VictorJuiz/thesis huggingface dataset:
Of land and Leviathan : how state-society bargaining shapes infrastructure in India

Infrastructure is crucial for development, yet it is chronically under-provided. This dissertation focuses on a key obstacle to infrastructure development: conflict over land acquisition. Using the case of India, this study aims to understand why and when resistance to the state's use of eminent domain powers arises, and investigates two key questions: 1) what prevents mutually-beneficial bargains between landowners and the state, and 2) under what conditions can the state enforce land acquisition? Chapter 1 argues that bargaining breakdown is not solely explained by state capacity, but rather by the strength of commitment problems between state and landowners. First, landowners with a high value for land cannot credibly commit not to use protests to extract additional resources from the state. Second, the state cannot commit to compensating all landowners fairly in an environment where land records are outdated and property rights are weak. Chapter 2 uses infrastructure project announcements as a proxy for land acquisition and analyzes the impact of a reform that improved landowners' collective action capacity and worsened their commitment problem. It provides empirical evidence that variation in landowners' commitment problems explains conflict prevalence. Chapter 3 explores the impact of the state's commitment problem on infrastructure investment and project implementation. Examining a land record digitization reform, the chapter shows that stronger property rights enhance the state's ability to implement infrastructure. Chapter 4 focuses on state responses to resistance and argues that bureaucrats with local ties can improve the state's internal coherence and penetration of society, thus reducing conflict around land acquisition. This chapter shows that infrastructure projects implemented with the involvement of local bureaucrats are less likely to face conflict and stall, due to bureaucrats' effective coordination of coercion with local politicians. Overall, this dissertation contributes to the understanding of land acquisition conflicts, the importance of property rights, and the role of embedded bureaucrats in policy enforcement. The findings hold implications for policymakers seeking to address the infrastructure deficit and mitigate conflicts over land acquisition in developing countries

Keywords: Humanities and Sciences.
Political Science.
Advisor: Grzymała-Busse Anna Maria
Scheve Kenneth F.
Gulzar Saad
Prillaman Soledad Artiz
Weinstein Jeremy M.
Oil and water; climate change attribution in Houston, Texas

This dissertation is about the political life of scientific evidence in Houston during intensified flood infrastructure planning process after Hurricane Harvey. It approaches what might be considered climate denial with ethnographic sensitivity to embodied custom to provide an anthropological account of climate change misinformation. Drawing on eighteen months of ethnographic and archival research, it analyzes anti-science sentiment at the center of oil-based capitalism where engineers, lawyers and business professionals negotiated facts and factuality across concrete and through floodwaters. This dissertation puts climate misinformation into a longer history of industrial story telling from nineteenth century booster narratives to present day public relations campaigns. It argues that the consequential lie in Houston was not "climate change isn't real." It was "fuel is cheap." This lie requires business people and politicians to discipline liability through idiom and affect to maintain the semantic separation between industrial action and industrial harm, between oil and water, without which fuel would not be cheap in this century or the last

Keywords: Humanities and Sciences.
Anthropology.
Advisor: Yanagisako Sylvia Junko
Bauer Andrew M.
Ebron Paulla A.
Inoue Miyako
Olvidados Entre la Cosecha: Understanding Social Needs and Health Disparities through Farm Workers' Perspectives

Background: Although farm workers are integral to the agricultural industry, they are systematically excluded from health and social resources. Such exclusion has created economic, social, and health disparities (Anthony et al., 2008; Liebman et al., 2021; Ramos A. K, 2018). Objectives: This study aims to explore farmworkers’ experiences related to health pre and post COVID, and to understand social disparities in their lives while narrating their priorities and concerns. Political and economic factors contributing to the unmet social and health needs of farmworkers are discussed. Methods: This qualitative study was conducted in full partnership with a farmworker community organization on the San Mateo coast. Testimonios and semi-structured interview questions were developed with input from farmworker advocates and farmworkers. Transcripts were coded and analyzed using an inductive approach. Results: Four core themes regarding the community’s lived experiences and needs emerged: (a) Racialization and Dehumanization; (b) Social Welfare and Basic Needs; (c) Cultural Wealth and Community Agency (d) Violence and Trauma. Conclusion: Findings yield implications for the impact of this methodology and of community organizations on improving farmworker lives and to center lived experiences to inform program development and policy priorities to aid farm workers and bridge social and health outcome gaps.

