have developed a strong regional-level network that provides effective mutual aid during disputes through industrial solidarity at the point of production. In contrast, within Latin America, dockworkers’ structural power in many countries, particularly outside of the Southern Cone, is effectively quite weak, despite their central position in the economic system, as a result of unfavorable industrial relations frameworks and pervasive violence at the national level. As a result, dockworkers have struggled to develop a cohesive regional-level network capable of delivering solidarity actions that are effective in the wide variety of national contexts for trade unionism found within the region. These national and regional-level observations are used to examine the challenges workers face in developing strong, cohesive and effective organizations that are truly global.

Advisor: Evans, Peter
Learning and Optimization for Mixed Autonomy Systems - A Mobility Context

Mixed autonomy characterizes problems surrounding the gradual and complex integration of automation and AI into existing systems. In the context of mobility, we consider: how will the gradual introduction of self-driving cars change urban mobility? In this dissertation, we develop machine learning and optimization techniques to address three key challenges: 1) quantifying the behavior of such complex systems, 2) addressing inherent sensing limitations, and 3) mitigating negative effects of introducing the automation./nWe demonstrate that deep reinforcement learning (RL) can serve as a unifying framework for studying the behavior of disparate and complex scenarios common in mixed autonomy systems. In particular, using deep RL, we find that automating a small fraction of vehicles in various traffic scenarios can result in a significant system-level velocity increase and numerous emergent driving behaviors. We demonstrate through the development of variance reduction techniques for policy gradient methods, that deep RL has the potential to scale to high-dimensional control systems, such as traffic networks and other mixed autonomy systems. We additionally present Flow, an open source RL platform with the goal of easing the design and study of disparate traffic scenarios. To address sensing limitations inherent when only parts of a system are automated, sensor fusion is explored. In particular, we introduce a convex optimization method for cellular network measurements from AT&T at the scale of the Greater Los Angeles Area, to address a flow estimation problem previously believed to be intractable. Finally, when automation reduces the cost of the activity (of transport), anticipated negative effects include induced demand and increased energy consumption. We study how the design of the mobility system itself can mitigate these effects. In particular, joint work with Microsoft Research provides insight into how high-occupancy vehicle lanes can simultaneously satisfy comfort and time preferences of users, and provide system benefits. We introduce combinatorial optimization methods based on clustering and local search for the resulting ridesharing problem. Together, these learning and optimization methods demonstrate that a small number of vehicles and sensors can be harnessed for significant impact on urban mobility, and shed light into the future study of mixed autonomy systems.

Advisor: Bayen, Alexandre M
New Optical Methods for Characterization of Materials and Biology

Optical methods, including light and fluorescence microscopies, have seen widespread/napplication in the characterization of materials and biology, but are not equally applicable to all/nsuch systems. This dissertation aims to describe new developments in select optical methods, and/ntheir subsequent application to answering scientific questions about material and biological/nsystems that were intractable using previous methodologies. We first develop a novel label-free/ninterference-based optical method for characterizing thin films on transparent substrates and/nsubsequently apply it to reveal reaction dynamics of graphene films in real time. We next/ndevelop novel methods in a different optical technique, point-localization super-resolution/nfluorescence microscopy. These include the application of spectrally-resolved super-resolution/nmicroscopy to organic adlayers on surfaces and the development of a remote focusing technique/nto image oblique planes through thick biological samples. We then combine our developments in/noptical methods and super-resolution microscopy to describe a novel correlative super-resolution/nimaging methodology, and overview the challenges and opportunities inherent to correlative/nsuper-resolution methods. We conclude with concrete applications of super-resolution/nfluorescence microscopy to biological systems and shed light on long-standing biological/nquestions. These include elucidation of the structure of meiotic chromosome axes in C. Elegans,/na mechanism for crossover-interference and crossover-regulation in the same meiotic/nchromosomes, discovery of a novel structural arrangement for nuclear pore complexes in meiotic/ncells, and discovery of a tube-shaped structure spanning the nucleus of breast cancer-like cells.

Advisor: Xu, Ke