VictorJuiz/thesis huggingface dataset:
Accent and Ideology among Bilingual Korean Americans

This dissertation documents a collection of sociolinguistic and sociophonetic studies of the speech of bilingual Korean Americans in California. Korean Americans are an ethnic minority in the United States whose speech patterns in Korean and English remain understudied. The goal of the studies is to begin sketching out the acoustic traits that characterize Korean American speech, insofar as the demographic can be considered to have a unified accent, or ethnolect, as well as to connect ideologies drawn from Korean Americans' own metalinguistic commentary to the patterns that emerge./nA portion of the data is drawn from a series of laboratory experiments which sampled and tested read speech in Korean and English by Korean Americans. The majority of the data comes from spontaneous bilingual speech collected in sociolinguistic interviews with forty Korean Americans residing in California. The acoustic data measured in the speech includes overall fundamental frequency (f0), formants of high back vowels, and voice onset time (VOT) of Korean consonants and affricates./nResults indicate that, on many different levels, bilingual Korean Americans are a unique speech community unto themselves. Unlike their same-age monolingual counterparts in Korea (native Koreans), they are not participating in a sound change marked by a merger of VOT in lenis and aspirated consonants and increased contrast in f0. Like many bilingual speech communities, they maintain phonological and prosodic distance between their two languages: bilingual Korean Americans speak in Korean with a higher f0 than they do in English, and they maintain cross-linguistic contrast in the articulation of their back vowels, avoiding overlap. However, Korean Americans demonstrate a unique cultural connection to the Korean language. In their own words, Korean Americans stress the importance of knowing Korean and remaining connected to their heritage, while at the same time, traditional or previously-cited definitions of what it means to be a Korean immigrant or the descendent of Korean immigrants appear to be shifting. Furthermore, most Korean Americans are in agreement that a particular way of speaking -- the Korean American ethnolect -- certainly exists, though its exact parameters remain elusive./nThese studies fill in a gap in our understanding of how to situate bilingual and bicultural ethnic minorities in the United States within ongoing issues in the literature on sound change, heritage language acquisition and maintenance, and ethnolect formation. In addition, this is the broadest collection of sociolinguistic and sociophonetic studies of Korean Americans in California to date. Yet in its breadth, it becomes clear that there are many stones left unturned; it is intended that the findings of this dissertation sow the seeds for many future studies of other heritage language and minority communities./nKey words: bilingualism, heritage speaker, Korean, California English, California Vowel Shift, sound change, ethnolect, Korean American, sociophonetics, sociocultural linguistics, sociolinguistic interview

Advisor: Johnson, Keith
Overcoming Model-Bias in Reinforcement Learning

Autonomous skill acquisition has the potential to dramatically expand the tasks robots can perform in settings ranging from manufacturing to household robotics. Reinforcement learning offers a general framework that enables skill acquisition solely from environment interaction with little human supervision. As a result, reinforcement learning presents itself as a scalable approach for widespread adoption of robotic agents. While reinforcement learning has achieved tremendous success, it has been limited to simulated domains; such as video games, computer graphics, and board games. Its most promising methods typically require large amount of interaction with the environment to learn optimal policies./nIn real robotic systems, significant interaction can cause wear and tear, create unsafe scenarios during the learning process, or become prohibitively time consuming to enable potential applications./nOne promising venue to minimize the interaction between the agent and environment are the methods under the umbrella of model-based reinforcement learning. Model-based methods are characterized by learning a predictive model of the environment that is used for learning a policy or planning. By exploiting the structure of the reinforcement learning problem and making a better use of the collected data, model-based methods can achieve better sample complexity. Previous to this work, model-based methods were limited to simple environments and tended to achieve lower performance than model-free methods. In here, we illustrate the model-bias problem: the set of difficulties that prevent typical model-based methods to achieve optimal policies; and propose solutions that tackle model-bias. The methods proposed are able to achieve the same asymptotic performance as model-free methods while being two orders of magnitude more sample efficient. We unify these methods into an asynchronous model-based framework that allow fast and efficient learning. We successfully learn manipulation policies, such as block stacking and shape matching, on the real PR2 robot within 10 min of wall-clock time. Finally, we take a further step towards real-world robotics and propose a method that can efficiently adapt to changes in the environment. We showcase it on a real 6-legged robot navigating on different terrains, like grass and rock.

