VictorJuiz/thesis huggingface dataset:
Landscapes of Locura: The Creative Possibilities of Queer Afro-Dominican Gender Performance in Digital Media

"Landscapes of Locura: The Creative Possibilities of Queer Afro-Dominican Gender Performance in Digital Media" dives into the intricacies of queer Afro-Dominican gender performance and its effects on black insurgency in the Dominican Republic through digital performance and creative world-building practices. Here, “queer Afro-Dominican” refers to black and afro-descendent Dominicans who actively queer notions of race, gender, and sexuality through radical performance and embodiments of the fluid identities they hold. With research grounded with a focus on the materiality of queer Afro-Dominicanidad through an analysis of gender performance from three feminine Afro-Dominican musical artists: Tokischa, Gailen La Moyeta, and La Pajarita La Paul, this paper explores themes relating to queer Afro-Dominican liberatory tactics rooted in performance. These three artists and their performance of gendered lyric, imagery, and aesthetics provide a case study for the ways in which embodied identities, desire and intimacy, and performance as autonomy act as entry points into discussions of queer Afro-Dominicanidad and the liberatory possibilities of queer black subjectivities in the Dominican Republic. This is a trek into the uncharted waters of non-normative femininities, imaginative decolonial work, and the demonic grounds where queer Afro-Dominican create spaces of temporal freedoms. Additionally, there is reflection on Landscapes of Locura, an installation that acts as a framework of observing, understanding, and assessing these queer Afro-Dominican performances of gender, is discussed. This use of installation art taps into the world-making practices of queer Afro-Dominicans by creating a physical landscape informed by such practices of freedom, and thus allows for another mode of analysis of queer Afro-Dominicanidad and its performances of gender.

Keywords: Dominican Republic
Afro-Caribbean
Feminism
Queer theory
Gender identity
African diaspora in art
Black people
Caribbean Area
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Thesis
Language, culture, and systems of evaluation : computational analyses of college admissions essays

Each year, high school students from all social strata apply to selective colleges and universities. One well known element to the application process for these schools is the solicitation of personal statements where applicants describe themselves, their interests, their communities, and other information they deem relevant to their college application. Other features of the same applications, specifically standardized test scores, have been consistent sources of controversy for decades given their strong correlations with social class (approximated by household income). This dissertation expands the literature on evaluation, culture, language, and college admissions by shifting the analytical lens onto admissions essays and leveraging tools from computational text analysis. Chapter two does this bluntly: using computationally derived features of admissions essays as predictors, we find that admissions essays are stronger predictors of income than SAT scores but also that the features are very strong predictors of SAT scores. Chapter three considers how students might see applying to college as a time to demonstrate multilingual competence, specifically Spanish, despite long histories of prejudice in K-12 education. And finally, chapter four shows how admissions essays written by transfer applicants are patterned by specific academic pathways (community college students vs. applicants enrolled in four year schools) but also how essay prompts might reduce or exacerbate these differences. Combined, this dissertation provides important clarifications for data science in education as well as the growing interest of computational methods in social science

Keywords: Education.
Advisor: Stevens Mitchell
Antonio Anthony Lising
Domingue Ben
Pearman Francis A.
A laser-driven fiber-optic gyroscope suitable for inertial guidance of transoceanic flights

The performance of commercially available fiber optic gyroscopes (FOGs) is limited by their scale-factor instability and noise, which are both due to high noise and poor wavelength stability of the superfluorescent fiber source (SFS) used to interrogate the FOG's sensing coil. A promising way forward is to replace the SFS with a laser, which has a much lower noise and better wavelength stability. Historically, lasers have not been used in FOGs because their high temporal coherence introduces several dominant sources of noise and drift in the fiber coil. However, recent investigations have shown that laser-driven FOGs can have exceedingly low noise and good scale-factor stability if the coherence of the laser is suppressed by broadening the laser linewidth with external phase modulation. In this thesis, we develop a new analytical model that derives the error in an aircraft's position from a gyroscope's known noise and drift, inferred from a measured Allan deviation of the sensor's output time trace. Using this model, we derive the noise and drift required to satisfy the Federal Aviation Administration's requirements for a trans-Pacific flight. We also developed a complete model of the coherence of a laser broadened by wideband phase modulation and use this model to optimize the coherence suppression. With most of the coherent effects suppressed by an optimally modulated laser, we performed one of the most exhaustive studies on the sources of drift conducted on a laser-driven FOG. In the process, we identified four dominant sources of drift, and implemented a variety of suppression techniques to reduce them. As a result, the long-term stability of the 3-km FOG tested in this research was improved by a factor of 30 relative to the previous world-record laser-driven FOG. This FOG exhibited a noise of 0.9 mdeg/√hour and a record-breaking drift of 12.7 mdeg/hour (characterized using the Allan-deviation maximum). The measured output of this FOG was used to simulate 20 10-hour trans-Pacific flights of an aircraft guided solely by inertial sensors. 95% of the flights landed within ±10 nmi of their intended destination. To our knowledge, this is the first report of a laser-driven FOG that meets the Federal Aviation Administration's criterion for aircraft navigation

