VictorJuiz/thesis huggingface dataset:
Power and the void : representations of sovereignty in unperformed plays of the Stalinist period
Keywords: Slavic Languages and Literatures.

This dissertation explores the relationship between sovereign power, its subjects, and the theatrical form through four unpublished and unperformed (or barely performed) plays from the time of Stalin's reign. In analyzing these plays, I approach sovereign power as, above all, a performance. This framing allows for the consideration of the position of the sovereign as a role to be inhabited, just as an actor on the stage gives life to their character. In light of this approach, the dramatic form becomes an ideal medium through which to meditate on how power-performances function: how they succeed, their varied impacts on their subjects, and also what weaknesses they may possess. The four plays featured in this project present a variety of modes through which to understand sovereignty and subjecthood. In some, the fleeting nature of performance and theatrical presence is emphasized, and sovereigns are depicted as ghostly phantoms who cannot generate the physical substance necessary for their rule to endure. In others, the elusive, intangible nature of the sovereign and the structures that surround him are shown in a more menacing light, as subjects can be trapped and seized by this power without a chance to resist. Placed together, these plays present a complex depiction of how theater can illuminate the power dynamics and struggles between sovereigns and subjects. Additionally, these plays are not prominent in their authors' respect oeuvres; this project thus sheds light on lesser-studied but textually and dramatically rich works of the Stalin era.
Predicting the Spatial Distribution of Snow Water Equivalent using Transfer Learning
Keywords: Mital, Utkarsh
Snow water equivalent
Transfer learning (Machine learning)
Deep learning (Machine learning)
Factor analysis
Text
Capstone
Report
Thesis
Student project report

Globally, water resources are under increasing stress that risks both water availability and quality. Hydrologic and watershed models have been used to forecast both water flow and quality. Furthermore, these models serve as decision making tools for sustainable water management. These models are subject to high uncertainties that can be reduced by accurate estimates of high-resolution spatial distribution of Snow Water Equivalent (SWE). The Airborne Snow Observatory generates Lidar maps that quantify SWE at a 50 m resolution. Although these maps provide detailed information, their infrequent temporal availability limits their use for modeling. For instance, in the Rocky Mountains of Colorado, only 12 maps are present in total for the period covering 2016 to 2019. In this work, we develop a neural network (NN) framework to model this temporally sparse Lidar data, which enables the estimation of SWE at unmapped time points in the Rocky Mountains of Colorado. To achieve this, we first incorporate meteorological and topographic data that describe the physical processes governing SWE. This data is used to extract seven predictor variables: accumulated snow, accumulated precipitation, mean seasonal temperature, the sum of positive degree days, elevation, slope, and aspect. Next, we tackle data scarcity by using transfer learning to integrate information from 80 Lidar maps in the Sierra Nevada mountains in California. An explanatory factor analysis is conducted to justify and reinforce the use of transfer learning. Results show that our NN framework, using the seven predictor variables, models SWE with a mean R2 of 0.44 prior to transfer learning. After applying transfer learning, we improve the mean R2 results to 0.56. Overall, transfer learning increases model predictability while decreasing both scatter and bias.
Preliminary Insights on TRAPPIST-1's Planetesimal Disk Architecture from N-Body Simulations
Keywords: TRAPPIST, N-body, exoplanet, habitability
Text
Thesis

One of the driving forces of modern astronomy is to detect and image habitable Earth-sized exoplanets to better understand the requirements for life and, by extension, our place in the universe. Recently, the TRAPPIST-1 system has become the focus of intense study because three of its seven Earth-sized terrestrial planets lie within the habitable zone of an ultra-cool red dwarf star, where water — if present at the surface — could be liquid. The orbital periods of all seven planets in the TRAPPIST-1 system are observed to be in small-integer ratios, with all seven planets linked via three-body resonances. Through N-body simulations, we use the long-term stability of the system and survivability of the resonance chain to constrain the feasible architecture of a theoretical planetesimal belt, in turn improving our understanding of potential volatile delivery to the planets within the habitable zone. Here we use the open-source REBOUND package to first improve on previous dynamical studies of the TRAPPIST-1 system with refined orbital parameters, and then introduce a custom collision model that can extract relative impact velocities at times of collision. We then show that planetesimal impactors preferentially target the outermost planet h, and most collisions occur in the first tens of thousands of Earth years of integration time. Our results imply that a theoretical planetesimal belt would preferentially affect the outermost planet on short timescales and initiate migration of the outer planets, potentially disrupting system stability. These N-body simulations contribute to long-running conversations regarding volatile transport via planetesimal impactors and the habitability of the TRAPPIST-1 system.
Preschool-Aged Children Can Infer What Speakers Know Based on How They Influence Others
Keywords: Cognitive Development
Social Cognition
Theory of Mind
Child Development
Text
Thesis

