VictorJuiz/thesis huggingface dataset:
Past, Present, and Future of Solar Neutrino Detection: SNO, SNO+, and Theia

This thesis represents a comprehensive study of solar neutrinos spanning three generations of solar neutrino detection techniques. Data from the Sudbury Neutrino Observatory (SNO), a heavy water detector instrumental in demonstrating flavor change in the neutrino sector, is used to set a limit on the lifetime of massive neutrino states within a model where said states can decay. The analysis of SNO data finds a limit of $k_2>8.08 \times 10^{-5}$~s/eV at 90\% confidence for the lifetime of $\nu_2$. Combining this limit with constraints from all other solar neutrino experiments, a new global best limit is found at $k_2>1.92 \times 10^{-3}$~s/eV at 90\% confidence for the lifetime of $\nu_2$./nThe upgrade of the SNO detector, SNO+, is currently in the process of migrating to a liquid scintillator target material. Prior to this, SNO+ produced a physics dataset with light water as the target, which was analyzed to extract the flux of $^8$B solar neutrinos. This analysis resulted in a measured flux of $\Phi_{\mathrm{^8B}} = 5.95^{+0.75}_{-0.71}\mathrm{(stat.)}^{+0.28}_{-0.20}\mathrm{(syst.)} \times 10^6\,\,\mathrm{cm}^{-2}\mathrm{s}^{-1}$. Further, this analysis demonstrated SNO+ has achieved very low backgrounds above 6.0~MeV visible energy. To aid in detector calibrations as SNO+ moves from water to scintillator, the design and evaluation of a Cherenkov optical calibration source is presented./nWith an eye on the future, the capabilities of the proposed Theia experiment are discussed, with particular attention to the novel water-based liquid scintillator target being evaluated for use by that experiment. A small scale experiment to evaluate the prospects of distinguishing scintillation and Cherenkov light in this target is described. Results are presented demonstrating the potential for identification of Cherenkov photons by timing and intensity information for this water-based liquid scintillator, and for a more traditional scintillator, LAB+PPO.

Advisor: Orebi Gann, Gabriel
Becoming Neoliberal Subjects: Power and Resistance in a School-Based Mentorship Program for Latino Boys

A growing number of school districts are seeking to remedy the high dropout rates and achievement gap experienced by Latino boys through Latino male mentorship programs. Indicative of neoliberal shifts in urban education, these programs often involve public-private partnerships and represent a new ideological blending of Latinx community interests and corporate philanthropic goals. This dissertation asks: How is Latino masculinity framed and constructed in the neoliberal era of urban education? To answer this question, I conducted an ethnographic case study of Latino Male Success (LMS; pseudonym), a Latino male mentorship program run by a non-profit organization servicing 10 middle and high schools in an urban school district in California. Data collection for this study consisted of two years of ethnographic research including 40 interviews, document and curriculum analysis, and over 500 hours of classroom observations./nThree main findings emerged from this study. First, dependence on philanthropy and the need to cater to wealthy donors resulted in a cultural deficit framing of Latino male mentorship in the program’s mission and curriculum. This approach obscured structural racism and centered the boys’ behavior as the primary source of underachievement by implicitly or explicitly positing them as disinterested in school, potentially violent, and in need of cultural change. Second, LMS day-to-day lessons and activities deprioritized a critical race and gender consciousness and cultivated an ideal Latino masculinity characterized by the neoliberal values of meritocratic individualism, benevolent hetero-patriarchy, and smart consumerism. Third, mentors and students also resisted the neoliberal logics that framed LMS. Select mentors taught extra-curricular and politically charged lessons centering racism and collective struggle, while students engaged in small moments of subversion enacted through repudiations of respectability, collective hopes, and loving, queer moments./nBy integrating critical and intersectional theories of race, gender, sexuality into my analysis, this research provides a timely addition to the literature on educational empowerment programs for boys and men of color. In contrast to scholarship that prioritizes an analysis of school punishment and pushout, my work reveals the profound impact educational empowerment projects have on influencing Latino male identity and learning experiences. My findings demonstrate the ways problematic racial and gender politics remain prevalent in these programs despite a rhetoric of racial uplift. Through tracing the intersectional racial politics of neoliberalism guiding Latino Male Success, this work sheds new light on the ways educational philanthropic foundations and non-profits shape racial formation in U.S. schools.

