VictorJuiz/thesis huggingface dataset:
Ultrafast Dynamics Studied by Few-Femtosecond Soft X-ray Transient Absorption Spectroscopy

X-ray Transient Absorption Spectroscopy (XTAS) has enabled real time measurements of numerous physical processes of great chemical importance. The technique is sensitive to electronic and molecular structure with element specificity and short time resolution. However, XTAS experiments that can probe carbon-containing molecules are still in their infancy. An apparatus is designed and constructed that provides efficient X-ray flux to probe transitions from the carbon 1s shell to the valence levels. The apparatus is able to use a visible pump pulse, less than 5 fs in duration, and a few-femtosecond cross-correlation is established. In vibrational studies of small molecules, core-excited potential energy surfaces along the symmetric stretch normal mode are extracted. Other vibrational modes are not observed, and the reasons are discussed as a limitation of XTAS. Dissociation of strong-field ionized CCl4+ is tracked in real time, measuring a time scale associated with Jahn-Teller distortion in this system. The signal also shows two previously unobserved intermediates in a symmetry-broken covalently bonded form of CCl4 + and a non-covalently bonded complex between a CCl3+ moiety and atomic Cl. Additionally, an excited state that results in Cl+ and CCl3 is observed. Finally, experiments measuring the electronic properties of solid-state systems are undertaken that show similar recombination times in semiconducting and metallic-containing single-walled carbon nanotube bundles, among other results.

Advisor: Leone, Stephen R
Neumark, Daniel M
Individual Differences in Visual Perceptual Biases

Human perceptual decisions are critically important. They can be socially significant, life-changing or even catastrophic if wrong, such as decisions made by radiologists, Olympic judges, TSA screeners, or vehicle operators. Many of these crucial judgments are made based on the perceptual expertise of individual observers. However, not everyone possesses the same level of expertise, even among professionals. There are two aspects that characterize the individual differences in expertise: variations in sensitivity and variations in bias. Past research has thoroughly investigated differences in sensitivity across many domains. However, the possibility of variations in perceptual bias has been largely ignored./nHere, we filled this gap by investigating the idiosyncratic variations in human visual perceptual biases. In the first study, we identified and characterized the unique perceptual biases in position perception and showed that these biases might originate from variations in visual acuity and they can influence the perceived appearance of objects, suggesting that idiosyncratic perceptual biases could propagate through different levels of visual processing and be associated with each other. To understand whether perceptual biases can have a real-world impact, in the second study, we further tested radiologists with artificial and realistic medical images and found that even expert radiologists were characterized with unique and systematic perceptual biases towards medical images. Given the prevalence of perceptual biases, the third study aimed to discover potential constructive ways to utilize the biases. We showed that when multiple observers make a collective decision, grouping individuals who have uncorrelated and thus independent perceptual biases can improve their combined performance./nTogether, these results highlighted the crucial importance of measuring and understanding individual perceptual biases and they have widespread implications for many real-world professions and situations such as driving, radiological or TSA screening and Olympic judges.

Advisor: Whitney, David
From Soot to Space: Using Quantum Mechanical Methods to Investigate Combustion and Astrochemical Processes

Theoretical models and simulations that describe chemical systems at the quantum mechanical level of electrons and nuclei have become essential components supporting experimental chemistry. The use of adequately accurate quantum mechanical simulations can help guide experiments in the laboratory, or assist in the interpretation of experimental results. Density functional theory (DFT) in particular has become the most widely used method in electronic structure due to generally good performance for a moderate computational cost. The work in this thesis primarily involves the application of DFT and other electronic structure methods to explore chemical processes in the realms of astrochemistry and combustion chemistry. For all studies, we employ the generally accurate $\omega$B97X-V density functional, and in some cases bolster our results using coupled-cluster methods with singles, doubles and perturbative triples correction (CCSD(T)). In Chapter 2, we characterize the cation, anion, and radical isomers of \ch{C4H4N}, as well as reaction pathways that may lead to these isomers. Small, nitrogenated species such as \ch{C4H4N} may be important precursors to pre-biotic molecules formed in non-Earth environments such as the atmosphere of Titan, a moon of Saturn. In Chapter 3, we investigate more Earthly processes that may lead to incipient soot particles during incomplete combustion reactions of hydrocarbons. Specifically, rate constants for hydrogen ejection reactions are calculated and reaction sequences are modeled for a suite of polycyclic aromatic hydrocarbons that are postulated to be quite important in radical-chain reaction pathways leading to soot. In Chapter 4, we return to the interstellar medium, this time exploring surface reactions on cold, icy grains. To this end, we explore the optimized complex geometries and binding energies of astrochemically relevant neutral closed-shell, neutral open-shell, anionic and cationic small molecules to water clusters of up to four waters. Such quantities are important for constructing accurate models of interstellar reaction chemistry, where cold grains play an important role in the production and observed abundances of gas-phase species.

