VictorJuiz/thesis huggingface dataset:
Imaging Spontaneous Neuronal Activity with Novel Voltage Sensitive Dyes

In order to fully characterize complex biological systems such as neural circuits, rapid and accurate mapping of cellular membrane potentials is highly desired. Voltage sensitive dyes offer a highly advantageous solution to this problem through inserting into cellular membranes and displaying changes in fluorescence quantum yield in response to changes in membrane potential. One class of voltage sensitive dyes, VoltageFluors (VF dyes), use a photoinduced electron transfer (PeT) mechanism to sense changes in membrane potential. These dyes consist of an electron-rich aniline donor group, a conjugated molecular wire, and a fluorophore acceptor group. In this dissertation, we examine various derivatives of VF dyes to investigate how modifications to the aniline donor and molecular wire regions affect the voltage sensitivities and signal-to-noise ratios of VF dyes. We also incorporate sulfone rhodamine chromophores into the VF Dye scaffold in order to generate voltage sensitive dyes which absorb and emit above 700 nm. Finally, we apply VF Dyes to dissociated neurons to optically investigate differences in neuronal excitability and connectivity at different stages of development and in different disease models.

Advisor: Miller, Evan W
It's important for us to get recognized and be somebody: Community health worker attributes, attitudes towards certification, and certification options in California

Community health workers (CHWs) are trusted members of the community who have an intimate understanding of the populations and communities they serve. They deliver culturally informed interventions to their communities and leverage their shared experiences and linguistic and cultural relationships to bridge their community to health care and social services. Historically successful in developing countries, CHWs promote chronic disease management, improve health outcomes, and reduce health care costs./nDespite the evidence, CHWs are not widely utilized within the health care system. The National Academy of Medicine declares barriers to working with CHWs, such as inconsistent scope of practice, variable training and qualifications, and lack of professional recognition by other health care providers. States are investigating ways to standardize the CHW workforce, such as certification, to set workforce entry standards and integrate CHWs into health care systems./nCurrently, the CHW workforce faces a crossroads. One path leads to a standardized CHW workforce integrated into health care systems via formalized training and qualifications. The other path holds CHWs as part of the communities where they live and work, valuing their relationships and embodied knowledge. While both paths are options for the CHW workforce, CHWs and CHW stakeholders must determine if or how these two paths can coexist. California is the perfect case study because there is continued debate across CHW stakeholders about the stakes of certifying CHWs. California’s diverse CHW workforce represents varying CHW types with contrasting ideologies of care, such as clinical and community-based CHWs and promotores de salud, a subset of CHWs who primarily serve Latinx communities and are grounded in a social, rather than medical, model of care. California’s size and social characteristics pose implementation factors that are relevant for the diverse issues other states will have to address for their own CHW workforce./nThis dissertation identifies the unique contributions offered by CHWs to fill health system gaps and challenges differently than other health care providers. The research then describes feelings of opportunity and exclusion related to CHW certification in California. Finally, this dissertation presents strategic options for California stakeholders to develop an appropriate CHW certification model.

Advisor: Snell Rood, Claire
Mobile Robot Learning

In order to create mobile robots that can autonomously navigate real-world environments, we need generalizable perception and control systems that can reason about the outcomes of navigational decisions. Learning-based methods, in which the robot learns to navigate by observing the outcomes of navigational decisions in the real world, offer considerable promise for obtaining these intelligent navigation systems. However, there are many challenges impeding mobile robots from autonomously learning to act in the real-world, in particular (1) sample-efficiency---how to learn using a limited amount of data? (2) supervision---how to tell the robot what to do? and (3) safety---how to ensure the robot and environment are not damaged or destroyed during learning?/nIn this thesis, we will present deep reinforcement learning methods for addressing these real world mobile robot learning challenges. At the core of these methods is a predictive model, which takes as input the current robot sensors and predicts future navigational outcomes; this predictive model can then be used for planning and control. We will show how this framework can address the challenges of sample-efficiency, supervision, and safety to enable ground and aerial robots to navigate in complex indoor and outdoor environments.

