VictorJuiz/thesis huggingface dataset:
Thriving Together: An Exploration of Nonbinary Asian/Asian American Identity Formation and Identity-Based Communities

This research explores two topics: the ways nonbinary Asian Americans conceptualize and reconcile with the intersections of their race and gender and the role of identity-based communities in identity formation. I conducted semi-structured interviews with five individuals who identify as Asian/Asian American and nonbinary, and all individuals either currently live or have lived in the Bay Area. All research participants, including myself, are also involved with APIENC (also known as API Equality - Northern California), an identity-based, queer and trans Asian and Pacific Islander organization based out of San Francisco Chinatown. Analysis of interview transcripts identified the different and unique experiences of nonbinary Asians/Asian Americans in order to better understand their processes of identity formation and their experiences in identity-based communities. Findings indicate that identity formation for nonbinary Asians/Asian Americans can involve internal and external tension, but the self-identification of nonbinary ultimately brought feelings of affirmation, completeness, and liberation. Furthermore, participants sought out identity-based spaces for community, and while some had previous negative experiences, all participants ultimately held deep love and appreciation for communities such as APIENC. APIENC was particularly successful at fostering belonging and community because of its focus on values-based organizing, accessibility, and deep relationships. Further research is needed to explore the impact of other axes of identity and the differences between intersectional and non-intersectional identity-based spaces in their capacity to foster inclusivity and community.

Keywords: Comparative Studies in Race and Ethnicity
CSRE
Center for Comparative Studies in Race and Ethnicity
CCSRE
Asian American Studies
ASNAMST
nonbinary
identity formation
community
Thesis
Advisor: Brody Jennifer
Wilcox Mike
Ties That Bind: U.S. Dual-Use Dependence on China

U.S.-China relations have become increasingly complex and competitive. This thesis seeks to explain why the United States government allowed key industries producing dual-use products, those with both commercial and defense applications, to become so dependent on China for their supply chains. Although many industries have sought lower-cost supply chains in China over the past several decades, dependencies for dual-use products pose particular vulnerabilities from a national security perspective: these products directly impact defense readiness and mobilization. Examples of dual-use products with high dependence on China include rare earth metals, printed circuit boards, lithium-ion batteries, pharmaceuticals, and more. I propose three theoretical models to explain high levels of dual-use product dependence on China: the formerly low prioritization of China as a security threat, the lagging integration of economic and security policies within the U.S. government bureaucracy, and lobbying by private sector industry associations. Examining evidence from a wide range of sources, including trade data, industry assessments, and government speeches and reports, I find that the U.S.’ dual-use dependence on China is significant, has been growing, and is widespread across industries and defense applications. Evidence further suggests that government dynamics better explain U.S. dual-use dependence on China than private sector factors like interest group influence do. I find that entities within the U.S. government long recognized the threat of dual-use dependence on China, but because China was not prioritized as a security threat or portrayed as a competitor, this dependence was allowed to persist and grow. Lagging integration of the U.S.’ economic and security policies exacerbated the lack of focus on this issue. Notably, private sector interest group influence does not appear to have played a major role. This thesis thus offers critical insights: it explains how America’s dual-use dependence on China came to be and reveals the economic and security interests that have shaped U.S.-China relations through time.

Keywords:  Center for International Security and Cooperation
Center for International Security and Cooperation
U.S.
China
U.S.-China relations
supply chains
supply chain security
economic interdependence
dependence
Thesis
Advisor: Zegart Amy
Tilli: A game-based, social-emotional learning tool on feelings, bodies, and boundaries

Every year, an estimated 246 million children are subject to some form of gender-based violence, including mistreatment, bullying, psychological abuse, and sexual harassment. Twenty-five percent of children experience physical violence and thirty-six percent experience emotional violence (WHO, 2016c) We envision a world where no child grows up to be either a victim or a perpetrator of sexual violence or assault. Tilli is a game-based learning tool for young learners (5-10 years) that helps parents and teachers curate meaningful conversations about trust, boundaries, bodies, and consent. The game helps kids practice empathy and critical thinking within real-world contexts, giving them a strong sense of agency and ownership over their own bodies, feelings, and relationships. The learning tool focuses on three skills; metacognition, empathy and critical thinking as means of achieving two key learning outcomes. Firstly ensuring that a young learner is able to recognize an unsafe situation (person or relationship) and seek help in avoiding it and secondly to ensure that a young learner has empathy and self-awareness to not create such a situation for someone else.

