VictorJuiz/thesis huggingface dataset:
Light harvesting for decarbonization through artificial and natural photosynthesis
Keywords: Chemical Engineering.

Photosynthesis offers promise in extending the reach of solar energy from the power sector further into the heating and transportation sectors while simultaneously enabling carbon sequestration on a global scale. While artificial photosynthesis (APS) might be used to provide sustainable fuels and chemicals, the field has demonstrated relatively slow progress over its ~80-year history. Despite select systems surpassing 10% energy efficiency, APS processes remain too costly to address our energy and material needs on any meaningful scale. Meanwhile natural photosynthesis (NPS) in inherently inefficient. Though the green revolution more than doubled the yields of essential grains, crops typically remain limited to energy conversion efficiencies below 1%. Given the urgency of today's climate crisis, these trajectories are unacceptable. The field of APS, especially photocatalysis, needs clearer direction to drive forward progress in material and system development. In parallel, attempts to improve NPS would benefit from innovative, scalable ideas. In this work, problems associated with experimental and reporting conventions in the field of photocatalysis are revealed that hinder the forward progress of photocatalytic material development. Common normalization conventions are seen to artificially inflate the apparent performance of multi-component systems by up to ~100%, and experimental instabilities result in up to ~400% variation in apparent performance for select multi-phase systems. A normalization technique and experimental guidelines are provided to address these issues. In the context of NPS, a novel approach to enhancing energy efficiency is introduced, employing luminescent materials to target the vast underutilization of available sunlight by plants. The approach is three-fold: spectral redistribution of sunlight to drive photosynthesis deeper within canopies; short-term temporal redistribution to smooth over light fluctuations; and long-term temporal redistribution to extend daylight hours. To enable temporal redistribution, a luminescence concentrating device is presented, with prototypes demonstrating peak intensities of ~1 W/m2, a nearly 30× increase over the raw material and approaching intensities relevant to photosynthesis. It is far from clear what the respective roles of APS and NPS will be in our future energy and environment landscapes. It is clear, however, that existing technologies are insufficient within political, economic, and social constraints. The trajectory of APS needs to be critically reexamined, efforts focused, and innovation in photosynthesis—artificial and natural—promoted
Limiting Students’ Capabilities: Drug Trafficking Organization Violence and School Leaving in Mexico, 2000-2017
Keywords: Mexican drug war
School leaving rates
Capabilities approach
Upper-secondary education
Violence
Stanford Graduate School of Education International Education Policy Analysis
Thesis

I investigate the relationship between drug-trafficking organization (DTO) presence and violence and school leaving in upper-secondary education in Mexico at the municipality level from 2000-2017. I rely on a fixed-effects model and find that both homicides and DTO presence increase school leaving rates among upper-secondary school students in Mexico by 0.1 and 1.2 percentage points, respectively. These results are understood through the capabilities approach framework to argue that violent crime and the presence of DTOs are influential factors in the level of educational attainment in a municipality because they limit the freedom of students to continue their education.
Line-intensity mapping with the CO mapping array pathfinder and beyond
Keywords: Physics.

Line-intensity mapping (LIM) is a relatively novel technique that, similar to studies of the cosmic infrared background radiation, uses spatial-spectral fluctuations in spectral line emission as proxy for faint and bright star-forming galaxies in the first several billion years of cosmic history. LIM surveys have strong scientific motivation in open questions about the history of cosmic reionisation and subsequent star-formation activity, and will be strongly complementary to space-based galaxy surveys through JWST and WFIRST. The CO Mapping Array Pathfinder (COMAP) is a blind CO intensity survey directly targeting redshift ~ 3 that will study the galaxy population at this redshift in aggregate. The present thesis reflects both experimental and theoretical work done in the course of the development of COMAP. This work ranges from test observations to isolate and characterise standing waves in the system, to forecasting foregrounds, cross-correlations, and line-of-sight anisotropies. The final part of this thesis also looks beyond COMAP, considering [C II] intensity mappers directly targeting the cosmic epoch of reionisation, and making signal and sensitivity forecasts for these surveys
Lobbyists, Public Relations and “Malign Activities”: The JCPOA as a Case Study of Foreign Influence
Keywords:  Center for International Security and Cooperation
Center for International Security and Cooperation
foreign influence
Iran
Saudi Arabia
Israel
JCPOA
Thesis

