VictorJuiz/thesis huggingface dataset:
The construction of nature and social categories

The idea that something "natural" is better than something not natural is regularly used to make decisions and justify beliefs in American life, in contexts as diverse as deciding what to buy in a grocery store to advocating for the righteousness of the gender binary. This dissertation identifies ways that concepts of nature and social categories (e.g. gender, race, and age) are socially constructed, and explores their consequences for inequality. It consists of three separate empirical papers. In the first, I present a theoretical framework of the cultural components of the cultural valuing of nature and naturalness. I argue that the idea that natural options are better consists of at least three separate cultural products: first, the "naturalness schema" that defines some things as natural and some as unnatural; second, the "natural-is-better cultural logic" that positions natural options as morally and instrumentally better than non-natural ones; and third, "appeal to nature frames" that cultural actors use to render meaning and make persuasive claims using the naturalness schema and natural-is-better cultural logic. Through a series of nationally representative surveys, I find that the idea that natural options are better can be understood as a cultural logic that helps people make decisions across myriad aspects of life. I further find that one consequence of this logic may reinforce an unequal status quo, thereby contributing to the maintenance of inequality. In the second empirical paper, I explore ways that nature is mobilized in cultural conversations through an analysis of an original database of 5700 print magazine advertisements. I find that there are two common and distinct strategies by which nature is used to frame persuasive messages: appeals to the outdoors and appeals to natural properties. These two types of appeals are associated with images that construct notions of rugged individualist masculinity and authentic care-focused femininity. In this way, appeals to the concept of nature may help reinforce the gender binary and harmful gender stereotypes that can reinforce gender inequality. The third empirical paper examines a different type of social process related to the construction of social categories: the ways that common American first names signal age, gender, and race. It shows that names are strong signals of all three types of social categories, and that people's perceptions of a name's age do not closely match the true population distribution. Together, these papers answer and raise empirical and theoretical questions related to the roles of social constructs of nature and social categories in processes of inequality

Keywords: Humanities and Sciences.
Sociology.
Advisor: Freese Jeremy
Saperstein Aliya
Ridgeway Cecilia L.
Contagion or Coincidence? Pacific Island Independence Movements Under the British Empire

Starting in 1970, the British Empire in the Pacific began to collapse, and within a decade, it was entirely dismantled. Compared to the remaining American and French colonial empires in the Pacific, this timeline is unique. Literature largely credits the rapid decolonization of British dependencies in the Pacific to British interests and their decision-making processes. There is only limited discussion of how these islands may have affected each other and influenced this rapid period of decolonization. My research seeks to answer the following question: To what extent did Pacific Islands influence each other’s decolonization movements, timing, and processes? This thesis pursues this question by closely examining two case studies: Fiji and New Hebrides. More specifically, I analyze their independence journeys through the lens of the first elected political leaders, who were also among the first advocates for independence during these formative years of state-building. Using rich archival material from the U.K. National Archives, Australian National University, and National Library of Australia, I examine the political leaders’ personal writings and identify specific sources of influence in the years leading up to Fijian and New Hebridean independence. My research finds that Pacific leaders and their independence movements drew inspiration from prior colonial dependencies that shared some significant deterrent to independence, for example ethnic division, and these precedents were often found outside the region. I find limited evidence of Pacific Islands influencing each other’s decolonization processes and movements, despite their close proximity.

Keywords: British Empire
Oceania
Autonomy and independence movements
Pacific Islands
Colonial legacy
Politics and government
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Thesis
Advisor: Rakove Robert
Contexts and contradictions : reasoning with science for literature-based drug repurposing

Many diseases, especially rare or emergent diseases, lack therapeutic options leading to grim outcomes. When time or money is limited, drug repurposing, using existing drugs in the pharmacy to treat different diseases than those for which they were developed, may be the only feasible option. The trouble is identifying which drugs might be effective for which diseases when human experiments are impossible. One strategy for repurposing is by piecing together the biological puzzle pieces of knowledge about interactions between drugs, proteins, genes, and diseases. These pieces exist scattered across biomedical research, and can get lost as literature in the field proliferates exponentially. Advances in AI, natural language processing, and knowledge graph methods can help us distill knowledge into a form that scientists can use to connect the dots and predict pre-clinical opportunities for drug repurposing in silico. Reasoning directly with scientific knowledge yields multiple challenge, which I address in this thesis. I present work on (1) framing the task of generating repurposing hypotheses using knowledge extracted directly from literature, (2) elucidating contradictory research claims in a domain rife with contradictions—COVID-19 drug treatment efficacy, and (3) augmenting extracted relational knowledge with essential biological cell type and tissue contexts. I present novel data and methods for addressing complexities when reasoning over scientific knowledge for drug repurposing