Keywords: Electrical Engineering.
Advisor: Digonnet Michel J. F.
Kahn Joseph M. (Joseph Mardell)
Miller D. A. B.
Learning and incentives in waitlist mechanisms

This dissertation is motivated by the deceased donor kidney allocation system in the United States. Through stylized models, I develop theories to examine the interplay of learning and incentives in such allocation markets. In Chapter 1, I consider a mechanism design problem where a social planner needs to decide whether and how to allocate a single object to a queue of strategic and privately informed agents. Using tools from operations research and voting literatures, I propose a simple mechanism that can effectively crowdsource agents' private information by balancing their strategic incentives and planner's learning goal. In Chapter 2, I add inter-temporal dynamics to study the equilibrium and optimal strategy in the presence of both dynamic incentives and observational learning in first-come-first-served waitlists. On the one hand, the availability of object implies preceding agents' remaining on the waitlist, which incentivizes agents to accept. On the other hand, it may also imply negative object quality via observational learning, incentivizing agents to reject. I show how these opposing forces come into play. Finally, in Chapter 3 I develop machine learning models to predict hard-to-place kidneys based on donor characteristics and signals from waitlist candidates

Keywords: Management Science and Engineering.
Advisor: Ashlagi Itai
Lo Irene (Management science professor)
Saban Daniela
Learning compositional and actionable visual representations for 3D shapes

Humans can easily accomplish a wide variety of daily household tasks by perceiving, understanding, and interacting with diverse 3D objects in unstructured 3D environments. It has been a long desire for vision and robotics researchers to build intelligent agents with human-level visual perception and dexterous manipulation skills. With the emergence of modern machine learning, especially deep learning techniques, the past decade has witnessed substantial progress in the field of 3D vision and robotics. However, today's vision and robotics systems still fall behind with what humans can achieve when generalizing to new tasks and environments. The representation of 3D objects, or 3D shapes, is one of the cornerstones of human-level 3D vision and robotics systems. This is notoriously challenging given that 3D shapes have extraordinarily diverse geometries, rich functional semantics, and complicated part structures. In the meantime, the complex nature of 3D tasks in vision, graphics, and robotics is another challenge we are facing. To this end, this thesis explores to tackle the core problems of building effective visual representations for diverse 3D shapes and designing scalable learning frameworks for various 3D tasks. This thesis focuses on building compositional and actionable visual representations of 3D shapes that can process large-scale 3D shapes and tackle various downstream 3D tasks. It is primarily structured in two parts. The first part introduces a line of compositional representations of 3D shapes for 3D vision and graphics tasks. Here, compared to prior 3D representations, smaller, simpler, and more reusable subcomponents of 3D geometry and functionalities in an object, such as different parts of an object, are discovered and assembled to reduce the complexity of 3D data. The second part further discusses actionable representations of 3D shapes to address the task complexity issue, especially when applied to robot manipulation tasks. Such actionable representations are learned using simulated interactions in a self-supervised manner, benefiting from scalable and inexpensive data generation in simulation. The thesis is then concluded with a discussion on the future work and directions toward a more general large-scale representation learning framework for 3D shapes

Advisor: Guibas Leonidas J.
Bohg Jeannette
Savarese Silvio
Learning from imperfect demonstrations

