VictorJuiz/thesis huggingface dataset:
Modeling the distribution of dark matter and its connection to galaxies [electronic resource]
Keywords: Physics.

Despite the mysterious nature of dark matter and dark energy, the Lambda-Cold Dark Matter (LCDM) model provides a reasonably accurate description of the evolution of the cosmos and the distribution of galaxies. Today, we are set to tackle more specific and quantitative questions about the galaxy formation physics, the nature of dark matter, and the connection between the dark and the visible components. The answers to these questions are however elusive, because dark matter is not directly observable, and various unknowns lie between what we can observe and what we can calculate. Hence, mathematical models that bridge the observable and the calculable are essential for the study of modern cosmology. The aim of my thesis work is to improve existing models and also to construct new models for various aspects of the dark matter distribution, as dark matter structures the cosmic web and forms the nests of visible galaxies. Utilizing a series of cosmological dark matter simulations which span a wide dynamical range and a statistical sample of zoom-in simulations which focus on individual dark matter halos, we develop models for the spatial and velocity distribution of dark matter particles, the abundance of dark substructures, and the empirical connection between dark matter and galaxies. As more precise observational results become available, more accurate models are then required to test the consistency between these results and the LCDM predictions. For all the models we investigate, we find that the formation history of dark matter halos always plays a crucial role. Neglecting the halo formation history would result in systematic biases when we interpret various observational results, including dark matter direct detection experiments, the detection of dark substructures with strong-lensed systems, the large-scale spatial clustering of galaxies, and the abundance of dwarf galaxies. Rectifying this, our work will enable us to fully utilize the complementary power of diverse observational datasets to test the LCDM model and to seek new physics.
Modeling the Labor Market Effects of the Affordable Care Act
Keywords:  Department of Economics
Affordable Care Act
Medicaid notch
simulation model
labor-leisure tradeoff
exchange subsidies
Stanford Department of Economics
Thesis

This paper develops a simulation model to predict the labor market effects of the 2010 Affordable Care Act (ACA). Two provisions of the ACA—the expansion of state Medicaid programs and the creation of public health insurance exchanges—are likely to have significant effects on individual labor supply. This paper incorporates these new provisions into the budget constraint of the individual labor-leisure choice model in order to predict optimal work hours in response to the ACA. Using Central Population Survey (CPS) and premiums data to empirically estimate model parameters, I predict an -11.8% decrease in work hours after the ACA, and bunching before Medicaid and exchange subsidy eligibility thresholds. I also predict heterogeneous results for different demographic groups, with retirement age workers predicted to exhibit the largest decrease in work hours, and low-income workers previously eligible for Medicaid predicted to increase work hours. The model serves as a base for future policy simulation and complements a growing empirical literature base on the Affordable Care Act.
Models and large-scale coordination algorithms for autonomous mobility-on-demand [electronic resource]
Keywords: Aeronautics and Astronautics.

Urban mobility in the 21st century faces significant challenges, as the unsustainable trends of urban population growth, congestion, pollution, and low vehicle utilization worsen in large cities around the world. As autonomous vehicle technology draws closer to realization, a solution is beginning to emerge in the form of autonomous mobility-on-demand (AMoD), whereby fleets of self-driving vehicles transport customers within an urban environment. This dissertation introduces a systematic approach to the design, control, and evaluation of these systems. In the first part of the dissertation, a stochastic queueing-theoretical model of AMoD is developed, which allows both the analysis of quality-of-service metrics as well as the synthesis of control policies. This model is then extended to one-way car sharing systems, or human-driven mobility-on-demand (MoD) systems. Based on these models, closed-loop control algorithms are designed to efficiently route empty (rebalancing) vehicles in very large systems with thousands of vehicles. The performance of the algorithms and the potential societal benefits of AMoD and MoD are evaluated through case studies of New York City and Singapore using real-world data. In the second part of the dissertation, additional structural and operational constraints are considered for AMoD systems. First, the impact of AMoD on traffic congestion with respect to the underlying structural properties of the road network is analyzed using a network flow model. In particular, it is shown that empty rebalancing vehicles in AMoD systems will not increase congestion, in stark contrast to popular belief. Finally, the control of AMoD systems with additional operational constraints is studied under a model predictive control framework, with a focus on range and charging constraints of electric vehicles. The technical approach developed in this dissertation allows us to evaluate the societal benefits of AMoD systems as well as lays the foundation for the design and control of future urban transportation networks.
Molecular mechanisms governing integrin-mediated force transmission in living cells [electronic resource]
Keywords: Chemical Engineering.

