VictorJuiz/thesis huggingface dataset:
Preachers of the great way : the Dharmabhanaka in Mahayana Buddhist sutras

In this dissertation I examine descriptions of preachers of the dharma (Sanskrit: dharmabhāṇaka) in select Mahāyāna Buddhist sūtras, dating from roughly the first century BCE to the sixth century CE. I analyze how Mahāyāna Buddhist authors represented these figures of religious authority and made claims about what these preachers should be like. I argue that these sūtras consistently use aesthetic descriptions of preachers; explicit statements about the blessings, empowerment, and authorization that they receive from buddhas, alongside promises of protection from powerful supernatural beings throughout the cosmos; and expressions of their venerable natures to articulate a normative vision of the ideal religious authority figure. Mahāyāna sūtras also exhort their readers and hearers to spread the scriptures and their teachings. In the context of these exhortations, propagation through preaching is made a central activity of Mahāyāna Buddhist practice—alongside a standard list of practices that includes accepting, memorizing, reciting, and copying the sūtras. Therefore, I further argue that such descriptions reveal that dharmabhāṇakas are envisioned as an ideal Mahāyāna Buddhist subject, because they embody one of the central practices of Mahāyāna Buddhism: propagating the dharma

Keywords: Humanities and Sciences.
Religious Studies.
Advisor: Harrison Paul
Gentry James Duncan
Gummer Natalie
Kieschnick John
Mayse Evan
Predictive uncertainty of fluid models

Fluid models play a critical role in a wide range of applications, such as oil and gas exploration and production, chemical process design and optimization, and CO2 enhanced oil recovery projects. Accurate predictions of phase behavior and thermophysical properties are key to ensuring the safety, performance, and profitability of these operations. However, predictions derived from fluid models inherently possess uncertainties due to model and parametric factors. These uncertainties are especially significant for hydrocarbon reservoir fluids, which exhibit complex phase behavior. This dissertation discusses the sources of uncertainty in reservoir fluid modeling, encompassing both empirical and compositional approaches, and provides a deeper understanding of their impact on fluid and flow model predictions. Initially, we demonstrate the uncertainty in model selection by comparing nine traditional empirical models for solution gas-oil ratios against experimental data. The results reveal that specific fluid compositions under different pressure and temperature conditions are better represented by distinct models. Consequently, we propose a new model that delivers superior overall performance. We also explore the uncertainty in input parameters for compositional fluid models, specifically highlighting how subjective choices of optimization algorithms and initial guesses impact the equation of state (EoS) regression process. As a result, EoS predictions remain uncertain even after tuning the uncertain inputs to a limited set of experimental data points. We present results for two hydrocarbon reservoir fluids, treating five properties of the heaviest carbon fraction as design variables. Although all considered optimization algorithms and initial guesses match experimental data for gas and liquid properties, the resulting EoS parameterizations lead to dramatically different predictions of the fluid's thermophysical behavior in unsampled pressure and temperature regions. Next, we investigate the uncertainty in input parameters for fluid models, introducing a framework to quantify the predictive uncertainty of multiphase pipe-flow models due to correlated random inputs. A case study evaluates the uncertainty in cumulative production for a reservoir with unknown fluid properties during the exploration phase. Global Sensitivity Analysis using Sobol's indices is employed to identify inputs significantly contributing to the model's predictive uncertainty. This framework facilitates improved risk management and informed decision-making within the energy industry. Lastly, we discuss the calibration of inherently uncertain flow models when field data is available. In particular, we propose a heuristic method for optimizing tuning factors applied to calculated pressure and temperature gradients and demonstrate its efficiency through a real case study

Keywords: Sustainability.
Energy Resources Engineering.
Advisor: Tartakovsky Daniel
Aziz Khalid
Kovscek Anthony R. (Anthony Robert)
Principal component analysis under extreme aspect ratios

