VictorJuiz/thesis huggingface dataset:
Analyzing the Economic and Political Implications of the Port State Measures Agreement for Multinational Fisheries Governance in the Northeastern Pacific
Keywords:  Earth Systems Program
Earth Systems
Stanford Center for Ocean Solutions
fisheries governance
IUU fishing
Port State Measures Agreement
Thesis

The Port State Measures Agreement (PSMA) is a top-down fisheries governance mechanism that employs port-level controls to deter, prevent, and eliminate illegal, unregulated, and unreported (IUU) fishing. Since coming into force in 2016, the PSMA has utilized binding enforcement and monitoring obligations on member parties (69 as of May 2021) to prevent vessels engaged in IUU fishing operations from landing their catches at PSMA-compliant ports. This international agreement focuses on IUU fishing as a major threat to the economic, social, and political well- being of nations around the world. The PSMA significantly affects power relations among nations, seafood companies, and fishing communities operating within national waters and on the high seas.1 The northeastern Pacific––defined here as being composed of Canada, the United States, and Mexico—is a particularly interesting region in which to analyze this problem (IUU) and potential solution (PSMA) due to the uneven and conflicting commitments to the PSMA (Canada and the US have ratified the PSMA, but at different times; Mexico has not) and related fisheries agreements in the region. The recently signed US-Mexico-Canada Commercial Agreement adds another layer of complexity to the IUU regulatory space, as the potential interactions between these two policies and implications for the region are currently unclear. This research takes a comparative analysis approach to explore initial motivations, economic incentives, policy responses, and geopolitical factors surrounding the PSMA and related agreements between the US, Canada, and Mexico. This paper analyzes the extent to which these national-international interactions are adding to or detracting from efforts to combat IUU, and proposes additional opportunities for alignment of existing institutions, policies, and multinational collaborations.
Ant Group and China's State Banks: Partners, Rivals, or Both?
Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
FinTech
China's Financial System
Ant Group
State Banks
China
Text
Thesis

Among the many Chinese Fintech companies, Ant Group has been a leading player in the market, used almost universally on the mainland and hailed internationally as a sign of China’s uniquely innovative financial technology. Its long-awaited IPO had raised $34.5 billion; valued at an astounding $313 billion, it was set to list as the world’s largest in history, and was considered proof of the mainland’s truly global capital markets. Despite the good press for China’s financial sector, on November 3, 2020 the IPO listing was halted by Chinese regulators, just two days before its debut. It was a startling reminder that China’s markets, despite their Western characteristics, are still confined within the bounds of a state-run economy.
The sudden implementation of strict regulations on Ant’s activities was nothing new: before the 2020 crackdown on their consumer loans arm, or CreditTech division, Ant Group’s Yu’e Bao—the money market fund within their InvestmentTech services—had come under fire from financial regulatory bodies in 2018. How did this Fintech company become so large that it posed a threat to the State not just once, but twice?
Weak points within China’s financial system both enabled Ant Group to reach historic heights and begot its precipitous decline. While Ant Group’s operations were certainly beneficial for some aspects of the economy, the company was also a source of competition to traditional banks, and posed significant risks to the stability of China’s financial system. This study examines how Ant’s CreditTech and InvestmentTech arms negatively impacted the Big 4 state banks’ access to individual deposits, exacerbated the riskiness of smaller commercial banks, and created the potential for a series of massive consumer loan defaults through targeting a historically present financing gap.
Application of spin squeezing in free space atomic sensors
Keywords: Applied Physics.

Atomic sensors measure a variety of physical quantities with ensembles of atoms. With careful engineering, the resolution of the atomic sensors will reach the quantum-projection limit. To beat this limit and further improve the resolution, spin squeezing, a specific entangled state, can be utilized. In this thesis, I will introduce efforts in integrating cavity-generated spin squeezed state into free space atomic sensors; specifically, atomic fountain clocks and atom interferometers. First, I will introduce two methods to retrieve spin squeezing in free space. The first method is based on an optical cavity and the second one uses a CMOS camera. The cavity method retrieves almost all the squeezing at short release time and the retrieved squeezing degrades at long release time. The study of this degradation leads to the understanding of the effects of coupling homogeneity loss. The application of the camera method successfully results in a free space Ramsey spectroscopy 5.8dB below quantum-projection limit, which is an important step to build an atomic fountain clock. Finally, I will detail the laser systems we constructed to do atom interferometry based on Raman transitions
Applications of deep learning in seismology
Keywords: Geophysics.