Keywords: Farmworkers, Social Health Disparities, Community Wellbeing, Race and Trauma
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Thesis
On some topics in statistical learning : cluster-aware Lasso and others

In this thesis, we visit four topics in statistical learning: Cluster-Aware Lasso, an adaptation of lasso dealing with correlated features in supervised learning, which uses a hierarchical clustering-based approach to adaptively select clusters of features. Confidence Intervals for Generalisation Error in Random Forests, an effort to extend the out-of-bag error point estimate in random forests to a confidence interval with appropriate coverage for generalisation error. Prediction of Gestational age using metabolite data, approaches to a specific problem type in supervised learning in which multiple observations are made at different time points for a given unit and the response is a time to or from a fixed event. Statistical Summaries of unlabelled evolutionary trees, techniques for summarising samples and distributions on a class of tree structures which are ranked and unlabelled

Keywords: Humanities and Sciences.
Statistics.
Advisor: Tibshirani Robert
Hastie Trevor
Palacios Roman Julia Adela
On the stability of solutions to random optimization problems under small perturbations

Consider the Euclidean traveling salesman problem with n random points on the plane. Suppose that one of the points is shifted to a new random location. This gives us a new optimal path. Consider such shifts for each of the n points. Do we get n very different optimal paths? In this work, we show that this is not the case --- in fact, the number of truly different paths is bounded as n becomes large. The proof is based on a general argument which allows us to prove similar stability results in a number of other settings, such as branching random walk, the Sherrington--Kirkpatrick model of mean-field spin glasses, the Edwards--Anderson model of short-range spin glasses, and the Wigner ensemble of random matrices

Keywords: Humanities and Sciences.
Statistics.
Advisor: Chatterjee Sourav
Dembo Amir
Diaconis Persi
On the theory of wall bounded turbulence : with applications to the problem of viscous drag determination for flows over smooth and rough walls

The study of turbulence has been ongoing for over seven decades now, and it is without a doubt one of the most challenging problems in classical physics. The immense difficulty of understanding and analyzing turbulent flows is why researchers have thrown all their resources at the problem, including supercomputers, in hopes of gaining some insight. While this thesis was written during the era of supercomputers and advanced CFD tools (in other words, in the 21st century), it takes on a different approach- we introduce analytical concepts to deal with turbulent flows, and we do so in a manner that encompasses all Reynolds number flows, from laminar to turbulent. It is our objective to show that the conventional view that turbulence cannot be analyzed analytically and that sophisticated numerical methods must be applied to solve fundamental turbulent flows of Newtonian fluids is not necessarily accurate. In this work, we develop physically meaningful and universal tools to analyze turbulent flows over smooth and rough walls, demonstrate their sufficiency in matching data very well and demonstrate their suitability for practical problems. As of now, direct numerical simulation is only suitable for very low Reynolds number and simple geometry investigations; we do not have enough computational power for the needs of most investigations. However, in far future developments might make it possible to supercompute the movement of each molecule in a fluid, providing a deeper understanding of the fluid dynamics of highly excited fluids. We can, in the meantime, gain a deeper understanding of turbulence through semi-analytical and practical methods. Incorporating semi-analytical models, such as those presented here, into numerical programs will also be extremely helpful for improving the computation of turbulence, since this can reduce computation times and improve the stability of numerical computations

Keywords: Engineering.
Aeronautics and Astronautics.
Advisor: Alonso Juan José
Cantwell Brian
Kroo Ilan
Operationalizing language for machine learning : supervision, interpretation, and communication

Language is a uniquely powerful tool for conceptualizing the world that allows humans to teach, understand, and collaborate with each other. This dissertation surveys three ways in which language can similarly improve the performance and usability of machine learning systems across a variety of tasks and modalities. First, supervision: we use language abstractions to help regularize models and agents for improved performance, even for downstream tasks in vision and reinforcement learning that do not necessarily require language understanding. Second, interpretation: we use language and compositionality to generate explanations of the neurons inside deep neural networks, allowing us to understand and even control model behavior. Finally, communication: we study a class of multi-agent signaling games, helping agents learn more robust and interpretable languages by encouraging them to express the generalizations encoded in human languages

Keywords: Engineering.
Advisor: Goodman Noah (Noah D.)
Andreas Jacob (Jacob Daniel)
Liang Percy
Opportunities for circular management of industrial waters to support the clean energy transition