Advisor: Levine, Sergey
Abbeel, Pieter
Brown v. Board of Education’s Midlife Crisis: Exclusionary School Discipline and Disproportionality in Special Education

Disproportionality is defined as the extent to which membership in a given group, such as gender, race/ethnicity, or socioeconomic strata, differentially affects the probability of a given outcome (e.g., being placed into special education or being assigned an out-of-school suspension). Underpinnings of disproportionality study include the broad sociohistorical and sociocultural issues of equity and social stratification by race, language, class, and ability that place already marginalized groups at a disadvantage. Though federal and state agencies have identified overrepresentation of students from racially and ethnically diverse backgrounds in both special education and exclusionary discipline as a significant problem in the field, quantitative studies have presented inconsistent findings on whether or not students from ethnically and racially diverse backgrounds are overrepresented in special education, and studies related to reduction of disproportionality in exclusionary discipline are scant. A broad understanding of social inequalities writ large may be the first step in mitigating these phenomena. This dissertation research aims to increase collective understanding of these processes.

Advisor: Rabe-Hesketh, Sophia
Rodl, Janelle E
Uncertainty quantification of reduced models

Detailed reaction models such as detailed soot models, describing complex phenomena in combustion are typically computationally intensive. Reduced reaction models derived from a detailed, full-size reaction model are, thus, necessary in combustion simulations. The validation of a reduced model typically requires its predictions for selected quantities of interest (QOIs) to be close to those of the detailed model. Moreover, an accurate reduced model should be capable of reproducing faithfully the propagation of uncertainty by the detailed model./nSeveral reduced models for syngas combustion were developed by the "detailed reduction" method. This method was also adapted to develop several reduced models for a stochastic soot oxidation model, which are typically difficult to develop with other reduction methods. Different measures were developed to assess how uncertainties in the model parameters and in the model predictions behave for reduced models as compared to those for the detailed model. The uncertainty quantification (UQ) analysis was carried out through a numerically efficient, deterministic, and optimization-based framework of Bound-to-Bound Data Collaboration (B2BDC) and a Gibbs sampling algorithm adopted for B2BDC. The measures developed can be categorized into the sampling- and optimization-based measures./nThe measures were applied to several reduced models of syngas combustion and several reduced models of soot oxidation in three different examples. The developed measures successfully quantified the propagation of uncertainty by the detailed and reduced models, and numerically demarcated the performance of different reduced models. The results demonstrated that assessment of the quality of a reduced model without considering parameter uncertainty may be misleading in that the deviation can be much larger when the uncertainties in the model parameters are taken into account, highlighting the significance of UQ analysis in the validation of reduced models. The performed analysis demonstrated that when the experimental data are of bad quality, the posterior region of the model parameters (the feasible set) could have a very complex shape, posing a substantial challenge to the sampling-based measures. If no verifiably accurate experimental data exist, computer-generated data from the solution of the detailed model offer a reliable alternative, in which the desired level of reduced-model accuracy can be prescribed by specifying the accepted ranges of variations in prediction of training targets. If a feasible set has a very complex shape, uniform sampling of the feasible set could be very expensive. In such situations, the B2BDC framework offers a more practical alternative by quantifying the propagated uncertainty through numerically efficient computations of uncertainty intervals and their overlap, all with the added benefit of obtaining the uncertainty sensitivities.

Advisor: Frenklach, Michael
The role of the prefrontal cortex in stress and motivation

Stress and motivation are inextricably intertwined. Importantly, stress, especially early in life, is associated with increased substance use and risk of addiction. Furthermore, there is high co-morbidity between stress-induced disorders such as PTSD and motivation disorders such as addiction and depression. This is suggestive of an overlapping neurobiological mechanism of vulnerability. The prefrontal cortex (PFC) is implicated in both stress and motivation circuitry and thus, may be a critical link between the two. It is highly connected to limbic regions, including the amygdala and hippocampus, as well as to regions crucial for motivation and reward such as the striatum and ventral tegmental area (VTA). The PFC is also highly sensitive to stress and has an extended period of experience-dependent plasticity as it continues to develop through adolescence. Thus, it is important to understand PFC circuitry, how it changes over development in response to stressors and how factors such as stressor type and sex might contribute to individual variability following stress exposure./nIn this thesis, I explore the role of prefrontal cortex circuitry in both stress and motivated behaviors using rodent models. In chapter one, I introduce the stress response, the relationship between stress and motivated behaviors, and describe the prefrontal cortex systems that connect them. In chapter two, I elaborate on part of this circuitry, describing the anatomy of VTA dopaminergic and GABAergic projections to PFC subregions, as well as to other limbic brain areas. In chapter three, I investigate how responding to the distress of another (vicarious stress) leads to biased motivated helping behavior depending on group membership and I describe the neural circuits underlying this behavior. Further, I address how helping behavior and the neural circuits involved change across development. I present evidence that in-group biases arise over development and that pro-social intent toward ingroup members recruits distinct neural circuits. In chapter four, I explore the effects of an acute traumatic stressor on PFC oligodendrocytes and myelination. I show there are sex-specific differences; females are preferentially affected, displaying stress induced increases in PFC myelin in the short term, yet decreased myelination in the long term relative to controls. Finally, I provide closing thoughts, offer future directions, and elaborate on the relevance of the PFC in human disorders. Overall, this body of work provides insight into stress and motivation circuitry, with a focus on the prefrontal cortex.

