VictorJuiz/thesis huggingface dataset:
Dach1 regulates the specification of coronary artery endothelial cells
Keywords: Biology.

Cardiovascular disease (CVD) is the most common cause of death in the world and new treatments are needed to combat this disease. An unexplored approach to improving outcomes of patients suffering from CVD, is the expansion of artery networks on the heart. In the event that one section of a coronary artery is blocked, more abundant artery networks could compensate for the loss of one artery. However, the underlying genetic and molecular factors that regulate artery specification and growth are incompletely understood. Here, I use two approaches to advance our understanding of artery development. First, I show that upregulation of the transcriptional regulator, Dach1, in endothelial cells increases artery specification during embryonic development in mice. Second, in order to better understand the formation of collateral arteries, I used a comparative approach by investigating heart development in Cavia porcellus (guinea pig). I found that this high altitude rodent has numerous collateral arteries which form during embryonic development. Together, these studies provide insight into new strategies for increasing coronary artery abundance or changing coronary morphology. Identification and characterization of arterializing factors such as Dach1 or the purported guinea pig collateralizing regulators, could provide the basis for new artery based treatment option for people at risk of CVD
Dark pool trading : stochastic control meets adaptive allocation
Keywords: Mathematics.

This thesis addresses an optimal liquidation problem in which a trader has access to a lit market, which offers guaranteed execution at a worse price, and dark pools, which offer the best price but do not guarantee execution. Given a fixed time window in which to liquidate her inventory, the trader must balance between these two venues to maximize her utility, while simultaneously estimating the inner workings of the dark pool from past execution data. The value function of this optimal control problem satisfies a nonlocal Hamilton-Jacobi-Bellman equation, and resolving the optimal strategy requires an exploration-exploitation assessment of the dark pools. Throughout, algorithmic complexity is kept as low as possible, with an eye towards the high-frequency setting
Data-driven analytics for clinical decision making, healthcare operations management and public health policy
Keywords: Management Science and Engineering.

Health care costs in the United States exceed $3.5 trillion annually, with between 60 billion and 35 billion considered waste. Data-driven analytics could reduce costs and provide higher quality care to patients by more efficiently allocating limited resources, just as analytics has done in other industries such as logistics, manufacturing and aviation. In this dissertation, I demonstrate three levels at which analytics provide value in health: clinical decision making, healthcare operations management and public health policy. Clinical decision making refers to decisions at the individual patient level: for example, determining which treatment to provide a patient or predicting an individual's risk of disease. Healthcare operations management refers to decisions about the system that delivers care to patients: for example, determining how to organize patient flow through a hospital or schedule procedures. Finally, public health policy refers to decisions about the overall health of a population: for example, determining how to control an infectious disease or distribute limited resources across different diseases
Data-driven modeling of dynamical systems
Keywords: Aeronautics and Astronautics.

Robots, automated decision systems, and predictive algorithms have become ubiquitous in our world, and we are becoming increasingly reliant on their ability to make intelligent decisions for us. We task these systems with choosing actions in sequential decision-making settings that will reap the best performance in the long-run, and hope to deploy them in environments about which they are uncertain. Uncertainty hampers the ability to make optimal decisions, and it arises from uncertainty about the state of the world, uncertainty about how the world changes, and uncertainty about what it means to act optimally. For a machine to overcome these sources of uncertainty, it must be able to learn from available data on its own, and others' interactions with the world. In many settings, this data is scarce and expensive to acquire. Moreover, this data is often incomplete - providing only a partial description of the state of the world and how it evolves. If machines are to be able to predict the outcomes of their actions, they must build models of their worlds from limited and incomplete data. In some settings, we may use experts to show machines how to act optimally - using them to correct the mistakes machines make. It is of paramount importance that we can guarantee the safety of the frameworks in which we allow the machines and experts to interact. The work in this thesis addresses the challenges of learning components of decision-systems from data. In the first part of this thesis, we present Structured Mechanical Models, a flexible model class that can learn the dynamics of physical systems from limited data. We then turn to the problem of partially observed systems, for which the data available does not reveal their full state. We present an algorithm called Certainty-Equivalent Expectation-Maximization, which can efficiently learn the dynamics of nonlinear, high-dimensional, and partially observed systems. We demonstrate the performance of this algorithm on multiple challenging domains such as an aerobatic helicopter, and apply it to the task of learning models of the spread of COVID-19. Finally, we study the problem of safely allowing an expert to correct the actions of a learned decision system to teach it optimal behavior. We propose an algorithm called EnsembleDAgger, which trains a Bayesian decision system on data from the expert, and uses the system's uncertainty to safely and effectively allow it to interact with an expert
Deciphering the implications of state-sponsored cyber operations for IR theory
Keywords: Political Science.

