VictorJuiz/thesis huggingface dataset:
Catalyst layer design and carbonate management for energy efficient electrochemical CO2 reduction to fuels and chemicals

Reducing anthropogenic CO2 emissions will be critical for mitigating the harmful effects of climate change on the environment. The ability to cost-effectively capture CO2 from point sources and utilize it as a chemical feedstock would allow several industrial processes to become carbon neutral. CO2 reduction (CO2R) powered by renewable electricity is a promising strategy for converting CO2 into valuable fuels and chemicals such as ethylene, ethanol, propanol, and acetate. To advance this technology for commercial applications, its operational costs must be reduced by improving energy and carbon efficiencies. Unfortunately, performing CO2R at low cell voltages at steady state is extremely challenging. Alkaline electrolytes minimize the cell voltage of CO2R, but CO2 rapidly reacts with OH-- to form (bi)carbonate (HCO3-- and CO32--), which drives the pH to 8 at steady state where the cell voltages are much higher. One approach to avoid (bi)carbonate formation is to use a two-stage electrolysis system, which consists of a CO2R step to generate CO, followed by CO reduction (COR) to multicarbon products in a separate electrolyzer. Solid oxide electrolysis cells can convert CO2 into high-purity CO streams at very high energy efficiency. Moreover, CO reacts with OH-- very slowly, which allows alkaline conditions to be maintained at steady state, thereby enabling low COR cell voltages. Both direct CO2R and the two-step process are being explored, but the energy efficiencies of both systems are still far too low. Improving cathode performance, developing ideas for managing (bi)carbonate formation, and constructing innovative cell designs will be necessary to advance these technologies towards commercialization. In this dissertation, we work towards improving the energy efficiency of CO2R and COR in several ways. We first provide a strategy for lowering the cathode overpotential for COR. We find that using a thick catalyst layer with percolated networks of hydrophobic pores and 5 µm Cu domains enables a high catalyst loading, which reduces cathode overpotentials. Such layers achieve a steady state COR cell voltage of 2.13 V at 200 mA cm--2, which is the lowest reported cell voltage for COR or CO2R at high current density. We then present an approach for mitigating CO2 uptake in direct CO2R by using highly concentrated (bi)carbonate electrolytes. This works towards maintaining alkaline conditions at steady state, which reduces operational cell voltages. Finally, we provide a platform for prototyping flow fields with 3D printing, which will be beneficial for scaling up CO2 and CO electrolyzers.

Keywords: Chemistry.
Advisor: Kanan Matthew William
Burns Noah
Chueh William
Causal aggregation of heterogeneous datasets and stabilization of feature selection procedures

Variable selection is increasingly becoming a key step in any data analysis pipeline. Identifying true relationships between a large number of covariates and a response of interest is a challenging problem that often requires strong modeling assumptions. In this thesis we develop new variable selection methodologies in two different directions. Our contributions build on the recently developed knockoff procedure as well as the causal invariance framework. We show how to aggregate data originating from heterogeneous datasets generated through different experimental settings to estimate causal effects and identify the relevant covariates in a causal sense. Our methodology efficiently uses all available information, and we propose an extension to high dimensions where the number of samples available per environment is small. Finally, we develop a Causal Boosting algorithm that efficiently recovers non-linear causal response functions from multiple datasets where different subsets of covariates are randomized. On a different topic, we propose some improvements on the knockoff procedure by extending the scope of datasets where such methodology can be applied. Going beyond Gaussian distributions, we propose a Bayesian Network knockoff sampling procedure that fits a much larger class of distributions. Also, we identify sources of instability in the procedure and devise an entropy based multi-knockoff procedure to mitigate the variability of the selected set of variables induced by the randomized nature of the procedure

Keywords: Statistics.
Advisor: Zou James
Candès Emmanuel J. (Emmanuel Jean)
Owen Art B.
Causal and selective inference in complex statistical models

During the past decades, advances in technology have led to a rapid growth of data in many fields. This abundance of data brings great opportunities as well as great challenges to data analysis: Datasets often measure hundreds of thousands of variables with complex dependence structures, which results in difficulties for statistical inference. For example, in a social network, each individual's behavior can be significantly influenced by the behavior of many others in complicated ways. Thus one cannot assume independence when analyzing the data. Similarly, in genetics, a trait could be associated with a large number of genetic variants, the relationship among which cannot be efficiently summarized by simple models. In both examples and many other real-world problems, datasets have dependence structures that standard statistical methods have difficulty dealing with. It is thus of interest to develop tools to conduct statistical inference under such complex dependence structures. This dissertation contributes to the toolbox of causal and selective inference in complex statistical models. One topic of interest is to develop methods to answer various causal questions in situations where individual subjects are interdependent. Chapter 2 and 3 develop statistical theory and methodology for treatment effect estimation under interference. Another interesting topic is to identify important dependency structures among many seemingly promising ones from a dataset. In settings where a specific variable is of scientific interest and many explanatory variables are potentially related to that variable, the goal is to build tools to select with confidence which of the variables are important for explaining the variable of interest. Chapter 4 and 5 tackle the problem of variable selection with false discovery rate control

