VictorJuiz/thesis huggingface dataset:
Transforming empire : the Genoese from the Mediterranean to the Atlantic, 1282-1492
Keywords: History.

Between the crusades and Columbus, the Genoese maritime empire centered in the eastern Mediterranean and the Black Sea transformed into a financial empire based on the Iberian peninsula. From the thirteenth century, Mongol, Mamluk, Turcoman, and Latin formations took advantage of Byzantine weakness; and against this backdrop the Genoese established a network of fortified ports and islands. With their maritime dominance, the Genoese rendered themselves essential to rulers from Plantagenet England to Mongol Baghdad, as mercenaries, envoys, navigators, bankers, and merchants. Yet the fifteenth-century rise of the Ottoman and Spanish empires eroded Genoese naval superiority. Traditionally, Venice is seen to have triumphed over Genoa in the geopolitical competition -- after all, the Venetian empire endured far longer than did that of the Genoese. Yet it was a pyrrhic victory, and the Genoese defeat a salutary trauma. Losing their eastern possessions to a rising Ottoman power and enduring foreign domination at home, the Genoese adapted and innovated, becoming dominant on the Iberian peninsula and in the Atlantic world in banking, the sugar industry, and the slave trade. Based on research conducted in two dozen archives and manuscript libraries around the globe, this dissertation weaves together Ottoman Turkish and Latin sources and brings together fragmented historiographies in an integrated narrative and analytical arc from the Mediterranean to the Atlantic. It was no accident that Columbus was Genoese. As pirates and slave traders, navigators and bankers, they brought extensive resources and long experience to their infiltration of the Iberian empires. Whether we demonize Columbus or lionize him, the Genoese trajectory brings us into an era we recognize readily as our own. Yet this study also forces a radical reassessment of a constellation of broad themes which we usually examine only in isolation: the legacy of the crusades and the interaction between Islam and Latin Christianity; the roots and consequences of Atlantic slavery; and the relationship of violence and colonial exploitation to the more subtle and hidden sovereignty of financial power
Transition metal dichalcogenides for next-generation photovoltaics
Keywords: Electrical Engineering.

Conventional silicon solar cells dominate the photovoltaics market with a market share of about 95% due to their low-cost manufacturing and reasonable power conversion efficiency. However, the low optical absorption coefficient and brittle nature of silicon lead to degraded performance in ultrathin, flexible silicon solar cells and therefore prevent their broader usage in next-generation photovoltaic applications demanding high specific power (power per weight) and flexibility, for example in aerospace, transportation, architecture and self-powered wearable and implantable electronics. In addition, for widespread adoption of solar energy, new materials need to be integrated on silicon to form tandem solar cells with higher efficiency, lower cost per Watt and therefore higher affordability. Semiconducting transition metal dichalcogenides (TMDs) are promising for flexible high-specific-power photovoltaics and for silicon-based tandem solar cells, due to their ultrahigh optical absorption coefficients, desirable band gaps, self-passivated surfaces, high stability and lifetime, biocompatibility, eco-friendliness, and compatibility with existing nanoelectronic fabrication processes. However, challenges such as Fermi-level pinning at the metal contact-TMD interface and the inapplicability of traditional doping schemes have prevented most TMD solar cells from exceeding 2% power conversion efficiency. In addition, fabrication on flexible substrates tends to contaminate or damage TMD interfaces, further reducing performance. In this work, we investigate the promise and challenges of TMDs as next-generation photovoltaic materials and provide novel and scalable solutions for their challenges, leading to record high performance on par with prevailing thin-film solar technologies
Transmission of the human microbiome : from infants to infections
Keywords: Genetics.

