VictorJuiz/thesis huggingface dataset:
Tributary: Rhythmic, Poetic, Mystical Experiences of African American Identity, Ancestry, and Spirituality

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.

Keywords: African and African American Studies
Institute for Diversity in the Arts
Ancestry
Traditional African Spirituality
Hip-Hop
Rememory
Hip-Hop Feminism
Thesis
Trust but Not Verify: Data Governance, Smart Cities, and The Public Interest—a Case Study of Sidewalk Toronto, Canada

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.

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
Truth and Justice: Understanding Franco’s Use of Sexual and Gender-Based Violence (SGBV) for Political Gain and Envisioning Justice for Impacted Communities

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.

Keywords:  Program in International Relations
Spain
gender-based violence
sexual violence
Francisco Franco
Spanish civil war
Spain History Civil War 1936-1939.
Thesis
Advisor: Van Schaack Beth
Twisted homology operations

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

Keywords: Mathematics.
Advisor: Galatius Søren
Vakil Ravi
Cohen Ralph L.
Two frameworks for reliable machine learning in biology and medicine

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

Keywords: Electrical Engineering.
Advisor: Zou James
Montine Thomas J.
Tse David
Weissman Tsachy
Two medical applications of deep neural networks

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

Keywords: Electrical Engineering.
Advisor: Thrun Sebastian
Pauly John (John M.)
Girod Bernd
Two theories of strange and bad metals

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

Keywords: Physics.
Advisor: Hartnoll Sean
Kivelson Steven
Qi Xiaoliang
ULTRAFAST X-RAY DIFFRACTION STUDY OF A SHOCK-COMPRESSED IRON-NICKEL METEORITE TO CONDITIONS RELEVANT TO PLANETARY CORES

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.

Keywords: School of Earth Energy &amp; Environmental Sciences
Geological Sciences
Mineral physics
Text
Thesis
Advisor: Mao Wendy
Gleason Arianna
Uncertainty and efficiency in adaptive robot learning and control

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

Keywords: Mechanical Engineering.
Advisor: Okamura Allison
Pavone Marco
Brunskill Emma
Finn Chelsea
Uncertainty and policy design for sustainable energy systems

Decarbonizing the electricity sector will require a massive increase in variable solar and wind generation. Maintaining the stability of that future grid will require new sources of flexibility to keep supply and demand in balance without relying on traditional fossil-fired generation. Battery-based storage and flexible demand are two promising sources of flexibility that could each play a large role in future electricity grids. New policies are necessary to support the growth and value of these resources, and to integrate them into existing markets, because they work very differently from the traditional electricity generators that markets were originally designed to coordinate. This dissertation contributes to ongoing policy discussions by identifying how policy and market design can efficiently support the growth of each of these up-and-coming resources. In doing so, it advances our knowledge of exactly how these resources can most effectively provide value to the electricity grid. This dissertation begins with a novel taxonomy of uncertainty analysis approaches available to macro-energy systems modeling and includes a summation of best-practice advice that is applied in the following chapters. The third chapter examines the design of Demand Response (DR) programs, which could provide flexibility to the electricity grid through programs that incentivize customers to reduce or shift their electricity usage. It examines the implications of several common DR program design parameters for the system value and customer experience, revealing which parameters may severely limit the value of DR, and which offer smart tradeoffs between utility and customer outcomes. Results show that advance notice requirements as well as duration, total-time and total energy limits could be valuable tools for providing good system value while minimizing customer impacts. DR program designers should avoid creating programs with poor reliability and time-of- day limits, as these can significantly reduce system value. The fourth chapter examines the economic outcomes, as well as greenhouse gas and criteria air pollutant emissions resulting from grid-scale lithium-ion battery storage across three different Independent System Operators in the US. It identifies situations where smart policy can support the growth of this industry and its value to society and shows that the financial attractiveness of batteries varies greatly by region. The frequency regulation (FR) market currently drives the profitability of batteries. Results suggest that policy supporting learning-by-doing and attractive financing terms can help batteries remain financially attractive despite expected declines in FR prices. Finally, it shows that batteries can increase the net emissions of the electricity system, but studies that do not incorporate ancillary services into battery dispatch decisions may not correctly estimate the magnitude of this effect

Keywords: Management Science and Engineering.
Advisor: Weyant John P. (John Peter)
Azevedo Inês M. L.
Benson Sally
(Un)changing inequality : economic life, social movements, and policymaking during years of inflation in urban Brazil, 1944-1978

