VictorJuiz/thesis huggingface dataset:
Olvidados Entre la Cosecha: Understanding Social Needs and Health Disparities through Farm Workers' Perspectives
Keywords: Farmworkers, Social Health Disparities, Community Wellbeing, Race and Trauma
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Thesis

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.
On some topics in statistical learning : cluster-aware Lasso and others
Keywords: Humanities and Sciences.
Statistics.

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
On the stability of solutions to random optimization problems under small perturbations
Keywords: Humanities and Sciences.
Statistics.

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
On the theory of wall bounded turbulence : with applications to the problem of viscous drag determination for flows over smooth and rough walls
Keywords: Engineering.
Aeronautics and Astronautics.

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
Operationalizing language for machine learning : supervision, interpretation, and communication
Keywords: Engineering.

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
Opportunities for circular management of industrial waters to support the clean energy transition
Keywords: Engineering.

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.
Optical deep ultra-violet characterization of ferroelectric twisted 2D hexagonal boron nitride
Keywords: Two-dimensional materials, hexagonal boron nitride, ferroelectricity, optics
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Thesis

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.
Optical properties of excited states in atomically thin black phosphorus
Keywords: Humanities and Sciences.
Physics.

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.
Optimal response to epidemics : models to inform policy
Keywords: Engineering.
Management Science and Engineering.

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.
Optogenetic engineering of BAR domain proteins
Keywords: Humanities and Sciences.
Chemistry.

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
The organizing principles underlying cortical visual processing streams
Keywords: Humanities and Sciences.
Psychology.

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.
Out of Many, Many: Political Polarization and Subnational Democratic Degradation in the Contemporary United States
Keywords: United States
Democracy
Federal government
State governments
Polarization (Social sciences)
Populism
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Thesis

The United States has not avoided the global democratic recession of the Twenty-First Century. Democratic degradation has taken hold at the federal level, but also, more subtly at the subnational level, where state governments have engaged in increasingly blatant anti-democratic action, cheered on by partisans in other states and at the national level. Gerrymandering, voting restrictions, and corrupt and legally questionable partisan action have become more common, accompanied by an atmosphere of political polarization and deep fear at the implications of opposition victory.
This thesis approaches the interplay between subnational democratic degradation in the United States and partisan polarization, analyzing the degree to which levels of political polarization in any given state predict the quality of democracy, and any shifts thereof, in that state. The primary hypothesis was that increased levels of polarization would correlate with lower democratic quality, as parties in government, fearing their opposition taking power, would impose anti-democratic measures to maintain their government.
The findings show little correlation between polarization and the quality of democracy. Political polarization increased in a largely linear fashion during the 2000 to 2018 period, while quality of democracy decreased slightly in a largely linear fashion over the same period in the vast majority of states. The exceptions were states where the party dominant in state government changed during the 2000 to 2018 period, with states that transitioned from Democratic- to Republican-controlled seeing significant democratic degradation, while states that transitioned from Republican- to Democratic-controlled saw significant improvement in quality of democracy. Analysis of the specific case of democratic degradation in Wisconsin suggests Republican party influence prompts such degradation. This raises further questions on the effects of partisan control of the post-Census redistricting process on state democracy, partisan transition and democracy more broadly, and the actual motivations behind state-level anti-democratic behavior.
An outline of outlines : typological reasoning in Plato and Aristotle
Keywords: Humanities and Sciences.
Philosophy.

My dissertation represents the first systematic study of the idea of an 'outline' (τύπος/typos) in Plato and Aristotle. Pushing back against the general consensus that Plato and Aristotle use typos vocabulary in a non-technical manner to indicate a 'rough sketch, ' I argue that language of typoi in both authors is consistently connected to a common conceptual structure. A typos: 1) marks out the object of inquiry within a larger domain of candidate objects and 2) identifies the primary elements of the essential definition of this object, 3) with the elements included in the outline subsequently requiring further clarification and articulation in order to produce a perfected account of the essence of that object. In Chapter I, I identify the key questions that arise when we consider various occurrences of the term in Plato and Aristotle together, with these questions serving to orient the dissertation as a whole. In Chapter II, I develop my understanding of the shared conceptual structure indicated by the language of typoi through a close reading of Nicomachean Ethics I.7. I argue that Aristotle's use of the term in the chapter must be understood in light of an epigraphic metaphor, show how his use conforms to Plato's own employment of the term, and suggest how the conceptual framework identified through the language of typoi fits into Aristotle's more general understanding of the structure of philosophical inquiry. Chapter III aims to confirm my claim that a typos includes the primary elements of an essential definition by showing how Plato uses the terminology in the Cratylus to indicate precisely those primary essential elements. Chapter IV turns to the Republic, showing how the tripartite common structure associated with the language of typoi in contexts of theoretical inquiry is also employed by Plato in practical contexts to indicate the structure of early character formation. Finally, Chapter V points forward to future avenues of research, suggesting how patterns of typological reasoning identified in my study of Plato and Aristotle are appropriated and transformed in the subsequent history of ancient philosophy
Painting the Bigger Picture: Social-Emotional Learning Interactions of Young Children in the Context of Collaborative Art
Keywords: Social-emotional learning
Dewey
Citizenship
Early childhood education
collaboration
Cooperation
conflict
Peer learning
Play
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Thesis

The modern era has revolutionized how people communicate but has led to a need for essential interpersonal skills. To succeed in navigating conflict and becoming critically engaged citizens, schools must support intra- and interpersonal development through social-emotional learning (SEL) as early as possible. This study examines social and emotional behavior development through collaborative art activities in 3-5-year-olds in an affluent preschool. The research aims to understand how collaborative art activities can support social and emotional development and cultivate prosocial behaviors on a larger scale of social democracy. The study highlights three types of collaboration: co-existence, cooperative play, and conflict. Collaborative art activities offer valuable opportunities for social-emotional and peer learning. Insights from this study can support individuals of all ages to establish shared goals, come to shared understandings, and learn together in our changing world. Practices like collaborative art may play a crucial role in supporting interpersonal communication.