Keywords: Medicine.
Biomedical Informatics.
Advisor: Altman Russ
Musen Mark A.
Potts Christopher
Contextual grounding in human-computer dialogue

Context is fundamental to human communication, shaping the meanings of our expressions and enabling the efficient and effective transmission of information. In the sphere of human-machine interaction, contextual grounding is paramount for facilitating interactions that feel as natural and intuitive as human dialogue. This research delves into the mechanisms through which machines can extract contextual information from a variety of sources: a conversational partner, a history of interactions, external world knowledge, and cooperative repair. We begin by introducing a model that optimizes speech with respect to an internal model of their conversational partner. This approach serves as an amortization of recursive social reasoning, where dialogue is shaped by considering a conversational partner's contextual understanding. This approach captures the nuances of pragmatic language use and provides good generalization to new contexts, all while maintaining reasonable processing costs. We extend the exploration of past interactions by shifting focus towards explicit recall of specific information gleaned in earlier dialogue. We introduce the Long Range Common Ground benchmark, a dataset comprised of GPT-generated dialogues, which aids in evaluating methods of maintaining common ground over extended interactions. This work elucidates that existing knowledge retrieval models are not yet proficient at the nuanced task of common ground retrieval. Next, we detail a framework for harnessing external world knowledge for dialogue tasks. Our framework produces a dialogue system augmented with an information retrieval module, capable of extracting pertinent documents from company policies. Our results demonstrate how such knowledge augmentation can enhance performance on action-oriented dialogue tasks by utilizing procedural instructions available in external knowledge databases, particularly in settings with minimal supporting data. Finally, we present a method of enabling dialogue systems to engage in a lightweight, data-efficient method of cooperative repair. We showcase how pre-trained goal-directed agents can generate insightful questions for visually-grounded tasks by using a pre-trained image captioner in conjunction with expected information gain. This approach enables a model to seek further clarification in cases of uncertainty, ultimately contributing to the broader goal of enhancing human-machine communication. In sum, this comprehensive exploration of contextual grounding strategies contributes to the evolving sphere of human-machine communication, aiming to foster more natural and effective interactions between people and technology

Keywords: Engineering.
Electrical Engineering.
Advisor: Goodman Noah (Noah D.)
Finn Chelsea
Sadigh Dorsa
Contingency model predictive control for automated vehicles

In the field of vehicle automation, contingent events are potential hazards that can be long-tail and high-severity. Their contextual warnings, however, enable contingency planning techniques to mitigate their risks. This dissertation presents Contingency Model Predictive Control (CMPC), a motion planning and control framework that optimizes performance objectives while simultaneously maintaining a contingency plan -- an alternate trajectory to avoid a potential hazard. By preserving the existence of a feasible avoidance trajectory, CMPC anticipates emergency and keeps the controlled system in a safe state that is selectively robust to the identified hazard. This is accomplished by augmenting the typical Model Predictive Control (MPC) prediction horizon with a second horizon, which is constrained to ensure safety from the contingent threat and is coupled to the nominal horizon at its first command. Thus, the two horizons negotiate to compute a command that is both optimized for nominal performance and robust to the contingent event. This dissertation develops several CMPC formulations and demonstrates their effectiveness in simulated and experimental applications. First, a General CMPC is introduced with maximum flexibility for arbitrary application. This general form is then honed to a Linear CMPC with a convex optimization that can converge in real-time. Linear CMPC is simulated on a toy problem and then experimentally demonstrated to control the steering angle of two automated vehicle platforms in obstacle avoidance and loss of tire-road friction scenarios. Finally, a Nonlinear CMPC is developed that controls a vehicle's steering, propulsion, and braking inputs, and is demonstrated experimentally in vehicle-following and loss-of-friction applications. In each demonstration, CMPC is presented in contrast to a Naive MPC and a Worst-Case Robust MPC to illustrate its properties. Analysis of the experiments and simulations herein reveal that Contingency MPC approaches potential emergencies with safe, intuitive, and interpretable behavior that balances conservatism with incentive for high performance operation.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Gerdes J. Christian
Boyd Stephen P.
Kochenderfer Mykel J.
Continual Learning of Dense Signals

Coordinate networks are neural networks that parameterize low-dimensional signals like images and signed distance functions. They fail to sequentially learn on a stream of signals as they catastrophically forget the earlier part of the stream. This paper proposes a method to mitigate forgetting based on a hash encoding coordinate network. Specifically, the hash table is augmented with a cache data structure along with additional regularizations. Experiments show that this method eliminates forgetting when the network learns dense signals for different data collection patterns, approaching the empirical upper bound with little margin.

