VictorJuiz/thesis huggingface dataset:
Unformed art : bad writing in the modernist novel

"Unformed Art: Bad Writing in the Modernist Novel" examines modes of writerly failure in the work of D. H. Lawrence, William Faulkner, James Joyce, and Samuel Beckett in order to rethink the status of failure in modernist literary aesthetics. Modernism, I argue, has made us too comfortable with the idea of failure. Between its unprecedented embrace of formal deviance and its philosophical valorization of failure itself, the modernist novel has long taught readers to equate apparent breakdowns of narrative form with artistic sophistication. Meanwhile, formalist literary criticism—true to its modernist origins even when its theoretical affiliations lie elsewhere—has often perpetuated this positive view of failure by focusing on moments of rupture, discontinuity, obscurity, and incoherence and reading them as sites of heightened meaning and value. Opposing both of these traditions, "Unformed Art" uses the techniques of literary formalism to study instances of writerly failure that resist transformation into secret aesthetic successes. The first chapter analyzes D. H. Lawrence's radical experiment with narrative time in Women in Love and argues that this novel's polemical opposition to the notion of "form" itself accounts for its traditional exclusion from the modernist canon. Chapter 2 historicizes the occlusion of genuine writerly failure in formalist criticism amid the discipline's larger fetishization of formal deviance, linking these twinned developments to modernist practices of reading that are vividly dramatized in Faulkner's fiction. Finally, chapter 3 examines the strange persistence of a descriptive trope—photomimesis, or the description of light—in works by Joyce and Beckett that ostentatiously embrace stylistic failure. I argue that photomimesis troubles the theoretical consistency and aesthetic hierarchies of these works while also revealing a fundamental commitment to beauty and good-faith representation at the far extreme of modernist negativity. Together these chapters reestablish the centrality of genuine failure to our experience of art (even in modernism), while exploring a dimension of literary form that has traditionally marked a practical limit for the discipline of close reading

Advisor: Woloch Alex
Jones Gavin Roger
Ngai Sianne
Unintended Consequences: The Impact of the CVRA Upon Superintendent Turnover

Since 2001, in its efforts to increase minority representation in elected bodies and boards, the California Voting Rights Act has largely catalyzed the switch from at-large to by-trustee area elections across the state. Up to this point, myriad studies have focused on determining the efficacy of the CVRA in achieving its more explicit endeavors. However, less attention has been paid to potential peripheral impacts of the policy. My analysis thus focuses not on whether the CVRA has accomplished its intended purpose; rather, my research endeavors to understand if, and to what extent, the CVRA’s change in election methodology from at-large to by-trustee area elections has affected superintendent turnover. While it’s clear that turnover is higher in the CVRA treated group as compared to the comparison group, the CVRA also seems to be slowing down this trend with statistical significance in terms of retention by about .11, to .13, and finally, to .25 superintendents – after 3, 4, and 5 years, respectively. If this is the case, then the CVRA might be smothering the flames of superintendent turnover, as opposed to fanning these flames – as it appears to do at first glance.

Keywords:  Public Policy Program
Stanford University
Humanities and Sciences
Public Policy Program
superintendent
district governance
CVRA
superintendent turnover
at-large elections
by-trustee area elections
school boards
California Voting Rights Act
Thesis
Advisor: Kirst Michael
Cox Geoffrey
Unmasking Identities and Healing in the Classroom: The Impact of Ethnic Studies on the Psychosocial Well-Being of First-Generation Latinx College Students