Advisor: Kaufer, Daniela
Discoverable Matter: an Optimist’s View of Dark Matter and How to Find It

An abundance of evidence from diverse cosmological times and scales demonstrates that/n85% of the matter in the Universe is comprised of nonluminous, non-baryonic dark matter./nDiscovering its fundamental nature has become one of the greatest outstanding problems/nin modern science. Other persistent problems in physics have lingered for decades, among/nthem the electroweak hierarchy and origin of the baryon asymmetry. Little is known about/nthe solutions to these problems except that they must lie beyond the Standard Model. The/nfirst half of this dissertation explores dark matter models motivated by their solution to/nnot only the dark matter conundrum but other issues such as electroweak naturalness and/nbaryon asymmetry. The latter half of this dissertation approaches the dark matter enigma/nfrom a different vantage point inspired by the null results at dark matter direct detection/nexperiments. The theory community has explored alternative dark matter candidates and/nproduction mechanisms while the experimental program has made progress on larger and/nmore sensitive experiments. In this dissertation, we take a complementary approach by/ninvestigating signals of novel dark matter models which may have been overlooked in current/nexperiments.

Advisor: Murayama, Hitoshi
SEParating the Cream: Selective Enrollment Public Schools and their Districtwide Effects

Fierce local debates throughout the United States surround the equity of admitting students to public schools using academic criteria. Although research has evaluated the central assumption of these debates—that Selective Enrollment Public (SEP) schools enhance the welfare of students who attend them—none has addressed the district-level outcomes associated with these schools. This is important because the selectivity and scope of SEP schools produce tiered school systems (SEP districts). This district-level process, in turn, calls for an analysis of district-level educational outcomes. To address this gap, I compile an original list of SEP schools using an innovative web scraping procedure. I combine these data with secondary sources from the Common Core of Data, the Education Demographic and Geographic Estimates, the Stanford Education Data Archive, and the Civil Rights Data Collection to evaluate the districtwide effects of SEP school on three sets of key educational outcomes: segregation, achievement, and access to college preparation./nBefore presenting these three quantitative analyses, I offer a detailed definition and background of SEP schools, which have been given little theoretical consideration by sociologists. The substantive analyses then begin by considering the first order question of whether sorting students on the basis of performance has implications for students sorting along other dimensions, namely race. I find evidence of some differences in patterns of segregation among white and under-represented minority (URM) students in SEP versus non-SEP districts, particularly in high school, which may suggest less stability for URM students attending predominantly white schools in SEP districts. With a better understanding of how SEP schools shape student sorting, I then evaluate districtwide academic outcomes. Using a grade-level Difference-in-Differences design, I find evidence of slower math achievement growth and widening white-Latinx gaps in SEP districts from third to eighth grade. Finally, evidence from Seemingly Unrelated Regression models suggests that, despite a narrower provision of Advanced Placement (AP) courses, AP enrollment rates are just as high (or higher) and exam passing rates are higher for white and Latinx AP participants in SEP versus non-SEP districts. However, white-URM gaps in college preparation are widest in SEP districts./nOverall, evidence of the effects of SEP districts on the average quality of education is mixed, but evidence suggesting that SEP districts exert significant influence on racial inequality in educational opportunity is relatively consistent. Even increases in efficiency appear to come at the cost of equity. Most importantly, this work highlights the importance of considering SEP schools as part of a differentiated school system, rather than isolated elite institutions.