How do we know what others know? Prior work has examined how children use evidence about isolated agents, like their perceptual access and actions, to infer what they know. However, humans are rarely fully isolated; instead, we are often surrounded by others whom we interact with, influence, and are influenced by. In these contexts, we can use a speaker’s communication and the way it causes a listener to behave to infer what that speaker knows - even if we do not know the specific content of what was communicated. The present studies investigated how preschool-aged children use two pieces of evidence about listeners to reason about what speakers know: changes in the outcomes of a listener’s actions following communication (Study 1) and changes in a listener’s actions themselves following communication (Study 2). In both studies, children observed two scenarios where a listener failed to activate a toy before succeeding. In Study 1, children observed a speaker produce nonsense language towards a listener after they failed but before they succeeded to activate a toy, as well as another speaker who spoke to a listener prior to initial failure. In Study 2, children observed a speaker communicate with a listener before a distinct change in action, followed by success, as well as another speaker who communicated with a listener resulting in no distinct change in action, followed by success. When asked which speaker knows how to make the toy work, 5 year-olds chose the speaker who appeared to cause the listener to succeed (Study 1) or change their action (Study 2). These results suggest that preschool-aged children are sensitive to the way speakers influence others via communication and can use evidence of that influence to infer what speakers know. More broadly, these studies highlight children’s ability to reason about the knowledge of one agent (a speaker) based primarily on evidence about another agent (a listener).
Prestige, Preference, and Preservation: Language Policies for Nahuatl and Guarani, from Empire to Nation
Keywords: Language policy
Mexico
Paraguay
Indigenous peoples > Languages
Nahuatl language
Guarani language
Text
Thesis

The Spanish colonization of the Americas has ultimately created a situation where no indigenous language is favored. In this thesis, I provide a comparative history of the language policies governing two indigenous languages, Nahuatl and Guarani, from the colonial period to the present. These languages were chosen because of their historic and demographic significance; Nahuatl was the language of the Aztec Empire and Guarani still remains the majority language of Paraguay. While much has been written about both languages, their histories have not been compared. My research was purely bibliographical. I consulted primary and secondary sources in English, Spanish, Nahuatl and Guarani; the thesis is a synthesis of these various histories, historical documents, and laws. My research has shown that Nahuatl and Guarani were subjected to similar policies, even after the independence of Mexico and Paraguay. In fact, Guarani’s policies were even less tolerant than Mexico’s at certain points in history. However, Nahuatl and Guarani’s policies were not always intolerant. Bans on both languages were lifted, relaxed, and overlooked by those in power many times when these languages were useful in gaining popular appeal. Even in times of official intolerance, there were impracticalities in enforcing such policies, depending on the situation of each country. Therefore, policy alone cannot account for the current disparity between each language’s vitality. Understanding the history of each language can allow us to identify the key actions for reversing language shift.
Prior-informed machine learning for biomedical imaging and perception
Keywords: Electrical Engineering.

Deepening our understanding of human health is more important than ever before for addressing real-world challenges in biomedicine and healthcare, especially with the recent pandemic. My research focuses on AI in medicine, to develop efficient ML models for biomedical imaging and perception for addressing clinically important problems. In this thesis, I will first explore the challenges in this emerging field and then present the two majors lines of my PhD research work. First, I will introduce my work on AI in biomedical imaging. Specifically, I will discuss how to integrate different kinds of prior knowledge to develop reliable data-efficient ML models, by exploiting the personalized priors, population priors and physics priors. With the prior-informed ML models, the proposed approaches can be applied to various applications including sparse-sampling CT and MRI image reconstruction, X-ray projection synthesis, and Cryo-EM imaging. These techniques show significant potential to impact cancer imaging and treatment. Second, I will introduce my work on AI in image perception. I will discuss how to develop ML-driven perception models that can adapt to the unique characteristics of biomedical data. Specifically, I will present a self-attention-guided ML model for quantitative image perception of fetal brain MRI images. This includes a cross-institute validation involving four U.S. clinical centers and a Turkish institute. This work demonstrates the ability of the developed ML model to characterize in utero neurodevelopment for anomaly detection in real-world clinical deployments
Privacy in the COVID-19 Pandemic: Analyzing the Impact of the Length of Data Privacy Law Existence on Variation in COVID-19 Contact Tracing Apps
Keywords: Law
contact tracing apps
data privacy
Center on Democracy Development and the Rule of Law
Text
Thesis