Imitation learning is one of the most promising robot learning paradigms, which attempts to learn robot policies from demonstrations. Standard imitation learning algorithms assume that the demonstrations are provided by optimal experts who are capable of perfectly performing the task on the robot of interest (i.e., the target environment). However, under these assumptions, imitation learning usually requires large amounts of demonstrations. This is limiting in many environments, where collecting optimal demonstrations is difficult due to various reasons such as difficulty of controlling robots with high degrees of freedom or limited interactions with the environment. In practice, we often have access to large amounts of imperfect demonstrations, which are possibly not optimal or are produced by different agents with different morphologies or dynamics. Such imperfect demonstrations contain valuable information that can be helpful for learning the optimal policy. However, directly imitating these imperfect demonstrations will lead to learning a suboptimal policy. Instead of direct imitation learning, we propose developing new algorithms to utilize these imperfect demonstrations towards learning an optimal robot policy. In this thesis, we categorize imperfect demonstrations into: i) suboptimal demonstrations, ii) cross-domain demonstrations, and iii) infeasible demonstrations. Suboptimal demonstrations often contain non-optimal sequence of states and actions. For example when reaching an object, the robot might take a longer path towards the goal. Cross-domain demonstrations are collected from agents with different morphologies or dynamics, but such demonstrations can still have correspondence to behaviors of the target agent. Finally, infeasible demonstrations are drawn from other agents that might not have any correspondence to the target agent. Prior works in learning from imperfect demonstrations only focus on one of these categories of imperfect demonstrations. In this thesis, we comprehensively address the problem of learning from imperfect demonstrations: We formalize the different categories of imperfect demonstrations and introduce a set of robot learning algorithms that tackle each category when learning from these demonstrations. We will further discuss under what assumptions each of our methods should be used with imperfect demonstrations. We conduct experiments in a number of robotics manipulation tasks in simulation and real to demonstrate the developed algorithms

Advisor: Sadigh Dorsa
Finn Chelsea
Rosman Guy
Learning in the forest : an exploration of Waorani education and its interaction with formal state schooling in the Ecuadorian Amazon

In this dissertation I seek to build upon global research examining differences between formal education and local learning systems in minority cultures like Indigenous Nations, Amazonian research focused on drivers of cultural change, and past anthropological research describing Waorani pedagogy and the impacts of formal State education. Across three chapters, I explore the Waorani system of learning by examining how they organize information, how they learn in and navigate through a landscape infused by magic and constrained by threats of danger, and how, where and when they learn on days with and without school. I explore Waorani mental concepts or "schema", epistemology, and the timing and spatial distribution of local and school learning experiences, while simultaneously contributing to anthropological literature more broadly by calling into question assumptions about the inefficacy of hunter-gatherer classification systems and the everyday vulnerability of perspective taking in Amazonian societies. Finally, I suggest that timing could be an essential element to consider when examining access to local learning contexts and seeking to help youth develop successful multicultural identities according to local objectives

Keywords: Anthropology.
Advisor: Durham William H.
Luhrmann T. M. (Tanya M.)
Ardoin Nicole M. (Nicole Michele)
Seetah Krish
Learning Multimodal Rewards from Rankings

Learning from human feedback has been shown to be a useful approach in acquiring robot reward functions. However, expert feedback is often assumed to be drawn from an underlying unimodal reward function. This assumption does not always hold, including in settings where multiple experts provide data or when a single expert provides data for different tasks. We thus have to go beyond learning a unimodal reward and focus on learning a multimodal reward function. We formulate the multimodal reward learning as a mixture learning problem and develop a novel ranking-based learning approach, in which the experts are only required to rank a given set of trajectories. Furthermore, as access to interaction data is often expensive in robotics, we develop an active querying approach to accelerate the learning process. We conduct experiments and user studies using a multi-task variant of OpenAI's LunarLander and a real Fetch robot, where we collect data from multiple users with different preferences. The results suggest that our approach can efficiently learn multimodal reward functions, and improve data-efficiency over benchmark methods that we adapt to our learning problem.

Keywords: Computer Science
HRI, reward learning, multi-modality, rankings, active learning
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Thesis
Learning Object-Centric Visual Representations for Common Sense Reasoning

People understand the world as a sum of its parts. Our effortless mental ability to simulate and imagine what will happen crucially depends on a scene representation that is compositional with respect to objects and the interactions between them. Similarly, learning representations with an emphasis on the perception and understanding of objects plays a key role in building human-like AI capable of supporting higher-level cognitive abilities such as common sense reasoning, causal reasoning, and goal-oriented planning. Many current methods in machine learning focus on learning structured representations in which objects are only implicitly represented, which poses a threat to model interpretability. Motivated by these observations, this work aims to develop algorithms for learning object-centric representations in which objects are explicitly represented. Given the importance of objects in human cognition, we draw inspiration from cognitive science to motivate and provide theoretical underpinnings. Specifically, we take a rationalist approach in augmenting deep neural network architectures to exhibit object-centric representations, and demonstrate empirically how these representations can be efficieintly leveraged for downstream tasks such as controllable image synthesis.