The human body is colonized by trillions of microorganisms, the vast majority of which reside in the large intestine. This collection of bacteria, fungi, archaea and viruses is collectively called the gut microbiome. Large enough to be considered its own organ, the gut microbiome has vast impacts on every day human function, including digestion, the immune system, even the brain and a host's mood. New research also suggests the gut microbiome can modulate an individual's response to anti-cancer immunotherapy. The anaerobic, nutrient rich environment of the mammalian intestine provides bacterial populations with everything needed to grow, proliferate and differentiate. While certain bacterial species that inhabit the gut microbiome may live within the food we eat, many species are uniquely adapted to the human intestine and do not live in other environments. The following conclusions logically follow: 1) Bacteria uniquely adapted to the human gut must be transmitted between individuals, 2) The human body has the ability to acquire new microbes from the environment and other individuals. Proper functioning of the gut microbiome is required for health, therefore this process is beneficial to the human body. 3) The gut microbiome is stable over time in most individuals, therefore selection takes place for which microbes engraft in the gut long-term. However, these conclusions raise many more questions about the acquisition and transmission of gut microbes, including: At what age do humans begin to acquire and develop a microbiome, and at what age does acquisition of new microbes slow down or stop? Where do the new microbes colonizing an infant come from? How are healthy, commensal microbes selected from pathogens? Does transmission between individuals also spread fungi, archaea and phages? Does transmission occur at the community level, or is it a single species at a time? Throughout my graduate work in Dr. Ami Bhatt's laboratory at Stanford University, I have attempted to shed light on a small portion of the microbiome transmission problem. Using advanced metagenomic sequencing techniques, I have tracked bacteria and phages down to the strain level to understand if and how they pass between individuals. This research has been limited to transmission between humans in non-experimental settings - no specific interventions or animal models were used. In these observational studies, I desired to capture acquisition and transmission events as they happened naturally by using the precise archaeological record stored in an individual's microbiome. I hope that my modest contributions will advance the field, improve understanding of how humans acquire and transmit members of their gut microbiota and provide stepping stones for future research to expand upon my findings
Transport in gapped topological surface states
Keywords: Applied Physics.

Three-dimensional topological insulators feature two-dimensional metallic surface states. These surface states are protected by the topological nature of the insulating bulk, meaning they must remain gapless present even when the surface is perturbed by, for example, disorder. Topological protection, however, has limits. Perturbations that affect the topological description of a system can open a gap in its surface states. Using electronic transport measurements of devices based on the topological insulator Sb2Te3, this thesis explores three such perturbations
Tributary: Rhythmic, Poetic, Mystical Experiences of African American Identity, Ancestry, and Spirituality
Keywords: African and African American Studies
Institute for Diversity in the Arts
Ancestry
Traditional African Spirituality
Hip-Hop
Rememory
Hip-Hop Feminism
Thesis

Within Black American ancestral consciousness, there exists a breath of perspectives that traverse the spiritual, historic, and mythical imagination. One way in which ancestral lineage can be traced is temporally and spatially across African American art forms. In this exploration, we can trace rhythmic dynamics, poetic expressions, and remix practices that have existed among African Americans since 1619, as they have responded to and innovated their experiences. In this work, I reflect – through my own music, poetry, and experience – on the legacy of creative expression for African American resistance and meaning making as well as the shared legacies of art-making and spiritual practice in the African Diaspora. Specifically, I hope to shine light on the legacy of resistance and remix that imbue Hip-Hop music. With this work, layers of knowing and healing are revealed within my own individual and communal experience. These knowings and healings are wrapped in a liberationist sentiment, working to uproot manifestations of white supremacy, imperialism, colonialism, and policing within the body and psyche.
Trust but Not Verify: Data Governance, Smart Cities, and The Public Interest—a Case Study of Sidewalk Toronto, Canada
Keywords: smart city
data governance
public interest
data economy
internet governance
digital ecosystem
risk
privacy
data monetization
corporation
civil society
data steward
data trust
multilevel governance
platform urbanism
platform governance
personal information
behavioral data
urban development
Big Tech
Google
Sidewalk Toronto
Quayside
Canada
Science Technology and Society
Thesis

As a pilot site for computational approaches to sustainable urban development, the 2017 Sidewalk Toronto project envisioned a mixed-use neighborhood that integrated physical infrastructure with digital networked technologies. The potential for ubiquitous collection, use, and sharing of diverse types of data, across different spaces and stakeholders, raised concerns about how to govern data in the public interest.
While previous research emphasized the need to govern data holistically to generate public value, little has been said of the complex and political exercise to define guiding principles to design data governance institutions, standards, and processes. Thus, this thesis asks: in Sidewalk Toronto, how were these data governance constructs sites of public-private contestation over the public interest? In this context, civil society denotes participants in the public consultation, and the private sector perspective denotes that of Google’s sister company, Sidewalk Labs, who designed data governance in the project.
To answer this question, I examined how these two stakeholders sought to gain leverage over design considerations by articulating their view of the public interest, and how the private sector sought to constrain the public interest through the design outcomes themselves. I analyzed public consultation materials, corporate documents, media articles, and secondary analysis between 2017-2020 and conducted expert interviews.
I found that the public and the private sector had starkly different perceptions of what modalities and levels of control were legitimate, what types of values data should generate for the local community, and what data should fall under local governance. These findings suggest the need to ground local data governance in collective ownership over the public’s physical and digital existence. To do so, communities must account for regulatory gaps governing behavioral data, and address public-private power asymmetries in the digital infrastructural ecosystem and the global data economy.
Truth and Justice: Understanding Franco’s Use of Sexual and Gender-Based Violence (SGBV) for Political Gain and Envisioning Justice for Impacted Communities
Keywords:  Program in International Relations
Spain
gender-based violence
sexual violence
Francisco Franco
Spanish civil war
Spain History Civil War 1936-1939.
Thesis

