VictorJuiz/thesis huggingface dataset:
Physics and machine learning based approaches to model energy storage systems
Keywords: Engineering.

Energy storage systems ensure the effective and reliable management of power supply and demand. Despite the widespread adoption, energy storage systems face substantial challenges (e.g., safety concerns, limited capacity, environmental impact), stemming from their relatively brief history in academic research and industrial applications. Motivated by these challenges, this thesis explores and develops physics and machine learning based approaches to model energy storage systems, focusing on lithium ion batteries and fuel cells. We first scrutinize the impact of mechanical and thermal factors on lithium plating in lithium ion batteries, employing both 1D and 3D physics-based models. We find that mechanical deformation can accelerate the onset of lithium plating, with folds and boundaries of jellyrolls being particularly susceptible due to distinctive mechanical conditions. Next, motivated by the crucial role of mechanical deformation in lithium plating, we develop SenseNet, a physics-informed deep learning model, for shape sensing applications. SenseNet is crafted to monitor the mechanical deformation of arbitrary structures without relying on prior knowledge of loading conditions, but utilizing a discrete network of strain sensors. Finally, given the complex nature of multiphysics problems and the notable efficiency and accuracy of machine learning methods, we explore and compare diverse data-driven approaches for fuel cell modeling.
Platforming the People’s Perspectives: The Role of Public Comment in Meta Oversight Board Decision-making
Keywords: Social media
Platform governance
Civil society
Content moderation
Meta Oversight Board
Text
Thesis

The Meta Oversight Board has become a hallmark of innovative private governance and platform accountability. This independent, quasi-judicial body created to check Meta’s content policies and practices features some of the world’s most esteemed experts in human rights and governance. Less talked about is its public comment process as a means for engaging those beyond the Board. While there has been much scholarly work on public participation in government processes, there is less understanding of its role within the context of private tech governance and online content regulation. In this thesis, I apply a mixed-methods approach to investigate the role of public comment in Meta Oversight Board decision-making. I undertake 1) a quantitative analysis of public comments submitted to the Meta Oversight Board in conjunction with the outcomes of the Board’s recommendations, and 2) a qualitative analysis of interviews I conducted with internal Board members and staff and civil society stakeholders.
I find that currently, there is no observable relationship between public comment engagement, whether as a whole or from civil society, and the effectiveness of Board recommendations. However, as revealed through my interviews, public comments indeed help the Board build more informed decisions and increase the legitimacy of these decisions. Additionally, internal and external Board stakeholders agree that the barriers to Board engagement, whether via public comment or otherwise, must be lowered to truly foster an inclusive, collaborative engagement process. Finally, I identify key weaknesses in the Board’s transparency efforts when it comes to meaningfully communicating its impact to stakeholders. In sum, my findings suggest a need for the Board to more closely consider how it actualizes its values of transparency, accountability, and accessibility as it pertains to its relationship with external stakeholders, particularly civil society.
Playing children : displacement and the performance of childhood
Keywords: Humanities and Sciences.
Theater and Performance Studies.

Playing Children investigates shifting conceptions of childhood and nation-statehood amidst the backdrop of the escalating global refugee situation. Focusing on plays and films from four contested regions and their diasporic counterparts -- the Pashtun Belt, Syria, India-Pakistan, and Israel-Palestine -- I chart how cultural producers across disparate conditions figure the displaced child towards and against dominant national imaginaries. A relational approach allows me to trace South-South solidarities, even as they unfold in countries like the United States and United Kingdom. Chapter One considers the complex network of actors, affects, and aspirations involved in both the creation and reception of Malala Yousafzai's multiple personas to argue that Malala is best read as a consummate performer. Chapter Two posits that Syrian refugee children are sympathetic to distant Euro-American spectators to the extent they can be conscripted into an "adoptive gaze." In Chapter Three, the repeated trope of a child accidentally crossing the India-Pakistan border reveals an ambivalence around ethno-national identity in the subcontinent that dates to the 1947 Partition. Chapter Four argues that the very notion of childhood becomes entirely discursive and disembodied in the region variably called Israel and Palestine. Ultimately, I find that displaced children exceed the statist, recognition-based frameworks within which both childhood and nation-state belonging are defined, thereby revealing the limits of the concepts themselves. Categories of performance, childhood, and the nation-state constrain while they come apart at the seams; like a child outgrowing their clothes, any imagination of the world as divided by national boundaries, by 'their' children and 'ours, ' no longer fits
Pluripotent stem cell morphogenesis in engineered hydrogels as a model of early human development
Keywords: Engineering.
Mechanical Engineering.

