VictorJuiz/thesis huggingface dataset:
“Neither Male Nor Female”: Resurrecting Spiritual Genderlessness in the Life of the Public Universal Friend, 1776 - 1819

This thesis examines spiritual performances of androgyny in the life of an eighteenth-century preacher named the Public Universal Friend. I argue that the Public Universal Friend’s gender identity has to be understood in analogous terms to their religious beliefs. Gendered in-betweenness reflected the spiritual in-betweenness the Friend embodied as an immortal spirit housed in a mortal body; simultaneously, androgyny visually reinforced their claims to spiritual otherworldliness. From their transformation in 1776, the Friend assumed a gender identity that flowed from their original claim to having been resurrected and reborn as a prophet. In this sense, we can reinterpret the Friend’s gender not as a secular expression of masculinity or femininity, but as a theological practice that expressed the preacher’s immaterial and metaphysical identity. Understanding the Friend in this light allows us to view the eighteenth-century spectrum of gender alongside the religious contexts the Friend existed within: one in which the boundaries between life and death, heaven and hell, mortal world and metaphysical realm were readily collapsed under the banner of God and in the body of the Friend.

Keywords:  Department of History
Public Universal Friend
gender
Department of History
Jemima Wilkinson
New York
early America
prophet
revivalism
burned-over district
nonbinary
genderless
queer history
jerusalem
Thesis
Advisor: Gienapp Jonathan
Winterer Caroline
Neural generation of open-ended text and dialogue

Advances in Deep Learning have enabled more fluent and flexible Natural Language Generation (NLG). While these neural generative systems achieved early success in machine translation, they encounter problems — such as repetition, incoherence, and uncontrollability — when applied to more open-ended tasks such as abstractive summarization, story generation and chitchat dialogue. Furthermore, open-ended neural generative models tend to be evaluated by crowdworkers in carefully-controlled environments; it is less well-understood how they behave in realistic environments with real-life users. This thesis analyzes and improves neural generative systems performing several open-ended tasks; in the case of dialogue, the systems are evaluated in their full social context. First, for abstractive summarization, I present a pointer-generator model to improve copying accuracy and a coverage mechanism to reduce repetition in the generated summaries. Next, for chitchat dialogue, I present a large-scale detailed human evaluation to reveal the relationship between bot behaviors (such as repetition, specificity, staying on topic, and question-asking) and human quality judgments, and show that by controlling these bot behaviors, we can improve user experience. Third, for story generation, I characterize the effect of large-scale pretraining, and of the decoding algorithm, on several syntactic, semantic, structural, and stylistic aspects of the generated text. Lastly, I present a study of a neural generative chitchat model in deployment as part of the Alexa Prize, talking to real, intrinsically-motivated users. By analysing bot-user interactions, I identify the bot's main error types, and how they relate to user dissatisfaction. Furthermore, I demonstrate a semi-supervised method to learn from dissatisfaction and thus improve the dialogue system

Advisor: Manning Christopher D.
Hashimoto Tatsunori 
Jurafsky Dan
A neural network with feature sparsity

First, we propose a neural network model with a separate linear (residual) term, that explicitly bounds the input layer weights for a feature by the linear weight for that feature. The model can be seen as a modification of so-called residual neural networks to produce a path of models that are feature- sparse, that is, use only a subset of the features. This is analogous to the solution path from the usual Lasso (L1-regularized) linear regression. We call the proposed procedure LassoNet and develop a projected proximal gradient algorithm for its optimization. This approach can sometimes give as low or lower test error than a standard neural network, and its feature selection provides more interpretable solutions. This thesis illustrates the method using both simulated and real data examples, and shows that it is often able to achieve competitive performance with a much smaller number of input features. We also discuss extensions of this work beyond supervised learning, which includes unsupervised learning, matrix completion, and sparsity in learned features. Second, we consider the problem of local feature attribution and selection for arbitrary black-box models. We introduce a geometric method which we call RbX, for Region-Based Explanations. This method relies on approximating the prediction model's level sets by convex polytopes, thus helping to simplify and interpret the model. We demonstrate the effectiveness of the method on a variety of synthetic and real data sets