Tradeoffs between energy security, cost, and environmental impacts are key features of both current and future energy systems. This dissertation will begin by describing analyses of wastewater discharge and water consumption tradeoffs of the current transitioning fossil fleet. The infrastructure requirements to replace this fossil fleet with renewable energy increases the demand for minerals, leading to increased extraction and associated environmental impacts. Recovery of these minerals, including rare earth elements (REEs), from unconventional sources is motivated by the need to increase supply and the potential for social and environmental benefits. The dissertation will cover structured methods to estimate downstream processing costs for unconventional sources of these minerals, with applications for the evaluation of critical mineral feedstocks and the development of the supply chain. The dissertation will also present case studies for applications of this framework, including technoeconomic performance of rare earth element extraction from REE-enriched acid mine drainage precipitate and recovery of lithium from various alternative brine sources. Studies of the economic feasibility of an unconventional critical mineral supply offer a path forward for environmentally benign sources that have previously been underdeveloped due to economic constraints.

Keywords: Engineering.
Advisor: Mauter Meagan
Caers Jef
Fletcher Sarah
Optical deep ultra-violet characterization of ferroelectric twisted 2D hexagonal boron nitride

Two-dimensional hexagonal Boron Nitride (2D hBN) is a uniquely rich 2D material due to its interesting opto-electronic properties. hBN’s large bandgap makes it an insulator, but also lends it interesting optical deep ultra-violet (DUV) properties that remain largely unexplored. A better understanding of these optical properties could provide us information about the modified band structure of mono- and few-layer hBN, as well as twisted, stacked hBN. In particular, hBN with a small twist angle close to 0 exhibits ferroelectricity. In this report, I explore the optical properties of twisted and ferroelectric hBN stacks on SiO2/Si substrates. Four main contributions were made: (1) The mechanical exfoliation of large, thin hBN flakes was significantly improved by using a micromanipulator during the transfer of hBN to the substrate. (2) A transfer matrix method model was programmed to rapidly estimate the thickness of few-layer hBN flakes based solely on their reflectance spectra, and was adapted to fit twisted hBN stacks with non-linear least squares optimization. (3) The amplitude of the interference patterns in reflectance spectra of annealed hBN stacks was shown to decrease with increased angle. (4) The different stacking sequence of ferroelectric and non-ferroelectric hBN stacks exhibit different optical signatures and excitonic features, but further research is required to better understand the relationship between ferroelectric stacking order, individual ferroelectric domains, excitons, and optical measurements.

Keywords: Two-dimensional materials, hexagonal boron nitride, ferroelectricity, optics
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Thesis
Advisor: Mannix Andrew
Optical properties of excited states in atomically thin black phosphorus

This thesis investigates exciton-mediated light-matter interactions in atomically thin black phosphorus (BP). The goal of this work is two-fold: firstly, to elucidate the complex many-body physics intrinsic to this distinct C2-symmetric two-dimensional semiconductor, and secondly, to manipulate these physical properties using external degrees of freedom, such as the density of free charge carriers. The success of this research is predicated upon the development of innovative sample fabrication techniques, which have resulted in high-quality samples. In the following chapters, I discuss projects that focus on the intrinsic optical properties of excitons in BP. Chapter 1 introduces BP, its key properties, and the physics describing exciton light-matter interactions. Chapter 2 outlines the primary experimental techniques used in this work, namely photoluminescence and reflectance contrast spectroscopy, and delves into the specifics of our sample fabrication process. In Chapter 3, I investigate the linear optical properties of high quality monolayer BP and determine key excitonic features. Enabled by narrow optical transition linewidths, I characterize the excitonic fine structure at the band edge of monolayer BP and identify signatures of the optically dark triplet exciton. Finally, in Chapter 4, I present the first direct measurement of gate-tunable charged excitons in monolayer BP. Here I discuss the remarkably large spectral shift between the optically bright exciton and its charged counterpart. This thesis not only quantifies key properties of excitons in atomically thin BP but also underscores the material's potential as a platform for exploring novel many-body physics.

