VictorJuiz/thesis huggingface dataset:
BAFFLES: Bayesian Ages For Field Lower-Mass Stars
Keywords:  Department of Physics
Bayesian
stellar ages
R'HK
lithium equivalent width
Thesis

Age is a fundamental parameter of stars, yet ages derived from spectroscopic indicators in field stars often are given without a derivation of the uncertainty of the measurement. We have developed a Bayesian framework, BAFFLES, to produce the age posterior for a star from its B-V color, calcium emission strength (log(R'HK)), and lithium abundance (log(EW Li)). Accurate stellar age prediction is essential for understanding directly imaged exoplanets since for self-luminous planets and brown dwarfs, the accuracy in the age of the host star sets the precision in the derived mass of the companion. Current methods to determine the ages of stars do not provide a statistically robust estimate for the uncertainty in the derived age, and do not often address physical priors like a constant star formation rate. We empirically determine the likelihood functions for calcium and lithium as functions of age from literature measurements of these indicators, which are of stars that are members of nearby moving groups and clusters with well-determined ages. In addition, combining lithium and calcium posteriors for a single star allows us to increase the precision on the inferred age. We are currently investigating extending our framework to additional age indicators including Hα emission, X-ray emission, rotation period, and vsin(i).
Behavioral and neural markers of self-regulation
Keywords: Psychology.

The ability to manipulate behavior in service of long-term goals, self-regulation, has a long history of research and thought behind it. Yet despite its long history and the multitude of related concepts and measures self-regulation research suffers from a lack of transferable measures, models and interventions. One overlooked reason for this might be the focus on creating mechanistic stories while sacrificing measurement rigor. This dissertation explores the meaning of a 'marker' of self-regulation from a psychometric perspective. Our work highlights the importance of the required and varying statistical properties of a self-regulation marker depending on the research question. With respect to within-subjects analyses we present the largest comparison of behavioral self-regulation measures in their suitability for individual difference analyses using retest-reliability. Regarding between-subjects designs we show how extensive model comparisons can reveal conflicting narratives and how neural group differences might lie in properties other than the central tendency of distributions.
“Better Than I Was Yesterday”: A Qualitative Analysis of Motivations to Self-Track
Keywords:  Department of Communication
Stanford University Department of Communication
digital media
self-tracking
motivation
Thesis

Despite the growing body of literature involving self-tracking data, relatively little research has studied why people choose to self-track of their own volition. This paper presents results from a questionnaire of 1200 college students, focusing specifically on their motivations to self-track, which behaviors they would want to track, and why. Results show a high overall motivation to self-track that supports the self-improvement hypothesis— that is, students are predominantly motivated to self-track by self-insight, behavior change, or both. Furthermore, qualitative analysis reveals numerous complex and interrelated sub-motivations to self-track, including intraindividual comparison, goal attainment, and habit maintenance. These results contribute to an understanding of what motivates individuals to self-track, what they hope to achieve from self-tracking, and what behaviors they would most like to understand or change. As self-tracking studies provide rich, comprehensive data from which to measure and predict behavior, these findings thus illustrate promising future directions for researchers interested in how to best design such studies to yield optimal results.
Between Tomboy Anxiety and the Cult of Girlishness: An Analysis of Gender Norms and Transgressions in China’s Post-socialist Children’s Literature
Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
China
Children's Literature
Gender
Thesis

Focusing on novels of Yang Hongying and Cao Wenxuan, which are the most popular and widely recognized works of children’s literature in twenty-first century China, this paper explores normative gender performances and the anxiety of gender transgressions, both of which form binary gender with assigned gendered roles and performances in Chinese society. This paper argues that, vacillating between the nation’s dominant ideologies of stabilizing gender norms and sociocultural trends of liberating people’s bodies and minds, children’s literature in the post-socialist period presents tomboy anxiety and the cult of girlishness, which were widely accepted and carried out by Chinese people.
Birth of the grotesque : dynamics of display in Neronian art and literature
Keywords: Classics.

This dissertation argues for shared dynamics within visual and literary culture in the Neronian period. By analyzing literary texts and Fourth Style wall paintings, my project reveals overlapping aesthetics in different media through an exploration of three essential themes: (1) the conflation of fantasy and reality, (2) the prevalence of hybrid forms, and (3) a style that accentuates ornament. In these areas, diverse cultural artifacts emerge as participants in a collective mode of display. As a hermeneutic framework for making these connections, I employ theories of the grotesque, a concept rooted in the Renaissance reception of Nero's Domus Aurea. Around 1480, antiquarians uncovered the subterranean ruins of the structure, and in these "grottoes" they marveled at wall paintings they named grottesche. Artists such as Pinturicchio and Raphael emulated these ancient forms to create a new artistic style, which over the centuries became broadly associated with the strange and fantastic as it developed into what we now call the grotesque. In my work, I define the grotesque as a mode of representation that disrupts normative ways of comprehending the world. It challenges preconceived notions about the stability of natural forms and provides alternative strategies for representing reality. By considering the grotesque, I help explain the Neronian predilection for fanciful and sometimes repulsive imagery as a desire to challenge expectations and to expand aesthetic limits.
Blind at the Museum: Conceptions of Access, Disability, and Inclusion from 1909 to Now
Keywords: Blindness
Museums
Accessibility
Inclusion
Science Technology and Society
Thesis

