VictorJuiz/thesis huggingface dataset:
Scalable Algorithms for Population Genomic Inference

Since the 1920s, researchers in population genetics have developed mathematical models to explain how a species evolves. With the rise of DNA sequencing over the past decade, we now have the data to use these models to answer real questions in evolutionary biology. However, the sheer amount of data and the time complexity of the models makes inference extremely challenging. Computer science has therefore become an essential tool for bridging theoretical models and modern sequencing data./nIn this thesis we present two novel algorithms that make use of DNA sequencing data in a principled yet practical way. The first method estimates the history of effective population sizes of a species using a coalescent hidden Markov model (HMM). Previous coalescent HMMs could only handle a few sequences, since the set of coalescent trees makes the state- space prohibitively large. Our algorithm uses a modified state-space to make inference computationally feasible while still retaining the essential genealogical features of a sample. We apply this algorithm, called diCal, to human data to learn more about major events in human history, such as the out-of-Africa migration. We also provide several extensions to diCal that make the computation faster, more automated, and applicable in a wider variety of scenarios./nThe second method is an algorithm for jointly estimating effective population size changes and natural selection. These two factors can leave similar traces in genomic data, and the models that would describe both are computationally intractable. Our method uses a machine learning technique called deep learning to make the inference procedure robust and efficient. Deep learning automatically teases out important features of the data, but previously had not been used in population genetics. We apply this method to African Drosophila melanogaster data to jointly infer their population size changes and classify each region of their genome as neutral or under natural selection. We considered three types of selection: hard sweeps, soft sweeps, and balancing selection. To create a sophisticated framework for population genomic inference, in the future it would be promising to combine machine learning algorithms with biologically-inspired coalescent modeling.

Advisor: Song, Yun S
Quantifying crowded and uncrowded letter recognition

Despite decades of research, significant mysteries remain concerning two/nfundamental aspects of human visual processing: how we perceive moderately complex line shapes such as letters of the alphabet, and how peripheral vision differs from foveal vision. This dissertation comprises studies that address both these topics, using experiments with letter recognition and visual "crowding"--the deleterious influence of flanking objects on identification of a target./nLetters are well-studied psychophysical stimuli that strike a balance between degenerate patterns such as Gabor patches or gratings, with more uncontrolled stimuli such as natural images. Letters contain distinct spatial features that are more straightforward to quantify than the characteristics of natural scenes, yet have a sufficient richness of information to probe the general-purpose mechanisms of object recognition./nThe last decade has seen a renewed interest in perception in the peripheral visual field, with a particular emphasis on the phenomenon of crowding. Many researchers acknowledge that this curious effect provides a unique window into the process of object recognition. Since there is such a marked difference between the periphery, where crowding is potent, and the fovea, where it is nearly absent, crowding sheds light on the dichotomy between visual processing in the high resolution fovea versus the information-limited periphery. Understanding crowding may illuminate general principles of biological visual processing.

Advisor: Chung, Susana T. L.
Synthesis and Verification of Networked Systems with Applications to Transportation Networks

Engineering advances have enabled systems that are increasingly complex while, simultaneously, expectations for the closed-loop behavior of these systems have become more demanding. The resulting combination of complexity and desired behavior requires correct-by-design approaches to system analysis and control. In this dissertation, we develop a class of scalable and automated verification and control synthesis techniques for networked systems. To motivate our study and to demonstrate the applicability of our results, we focus on transportation networks./nThe formal analysis and synthesis approaches which are the emphasis of much of this dissertation require a discrete representation of the system in the form of a finite abstraction for the system's behavior. A finite abstraction models the states and dynamics of the system with a finite set of properties and transitions which capture all the phenomena of interest. We first present a broad class of systems which are mixed monotone, that is, systems for which the state evolution can be decomposed into an increasing and decreasing component. For such systems, we develop an efficient abstraction algorithm to enable correct-by-design control synthesis. We then use this approach to synthesize control strategies for transportation flow networks./nWe next turn our attention to qualitative analysis of the dynamics present in traffic flow networks. We propose a macroscopic network flow model in continuous time suitable for analysis as a dynamical system, and we analyze equilibrium flows as well as convergence. We show that the same mixed monotone property that enables efficient abstraction allows us to prove global asymptotic stability by embedding the flow network in a larger system./nThese contributions provide the theoretical foundation for correct-by-design control of a large class of networked physical systems. By focusing on transportation networks, we show concretely how these results apply to a domain that is of considerable practical importance.