This dissertation seeks to understand to what extent state-sponsored cyber operations should motivate revisions in traditional international relations theory. In Chapter 2, I test the applicability of existing theories of state conflict initiation and foreign policy on the cyber domain. I find that democracies are no less likely than autocracies to initiate a cyber operation in a given year, although they initiate with less frequency. Heterogeneity among authoritarian regime types exists; however, contrary to theories of traditional conflict initiation, personalism is not significant. These findings provide suggestive evidence that institutional mechanisms of conflict initiation and foreign policy do not restrain cyber initiation in the same way they have been theorized to restrain conventional conflict. Chapter 3 is a co-authored paper that utilizes a survey experiment to test whether the public support for retaliation is distinct in response to cyber and kinetic operations. We argue that certain characteristics, namely attribution certainty, timing, and perceived intentionality of harm, may account for the public's distinct attitudes to cyber operations. Finally, in Chapter 4, I advance the literature by arguing against a narrow focus on the impacts of the attribution problem, and I contribute to the development of a common taxonomy for target state communication of cyber operations. I argue that the attribution problem provides the target state increased agency in deciding when, and to what extent, to communicate cyber operations. This discretion allows states flexibility in their response and provides greater control over escalation and domestic political costs at the expense of deterrence by punishment. Taken together, these papers demonstrate how the clandestine and covert nature of cyber operations has implications for the domestic political costs and, subsequently, considerations for the initiation and response to cyber operations
Decoding transcription regulatory patterns in health and disease
Keywords: Biomedical Informatics.

Transcriptional control of gene expression is known to be a cell-type specific process that depends on many different factors: including the underlying DNA sequence, combinatorial patterns of transcription factors (TFs) and their associated motifs, chromatin accessibility, and DNA-DNA looping contacts. However, modeling the relationship between DNA and its regulatory elements while learning how these regulatory complexes lead to cell-type specific function within a 3-dimensional context remains difficult. Additionally, sequence variants within DNA are known to modulate risk for many polygenic diseases--with oncogenic, neuropsychiatric, and inflammatory etiologies, among others. As these variants are mostly in non-coding regions of the genome, assessing the functional impact of variants within the cell and how they might contribute to disease pathogenesis remains a computational and experimental challenge. In this dissertation, I present data representation and modeling strategies to understand cell-type specific transcription regulatory mechanisms in healthy cells and analyze how these mechanisms are disrupted in complex polygenic diseases. First, I use an epigenomic and transcriptomic resource of 15 different epithelial cell types to extract features of transcription regulation from the dataset in an unbiased manner and to build a data representation of transcription regulation that accurately predicts cell type. I then show that different patterns of transcription factor motif cooperativity exist in healthy cells and that this synergistic behavior is altered in the context of cancer. Next, as part of a collaborative effort, I generate a compendium of non-coding single nucleotide variants (SNVs) for neuropsychiatric disorders that alter transcription-regulatory activity in a developing neural cell system. The analytical framework built from this dataset links regulatory variants to disease-relevant genes, pathomechanisms, therapeutics, and clinical manifestations of disease using expression-gene mapping, network analyses, chromatin looping, and statistical enrichment methods. Additionally, I develop a network-based approach to identify genes relevant for psychiatric disease diagnosis and to prioritize treatment options. Finally, I add a perspective on the strengths and limitations of modern network methods--including network building strategies, machine learning techniques, and inference applications--for biological applications. In summary, I hope that these resources and methods--which provide further insight into transcription regulation--have translatable uses in the clinical setting for complex disease prognosis, diagnosis, and therapy selection
Deconstructing the American Classroom during a Pandemic: Architects and Teachers Reconsider Learning Spaces
Keywords: Stanford Graduate School of Education
Stanford University
design
classroom
learning
space
pandemic
Covid-19
adaptable
flexible
useable
outdoors
ventilation
school
Thesis