Advisor: Leonardo, Zeus
Perfectly Imperfect: Law Students with Non-Apparent Disabilities and Disability Law

How does disability rights law map on to the lives of law students with non-apparent disabilities? This case study uses legal analysis of case law to show how the law maps on to the lives of two law students with non-apparent disabilities. The study takes place in one public law school in California. It explores the prevalence of the stigma surrounding disability in law school compelling law students to continue trying to pass as able-bodied, sending a constant message that their unique gifts and perspective do not matter, as long as they continue to pass. The Department of Justice’s Operation Varsity Blues is prosecuting one of the largest college admission scams of wealthy students faking disabilities, makes this study all the more relevant adding to the stigma around non-apparent disabilities; viewing such students as frauds trying to game the system, not smart, and a drain of resources./nThe challenges in recruiting for the study highlighted the deep-rooted fear in talking about their disability all the more supported by legal precedent that acknowledged “professional stigma” if a student is found out. A normative law is proposed to help combat the environment that creates the reluctance of law students to speak out against their current marginalization. Laws are necessary but at times not sufficient. Thus this proposal cautions the reader to keep in mind that the law alone is not enough, but it is a first step in attempting to mitigate the repression of students with non-apparent disabilities in the hopes of remedying their unequal share in our democracy, in turn, strengthening it. The countless obstacles encountered by these law students at times creates a perfect legal advocate because these students have had to advocate for themselves in an imperfect world. Law students with non-apparent disabilities do not have the Varsity Blues because they are perfectly imperfect.

Advisor: Garcia Bedolla, Lisa
Robust Deep Learning Algorithms for Selective Laser Melting Monitoring

The objective of this research is to enable real-time in-situ monitoring for the Selective Laser Melting (SLM) process, by providing diagnostic feedback from monitoring that can be used to automate and adjust SLM system parameter settings. The ultimate goal is to improve SLM product quality and manufacturing productivity. We propose a deep learning approach to monitoring that takes in-situ videos as input data fed to convolutional neural networks (CNNs). We describe the entire monitoring framework, including running SLM experiments, collecting SLM video data, image processing for ex-situ generated height maps, generating labels for in-situ data from ex-situ measurement, and training CNNs with labeled in-situ video data. Experimental results show that our approach successfully recognizes the desired SLM process metrics (e.g. size, continuity) from in-situ video data./nIn order to train effective CNNs, besides collecting extensive SLM video data, we also need to label it. We have automated the process of generating labels from ex-situ measurements of the corresponding finished SLM experimental output. The ex-situ measurements provide high-precision height maps for the product surface, to which we apply our proposed image processing algorithm to calculate process quality metrics as labels./nHowever, our proposed automated labeling approach requires high-precision height maps, which are generated from an expensive Structured Light Microscope. It might not be readily available to other researchers and institutions, or not enough machine time may be available to label all experiments for which there is video. Thus there might not be enough labeled data to train effective CNNs. This research also combines semi-supervised learning with our original approach to address this problem. Semi-supervised learning method enables other researchers to address the problem without requiring a huge amount of labeled data./nIn addition, in practice, another issue is label noise. Even though the data labels were generated using high-precision height maps, the labels are not perfect and might still contain incorrect labels, known as ``noisy'' labels. We propose novel approaches to improve neural networks' performance when they are trained under label noise. The proposed approaches can be easily combined with other existing approaches that address the label noise problem to further improve the prediction accuracy, with very few additional hyperparameters that need to be tuned. Experimental results demonstrate that our approaches can significantly improve CNN models' prediction accuracy when training neural networks with noisy labels.

Advisor: McMains, Sara
Visualization, Prediction, and Causal Inference: Applications in Healthcare