Advisor: Abbeel, Pieter
Growth of Highly Crystalline MoS2 Thin Films by Pulsed Laser Deposition

This dissertation presents a growth and characterization study of MoS2 thin films/ndeposited by pulsed laser deposition, with specific interest in demonstrating precise/nthickness control down to a single monolayer and high film uniformity over large/nareas (0.25 cm2). Limits to traditional methods for scaling field effect based switching/ndevices have necessitated the development of alternative device architectures./nDevices utilizing two-dimensional materials such as MoS2 have demonstrated/nimproved electrostatic integrity at small scales while tunneling field effect transistors/nhave demonstrated a pathway toward reduced power consumption. However,/ndrawbacks to previously explored synthesis techniques have limited the applicability/nof MoS2 for these applications. Toward this goal, the work presented establishes/nrepeatable growth conditions to fabricate highly crystalline and uniform MoS2 thin/nfilms over technologically significant dimensions and establishes the efficacy of pulsed/nlaser deposition as a suitable synthesis technique. Films grown herein are found to be/nP-type with significant defect densities suggesting avenues for future research.

Advisor: Ramesh, Ramamoorthy
Blighted Ambitions: Federal Policy, Public Housing, and Redevelopment on the West Coast, 1937-1954

In 1937, the U.S. Congress passed the Wagner-Steagall Housing Act, authorizing federal funds for the development of public housing. Twelve years later, Congress reauthorized this legislation with the American Housing Act of 1949. In the process,Congress converted a federal housing program into a redevelopment program that only sometimes funded housing./nKey to understanding this change is the rise of a planning concept called “blight.” In the first half of the 20th century, planners, public officials, scholars, and intellectuals struggled to reshape the word “blight” into a description of urban conditions, with little resulting consensus. Despite this, Congress included no legislative definition for “blight,” allowing local leaders great discretion about where and what parts of the city were suitable for clearance and replacement. While previous legislation had restricted federally funded intervention to addressing “slum” conditions, “blight” freed cities from this requirement./nThis dissertation has two components. First, at the national scale, it examines struggles to define “blight,” how this term came to be excluded from the Housing Act of 1937, and how pressure from the real estate industry placed it into the Housing Act of 1949. Second, it examines two case studies from the U.S. West Coast: Oakland, California’s efforts to map “blight” citywide from 1949-1951, and Portland, Oregon’s first attempt to create an urban redevelopment program from 1950-1953. Both show how cities without “slum” conditions attempted to leverage the logic of “blight”--seemingly empirical, but actually political--to achieve goals that had little or nothing to do with public housing./nThis, then, is the twin story told in this dissertation—first, of how the term “blight” appeared to have empirical meaning without in fact having empirical dimensions, and second how the real estate industry made use of the slipperiness of “blight” to justify projects that largely supplanted public housing.

Advisor: Shanken, Andrew M
Molecular Design as a Tool for Synthesis and Applications of Graphene Nanoribbons: A Chemist’s Approach to Carbon Nanomaterials

Graphene nanoribbons (GNRs) are pseudo-1D carbon nanostructures that display similar, unique electronic properties to 2D graphene. However, lateral confinement of electrons within GNRs generates a sizeable bandgap thus elevating these materials to a privileged position for high-performance electronic device applications. While traditional synthetic approaches toward GNRs (i.e. top-down techniques) lack complete control over edge structure, width, and chemical functionality, recent examples of bottom-up synthetic routes, both in solution and on catalytic metal surfaces, have provided an avenue toward atomically defined, smooth edge GNRs. Nonetheless, there are still many major challenges that need to be addressed in order to apply these materials into the next generation of electronic device applications. Several of those challenges are related to: (1) the deposition, proper alignment, and orientation of solution synthesized GNRs (Chapter 2), (2) the design and synthesis of low band gap GNR structures for uses in traditional field effect transistor applications (Chapter 3), and (3) the rational synthesis of GNR heterostructures (block polymers) in order to access exotic physical phenomena (Chapter 4). Herein, I have developed several methods to address all three of these challenges, providing a major steppingstone toward incorporation of GNRs into next generation electronic devices.

Advisor: Fischer, Felix
Essays in Asset Pricing

What explains the cross-sectional variation of expected returns? This dissertation contains two essays that study this question both theoretically and empirically for two popular puzzles in the asset pricing literature./n/nIn the first chapter of the dissertation, I study the asset pricing implications of learning-by-doing in a partial equilibrium model where firms optimally choose to adopt the newest technology. Firms are heterogeneous in the sense that they have different learning curves. Adopting the newest technology is costly, however, because the firms forgo the experience they accumulated in the past. The model implies that firms with (1) obsolete technology, (2) little accumulated experience, (3) low forgetting rate, or (4) high learning rate are more likely to adopt earlier, which I label as 'early innovators'. I show that these firms load more on growth options as opposed to assets-in-place, are more exposed to technological shocks, and earn a lower risk premium. The model can match the magnitude of the value premium as well as the size premium./nIn the second chapter of the dissertation, I document that idiosyncratic volatility and future returns are not simply negatively related. Past performance of the market predicts whether high or low idiosyncratic volatility stocks generate positive returns. A signed idiosyncratic volatility (SIV) factor, which is long high idiosyncratic volatility and short low idiosyncratic volatility following bull markets and vice versa following bear markets, produces significant positive risk-adjusted returns. A model with extrapolative agents and market segmentation can capture these facts.