Advisor: Head-Gordon, Martin
Meritocracy Reconsidered: The Politics of Civil Service Recruitment

A prominent literature in political science holds that the meritocratic recruitment of public servants leads to gains in bureaucratic performance. It is also believed that this institution ought to also have positive effects on social cohesion, since the meritocratic distribution of civil service jobs theoretically enables members from all groups---ethnic minority or otherwise---to win coveted employment in the public sector. Looking predominantly at Southeast Asia, and drawing on large scale surveys and archival documents, this dissertation presents an argument and evidence to the contrary. Instead, under certain conditions, the introduction of meritocratic civil service reforms perpetuates existing inequalities, as privileged groups outperform marginalized groups on entrance exams and go on to staff administrative posts at disproportionately high rates, an outcome that heightens group-based resentment and weakens national solidarity./nThis dissertation develops its argument in the context of an important but understudied tension between the twinned goals of state-building and nation-building---a trade-off that comes most into focus in the Asian context. At the moment of independence, the leaders of Asian states faced the urgent task of state-building, which mostly involved recruiting a competent corps of public servants to staff the organs of their new governments. But these leaders were also tasked with nation-building to generate a sense of solidarity across their diverse populations. These twinned goals often existed---and continue to exist---in tension with one another. The most "competent" applicants for public service typically hailed from historically privileged groups who had received formal education: the forward castes in India, the Javanese in Indonesia, or the Chinese in Malaysia. A narrow focus on state-building would have led to a disproportionate representation of certain groups in the apparatus of the state, which would have surely detracted from the task of nation-building.

Advisor: Dunning, Thad
Bridging Safety and Learning in Human-Robot Interaction

From autonomous cars to systems operating in people's homes, robots must interact with humans. What makes this hard is that human behavior--especially when interacting with other agents--is vastly complex, varying between individuals, environments, and over time. A modern approach to deal with this problem is to rely on data and machine learning throughout the design process and deployment to build and refine models of humans. However, by blindly trusting their data-driven human models, robots might confidently plan unsafe behaviors around people, resulting in anything from miscoordination to potentially even dangerous collisions./nThis dissertation aims to lay the foundations for formalizing and ensuring safety in human-robot interaction, particularly when robots learn from and about people. It discusses how treating robot learning algorithms as dynamical systems driven by human data enables safe human-robot interaction. We first introduce a Bayesian monitor which infers online if the robot's learned human model can evolve to well-explain observed human data. We then discuss how a novel, control-theoretic problem formulation enables us to formally quantify what the robot could learn online from human data and how quickly this learning could be achieved. Coupling these ideas with robot motion planning algorithms, we demonstrate how robots can safely and automatically adapt their behavior based on how trustworthy their learned human models are. This thesis ends by taking a step back and raising the question: "What is the ‘right’ notion of safety when robots interact with people?'' and discusses opportunities for how rethinking our notions of safety can capture more subtle aspects of human-robot interaction.

Advisor: Dragan, Anca
Tomlin, Claire
Inferring evolutionary history from whole genomes: outlier tests and methods based on the coalescent with recombination

Whole genome sequences allow for new, powerful inferences of evolutionary history. In addition to increasing the scale of application of traditional population genetic inferences by using them on an enormous amount of loci, genomic data also provide valuable information about the location of those sites and correlations among them, which can be leveraged for inference of evolutionary parameters. In this dissertation, I explore three types of evolutionary inferences that are thriving with the increasing availability of genomic data and new methods. In Chapter 1, I use simulations to evaluate methods for inference of ancestral recombination graphs. In Chapter 2, I apply a new method based on the pairwise sequential Markovian coalescent to infer population split times and migration rates from a pair of diploid genomes. In Chapter 3, I use population-level genomic data from the population of a rural village in Ecuador to infer signatures of selection possibly related to the beneficial effects of diet on their cardiovascular health.