The rapid creation of contact tracing apps at the beginning of COVID-19 is unprecedented given that 109 countries adopted these apps in the span of just a few months. Governments turned to contact tracing apps to speed up the process of manual contact tracing, piloting a system that had never been used before. This was often presented as a solution to the pandemic, but these apps came with the innate drawback of collecting sensitive personal information. This data collection gave governments an opportunity to use contact tracing apps as a way to expand surveillance of their populations. There are many concerns that this surveillance will persist after the end of the pandemic, which poses a significant risk to privacy and civil liberties at large. In this thesis, I seek to understand how the length of time that a country has had a data privacy law impacts the implementation of its contact tracing app. I find that the duration of a data privacy law has no impact on a country’s contact tracing app implementation. Instead, country-specific factors play a role. I look at the cases of Australia and South Korea, as they both have had a data privacy law for a long period of time but implemented their contact tracing apps in very different ways. I argue that Australia’s previous mishandling of digital projects and South Korea’s experience with the 2015 MERS outbreak alongside the government’s skillful deployment of technology shaped how the two countries designed their contact tracing apps.
Private data analysis beyond the worst case
Keywords: Electrical Engineering.

Differential Privacy (DP) provides strong guarantees on the risk of compromising a user's data in statistical and machine learning applications. However, privacy-preserving algorithms often suffer considerable loss in accuracy compared to non-private algorithms, both in theory and practice. In this thesis, we investigate and study several approaches towards improving the cost of private algorithms and closing the gap between private and non-private performance. While there exist many privacy-preserving algorithms with optimality certificates, most of these hold only in the worst case setting; that is, the performance of the algorithm is measured by it's performance on the worst-case input. Therefore a (worst-case) optimal algorithm may not be achieving the best possible performance for certain functions with more natural inputs. Building on this observation, we propose three general directions for designing algorithms that can improve the privacy cost and outperform existing (worst-case) optimal algorithms. In our first approach, we move beyond worst-case optimality and define a new notion of instance-optimality in differential privacy. Instance-optimal algorithms adapt to the difficulty of the underlying input and therefore result in improved performance for natural inputs. We develop an algorithm, namely the inverse sensitivity mechanism, which is nearly instance optimal and present several instantiations for statistical and machine learning applications such as median estimation, regression problems, and principal component analysis. As our instance-optimal algorithm may be computationally intractable in some settings, our second approach moves beyond worst-case optimality by identifying natural properties of the inputs that hold in practice. This will allow to (i) design efficient and optimal algorithms for these families, and (ii) improve the rates compared to worst-case inputs as these inputs do not satisfy the properties we consider. Our final approach builds on the observation that differential privacy may be too stringent for some use cases. To address this, we propose element level differential privacy, which extends differential privacy to provide protection against leaking information about any particular ``element'' a user has, allowing better utility and more robust results than classical DP. By carefully choosing these ``elements, '' it is possible to provide privacy protections at a desired granularity and result in better utility
Probing extreme states of matter using high resolution inelastic X-ray scattering at hard X-ray free electron lasers
Keywords: Aeronautics and Astronautics.

This dissertation focuses on the investigation of the stiffening of the interatomic potential of a gold lattice under intense optical irradiation. Even though this exotic behavior, predicted by theory, has been investigated experimentally using electron diffraction, its observation is still debated. Here, nanometer-thick foils of gold have been irradiated using a femtosecond optical laser pulse to energy densities higher than previously reported values and subsequently probed using X-rays. At these conditions, the response of laser-excited gold was found to be consistent with theoretical predictions. To go beyond the previous measurement, an experimental platform based on inelastic X-ray scattering at an X-ray Free Electron Laser is presented to circumvent the need for a theoretical model and thus bring an unambiguous observation of this exotic phenomena in laser-excited gold. Such observation would be important to deepen our understanding of the melting behavior of gold under extreme conditions such as the ones generated during ultrafast laser processing of materials
Processes that shape the avian gut microbiome
Keywords: Biology.