Advisor: Gârleanu, Nicolae
Fission plasmas and their novel application to power producing nuclear reactors in space

The specific power (We/kg) requirements for high powered space missions greater than 1MWe present a significant design challenge to the nuclear engineering community. Energy conversion, and the temperature at which it occurs, is central to this problem and remains a limiting factor to realizing high specific power systems. This thesis investigates a static, in-core thermionic energy conversion (TEC) cycle that uses kinetic energy from fission fragments and their resulting high energy beta decay to produce both heat for thermionic emission and ionization of an interelectrode low temperature plasma. This novel plasma ionization scheme allows for dramatically increased device efficiency and loosened design constraints; both are needed for TEC to be competitive with the dynamic Brayton cycle – currently the only technology slated for high powered space missions. We develop an electrical power model, called the Heavy Ion Thermionic Energy Conversion (HITEC) model in order to investigate electrical power characteristics of both single cell and nuclear reactor scale devices in order to assess this reactor’s performance metrics and its applicability to high powered space missions. HITEC was benchmarked against the only existing experimental data set from a de-classified joint research effort [14], and its power characteristics are in good agreement with this experimental data. The reactor scaling portion of HITEC was used to calculate the electrical performance of a 2.5MWth nuclear reactor. Four separate cases were tested on a single core design: we found that in three of the four cases, the HITEC reactor exceeded NASA’s specific power limits for the 1-10 MWe power output range (> 60We/kg). When auxiliary power was implemented for additional plasma ionization, the maximum specific power attained was 348 We/kg. This makes HITEC, an attractive static alternative to the dynamic Brayton cycle for power producing nuclear reactors applied to high power mission sets.

Advisor: Bernstein, Lee
Selective Silylation of Aromatic and Aliphatic C–H Bonds

The following dissertation discusses the development of silylations of C–H bonds that occur with high selectivity and investigations of the mechanisms by which they occur. The work includes intramolecular silylations of aliphatic C–H bonds, catalyzed by Rh complexes, and intermolecular silylations of aromatic C–H bonds, catalyzed by Ir complexes./nChapter 1 provides a review of catalysts, reagents, and conditions for the undirected functionalization of C–H bonds with high selectivity. This review focuses on catalysts that alter or improve the selectivity of borylation and silylation reactions./nChapter 2 discusses the development of a rhodium complex containing the Xantphos ligand which catalyzes the intramolecular silylation of alkyl C–H bonds with unusual selectivity for the C–H bonds located δ to the oxygen atom of a silyl ether over typically more reactive C–H bonds more proximal to the same oxygen atom. This chapter describes experimental studies on the mechanism of the silylation and DFT studies on the origin of the selectivity with which it occurs./nChapter 3 describes the discovery of new catalysts for the silylation of aromatic C–H bonds. Analysis of initial rates uncovered the high reactivity of iridium catalysts containing a hindered phenanthroline ligand but accompanying rapid inhibition by hydrogen gas. With this catalyst, under a flow of nitrogen to remove hydrogen, electron-rich arenes, including those containing sensitive functional groups, undergo silylation in high yield for the first time./nChapter 4 presents a detailed mechanistic analysis of the iridium-catalyzed silylation of aromatic C–H bonds. Until this study, no experimental data on the identity of complexes related to the mechanism of this process or the mechanisms by which they react to functionalize C–H bonds had been reported. In this chapter, we identify the resting state of the catalyst as an iridium disilyl hydride complex. Studies on the kinetics of the reaction revealed that the rate-limiting step varies with the electronic properties of the arene./nChapter 5 describes the discovery of new catalysts for the silylation of heteroaromatic C–H bonds with high sterically derived selectivity. A novel pyridyl-imidazoline ligand is shown to improve the selectivity of the reaction for silylation of the least hindered C–H bond. The origin of selectivity was investigated and we determined that the rates of formation of the C–Si bond from isomeric heteroaryliridiuim complexes influences the selectivity of the silylation reaction more than the rates of the cleavage of C–H bonds to form these complexes.

