VictorJuiz/thesis huggingface dataset:
Does Ethnic Fractionalization Matter for Development?
Keywords:  Department of Economics
Stanford Department of Economics
Thesis

An existing literature finds that ethnic fractionalization (a measure of ethnic diversity) is negatively correlated with various development outcomes, including growth and public goods provision (Alesina and La Ferrara, 2005). This paper attempts to deal with issues of endogeneity in work related to ethnic fractionalization. I develop an instrument for fractionalization based on the arbitrary construction of African borders, and also use a control function approach to deal with omitted variables bias. Both methods generate evidence that the endogeneity of ethnic fractionalization causes substantial bias in regressions of growth on fractionalization, and the true effect substantially smaller, although very difficult to estimate precisely. I develop a game theoretic model to explain how to reconcile micro-level evidence of harmful ethnic favoritism with macro-level effects of ambiguous sign.
Driver adaptation to changes in automobile handling properties [electronic resource]
Keywords: Mechanical Engineering.

Automobile drivers frequently encounter changes in vehicle handling properties that require them to adjust their steering control actions. These changes may be benign, such as slightly different steering ratios in different cars, or potentially life-threatening, such as suddenly encountering a patch of black ice on the roadway. Understanding how drivers adapt to such handling changes is important for informing the design of new driver assistance systems and training methods. Although adaptation has been studied extensively in the laboratory for applications such as arm reaching and walking, there are still many unanswered questions about the adaptation process in the context of driving. This dissertation addresses the need to understand how drivers adapt to handling changes by designing and implementing a novel user study with an experimental vehicle. Drivers complete several trials of a lane change task while the vehicle handling properties are mod- ified in one of four ways that represent scenarios similar either to common laboratory ex- periments from arm reaching studies or to real-world situations specific to driving. The specific handling changes are (1) scaling the steering ratio, (2) reversing the steering direc- tion, (3) scaling the steering wheel torque, and (4) simulating low friction vehicle dynamics. To enable the latter type of handling change, a new approach to handling modification has been developed using online simulation of the dynamics and tire forces of a reference vehicle model. The resulting control scheme provides a method of emulating a range of planar vehicle dynamics, in particular those of a vehicle traveling on an icy road. The results of user studies with the four handling modifications demonstrate that adaptation exists in automobile steering control tasks. The studies provide evidence that drivers adapt their steering control based on steering wheel angle, not on steering wheel torque. Finally, individual driving style influences the degree of adaptation necessary when encountering a low friction surface.
Drosophila melanogaster natural variation affects infection and microbial growth dynamics when infected with Listeria monocytogenes [electronic resource]
Keywords: Microbiology and Immunology.

The immune response is a dynamic system, which balances microbe killing with host health. In this dissertation I used a Drosophila melanogaster -- Listeria monocytogenes model to define parameters that define the dynamics of infection, and to measure the variance of these key parameters. In Chapter 2, I performed a screen to identify genes that affect the phenotypic natural variation of host survival and pathogen growth control after an infection. In Chapter 3, I investigated the shape of disease- tolerance curves, which summarize infection outcome by plotting health by microbe load. We found tolerance curves are sigmoid, and can be warped in predictable ways. Using this information, we defined a simple mathematical model of infection comprised of three parameters: microbial growth, immune activation and host health. In Chapter 4, I report experiments that focused on microbe growth, measuring how fly genetic variation alters Listeria growth curves. I found microbe growth curves changed in two ways: maximal growth rate and/or growth plateau. I found that these two growth parameters could change independently and both affected host health. This work provides a foundation for a dynamic model of immunity, which is essential to explain how the balance between microbe killing and host health is maintained.
Dynamic models of electron transport in hall thruster simulations [electronic resource]
Keywords: Mechanical Engineering.