Advisor: Arcak, Murat
Perimenopause as a Sensitive Period for Women’s Health and Aging: A Review of the Chronic Disease Literature and Two Empirical Tests of Significance

The critical and sensitive periods model, a key component of the life course health and development (LCHD) framework, describes windows of growth, development, or change during which exposures can permanently affect the structure or function of the body and influence its trajectories for health. Research on critical and sensitive periods has traditionally focused on the impact of early life influences on later life health. This focus on infancy and youth is due to the rapid pace at which development and growth occurs, which can make individuals particularly susceptible to the influences of risk and protective factors./nPerimenopause—the transition period bridging women’s reproductive and post-reproductive years—is another window, however, in which rapid physiological changes occur, yet minimal research has investigated this time as a critical or sensitive period. The three papers included in this dissertation investigate the health of women at mid-life and explores how their experiences during the menopausal transition affect their chronic disease risk and longevity./nThe first paper, a scoping review, compares the physiological similarities between perimenopause and puberty and their respective associations with some of the most prevalent chronic conditions in the United States. The second paper, a longitudinal cohort analysis of women undergoing the menopausal transition, explores whether perimenopause represents a sensitive window for stress responsivity. Using survey data from the Study of Women’s Health Across the Nation (SWAN), a national study of 3,300 women at midlife, the relation between psychological stress, perimenopause, and fibrinogen—a biomarker for systemic inflammation—is examined. The third paper, a time series analysis of population cohorts in France and England in Wales during the 19th Century, tests whether the age range at which perimenopause occurs at the population level is a sensitive window for longevity. The relation between cohort mortality at ages 45-49 and life expectancy at age 60, specifically, is examined using data from the Human Mortality Database./nThe primary findings from these three papers are as follows: 1) Perimenopause shares many physiological similarities with puberty, a well-documented sensitive window for a number of health outcomes. The interaction of certain behaviors or exposures with the significant hormonal shifts during perimenopause, moreover, appears associated with risk for mood disorders, metabolic-related morbidities, autoimmune diseases, cardiovascular disease, cancer, musculoskeletal disorders, and premature mortality. 2) Women’s perceptions of stress appear to change during the course of the menopausal transition; however, such changes are not associated with adverse physiological effects, as measured through changes in fibrinogen. 3) Adverse environmental conditions during perimenopause appear significantly related to decreased longevity among women in France and England and Wales during the 19th Century.

Advisor: Catalano, Ralph
Aquatic organisms provide insight into environmental problems: From genes to communities

An understanding of environmental responses to current and emerging problems and the capacity to forecast future threats requires appropriate indicators. The temporal scale of impact to animal health and species biodiversity can vary from centuries in the case climate change, to immediate, such as chemical spills. This dissertation uses genetics, physiology, and community ecology to understand current and emerging environmental threats and appropriate biomonitoring responses across a range of temporal scales./nPatterns of genetic diversity across a species range can suggest past glacial refugia and recolonization, dispersal barriers, and evidence of vulnerabilities to future change. However, population genetic structure and historical refugia among co-occurring species is not well characterized for many groups of aquatic organisms. Therefore, I compared phylogeographies of five, large bodied co-occurring aquatic insect species (four stoneflies: Calineuria californica, Hesperoperla pacifica, Pteronarcys californica, and Pteronarcys princeps and one caddisfly: Dicosmoecus gilvipes) across their ranges./nWidespread and persistent environmental contaminants may be monitored in the marine environment using top predators such as marine mammals. I sampled serum, inner blubber, and outer blubber and determined predictive equations between tissues for a suite of persistent organic pollutants in northern elephant seals (Mirounga angustirostris), which indicated strong relationships, particularly between serum and inner blubber POP concentrations./nAlthough benthic macroinvertebrate communities respond to natural environmental fluctuations they can still serve as indicators of acute anthropogenic stressors. Multiple years of macroinvertebrate sampling (2004, 2010-2013) in an urban creek quantified the community response to an accidental, 3000 L, oil spill using a Before-After Control-Impact approach. Taxa richness and sensitive taxa decreased immediately, but recovered one year later, likely aided by drift from upstream reference sites and winter rains. Additionally, I identified five indicator taxa that will enhance future biomonitoring efforts./nTo better understand population dynamics and response to drought in seasonal wetlands, I investigated successional changes in macroinvertebrate occurrence and abundance in northern California. The early-season predator assemblage was dominated by dytiscid beetles and late-season by Lestes damselflies. The phenology of taxa and life history strategies may affect how macroinvertebrate populations respond to increased annual variation in hydroperiod as a result of future climate change.