Keywords: neural network
coordinate network
signal representation
continual learning
catastrophic forgetting
computational imaging
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Article
Thesis
Advisor: Wetzstein Gordon
Contrast-invariant orientation tuning and efficient selection in human visual cortex

The visual system is a complex network of structures that work together to process and make sense of visual information derived from the surrounding environment. This dissertation explores the challenges faced by the visual system, both externally and internally, in achieving its goal of providing a coherent perception of the visual world. Chapter 2 focuses on contrast-invariant orientation tuning, a remarkable ability of the visual system to maintain stable perception regardless of changes in stimulus contrasts. While extensively studied in non-human primates, the existence of contrast-invariant orientation tuning in human visual cortex has been uncertain. I present evidence that orientation tuning in human visual cortex remains invariant to changes in stimulus contrast. Chapter 3 investigates the neural mechanism of selective attention and its impact on contrast-discrimination behavior. By employing different task designs with varying numbers of targets, the association between the number of targets and different attention mechanisms is examined. It is demonstrated that the efficient selection mechanism is utilized when multiple targets are present, while both the efficient selection and response gain mechanisms are associated with fewer targets. Also, the efficient selection model proves to be effective in explaining the behavior in attention tasks. In investigating these mechanisms, I utilize the steady-state visual evoked potential (SSVEP) frequency tagging technique involving multiple input stimuli, which is an effective tool for studying both sensory and cognitive processes. This dissertation contributes to our understanding of how the human visual system adapts to external changes and internal demands, shedding light on the mechanisms of contrast-invariant orientation tuning and selective attention

Keywords: Humanities and Sciences.
Psychology.
Advisor: Gardner Justin
Gweon Hyowon
Norcia Anthony Matthew
Control in action

This dissertation proposes a theory of action---the control theory of action---whose central commitment is that action is movement that is controlled by the mover. On the theory, movement is understood capaciously and control is characterized by a trio of conditions consisting of an aim condition, a modal condition, and an explanatory condition. The control theory of action is then used to answer a number of foundational questions about the nature of intentional action, the particular kind of action which has been the focus of much of contemporary philosophy of action

Keywords: Humanities and Sciences.
Philosophy.
Advisor: Bratman Michael
Peacocke Antonia 
Hussain Nadeem J. Z.
Controlled Discrimination: The Perceptions and Policies of Sino-Muslims and the High Qing Statecraft

This thesis examines the Qing dynasty's attitudes and policies towards its Sino-Muslim population from the Kangxi to the Jiaqing period. Contrary to conventional views, it argues that the Qing state's stance towards the Sino-Muslims was consistent throughout the High Qing period. The Qing emperors shared with their Neo-Confucian officials skepticism towards the religious solidarity of the Sino-Muslims, while fearing the cultural chauvinism these officials harbored against Islam and its followers. Consequently, despite publicly affirming the legitimacy of the Sino-Muslims' cultural and religious practices, the Qing emperors consistently implemented administrative measures to tighten state control and surveillance over these communities under the guise of security concerns.

Keywords:  Stanford Global Studies
Sino-Muslims
Qing Dynasty (China)
Ethnic relations > Political aspects
History
Text
Thesis
Advisor: Sommer Matthew
Control-oriented learning for robotics and dynamical systems