Keywords: metacognition
social-emotional learning
empathy
critical thinking
sexual assault prevention
Graduate School of Education
Thesis
Time-lapse inversion of anisotropic velocity linked to geomechanics

Seismic reservoirs undergo production-induced changes, such as fluid saturation, realignment of the stress field, and variations in pressure. Repeated seismic surveys, conducted before and after production, can provide a wealth of information about those changes. Traditionally, the analysis of these surveys is limited to seismic attributes, such as reflectivity, impedance, etc, from the backscattered energy on reflections. In this study, we show how to retrieve reservoir properties directly from the seismic data by analyzing the transmission effect on reflections. We use fully-coupled flow and geomechanical simulations to invert for those properties in an integrated approach. The methodology relies on a time-lapse application of the full-waveform inversion (FWI) algorithm. By linking geomechanical models to seismic properties, we can build an initial anisotropic seismic model that represents the study area. Production-induced changes in the seismic velocity and anisotropy parameters are estimated using third-order elasticity theory. From numerical studies of the model, we conclude that velocity changes can be estimated from the seismic data with limited offset (less than 5 km), but changes in anisotropic parameters are only sufficiently constrained with long offset data. We then apply the time-lapse FWI approach to a 3-D dataset acquired at the Genesis field in the Gulf of Mexico, using a total-variation regularization on the model differences. To address the computational challenges this poses, we use large-scale cloud computing and manage to finish time-lapse FWI on a 3D model within one day. A velocity decrease is recovered in the overburden above the reservoir, extending from the reservoir to the ocean bottom. This phenomenon can be explained by production-induced reservoir compaction and overburden dilation. The agreement of the numerical results from the seismic data and the geomechanical modeling verifies the validity of our method

Keywords: Computational and Mathematical Engineering.
Advisor: Biondi Biondo
Clapp Robert G. (Robert Graham)
Harris Jerry M.
Timescales of magma genesis and the dynamics of processes that produce differentiated alkalic volcanism

Volcanic provinces that erupt compositionally diverse lavas are indicative of dynamic and evolving magmatic systems at depth. Determining the processes and the timescales required to produce such a variety of magmas is a fundamental question in understanding the eruptive potential of volcanoes and the hazards associated with volcanic evolution. This dissertation explores two intraplate volcanic regions where basaltic magmas are the dominant erupted compositions, yet small volume differentiated alkalic magmas are also present. Textural, geochemical, and geochronologic methods are integrated to decipher how and when the differentiated magmas are produced. The first part of this study focuses on trachytic lavas from the intraplate volcanic field of Harrat Rahat in western Saudi Arabia. Chapter 1 relies on analysis of mineral phases present in the trachyte lavas to determine the generation processes and storage conditions of the evolved magmas. In addition, U-series disequilibrium dating of bulk mineral separates was employed to determine crystallization timescales of the trachyte magmas prior to eruption. These data are compared to previously determined 40Ar/39Ar eruption ages of the young (< 180 kya) trachyte lavas and provide necessary information for assessing the potential of future differentiated eruptions. The second part of this study investigates the differentiated alkalic lavas that were erupted during the waning volcanic period of hotspot magmatism, referred to as the postshield stage, at Kohala volcano in Hawaii. Chapter 2 presents whole rock chemistry of the postshield lavas at Kohala as well as new 40Ar/39Ar eruption ages. The new geochronologic data are in good agreement with the limited previous age constraints but show a clustering of eruptions occurring over a brief period during the stage. This high frequency of eruptions along with changes in lava chemistry over time likely requires incorporation of a different melt source during the postshield stage. Here, it is hypothesized that this period of frequent eruptions could correspond to a mass wasting event at Kohala that enhanced melting. Chapter 3 further investigates the postshield processes at Kohala through textural and chemical analysis of the lavas and reveal pervasive mixing textures throughout the volcanic suite. Major and trace element chemistry of rare apatite phenocrysts in the Kohala lavas show near-rim zoning in the crystals which is utilized to assess chemical changes that occurred in the magmatic system. Modeling of strontium diffusion in apatite is used to interpret mixing timescales that occur before eruption of the postshield lavas. These two chapters combined illustrate the necessity of new inputs of magma to the system prior to eruption of postshield lavas for Hawaiian volcanoes

Keywords: Geological and Environmental Sciences.
Advisor: Grove Marty
Pamukcu Ayla Susan 
Suckale Jenny 
Vazquez Jorge
Tingets