Among the many challenges the Covid pandemic has brought, education is one that will be forever reformed. Having children in school is critical because it dictates what our future will be, and to make this possible during a pandemic, we need better designed schools. Therefore, to imagine the ideal covid-safe classroom, I decided to explore what architectural lessons for learning spaces in terms of an “ideal classroom” can be drawn from changes in architects’ thinking and teachers’ experiences during the pandemic. I conducted interviews with eight architects and seven educators, asking the architects how the pandemic changed their thinking about educational spaces and prompting elementary school educators to reflect on what they’ve learned about the distribution of space in their classroom. In analyzing the interviews, four intangibilities emerged on which architects and educators agree that a classroom must have: useability, a useable and multipurposed space; adaptability, being able to transform and change; breathability, good air ventilation; and egressability, access to the outdoors. Through the analysis and coding of the architects’ and educators’ perspectives, I was inspired to create my own architectural design of a learning space that embodies the four intangibles, which moves away from traditional, cubic rooms to undulating roofs and walls that mimic students’ movement and learning. With this reshaping, I create a fluid, mobile, and flexible space that allow teachers and students to adapt to the next crisis. The results of the study suggest the value of continuing the conversation with educators and architects across America to expand our thinking of classroom design for a dynamic and developing world.
Deep cuts : transgender history in US-American art after World War II
Keywords: Art and Art History.

During World War II, Nazi occupation forced the centers of modern art and transsexual medicine to move from Europe to the United States. This dissertation explores the legacy of this convergence in modern and contemporary American art and supports the continued growth of transgender art history as a distinct subfield. In this project, I contend that works by and featuring Forrest Bess (1911-1977), Candy Darling (1944-1974), Greer Lankton (1958-1996), Cassils (b. 1975), and Chris E. Vargas (b. 1978) do at least three things: first, they show how social and scientific histories have shaped the ways in which gender transformation has appeared (or disappeared) in art history from WWII to the present moment; second, they demonstrate why works of art in particular are central to constructing a visually and materially rich transgender history; and third, they further argue for the strengths and limitations that painting, photography, and sculpture each have in addressing transgender embodiment
Deep learning for anomaly detection and representation learning
Keywords: Mechanical Engineering.

Anomaly Detection (AD) refers to the process of identifying abnormal observations that deviate from what is defined as normal. With applications in many real-world domains, anomaly detection has become an important research field in machine leaning and artificial intelligence. However, detecting anomalies in high-dimensional space is challenging. In some high-dimensional cases, previous anomaly detection algorithms fail to correctly model the normal data distribution. Also the understanding on the detection mechanism of AD models remained limited. How are anomalies detected by a AD algorithm? Why is a specific instance detected as anomalous? To address these challenges and questions, first we propose the Regularized Cycle-consistent GAN (RCGAN) that introduces a penalty distribution in the modeling of normal data distribution. We theoretically show that the penalty distribution regularizes the discriminator and generator towards the normal data manifold. This results in higher detection accuracy and more precise normal data modeling. Second, we introduce the memory augmented GAN (MEMGAN) for anomaly detection. By leveraging adversarial learning and enforcing cycle-consistency, the trained memory units enclose the encoded normal data. We also theoretically and numerically show that the encoded normal data reside in the convex hull of the memory units, while the abnormal data are expected to be located outside. Third, we explore anomaly detection with domain adaptation where the normal data distribution is non-static. We propose to extract the common features of source and target domain data and train an anomaly detector using the extracted features. The second part of the thesis is on language representation learning. Language representation learning refers to the task of mapping language token to a numerical vector. Previous sentence representation models largely rely on labeled data and supervised training. However, these data are expensive to obtain, making it hard to to cover more domains and languages. To solve this challenge, we propose Conditional Masked Language Modeling (CMLM) that integrates sentence representation learning into unsupervised Masked Language Modeling (MLM) training. We also propose GEM, a non-parameterized method that exploits the geometric structure of a sentence subspace to build the sentence representation. Finally, we explore the problem of out-of-vocabulary (OOV) embeddings imputation by using graph convolutional networks and grounded language information
Deep learning for house prices
Keywords: Management Science and Engineering.