This dissertation is a study of inflation and inequality in urban Brazil from 1944-1978. Examining the feedback loop among economic life, social movements, and policymaking, it analyzes how different people experienced inflation, the social movements they formed to protest against it, and how some policies enacted to address it helped exacerbate inequality. The first half features chapters about the unequal lived experience of the high cost of living in the 1940s and the public protest campaigns carried out by women and students in Rio de Janeiro in the 1940s and 1950s. The second half examines the American consulting firm Klein & Saks' 1954 mission that studied why food prices were so high in urban Brazil; the wage policy in 1964's PAEG that reduced workers' wages as a way to fight inflation; and the anti-inflation advertising campaigns in 1973 and 1977 that blamed urban women, shopkeepers, and consumers for inflation. Integrating methods from social, cultural, and intellectual history with those from economic history and political economy, while using an array of qualitative and quantitative primary sources, this dissertation makes several arguments. First, we cannot understand urban Brazilians' lived experience from the 1940s-1970s without appreciating the degree to which inflation influenced their daily lives, and how this varied by socioeconomic status and gender. Second, analyzing the women- and student-led social movements in the 1940s and 1950s not only enhances our knowledge of these groups' agency and political action but also of their successes and failures in influencing policymakers. Finally, we have an incomplete conception of both the range of policy options available to policymakers and the results of policies that scholars have overlooked. Ultimately, this dissertation contributes to our understanding of state-society relations, political legitimacy, and the persistence of inequality in Brazil, and it provides lessons for countries today and in the future facing economic imbalances

Keywords: History.
Advisor: Frank Zephyr L.
Borges Dain Edward
Haber Stephen H.
Wolfe Mikael
Uncovering molecular innovation in the malaria plastid organelle through forward genetics

Plasmodium parasites and related apicomplexans are important human pathogens with major global burdens. These parasites represent a highly divergent and understudied branch of eukaryotes, and as such often defy the expectations set by model organisms. One striking example of unique apicomplexan biology is the apicoplast, an essential but non-photosynthetic plastid derived from a secondary (eukaryote-eukaryote) endosymbiosis. Endosymbioses are a major driver of molecular and cellular innovation, and biogenesis of the four-membrane apicoplast poses a unique challenge requiring evolution of new machinery. As such, apicoplast biogenesis pathways represent a hot spot for cellular innovation, yet few molecular players have been identified. To discover organellar pathways with evolutionary and biomedical significance, we conducted a mutagenesis screen for essential apicoplast biogenesis genes in P. falciparum using a novel fluorescent reporter and a chemical rescue that permits conditional disruption of the apicoplast. We identified fourteen gene candidates, and validated that six are specifically essential for apicoplast biogenesis. Three validated genes had no previous functional annotation, including a triosephosphate isomerase (TIM)-barrel which we show is derived from a core metabolic enzyme but evolved a new activity for its role in apicoplast biogenesis. We also validate a CaaX protease and bacteriocin processing (CPBP) gene, which based on gene annotations was expected to function as a CaaX protease in the eukaryotic post-prenylation processing pathway. However, we unexpectedly find that it does not act as a eukaryotic CaaX protease, but is instead derived from a cyanobacterial gene and likely performs a conserved chloroplast function that has been retained for complex plastid biogenesis. Altogether, our screen identified both unknown and unexpected genes, highlighting the abundance of molecular innovation in apicoplast biogenesis pathways. Furthermore, this proof-of-concept screen enables future expansion to a genome-scale screen, allowing broad discovery of these unique and essential pathways

Keywords: Molecular and Cellular Physiology.
Advisor: Yeh Ellen
Boothroyd John C.
Krasnow Mark
Maduke Merritt C.
Understanding and analyzing the effectiveness of uncertainty sampling

Active learning techniques attempt to reduce the amount of data required to learn a classifier by leveraging adaptivity. In particular, an algorithm iteratively selects and labels points from an unlabeled pool of data points. Over the history of active learning, many algorithms have been developed, though one heuristic algorithm, uncertainty sampling, stands out by its popularity, effectiveness, simplicity, and intuitiveness. Despite this, uncertainty sampling has known failure modes and lacks the theoretical underpinnings of some other algorithms such as those based on disagreement. Here, we present a few analyses of uncertainty sampling. First, we find that uncertainty sampling iterations implicitly optimizes the (generally non-convex) zero-one loss, explaining how uncertainty sampling can achieve lower error than labeling the entire unlabeled pool and highlighting the importance of a good initialization. Second, for logistic regression, we show that the extent to which uncertainty sampling outperforms random sampling is inversely proportional to the asymptotic error, both theoretically and empirically. Finally, we use the previous insights to show uncertainty sampling works very well on a particular NLP task due to extreme label imbalance. Taken together, these results provide a sturdier foundation for understanding and using uncertainty sampling

Advisor: Liang Percy
Ré Christopher
Sadigh Dorsa