Seismic waveforms contain valuable information about earthquakes and earth structure. Dense seismic monitoring networks are deployed across the world and collect massive amounts of observational data; however, the sheer amount of this data poses a challenge for seismic data processing and analysis. Developing effective and efficient algorithms and models for seismic data analysis is thus important for studying earthquake physics, improving earthquake forecasting, and mitigating earthquake hazards. In the work presented in this thesis, I have explored a promising approach to advancing earthquake detection and inversion using deep learning. Deep learning has in recent years achieved super-human performance in solving many challenging problems, such as image recognition, protein folding, and playing Go or Atari games. In contrast to conventional algorithms that rely on expert-designed features and decision rules, deep neural networks can automatically learn characteristic features and statistical criteria from large training data sets accompanied by manual labels. The huge amount of archived seismic data collected in the past few decades provides excellent training resources for deep learning, making it a very promising approach to studying seismic signals and addressing research challenges, such as detecting hidden small earthquakes whose numbers dominate earthquake catalogs. However, at the time I started my PhD research, there was limited work on deep learning applications in seismology. To explore the potential of deep learning in seismology, I focused on two directions: First, I developed modular deep learning algorithms to improve earthquake monitoring including signal denoising, phase picking, phase association, and earthquake detection. The results of my work show that these deep learning algorithms significantly improve earthquake monitoring by detecting up to orders of magnitude more small earthquakes than are detected in standard catalogs, and by doing so reveal a far more detailed picture of earthquake sequences and fault structures. In addition, I utilized cloud computing to scale-up our detection workflow to solve the big data challenge in mining large archived data sets. Second, I studied the connection between deep learning optimization and conventional seismic inversion, such as full-waveform inversion. I developed a new inversion approach to solving seismic inverse problems using automatic differentiation and proposed a new regularization method by parameterizing inversion targets using neural networks. The results show that the rapid development of deep learning frameworks and neural network architectures can improve seismic inversion to constrain physical parameters of interest from detected seismic waveforms. In all, these applications of deep learning to both earthquake monitoring and inverting underlying parameters demonstrate that deep learning is an effective tool to improve the extraction of useful information from seismic data and that it holds great promise for future developments in seismology
Applied statistical methods for high-dimensional generalized linear models
Keywords: Statistics.

The Generalized Linear Model (GLM) is a fundamental statistical model to describe the relation between a response variable and a set of covariates. The model coefficients of a GLM are usually estimated using the maximum likelihood estimator (MLE) and confidence intervals for the coefficients are constructed using the classical asymptotic theory of the MLE. While the classical theory is valid under the condition that the number of variables p is vanishing compared to the number of observations n, it is invalid when p is comparable to n. To infer model parameters in the high-dimensional setting, researchers have been studying the asymptotic distribution of the MLE when p grows with n at a constant ratio, which they found to be informative in practical settings. These works typically focus on the setting when the covariates are i.i.d. or multivariate Gaussian. One open question is how to estimate the MLE distribution for general covariates. In this work, we study the distribution of the MLE with the objective of achieving valid inference for a high-dimensional GLM. We take two approaches in our study. First, we derive the theoretical distribution of a high-dimensional logistic regression when the covariates are multivariate Gaussian, and we demonstrate that our theory is accurate for moderate sample sizes. Second, when covariates are not Gaussian, we develop a resized bootstrap method to approximate the MLE distribution. We observe in simulated examples that the resized bootstrap method provides valid inference for a variety of GLM and covariate distributions. One application of our method is constructing confidence intervals for GLM coefficients
Approaches for the functional interpretation of rare genetic variation
Keywords: Biomedical Informatics.

Rare variants, due to their abundance and often relatively recent appearance, are expected to play a large role in genetic contributions to disease. However, determining rare variants with potential to impact the development of rare or common diseases remains a challenge. In this dissertation, I discuss several projects assessing multi-omics data integration to identify functional rare variation and connect variants to both rare and common disease. In chapter 2, I present work analyzing transcriptomic data to improve rare disease diagnosis and in particular, identifying patterns of allele-specific expression in rare disease individuals that highlight genes associated with the patient's phenotype. In chapter 3, I present approaches using transcriptomes across ~50 tissues for ~800 individuals with matched whole genome sequencing to characterize rare variation leading to multitissue changes in gene expression, allelic imbalance and alternative splicing, a subset of which show strong associations with common diseases in an external cohort. In chapter 4, I assess additional functional signals, DNA methylation and plasma proteome abundance, in addition to transcriptomic information to further refine our understanding of how rare variants can contribute to downstream phenotypes, and identify rare variation contributing to large changes across DNA methylation, gene expression, and protein abundance in a multi-ethnic cohort. Together, this work provides several approaches for integrating functional multi-omics data into rare variant interpretation pipelines and evaluates their use across multiple contexts
Approaching the limits of low resistance contacts to n-type germanium
Keywords: Electrical Engineering.