What does it mean to be blind in a museum? How does blindness operate in a space centered on the practice of seeing? Scholars have frequently looked at disability through the lens of physical access, but this research seeks to understand the connections between disability and art by investigating how museums attempt to be accessible to the blind and visually impaired. By way of science and technology studies and disability studies, this analysis will argue that developments in how access is conceived has mirrored changes in the boundary between who counts as able-bodied and who is considered disabled. Through an examination and synthesis of fieldwork, annual reports, news articles, and websites, it situates museum accessibility initiatives for those who are blind within a historical arc by outlining three different periods from the early 20th century to now, and it explores what these initiatives can tell us about the ways societal assumptions about blindness can permeate into art and entertainment spaces. By placing museums in contrast with the individuals who lack the dominant sense typically assumed necessary for participation in museums, this research will provide insight into how cultural understandings of disability affect the ways we think about art and knowledge, as well as who is or isn’t allowed to be a part of these structures.
BOND
Keywords: ldt
Thesis

One of the most crucial predictors of later academic and broader life success is the quality of language input children receive during the early years (Hoff, 2013). While families that engaged in more intergenerational storytelling showed better emotional well-being than their peers whose families shared less personal stories amongst themselves (Fivush, Bohanek & Zanam, 2011), need finding results suggest that there are missed opportunities in families for engaging in deeper conversations. BOND is a suite of free applications that allows families to have meaningful conversations, even if they have only limited time or are on the go. The first piece of the suite that is developed is The BOND Show, a mobile based talk-show, where the parent and their child get to be star guests in a conversation hosted by a cartoon host of their choice. The host asks deep, generative questions from both the parent and the child, making it easier and more fun to engage in rich conversations.
Boosting like path algorithms for L1 regularized infinite dimensional convex neural networks
Keywords: Statistics.

This work falls at the intersection of three fields or research: Neural Networks, Boosting and Sparse Inverse Problems. Neural Networks, despite being very successful, are poorly understood. Convexification has been an important tool in analyzing a single hidden layered network in recent years. Boosting is one of the most popular tools available for non-linear regression and classification. While boosting is a very general idea, only one variety (greedy gradient boosting with trees) seems to completely dominate the field, leaving scope for expanding the applicability of boosting; and also the algorithms used for fitting it. We employ a model for convex neural network which has been previously studied, but our focus will be on actually fitting it. In doing so, we also see that we can better understand boosting. This leads us to greatly expand both the applicability and algorithms for boosting. Given that our formulation of the convex neural network is a sparse inverse problem, the Julia based framework we develop can be used to solve any sparse inverse problem in the various methods we describe with minimal code development. Finally, as a bonus, we see that the methods we present are not just pedagogical but they do have competitive performance on real datasets. They match the performance of not only Neural Networks of comparable complexity that are hard to train, but also that of state-of-the-art boosting technologies like XGBoost with trees.
Bootcamps : a new path for occupational entry
Keywords: Management Science and Engineering.

This dissertation utilizes a unique setting—coding bootcamps—to examine how workers attempt to reskill themselves for growing job areas without traditional organizations serving as the backdrop for their actions. I draw on 80 semi-structured interviews and observations conducted at two bootcamps in Silicon Valley over the course of 17 months of fieldwork. My findings suggest that bootcamps resembled learning collectives where self-learning and learning from peers and near-peers figured more prominently than expert instruction. Under conditions of minimal expert instruction and obstacles to legitimate peripheral participation, I show how aspiring software developers sought out an occupational community in virtual spaces, learned asynchronously from unknown others, developed their practice through mock work among themselves and managed to get hired as a new category of occupational entrant—the bootcamp graduate. This dissertation contributes to our understanding of employability management practices and under-institutionalized learning and socialization processes in contemporary careers.
Bounds for sets with no nontrivial polynomial progressions
Keywords: Mathematics.