Living cells are exquisitely tuned to the mechanical properties of their surroundings. Indeed, physical forces such as substrate rigidity, fluid shear, and stretch guide cellular processes such as stem cell differentiation, wound healing, and development. Additionally, disruption in the ability of cells to sense the mechanical properties of their surroundings represents a hallmark of many diseases, including muscular dystrophy, arteriosclerosis, cardiomyopathies, and cancer. Although cells have numerous mechanisms for detecting mechanical inputs, one of the most prominent is through integrins, heterodimeric transmembrane proteins that cluster into micrometer-sized assemblies termed focal adhesions (FAs). FAs link the extracellular matrix (ECM) to the cell cytoskeleton and are composed of hundreds of different proteins, many of which are proposed to sense force. How the many proteins within FAs are organized into force sensing and transmitting structures is poorly understood. Additionally, the mechanisms describing how FAs regulate force transduction is not well established, due primarily to a lack of tools to visualize cell-generated forces at molecular resolution. Here, we describe Förster resonance energy transfer (FRET)-based molecular tension sensors that allow us to directly visualize cell-generated forces with single-molecule sensitivity. We apply these sensors to measure forces transmitted by integrins in living cells and find that relatively modest tensions at the molecular level are sufficient to drive robust cellular adhesion. We observe strikingly complex distributions of tension within individual FAs, and characterize the organization of various adhesion components with respect to tension generation. We combine fluorescent molecular tension sensors with super-resolution light microscopy to visualize traction forces within FAs at < 100 nm spatial resolution. Finally, using these sensors we establish that metastatic cancer cells generate modest increases in integrin-mediated forces and we use proteomics to identify potential molecular associations that contribute to this increase in tension and thus the progression of cancer.
Molecular tuning of synaptic vesicle fusion mode probed using novel pHdot optical nanosensor [electronic resource]
Keywords: Molecular and Cellular Physiology.

Regulated exo-endocytosis is essential for the function of many intercellular signaling networks. Using a new optical nanosensor termed pHdot, I explore the molecular and cellular signals that tune the balance between two modes of vesicle fusion and recycling: kiss-and-run (KR) and full collapse fusion (FF) in hippocampal synapses in primary culture. Stimulation after a period of rest generates a frank initial rise in pHdot fluorescence, an unmistakeable signature of early phase KR. The prevalence of KR was dependent on the available pool of readily releasable vesicles and modulated by internal Ca2+ dynamics. Intriguingly, pharmacological inhibition of the Ca2+-dependent phosphatase calcineurin and stimulation of synapses with phorbol esters had dramatic yet opposing effects on fusion mode balance; providing a means of turning KR "on" or "off" respectively. Emerging data further suggests calcineurin facilitates a molecular switch from KR to FF that is dependent on actin dynamics and the F-BAR domain-containing protein syndapin I. Altogether, this body of work provides mechanistic insight into what appears to be a rather complex molecular choreography underlying kiss-and-run fusion at small, central synapses. The rich set of modulatory effects described could conceivably account for disparate reports on the prevalence of kiss-and-run at nerve terminals, a source of longstanding controversy. More exciting however is the likelihood that these findings reflect the diversity of signals and proteins that participate in shaping the energetics of fusion pore formation thus providing a glimpse into the intricate biochemical milieu that a vesicle navigates to direct its dynamic release properties.
Monolithic integration of germanium-on-insulator platform on silicon substrate and its applications to devices [electronic resource]
Keywords: Electrical Engineering.

Due to its higher carrier mobilities and lower optical bandgap, germanium (Ge) has been considered as an attractive material for high performance CMOS and optical applications. High performance electrical and optical devices have already been demonstrated on a germanium-on-insulator (GOI) platform. To employ high performance GOI devices side by side with a silicon (Si) CMOS circuitry, monolithic integration of GOI platform on Si is needed. In this work, a lateral overgrowth technique for the monolithic integration of GOI on Si is demonstrated. Silicon dioxide (SiO2) is used as a growth mask. Ge is over-laterally grown from the growth windows defined in the SiO2 to form the GOI platform. The technique gives a high quality GOI platform. Based on the lateral overgrowth approach, lateral p-i-n photodiode with excellent diode characteristics and high optical response is demonstrated. On the lateral overgrowth GOI, p-i-n and metal-semiconductor-metal (MSM) photodiodes are demonstrated. Ge MSM photodiodes typically show high dark current, due to the strong metal Fermi level pinning. To suppress the high dark current, photodiode with metal-insulator-semiconductor (MIS) contact is demonstrated using titanium oxide as a Fermi level de-pinning layer. The MIS contact allows transport of electrons freely but blocks holes to reduce dark current.
Moonshine and SW(3/2, 2) [electronic resource]
Keywords: Physics.