The Hall-effect thruster (HET) is an electrostatic propulsion device that relies on the Hall effect to generate a dense ExB electron current to ionize the propellant gas. In simulating Hall thrusters, describing electron cross- eld transport has been one of the greatest challenges because the electron transport in a Hall thruster is anomalously higher than that predicted by classical collision theory. Researchers have suggested some explanations of the anomalous transport, but they have failed to establish a reliable physical model for general applications. Establishing a physical model that is applicable to various types of Hall thrusters in various operating conditions is an objective of this work. In this thesis, a 2-D hybrid particle-in-cell (PIC) simulation for the Stanford Hall thruster (SHT) is used to implement the transport (electron mobility) models. Among various attempts, an entropy closure model, as well as a turbulent transport model were successfully implemented and demonstrated results that show reasonable agreement to measured data. The entropy closure model uses a 1-D entropy transport equation in the plasma of a Hall thruster discharge to derive a relation for electron mobility as a function of other plasma properties. The simulated results show a reasonable agreement with experiments. The turbulent transport model seeks for a more straightforward way to incorporate the entropy production mechanism into the simulation. By assuming that the Joule heating is the main source of entropy production, we adopted the turbulent kinetic theory to relate the energy dissipated from the largest eddies with the energy production rate. Through a scaling analysis, electron mobility is expressed as an explicit function of other plasma properties of the simulation. The simulated electron mobility captures the electron transport phenomenon measured experimentally. To test the transportability of the turbulent model, the simulation was modi ed for an SPT-type thruster with a different geometry than the SHT. Also, an alternative propellant, molecular nitrogen, was simulated on the geometry of the SHT using the turbulent model. The dynamic mobility models make it possible to observe the dynamic characteristics of the Hall thruster. The mobility models in this study magnify the capability of Hall thruster simulations to explore design space cost effectively.
Dynamic resource and power management in virtualized data centers [electronic resource]
Keywords: Management Science and Engineering.

Cloud computing is a multi-billion dollar industry that has permanently changed the internet landscape. As public demand for cloud computing and cloud-based services grows, technology companies are required to build a progressively larger network of data centers to keep apace with expanding traffic loads. The operational cost of powering and cooling many thousands of servers is a growing financial strain that has already reached a critical point across the industry, with data centers additionally posing an emerging environmental hazard as a significant contributor to carbon emissions. Energy-efficient resource management is a looming imperative that can save companies billions of dollars and curb the adverse effects of data centers on the environment. Resource utilization rates in modern data centers are low, typically operating below 30-50\% of capacity. Dynamic Power Management is a multidisciplinary research area that aims to reduce energy consumption by dynamically adjusting the power draw of elements within a data center to match an evolving compute load profile, maximizing utilization and minimizing the energy used on idle processes. A natural tension arises, however, between decreased energy consumption and increased congestion or latency, which is exacerbated by stochastic forces that introduce uncertainty and impede full elasticity with added risks, delays, and switching costs. In this thesis, we explore various stochastic models to optimize the energy and congestion tradeoff in Dynamic Power Management applications for virtualized data centers. We use Dynamic Programming as a template to frame and solve these systems, as well as related methods in Approximate Dynamic Programming to reach beyond the practical limitations of standard Dynamic Programming solution concepts. In this way, we are able to address and implement models at multiple layers of the technology stack. In the first part of this thesis, we consider cloud applications with partial execution times. Iterative algorithms, which are increasingly popular in internet search and traffic classification, introduce a dimension of quality that induces a three-way tradeoff among output quality, power draw, and latency. Next, we study resource management for virtual machines. The management of resources in a virtual environment is complicated by primitives that are constantly in flux, including bursty traffic and supply and demand dynamics that drive the effective delays and costs of acquiring computing resources. We develop Markov models to capture these effects and leverage their Markov properties to find resource management policies in systems with incomplete information and exploitable market trends. Finally, we consider voltage and frequency scaling in a network switch. We discuss the limitations of Dynamic Programming in this setting and implement two alternative methods that exploit load-balancing and convexity properties to derive an approximate solution.
Economies of expertise [electronic resource] : knowledge and skill transfer in classical Greece
Keywords: Classics.

How did 4th-century autocrats like Philip II of Macedon (or Mausolus of Caria, etc.) import expertise from Greek city-states? The evidence available to us indicates they did do this----deliberately and quickly----and so the mystery becomes: how? It is particularly mysterious in light of modern studies that show substantive expertise transfer to be very challenging (see, for instance, the field of development economics). This dissertation offers an answer to this problem: the new capacity of states for expertise transfer was made possible by new frameworks for understanding expertise, developed mainly at Athens (for instance, in the works of Thucydides, Plato, and Aristotle), but disseminated to Greek speaking autocrats over the course of the 4th-century. These new accounts----centered on notions like technē, epistēmē, and empeiria----opened up new possibilities for 4th-century monarchs, allowing them to pinpoint technical fields they wished to target, capture, and recreate (something impossible in absence of an agreed upon, well- developed language of expertise). Plugging into a Greek world of high specialization and expert mobility, opportunists like Philip were thus able to rapidly develop state capacity in fields like land and sea warfare, siege engineering, fortification building, and fiscal administration, allowing them to compete with, and eventually dominate, the Greek city-states.
The effects of cover crops on phosphorus cycling in agricultural soils of California [electronic resource]
Keywords: Biology.