Recent studies of high-dimensional principal component analysis (PCA) often assume the proportional growth asymptotic, where the sample size n and number of parameters p are comparable, with n and p tending to infinity and their ratio converging to a positive constant. Yet, many datasets---perhaps most---have very different numbers of rows and columns. This thesis considers disproportional growth, where n and p are large though their ratio tends to zero or infinity. Either disproportional limit induces novel phenomena distinct from the proportional and fixed-p limits. Theory derived here shows that the displacement of the empirical singular values and vectors from their noise-free counterparts and the associated phase transitions---well-known under proportional growth asymptotics---still occur in the disproportionate setting. They must be quantified, however, on a novel scale of measurement that adjusts with the changing aspect ratio as the matrix size increases. In this setting, the top singular vectors corresponding to the longer of the two matrix dimensions are asymptotically uncorrelated with the noise-free signal. We apply this theory to covariance estimation under the spiked model, where the population covariance is a low-rank perturbation of the identity. For each of 15 different loss functions, we exhibit in closed form new optimal shrinkage and thresholding rules; optimality takes the particularly strong form of unique asymptotic admissibility. Our optimal procedures demand extensive eigenvalue shrinkage and offer substantial performance benefits over the standard sample covariance estimator. These procedures are closely related to optimal shrinkage rules for denoising of spiked Wigner matrices. Practitioners may ask whether to apply the procedures of the proportional or disproportional asymptotic frameworks. Conveniently, we show that one can be framework-agnostic: one unified set of closed forms (depending only on the aspect ratio of the given data) offers full asymptotic optimality under either framework. Finally, we consider estimation of the principle components of large, asymmetric tensors of arbitrary order. We study matricization approaches, which construct specific matrices from such tensors and then apply spectral methods; these strategies produce extremely tall matrices that naturally fit within the disproportional growth framework. For tensor unfolding, partial tracing, and a new method---successive contraction---we identify sharp thresholds in signal-to-noise ratio above which the signal is partially recovered. Moreover, we prove that above the partial recovery threshold of unfolding, successive contraction asymptotically achieves exact signal recovery

Keywords: Humanities and Sciences.
Statistics.
Advisor: Donoho David Leigh
Johnstone Iain
Montanari Andrea
A principals' principles : tangled hierarchies in charter management organizations

The study of schools as institutions has been central to the fields of sociology of education and organizational sociology long before charter schools started to spread across the US in the 1990s. Since then, business thinking and its related practices have enjoyed unprecedented influence on K-12 schools. Despite thirty years of operations and nearly as much robust research on academic outcomes and community effects, there remains little study of how charter schools are led. I attend to this gap through participant and ethnographic observation across several charter schools and their respective networks, as well as in-depth interviews of charter school teachers and principals, the two most critical positions in schools for student learning. I find tangled hierarchies are endemic to leadership of networked charter schools, where governing boards depend heavily on principals for expertise but rarely grant them autonomy to meaningfully lead, preferring instead they adopt a role akin to that of a middle manager. Principals in a network then are forced to contend, resist, or acquiesce to innumerable decisions of a central office executive staff and their governing board of directors, a process known as managing up. This research has implications for the study of institutions, furthering the notion that they are "inhabited" by agentic actors, but also noting the continuing influence of institutions on organizational behavior. This research also has implications for school leadership and preservice training and education policy, particularly charter schools in noting how the institutional environment of accountability that charter schools bring to K-12 education invariably hamstrings talented leadership

Keywords: Education.
Advisor: Carnoy Martin
Powell Walter W.
Valdés Guadalupe
Principled algorithms for domain adaptation and generalization

Machine learning models are increasingly applied to datasets different from the training datasets. The performance of models often degrades when tested on unseen scenarios. Empirically, many algorithms have been used for domain adaptation and generalization, but few methods have been able to surpass empirical risk minimization consistently on common benchmarks. Theoretically, traditional learning theory offers limited insights for distributional shift problems. The main goal of this thesis is to bridge the gap between the theory and practice for domain shift problems, and to develop principled algorithms that have better robustness guarantees. We study three domain shift problems with increased supervised from the target domain. We first study domain generalization where no target data is available during training. We show that feature-matching algorithms generalize better when the distinguishing property of the signal feature is indeed conditional distributional invariance. Next, we study domain adaptation where unlabeled target data is available. We show that self-training helps when the target is more diverse than the source. Lastly, we study active online learning under domain shift. We show that uncertainty sampling leads to better query-regret tradeoff when there is hidden domain structure. In all three problems, the synergy of explicit bias from the algorithm and implicit bias from the domain shift structure contributes to successful transfer between domains

Keywords: Engineering.
Advisor: Ma Tengyu
Ermon Stefano
Hashimoto Tatsunori
Privacy and efficiency in personalized decision-making and recommendation

This dissertation explores challenges concerning data privacy and efficiency in decision-making and recommendation systems. The first chapter discusses difficulties of model learning under misspecification in contextual bandits for adaptive decision-making. We propose a reduction of contextual bandits to general-purpose heterogeneous treatment effect estimation, and demonstrate that this approach improves data efficiency and robustness to model misspecification in contextual bandits. The second chapter explores heterogeneous domain adaptation in decision-making in a data-constrained setting. Specifically, we consider learning personalized decision policies on historical observational data from heterogeneous data sources in a federated setting. A federated policy learning algorithm is proposed for this setting and a novel regret analysis is presented that highlights the extent of benefits of heterogeneity in policy learning. The final chapter focuses on data privacy in recommender systems. It proposes a novel approach for developing privacy-preserving large-scale recommender systems with synthetic data generated using differentially private language models. We demonstrate this approach ensures query-level privacy guarantees without sacrificing on recommendation quality, a key step towards the realization of secure and efficient data-driven personalized recommendation

