VictorJuiz/thesis huggingface dataset:
Statistical Investigation of Surface Expressions in the Midway Sunset Oil Field

In the Midway Sunset Oil Field in Central California, operators inject steam into the shallow diatomite formation to lower the viscosity of heavy oil and flow it out of wells. The injected steam, however, does not always remain in the reservoir or flow out of the wells. In two zones in the study area, the steam comes out at the surface, creating sinkholes, seeps, and steam outlets. These phenomena, called "surface expressions," pose safety hazards, including death.
This study has aggregated a complete list of all reported surface expressions in the study area and their characteristics, showing that surface expressions are more widespread than a single incident. While previous earth models of the area have been proprietary, this study describes the creation of an earth model, based on a geological literature review and synthesis of available data that is now in the public domain.
The highlights from the study are in the spatial statistical analysis. The spatial analysis examines what is unique about the zones with surface expressions that may contribute to their occurrence. It is hypothesized that the surface expressions are caused by leakage of steam through old improperly abandoned wells, high injection pressures, structurally controlled flow patterns, high injection volumes, or flow along naturally occurring faults among other possible factors. Spatial statistical analysis using logistic regression and classification trees is used to explore the relationship between the surface expressions and spatial attributes. The results point to a significant spatial correlation between the surface expressions and two predictors: concentration of plugged wells and seal thickness. These predictors emphasize the possible role of plugged wells in causing surface expressions as well as the importance of having sufficient seal between the producing zone and the surface.
Initial flow simulation results also point in the direction of damaged wells as likely pathways of surface expressions. This research implies that more attention needs to be given to analyzing the abandonment procedures of plugged wells near steam injectors and re-abandoning wells if necessary. In addition, wells that inject fluids at very shallow depth near areas

Keywords:  Energy Resources Engineering Department
School of Earth Energy &amp; Environmental Sciences
Surface Expressions
Midway Sunset
oil leaks
Energy Resources Engineerwith an eroded seal should also be subject to further scrutiny.ing
Thesis
Advisor: Mukerji Tapan
Statistical learning of reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics [electronic resource]

Complex chemical processes, such as the decomposition of energetic materials, the chemistry of planetary interiors, and surface catalysis, are typically studied using large-scale molecular dynamics simulations that can run for weeks on high performance parallel machines. These computations may involve thousands of atoms forming hundreds of molecular species and undergoing thousands of reactions. It is natural to wonder whether this wealth of data can be utilized to build more efficient, interpretable, and predictive models. In this thesis, we use techniques from statistical learning to develop a framework for constructing kinetic Monte Carlo (KMC) models from molecular dynamics data. We show that our KMC models can not only extrapolate the behavior of the chemical system by as much as an order of magnitude in time, but can also be used to study the dynamics of entirely different chemical trajectories with a high degree of fidelity. We also develop a new and efficient data-driven method for reducing our learned KMC models using L1-regularization. We describe how our L1-regularization based algorithm can also be applied to complex systems of reaction rate equations such as those studied in the combustion community, providing a novel data-driven method for reducing nonlinear dynamical systems.

Advisor: Reed Evan J. primary advisor. Thesis advisor
Cai Wei
Candès Emmanuel J. (Emmanuel Jean) advisor. Thesis advisor
Statistical models for phenotypic and genotypic expression [electronic resource]

Over the last decades, genomic data has become significantly cheaper to produce and more ubiquitous. The analysis of such data can shed light on the functioning of organisms and how phenotypes are encoded in the genetic material of each cell. It is thus of interest to create informative statistical models of cellular phenotypes, such as gene expression, using this genomic data. In this work, we develop models to understand how gene expression is regulated by the genetic code and key regulatory proteins, and how these regulatory programs give rise to diverse phenotypes. The first part of the thesis focuses on inferring phenotypic traits directly from genotypic variants. To this end we introduce a new sparse regression method called the component lasso. The method is suited for datasets with highly correlated groups of variables, which often occur in genetics. In particular, we consider predicting traits from correlated sets of mutations in genes. The method estimates and uses the connected-components structure of the sample covariance matrix during inference to achieve a lower mean squared error as well as better support recovery. We evaluate the performance of the component lasso on simulated and real data examples. In the second part of the thesis, the focus is on the problem of genotypic expression and methods for modeling gene regulatory networks in different cells. We assume a simplified model in which mechanisms responsible for gene expression involve only two main elements: 1) transcription factors that bind to the DNA molecule; and 2) motifs that exist in the regulatory regions of genes. We first present a solution within a boosting framework that represents a regulatory network with alternating decision trees. We use the cell differentiation hierarchy to infer different networks for different cell types, while restricting the differences for models of closely related cells. We evaluate the boosting method on simulated data as well as on a real hematopoiesis dataset that has an inherent hierarchy over blood cells that stem from a single progenitor. We then present a deep learning approach for the classification of gene expression. We use a multimodal neural network on the raw DNA sequence and regulator expression data, which allows us to automatically discover relevant and new motif sequences.