Phosphorus (P) limits agricultural productivity because most soil P is found in pools of low plant-availability and external inputs that are used to increase plant-available P are only partially recovered in crops, resulting in low P use efficiency (PUE). Cover crops could reduce external P input requirements, increase PUE and stimulate soil P cycling by mobilizing soil P and by retaining soil P via plant uptake, especially in low-input agricultural systems. This dissertation seeks to determine if cover crops have similar effects on soil P cycling in intensive agricultural systems with relatively high soil P, using two long-term experiments in California, greenhouse experiments and nutrient budgets. In both field and laboratory conditions, legume cover crops had a greater potential to mobilize soil P than other cover crops, although in practice they did not mobilize soil P. In contrast, cereals had the strongest effect on soil P availability and P cycling by taking up more soil P than other cover crops. Regardless of cover crop type, P taken up in cover crop biomass was recycled rapidly in these systems: cover crop residues and mineral fertilizer contributed similarly to soil pools and wheat P uptake, with a greater contribution at lower soil P availability. However, cover crops had relatively small effects on long-term soil P dynamics relative to compost addition that was the main factor driving differences in P budgets computed at the farm-scale. Overall, cover crops have the potential to affect soil P cycling in these systems with relatively high soil P, although to a lesser degree than do composts.
Effects of TLR9-Dependent Activation of Plasmacytoid Dendritic Cells on Post Stroke Recovery
Keywords:  Department of Biology
Stroke
pDC
Thesis

Stroke is a leading cause of human disability and death, yet continues to lack an effective treatment, as well as a thorough understanding of its pathological mechanisms. Currently, the post-stroke immune response is believed to be a pathological mechanism that exacerbates tissue damage and inhibits recovery. Within the immune system, a relatively rare leukocyte known as the Plasmacytoid Dendritic Cell (pDC) has been demonstrated to produce a disproportionately large amount of type 1 IFNs and pro-inflammatory cytokines through a TLR9-dependent pathway. Consequently, it is thought to serve as an orchestrator of the immune response. Therefore, this project sought to answer the question: How does pDC activity affect recovery after stroke? To answer this question, the effect of pDC activation via the TLR9-dependent pathway on post-stroke recovery was examined through usage of the widely accepted Middle Cerebral Artery Occlusion (MCAO) mouse model for stroke induction, the rotating beam test to assess recovery of motor function, neurological scoring, and confocal microscopy to visualize pDCs within the brain after stroke. It was found that activation of pDCs via the TLR9-dependent pathway leads to increased (statistically insignificant) infarct size. As well, pDC depletion facilitates short-term motor function recovery (partially significant), and a decrease in long-term development of infarct (statistically insignificant). This research is novel because it places pDC activity within the context of the post-stroke immune response, and has translational potential through elucidating a neuropathological mechanism of stroke.
Efficient learning in sequential optimization [electronic resource]
Keywords: Management Science and Engineering.

We consider a broad class of online optimization problems in which a decision-maker must balance between exploration and exploitation while learning from partial feedback. In these problems, the decision-maker repeatedly chooses among a set of possible actions, observes an outcome, and receives a reward representing the utility derived from this outcome. She is uncertain about the underlying system and is therefore initially unsure of which action is best. However, as outcomes are observed, she is able to learn over time to make increasingly effective decisions. Her objective is to choose actions sequentially so as to maximize the expected cumulative reward. We focus on three algorithmic approaches that accommodate flexible statistical modeling, and are capable of experimenting efficiently in broad classes of problems. The first part of the thesis focuses on a design principle known as optimism in the face of uncertainty, which underlies many of the most effective exploration algorithms. We provide a regret bound for an optimistic algorithm that applies broadly and can be specialized to many specific model classes. Our bound depends on a new notion of dimension that measures the degree of dependence among actions. We compare our notion to the Vapnik-Chervonenkis dimension, and explain why that and other measures of dimension used in the supervised literature do not suffice when it comes to analyzing optimistic algorithms. We then turn our attention to Thompson sampling, an elegant algorithm for learning in online optimization problems with partial feedback. We derive a close theoretical connection between Thompson sampling and optimistic algorithms. Due to the connection we derive, existing analysis available for specific optimistic algorithms immediately translates to expected regret bounds for Thompson sampling. The second part of the thesis pushes beyond the optimistic principle, and offers a fresh, information-theoretic, perspective on the exploration/exploitation tradeoff. We first revisit Thompson sampling from this perspective and provide novel regret bounds that scale with the entropy of the optimal action distribution. Then, we propose a new algorithm--information-directed sampling (IDS)--and study its performance. IDS quantifies the amount learned by selecting an action through an information theoretic measure, and selects actions by optimizing an objective that explicitly balances attaining a high immediate reward and selecting informative actions. We provide a general regret bound for IDS, demonstrate strong performance in simulation, and show through simple analytic examples that it can dramatically outperform Thompson sampling due to the way it quantifies information.
Electrochemical CO2 and CO reduction on oxide-derived copper catalysts [electronic resource]
Keywords: Chemistry.