Human embryogenesis involves a series of complex morphogenetic events, wherein human pluripotent stem cells (hPSCs) give rise to all the tissues in the human body. Mechanisms of embryogenesis cannot be studied directly in human embryos due to ethical concerns and technological limitations. Yet, these mechanisms hold the key for taking stem-cell-based therapies to the clinic. To tackle this, there has been a tremendous effort recently toward developing hPSC-based models of the embryo, but current studies lack mechanistic insights as well as reproducibility, due to use of extracellular matrices such as Matrigel which are poorly defined and heterogeneous with limited tunability of matrix properties. In this dissertation, I will first demonstrate the use of engineered alginate hydrogels for studying the impact of hydrogel mechanical properties on hPSC behavior, with hPSCs forming human epiblast-like structures in specific hydrogels. The human epiblast forms a central fluid-filled lumen during embryogenesis which is crucial for proper establishment of the body axes. Using the hPSC-based model of human epiblast, I next unearth the mechanism of epiblast lumen formation. Overall, this body of work demonstrates the power of embryo models and engineered hydrogels, and greatly advances our understanding of the self-organization mechanisms that drive human embryo development.
A plurisubharmonic analogue to Igusa's theorem
Keywords: Humanities and Sciences.
Mathematics.

This dissertation is concerned with removing singularities of plurisubharmonic functions. Igusa demonstrated that there is an h-principle for functions without higher singularities, and later, Eliashberg and Mishachev extended these results. In this thesis, we prove an analogue of Igusa's theorem for families of plurisubharmonic functions on a complex manifold under an additional assumption restricting to the case when all singularities are of corank 1
Polymer materials design for lithium batteries
Keywords: Engineering.
Materials Science and Engineering.

Battery technology is crucial for our modern world, enabling the storage of electrical energy and driving the transition to clean power sources. My research focuses on designing polymer materials to improve battery safety and energy density. I work on non-flammable polymer electrolytes for Li batteries and polymer coatings for the lithium metal anode. Chapter 1 provides background information, while Chapter 2 focuses on a specific safe polymer-based electrolyte design. Chapters 3, 4, and 5 cover the development of polymer coatings. Chapter 6 concludes the research and discusses future prospects
Polymer physics driven design and understanding of biological materials
Keywords: Engineering.
Chemical Engineering.

Polymers make up much of the world around us, from man-made materials like plastics and nylon to natural biological materials like mucus and DNA, and their physical behavior plays a large role in their function. Understanding how a polymer's composition can influence its final bulk physical behavior and function is crucial to creating novel classes of materials for new application spaces. Additionally, in the context of biology, knowing what structure-property relationships exist for biopolymer-based fluids can explain how and when there is a dysfunction. For example, the rheological properties of mucus, which lines every wet epithelial surface of our bodies, are carefully tuned to be a selective barrier to allow nutrients through and keep pathogens out, and small changes in elasticity could lead to infection. Connecting microscopic properties of a polymer chain to the macroscale physics of the polymeric material, however, still remains a challenge. In my dissertation, I present theoretical polymer physics models that link molecular-level parameters to predictions of bulk rheological behavior and combine these models with experimental techniques to develop a physical understanding of polymer-based complex biological fluids. I then demonstrate how this understanding can be leveraged to support certain therapeutic strategies.
POROSITY AND PERMEABILITY CHANGES DURING CYCLIC LOADING AND UNLOADING OF CARBONATE RESERVOIRS FOR GAS STORAGE APPLICATIONS
Keywords: Porosity
Permeability
Loading and unloading
Underground gas storage
Text
Thesis