Advisor: Resh, Vincent H
Large-Margin Structured Prediction Extensions of Neural Networks for Automatic Speech Recognition

Neural networks, especially those with more than one hidden layer, have re-emerged in Automatic Speech Recognition (ASR) systems as replacements to emission models based on Gaussian Mixture Models (GMMs). While the use of these so-called Deep Neural Networks (DNNs) has enjoyed widespread success due to improvements in recognition results, the exact source of better recognition accuracy is not entirely understood. Using a bootstrap resampling framework that generates synthetic test set data satisfying conditional independence assumptions of the model while still using real observations, I show that DNNs used for both feature generation and hybrid acoustic modeling help compensate for incorrect conditional independence assumptions and help fix poor phone duration estimates of the hidden Markov Model (HMM)./nDespite these improvements, the large increase in word error rates for DNN-HMM systems on real data compared to synthetic data suggests that one can improve recognition performance by modifying the training criterion. Since neural networks are log-linear at the output layer, I propose using sequences of last hidden layers as input to a log-linear model, and training that model with large-margin criteria. These Structured Support Vector Machine (SVM) approaches allow us to more directly minimize errors relevant to automatic speech recognition, and provide some guarantees on test set error. First, I show how one can generate better features by combining a neural network with a hidden Markov Support Vector Machine (HMSVM). Then, I propose a hybrid DNN-Structured SVM acoustic model and an online training algorithm that iteratively updates alignments for faster convergence. Training of this model falls under a class of approaches known as sequence-discriminative training, which are used to train state-of-the-art systems. This DNN-latent Structured SVM model beats alternative methods to sequence-discriminative training by 1.0% absolute, while needing 33-66% fewer utterances to converge./nFinally, I analyze the Structured SVM approach to sequence-discriminative training and compare it to standard methods. I show how the loss function for boosted Maximum Mutual Information is an upper bound of the hinge loss for the Structured SVM, and how such a relaxation precludes the use of aggressive boosting parameters needed for better results. Finally, I analyze four of the most popular sequence-discriminative training criteria – Maximum Mutual Information, boosted Maximum Mutual Information, Minimum Phone Error, and state-level Minimum Bayes Risk – and the latent Structured SVM using the bootstrap resampling framework, and compare how different sequence-discriminative training criteria compensate for data/model mismatch. Structured SVM models perform better for real rather than synthetic data, likely because the model makes fewer distributional assumptions about the underlying data.

Advisor: Morgan, Nelson H
Climate Change from the Streets: A Community-based Framework for Addressing Local and Global Environmental Health Impacts

This dissertation analyzes the emerging epistemologies of climate change in California as articulated by social movements, experts, and subnational governments. As the world’s eighth-largest economy and the only state in the U.S. to implement a comprehensive program of regulatory and market-based mechanisms to achieve reductions in greenhouse gas (GHG) emissions, California represents an important site of inquiry. The passage of Assembly Bill 32, the Global Warming Solutions Act of 2006 has made the state a global leader on climate change science and policy innovation. While no subnational government can halt climate change alone, California’s environmental policies have a long history of success and replication. Through an extensive analysis of the state’s climate policies and interviews with key stakeholders, this dissertation highlights the challenges California faces in influencing global climate policy while addressing the needs of local communities that are already adversely impacted by air pollution./nAs cities and public agencies appropriate leadership roles in climate governance, policy formulation is increasingly emerging as an expert-driven process that emphasizes global GHG reductions as the goal and geographically-neutral economic and technological fixes as the solution. In this process, community-based strategies that integrate climate change interventions with population health outcomes are often excluded. This dissertation asks how environmental justice advocates are engaging strategically in the policymaking process in order to legitimize or contest regulatory policies regarding climate change in the face of ongoing pollution, illness, and injustice. In answering this question, the dissertation centers on three areas of inquiry: (1) the public health and environmental justice aspects of municipal climate action plans; (2) the conflict over statewide carbon pricing and use of its revenue for investment in communities most impacted by air pollution; and, (3) the social implications of international forest carbon-offset projects allowable under California’s market-based climate change law. These cases provide critical insights into environmental inequities and the emerging epistemologies of climate change on multiple scales. The dissertation findings demonstrate that the implementation of climate policies can either serve to exacerbate or redress underlying environmental health inequities in urban communities. In particular, these cases highlight the environmental justice strategies that are challenging a priori policy expertise to produce new local, place-based conceptualizations of climate change that underscore population health and community well-being.