Young children (ages 3 - 6) are struggling with labeling, expressing, and managing their emotions, especially when it comes to managing negative emotions. Children who have a higher emotional intelligence experience long term benefits to their mental health and wellbeing. They will develop resilience and coping skills, display fewer behavioral problems, and feel more confident and capable. Tingets is an interactive storytelling app that leverages the power of tangible play to take children through activities that strengthen their emotional communication and expression skills. With Tingets, children explore the world crafts by designing different characters with unique facial expressions and utilize artificial intelligence to make their creations come to life. Children will take their characters through different activities as they build their own voice and develop more emotional communication skills. With Tingets it is our goal to help children better understand their own and others’ emotions, develop a more granular emotional vocabulary, express emotions in a more constructive way, manage negative emotions and celebrate & cultivate positive emotions as well.
With over 100 different participants we have conducted a number of different research studies. We have run concept tests, play tests, parent surveys, in-person and digital interviews. We have seen kids share personal stories about how they feel with their parents and utilize the new words they are learning with Tingets to express themselves. It is our vision to make social and emotional learning accessible and fun for all kids.

Keywords: Social-Emotional Learning
Tangible Play
Emotional Intelligence
Artificial Intelligence
Interaction Design
Learning Design and Technology
Graduate School of Education
Thesis
Today’s Challenge of Caring for Elders in China and Its Implications for Social Work Practice

In my research, I will first review the definition of Chinese filial piety and the requirements of filial obligations in imperial China. Then, I will discuss how political, economic and social changes influence families to modify their filial obligations to old parents. Because the shift of filial responsibilities from families to health and social service providers put social work to an increasingly important role, I will discuss the challenges faced by the social work industry. I will trace the development of social work in China and summarize why current social work industry in China is not satisfying in delivering qualified service to the senior people who live in nursing homes or private communities. In the penultimate section, I will give recommendations about the state-level public policies and suggestions of social workers qualification enhancement. In the conclusion part, with the new encouragement and requirements by the State of Council at the end of 2019, it is hoped that filial piety will be maintained, aging service in social work practice will be strengthened, and senior citizens will have a decent life in their retirement.

Keywords:  Center for East Asian Studies
Center of East Asian Studies
Filial Piety
Aging
Social Work
China
Thesis
Advisor: Karen Eggleston
TOMBED IN BY TomB: THE ROLE OF TomB IN TYPE 6 SECRETION SYSTEM ACTIVATION IN SALMONELLA ENTERICA SER. TYPHIMURIUM

The pathogen Salmonella enterica serovar Typhimurium encodes a tightly regulated Type 6 Secretion System (T6SS) which plays a large role in interbacterial competition. In an effort to further understand how this secretion system is regulated, our lab recently conducted a transposon screen to identify regulators of expression of T6SS genes. Transposon insertions immediately upstream of TomB, the Hha antitoxin, led to increased T6SS expression. Hha is a conditional toxin that interacts with Histone-like Nucleoid Structuring (HNS) proteins which have been previously identified as repressors of Salmonella’s T6SS. Previous studies have shown that a strain deficient in Hha and its paralog, Ydg-T, increases expression of several Salmonella Pathogenicity Island 6 (SPI-6) genes. I hypothesized TomB plays a role in regulating Salmonella’s T6SS. In this thesis, I report that the overexpression of TomB using an arabinose inducible plasmid increases expression of T6SS proteins such as ClpV, Hcp1, and Tae4. Furthermore, through in vitro competition assays, I show evidence of increased T6SS-dependent killing of prey in strains of S. Typhimurium that overexpress TomB.

Keywords:  Department of Biology
Biology
Microbiology and Immunology
Salmonella
Type 6 Secretion Systems
Interbacterial Competition
TomB
Thesis
Advisor: Monack Denise
Mudgett Mary Beth
Topics in convex optimization for machine learning

Convex optimization has been well-studied as a mathematical topic for more than a century, and has been applied in practice in many application areas for about a half century in fields including control, finance, signal processing, data mining, and machine learning. This thesis focuses on several topics involving convex optimization, with the specific application of machine learning

Keywords: Electrical Engineering.
Advisor: Boyd Stephen P. 
Leskovec Jurij 
Weissman Tsachy
Topics in machine learning for causal inference with applications in social science

Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals. Taking the perspective of random forests as an adaptive kernel method, we pair the forest kernel with a local linear regression adjustment to better capture smoothness. The resulting procedure, local linear forests, enables us to improve on asymptotic rates of convergence for random forests with smooth signals, and provides substantial gains in accuracy on both real and simulated data. We prove a central limit theorem valid under regularity conditions on the forest and smoothness constraints, propose a computationally efficient construction for confidence intervals, and discuss an extension to local linear causal forests for learning heterogeneous treatment effects. Following this deep dive into local linear forests, we discuss two applications of machine learning for causal inference. The first is a retirement reform in Denmark, in which shifting eligibility ages for an early retirement program provide an opportunity to analyze heterogeneous treatment effects of the age of retirement eligibility. The second is a randomized controlled trial in Nairobi, Kenya, aiming to lower rates of gender-based violence against adolescent students living in informal settlements. In the latter example, we explore how local linear causal forests help to uncover and emphasize trends in marginalized student responses to the intervention. In both cases, we address how machine learning and causal inference are powerful tools to discover patterns in individual treatment effects, and to advocate for marginalized groups when estimates reveal troubling patterns in the data

Keywords: Statistics.
Advisor: Athey Susan 
Hastie Trevor 
Owen Art B.
Topics in Markov chain Monte Carlo methods, with applications in statistics

Markov chain Monte Carlo (MCMC) methods, such as the Metropolis-Hastings algorithms, the Gibbs sampler, are ubiquitous in almost every quantitative subject of study such as physics, chemistry, statistics, biology, and computer science. In this thesis, we focus on the following two kinds of sampling problems: 1. How to efficiently sample binary matrices with fixed row and column totals uniformly at random? 2. How to draw samples from 'doubly intractable' distributions? For both of the two problems, we explore the theoretical properties of the existing MCMC algorithms and develop new methods to improve the existing algorithms

Keywords: Mathematics.
Advisor: Diaconis Persi
Palacios Roman Julia Adela
Wong Wing Hung
Toward the emergence of time in quantum gravity

Space and time are fundamental to our description and understanding of physical laws. But are space and time themselves only approximations to an even more fundamental description of reality? Recent developments in quantum gravity have shown how space can be emergent from more fundamental principles. This dissertation provides a review of tools and results developed over the last five years which shed light on a more fundamental description of time. We first discuss a way of understanding time using quantum information theoretic techniques. These techniques are put to use to study how causality and the arrow of time can emerge from the pattern of entanglement in tensor networks. Next we discuss concrete models of emergent time in quantum gravity, culminating in a non-perturbative model in which both space and time are emergent from random matrices. This model describes low-dimensional de Sitter quantum gravity, which has a positive cosmological constant

Keywords: Physics.
Advisor: Hayden Patrick (Patrick M.)
Kivelson Steven
Susskind Leonard
Towards an extensible local magnitude scale

A consistent earthquake catalog containing homogeneous event size estimates is very crucial for many applications, including determination of long-term seismic activity rates, estimation of average site conditions, and most importantly, for seismic hazard assessment. The local magnitude, ML, is one of the most widely-used magnitudes for local and small earthquakes. However, ML as it was originally de ned by Richter can be very biased due its arbitrary anchor at 100 km, and the fact that Richter derived it specifically for southern California. This thesis aims to redefine the local magnitude scale in a more intentional and physical way, as Richter had intended it to be. The primary purpose for chapter 2 is to introduce a physically-meaningful, data-driven approach to calibrate the local magnitude scale. We apply this approach to southern Kansas, Texas, Oklahoma and southern California. In chapter 3, we develop a model-driven approach to calibrate ML where earthquake data is scarce or unavailable. This approach utilizes information about the crustal structure and gives results that are consistent with the empirical approach. In chapter 4, we investigate the relationship between ML and seismic moment, M0, and use time-domain measurements of the maximum displacement amplitude, filtered using seven one-octave band-pass filters, to analyze the frequency content of S-wave and gain knowledge about the regional wave propagation characteristics as well as source parameters. The last chapter discusses the secondary project, in which we perform ultrasonic velocity experiments on Utica shale samples in order to characterize their anisotropy, and to estimate their dynamic elastic parameters. It is an attempt to better understand the in-situ behavior of the rock

Keywords: Geophysics.
Advisor: Beroza Gregory C. (Gregory Christian)
Ellsworth William L.
Zoback Mark D.
Towards explainable AI : feature significance and importance for machine learning models

Recent machine learning models such as neural networks underpin many of the best-performing AI systems. Their success is largely due to their strong approximation properties, superior predictive performance, and scalability. However, a major caveat is explainability: these models are often perceived as black boxes that permit little insight into how predictions are being made. We tackle this issue by developing various tests to assess the statistical significance and importance of the input features of machine learning models with a special emphasis on neural networks. The tests enable one to discern the impact of individual variables as well as interactions of variables on the prediction of a model. The test statistics can be used to rank variables according to their influence and be used to perform model selection. Simulations and applications on various real data illustrate the computational efficiency and performance of the tests

Advisor: Giesecke Kay 
Lai T. L.
Pelger Markus