This study explores the loneliness, self-esteem, and belonging that first-generation Latinx college students at a private elite university experience both within and outside of the Ethnic Studies classroom. More specifically, this study aims to 1) uncover the psychosocial effects (e.g., levels of loneliness, self-esteem, and sense of school membership) of engaging with culturally relevant Ethnic Studies courses and 2) understand the mechanisms by which Ethnic Studies impacts the psychosocial well-being of first-generation Latinx college students traversing a highly selective, exclusionary space within Higher Education. The sample population is comprised of 61 Stanford University students who self-identify as both first-generation college students and Latinx individuals. Group 1 is formed by 29 students who have taken two or more Ethnic Studies courses and Group 2 is formed by 32 students who have not taken two or more of these courses. Applying a mixed-methods approach, data were collected through an anonymous virtual survey and semi-structured exploratory interviews about the well-being and experience of participants in Ethnic Studies courses. Drawing upon the UCLA Loneliness Scale (Version 3), Rosenberg Self-Esteem Scale, and an adapted version of the Psychological Sense of School Membership Scale within the online survey, t-test results revealed a significant difference between Group 1 and Group 2 solely for the school belonging scale. Group 2 students who had not taken two or more Ethnic Studies courses demonstrated higher levels of school belonging overall, yet 86.2% of Group 1 students reported that Ethnic Studies helped cultivate their belonging on campus. Future studies and interventions should explore ways to mitigate the alienation first-generation Latinx students feel within the wider school community, a magnified contrast to the belonging they experience specifically in their culturally affirming, identity safe classrooms through community match and validation. Additionally, neither group scored exceptionally high in self-esteem or low in loneliness, illuminating the need for universities to acquire a whole student approach that centers well-being and culture in all classrooms as a means to promote educational equity and bolster the psychosocial well-being of underrepresented racially minoritized students.

Keywords: Graduate School of Education
Stanford University
first-generation college students
Latinx students
Ethnic Studies
psychosocial well-being
belonging
loneliness
self-esteem
ethnic identity affirmation
higher education
elite institutions
Thesis
Advisor: Padilla Amado
User community innovation : implications for firm strategy, organizing and performance

Community-based innovation has recently emerged as a focal point of research on strategy and innovation. Scholars have argued that communities have several advantages over firms, such that managers must increasingly consider how to best compete and collaborate with this unique organizational form. Extant empirical work provides significant insight. However, it remains unclear how and when firms benefit from user community-based innovation. Across three tightly-linked papers, this dissertation addresses this gap. The first is a comprehensive review of prior work that studies user communities as they relate to firm strategy and performance. The second paper is a comparative case study of innovation by two civilian drone ventures—one that organized as a community, the other that organized as a firm. Finally, using a novel panel dataset of 2,586 video game development projects, the third paper develops and tests theory on whether firms can improve performance via learning from communities. Together, these papers contribute several interrelated findings to literature on firm strategy, organization theory and user community innovation, as well as several insights for management practice

Keywords: Management Science and Engineering.
Advisor: Eisenhardt Kathleen M. 
Eesley Charles 
Katila Riitta
Using AI and Deep Learning to Predict IQ and Reading Skills in Humans through Analysis of Human Brain White Matter Fascicles

This study uses Artificial Intelligence techniques and white matter data in the human brain, to analyze potential pathways to transhumanism: enhancing human cognitive abilities, and eventually creating a brain-computer interface that could result in brains enhanced by artificial intelligence. For example, using machine learning technologies, we could predict the particular development curves for some brain regions or tracts, and teach particular skills at the right time, when that area’s development is taking place. Moreover, by imaging the brain, we could identify specific brain substrates to stimulate while learning new skills, as performed by some existing researchers and companies,.
Deep Machine Learning models could be used to personalize content for different subjects based on their specific brain psychology, which can be applied in multiple fields, from education to entertainment, marketing, business and sales..
In this research, we have used brain magnetic resonance imaging techniques to identify brain white matter structure (brain tracts in particular) to train deep learning and other shallow artificial intelligence models which predict reading abilities and cognitive skills' scores.
The fractional anisotropy, a measure of how water diffuses within a brain location can be measured along the length of a tract (the FA profile). These profiles correlate with reading proficiency. We use these profiles to analyze the relationship between white matter in the human brain and reading and cognitive abilities. We examine multiple machine learning models that could help predict cognitive skills measures such as IQ and reading proficiency in subjects based on their fractional anisotropy profiles.