Advisor: Lucas, Samuel R
Encountering Loss: An Ethnographic Account of Chinese War Survivors’ Pursuits of Redress in the Fin-de-siècle Japanese Law Courts

In the early 1990s, as many parts of the post-Cold War world went through a resurgence of WWII memories, numerous Chinese victims of the Japanese invasion and occupation between 1931 and 1945 began to seek individual redress for their wartime suffering. Some of their grievances, by gaining support from Japanese progressive lawyers and activists, reached the Japanese civil law courts as a series of compensation claims. This dissertation is an ethnographic and historical exploration, and in part, an attempt at philosophical elucidation, of what we are left with after the Japanese judiciary’s dismissals of these numerous lawsuits; namely, a pile of documents produced through and around the litigation./nWhereas a few other anthropologists of East Asia, by studying various failed pursuits of legal redress concerning Japanese-inflicted atrocities, have astutely observed law’s failures and limits of institutional justice, I propose to take an ethnographic return to the testimonial weight of those pursuits. How did the series of litigation serve as a (limited) medium through which to encounter the plaintiffs’ wartime losses and wounds? Now that most of these war victims have passed away, can we still hear their voices by reading the testimonial documents left behind? With these questions, I aim to move away from a “what happened” kind of inquiry to “how” we can(not) encounter the pain of others, and thereby explore the limits of our empathy./nTo pursue this objective, I engage in discourse analyses combined with field observations and interview research, examining the said series of litigation as an analytical space in which global, national, and local discourses intersected with one another. Without relying on a dichotomized view on global humanitarianism vs. national sentiments, I carefully sort out the multiple levels and directions of inter-discursive translation — and the limits of such translation — occurring in this space, and illustrate how the Chinese war survivors’ memories traveled across space and time through such translation. I demonstrate this point by closely studying two specific cases chosen from the series of litigation: the Pingdingshan Massacre (1932) in the territory of Manchukuo; and wartime sexual slavery in a rural area of Shanxi Province. At the same time, these ethnographic case studies highlight my own troubled efforts to encounter the pains of the Chinese litigators. To reflect upon this issue, the last chapter examines the concept of trauma by engaging in an anthropological reading of philosophical and psychoanalytical texts. I argue that the concept of trauma enables us to grasp how our inextricable relationship to the loss experienced by the vanquished is effected, sustained, and at the same time alienated by the work of language.

Advisor: Liu, Xin
Pandolfo, Stefania
Reconstruction of Cosmological Fields in Forward Model Framework

The large scale structures (LSS) of the Universe contain a vast amount of information about the birth, evolution and composition of our Universe. To mine this information over the next decade, large scale imaging surveys such as the Dark Energy Spectroscopic Survey (DESI), Large Synoptic Survey Telescope (LSST), Euclid and WFIRST will probe the Universe with unprecedented precision, and on the largest scales. This provides an opportunity to shed light on the longstanding mysteries regarding the true nature of dark matter and dark energy, as well as to resolve tensions between different probes of the past decade. However to utilize the full potential these surveys which are no longer statistically limited, it is critical to develop analytic methods that extract the maximum amount of information across all scales. In this regard, the massive data volumes, the non-linearity, and non-Gaussianity of the signal in the regimes probed by these surveys will pose outstanding challenges./nThe focus of this thesis is to tackle aforementioned challenges with a novel framework for the analysis of the large scale structures by reconstructing cosmological fields using forward models. These forward modeling approaches are the most promising way to jointly model different cosmological observations with requisite accuracy at all scales, while accounting for their individual systematic biases and noise. We approach reconstruction within a Bayesian formalism - by optimizing the likelihood of the observed data and maximizing the corresponding posterior of initial conditions. This requires solving optimization problems in multi-million dimensional space and we develop novel differentiable forward models as well as adiabatic optimization algorithms to assist with this./nWe begin by using this approach to reconstruct the initial density field from a continuous dark matter field, as well as discrete observables like dark matter halos. We also construct a power spectrum estimator for this reconstructed Gaussian density field, which is the summary/nstatistic for optimal analysis. With 21-cm intensity mapping, we show how this framework can instead be used for secondary analysis such as de-noising the observed data and recovering lost cosmological information. For this, we also introduce Hidden Valley simulations/n- a suite of high resolution N-Body simulations and develop models to simulate HI clustering. Having demonstrated the efficacy of this approach, we finally introduce FlowPM - a GPU-accelerated, distributed, and differentiable N-Body solver in TensorFlow that naturally/ninterfaces with the most advanced machine learning tools. This will enable the community to efficiently solve large scale cosmological inference problems and develop differentiable forward models for the reconstruction of cosmological fields with the next generation of LSS surveys.

Advisor: Seljak, Uros