For over half a century, we have witnessed an incredible increase in the performance of digital electronics by virtue of Moore's Law. This improvement has largely been powered by aggressive scaling of silicon (Si) transistor technology. However, as device dimensions continue to be reduced to smaller dimensions, Si may be reaching its limits as a high-performance channel material. Germanium (Ge) is a promising candidate to augment or replace Si due to its high electron and hole mobilities, and its compatibility with existing Si process technology. High-performance Ge PMOS transistors have been demonstrated, but NMOS lags behind due to the difficulty in making low resistance contacts to n-type Ge. In this work, I investigate the factors limiting the contact resistance and explore techniques to reduce the electron Schottky barrier height and increase n-type dopant activation in order to achieve low resistance contacts to n-type Ge. First, I investigate the use of metal-interlayer-semiconductor (MIS) contacts to alleviate the strong Fermi level pinning on Ge. I experimentally determine the pinning properties of zinc oxide (ZnO) through electrical measurements and show that it is a suitable choice for the interlayer material. Fabrication of ZnO MIS contacts, however, reveals some practical limitations, including a high tunneling resistance and reaction at the metal-ZnO interface. I demonstrate that interlayer doping can be used to overcome these issues and effectively reduce contact resistance. Next, I introduce differential Hall effect metrology (DHEM) as a technique to accurately measure high-resolution electrically-active carrier concentration depth profiles. Using this technique, I demonstrate the first measurement of a large decrease in electron concentration within 10 nm of the surface for Ge ion-implanted with both a single phosphorus (P) implant and with antimony (Sb) and P co-doping. These measurements indicate that ion implant damage causes severe near-surface dopant deactivation. Finally, I present a thorough investigation into the effects of various process parameters on contact resistance to n-type Ge. I show that removal of Ge native oxide (GeOx), nickel germanide (NiGe) formation, and co-doping can improve contact resistance but are ultimately not enough. Finally, I demonstrate that low temperature nonthermal equilibrium molecular beam epitaxy (MBE) of Ge with in-situ Sb doping can be used to realize heavy n-type electrically-active doping. I fabricate nickel (Ni) contacts on this substrate to achieve an ultra-low specific contact resistivity which is among the lowest reported values to date for contacts to n-type Ge. This work demonstrates that low resistance contacts to n-type Ge can be achieved and provides a pathway towards the realization of Ge NMOS technology
An archaeology of Chinese transnationalism
Keywords: Anthropology.

Between the late nineteenth and early twentieth century, tens of thousands of Chinese immigrated to the United States to labor as miners, railroad builders, cooks, farmers, laundry workers, and merchants. My dissertation project uses archaeological methods to understand the transnational linkages that Chinese migrants maintained between their home villages in southern China and the Chinese communities they established in America. Specifically, I examine two diasporically connected areas: two Chinatowns in Southern California, and Wo Hing, a remittance-built village in Gom Benn, Taishan County. Established in 1902, Wo Hing was built in part by residents living in the Riverside Chinatown (1885-1940s) and San Bernardino Chinatown (1878-1940s). I use multiple lines of evidence—artifacts collected from archaeological survey, legacy archaeological collections, architecture, archival documents, genealogies, and oral history interviews—to examine how the transpacific movement of people, objects, and information impacted the diasporic communities and home village
Architecting small and bright upconverting nanoparticles as a toolkit for bioimaging
Keywords: Materials Science and Engineering.

Upconverting nanoparticles (UCNPs) have the unique ability to absorb light in the near-infrared and emit light in the visible and ultraviolet via a multiphoton process. UCNPs have emerged as a new optical toolkit for tagging, sensing, and drug delivery in biological systems, where they have gained attention due to their: 1) near-infrared rather than visible or ultraviolet excitation, enabling low background noise and high penetration depth through biological tissue, 2) stable emission, without photobleaching or blinking, and 3) biocompatibility. However, small UCNPs are limited in emission efficiency and brightness due to surface quenching of emissive states. Here, we explore ways to improve and alter the emission properties of sub-20 nm UCNPs using nanoparticle architecture, dopant concentration, and host lattice. First, we design and synthesize a unique core-shell-shell nanoparticle architecture for bright upconversion within a sub-20 nm footprint that features: 1) maximum sensitization in the core, 2) efficient energy transfer from sensitizer core to emitter shell, and 3) emitter localization near the surface to enable efficient resonant energy coupling to an external optical probe. We synthesized this novel architecture and compared it with the field-standard core-shell architecture, showing that our novel architecture exhibits up to 2x greater single particle emission and up to 8x greater emission enhancement when coupled to a fluorescent dye. Next, we investigate host lattice effects by synthesizing and comparing sub-20 nm UCNPs composed of 8 different alkaline earth host lattices doped with Yb3+ and Tm3+, passivating these nanoparticles with a biocompatible CaF2 shell. We further explore improvements in efficiency and ultraviolet emission via core-shell design and Yb3+/Tm3+ concentrations in Sr-based host materials. We synthesize and measure sub-20 nm UCNPs with upconversion quantum yield as high as 2.5% and up to 16.5% of their emission in the ultraviolet. Finally, we explore the capabilities of more broadly lanthanide-doped nanoparticles for sensing applications and how we may be able to leverage small and bright nanoparticles for single particle sensing and other future applications. Through this work, we show methods to maintain bright and effective UCNPs in sub-20 nm size regimes, enabling the unique capabilities of UCNPs to applications that require small optical probes for tagging, sensing, and photochemistry
Aristotelianism and English Political Thought: Variations on an Elizabethan Theme
Keywords: history
political thought
England
Aristotle
Aristotelianism
monarchical republic
Sir Thomas Smith
John Case
Richard Hooker
political epistemology
citizenship
Thesis