Keywords: iq
plasticity
connectome
tensor
education
white matter
human brain
biomarker
tractography
oligodendrocytes
Thesis
Advisor: Wandell Brian A
Lerma-Usabiaga Garikoitz
Using chromatin dynamics to understand transcriptional regulation and genetic variance

The genome is the same in every cell of an organism, but complex organisms have multiple cell types. This is due to the epigenome specifying a cell's transcriptional state resulting in distinct cell identities. Despite 98.5% of the human genome is non-coding and the site of epigenomic activity, which specifies instructions for regulating transcription of the coding region, relatively little is known about its function. A major hurdle with studying non-coding regulatory elements is identifying their distal gene and regulatory targets. Here, I solve this issue by using the three-dimensional chromatin conformation to identify targets that are in close physical proximity of the regulatory element. By integrating data on the three-dimensional chromatin state with other epigenomic measures including the open chromatin regions, transcription factor binding sites, and histone modifications I provide genome-wide insight into how the epigenome establishes the transcriptional state of the cell. In addition, I have developed a new method for prioritizing non-coding genetic variants in complex phenotypes. I validated this method in the context of Type 1 Diabetes and craniofacial disorders. In this dissertation, I demonstrate how data integration of multiple measurements of the epigenome and transcriptome provides insight into methods of transcriptional regulation and how this regulation is influenced by genetic variance

Keywords: Stem Cell Biology and Regenerative Medicine.
Advisor: Oro Anthony
Greenleaf William James
Wernig Marius
Wysocka Joanna
Using Complex Geometry to Rederive the BPS Spectrum of Seiberg-Witten Theory

This thesis reports on a novel procedure to derive the BPS spectrum of supersymmetric quantum field theories using the geometric properties of their vacuum moduli spaces. This procedure is demonstrated with a 4D, N=2 supersymmetric field theory, Seiberg-Witten theory. We examine the Coulomb branch of the vacuum moduli space of Seiberg-Witten theory compactified from 4D to 3D. It is known that the compactified metric is hyper-Kähler and therefore Ricci flat (Seiberg and Witten, 1996). Through using the wall-crossing formulae outlined by Gaiotto, Moore and Neitzke (Gaiotto, Moore, and A. Neitzke, 2010) and by imposing Ricci flatness on the compactified metric, it should be possible to extract the counts of BPS particles. This thesis reports on a method a validating this hypothesis using Seiberg-Witten theory, where the exact counts of BPS particles are known. In particular, I look at the strong coupling region of Seiberg-Witten theory and find that the integer count of the magnetic monopole can be derived through this approach. Through investigating the Ricci scalar at certain points in the Coulomb branch, the integer count of the monopole minimises the Ricci scalar numerically. This project was undertaken with Lark Wang, another undergraduate student

Keywords:  Department of Physics
Complex Geometry
Seiberg
Witten
Supersymmetry
Compactification
Wall-Crossing
Thesis
Advisor: Kachru Shamit
Zimet Max
Kallosh Renata
Using high-throughput genomics to open and understand new biological questions