Keywords: Statistics.
Advisor: Candès Emmanuel J. (Emmanuel Jean)
Wager Stefan
Hastie Trevor
Causal Distillation for Language Models

Distillation efforts have led to language models that are more compact and efficient without serious drops in performance. The standard approach to distillation trains a student model against two objectives: a task-specific objective (e.g., language modeling) and an imitation objective that encourages the hidden states of the student model to be similar to those of the larger teacher model. In this paper, we show that it is beneficial to augment distillation with a third objective that encourages the student to imitate the causal computation process of the teacher through interchange intervention training(IIT). IIT pushes the student model to become a causal abstraction of the teacher model - a simpler model with the same causal structure. IIT is fully differentiable, easily implemented, and combines flexibly with other objectives. Compared with standard distillation of BERT, distillation via IIT results in lower perplexity on Wikipedia (masked language modeling) and marked improvements on the GLUE benchmark (natural language understanding), SQuAD (question answering), and CoNLL-2003 (named entity recognition).

Keywords: Natural language processing (Computer science)
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Thesis
Causal Structure and Horizon Stability of Black Holes in Curved Spacetimes

The causal structure of spacetime encodes basic information about how different events influence each other, allowing us to better understand our universe. In this thesis, we study the causal structure of Schwarzschild and Reissner–Nordström (RN) black holes in de Sitter (dS) and Anti-de Sitter (AdS) spacetimes. We find that the inner horizon of RN black holes is highly unstable and analyze how its conformal diagram evolves from the Schwarzschild conformal diagram. We illustrate the Penrose diagrams for uncharged and charged black holes within the constant curvature spacetimes and analyze their properties. We examine the smallest black holes in dS, finding their probability of nucleation, as well as the largest dS black holes, which lead to the Nariai solution. We demonstrate two methods for finding the Nariai geometry of RNdS, with the caveat that the inner horizon remains highly unstable. Finally, we find a parallel between gravitational and matrix methods, confirming that the underlying holographic description of dS relies on matrix quantum mechanics.

Keywords: Physics
de Sitter
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Thesis
Advisor: Susskind Leonard
Stanford Douglas
Causes and consequences of damaging mutation accumulation in cancer

Cancer develops from an accumulation of somatic mutations over time. While a small subset of these mutations contribute directly to tumor progression, the vast majority of the remaining mutations that accrue in cancer - which can often reach up to thousands and even tens of thousands - are thought to have little functional relevance to tumor growth. However, a growing body of literature also supports that many of these mutations could instead be damaging to cancers - potentially through gene loss of function, immune surveillance, or induction of protein misfolding stress. This dissertation explores the causes and consequences of damaging mutation accumulation in cancer in two parts. First, I use a combination of evolutionary simulations from population genetics modeling and analysis of empirical cancer sequencing data to demonstrate that Hill-Robertson interference causes negative selection to become rapidly inefficient in most cancers. I find that low mutational burden tumors, which experience weak linkage effects, demonstrate strong patterns of positive and negative selection, while high mutational burden tumors, which endure strong linkage effects between mutations, exhibit patterns of neutral tumor evolution. Collectively, these results suggest that the vast majority of tumors (> 95%) are accumulating damaging mutations which are not being removed. Second, I explore how tumors can successfully survive the deleterious consequences of this damaging mutation accumulation. I use functional genomics and statistical analysis to examine how tumors phenotypically respond to damaging mutation accumulation by examining changes in gene expression. I combine this analysis with loss-of-function experiments in cancer cell lines to demonstrate that high mutational burden cancer cell lines maintain their viability by utilizing mechanisms that largely mitigate and prevent protein misfolding stress. Ultimately, these results elucidate potential mechanisms and physiological consequences of inefficient negative selection in somatic evolution, and add to potential therapeutics that can be applied to target high mutational load tumors

Keywords: Biology.
Advisor: Curtis Christina
Petrov Dmitri Alex
Good Benjamin H. 
Winslow Monte
Cell Surface Sialylation Modulates Neuronal Excitability and Network Integration