Superstring compactification on a manifold of Spin(7) holonomy gives rise to a 2d worldsheet conformal field theory with an extended supersymmetry algebra. Such manifolds have a c=12 realization of the symmetry group SW(3/2, 2). In this thesis, I explore the characters of this algebra, and decompose the elliptic genus of a general Spin(7) compactification in terms of these characters, giving results that are suggestive of a large symmetry group. I make this suggestion manifest by constructing a SCFT that permits an M24, Co2 and Co3, and which has an action by the SW(3/2, 2) algebra.
Multiple testing and minimax estimation in sparse linear regression [electronic resource]
Keywords: Statistics.

In many real-world statistical problems, we observe a response variable of interest together with a large number of potentially explanatory variables of which a majority may be irrelevant. For this type of problem, controlling the false discovery rate (FDR) guarantees that most of the selected variables, often termed discoveries in a scientific context, are truly explanatory and thus replicable. Inspired by ideas from the Benjamini-Hochberg procedure (BHq), this thesis proposes a new method named SLOPE to control the FDR in sparse high-dimensional linear regression. SLOPE is a computationally efficient procedure that works by regularizing the fitted coefficients according to their ranks: the higher the rank, the larger the penalty. This adaptive regularization is analogous to the BHq, which compares more significant p-values with more stringent thresholds. Under orthogonal designs, SLOPE with the BHq critical values is proven to control FDR at any given level. Moreover, we demonstrate empirically that this method also appears to have appreciable inferential properties under more general design matrices while offering substantial power. The thesis proceeds to explore the estimation properties of SLOPE. Although SLOPE was developed from a multiple testing viewpoint, we show the surprising result that it achieves optimal squared errors under Gaussian random designs. This optimality holds under a weak assumption on the l0-sparsity level of the underlying signals, and is sharp in the sense that this is the best possible error any estimator can achieve. An appealing feature is that SLOPE does not require any knowledge of the degree of sparsity, and yet automatically adapts to yield optimal total squared errors over a wide range of l0-sparsity classes. Finally, we conclude this thesis by focusing on Nesterov's accelerated scheme, which is integral to a fast algorithmic implementation of SLOPE. Specifically, we prove that, as the step size vanishes, this scheme converges in a rigorous sense to a second-order ordinary differential equation (ODE). This continuous time ODE allows for a better understanding of Nesterov's scheme, and thus it can serve as a tool for analyzing and generalizing this scheme. A fruitful application of this tool yields a family of schemes with similar convergence rates. The ODE interpretation also suggests restarting Nesterov's scheme leading to a new algorithm, which is proven to converge at a linear rate whenever the objective is strongly convex.
Multiple views on multiple metrics [electronic resource] : how teachers perceive the validity and utility of metrics in a multi-measure evaluation system
Keywords: Education.

Over the past five years, policy makers have shown a renewed interest in using teacher evaluation as a lever for increasing the quality of the teacher labor market. As such, states have been spurred by federal policies such as Race to the Top and influential foundations such as the Bill & Melinda Gates Foundation to use a more reliable, more nuanced method of evaluation: multiple metrics. Unlike relying solely on test scores or observation checklists, these new composite metrics were touted as more stable estimates for both human resource decisions and for targeting professional development. These multiple measures vary from district to state, but they are largely a combination of growth-based student test metrics, standards-based observation metrics, and surveys from stakeholders such as students or teachers. This study focuses on a state that uses the former two metrics, student growth and teacher observation, to ask whether such metrics are providing information to teachers that they can use to inform their instruction. This mixed-methods study takes place in Delaware, which was the first winner of President Obama's federal education initiative, Race to the Top. As such, it won
Multiplexed immuno-magnetic enrichment of cells and downstream molecular analysis [electronic resource]
Keywords: Chemical Engineering.

This thesis is about the characterization, and applications of the magnetic sifter. The magnetic sifter is a microfabricated cell separation device that enables high throughput cell separation with good capture and release properties. The unique magnetic and fluidic properties of the magnetic sifter that contribute to its unprecedented capture and release performance are discussed based on finite element analysis, and experimental characterization. In addition, this thesis will also cover the extension of the magnetic sifter towards accomplishing multiplexed magnetic separation. In particular, it will be shown how judicious manipulation of the competing magnetic and hydrodynamic forces, the dominant factors in the magnetic sifter, allow for multiplexing by a variety of methods. Lastly, this thesis will also expand on the various downstream molecular methods that have been shown to integrate well with the magnetic sifter. It shall be made evident that the magnetic sifter causes no adverse effects in the phenotype of the isolated cells, and that various downstream molecular analysis methods all combine well with the magnetic sifter. Data is presented for all these methods, and will illustrate the potential the magnetic sifter has, for various clinical applications in the near future.
Mutual Fund Age, Performance, and the Optimal Track Record
Keywords:  Department of Economics
Stanford Department of Economics
mutual fund performance
age and performance
Morningstar funds
Thesis