Keywords: Statistics.
Advisor: Tibshirani Robert
Duchi John
Hastie Trevor
‘Neuralizing’ Injustice: How Brain Science Misunderstands Racism, Addiction, and Crime

In his capstone thesis, Christopher Iyer examines the pitfalls of applying modern psychology and neuroscience to racism, addiction, and criminality. He considers how the individual-focused methods of brain science have "over-individualized" our views of (and proposed solutions to) these issues, obscuring structural and environmental root causes. Moreover, he criticizes how applying brain science to these issues has "over-neuralized" them, leading us to believe that racism, addiction and crime should be understood (and intervened upon) as features of our brains—divorced from social conditions, and divorced even from ourselves as empowered agents. Scapegoating the brain allows us to show mercy towards individuals without grappling with collective responsibility for the conditions of social injustice truly at the heart of racism, addiction, and criminality. Finally, he outlines recommendations for conscientious and socially embedded brain science research that is responsive to social injustice.

Keywords: Drug addiction
Neurosciences > Moral and ethical aspects
Racism in the social sciences
Text
Thesis
Neuroimmune connections in the spleen : a first look at spleen glia

Non-myelinating Schwann cells (NMSCs) are the most abundant cells in the peripheral nervous system. These cells ensheath the unmyelinated autonomic nerves innervating every visceral organ. However, outside of the enteric nervous system, very little is known about what they do. Glia are best studied in the brain, can support neurons, and also modulate neuronal signaling and inflammation. In the peripheral nervous system, neuronal signaling itself may be a key immune modulator as immune organs are sympathetically innervated and stress modulates immune responses. Increased sympathetic signaling after stressful events results in profound changes in the spleen, the largest secondary lymphoid (immune) organ. For example, after a large stroke, sympathetic signaling within the spleen increases and leads to the death of B-cell populations. This thesis advances our knowledge of non-myelinating Schwann cells in the spleen (spleen glia), and how sympathetic innervation of the spleen may modulate inflammation. Chapter 1 provides an overview of what is known about the roles of the spleen and our sympathetic nervous system in inflammation. Moreover, we discuss what is known about how responses between the two systems are coordinated and what the possible key mechanisms are, particularly the importance of the splenic nerve. We also explore the role of glia in both the CNS, ENS, and PNS and mechanisms by which they alter neurotransmission and immunity. Chapter 2 contains a detailed immunohistochemical and transcriptomic characterization of spleen glia. Here I utilize immunohistochemistry to provide the first in-depth characterization of the cells. This analysis reveals the expression of two common glial markers (GFAP and S100B), a close association with sympathetic nerves, vasculature, and immune cells, as well as the density and location of the cells throughout the organ. I combine this with RNA-sequencing to further characterize spleen glia. I report that they express a host of glial genes, genes involved in neurotransmission as well as genes involved in immune responses. Lastly, I use the sequencing to provide a transcriptomic comparison of spleen glia to brain glia and enteric glia, determining that spleen glia are unique when compared to these different populations. In Chapter 3, I explore the relationship of spleen glia to the splenic nerve which they ensheath. Here, I utilize SARM1 knockout animals that are protected against Wallerian degeneration after nerve injury. The work here reveals robust physical changes to spleen glia indicating potential trophic factors involved in maintaining this relationship. Chapter 4 provides a summary of the findings reported in this dissertation and describes future studies. In total, this dissertation provides a deeper context to neuroimmune interactions in the spleen and the first characterization of a novel cell type, spleen glia

Advisor: Ricci Anthony
Andreasson Katrin
Idoyaga Juliana
Talbot William S.
Neutrophils eradicate tumors via reactive oxygen species in response to immunotherapy