The recycling of CO2 to liquid fuel using renewable energy is a longstanding problem that has been stymied by the lack of efficient electrocatalysts for this reaction. We demonstrate here that the reduction of metal oxide layers to generate "oxide-derived nanocrystalline metals" is a general method to increase the energetic efficiency of CO2 reduction and suppress the competitive H2 evolution reaction. Copper is the only metal with appreciable CO electroreduction activity, but requires extreme overpotentials to reduce CO in preference to H2O. Oxide-derived Cu (OD-Cu) lowers this overpotential requirement by at least 500 mV, producing ethanol and acetate with a combined Faradaic efficiency of 57% at --0.3 V vs the reversible hydrogen electrode. Commercial Cu nanoparticles of the same size and morphology exhibit very low selectivity for CO reduction under identical conditions, implicating the participation of surface defects or grain boundaries in the catalysis on oxide-derived nanocrystalline Cu. Using TEM electron diffraction and temperature-programmed desorption, we build grain boundary structure-activity relationships and probe the adsorption energy of CO to the oxide-derived Cu surface. Based on these data, we hypothesize that grain boundary surfaces provide active sites that bind CO more strongly than conventional Cu terrace or stepped facets, and these active sites are implicated in CO reduction catalysis.
Electrochemical CO2 reduction on transition metal surfaces - from thermodynamics to kinetics [electronic resource]
Keywords: Chemical Engineering.

Electrochemical CO2 reduction is a promising technology for the storage of renewable energy in the form of energy-dense hydrocarbons. Although this reaction has been studied experimentally for over a century, only in the last decade have advancements in density functional theory (DFT) and computing power allowed for the theoretical study of heterogeneous electrochemical CO2 reduction to thrive. Our work builds upon advances in the understanding of electrochemical interfaces to model electrochemical CO2 reduction at the fundamental level. First we explore the thermodynamics of CO2 reduction on transition metal surfaces with DFT calculations of binding energies. Then we use explicit simulations of the water-metal interface to calculate proton-electron transfer barriers. Finally we apply trends in barriers to predict electrochemical CO2 reduction reactivity from mean-field microkinetic modeling.
Electrochemical studies of III-V heterojunctions and nanowires [electronic resource]
Keywords: Electrical Engineering.

III-V semiconductors exhibit light absorption and charge transport properties that make them ideal for solar energy conversion. However, these materials have been relatively unexplored, as compared to silicon and metal oxides, for photoelectrochemical water splitting. They have also not seen much development in photovoltaics apart from expensive and resource-hungry multijunction architectures. The focus of this work is in the growth of III-V thin film heterojunctions and nanowires, and in electrochemical analysis as an evaluation as electrodes in photoelectrochemical energy conversion and in photovoltaic devices. The first part of this work focuses on the design of two epitaxial heterojunction III-V stacks: InGaP grown on GaAs, and GaP grown on Si. The photovoltaic parameters of these heterojunctions are extracted, and solar-driven hydrogen evolution is demonstrated. Additionally, a new material, AlPN, is grown on GaP. Despite its apparent lack of crystallinity, it demonstrates photoactivity in the visible light range. The second part of this work demonstrates a reduction photocurrent in III-V phosphide nanowires that, due to their reduced dimensionality, can aid in the performance of solar-to-hydrogen systems. Controlled non-aqueous measurements are performed to examine the reduction photocurrent generated in the nanowires, and electrochemical impedance measurements are used to characterize the energetics and electrostatics in the junction created between the nanowires and the electrolyte. Novel phenomena are observed in the materials growth of phosphide nanowires on GaAs, specifically substrate tuning of the growth directions for GaP nanowires, and the formation of an InGaAs interlayer during InP nanowire growth. The third and final part of this work investigates the development of III-V nitride materials for solar energy conversion. With growth control, various micro and nanoscale morphologies of GaN-on-Si heterojunctions are created to drive an oxidation photocurrent in non-aqueous environments. This photocurrent shows that a structured nanowire/film electrode exhibits promising performance in the context of materials usage, and impedance measurements help to build a band diagram of the Si/GaN/electrolyte junction created for all electrodes under study. Single crystal InN nanowires are grown with different morphologies and generate a photovoltage under solar illumination within an electrochemical junction, opening up research in using high-indium InGaN alloys for solar cell and photoelectrode applications.