Keywords: Humanities and Sciences.
Physics.
Advisor: Heinz Tony F.
Feldman Ben (Benjamin Ezekiel)
Jornada Felipe
Optimal response to epidemics : models to inform policy

Epidemics, such as COVID-19, can have profound and far-reaching impacts on societies and individuals worldwide. Infectious disease outbreaks can result in millions of lives lost and place a tremendous burden on public health resources. Additionally, the opioid epidemic in the U.S. has been further fueled by COVID-19, leading to record drug overdose deaths in 2021. In the face of these epidemics, policy makers face difficult decisions in allocating limited resources to improve population health. To address these challenges, this dissertation focuses on the development of mathematical models to inform critical decisions in public policy, specifically optimizing resource allocation to control epidemics. Chapter 2 centers on the opioid epidemic. We develop a dynamic model to assess the effectiveness of interventions for controlling the US opioid epidemic. We show that reductions in opioid prescriptions are necessary but may lead to a short-term increase in heroin overdose deaths, and thus must be combined with scale up of treatment for addicted individuals -- but that even with immediate policy changes, significant morbidity and mortality will still occur. Our analysis provides critically needed evidence-informed recommendations for reducing opioid-related morbidity and mortality in the U.S. Chapters 3, 4 and 5 focus on developing interpretable models to guide the allocation of limited vaccines to control the spread of an infectious disease. In Chapter 3 we first consider an SIR (susceptible, infected, recovered) model with interacting population groups and a single allocation of vaccine. By approximating the disease dynamics, we derive intuitive analytical conditions characterizing the optimal solution for four different objectives: minimize infections, deaths, life years lost and QALYs lost due to deaths. We extend the work in Chapter 4 to a dynamic setting and develop a method for allocating vaccines over time. In Chapter 5, we further extend the work to an endemic setting, considering vaccine booster doses to take into account waning immunity from vaccination. We show that the approximated optimal solution is an all-or-nothing allocation based on a prioritized list of population groups given by the analytical conditions. Numerical simulations show that the analytical solutions achieve near-optimal results with objective function values significantly better than would be obtained using simple allocation rules such as allocation proportional to population group size. By leveraging interdisciplinary approaches, this dissertation aims to aid in decision making in the areas of opioid abuse, COVID-19, and epidemic control. Importantly, the work provides general theoretical frameworks that can be adapted to other public health challenges for epidemic control.

Keywords: Engineering.
Management Science and Engineering.
Advisor: Brandeau Margaret L.
Ashlagi Itai
Humphreys Keith
Optogenetic engineering of BAR domain proteins

Nanoscale membrane curvature is understood to play an active role in essential cellular processes such as endocytosis, exocytosis, and actin dynamics. Few methods, however, can precisely manipulate membrane curvature in live cells. Making use of BAR domain proteins, a well-studied superfamily of membrane-remodeling proteins, and the improved Light-Inducible Dimer (iLID) system, we developed a new method of generating nanoscale membrane curvature in live cells that is controllable, reversible, and capable of precise spatial and temporal manipulation. As proof of concept, we first engineered two optogenetic systems, opto-FBAR and opto-IBAR, that allow light-inducible formation of inward and outward membrane curvature, respectively. We then expanded upon this approach by engineering other BAR domain superfamily members to be light-inducible, including members of the FCHO, PACSIN, Amphiphysin, and NOSTRIN subfamilies. These systems present a novel approach for light-inducible manipulation of nanoscale membrane curvature in live cells

Keywords: Humanities and Sciences.
Chemistry.
Advisor: Cui Bianxiao
Dassama Laura
Lin Michael Z.
The organizing principles underlying cortical visual processing streams

Confronted by a chaotic and rapidly changing world, as humans we are constantly performing computations on the torrent of visual input we receive, deciding whether the shape in front of us is a building or a car (what), where said car is (where), and whether its currently moving towards us (what it is doing). To determine this quickly, efficiently, and ideally in parallel, the human visual system is thought to be divided into multiple, distinct processing streams. In the experiments described here, I use functional magnetic resonance imaging (fMRI), diffusion magnetic resonance imaging (dMRI) and neural networks to investigate how structure and function are linked to produce the organization of the visual cortex into differentiated streams. In Study 1, I focus in on the case study of the well-characterized system of face processing to ask how the most basic computations differ across processing streams and then how anatomical connections from the earliest stages of the cortical visual system to higher-level face-selective areas reflect different computational demands across streams. In Study 2, I zoom out to ask what are the underlying principles behind the organization of visual cortex into processing streams. While the streams are typically discussed in the context of what behaviors they each may serve, I find that instead their emergence can be explained by optimization pressures to learn generally useful visual representations, while fitting those representations in the constrained physical space of the skull. Together, these findings provide a window of insight into why and how the functional organization of human visual cortex into specialized processing streams emerges, and may have implications for how to efficiently build more general artificial intelligence systems.

Keywords: Humanities and Sciences.
Psychology.
Advisor: Grill-Spector Kalanit
Druckmann Shaul
Gardner Justin
KayKendrick
Wandell Brian
Yamins Dan