In July 2015, the P5+1 announced the conclusion of the Joint Comprehensive Plan of Action (JCPOA), known colloquially as the “Iran nuclear deal,” an agreement between Iran and the P5+1 to place limits on the Iranian nuclear program. The agreement was controversial not only within American domestic politics, but also in the eyes of American allies in the Middle East that are commonly thought to exercise significant influence over American foreign policy. Using the JCPOA as a case study on foreign influence in American policymaking, this thesis analyzes the influence strategies of Israel, Saudi Arabia, and Iran from 2013 to 2018 while assessing the efficacy of these strategies. Based on the theoretical literature, this thesis proposes four different pathways to influence: direct and indirect lobbying, and direct and indirect information operations. As a result of forty interviews with former government officials, ambassadors, policy advocates, and regional experts, this thesis finds that while foreign influence does not drive US foreign policy, it can act as an important intervening factor, raising or lowering the costs of political decision-making and amplifying existing preferences within the American political system. Thus, this thesis provides a nuanced perspective on foreign influence in American policymaking, showing the real impact that such campaigns can have while eschewing the idea that American policymakers lack agency in important foreign policy matters.
The long arm of God : how religious commitments shape adolescents' academic outcomes in secondary and post-secondary institutions
Keywords: Education.

Religion permeates every aspect of American society. Religion is a particularly salient feature for millions of American teenagers: half see faith as central to their daily life, and one in three say they pray daily. However, scholars of education have paid little attention to the way that religion affects academic outcomes in non-sectarian educational settings. In my dissertation, I examine how adolescents' religious commitments shape their academic outcomes in secondary school and post-secondary institutions. My empirical evidence is based on quantitative and qualitative analyses of two longitudinal and nationally representative data sets of American adolescents: The National Study of Youth and Religion and the National Longitudinal Study of Adolescent to Adult Health. My dissertation consists of five papers. The first paper synthesizes prior literature about how religion is associated with academic outcomes. In the second paper, I examine whether the "effect" of religiosity on academic outcomes is causal or spurious. Having established that the positive relationship between religiosity and academic outcomes is likely causal, my third and fourth papers asks why more religiously engaged high school students earn better grades. In the fifth paper, I examine how religion complicates narratives about women's pathways through college. Overall, my dissertation findings show that the synergy between Christianity and secondary schooling creates an academic advantage for students with conventional Christian commitments, while their commitment to rule-based orderliness shapes their path in unexpected ways once they transition into higher education. By highlighting the ways in which religious commitments and educational norms coalesce and the ways in which they collide, this dissertation reveals the powerful yet invisible role that religion plays in shaping the educational outcomes of young adults in the US. The dissertation also complicates traditional narratives of educational inequality that focus exclusively on the role of race, social class and gender, without accounting for religion
A loud voice and a big stick : essays on military influence in America
Keywords: Political Science.

This dissertation is devoted to studying the role of military elites in American politics. I argue that American military elites are political actors: they have discernible patterns of partisan affiliation, they influence presidential decision-making on whether to use military force during foreign policy crises, and they can exert broad influence on Americans' attitudes. In contrast to President Teddy Roosevelt's philosophy, American military elites have a loud voice and carry a big stick: they wield political influence while commanding the vast military capabilities of the United States. However, contrary to normative concerns over military influence found in the civil-military relations literature, I argue that military influence is not necessarily a malignant feature of contemporary American politics, as military influence appears to be constrained in several important ways. Over several empirical chapters, I assemble evidence for this interpretation of military elites as political actors. In doing so, I advance the civil-military relations literature by introducing novel data into the literature, by leveraging innovative methods such as survey experimentation, and by applying rigorous quantitative methods to the study of civil-military relations
Lovecraft and the Question of an Uninhabitable Universe
Keywords: Comparative Literature
DLCL
Honors
Thesis

This thesis critically analyzes the work of H.P. Lovecraft, long immured in the obscurity of pulp magazines. Posthumously recognized as the doyen of cosmic horror, Lovecraft occupied a unique and lingering slice of horror literature, rich with philosophical content. This thesis answers the question of why one, as both a scholar and a reader, should concern oneself with the unrelentingly bleak literature Lovecraft produced. I argue that his work can be understood as a continual attempt to convince its reader that the universe is uninhabitable. My case rests on a belief that Lovecraft's arguments for this provocative claim could not be made through philosophy alone -- the structure of his thought closely resembles that of a theologian. I locate in his work a rich, albeit unwitting, correspondence with philosophers and theologians of history such as Giambattista Vico, Friedrich Nietzsche, and Karl Löwith. Ultimately, this theological and philosophical interrogation of Lovecraft's work allows us to properly understand those literary tropes forever associated with the word "Lovecraftian," and lays a foundation for future scholarship and recognition of Lovecraft's body of work. Winner of the DLCL Award for Outstanding Thesis (2020).
Luminaries Working Toward Change: An Analysis of the Barriers Preventing the Advancement of Women Leaders and a Proposition of How We Move Forward Together
Keywords: leadership
luminaries
women leaders
identity
diversity
career advancement
barriers
Science Technology and Society
Thesis