The recent wave of data collection in the field of healthcare has opened up an ocean of possibilities to learn and develop new exploratory, diagnostic, and prognostic methods. This thesis explores how three fields of statistics (1) data visualization, (2) prediction, (3) and causal inference, can help us leverage this data in order to answer a wide range of questions in healthcare./nPart I of this thesis presents a software package called superheat that can be used by researchers to visualize complex datasets and multi-faceted modeling results. The primary users of this software so far have been those in the medical research industry. In this thesis, we apply superheat in three case studies including (1) using a publicly available global organ donation database curated by the World Health Organization to understand and summarize the global organ donation trends, (2) visualizing groups of topics that appear in text data scraped from Google News, (3) examining model performance for a model designed to predict the brain's response to images using fMRI data. The theme of Part 1 of this thesis is visualization in healthcare./nPart II of this thesis introduces an analysis for predicting a patient's risk of developing a Surgical Site Infection (SSI) following surgery. A SSI is an infection that occurs at the site of a surgery within 30 days post surgery, and is responsible for up to 30% of hospital acquired infections. This method was developed in collaboration with healthcare professionals including infectious disease experts and surgeons at UC Davis. The theme of Part 2 of this thesis is prediction in healthcare./nPart III of this thesis presents a novel application of instrumental variables in causal inference, asking about the possible effectiveness of a "survival-benefit"-based liver transplant allocation scheme. The conclusion is that while there could be substantial benefit yielded from rethinking how organs are allocated, the feasibility of implementing such a scheme that relies drawing causal inferences from complex observational data is extremely difficult. The theme of Part 3 of this thesis is causal inference in healthcare.

Advisor: Yu, Bin
Machine Learning: Why Do Simple Algorithms Work So Well?

While state-of-the-art machine learning models are deep, large-scale, sequential and highly nonconvex, the backbone of modern learning algorithms are simple algorithms such as stochastic gradient descent, gradient descent with momentum or Q-learning (in the case of reinforcement learning tasks). A basic question endures---why do simple algorithms work so well even in these challenging settings?/nTo answer above question, this thesis focuses on four concrete and fundamental questions:/n- In nonconvex optimization, can (stochastic) gradient descent or its variants escape saddle points efficiently?/n- Is gradient descent with momentum provably faster than gradient descent in the general nonconvex setting?/n- In nonconvex-nonconcave minmax optimization, what is a proper definition of local optima and is gradient descent ascent game-theoretically meaningful?/n- In reinforcement learning, is Q-learning sample efficient?/nThis thesis provides the first line of provably positive answers to all above questions. In particular, this thesis will show that although the standard versions of these classical algorithms do not enjoy good theoretical properties in the worst case, simple modifications are sufficient to grant them desirable behaviors, which explain the underlying mechanisms behind their favorable performance in practice.

Advisor: Jordan, Michael I
Towards More Predictive Models of Galactic Center Accretion

Sagittarius A* (Sgr A*), the roughly 4 million solar mass black hole at the center of our galaxy, is arguably the best natural test-bed for supermassive black hole accretion models. Its close proximity allows for detailed observations to be made across the electromagnetic spectrum that provide strong, multi-scale constraints on analytic and numerical calculations. These include exciting new event horizon scale results that directly probe the strong field regime for the first time. Spanning roughly 7 orders of magnitude in radius, the accretion system begins at ~ parsec scales where a large population of Wolf-Rayet (WR) stars interact via powerful stellar winds. A fraction of the wind material accretes to the event horizon scales, heating up and radiating on the way down to provide the observed multi-wavelength emission. Detailed modeling of this process requires 3D numerical simulations spanning as large of a dynamical range as possible. The goal of this thesis has been to improve the predictive power of such simulations applied to Sgr A* by limiting the number of free parameters that they contain. This is done by incorporating more theoretical and observational knowledge into the calculations, for example, what collisionless physics tells us about how electrons/ions are heated in a turbulent medium and the properties of the winds of the WR stars. The resulting simulations have shown excellent agreement with a wide range of observational constraints and could have major implications for how the accretion flow around Sgr A* is modeled in the future. The techniques presented here are also applicable to other low luminosity AGN and X-ray binaries.

Advisor: Quataert, Eliot
Discovering and Drugging Anti-Cancer Targets of Natural Compounds Using Chemoproteomics