Animals harbor and maintain a diverse assemblage of bacteria, viruses, fungi, and other eukaryotic organisms that reside and interact at a particular body site. The net effect of these interactions, most notably in the gut, is responsible for mediating host health. The gut community consistently demonstrates its importance in animal hosts. However, most studies focus on humans or model organisms. As such, I applied the techniques developed in microbiology to wildlife, particularly avian hosts. In my dissertation, I independently developed research questions, acquired funding for projects, and established collaborations with experts in various fields to investigate: (1) how land-use change influences Neotropical passerine birds, (2) how dietary guild affects the gut microbiome of a diverse group of bird species, (3) how captivity changes the Brown Kiwi (Apteryx mantelli) gut microbial communities, and (4) how the gut microbiome assembles and changes throughout Brown Kiwi development. In the first chapter, I quantified the impact of land-use change on six species of Neotropical passerines and their gut microbiomes (San Juan et al. 2020). I found bacterial communities responded more strongly to host species identity than to the habitat type, where sites ranged from primary forests to intensified coffee farms. However, the microbiomes of two out of the six bird species were modified, suggesting some bird species may be more vulnerable to land-use change than others. In chapter 2, I assessed how host species and family, dietary guild, habitat, and migratory status contribute to the gut bacteria of 79 bird species. I found host species identity to be the strongest predictor of the gut microbiome, followed by dietary guild. Migratory status had little influence on the gut microbiota. In chapter 3, I investigated how captive rearing, a practice crucial in the recovery of endangered species, impacts Brown Kiwi gut bacteria and fungi. I found a significant drop in alpha diversity and shifts in community composition between wild and captive kiwi (San Juan et al. 2021). To further elucidate captivity's influence on the gut microbiome, I performed a comprehensive longitudinal study on captive kiwi to explore the temporal dynamics of their gut microbes. I found that the gut microbiome fluctuates day-to-day, especially early on in life. Age was a stronger predictor of gut microbiome variation compared to a common disease (coccidiosis). Overall, my research considers host identity, life history traits, and environmental factors including their habitat as explanatory variables of wildlife gut microbiomes. I generated datasets that emphasize the importance of the gut microbiome, an often neglected aspect in conservation practices. Understanding the factors that contribute to avian gut microbial communities may highlight areas we have not considered in other host species
The production, retention, and measurement of teachers
Keywords: Education.

This dissertation is comprised of three papers relating to the American public school teacher work force. I use large scale records covering millions of teachers to ask: 1) How many teachers are quitting their jobs, and can we use public records to better answer this question? I assemble public records to create a database that includes individual teacher records from 13 states covering roughly 35\% of American public school teachers. I find that across states between 10 and 15 percent of teachers stop teaching in their district in a given year, and also that there is substantial variation across districts in that rate. I discuss implications for future research from the widespread availability of individual level, longitudinally linked, teacher employment data. 2) When Oklahoma boosted teachers' salaries by \$6,000, did their retention rates improve? Descriptive time series analyses in Oklahoma find no noticeable change in trend for turnover rates. I use a difference in difference analysis to compare district turnover rates in Oklahoma to Texas and Kansas, and can rule out a change as small as 0.72 percentage points. This lack of effect is robust across multiple specifications and comparison groups. 3) If we change the passing standards on a teacher licensure exam, will that change who attempts to take the test? Given the current concerns about increasing the number and diversity of new teachers, policy makers are re-examining the role of teacher licensure tests. I use difference in difference techniques to compare who shows up to take a teacher licensure exam before and after the passing "cut score" is changed. I find suggestive evidence that when standards are lowered from high levels, the share of Black candidates and candidates from alternative preparation programs increases. However, an analysis of 32 cut score change events, most of which were small changes, did not find a consistent relationship between the cut score change and changes in the composition of test takers
Progress on the practical integration of cryogenics to gravitational-wave detectors
Keywords: Physics.

The success of the LIGO and Virgo observatories has launched a new era of gravitational-wave astronomy. Despite their achievements, there is still plenty of room for improving the technologies that have made gravitational-wave detection possible. One avenue for improvement comes in the form of using silicon masses cooled to 124 Kelvin to reduce the thermally driven noise in the midband of the observatories. This idea, while promising, carries a lot of technical challenges that must be overcome before it can be put to practice. This work covers investigations on some practical aspects of integrating cryogenics to gravitational-wave interferometers, including estimation of technical noises introduced by cryogenic equipment---such as Newtonian or scattered light noises---selection of a proper cryogen, and adequate procedures for faster iteration times for research and development of the cryocooled interferometer technologies