Through an extensive discussion of the current discourse on women’s leadership in the United States today as well as data collected from five in-depth interviews with women leaders from across industries, this research shines light on the following proposition: positive change for women pursuing leadership roles happens through connection at the level of the self and connection to others. A competing narrative emerges in the current discourse. Some experts and practitioners point to structural and systemic issues such as gender bias and the double bind within the workplace that prevent women from advancing in their careers, while others claim that self-imposed barriers such as the impostor syndrome prevent women from advancing. Results from the five interviews with a diverse group of women in top leadership roles highlight how these deep-rooted and multifaceted barriers underscore the need for a reframing of the discourse and for transformative change. Key themes addressing how we move forward together became apparent in these interviews, such as authenticity, shared vision, and relationship-building. Authentically embracing one’s intersectional identity, leading with a shared vision, and focusing on relationship-building are strategies applicable to women and men’s advancement - noting how leadership can be shared across gender identity and is not a zero-sum game. Rather, men and women can advance together. However, there is no one-size-fits-all solution. As luminaries, we must break through those barriers and employ new strategies collaboratively to ensure that leadership is more representative of the populations that those leaders serve.
Machine learning analytics for data-driven decision support in healthcare
Keywords: Electrical Engineering.

Machine learning has the potential to revolutionize the field of healthcare. With the increasing availability of electronic healthcare data, machine learning algorithms and techniques are able to offer novel data-driven insights in the form of descriptive, predictive, and prescriptive analytics. Research efforts in machine learning-driven clinical decision support systems have demonstrated performance comparable to, or surpassing, that of doctors across a wide range of disciplines. However, very few of these solutions are implemented and used. This may be due to the solution being too specialized, too difficult to operationalize, or both. My research in machine learning for clinical decision support has focused on delivering broadly applicable and clinically actionable predictions for heart disease and opioid use and misuse. As some of the leading causes of death in the US and worldwide, these are important public health concerns. A less-explored facet of decision support in healthcare lies on operational delivery of care: improving hospital efficiency, modeling patient admissions and discharges, and preventing medical errors. While these research topics are not as popular as their clinical counterparts, the potential for real-world improvement through the study of these issues is far greater in the near-term. In this dissertation, I present novel contributions spanning both the clinical and operational delivery of care. I focus on four lines of data-driven research which have the potential to deliver widespread impact: heart disease prediction, opioid use prediction in pediatric patients, medical error reduction, and hospital discharge planning and resource allocation
Machine learning for small molecule lead optimization
Keywords: Chemistry.

The development of small molecule drugs is a lengthy and expensive process that could potentially be improved by new technologies. Lead optimization is an important part of small molecule drug discovery, where initial hit molecules are gradually developed into suitable drug candidates. It can be described as an iterative cycle of design, make and test phases, which can be further broken down into a series of concrete sub-problems, namely: molecular property prediction, molecule generation, chemical synthesis planning, experimental chemical synthesis, and experimental testing. This thesis explores machine learning methods to tackle a few of the sub-problems in small molecule lead optimization, with a focus on the early design phases
Magnetoresistive and magnetostrictive sensors for the mobile era
Keywords: Materials Science and Engineering.

Magnetoresistance is the property of a material to change the value of its electrical resistance in an external magnetic field, which has been utilized in a variety of sensing applications. Magnetostriction is the phenomenon of a magnetic body to change its shape and dimension as a function of an applied magnetic field. Magnetostrictive materials have been widely used as bulk resonators in electrical article surveillance system and studied for small-mass-detection-based portable sensing applications。 The first part of this work focuses on developing magnetoresistive and magnetostrictive sensors towards higher accuracy, higher sensitivity, and smaller physical sizes for mobile applications. Here a new methodology has been developed to guide the sensing system design. Besides, we have fabricated magnetostrictive resonators with miniaturized sizes and much larger resonant frequencies. Moreover, the resonant signals maintain good signal-to-noise ratios and quality factors. Second, a novel anisotropic magnetoresistive (AMR) angle sensor with multilayer material stack has been proposed and fabricated. Compared with traditional single layer sensors, our fabricated quadruple-layer AMR angle sensors have improved the sensing accuracy by a factor of 5. In addition, the quadruple layer AMR angle sensor demonstrates good detection accuracy even at small magnetic fields, which helps to lower the requirement of hard magnets in AMR angle sensing system Finally, the development of AMR angle sensors with stray field immunity is discussed. The signal of AMR angle sensors can be corrupted with significant errors due to the existence of stray fields in real application environments. Here we have designed a new system consisted of four AMR angle sensors along with a multipole magnet. Using the output signals from all the four sensors, the errors from stray fields are effectively reduced, which enables the implementation of AMR angle sensors in a wider range of applications
Making causal conclusions from heterogeneous data sources
Keywords: Statistics.