Content Warning: This abstract mentions sexual and gender-based violence
In this thesis, I argue that Franco sought to systematically use sexual and gender-based violence (SGBV) through a variety of methods to gain political power in Spain during the Spanish Civil War and his subsequent dictatorship. Through my analysis of victim and witness testimony, legal documents, and expert opinion, I find that women’s and LGBTQ+ liberation was viewed as a threat to the regime’s power and therefore the bodies and actions of women and LGBTQIA+ individuals were heavily policed. Additionally, Spanish culture was rapidly shaped to encourage violence against women, sexual violence was used as a weapon during the war and method of repression during the dictatorship, and children were trafficked from mothers to Franco sympathizers in Spain and abroad to prevent the continuation of a political identity he considered a threat. After decades of the Spanish government blocking legal pathways to criminal prosecution and reparations, opportunities for justice, while limited, have started to become available. Finally, this thesis concludes by considering domestic, regional, and international options to pursue justice for individuals and communities that have been impacted by sexual and gender-based violence perpetrated by Franco's forces and regime.
Twisted homology operations
Keywords: Mathematics.

This thesis develops a theory of operations on the twisted homology of E∞-algebras, generalizing a classical theory developed by J.P. May. First we describe a framework suitable for discussing twisted coefficients, which requires working with E∞-algebras in certain categories of functors. In this context, we define twisted versions of the classical Dyer--Lashof operations, as well as a product. Moreover, we prove that these distinguished operations generate all operations on twisted homology by giving a (non-canonical) explicit basis for the homology of free E∞-algebras in terms of these operations. We also make this statement functorial by proving that the homology of a free E∞-algebra is a free object in an appropriate category of objects equipped with an action of the Dyer--Lashof operations. This theory has applications to the study of E∞-spaces with local coefficients, though these are not discussed in detail here
Two frameworks for reliable machine learning in biology and medicine
Keywords: Electrical Engineering.

Machine learning models are being deployed to biological and clinical settings, including here at Stanford, e.g. to analyze ultrasounds automatically or map ancestries from genomics data. However, machine learning models suffer from issues of reliability: even models with good test performance often fail in unpredictable ways when deployed to real-world settings. In this thesis, I present two frameworks for more reliable machine learning: one for supervised learning and one for unsupervised learning. In the case of supervised learning, I present Gradio (www.gradio.app), an open-source Python framework for interactively testing models on real-world data. Gradio is being used to run the first real-time clinical trial of a machine learning model in the Stanford Department of Dermatology, and has been used to validate models at Google, Siemens, Amazon, Mercy Hospital, and Harvard. In the thesis, I describe the core questions that led to the development of Gradio, and showcase applications that demonstrate the usefulness of the framework. I further describe a novel explanation method that we have developed that allows debugging of faulty models with Gradio. On the unsupervised side, I introduce the framework of contrastive datasets, which provides a more reliable way to find patterns in unlabeled data. Our framework is quite general and has been adopted for purposes such as denoising images and mapping ancestries of admixed populations. Together, these frameworks provide a way to do more reliable unsupervised and supervised machine learning
Two medical applications of deep neural networks
Keywords: Electrical Engineering.

Many life threatening conditions can be cured if treated early enough. Further, many life threatening conditions are obvious to a trained medical expert. Recent advances in deep learning can be leveraged to distill some of this highly valuable medical expertise. A novel large scale dataset of labeled skin lesion images is collected from the internet. An ImageNet pretrained model is fine-tuned on the dataset to achieve board-certified dermatologist level performance at detecting skin cancer. Separately, a neural net is trained to detect ICD codes from biological waveforms such as electrocardiogram and photoplethysmogram. With this net, users can be reliably placed into lower and higher risk groups. Users can be triaged with an unreliable test to improve the effective sensitivity of a limited reliable test. A semantic embedding of ICD codes self-organized by organ (heart, liver, brain, etc.) is demonstrated. The neural net learned this semantic embedding of ICD codes as a byproduct of being trained to detect ICD codes from raw waveforms
Two theories of strange and bad metals
Keywords: Physics.