This thesis traces the development of a vocabulary of "political Aristotelianism" in the English Renaissance. That vocabulary is defined and its historicity defended by an interpretation of major Aristotelian texts in the first chapter. The second through fourth chapters treat, in turn, the Aristotelian dimensions the thought of Sir Thomas Smith, John Case, and Richard Hooker in order to establish how their contributions to the history of English political thought were significantly inflected by their commitments to an Aristotelian theory of politics defined, chiefly, as an epistemology of citizenship. The encounter between that theory and the politics of Elizabethan England generated novel accounts of the English polity (especially as a "monarchical republic") and of political knowledge, itself.
Artifacts of accountability : a case study of California's local control funding formula
Keywords: Education.

Accountability and evaluation systems are pervasive throughout the U.S. K-12 education system. Former reform efforts, such as No Child Left Behind (NCLB), used high stakes testing and punitive sanctions in combination with incentives to shape organizational practices according to desired outcomes. More recent developments in federal and state legislation, however, have given rise to a new type of accountability system that relies less on singular rankings or ratings, provides support rather than punishment, and extends local control to school districts. The changes occurring in California via the Local Control Funding Formula (LCFF) set the stage for this new era of accountability, which incorporates some elements from the previous system while also introducing new evaluation components. These shifts provide an opportunity to examine accountability under a new policy context. How do school districts respond to these changes? Through qualitative content analysis of accountability documents, known as Local Control Accountability Plans (LCAPs), I conduct two studies to better understand how school districts justify resource allocation to their varied institutional audiences and to investigate how school districts make sense of performance outcomes in narrative form. Ultimately, I claim that these documents represent artifacts of accountability, which reveal how organizations avoid, negotiate, or resolve tensions in public priorities and values
Aspects of locality in quantum many-body physics
Keywords: Physics.

These collected works address several basic questions about the evolution of quantum lattice systems. While a choice of local tensor factorization of the Hilbert space is often implicit in the writing of a Hamiltonian or Lagrangian, the identification of local tensor factors is not intrinsic to the Hilbert space itself. In Chapter 1, we show that generically, the tensor factorization of the Hilbert space into local degrees of freedom is uniquely determined by the Hamiltonian, at least when such a factorization exists. In Chapter 2, we discuss how a generic local Hamiltonian may be uniquely recovered from a single eigenstate such as the ground state. In Chapter 3, we characterize when local dynamics can be generated by local Hamiltonians, providing a converse to the Lieb-Robinson bounds in one dimension. In Chapter 4, we explore the emergence of classicality in large quantum systems. In particular, we show that for any evolution of the system and environment, for everywhere in the environment excluding an O(1)-sized region, any locally accessible information about the system must be approximately classical, i.e. obtainable from some fixed measurement
Assessing Measures of Explanatory Power
Keywords: Epistemology
Philosophy
Symbolic Systems Program
School of Humanities &amp; Sciences
Thesis

Measures of explanatory power have a key point of discussion recently in the context of epistemic research into abduction or inference to the best explanation. Many recent works have integrated explanatory power into Bayesian inference rules, arguing that such inference rules converge more quickly or are otherwise better than inference using Bayes' rule. This thesis proposes a pragmatic test, explanation task superiority, which attempts to determine if belief inference rules capture more or less valuable information than one another. This test borrows the decision theory literature's partially-observable Markov decision process (POMDP) formulation to separate epistemic and pragmatic rationality to assess a belief update rule by the value of its epistemic rationality. Through simulation, the thesis then shows that currently proposed inference rules which incorporate explanatory power fail to capture valuable information that inference by Bayes' rule does not and thus are not explanation task superior.