High-throughput genomics has enabled the profiling of cells and cell states at previously unprecedented resolution. These genomic technologies include methods such as RNA-sequencing, which provides insight into the transcriptional state of cells, and ATAC-sequencing, which provides insight into the epigenetic chromatin state of cells. These methods can be performed at single-cell levels, providing a fine-grained look into heterogeneous tissues. In this dissertation, I describe the use of genomic technologies toward three areas of interest. First, using large-scale bulk RNA-sequencing datasets to computationally predict the transcriptional start site of a circular RNA, ciRS-7. This allowed the identification of the linear host RNA from which ciRS-7 is transcribed. Second, I describe the combining of single-cell ATAC-sequencing with CRISPR profiling in a new method, Perturb-ATAC. Perturb-ATAC allows the analysis of the regulatory landscape in single cells after a variety of CRISPR perturbations, allowing the linkage of transcriptional regulators to their epigenetic targets. Third, I apply single-cell RNA-sequencing analysis to understanding the clinical profile of CD19-directed CAR-T therapy. CD19-directed CAR-T has shown tremendous promise for the treatment of B cell malignancies, but also a high incidence of neurotoxicity, and through the use of single-cell analyses, I identify a CD19-positive population within the brain. These three applications of high-throughput genomics demonstrate the power of genomic analyses to understanding novel questions in biology, and individually resolve particular questions of interest while posing new questions for future work in their respective areas

Advisor: Chang Howard Y. (Howard Yuan-Hao)
Khavari Paul A.
Majeti Ravindra
Satpathy Ansuman
Using Poison Frogs to model sex differences in spatial navigation

The ability to navigate one’s environment is a key aspect of individual survival and overall fitness in many species. For example, spatial navigation is important for foraging, finding potential mates, or returning home. A critical part of spatial navigation is exploration behavior, or how an individual behaves in a novel environment. Exploration is also an important step in building a cognitive map of a new environment. Interestingly, the propensity to explore a new environment can differ between males and females in many species. In mammals, females often have decreased exploratory behavior compared to males and some have suggested this difference is linked to reproductive strategy where females must stay close to home to care for offspring. However, little work has been performed on exploratory behavior in animals with contrasting reproductive strategies. To better understand how sex differences in exploratory behavior is linked to reproductive strategies, I quantified exploratory behavior in an open field assay with several species of South American poison frogs (Family Dendrobatidae) that vary in parental care strategies, including male- and female-uniparental and biparental care. I found no significant sex differences in exploration rate or average speed between sexes for any of the species, though there were significant differences among species, with the male uniparental species exhibiting higher exploratory behaviors than female uniparental and biparental species. Overall, this work shows exploratory behavior in dendrobatid poison frogs differs across species, but not sex, which may be linked to general life-history differences rather than parental care strategies.

Keywords:  Department of Biology
Stanford Department of Biology
O'Connell Lab
Poison Frogs
Ranitomeya imitator
Oophaga Sylvatica
Dendrobates Tinctorius
Open Field test
Exploratory behaviors
Thesis
Advisor: O'Connell Lauren
Gordon Deborah
Utilizing synchrotron X-ray diffraction and X-ray absorption spectroscopy to study materials for electrochemical reactions involving hydrogen and carbon dioxide

Developing a more sustainable energy economy would offer several benefits, including mitigation of the most devastating aspects of climate change. The hydrogen evolution reaction (HER) and electrochemical CO2 reduction (CO2R) have been proposed as ways to sustainably generate chemicals and fuels using renewable electricity. Before these reactions can be adopted on a large scale, however, electrocatalysts with higher activity and selectivity need to be developed. A lack of understanding of how the surfaces of these electrocatalysts develop during the reaction complicates the connection between the structure of these materials and their activity, making the rational design of electrocatalysts difficult. This dissertation discusses efforts to develop an improved understanding of the materials used in electrochemical reactions involving carbon dioxide and hydrogen. First, a variety of metal phosphide and sulfide materials are evaluated as electrocatalysts for CO2R. These materials were found to be highly selective for the HER over CO2R, and collaboration with computational chemists showed the need to calculate the energetics for the competing HER when predicting new catalysts for CO2R. Next, this dissertation discusses the development of an electrochemical cell which allows for the in situ characterization of electrocatalyst surfaces under operating conditions where catalyst performance is typically benchmarked. This approach uses grazing incidence X-ray diffraction (GIXRD) and grazing-incidence X-ray absorption spectroscopy (GIXAS) to characterize the top 2-3 nm of planar electrocatalysts at reaction rates in excess of -100 mA cm-2, providing insight into the active surface of the electrocatalyst under operating conditions. To extract quantitative lattice parameters from these GIXRD measurements, a model for the refraction of X-rays as they pass through the electrolyte-electrode interface was developed. Lastly, in situ XRD is utilized to study hydrogen intercalation in palladium electrodes. This process plays a role in electrocatalysis, hydrogen storage, and hydrogen-selective membranes, and understanding the formation of palladium hydride as a function of electrochemical potential could improve the design of these materials. These experiments show a clear hysteresis in the potential where hydrogen intercalates and deintercalates, showing that hydrogen intercalation and deintercalation are not microscopic reverses. This thesis has enabled a deeper understanding of how the structure of electrocatalysts and other electrode materials evolves under in situ conditions, aiding the rational design of improved materials for electrochemical reactions involving hydrogen and carbon dioxide