In this thesis, we prove quantitative bounds in the polynomial Szemer\'edi theorem in several situations in which no bounds were previously known. In Chapter 2, we prove that if P_1, P_2\in\mathbb{Z}[y] are affine-linearly independent, then any subset of \mathbb{F}_q with no nontrivial polynomial progressions of the form x, x+P_1(y), x+P_2(y) must have size \ll_{P_1, P_2}q^{23/24}, provided the characteristic of \mathbb{F}_q is large enough. In Chapter 3, we prove that if P_1, \dots, P_m\in\mathbb{Z}[y] are affine-linearly independent, then any subset of \mathbb{F}_q with no nontrivial polynomial progressions of the form x, x+P_1(y), \dots, x+P_m(y) must have size \ll_{P_1, \dots, P_m}q^{1-\gamma_{P_1, \dots, P_m}} for some \gamma_{P_1, \dots, P_m}> 0, again provided that the characteristic of \mathbb{F}_q is large enough. In Chapter 4, we prove that any subset of \{1, \dots, N\} with no nontrivial progressions of the form x, x+y, x+y^2 must have size \ll N/(\log\log{N})^{2^{-157}}. In Chapter 5, we prove that if P_1, \dots, P_m\in\mathbb{Z}[y] have distinct degrees, then any subset of \{1, \dots, N\} with no nontrivial polynomial progressions of the form x, x+P_1(y), \dots, x+P_m(y) must have size \ll N/(\log\log{N})^{1-\gamma_{P_1, \dots, P_m}} for some \gamma_{P_1, \dots, P_m}> 0. In the final chapter, Chapter 6, we move to the nonabelian setting and prove power-saving bounds for subsets of nonabelian finite simple groups with no nontrivial progressions of the form x, xy, xy^2.
BPS states from geometry and geometry from BPS states
Keywords: Physics.

Some of the most-studied vacua of string theory are obtained by compactification of the 10d perturbative theories on a compact Calabi-Yau manifold. Such vacua play a central role in our understanding of string dualities, have provided a setting for the development of black hole statistical mechanics, and provide starting points for quasi-realistic string phenomenology. However, in spite of the importance of these manifolds, their defining characteristic -- their Ricci-flat metrics -- has never been determined (except for tori). This is the case even for the simplest of these manifolds -- K3 -- and until recently this provided a significant obstacle to the study of black hole statistical mechanics for many 4d string vacua with half-maximal (N=4) supersymmetry. I explain how the phenomenon of wall crossing can be exploited to solve these problems. I also describe other contexts that naturally relate string theory, geometry, and BPS state counting problems. A recurring theme will be the utility of approaching problems from a number of perspectives.
Bridging the gap between processing and performance : morphological characterization of organic photovoltaics using X-ray methods
Keywords: Electrical Engineering.

How can we provide renewable energy with low upfront cost and adaptable installation? This is the question that organic photovoltaics (OPV) seek to answer. OPV devices benefit from inexpensive production, flexibility, and low weight. The performance of lab-made devices has been steadily improving, but there is still a barrier to scaling up production. This is because the efficiency of the device depends heavily on the morphology of the active layer as it self-assembles from solution. In this dissertation, I develop and use X-ray methods to measure the morphology of OPV materials in order to link processing, morphology, and performance. I start with an overview of OPV devices, including a summary of morphological parameters that are of interest. Then I outline the characterization strategies that are currently available. I apply these methods to investigate the links between fabrication, morphology, and performance for two fabrication strategies. In the first part, I analyze the effect of various annealing temperatures on a polymeric material. I conclude that moderate-temperature annealing improves the morphology and performance, while high-temperature annealing results in a side chain cleavage reaction which is favorable to morphology but detrimental to performance. This discrepancy is likely caused by reaction by-products remaining in the film. The similarity of this reaction to degradation caused by irradiation gives clues to the burn-in mechanism which has been widely observed in OPV materials. The second part examines the effect of side chain manipulation on morphology and performance in a polymer:fullerene system. I conclude that high performance is achieved through favorable domain sizes, crystallinity, and packing structure, all of which intimately depend on side chain selection. On the other hand, high performance does not require the existence of a mixed phase, which also depends on side chain selection. I also find that the highest performing system benefits from the self-assembly of the polymer into small aggregates, which is aided by its unique side chain configuration. Next, I develop new strategies for analyzing OPV morphology. The first of these describes a software package that I wrote to help researchers analyze various aspects of X-ray scattering data: normalization and uncertainty in scattering intensity, indexing a scattering pattern to extract a crystal structure, and correction of a scattering pattern for refraction. Lastly, I demonstrate that ptychography may be used for measuring OPV morphology with unprecedented resolution and low beam damage when beam energy is decreased.
Building high-performance distributed systems with synchronized clocks
Keywords: Electrical Engineering.