Although neutrophils are powerful cytotoxic cells capable of killing microbes and injuring host tissues, they are often associated with tumor-promoting inflammation and immunosuppression rather than tumor killing. We demonstrate here that neutrophils can be therapeutically harnessed to eradicate tumors. Following intratumoral co-administration of tumor-binding antibodies, CD40 agonist, and tumor necrosis factor, neutrophils rapidly infiltrate tumors and mediate tumor clearance. This is accomplished through an inflammatory cascade wherein complement activation induces leukotriene B4 (LTB4) production, which in turn activates neutrophil-dependent reactive oxygen species (ROS) production via xanthine oxidase (XO), ultimately resulting in tumor cell death and induction of adaptive anti-tumor immunity. This is interrupted by selective blockade of complement, LTB4, XO, or by scavenging ROS, indicating a necessary role for each of these elements. These results demonstrate the therapeutic capacity of neutrophils to mediate tumor-eradicating immunity and define a mechanism of tumor clearance with multiple promising targets for therapeutic intervention

Advisor: Engleman Edgar G.
Davis Mark M.
Sunwoo John B.
Winslow Monte
New optics for low-damage electron microscopy

While transmission electron microscopes are in principle capable of atomic resolution, the practical resolution limit for many samples of critical interest (i.e. biological molecules, polymers, battery materials) is determined by beam-induced damage. This leads to a dose-limited resolution far inferior to the resolution limit set by diffraction and aberrations. Multi-Pass Transmission Electron Microscopy (MPTEM) is a new technique for improving dose-limited resolution. I will motivate MPTEM with theory and simulation and present a detailed design of the novel optical elements which enable this protocol. I will also discuss the design of a programmable electron phase plate, which controls electron wavefronts and enables a variety of new measurement techniques. Finally, I will describe a new approach to understanding phase imaging for strongly scattering samples, which will be critical for extracting the maximum possible information from a multi-pass measurement

Keywords: Applied Physics.
Advisor: Kasevich Mark A.
Chu Steven
Schleier-Smith Monika
New results in the mathematics of string theory

In addition to being a promising candidate for a UV-complete theory of quantum gravity, string theory provides a rich breeding ground for relationships between physics and pure mathematics. Importantly, these relationships can go in both directions: string theory has both been enriched by the direct application of existing results in mathematics, and has also inspired profound new ideas in math. In this thesis, consisting of four papers co-written by the author, we present new developments in both of these directions. In the former direction, we use existing results in number theory and dynamical systems to determine aspects the BPS spectra of certain four-dimensional supersymmetric theories, including both string compactifications and quantum field theories. In the later, we conjecture a physical criterion for the modularity of some Calabi-Yau threefolds defined over the rational numbers

Keywords: Physics.
Advisor: Kachru Shamit
Silverstein Eva
Susskind Leonard
New theoretical insights for oxygen electrocatalysis

Oxygen electrocatalysis, defined here as the study of electrocatalytic reactions consisting of O2, H2O, H2O2, has applications in a wide variety of industries from clean transportation to water purification. By utilizing electrochemical synthesis via either the 2-electron water oxidation reaction (2e-WOR) or the 2-electron oxygen reduction reaction (2e-ORR), we can produce hydrogen peroxide remotely for use as a water disinfectant in places with limited access to clean drinking water. By reducing oxygen to water in fuel cells, we can produce electricity to propel vehicles and power cities. In this work, we use a combination of density functional theory (DFT), microkinetic modelling, and ab initio molecular dynamics (AIMD), to develop new understanding of catalysts for these reactions.Specifically, we show that binding energies can help us understand and predict catalysts for the2e-WOR and 2e- and 4e-ORR, as well as predict more general electrochemical behavior of transition metal surfaces. First we use limiting potential analysis to predict ZnO as an active and selective catalyst for the 2e-WOR. We demonstrate that ZnO is not only the most active catalyst for this reaction known to date, but is also remarkably stable in reaction conditions. This result demonstrates the power of simple tools like limiting potential analysis in predicting catalysts for electrochemical reactions. Next, we demonstrate some of the limitations of limiting potential analysis in the ORR in two specific scenarios. First, we show that on transition metals, incorporating electric field ejects into microkinetic modeling allows us to accurately predict varying pH dependencies on several model catalysts. We use this newly developed model to create activity volcanoes which help us understand how and why electric field affects different catalysts in different ways in both the 2e- and 4e-ORR. We extend this understanding to transition metal oxides, where the intercept of the O-OH scaling relation is much higher than in metals. We show that by applying a similar microkinetic model to these new scaling relations, we can explain the relative inactivity of oxides in the 4e-ORR. Finally, we demonstrate that using similar principles as used in oxygen electrocatalysis, we can predict trends in work function reduction, and consequently, potential of zero charge (PZC), across transition metal surfaces. We connect work function reduction directly to water coverage using AIMD, and show that even simple vacuum binding energy calculations have predictive power for PZC. Throughout this thesis we attempt to show that by using simple descriptors to explain binding energies, reaction rates, and other electrochemical properties, we are not only able to predict new catalysts, but also better understand the physical phenomena which govern the way existing catalysts behave