Neurons are coated in a thick layer of glycoproteins and glycolipids called the glycocalyx. The neuronal glycocalyx is highly sialylated, and depletion of these surface sialosides is associated with neuroinflammatory disorders through an uncharacterized mechanism. The basic role of the glycocalyx in affecting electrical activity in neurons also remains unknown despite studies demonstrating that global desialylation leads to a reduction in action potential firing rate. This is due in part to the dual technical challenge of manipulating glycans in a subpopulation-specific manner and measuring the internal electrophysiology of several neurons simultaneously. To meet these challenges, this work aims to develop a voltage imaging platform to investigate how network activity in mouse neuron cultures change in response to linkage-specific sialoside removal. We also characterize a pathway by which activated microglia, the resident phagocytes of the brain, alter the functional connectivity of nearby neuronal networks via secretion of extracellular vesicle-bound Neuraminidase 3 (Neu3). Altogether, these results reveal that distinct subpopulations of sialosides play critical roles in network activity and uncover a new molecular mechanism by which microglia may influence neuronal electrophysiology. These discoveries present Neu3 as a potential therapeutic target for neuroinflammatory conditions and demonstrate the importance of glycosylation in neuronal communication.

Keywords:  Department of Biology
Biology
glycocalyx
sialic acid
electrophysiology
microglia
neuroinflammation
voltage imaging
Stanford ChEM-H
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Thesis
Advisor: Bertozzi Carolyn
Chen Xiaoke
Centering the Voices of First-Generation Immigrant Youth: Multilingual NLP Methods in the Translanguaging Context

Translanguaging, or the act of using multiple languages within a speech utterance (e.g., sentence and/or word), is a global phenomenon for multilingual communities. In the context of the United States, translanguaging is a frequent occurrence among Latin American immigrant communities. While there are several large multilingual models such as XLM-RoBERTa and multilingual BERT, these models have been trained on and evaluated with parallel monolingual data. Upholding parallel monolingualism as the standard definition of multilingualism erases the language practices of many communities of color, including Latin American immigrants in the United States. The consequences are even worse for racialized children in the schooling system who may be labeled as English Language Learners (ELL) for the very notion that their fluency in multiple languages must be separate and apart. This ELL label has immediate consequences regarding future classes they have access to, as well as their own sentiment around and through their language practices. Moreover, there is currently no labeled NLP dataset that includes translanguaging between Spanish and English for the task of sentiment analysis. In collaboration with the Stanford Graduate School of Education, this research aims to center the voices of first-generation Indigenous Latin American immigrant students in NLP research through the task of sentiment analysis. Specifically, this thesis constructs the Interview Transcripts Dataset, an innovate trilingual dataset composed of transcribed interview data that contain instances of translanguaging, as well as a framework for developing these datasets. The findings of this project provide a promising starting point, and emphasize the need to leverage current pre-trained models on similar domains as well as develop a more robust large-scale dataset that centers translanguaging. Ultimately, translanguaging remains an open problem in NLP research tasks.

Keywords: Computer Science
Translanguaging (Linguistics)
Education
Natural language processing (Computer science)
Multilingualism
Multilingualism in children
Transcription
Interviewing
Spanish language
Native language and education
Children of immigrants > Attitudes
Children of immigrants > Education (Elementary)
Zapotec language > Study and teaching (Elementary) > Spanish speakers
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Thesis
Advisor: Manning Christopher
Martínez Ramón
Challenging the State: Western European Regionalism in the Era of Financial Crisis

The Global Financial Crisis and its aftershocks have substantially altered the Western European political landscape. But while the literature has extensively focused on the impacts of the economic hardship on traditional party competition, it has often failed to consider the center-periphery dimension. This thesis addresses both the demand for and supply of regionalist ideologies during the crisis. Using an original dataset containing 8 countries, 35 regions, and 128 regionalist parties, it finds that voters did not increase their support for regionalist parties during the crisis and may have even turned their backs on these political actors. In addition, I consider the reactions of regionalist parties in three Spanish autonomous communities, Catalonia, the Basque Country, and Galicia, to the crisis. I find evidence of regionalist mobilization on the issue and even some indications of radicalization of regionalist demands. Taken together, these findings raise interesting implications for the impacts of the financial crisis and the interaction between economic indicators, party competition, and voting patterns.

Keywords: Law
Regionalism
Nationalism
Global Financial Crisis (2008-2009)
Economic Voting
Electoral Manifestos
Western Europe
Spain
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Thesis
Advisor: Crombez Christophe
Chance Me!: Competition Culture in Online College Admission Forums

It is no secret that the attainment of a college degree is one of the single most impactful choices in one’s life that has the ability to positively impact lifetime financial and social status. As the technological landscape of the workforce continues to develop exponentially and the demand for qualified employees who can meet and adapt to modern challenges—i.e. college graduates—increases, students are responding to this pressure, enrolling in record numbers year after year. While access to entry into and information about these institutions has been historically stratified by class, race, gender, and other identities, the proliferation of the participatory internet has given rise to a host of readily accessible resources and tools for all students. Discussions about college pursuit have historically been internal amongst schools, education professionals, and families; however, online admissions communities serve as sites where all members of the college application process co-engage with one another, shedding light on their interpretation of the qualities that warrant admission into institutions of higher education. While literature exists about the influences and motivations that allow students of all backgrounds to pursue college, this thesis, through the textual analysis of content submitted to popular college admissions online forums, aims to explicate the nature of admissions discussions in novel spaces online, against the backdrop of an intensely competitive culture. Despite their neutral and beneficial flagging, I find that messages within these platforms can contribute to and reinforce legacies of stratification and exclusion in education. Forum users and moderators can be more active in critically engaging with the messages circulated in these spaces and the systems they serve to benefit.