Keywords: Electrical Engineering.
Advisor: Kundaje Anshul
Tibshirani Robert primary advisor. Thesis advisor
Duchi John advisor. Thesis advisor
Weissman Tsachy advisor. Thesis advisor
Stein's lemma and subsampling in large-scale optimization [electronic resource]

Statistics and optimization have been closely linked since the very outset. This connection has become more essential lately, mainly because of the recent advances in computational resources, the availability of large amount of data, and the consequent growing interest in statistical and machine learning algorithms. In this dissertation, we will discuss how one can use tools from statistics such as Stein's lemma, subsampling, and shrinkage to design scalable, and efficient optimization algorithms. The focus will be on the large-scale problems where iterative minimization of the empirical risk --or maximization of the log-likelihood-- is computationally intractable, i.e., the number of observations n is much larger than the dimension of the parameter p. In each chapter, we will discuss an efficient estimator or optimization algorithm designed for training a statistical model when the dataset is large, i.e. in the regime n > > p > > 1. The proposed algorithms have wide applicability to many supervised learning problems such as binary classification with smooth surrogate losses, generalized linear problems in their canonical representation, and M-estimators. The algorithms rely on iterations that are constructed through Stein's lemma, subsampling, and/or shrinkage techniques that achieve quadratic convergence rate, and that are cheaper than any batch optimization method by at least a factor of O(p). We will discuss theoretical guarantees of the proposed algorithms, along with their convergence behavior in terms of data dimensions. Finally, we will demonstrate their performance on well-known classification and regression problems, through extensive numerical studies on large-scale real datasets, and show that they achieve the highest performance compared to other widely used and specialized algorithms.

Keywords: Statistics.
Advisor: Bayati Mohsen primary advisor. Thesis advisor
Montanari Andrea primary advisor. Thesis advisor
Candès Emmanuel J. (Emmanuel Jean) advisor. Thesis advisor
Steps toward Huntington’s Disease Therapeutics: Application of Microfluidic Co-Culture System to Assess ProBDNF Treatment

Huntington’s disease (HD) is a devastating neurodegenerative condition that results in uncontrolled movements, deterioration of intellectual capabilities and emotional instability. HD is classified as an autosomal dominant, tri-nucleotide repeat disorder caused by abnormal expansion of “CAG” repeats in the HTT gene on chromosome 4. HTT codes for the protein huntingtin (HTT), which is linked to diverse cellular functions. Mutant HTT tends to misfold and form protein aggregates. Mutant HTT toxicity affects the entire brain, but it is particularly devastating within the corticostriatal axis. Although the exact mechanisms are currently debated, mutant HTT leads to a decrease in the transcription and delivery of brain-derived neurotrophic factor (BDNF) from the cortex to the striatum. BDNF plays a fundamental role in the survival and terminal differentiation of medium spiny neurons (MSNs) in the striatum. I hypothesize that bypassing the defective mechanism of BDNF transcription and delivery through the use of virus-induced BDNF expression in the striatum and cortex can rescue MSNs. In this thesis, I produce proBDNF viruses and assess their effects in-vitro through a striatal autocrine pathway and a corticostriatal paracrine pathway. The paracrine pathway requires a reconstruction of the corticostriatal axis that is only possible with the novel microfluidic co-culture platform that is employed in this study. The ability to recreate and manipulate the in-vivo corticostriatal architecture in a cheap, replicable in-vitro platform is extremely powerful in pushing the frontlines for HD therapeutics. This thesis strives to unite the promising strategy of BDNF viral gene therapy with an innovative research platform: the microfluidic cell co-culture. Huntington’s disease does not currently have a cure, giving great urgency to the search for therapies.