The modern proliferation of large observational databases -- in fields such as e-commerce and electronic health -- presents challenges and opportunities for applied researchers. Such data can contain rich information about causal effects of interest, but the effects can only be estimated if we make untestable assumptions and carefully model the assignment mechanism. Experimental data provides a "virtuous" counterpart for the purposes of inferring causal effects, but randomized trials are often limited in size and, consequentially, lack precision. In this thesis, we consider problems of "data fusion, " in which observational and experimental datasets are used together to estimate causal effects. The problem is considered from three angles. First, we develop methods for merging experimental and observational causal effect estimates in the case when all confounding variables are measured in the observational studies. Next, we remove the unconfoundedness assumption, which leads to a new class of estimators based on a shrinkage approach. Finally, we propose a novel solution for designing experiments informed by observational studies, making use of the regret minimization framework. Throughout, we deploy tools from disparate areas of the literature, including Empirical Bayes, decision theory, and convex optimization
Making sense of the feedback loop between the media and the criminal justice system
Keywords: Law JSD.

My dissertation focuses on the connections between criminal law, race, media, and society. Adopting an interdisciplinary approach, I focus on three interrelated components: the criminal law in action, in particular sentencing decisions; media representations of crime, criminals and victims; and how the interactions between these institutions and representations shape the criminal justice system. My dissertation aims to push the scholarship on the effects of mass media on the criminal justice system a step forward, and to empirically assess and explain both the existence and the magnitude of such effects, with particular attention to the treatment disadvantaged minorities receive from the criminal justice system. My first two papers explore racially skewed media representations of defendants and victims, and discuss how these representations dominate the criminal justice system and affect criminal law and policy. The third paper investigates how increased coverage on crime leads to increased harshness in sentencing among judges in state trial courts, and explain these relationships based on the media's role in instilling fear of crime in society. Utilizing mainly quantitative research methods, I bridge a gap in the empirical scholarship and provide evidence to better understand the feedback loop between the media and the criminal justice system. I demonstrate how institutional dynamics shape social learning on crime in society, corroborate stereotypical thinking, and preserve and perpetuate inequalities in the criminal justice system. By doing so, I go beyond traditional institutional exploration of the criminal justice system to provide a more holistic view of how Americans in general, and legal professionals in particular, perceive and discuss crime in society. Moreover, I offer a novel multidisciplinary lens to address recurrent themes in the scholarship on racial disparities in the criminal justice system
Many Faces of Many-Body Quantum Chaos: From Periodic Orbits to Semiclassical Fields and Back
Keywords:  Department of Physics
Quantum Chaos
BGS conjecture
Large N systems
Floquet systems
Quantum Dynamics
Thesis

The conventional paradigm for statistical physics starts with the assumption that all microstates of an isolated system are equally probable in equilibrium. In classical mechanics, due to inherent non-linearities in the equations of motion, an ensemble of states concentrated in a small region of phase space very rapidly explore the entire allowable phase space. This ergodic property justifies the fundamental statistical assumption. In quantum mechanics, however, Schrodinger evolution is always linear in Hilbert space and ergodicity in the naïve sense does not apply. In this thesis, we explore one notion of quantum ergodicity: random matrix statistics of the energy spectrum. For few-body quantum systems that admit a semiclassical limit, Andreev et al. (arXiv:cond-mat/9601001) have proven that this notion of quantum ergodicity follows directly from the classical notion of ergodicity. We extend this work in two ways, first to many-body quantum systems (including various large N systems familiar in high energy physics) with emergent semiclassical limits, and then to a class of many-body Floquet spin chains with no semiclassical limit. In the end, we discuss limitations of our approach and make some comments on future directions to pursue.