Synchronized clocks can be used to improve the performance of distributed algorithms. They put the computers in a distributed system onto a single timeline, allowing the events that happen in the system to be ordered in a consistent way. This dissertation presents two work related to synchronized clocks. The first work is the CRaft consensus protocol. Consensus protocols allow a collection of machines to work reliably in the presence of failures. Today's most widely used consensus protocols, such as Paxos and Raft, usually require a single leader server during normal operation. All client requests go to the leader. This limits the throughput and scalability of such protocols. CRaft is a scalable multi-leader extension to Raft. It runs multiple Raft groups, and uses precisely synchronized clocks to combine the logs from Raft groups. By distributing the load to multiple servers, CRaft achieves higher throughput compared to Raft, while providing the same consistency guarantees as Raft. The second work is the Self-Programming Network dashboard. During the past decade, the networks have evolved to provide more complex functions, such as supporting data-intensive or computation-intensive tasks. The Self-Programming Network dashboard aims to provide network operators and application users a transparent and interactive view into the network, facilitating the analysis and diagnosis of network performance problems. The dashboard visualizes detailed network states, where synchronized clocks are used to process the data.
Building informative priors for the subsurface with generative adversarial networks and graphs
Keywords: Energy Resources Engineering.

Uncertainty quantification of flow in porous media in the subsurface is important for applications such as aquifer management, CO2 storage, hydrocarbon production and contaminant transport modeling. The main goal of this dissertation is to develop new approaches to build fast and geologically consistent priors for the subsurface. We first review the physics of computational sediment transport and the consequences for simulation in different depositional environments. A comparison of a data set of 110 modern analogs is made with a large number of Delft3D realizations of river deltas using the channel networks as a framework. Because of the prohibitive computational cost associated with using computational hydrodynamics on geological time scales, it is necessary to speed up such experiments significantly or to somehow extract the essence of the information. One possibility is to use the final output as training images. Thus, a Generative Adversarial Network, which is a modern family of generative machine learning models, is here trained using a data set of satellite images of river delta networks. These types of methods hold much promise for the future but are shown to have limitations when realizations need to be generated in data-dense environments. As an alternative to artifical neural networks, which require large amounts of training data, we propose a computationally cheaper and grid-free alternative, namely the framework of spectral graph theory. The mapped graph Laplacian spectrum is shown to hold strong discriminative power both for real and synthetic river deltas, digital rock samples and heterogeneous porous media. We argue that there should be done more research on generating such geological graphs directly based on prior distributions of more detailed geological patterns.
Building the Mothership: “School Bussing” on Public Transportation
Keywords: Science Technology and Society
education
students
Stanford University
racism
school
mothership
mothership connection
P-Funk
Parliament Funkadelic
Parliament Funk
Afrofuturism
bussing
gentrification
white flight
urban renewal
Japanese
Fillmore
Sunnydale
Bayview-Hunters Point
Bayview
San Francisco
Black Lives Matter
Black
white
Latin
Chinese
Filipino
Elderly
Children
Mothers
User Interface
design
love
empathy
empowerment
adolescent development
youth
music
singing
voice
elderly
senior citizen
generational divide
culture
cultural understanding
cultural competence
code switching
mixed kids
mixed children
mixed
multicultural
Thesis

This thesis is about the beautiful youth of San Francisco, specifically the youth of Sunnydale and Bayview-Hunters Point. It amplifies their voices, brilliance, and experiences taking public transportation to and from school.
Please copy and paste this URL into your browser to watch the full abstract on YouTube:
https://youtu.be/Ma6Xcxb1DiU
This thesis was finished during the 2020 Pandemic COVID 19. The author finished it under great duress and personal struggle. The author finished it while watching the world finally acknowledge the cries of Black and Brown folks around the world.
Black Lives Matter. Love Heals All. Change Must Come. Listen. Empower. Envision. Dream. Share. Speak. Stand. Sing. Be You. Love Difference. See HUMANITY.
Thank you for your time <3
Camera systematics and three-point correlations in modern photometric galaxy surveys
Keywords: Physics.

The goal of modern cosmology, broadly speaking, is to understand the behavior of the Universe at large scales, including the evolution of dark matter and dark energy over cosmic time. In the context of the modern paradigm of a universe dominated by dark energy and cold dark matter (LCDM), the goal is to detect deviations from LCDM predictions (new physics), and in the absence of those, to infer the value of the LCDM parameters. Advances this endeavor will require both improved constraints on systematic errors in raw astronomical data as well as improved statistical methods for extracting cosmological information from galaxy catalogs. Toward these ends, the first half of this thesis discusses methods for improving our ability to make precise and accurate measurements of galaxies in the universe using astronomical CCD imaging cameras. The second half of this thesis discusses a novel application of a statistical probe of the cosmic web of dark matter, the galaxy three-point correlation function, to photometric galaxy surveys, that allows us to extract more information of cosmological interest from the observed galaxy distribution. Both lines of research discussed in this thesis will be useful in future analyses of data from upcoming optical galaxy surveys, including the Large Synoptic Survey Telescope.