This paper investigates the relationship between mutual fund age and performance, and what this might imply about the track record that investors should require when evaluating mutual funds. While this relationship has been explored mostly through the lens of performance persistence, primarily by Berk and Green (2004), the age/performance relationship alone is still relatively unexplored, particularly in regard to new funds. This relationship has significant implications for the decisions of mutual fund investors. If age and performance have a negative correlation, then waiting several years for a mutual fund to establish a track record before investing may be unwise, as the fund’s best performance will already be behind it. In this paper, I utilize data from the CRSP mutual fund database to quantify the age/performance, age/risk, and age/risk-adjusted performance relationships for Morningstar’s listed U.S. equity funds. I conclude that in general, older funds are riskier, but this relationship reverses for very young funds, which exhibit more risk. I also find that older funds have slightly lower risk-adjusted returns, but this relationship again reverses for very young funds. Though both of these relationships are somewhat weak, my results imply that investors may not want to flock to new funds in the hope of outsized riskadjusted returns—requiring a track record does not come at a significant cost and may, in fact, help investors avoid undue risk.
MYC and RAS induce lipogenesis in lung cancer and generate distinct tumor subtype lipid signatures
Keywords:  Department of Biology
Biology
MYC
KRAS
RAS
oncogene
metabolism
lipogenesis
lung cancer
Thesis

Lung cancer is the deadliest type of cancer and a cure for advanced stages of this disease does not currently exist. MYC and KRAS are two oncogenes commonly implicated in the development and maintenance of lung cancer. Emerging evidence has revealed that lung cancer reprograms cellular metabolism and previous studies have demonstrated that MYC and KRAS alter glucose and glutamine metabolism as a means of supporting cancer cell survival. However, the role that MYC and KRAS play in lipid metabolism in lung cancer has not been addressed. Here, preliminary data using Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) for lipid profiling of lung tumor tissue as well as gene expression analysis of key lipogenesis genes show that MYC and KRAS not only induce lipogenesis in lung tumors but do so through distinct pathways. Additionally, by administering inhibitors of enzymes involved in the fatty acid synthesis pathway to human lung cancer cell lines, I demonstrate that blocking lipogenesis reduces the proliferation of lung tumor cells in vitro. These results suggest that lung tumors are dependent upon oncogene-induced lipogenesis and that inhibition of lipogenesis pathways may be a promising therapeutic approach for the treatment of lung cancer.
Nanoscale reaction, transport, and phase transformation in LixFePO4 battery electrodes [electronic resource]
Keywords: Materials Science and Engineering.

Interfacial reaction, transport, and phase transformations are the primary mechanisms by which materials change. Understanding these processes, especially on the nanoscale, is key to understanding how material properties change through external physical and chemical stimuli. To obtain a holistic understanding, it is also necessary to study how these mechanisms are coupled to one another. The lithium-ion battery is an ideal model system for studying the coupling of reaction, transport, and phase transformations on the nanoscale. Not only do batteries use all three mechanisms during charge and discharge, it is also possible to precisely control the lithium composition, an extrinsic variable, at the nanoscale by controlling the electrochemical current. Fundamental studies of Li-ion battery materials, especially phase transforming ones, are hindered by the porous electrode architecture. Such porous electrodes contain an ensemble of nanoparticles with nonuniform electronic and ionic connectivities. With heterogeneous current densities and lithium compositions, it is very difficult to correlate macroscopic current-voltage or structural measurements to microscopic, single-particle phenomena. My thesis overcomes this problem by using nanoscale imaging study individual battery particles. By being able to probe the lithium composition and current density of single battery particles on the nanoscale, I obtain a local, microscopic measurements of reaction, transport, and phase transformation dynamics. While I initially conduct these experiment ex situ by cycling and disassembling the battery electrodes, I also develop in situ techniques that enable me to track the evolution of the lithium composition and current density on the nanoscale as the battery charges and discharges. I specifically aim to understand four different ways that reaction, transport, and phase transformation are coupled on the nanoscale. First, I study how transport at the porous electrode affects reaction and phase transformation within individual particles. Second, I investigate how ion insertion reaction rate affects the phase transformation pathway. Third, I examine how the lithium composition and phase affects the ion insertion reaction rate. Finally, I study how diffusion affects the ion insertion rate. I developed a coherent model that quantitatively explains how the rate of ion insertion reaction and the rate of diffusion ultimately affect the phase transformation pathway, both within individual battery particles and in a many-particle electrode. This model shows how reaction, transport, and phase transformation are coupled together to determine how this battery electrode undergoes charge and discharge.