Keywords: Chemistry.
Advisor: Jaramillo Thomas Francisco
Chidsey Christopher E. D. (Christopher Elisha Dunn)
Kanan Matthew William
A vagal pulmonary hypoxia sensing pathway regulates breathing

The carotid body is rapidly activated by hypoxemia and regulates ventilation to maintain blood oxygen levels. Other peripheral hypoxia sensors are not well understood. We molecularly profiled vagal pulmonary sensory neurons by single-cell RNA-sequencing, identified 10 subtypes, and predicted the sensing properties of each subtype based on their mRNA expression profiles. We used genetic strategies and viral tools to characterize a vagal pulmonary sensory neuron subtype (PSN4) that expresses Olfr78, a lactate receptor involved in hypoxia sensing in the carotid body. Individual PSN4 neurons project to multiple sites in the lungs, innervating neuroepithelial bodies, airway smooth muscle, and the alveolar region. They are upstream of neurons in the hypoxia-sensitive circuit of the caudal nucleus of the solitary tract. When optogenetically, or chemogenetically activated, or pharmacologically activated with an Olfr78 agonist, the PSN4 neurons decrease expiratory time, which increases respiratory rate and minute ventilation. They also increase sigh rate. We propose that lactate, produced when airway oxygen levels decline, activates a subset of pulmonary sensory neurons through Olfr78 to increase ventilation and initiate sighs to reinflate collapsed alveoli. We discuss the physiological and disease implications of this second acute hypoxia sensor that regulates ventilation

Keywords: Kaltschmidt, Julia
Chemical Engineering.
Advisor: Khosla Chaitan
Krasnow Mark
Chen Xiaoke
Validation and generalization of pixel-wise relevance in CNNs trained for face classification

The increased use of convolutional neural networks for face recognition in science,
governance, and broader society has create an acute need for methods that can
codify how these ’black box’ decisions are made. To be interpretable and useful
to humans, such a method should convey a model’s learned classification strategy
in a way that is robust to random initializations or spurious correlations in input
data. In this work, we applied the decompositional pixel-wise attribution method
of layer-wise relevance propagation (LRP) to this end, quantifying pixel-wise
information use of several classes of VGG-16 models trained for face recognition.
We evaluated the models on relevance-based occluded images to analyze how
each model’s relevance distributions vary with and generalize across the type of
pretraining dataset (ImageNet or VGGFace), the finetuning task (gender or identity
classification), and random initializations. We found that relevance distributions
produced by these various types of VGG-16 models prove generally stable across
random initializations and can generalize across finetuning tasks. However, there
is much less generalization across types of pretraining data. So, we find that
LRP relevance distributions generalize asymmetrically and to varying degrees
across these parameters, suggesting it may be possible to find an underlying set of
important facial image pixels that are relevant to most CNNs and tasks.

Keywords: convolution neural networks
CNNs
facial recognition
visual perception
explainability
Thesis
Advisor: Grill-Spector Kalanit
Poltoralski Sonia