The behavior of the strongly correlated strange and bad metals have eluded theoretical understanding for some time. They exhibit anomalous transport and thermodynamic properties that defy typical predictions according to the quasiparticle picture of Fermi liquid theory. I will first discuss a theory for the widely studied strange metal, strontium ruthenate. Specifically, strontium ruthenate contains "hot" electrons that are less quantum mechanical than the other "cold" electrons. A scattering process in which a cold electron becomes hot after colliding with a second cold electron is unusually strong. This fact is seen to underpin the strange metallicity of strontium ruthenate. I will also discuss a tractable microscopic model for bad metals, by supplementing the well-known Hubbard model with a screened Coulomb interaction. Throughout the phase diagram, we observe T-linear resistivity above the Mott-Ioffe-Regel limit. At intermediate temperatures, this arises from a cancellation between the nontrivial temperature dependence of both diffusivities and thermodynamic susceptibilities
ULTRAFAST X-RAY DIFFRACTION STUDY OF A SHOCK-COMPRESSED IRON-NICKEL METEORITE TO CONDITIONS RELEVANT TO PLANETARY CORES
Keywords: School of Earth Energy &amp; Environmental Sciences
Geological Sciences
Mineral physics
Text
Thesis

Natural kamacite samples (Fe92.5Ni7.5) from a fragment of the Gibeon meteorite were studied as a proxy for terrestrial core material to examine phase transition kinetics under shock compression for a range of different pressures up to 140 GPa. In situ time- resolved x-ray diffraction (XRD) data were collected from a body-centered cubic (bcc) kamacite section that transforms to the high-pressure hexagonal close-packed (hcp) phase with sub-nanosecond temporal resolution. The starting coarse-grained kamacite crystals rapidly transformed to highly oriented crystallites of the hcp phase at maximum compression. The hcp phase persisted for as long as 9.5 ns following shock release. Comparing the c/a ratio with previous static and dynamic work on Fe and Fe-rich Fe-Ni alloys, it was found that some shots exhibit a larger than ideal c/a ratio, up to nearly 1.65. This work represents the first time-resolved laser shock compression structural study of a natural iron meteorite, relevant for understanding the dynamic material properties in metallic planetary bodies during impact events and the evolution of the elastic behavior of the Earth’s core.
Uncertainty and efficiency in adaptive robot learning and control
Keywords: Mechanical Engineering.

Autonomous robots have the potential to free humans from dangerous or dull work. To achieve truly autonomous operation, robots must be able to understand unstructured environments and make safe decisions in the face of uncertainty and non-stationarity. As such, robots must be able to learn about, and react to, changing operating conditions or environments continuously, efficiently, and safely. While the last decade has seen rapid advances in the capabilities of machine learning systems driven by deep learning, these systems are limited in their ability to adapt online, learn with small amounts of data, and characterize uncertainty. The desiderata of learning robots therefore directly conflict with the weaknesses of modern deep learning systems. This thesis aims to remedy this conflict and develop robot learning systems that are capable of learning safely and efficiently. In the first part of the thesis we develop tools for efficient learning in changing environments. In particular, we develop tools for the meta-learning problem setting---in which data from a collection of environments may be used to accelerate learning in a new environment---in both the regression and classification setting. These algorithms are based on exact Bayesian inference on meta-learned features. This approach enables characterization of uncertainty in the face of small amounts of within-environment data, and efficient learning via exact conditioning. We extend these approaches to time-varying settings beyond episodic variation, including continuous gradual environmental variation and sharp, changepoint-like variation. In the second part of the thesis we adapt these tools to the problem of robot modeling and control. In particular, we investigate the problem of combining our neural network-based meta-learning models with prior knowledge in the form of a nominal dynamics model, and discuss design decisions to yield better performance and parameter identification. We then develop a strategy for safe learning control. This strategy combines methods from modern constrained control---in particular, robust model predictive control---with ideas from classical adaptive control to yield a computationally efficient, simple to implement, and guaranteed safe control strategy capable of learning online. We conclude the thesis with a discussion of short, intermediate, and long-term next steps in extending the ideas developed herein toward the goal of true robot autonomy