Keywords: Engineering.
Advisor: Athey Susan
Wager Stefan
Weintraub Gabriel
Probabilistic modeling of air and ground vehicle trajectories

Safe integration of autonomous vehicles into their traffic environments requires modeling human behavior. This thesis presents techniques to model vehicle trajectories that are representative of human driver and pilot behavior in the real world. These models can be used to build simulations for developing and verifying safety-critical traffic systems. Modeling vehicle trajectories can be challenging due to several reasons specific to the context. This thesis presents modeling approaches that address the challenges in each context. For autonomous cars, modeling trajectories can be challenging due to the inherent uncertainty in human behavior and complex interactions among drivers. While a driver model can be learned directly from data, we generally want our models to be interpretable. This thesis addresses this challenge by proposing a framework that combines physics-based driver models with a data-driven learning approach. For conventional types of aircraft, trajectories depend on the flight procedures and instructions from air traffic controllers. In dense airspace, however, aircraft behavior may vary due to factors including the preferences of air traffic controllers and the correlations among multiple aircraft. This thesis proposes a probabilistic model that learns the relation between aircraft and procedures in each flight stage. The trained model then can be used for generating trajectories given unseen traffic environment with procedural information. Further, the thesis proposes a method to incorporate correlations among multiple aircraft. For new types of aircraft such as urban air mobility systems, the major challenge for building models is the lack of available data. This thesis introduces an interactive software package called CONTRAIL, which provides an interface for building an aircraft encounter model by geometrically defining the nominal encounter paths and specifying a probability distribution over the nominal paths. Instead of learning aircraft behavior in a data-driven way, field experts can use this tool to create encounter models and generate realistic trajectories for new categories of aircraft

Keywords: Engineering.
Aeronautics and Astronautics.
Advisor: Kochenderfer Mykel J.
Alonso Juan José
Schwager Mac
Probing and designing electrolyte solvation for lithium batteries

Capturing anthropogenic carbon dioxide is essential to meet the climate targets.1 Despite the efforts to decarbonize the grid and transportation, some sectors remain difficult to decarbonize, and carbon capture is necessary to reach carbon neutrality.2 The incumbent technology for carbon capture utilizes amine sorbents to scrub CO2 using thermal stimuli suffers from high energy costs; 3 however, the thermodynamic penalty for CO2 capture and release is high and the heating of non-active water solvent with high heat capacity contribute to the high energy cost. As an alternative, electrochemical carbon capture has recently been gaining attention.2 Electrochemical activation prevents energy input into water heating, and also it is possible to achieve high efficiencies by tuning the absorption chemistry. Key prerequisites of candidate sorbents molecules include: 1) low thermodynamic energy cost; 2) facile reaction kinetics; 3) high stability upon oxidation and reduction; 4) preferred compatibility with water.2 In particular, attaining highly stable redox chemistries is a non-trivial challenge, as the redox cycle often involves free radicals. We propose the idea of deploying redox active amine molecules that have been widely deployed in batteries as redox mediators. Redox mediators are molecules that undergo redox cycles to facilitate sluggish reactions at the battery electrode interfaces.4 Because the stability of these chemistries have already been confirmed, and the reaction pathways are well-studied, we believe that they are highly promising candidates for electrochemical carbon capture. Through preliminary studies we have identified a molecule, 4-amino-TEMPO, that stably undergoes reduction and oxidation to capture and release CO2. There are two aspects of this molecule that makes it so promising: 1) it is a stable radical in its native form, making it stable upon repeated cycling; 2) it shows a remarkably small energy cost to capturing and releasing CO2 (Fig. 1). We plan to investigate the capture and release mechanism using a multitude of analytical techniques, including nuclear magnetic resonance spectroscopy (NMR) and liquid chromatography mass spectrometry (LC-MS). In addition, we plan to design a novel flow cell with symmetric reactions (Fig. 2). This should be a breakthrough because all other electrochemical cells for carbon capture have utilized a placeholder reaction on the counter electrode to balance the charge, which calls for additional materials and bulkier setups. We believe that our novel cell design and reaction chemistry will offer an avenue towards practical deployment of electrochemical carbon capture

Keywords: Engineering.
Materials Science and Engineering.
Advisor: Cui Yi
Chueh William
Prinz F. B.
Probing charge carrier properties in organic semiconductors with optical spectroscopy