Keywords:  Department of Biology
Biology
Neurobiology
Huntington's Disease
Microfluidics
Viral Gene Therapy
ProBDNF
Thesis
Advisor: Yang Yanmin
Feldman Jessica
Stochastic hydrologic modeling for converting PAR to net solar radiation and estimating potential evapotranspiration rate using empirical PM equation [electronic resource]

Evaporation is the process that liquid water in converted into water vapor. In order to evaporate water, Energy is required to change the state of the molecular of water from liquid to vapor, and direct solar radiation and the ambient temperature of the air provide this energy. Photosynthetically Active Radiation (PAR) plays important roles in agriculture, ecology, and atmospheric chemistry. In practical reason, certain area only available PAR, however, to estimate reference evapotranspiraion, total solar radiation is required instead. This paper provides the conversion model from PAR to Total solar radiation, and its application to estimate reference evapotranspiration (Chapter 1), and Pan coefficient (Chapter 2). Conversion model perform well, especially practical purpose, and Simple form of model can be easily understood, and requires only minimum computing power, so using this model might provide resalable performance in field practical purpose.

Advisor: Freyberg David L. advisor. Thesis advisor
Stories Courts Tell: The Problematic History of the Yugoslav Tribunal in Bosnia and Herzegovina

In 1993, the United Nations Security Council established the International Criminal Tribunal for the former Yugoslavia, the first of a number of contemporary international and hybrid tribunals that have been established since the 1990s. The proliferation of these courts has largely been driven by their potential to contribute to transitional justice, or the process of transitioning from war to peace by addressing the legacy of violence through accountability. The founders of the Yugoslav Tribunal believed that the court would positively impact peace and reconciliation in Bosnia and Herzegovina.
In this thesis, I argue that the Yugoslav Tribunal has not positively impacted Bosnia and Herzegovina because the Tribunal operated as if the court was part of a domestic judicial system, rather than a transitional justice process. Many among the Tribunal’s staff and supports believed that the purpose of international courts was simply to try alleged criminals; therefore, they thought that the Yugoslav Tribunal should simply duplicate the structures and goals of domestic courts.
I argue that transitional justice trials are not like domestic trials: they are pedagogical tools that can be used to support or craft a collective narrative about the past. Through the drama of a trial, these types of courts contribute to society’s understanding of history. Because the Yugoslav Tribunal largely operated as if it were a domestic court on a global scale, it did not prioritize communicating its findings or crafting a narrative for the public. Therefore, the Yugoslav Tribunal has been used by other actors within Bosnia and Herzegovina to reinforce their own entrenched narratives.

Keywords: international justice
transitional justice
international criminal tribunal for the former Yugoslavia
Balkans
Bosnia and Herzegovina
history
center for democracy development and the rule of law
Thesis
Advisor: Naimark Norman
Strategic responses to categorization [electronic resource]

This dissertation considers a number of theoretical alternatives to the work on category theory. By integrating category theory with insights from the behavioral theory of the firm, it focuses attention on the role producers play in reacting to and generating the system of categories in markets. Producers react strategically given the limited information available to them and given the competitive incentives imposed on them by the market. The consequences of their decisions can both replicate and differ from the predictions of category theory. The first chapter introduces and situates these ideas in prior work. In Chapter 2, I extend the work on middle-status conformity into a general framework of quality thresholds in markets. Quality thresholds affect producer incentives to innovate, and come in two forms: fixed thresholds encourage conformity from above-threshold producers while responsive thresholds encourage innovation. The chapter presents a typology of market innovation, showing how market structures predict when innovation will emerge from top producers in a market, when from mid-tier producers, when from both, and when from neither. Chapter 3 evaluates the theoretical model presented in the second chapter in an experimental setting. In addition it discusses the role of quality thresholds as policy instruments in determining the locus of innovation in many real world settings. Chapter 4 provides an alternative mechanism for the effects identified in category theory by minimizing the key role of the audience. It argues that producer competition in uncertain markets generates a dynamic in which producers tend to imitate their successful peers and avoid their unsuccessful peers. Over time, clusters tend to form around successful producers, creating a correlation between producer appeal or success, and producer density. If audiences rationalize dense clusters of producers as categories, the correlation between clustering and success may be misinterpreted as a penalty against category spanning or against mis-categorized producers. While replicating existing effects, this model also makes a number of novel predictions about category dynamics and category emergence that distinguish it from prior theory.