Keywords: Chemical Engineering.
Advisor: Bent Stacey
Noerskov Jens
Jaramillo Thomas Francisco
"No, I'm Puerto Rican": An Exploration of Identity Formation and Anticolonial Resistance in Chicago's Puerto Rican Community

The story of migration from the island of Puerto Rico to and throughout cities such as Chicago is one of serial displacement and resistance to the colonial structure of the United States. Puerto Ricans residing in mainland US cities are pulled between a national identity that characterizes the island and a dualistic racial identity that categorizes American people. Because the values that inform the formation of identity are intertwined with the social context one is raised in, where Puerto Ricans are raised can powerfully shape how they accept or reject various identities. Humboldt Park became known as a nucleus of Puerto Rican culture in Chicago after decades of social and residential discrimination and displacement as Puerto Rican migrants worked to settle and gain access to resources and rights. Initiatives led by Puerto Rican activists in Chicago led to the creation of organizations such as Casa Central Evangelical and the Puerto Rican Cultural Center, which were instrumental in the prevalence of a united Puerto Rican community. I am seeking to answer two questions in this research project: 1. How has the creation of Casa Central Evangelical and the Puerto Rican Cultural Center created and upheld a national identity? And 2. How has this sense of national pride impacted the racial identities of Puerto Ricans in Chicago?
My findings show that the creation of the Puerto Rican Cultural Center (PRCC) and Casa Central Evangelical strengthened the presence of Puerto Rican pride in Humboldt park by preserving traditional Puerto Rican culture and providing organized spaces for Puerto Rican identities. More specifically, the PRCC is modeled after principles and societies with anticolonial frameworks. The anticolonial nature embedded in PRCC has strongly shaped the paradigms of Puerto Ricans in Chicago, centering their Puerto Rican identity as an act of resistance to colonialism.

Keywords: identity
race
Puerto Ricans
Chicago
anti colonialism
resistance
displacement
Thesis
Advisor: Rosa Jonathan
The nonaligned self : Asian redeployments of the American renaissance

This dissertation examines a network of affiliations that emerged between a group of Asian American and Chinese diasporic writers and their nineteenth-century predecessors from the American Renaissance. In the twentieth century, Asian writers living in the West revived the defense of the free individual found in the Anglo-American tradition of liberalism in order to imagine new forms of provisional belonging in a pluralistic society. I draw on critical translation studies, postcolonial theory, and recent studies of the global Cold War in order to explore the links between the Asian American and Chinese diasporic traditions through their mutual commitment to redeploying ideas they encountered from the American canon. Sustained transhistorical exchanges with the literary past enabled Asian writers living in exile as well as their Asian American counterparts to articulate muted critiques of American capitalist hegemony as well as Communist repression in places like mainland China. Asian writers instrumentalized the defense of liberalism they encountered in the writings of Emerson, Whitman, Hawthorne, and Douglass in order to invent a vision of nonaligned selfhood that was global in its aspirations, even as it sought to challenge the exclusionary limits of nation-states, whether they were Communist totalitarian regimes or Western multicultural democracies