Advisor: Hartwig, John F
Design, Control, and Motion Planning of Cable-Driven Flexible Tensegrity Robots

Tensegrity structures are an emergent type of soft-robotics that are compliant, lightweight, and impact-resilient. In collaboration with NASA Ames Research Center, research in the Berkeley Emergent Space Tensegrities Lab at UC Berkeley has largely focused on the design and control of these novel structures as potential surface exploration robots which could act as both landers and rovers. More recently, tensegrity robots have also been proposed for applications closer to home – working as disaster response and emergency co-robots to help first responders obtain situational awareness faster and safer. Constructed using isolated rigid bodies suspended in a tension network of elastic elements, tensegrity structures exhibit unique and advantageous mechanical properties for applications in uncertain and potentially hazardous environments, albeit at the cost of increased complexity for dynamic feedback control./nIn addressing these challenges, this work explores possible approaches for feedback control and state estimation for ground-based rolling locomotion with six-bar spherical tensegrities. In this dissertation, we explore problems pertaining to practical implementation – state estimation, modeling, motion planning, and optimal control of tensegrity robots under uncertainty. Leveraging the well-structured dynamics of Class-1 tensegrity robots, we implement and evaluate model-based Model Predictive Control and iterative local quadratic methods for tensegrity motion planning. Additionally, we consider alternative tensegrity topologies and actuator schema which may enable improved performance for task-specific objectives. Due to the many degrees of freedom and compliant nature of tensegrity structures, however, excessive state estimate errors may propagate catastrophically. To evaluate these effects, Bayesian state estimators are applied to tensegrity ground mobility in simulation, evaluating their performance under the additional constraints of low-cost sensors and potentially scarce and noisy sensor data. An imitation learning approach is introduced to achieve directed rolling motion using a contextual neural network policy, combining deep learning and optimal control for real-time feedback control of highly nonlinear tensegrity systems. Finally, a robust minimax control approach is proposed in order to address challenges which arise at the intersection and interaction of state estimation and trajectory optimization for flexible tensegrity robotics. Combined, these pragmatic research developments help advance the progression of this novel technology towards becoming a viable and more widely adopted robotics paradigm.

Advisor: Agogino, Alice M.
Efficient Algorithms Inspired by Integer Programming Formulations

Integer programming formulations play a key role in the design of efficient algorithms and approximation algorithms for many discrete linear optimization problems as found in previous research. A specific integer programming formulation of a discrete linear optimization problem may lead to algorithms with better running time or approximation algorithms with better approximation factors than the other algorithms for the same problem. Inspired by this, we introduce here new integer programming formulations to devise efficient algorithms and approximation algorithms for two classes of problems, the covariate balancing problems and the Replenishment Storage problems.The existence of certain special integer programming formulations is an evidence of the existence of polynomial time algorithms for some discrete optimization problems. For a variant of the covariate balancing problems, our new integer programming formulation has a network structure that was not previously known. We use this new formulation to devise network flow algorithms, which have better running time than an existing polynomial time algorithm. Other integer programming formulations we introduce show that several variants of the class of covariate balancing problems are fixed-parameter tractable. Integer programming formulations are often important in designing approximation algorithms for intractable problems. The Replenishment Storage problems were known to be NP-hard and one approximation algorithm was known for a special variant of the class. We derive for this variant a polynomial time approximation scheme using a new integer programming formulation. Our new formulation also leads to the first known approximation scheme for another variant of this class. Moreover, this resolves the complexity status of some variants of the Replenishment Storage problems as weakly NP-hard.

Advisor: Hochbaum, Dorit S
Improvements in Density Functional Theory Calculations, Analysis, and Applications

Density functional theory (DFT) is the most widely used quantum chemistry method. This dissertation explores and advances different aspects of DFT. The computational cost of DFT scales as the third power of the system size. In Chapter 2, we introduce an embedded many-body expansion, called the polarized many-body expansion, which removes the computational bottleneck steps in DFT calculations by exploiting the pairwise additivity of some components of interaction energy in molecular clusters. Energy decomposition analysis fragments interaction energy into physico-chemically meaningful components like permanent electrostatics, dispersion, polarization, and charge transfer. In Chapter 3, we introduce a non-perturbative approach to decompose charge transfer energy into pairwise additive terms. This approach improves upon the previous perturbative approach and can be applied to chemical complexes with both weak and strong charge transfer interactions. Different density functional approximations (DFAs) show varied performance for predicting properties of different chemical systems. In Chapters 4 and 5, we identify DFAs which give good accuracy for predicting the interaction energy of hydrogen with different model binding sites. These DFAs can potentially be used for in silico high-throughput screening of materials capable of storing hydrogen at desired conditions.

Advisor: Head-Gordon, Martin P.