Keywords: Business.
Advisor: Goldberg Amir primary advisor. Thesis advisor
Barnett William P. advisor. Thesis advisor
Carroll Glenn advisor. Thesis advisor
Stratigraphic and depositional controls on source rock heterogeneity and composition of expelled petroleum in the triassic shublik formation of Arctic Alaska [electronic resource]

Petroleum source rocks display significant variability in lithology, and quality, quantity and thermal maturity of organic matter. However, many regional geochemical studies focus on a few selected rock samples that may not represent the entire source rock section, which can affect estimates of resource potential assessment (conventional and unconventional). For nearly thirty years, the Triassic marine carbonate Shublik Formation has been suggested and confirmed as a key source rock for hydrocarbons in the North Slope of Alaska. The formation accounts for roughly one third of the oil in the supergiant Prudhoe Bay Field, and for nearly all of the oil in the second largest Kuparuk River Field. This dissertation examines oil-source rock correlation, source rock heterogeneity, distribution of organic-rich and -lean intervals, and evidence for migrated hydrocarbons in a stratigraphic framework with implications for unconventional shale resources evaluation. While different workers have conducted lithostratigraphic analysis of the Shublik Formation, and geochemical analyses of North Slope oils, this work links geochemistry, sedimentology, and petroleum system analysis, providing detailed shale resource system evaluation, which ultimately contributes to the growing body of knowledge in such exploration frontiers.

Keywords: Geological and Environmental Sciences.
Advisor: Graham S. A. (Stephan Alan)
Moldowan J. M. (J. Michael)
Peters Kenneth E. (Kenneth Eric)
Structural and electronic correlations in hybrid materials [electronic resource] : high-pressure behavior, optoelectronic properties, and energy-storage capabilities

Organic-inorganic hybrid materials combine the robust electronic properties of extended inorganic solids with the diversity of organic molecules. Their mild synthetic conditions and versatile nature encourage their application as energy storage and conversion materials. I have placed emphasis on understanding the fundamental relationships between structural and electronic properties in these hybrids. To this end, I have utilized gigapascal-scale pressure to explore the structural evolution of halide perovskites under compression and the concomitant changes to their electronic behavior. I have observed dramatic piezochromism, increased conductivity, and the emergence of new photoluminescent states and altered electronic structures, thereby demonstrating the modification of these important semiconductors' thermodynamic and transport properties. In addition to the examination of fundamental structural and electronic correlations in hybrid materials, I have focused on designing, synthesizing, characterizing, and applying new hybrid materials—most often crystalline halide perovskites—as battery electrodes or white-light phosphors.

Keywords: Chemistry.
Advisor: Karunadasa Hemamala
Kanan Matthew William
Mao Wendy (Wendy Li-wen) advisor. Thesis advisor
Structural and functional imaging of intact neural systems [electronic resource]

Revolutionary methods developed in the last decade have yielded new insights into many biological systems. Despite these technological advances, many complicated structures such as the brain have continued to defy our understanding. In part, this veil exists because of the necessary trade-offs made by most methods, sacrificing resolution of some dimensions (e.g. functional, temporal) in order to more precisely measure others (e.g. structural, molecular). Recent methods for unbiased, whole-sample analysis with high resolution could eliminate some of these trade-offs and yield a more complete understanding of systems-level interactions in complex structures. In this thesis, I apply whole-system methodologies to characterize a behavioral neural network spanning multiple brain regions and a developmental process in an entire organ, the pancreas. In the first section of this work, I use a model organism, the larval zebrafish, to study the whole-brain response in passive coping. I develop a behavioral challenge protocol that induces passive coping in the larval zebrafish and perform brain-wide calcium imaging of neural activity during the behavioral transition. Recordings of neural activity reveal a slow but striking ramping of activity confined to the lateral habenula, as well as a gradual transition to a reduced level of activity in both the raphe nuclei and the dorsal thalamus. Additionally, I use optogenetic stimulation of the lateral habenula combined with brain-wide imaging to show that activation is sufficient to reduce mobility as well as reduce activity in the raphe. These results provide unbiased evidence of a critical role for the lateral habenula in regulating both immediate and prolonged effects of stress on action selection, whereby either synaptic or membrane properties of lateral habenula neurons encode both prior and on-going experiences. In the second section of this work, I adapt CLARITY, a tissue-clearing technique, to be easily compatible for clearing a variety of heterogeneous and soft tissues and for integration into a standard clinical workflow. After developing a biphasic hydrogel methodology and an automated analysis platform for high-throughput quantitative volumetric analysis of biological features, I validate and apply this approach in the examination of a variety of organs and diseased tissues with a specific focus on the dynamics of pancreatic innervation and islet development in laboratory mouse and human clinical samples. Together, these two sections demonstrate unbiased, whole-sample techniques for: (1) probing the brain-wide neural response in disease-relevant behaviors in a model organism; and (2) characterizing molecular-level phenotypes and development processes in a variety of intact systems.

Keywords: Chemical and Systems Biology.
Advisor: Deisseroth Karl primary advisor. Thesis advisor
Bao Zhenan advisor. Thesis advisor
Meyer Tobias advisor. Thesis advisor
Wandless Thomas advisor. Thesis advisor