Advisor: Nielsen, Rasmus
Ecological Outcomes of Cannabis Legalization: A multidisciplinary study of cannabis land use change, its social drivers, and environmental outcomes

Recent state-level legalizations of recreational cannabis across the US have created a large-scale policy experiment that could alter land use patterns and shape wildlife communities. Cannabis legalization provides a rare opportunity to study the consequences of land use change in a rural agriculture frontier. This dissertation explores the ecological outcomes of cannabis legalization by approaching cannabis landscapes as social-ecological systems and combining multiple disciplinary approaches. My research takes an interdisciplinary approach to quantifying land use change and contributes to our context-specific understanding of wildlife responses to human development. Ultimately, this research provides results that are timely and may be useful for policy, management, and land use decision-making./nI begin with an introduction on cannabis landscapes as social-ecological systems. I outline a brief history of cannabis farming in the western US, as well as an explanation of my focus on small-scale legacy cannabis farming. Then, in Chapter 1, I produce baseline distribution data on cannabis land use in southern Oregon, and examine the overlap with sensitive ecological features. This chapter addresses the questions: How is cannabis production distributed? Where might we be concerned about its environmental impact? In Chapter 2, I use cannabis farmer interviews to generate model covariates which contextualize the cannabis land use data from the first chapter. Here, I ask: What drives cannabis land use over time? What is the socio-ecological context for these drivers? In Chapter 3, I use wildlife camera data to examine the outcomes of cannabis land use for animals on and surrounding cannabis farms. This chapter focuses on the question: How do wildlife respond to cannabis farming? In Chapter 4, I detail the methodology for field experiments that address specific mechanisms of wildlife response to cannabis cultivation, asking: What is the role of light and noise disturbance in multi-taxa wildlife response to cannabis? Finally, I conclude with a summary of the broad implications of this work, as well as future research and policy recommendations.

Advisor: Brashares, Justin
Exciton-Phonon Interactions in Complex Materials

The interaction between excitons and phonons is fundamental to the nature and fate of photoexcitatons in solids, with important implications for spectroscopy and transport measurements, and for applications in optoelectronics and clean energy. In this dissertation, we present recent advances in computing exciton-phonon interactions from first principles. We implement a recently proposed linear-response reciprocal space-based framework which involves contracting electron-phonon matrix elements computed from density functional perturbation theory (DFPT) with exciton expansion coefficients obtained after building and diagonalizing the ab initio Bethe-Salpeter Hamiltonian, which is built on density functional theory and the GW approximation. We apply this formalism in unique ways to study phenomena related to exciton-phonon interactions, namely the nature of exciton diffusion in acene crystals, the dynamical screening of excitons in halide perovskites due to lattice vibrations, and the asymmetric lineshapes in MoS2 stemming from off-diagonal exciton-phonon coupling.

Advisor: Neaton, Jeffrey B
Empirical Study of Connected Vehicle Data and Its Application to Traffic Measurement

Connected Vehicle data is an emerging technology that offers to broadly measure traffic at ascope not accessible with traditional traffic measurement systems. Using GPS and vehicles onboard data, we can gain a broader perspective of traffic. The current adoption rate of connected vehicles is near 2-3 percent, but offers wide-area coverage. The resulting sparsity of data offers new challenges in extracting useful measures of the traffic state. This work focuses on an empirical study of available connected vehicle data collected from the San Francisco and Los Angeles areas. We explore how the penetration rate of connected vehi- cles in these areas influence the fidelity of extracted traffic flow measurements. Leveraging the accuracy of the GPS samples, we explore separating data into lane-level distinctions. We developed penetration rate agnostic estimates of queue length distributions at intersec- tions. Utilizing the repeatability of timed intersections, we derive spatiotemporal diagrams of a major throughway and connect them to a macroscopic fundamental diagram. Finally, we explore new accident risk calculations based on extracted maneuver level information. Connected vehicle data offers new and exciting insights that will continue to improve with increased penetration rate.