This dissertation develops, estimates, and analyzes a deep learning model for home prices using an unprecedented dataset containing over 150 million sale transactions between 1970 and 2019 in the United States. We contrast our model's superior performance with methods commonly used in the literature, and uncover previously unstudied pricing factors. The last sale amount figures as the most influential variable, and we identify localized statistics, house characteristics, mortgage rates, and macroeconomic conditions to be among the most salient features. We also contribute to the literature in demonstrating our model's capacity to capture non-linear effects, exposing, for example, the model's increased sensitivity to local statistics in times of high and low economic prosperity. In the second part, we use the trained network to scrutinize the efficiency of housing markets —which assumes all information is contained in home prices— in a set of novel approaches. We challenge the efficiency notion by finding that model-induced price trends can be predictive of future returns, and devise a trading strategy that exploits such effects to produce unparalleled profits. In an out-of-sample simulation of house trading from 1995 to 2019, our deep learning-induced strategy produces the highest average returns (at an annualized rate of over 25%) when compared to benchmark methods, and we find that short term resales yield the highest performance. Finally, we find evidence that suggests the market is not efficient (there are large gains to be made using only past information), and expose the areas with the highest price exploitability, such as California, Washington State, Wyoming, Colorado, New Mexico, Massachusetts, New York, Connecticut, and New Jersey
Deep learning for protein design
Keywords: Bioengineering.

Advancements in computational modeling methods have led to remarkable outcomes in protein design, including the development of new therapeutics, enzymes, and biosensors. However, the protein design problem remains a major engineering challenge, as the current design process relies heavily on heuristics, requiring subjective expertise to negotiate pitfalls that result from optimizing imperfect scoring functions. The ability to quickly and accurately generate protein structures and sequences would constitute a major advancement in the field of protein design. Improved methods for function approximation, in particular deep learning, might allow us to move beyond the limitations of current methods. My thesis work explores the development of deep learning systems to learn and sample from the distributions of protein structures and sequences. I first describe my work on training deep generative models to generate protein backbones and will further describe applications of this method to loop modeling. Next, I describe efforts to move toward practicable protein backbone generation including generating backbone coordinates in a fully differentiable manner, as well as generating domain-specific backbones or backbones conditioned on properties of interest. Next, I describe an entirely learned method for protein sequence design on fixed protein backbones, highlighting some interesting experimental results validating the method. Finally, I conclude with a perspective on future directions in this field
Deep neural network surrogates for inverse problems
Keywords: Energy Resources Engineering.

Inverse problems in subsurface flow are generally challenging due to the need for a large number of expensive numerical solutions to partial differential equations (PDEs). Inverse modeling typically consists of generating realizations of the unknown model parameters and matching the corresponding model's prediction to the measurements. Model errors, measurement errors, and data scarcity require one to quantify predictive uncertainty in model predictions, which further exacerbates the computational cost of inverse modeling. The latter can be ameliorated either by devising inversion frameworks that require fewer forward model runs to converge, or by constructing a much more efficient forward surrogate model that replaces the PDE solver. In this dissertation, we pursue these two strategies and apply them to three inverse problems of practical importance in subsurface applications. The common thread in this investigation is the use of deep neural network (DNN) surrogates that accelerate forward modeling by several orders of magnitude. In the three applications, we first present a study on identification of the statistical parameters of a discrete fracture network (DFN); we further focus on a higher-dimensional inverse problem on contaminant source identification; finally, we expand the unknown parameter dimension and tackle a realistic three-dimensional inverse problem, in which both a heterogeneous conductivity field and the contaminant release history are identified from sparse observations