Underground gas storage is utilized to level seasonal variations in the supply and demand of natural gas, or hydrogen, and provide energy stability and security. In such projects, the reservoir undergoes cyclic injection, storage, and production of the gas. Cycles vary between days to years. Due to the cyclic nature of injection and production from the reservoir, it is important to understand the effect of cyclic loading and unloading, and the accompanying changes in stress, caused by pressurization and depressurization on rock porosity and permeability.
In this report, we have examined the effect of cyclicity on two Texas Cream carbonate samples. To simulate reservoir conditions, we hold the confining pressure constant while loading and unloading the pore pressure within the elastic limits. We ran several cycles for each sample and the porosity and permeability were recorded during the cycle. The results show a slight decrease in the porosity and permeability with cycling. Results also emphasize that mechanical rock damage needs to be considered with the cyclic nature of the gas storage operation as it leads to rock fatigue damage and deformation.
Most of the research in this area focuses on reservoir depletion or one cycle of loading and unloading. Therefore, these results highlight the importance of considering the changes in rock characterization with pore pressure cyclicity. The characterization of the reservoir rock porosity and permeability change is essential for modeling and to set guidelines for better reservoir management.
Potential automorphy for general linear groups
Keywords: Humanities and Sciences.
Mathematics.

We prove potential automorphy results for a single Galois representation of global Galois field with p-adic coefficients. The strategy is to use the p, q switch trick to go between the p-adic and q-adic realisation of a certain variant of the Dwork motive. We choose this variant to break self-duality shape of the motives, but not the Hodge-Tate weights. Another key point to prove is that certain p-adic representations we choose that come from the Dwork motives is ordinarily automorphic. The majority of the work is published on Inventiones Mathematicae volume 231, pages 1239--1275 (2023)
Power, exclusion, and identity : the politics of muslim marginalization in India
Keywords: Humanities and Sciences.
Political Science.

What are the causes and consequences of the political exclusion of marginalized groups? This dissertation studies this question by focusing on the over 200 million Muslims in India, the world's largest democracy. Over the past several decades, India has progressed in the political inclusion of several marginalized groups, including Scheduled Castes, Scheduled Tribes, and women. However, Muslims have experienced no measurable progress and their political and socio-economic condition has deteriorated. Indian Muslims further represent an understudied type of marginalized group: those who do not have institutional protections to help them gain political power. This dissertation studies the origins of the political exclusion of Indian Muslims, when they can break out of this exclusion, and why—even when they break out—they cannot sustain political power. I propose a framework to understand the causes and consequences of Muslim representation that considers how changes among elites and voters from the dominant and marginalized groups can help account for the contours of Muslim political representation in India. First, I theorize that structural factors, political incentives, and party characteristics help explain where, when, and through which parties Muslims can gain representation. Second, I challenge the conventional wisdom that power begets power by arguing that when a Muslim comes to power it can lead to a representation trap where they have a lower likelihood of subsequently winning. I argue that this process is driven by uniting Hindus and dividing Muslims. I test the argument using descriptive and causal analysis of election data across India, an original in-person survey of nearly 5000 Hindu and Muslim voters that contribute to natural and survey experiments, and 150 elite and voter interviews during a combined eight months of in-depth fieldwork. By providing a comprehensive story of Muslim marginalization in India, this project contributes to our understanding of political representation, identity politics, and democracy
The pragmatics of image description generation
Keywords: Humanities and Sciences.
Linguistics.

Finding true words that describe an image seems easy, but it is finding the right words for an image that poses a communicative challenge. Describing an image to make it accessible for someone who can't see it additionally carries a lot of social responsibility when selecting which information to put into words, since those words fundamentally determine what our interlocutor will learn and what they might miss. With recent advancements in computational vision-language models comes an opportunity for building systems that can describe the visual world (e.g., images) for us -- and that instantly. This bears special promise for advancing nonvisual accessibility for people who are blind or have low vision, as their access to visual content online is often restricted to rarely present alt text descriptions. In this dissertation, I argue that the development of vision-language systems that are useful for social needs such as accessibility needs to be centered around pragmatic communicative principles. This includes the datasets we construct, the way we design and train our models, and how we evaluate them. The opportunities are vast, but now is the time where we can start to center computational modeling progress around the intricacies and complexities that make our communication so rich and effective.
Preachers of the great way : the Dharmabhanaka in Mahayana Buddhist sutras
Keywords: Humanities and Sciences.
Religious Studies.

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
Predictive uncertainty of fluid models
Keywords: Sustainability.
Energy Resources Engineering.

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
Principal component analysis under extreme aspect ratios
Keywords: Humanities and Sciences.
Statistics.

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