Keywords: Machine learning
computer vision
cognitive science
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Thesis
Learning preferences for interactive autonomy

When robots enter everyday human environments, they need to understand their tasks and how they should perform those tasks. To encode these, reward functions, which specify the objective of a robot, are employed. However, designing reward functions can be extremely challenging for complex tasks and environments. A more promising approach is to learn reward functions from humans. Recently, several robot learning works embrace this approach and leverage human demonstrations to learn the reward functions. Known as inverse reinforcement learning, this approach relies on a fundamental assumption: humans can provide near-optimal demonstrations to the robot. Unfortunately, this is rarely the case: human demonstrations are often suboptimal due to various reasons, e.g., difficulty of teleoperation, robot having high degrees of freedom, or humans' cognitive limitations. This thesis is an attempt towards learning reward functions from humans by using other more reliable data modalities. Specifically, we study how reward functions can be learned using comparative feedback, in which the human user compares multiple robot trajectories instead of (or in addition to) providing demonstrations. To this end, we first propose various forms of comparative feedback, e.g., pairwise comparisons, best-of-many choices, rankings, scaled comparisons; and describe how a robot can use these various forms of human feedback to infer a reward function, which may be parametric or non-parametric. We discuss the pros and cons of each comparative feedback modality in detail, and show how such feedback enables us to outperform standard inverse reinforcement learning that only utilizes demonstrations. To increase data-efficiency, we also propose active learning techniques to enable the robot to ask for comparison feedback that optimizes for the expected information that will be gained from that user feedback

Keywords: Electrical Engineering.
Advisor: Sadigh Dorsa
Brunskill Emma
Finn Chelsea
Learning to be a human alongside the more-than-human world : navigating information, morality, and sense of self in a social and environmental context

Humans are unique in our cognitive capacity to understand the nuances of how complex systems function, our ability to attach social, cultural, and symbolic meaning to elements of those systems, and our propensity to self-reflect and experience emotions about our roles in them. These abilities make our lives richer, but also more complicated, as they mean that navigation of the world involves not only procurement of the resources we need to survive but also cognition about those processes, and integration of reasoning, morality, and emotion into our choices. This dissertation examines the processes by which humans navigate the agency and responsibility that come with our human gifts-how we develop ideas about what it means to be a human alongside the more-than-human world, and how those ideas manifest in narratives we create and behaviors we adopt. In Chapter 1, I use semi-structured interviews with visitors to a local farmers' market to explore how people navigate the vast and complicated information landscape to develop mental models of how systems around us work, and how and when we apply those models to real-world decision-making. Chapter 2 focuses more deeply on the role of identity and morality in our understanding of how to interact with the world, using a set of interviews with office workers about their use of paper towels in public restrooms to reveal the various ways we narrate, or rationalize, our quotidian consumption-related behavior. Using video data from cameras worn or carried by hikers in a redwood forest, Chapter 3 explores how the emotional experience of awe in nature influences both how we understand our role in the larger world and how we construct and update our mental models in the face of awe. Taken together, these chapters provide insight into the cognitive and emotional processes that underlie or explain our choices about how to navigate the world. They may inform behavioral and/or educational interventions to help us make more environmentally, but also more psychologically sustainable, choices in a complex world

Advisor: Algee-Hewitt Mark
Ardoin Nicole M. (Nicole Michele)
Gardner C. D. (Christopher David)
Williams Dilafruz R.
Learning to generate data by estimating gradients of the data distribution

Generating realistic data with complex patterns, such as images, audio, or molecular structures, often relies on expressive probabilistic models to represent and estimate high- dimensional data distributions. However, even with the power of deep neural networks, building powerful probabilistic models is non-trivial. One major challenge is the need to normalize probability distributions; that is, to ensure the total probability equals one. This necessitates summing over all possible model outputs, which quickly becomes impractical in high-dimensional spaces. In this dissertation, I propose to address this difficulty by working with data distributions through their score functions. These functions, defined as gradients of log data densities, capture information about the corresponding data distributions without requiring normalization, hence can be modeled with highly flexible deep neural networks. This dissertation is organized into three parts. In Part I, I show how to estimate the score function from a finite dataset with expressive deep neural networks and efficient statistical methods. In Part II, I discuss several ways to generate new data samples from models of score functions, building upon ideas from homotopy methods, Markov chain Monte Carlo, diffusion processes, and differential equations. The resulting score-based generative models (also known as diffusion models) achieved record-breaking generation performance for numerous data modalities, challenging the long-standing dominance of generative adversarial networks on many tasks. Importantly, the sampling procedure of score-based generative models can be flexibly controlled for solving inverse problems, demonstrated by their superior performance on multiple tasks in medical image reconstruction. In Part III, I show how to evaluate probability values accurately with models of score functions. Taken together, score-based generative models provide a flexible, powerful and versatile solution for data generation in machine learning and many other disciplines of science and engineering

Advisor: Ermon Stefano
Hashimoto Tatsunori
Ma Tengyu