Data-driven learning for robotics is ultimately done in the interest of informing decision-making and control, particularly in unseen and unstructured environments. This thesis is centered on the concept of control-oriented learning, whereby learning is attuned to this desired goal, rather than to naively fit models to data. To this end, this thesis presents novel methodologies that tailor model learning towards closed-loop feedback control for robotic systems. In Part I of this thesis, we focus on the offline learning setting. We leverage nonlinear control theory to regularize data-driven model fitting optimization problems to be better aligned with the task of trajectory tracking. In Chapter 2, we propose to do this by adding constraints to the learning problem that encourage the learned model to be stabilizable by jointly learning a dynamics model and a control-theoretic certificate which guarantees the existence of robust tracking controllers for arbitrary open-loop trajectories generated with the learned model. In Chapter 3, we take a different approach by embedding intrinsic factorized structure into our learned dynamics model that engenders the immediate synthesis of a performant tracking controller. Whether through constraints or model structure, we demonstrate that imbuing the offline learning problem with control-theoretic notions results in a model that performs better in closed-loop control online. In Part II of this thesis, we shift our focus to the online learning setting through the lens of adaptive control. In Chapter 4, we propose to learn a feature basis for adapting dynamics models offline through differentiable closed-loop simulation of classical adaptive control laws with parametric dynamics models. Ultimately, this takes the form of a meta-learning framework that links gradient descent on a trajectory tracking performance metric with a parametric representation of any unknown dynamics residual and feedback gains defining the adaptive control law. In Chapter 5, we generalize classical adaptive control concepts, namely stable concurrent estimation and certainty-equivalent cancellation of uncertain dynamics terms, to the robust model predictive control setting. Our approach allows us to couple classic online adaptation schemes, parameter uncertainty bounds, and robust model predictive control feedback that maintains safety through state and input constraint satisfaction, while decreasing conservatism in our uncertainty bounds over time. We conclude this thesis with a discussion of possible future avenues of work towards better synergy between learning modules in the autonomy stack and online closed-loop deployment objectives.

Keywords: Engineering.
Aeronautics and Astronautics.
Advisor: Pavone Marco
Boyd Stephen P.
Schwager Mac
Convergent dopamine and serotonin reward signals govern associative learning

In mammals, midbrain dopamine (DA) neurons in the ventral tegmental area (VTA) have long been thought to play a critical role in pavlovian learning because they respond to rewards, acquire responses to reward predictive cues, and project to limbic structures important for motivated behaviors. Now, there is mounting evidence that serotonin (5-hydroxytryptamine; 5HT) neurons in the dorsal raphe (DR) share these properties. This suggests limbic circuits are under coordinated neuromodulatory control as animals form new associations, and begs the question: how are converging DA and 5HT signals integrated to drive learning? In Chapter 1, I review current hypotheses about the functions of the DA and 5HT systems in reward learning, both individually and together. In Chapter 2, I present original research beginning with the development of a double transgenic model enabling simultaneous genetic access to the brain's DA and 5HT systems in mice. Neural circuit tracing with these mice identified the nucleus accumbens posterior medial shell as the limbic area with the greatest density of converging VTA DA and DR 5HT inputs. VTA DA axons in this target region were excited by rewards while DR 5HT axons were inhibited. Loss-of-function manipulations blunting both of these reward responses together, but not either one alone, disrupted learning and reduced reward consumption without being inherently aversive. Gain-of-function manipulations reproducing both reward responses, but not either one alone, were sufficient to drive acquisition of new cue-reward associations. Taken together, these data show that striatal integration of convergent DA and 5HT reward signals governs learning by gating reinforcement. Finally, in Chapter 3 I provide a discussion of these findings, potential mechanisms, and future directions

Keywords: Medicine.
Advisor: Malenka Robert C.
Eshel Neir
Lammel Stephan
Luo Liqun
Convex neural networks

Neural networks have made tremendous advancements in a variety of machine learning tasks across different fields. Typically, neural networks have relied on heuristically optimizing a non-convex objective, raising doubts into their transparency, efficiency, and empirical performance. In this thesis, we show that a wide variety of neural network architectures are amenable to convex optimization, meaning that their non-convex objectives can be reformulated as convex optimization problems using semi-infinite dual formulations. We first show that for two-layer fully connected neural networks with ReLU activations, the optimization problem is convex and demonstrates a unique link to copositive programming, with a regularizer which promotes both sparsity in the number of activation patterns used in the network, and sparsity in the number of neurons that are active for each activation pattern. We show that this formulation admits closed-form solutions in certain data regimes, and use copositive programming to relax the problem into one that is polynomial-time in the problem dimensions for data matrices of a fixed rank. We show that solving the convex reformulation results in a better solution than that found by heuristic algorithms such as gradient descent applied to the original non-convex objective. In the rest of this thesis, we explore different neural network architectures and training regimes which pose new challenges to the convex optimization formulation. We show that for convolutional neural networks and transformer architectures, the optimization problem also admits a convex reformulation. We also show that for neural networks with batch normalization and generative adversarial networks, the same convex reformulation techniques can disentangle uninterpretable aspects of non-convex optimization and admit faster and more robust solutions to practical problems in the field. Finally, we show that these approaches can be scaled to deeper networks using a Burer-Monteiro factorization of the convex objective which maintains convex guarantees but allows for layerwise stacking convex sub-networks in a scalable fashion.

Keywords: Engineering.
Electrical Engineering.
Advisor: Pauly John
Pilanci Mert
Vasanawala Shreyas