Advisor: Poolla, Kameswhar
Policing the Demos: Liberalism and the Birth of Police Discretion

This dissertation examines the changing relationship between democracy and the police in the last half-century of police form. Specifically, it offers a genealogy of the concept of police discretion in several foundational texts in police science and explores the ways in which the shifting meaning and dangers of police discretion have mediated that relationship. Beginning with President Johnson’s Crime Commission, the dissertation charts how police discretion transformed from the central dilemma of policing in a democratic society to near total disappearance as an object of police reform. In so charting, it shows how the Crime Commission’s preference for controlling discretion through administrative legalism was replaced by a concern for squaring discretion with the norms and standards of legitimacy that underwrite law. That concern is evidence not only in academic studies of police behavior like those developed in the works of Jerome Hall, Jerome Skolnick, James Q. Wilson, and Egon Bittner, but appears as well in the Supreme Court’s Due Process Revolution cases. The dissertation submits that police discretion is best understood in terms of political decisionism, and it argues that these transformations are the result of liberalism’s failure to coherently account for decisionistic power. Consequently, the dissertation contends, police reform has expanded rather than curtailed police discretion and its attendant violence. Moreover, it demonstrates how discretion is reprogrammed in the era of community policing, and it argues that not only is the impasse between police abolition and police reform best understood as a result of liberalism’s faults, but that community policing’s moral-political rationality complicates the prospects for both.

Advisor: Simon, Jonathan
Zimring, Franklin
Critical Literacies and Cosmopolitan Sensibilities: Traversing Digital Media Spaces, Building Socially Just Global Places

My dissertation seeks to explore opportunities for disruptions—moments of learning in (digitally) networked spaces that seek to challenge, subvert, and reimagine our worlds. Drawing primarily on sociocultural theories of literacy and learning, I explore the ways that learning, mediated by digital technologies, takes place in formal and informal educational spaces. The world that students inhabit is increasingly characterized by rapidly spreading information (and mis-information) and the ease of connectivity made possible by the proliferation of technology can significantly affect the lives of others near and far, for better or worse. As such, my dissertation consists of five chapters that seek to: (1) add to conversations conceptualizing the kinds of skills and dispositions entailed in our current world and (2) to interrogate how learning occurs through the quotidian interactions in the everyday lives of children, adolescents, and young adults. The central chapters of my dissertation are three empirical pieces that draw on data collected across different educational contexts wherein digital media are regularly used by students as tools in creating and sharing knowledge.

Advisor: Hull, Glynda A
Murphy-Graham, Erin
Socioeconomic Background and Judgments of Economic Inequality

The current investigation documented how individuals of different socioeconomic backgrounds differ in their judgments of the severity of economic inequality. Seven empirical studies provide robust support for the central hypothesis that those of higher (vs. lower) socioeconomic backgrounds will judge wealth distributions as less severely unequal. Further analyses suggested that these differences (in economic inequality judgments) can, in part, be explained by differences in economic system justification (i.e., stronger among those of higher socioeconomic backgrounds). Toward the end, I demonstrated how such differences can lead to, and explain, diverging reactions (in emotions and attributions) toward other societal issues – in current data, the unhousedness problem.

Advisor: Keltner, Dacher
Randomized methods and statistical inference

When faced with difficult problems, randomized approximations are a tool commonly employed by quantitative scientists which can offer alternative algorithms and analyses. In this dissertation, we consider a range of problems at the intersection of randomized methods and statistics. Throughout our work, randomized methods are a unifying theme used in the first section to construct tractable approximations and later as tools for automating computational Bayesian statistics./nInitially in the first three chapters, we develop novel analyses to recent problems in experimental design, double descent, and random projections by using determinantal point processes (DPP). We first consider approximately optimal Bayesian experimental design using an adaptive row sampling algorithm based on DPPs. In the subsequent chapters, we generalize the proof techniques to study double-descent in over-parameterized least squares linear regression and establish expectation formulae for sub-Gaussian matrix sketching./nIn the later chapters we focus on probabilistic programming and developing theory and tools to automate statistical inference using randomized algorithms based on Monte Carlo Markov chain (MCMC) and variational inference (VI). We first consider lightweight inference compilation (LIC) whichcombines deep learning with MCMC through parameterizing proposers q(x) by graph neural networks which condition each node on its Markov blanket. The next chapter considers tail anisotropy in multivariate heavy tailed target densities p(x) and proposes fat-tailed variational inferene (FTVI) to approximate them. Our work concludes on the generalized Gamma algebra (GGA), where we address the analysis of fat tails during static analysis of a probabilistic program’s source code. This enables a priori computation of tail parameters, which we show improves the stability and convergence of a number of inference tasks.

Advisor: Mahoney, Michael
Hammond, Alan