Advisor: Jones Gavin Roger
Fishkin Shelley Fisher
Palumbo-Liu David
Ruttenburg Nancy
Non-canonical hedgehog signaling mechanisms of resistance in basal cell carcinoma

Tumor heterogeneity and lack of knowledge about resistant cell states remain a barrier to targeted cancer therapies. Basal cell carcinomas (BCCs) depend on Hedgehog (Hh)/Gli signaling, but can develop mechanisms of Smoothened (SMO) inhibitor resistance. We have identified a nuclear myocardin-related transcription factor (nMRTF) resistance pathway that amplifies noncanonical Gli1 activity, but characteristics and drivers of the nMRTF cell state remain unknown. Here, we use single cell RNA-sequencing of patient tumors to identify three prognostic surface markers (LYPD3, TACSTD2, and LY6D) which correlate with nMRTF and resistance to SMO inhibitors. The nMRTF cell state resembles transit-amplifying cells of the hair follicle matrix, with AP-1 and TGF-beta cooperativity driving nMRTF activation. JNK/AP-1 signaling commissions chromatin accessibility and Smad3 DNA binding leading to a transcriptional program of RhoGEFs that facilitate nMRTF activity. Importantly, small molecule AP-1 inhibitors selectively target LYPD3+/TACSTD2+/ LY6D+ nMRTF human BCCs ex vivo, opening an avenue for improving combinatorial therapies

Advisor: Oro Anthony
Sage Julien
Jackson Peter K. (Peter Kent)
Khavari Paul A.
Nonlinear analysis of biological models and stochastic analysis of machine learning methods

This dissertation consists of two independent parts: nonlinear analysis of biological models (Part I), and stochastic analysis of machine learning methods (Part II). In the first part, we investigate two nonlinear partial differential equations arising in biology. The first model we consider is the Euler alignment system, which is a hydrodynamics limit of the Cucker-Smale model describing the collective dynamics of a flock of $N$ individuals (birds, fish, etc.) that tend to align their velocities locally. We prove that, as long as the weakly singular interaction kernel is not integrable, the solutions of the Euler alignment system stay globally regular. This result extends the previous regularity results to the critical case. The second model we consider is the Burgers-FKPP equation, which is a reaction-diffusion equation equipped with an advection term of the Burgers type. The Burgers-FKPP equation appears in many applications from chemical physics to population genetics, but the large time behavior of its solutions is rarely studied. An interesting feature of Burgers-FKPP is that when the coefficient of the Burgers nonlinearity increases, the propagating solutions have a phase transition from pulled fronts to pushed fronts. In this work, we show the convergence of a solution to a single traveling wave in the Burgers-FKPP equation, as well as some side discoveries including front asymptotics in higher orders. In the second part, we study the several machine learning models from the stochastic analysis viewpoint. Taking the continuous-time limit and using approximate stochastic differential equations (SDE) to analyze stochastic gradients algorithms has become popular in recent years, since it provides many new insights and compact proofs using developed toolkits. We exhibit the power of stochastic analysis in machine learning from two independent projects. In the first project, we consider the asynchronous stochastic gradient descent (ASGD) algorithm that updates iterates with a delay read, which plays an important role in large scale parallel computing. We derive corresponding SDEs to characterize the dynamics of the ASGD algorithm. Based on that, we can further explore algorithmic properties by considering the temperature factors in Langevin type equations, as well as identifying optimal hyper-parameters by using optimal control theory. In the second project, we consider data, model, and stochastic optimization algorithms as an integrated system. On the one side, we focus on comparing resampling and reweighting for correcting sampling biases. On the other side, we propose a combined resampling and reweighting strategy to handle the data feature disparities. Both problems arise as the models are non-convex, and by SDE approaches we explain how stochastic gradient algorithms select the minimum in different regions

Advisor: Ryzhik Leonid
Ying Lexing
Papanicolaou George