Advisor: Corburn, Jason
Egyptian Airs: The Life of Luxury in Roman Wall Painting

After Rome conquered Egypt, Egypt invaded Rome. Or so it would seem, judging from the explosion of Egyptian-inspired art in Rome beginning in the mid-first century BC—including a host of Egyptian motifs incorporated into Roman wall painting. These motifs are based on pharaonic art, the art of Egypt under the pharaohs until the Hellenistic period, and they carefully reproduce the iconography and even the artistic style of this tradition. Roman walls in this period thus depict for the first time miniature pharaonic figures, crowns, and animals, lined up in delicate friezes, framed in elaborate panels, and tucked among bushes in lush garden scenes. In these frescoes—an artistic genre that has been called “quintessentially Roman”—the Egyptian elements seem to produce a striking contrast to their surrounding Roman framework. This marked shift in domestic decoration prompts the question: Why would a Roman homeowner choose such foreign elements for his walls?/nThe present project proposes that the pharaonic subjects in Roman painting relate primarily not to the world of religion, politics, or fashion, as has been argued, but to the practice of collecting art and luxury goods; and that, in this respect, they act analogously to the Greek elements in Roman painting—elements which, until now, have been treated as a discrete and unrelated phenomenon. Investigating how Roman collections of art inform both sets of material, and what this means for our understanding of Egyptian and Greek elements in Roman wall painting, lies at the heart of this project./nUsing diverse modes of inquiry and a wide range of evidence—close analysis of the paintings, comparison with archaeological material, and interpretation of literary sources—this study makes two major contributions to Roman painting studies. First, it encourages a revision of the widespread political readings of Roman domestic art, especially painting. Second, it drives home the importance of studying the manifold visual culture of the Roman house as a unit, rather than separating wall painting from the other materials with which it was clearly in dialogue. In so doing, this study casts new light on Roman collecting, artistic practice, and domestic life.

Advisor: Hallett, Christopher H.
Reanimated City: A Spatial Analysis of State Rule, Rupture, and Repurposing in Phnom Penh, Cambodia (1979-1993)

This dissertation explicates the remaking of sovereign rule through land reform in Phnom Penh, Cambodia through moments of social, political and economic reconfiguration. Depopulated by the Khmer Rouge in 1975, Phnom Penh was repopulated after the genocide by a mostly new urban citizenry. Then, in the spring of 1989, the socialist state introduced a comprehensive land reform program, that was critical to the regime’s retention of power in the liberal era. What followed was a brief but highly contentious transitional period in which new land reform policies led to property disputes across the country, becoming especially violent in Phnom Penh. These territorial politics shaped and were shaped by new forms of authority, political economy, and rule of law, articulating shifting conditions of ownership and belonging. Through five empirical chapters, I explore how rupture becomes the basis upon which diverse claims to sovereignty are made. In particular, drawing from in-depth interviews with sixty-four state officials, property owners and informal settlers and archival research in the People’s Republic of Kampuchea/State of Cambodia state papers, and the United Nations Transitional Authority in Cambodia land dispute files, I consider how property and political regimes were co-constituted in the wake of the Cambodian genocide. I argue that by claiming a rupture with past, the state invites new urban populations to claim land in line with new conditions of ownership and belonging. In so doing, claimants confer power onto the regime’s reformed political regime. Critically, these shifting conditions of land ownership in Phnom Penh are deeply entwined with a racialized state rule. In co-producing property and political regime, the state is simultaneously producing the normative national subjects that can rightfully claim a space in the city.

Advisor: Hart, Gillian P.
Linking Historical Market Crashes: A Market Microstructure Model and Statistical Evidence

Studies of stock market crashes are as sparse as the occurrence of/ncrashes. The mainstream theoretical models on stock market crashes are rooted in rational/nexpectations equilibrium models and classical/nmarket microstructure models. Compared to theoretical works, there are/neven fewer works done on the empirical side. This is because most of/nthe theoretical models do not provide straightforward tests against/nempirical data. Secondly, the relatively small/nsample size (rare occurrence) of stock market crashes is always an obstacle for/nempirical testing./nIn this dissertation, we build a strategic trading model to link two major US stock market/ncrashes: the 1987 crash and the 2010 Flash Crash. We then provide/ncross-sectional empirical evidence to verify our model hypothesis and evaluate price impact due/nto the information asymmetry effect and the limited risk-bearing capacity effect. We use statistical learning/nmethods to compare our model based predictors with other predictors/nfor the maximum cross-sectional price drawdown of SP500 stocks during the 2010 Flash Crash and/ncheck the robustness of our/nfindings.

Advisor: Guo, Xin