Though many proteins have been identified as relevant to cancer pathogenicity, very few are now targeted clinically due to a lack of both a mechanistic understanding of the protein(s) and the identification of small molecule modifiers. Even highly-studied oncogenes are rarely therapeutically utilized, since solvent-accessible amino acid residues to which small molecule drugs can bind have often not yet been identified. In fact, it is currently thought that such small molecule binding pockets have only been identified on 10-15% of human genes, and less than 5% are currently exploited therapeutically.(1) In order to develop targeted cancer therapies, it is imperative to both identify and effectively pharmacologically manipulate small molecule binding pockets on disease-relevant protein targets./nMany existing targeted cancer therapies utilize or build upon naturally-occurring compounds that exert anti-cancer activity. Many of these natural products contain electrophilic moieties, reacting covalently with nucleophilic amino acids. While the use of natural products themselves as drugs seems like a promising idea, such complex compounds often interact widely in the proteome, resulting in non-therapeutic interactions that could potentially result in unwanted physiological side effects. A more effective route toward natural product-based drug development, therefore, requires identifying the particular covalent modification responsible for the anti-cancer activity and developing a compound that binds to the same residue more selectively. A variety of approaches to identifying targets of natural products have arisen, but most rely on piecemeal methods, such as derivatizing the natural product or assessing its reactivity one protein at a time./nChemoproteomics have arisen as strategies to address these two challenges, allowing for proteome-wide assessment of a compound’s reactivity directly in biological systems. The use of chemoproteomic technologies has contributed to a deeper understanding of natural products’ protein interactions while also allowing for the identification of more selective, covalently-acting small molecules, whose smaller size also renders them more synthetically accessible./nIn this work I provide a comprehensive discussion of the current understanding of metabolic pathways implicated in cancer and the associated therapies in development and use. In addition, I demonstrate the efficacy of chemoproteomic technologies in a study that identifies a novel protein target and druggable site targeted by an anti-cancer natural product, followed by the discovery and development of small molecules that bind more selectively to the protein target.

Advisor: Nomura, Daniel K
Game-Theoretic Safety Assurance for Human-Centered Robotic Systems

In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high-confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible./nThis dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. It first introduces a suite of novel robust optimal control formulations and algorithmic tools that permit tractable safety analysis in time-varying, multi-agent systems, as well as safe real-time robotic navigation in partially unknown environments; these approaches are demonstrated on large-scale unmanned air traffic simulation and physical quadrotor platforms. After this, it draws on Bayesian machine learning methods to translate model-based guarantees into high-confidence assurances, monitoring the reliability of predictive models in light of changing evidence about the physical system and surrounding agents. This principle is first applied to a general safety framework allowing the use of learning-based control (e.g. reinforcement learning) for safety-critical robotic systems such as drones, and then combined with insights from cognitive science and dynamic game theory to enable safe human-centered navigation and interaction; these techniques are showcased on physical quadrotors—flying in unmodeled wind and among human pedestrians—and simulated highway driving. The dissertation ends with a discussion of challenges and opportunities ahead, including the bridging of safety analysis and reinforcement learning and the need to ``close the loop'' around learning and adaptation in order to deploy increasingly advanced autonomous systems with confidence.

Advisor: Sastry, Shankar
Tomlin, Claire J
Machine Learning-based Inverse Solution for Predictions of Impact Conditions during Car Collisions

In this work, a novel deep learning computational framework is developed to determine and identify the damage load conditions of different types of structures, including cantilever beams of inelastic materials, elasto-plastic shell structures of inelastic materials and crashed cars subjected to mechanical forcing actions./nThere are a variety of methods to measure engineering responses based on the corresponding load conditions. The aim of this work is to establish reverse analysis algorithms. This artificial intelligence framework offers a practical solution to solve the inverse problem of engineering failure analysis based on final material and structure damage states and fields. More precisely, the machine learning inverse problem solver may be a practical solution to characterize failure load parameters and conditions based on the permanent plastic deformation distribution or the residual displacement condition of beam, shell structures and cars./nThe study presents the detailed machine learning algorithm, data acquisition and learning processes, and validation and verification examples. Neural Network Modeling offers a cohesive approach to the computational mechanical problems based on TensorFlow. Different activation functions and loss functions are compared theoretically and numerically during implementing neural network./nFeature selection is used in model construction for simplification of models to make them easier to interpret and lead to shorter training times. It is demonstrated that the developed machine learning algorithm can accurately identify a practically unique prior static loading as well as impact loading state for different structures, in an inverse manner, using the permanent plastic deformation or the residual displacement as the forensic signatures./nThe data-driven based method developed in this work, employs Artificial Neural Networks to provide a powerful tool for forensically diagnosing, determining, and identifying damage loading conditions for engineering structures in accidental failure events, such as car crashes and infrastructure or building structure collapses. The machine learning inverse problem solver developed here may have potential broader impacts on general forensic material and structure analysis using permanent plastic deformations.

Advisor: Li, Shaofan