Keywords: Universities and colleges > Admission
Education, Higher
Electronic discussion groups
Reddit
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Thesis
Advisor: Rosa Jonathan
Changing the meaning of motherhood : gender, family, and politics in Côte d'Ivoire, 1893-1983

Queen mothers and women warriors figure prominently in African tradition. In modern African political systems, however, women's influence often appears marginal. This dissertation examines this apparent gulf between "tradition" and "modernity" by tracing the history of changing meanings of motherhood in Côte d'Ivoire in the twentieth century. Motherhood has long been the source of moral and political authority for women in African societies. Beyond connotations of reproduction and caregiving, African motherhood is a social institution that encapsulates women's political, economic, and ritual responsibilities in their communities. In excavating the relationship between motherhood and politics, this study demonstrates how maternal authority emerged as both a site of female power and a locus of political contestation in Côte d'Ivoire. Over the course of the twentieth century, motherhood empowered women to make potent political claims, most notably in resistance to colonial rule. But as colonial officials, missionaries, and Ivoirian leaders vied for power against a backdrop of turbulent political change, meanings of motherhood began to shift. From public mothers, to domestic housewives, to citizen-mothers, the ideal Ivoirienne took on new forms as women and men debated whether to harness or suppress women's authority. As women sought recourse in public motherhood, those opposed to or threatened by their action leveraged changing meanings of motherhood in order to coopt their militancy. Through careful readings of colonial archives, interviews, and news media, this dissertation follows women as they negotiated and resisted these conflicting imperatives

Keywords: History.
Advisor: Cabrita Joel
Roberts Richard L.
Daughton J. P. (James Patrick)
Characterizing the greenhouse gas impacts of natural gas resources : a life-cycle assessment and evaluation of new aerial technologies

Greenhouse gas emissions from oil and natural gas production will need to be reduced as society transitions to a renewable energy economy and production declines. However, the energy system has significant inertia: even in a world meeting 1.5 ºC of warming targets, natural gas consumption is estimated to be 50% of 2020 levels in 2050. Previous research has shown that significant differences can exist in production emissions between different fossil fuel resources, and thus it is critical to develop metrics to minimize emissions during the energy transition. Based on an updated synthesis of measurements from component-level field studies, in Chapter 2 we develop a new inventory-based model for CH4 emissions, for the production-segment only, that agrees within error with recent syntheses of site-level field studies and allows for isolation of equipment-level contributions. We find that unintentional emissions from liquid storage tanks and other equipment leaks are the largest contributors to divergence with the US EPA Greenhouse Gas Inventory. If our proposed method were adopted in the United States and other jurisdictions, inventory estimates could better guide CH4 mitigation policy priorities. In Chapter 3, we calculate life-cycle emissions for natural gas production in the United States at the producing-county level. Results are aggregated and reported by basin. The bottom-up model from Chapter 2 is combined with an open-source life-cycle emissions calculator. We find that in both gas and oil rich basins, carbon intensity of the produced gas is dominated by methane emissions in all cases except when there is exceptionally high flaring. For dry gas fields (such as the Appalachian basin), carbon intensity is dominated by equipment leaks and pneumatic device venting. For mixed and oil dominant fields (such as the Williston basin), carbon intensity of the produced gas is dominated by tank emissions and flaring. We demonstrate that allocation choices (i.e., whether particular emission sources are assigned to oil and/or gas) have a large impact on the resulting carbon intensity. In Chapter 4, we perform a single-blind evaluation of the quantification capabilities of three aerial, airplane-based technologies (Bridger's Gas Mapping LiDAR, Carbon Mapper's Global Airborne Observatory, and GHGSat-AV) in blinded, controlled release trials with a focus on large emitters (10-2,000+ kg hr-1 CH4). In two 2021 campaigns, metered natural gas was released concurrently with overpasses by the tested technologies. Results were submitted by operators in a three-stage unblinding process. Detection rates of non-zero releases were 100%, 85%, and 86% for Bridger, Carbon Mapper, and GHGSat, respectively. The teams report parity slopes of 0.35 to 1.06, with R2 values of 0.35-0.78. We discuss implications for measurement methods and practical emissions reductions

Keywords: Energy Resources Engineering.
Advisor: Brandt Adam (Adam R.)
Jackson Rob
Ravikumar Arvind