Polymeric organic semiconductors (OSCs) have complicated, heterogeneous microstructures with significant nanoscale disorder. This thesis broadly shows how spectroscopic characterization via mid-Infrared charge modulation spectroscopy (CMS) and UV-Vis-NIR absorption provide powerful tools to understand disorder effects on electronic charge carriers. The first section shows how CMS spectra can be quantified to yield information on spatial localization of charge carriers, and then this quantification is expanded in the second section to focus on understanding charge transport in a modern, high-mobility OSC. In this section, it is shown that one-dimensional carrier delocalization along polymer chains (intrachain) is sufficient for good electronic charge transport, provided occasional, short-range contacts exist between polymer chains (interchain). Finally, the last two sections focus on understanding charge transport and carrier delocalization in electrolyte gated polymeric OSCs at high carrier densities exceeding one charge per ten monomers. In these sections, the importance of local, short-range intermolecular aggregates on charge transport is explored, and the remarkable result of charge carrier induced structural ordering via increasing polymer chain planarity is shown through a combined spectroscopic and X-ray scattering study. Overall, this thesis broadly shows that intrachain effects appear to dominate charge transport in polymeric OSCs.

Keywords: Engineering.
Materials Science and Engineering.
Advisor: Salleo Alberto
Mukherjee Kunal (Researcher in materials science)
Spakowitz Andrew James
Progress and Neglect Within the Same City: Analyzing the Perspective of the Community in Southwest Detroit on Gentrification

Southwest Detroit, a predominantly Latine neighborhood, is located near Detroit’s rapidly gentrifying downtown area. In response to devastating economic challenges, Detroit has taken a “growth first” approach to revitalization. In the past decade, this approach has become apparent through the city’s focused investments of its limited resources in Downtown Detroit and the improvement of pathways to the business district, such as the construction of the Gordie Howe International Bridge. Drawing on the concept of revitalization as a paradox and the significance of culture and place, I investigate how residents of Southwest Detroit have been affected by these changes and how their sense of belonging and place is affected. I conducted 21 semi-structured interviews to study how residents in Southwest Detroit perceive these changes. The findings of this study suggest that the “growth first” approach results in poor infrastructure outcomes within Southwest Detroit due to disinvestment and exacerbates issues, including legal insecurity, displacement, and pollution. This approach has also brought gentrification to Southwest Detroit, along with a declining sense of belonging and sense of place among Southwest Detroit residents. This study furthers research on the role of gentrification in shaping belonging and attachment to place. Within Detroit, it contributes to expanding conversations about revitalization beyond Black-white relationships by capturing the perspectives of a predominantly Latine community.

Keywords: Gentrification
Urban policy
Belonging (Social psychology)
Text
Thesis
Progress towards a total synthesis of the bis-guanidinium neurotoxin zetekitoxin AB

Zetekitoxin AB (ZTX) is a potent neurotoxin isolated from the endangered Panamanian golden frog, Atelopus zeteki. ZTX belongs to a family of bis-guanidinium toxins, which includes (+)-saxitoxin (STX) and the gonyautoxins (GTXs). ZTX, however, is ~200 times more potent than STX as an inhibitor of voltage-gated sodium ion channels (NaVs). Two structural features are unique to ZTX: a bridging macrolactam and an unusual guanidinium N,O-acetal. Given that the total isolate supply of ZTX is 300 μg, de novo synthesis of ZTX is the only way to enable structure-activity studies and to confirm the assigned structure. To this end, a route to the tricyclic core of ZTX has been developed to enable access to the natural product. This route also provides access to the related bis-guanidinium toxin GTX.

Keywords: Humanities and Sciences.
Chemistry.
Advisor: Du Bois Justin
Burns Noah
Wender Paul A.
Project recon : a computational framework for and analysis of the California parole hearing system

Parole decisions can tip a sentence toward fifteen years or fifty. Despite the great power that parole boards hold, their decision processes are poorly documented and largely hidden from public scrutiny. Parole hearings produce almost no structured data, only an unstructured transcript of hearing dialogue several hundred pages in length. In the following dissertation, we use natural language processing to analyze the transcripts of 35,105 parole hearings held between 2007 and 2019 for candidates serving life sentences in California, totaling approximately five million pages. Through regression analyses of data extracted from the transcripts, after controlling for relevant case factors, we find that several factors outside of the candidate's control explain hearing outcomes. We find that commissioners vary widely in their punitiveness in previously unobserved ways; the assignment to a particular commissioner significantly influences the hearing outcome. Racial disparities limit the quality of legal representation that parole candidates receive as well as their voice in the hearing dialogue, and both significantly predict the parole outcome after again controlling for case factors. Previous analyses of parole systems have been limited by the unavailability of structured data or the task of hand-annotating hearing transcripts. Our results thus provide the most comprehensive picture of a parole system studied to date. While our results carry direct implications for legislative parole reform, our methodology—using machine learning to analyze legal hearings—can be extended to many other procedures in criminal and administrative law with limited structured data

Keywords: Engineering.
Management Science and Engineering.
Advisor: Manning Christopher D.
Ugander Johan
Ashlagi Itai
Goel Sharad