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Recent Dissertations

Physics ph.d. degrees - spring 2022.

Juan Camilo Buitrago Casas Advisor: Stuart Bale On the Sun's faintest coronal hard X-rays

David Dunsky Advisor: Lawrence Hall Fingerprints of High Energy Physics Beyond Colliders

Satcher Hsieh Advisor: Norman Yao Quantum sensing at high pressures using nitrogen-vacancy centers in diamond

Francisco Leal Machado Advisor: Norman Yao Out-of-equilibrium dynamics and phases of matter in Atomic, Molecular and Optical systems

Zengyi Li Advisors: Mike DeWeese and Friedrich Sommer Entropy in Unsupervised Machine Learning

Nikola Maksimovic Advisor: James Analytis Advances in nearly-magnetic superconductivity

Bradley Mitchell Advisor: Irfan Siddiqi Investigating Microwave-Activated Entangling Gates on Superconducting Quantum Processors

Christopher Olund Advisor: Norman Yao State Structure and Operator Dynamics in Quantum Many-Body Systems: from s-Sourcery to Strong Zero Modes

Leon Otis Advisors: Jeffrey Neaton and Eric Neuscamman Optimization Algorithms in Variational Monte Carlo for Molecular Excited States

Eric Parsonnet Advisor: R. Ramesh Dynamics and Methods of Manipulating Ferroic Order in BiFeO3 and Related Materials

Elizabeth Peterson Advisor: Jeffrey Neaton First-principles studies of complex functional oxides and chalcogenides

Sai Neha Santpur Advisor: Marjorie Shapiro Search for Non-pointing and Delayed Photons in pp collisions at √s=13 TeV using the ATLAS detector

Conrad Stansbury Advisor: Alessandra Lanzara Cohesive Experimental and Analysis Techniques for Angle Resolved Photoemission Spectroscopy

QinQin Yu Advisor: Oskar Hallatschek Empirical tools for studying genetic drift in microbial populations

Physics Ph.D. Degrees - Fall 2021

Roger Huang Advisor: Yury Kolomensky Searching for 0νββ Decay with CUORE and CUPID

Oliver Jeong Advisor: Adrian Lee Development of Simons Array Optics for Cosmic Microwave Background Polarimetry

Matthew Kramer Advisor: Kam-Biu Luk Robust Measurement of Mixing Parameters $\sin^2 2\theta_{13}$ and $\Delta m^2_{ee}$ with Reactor Antineutrinos at Daya Bay

Jonathan Han Son Ma Advisors: Naomi Ginsberg and Patrick Naulleau Understanding Radiation Physics and Chemistry of Extreme Ultraviolet Resists

Nathan Ng Advisors: Jeffrey Neaton and Eran Rabani Aspects of localization in centrally coupled systems

Kelsey Oliver-Mallory Advisors: Robert Jacobsen and Kevin Lesko Backgrounds in LUX and LZ: Extending the Sensitivity of LUX to Low-mass Dark Matter

Dylan Rees Advisor: Joseph Orenstein Nonlinear Optical Properties of the Chiral Weyl Semimetal RhSi

Fernando Torales Acosta Advisor: Barbara Jacak Isolated Photon Hadron Correlations in √sNN = 5.02 TeV pp and p–Pb Collisions

Physics Ph.D. Degrees - Summer 2021

Vyassa Baratham Adviser: Michael DeWeese and Kristofer Bouchard Constraining Ill-Posed Inverse Problems in Neural Electrophysiology via Biophysically Detailed Forward Simulation

Micah Brush Adviser: Oskar Hallatschek and John Harte Macroecological Patterns Out Of Steady State

Venkatesa Chandrasekaran Adviser: Raphael Bousso Classical and Quantum Aspects of Black Holes and Spacetime

Ahmet Coskuner Adviser: Lawrence Hall and Kathryn Zurek Dark Matter Detection Phenomenology

Siva Darbha Adviser: Daniel Kasen Signatures from Aspherical Kilonovae and Unconventional Tidal Disruption Events

Hannah Klion Adviser: Eliot Quataert Monte Carlo Radiation Transport Simulations of Asymmetric Neutron Star Mergers

Jonathan Liu Adviser: Hernan Garcia Investigating the Dynamics of Non-Equilibrium Behavior in Eukaryotic Transcriptional Regulation

Stephen Martis Adviser: Oskar Hallatschek Eco-evolutionary dynamics in high dimensions

Christopher Mogni Adviser: Petr Horava Quantum Gravity Beyond Equilibrium

Stephen Randall Adviser: Petr Horava Topological Quantum Gravity of the Ricci Flow

Pratik Sachdeva Adviser: Michael DeWeese and Kristofer Bouchard The impact of correlated variability on models of neural coding

Tianrui Xu Adviser: Joel Moore Quantum Dynamics of Correlated Fermions In- and Out-of-Equilibrium

UC Berkeley

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Digital Twins as Testbeds for Iterative Simulated Neutronics Feedback Controller Development

• Ong, Theodore Kay Chen
• Advisor(s): Peterson, Per F

Before a new nuclear reactor design can be constructed and operated, its safety must bedemonstrated using models that are validated with integral effects test (IET) data. However, because scaled integral effects tests are electrically heated, they do not exhibit nuclear reactor feedback phenomena. To replicate the nuclear transient response in electrically heated IETs, we require simulated neutronics feedback (SNF) controllers. Such SNF controllers can then be used to provide SNF capabilities for IET facilities such as the Compact Integral Effects Test (CIET) at the University of California, Berkeley (UC Berkeley). However, developing SNF controllers for IET facilities is non-trivial. To expedite development, we present the use of Digital Twins as testbeds for iterative SNF controller development. In particular, we use a Digital Twin of the Heater within CIET as a testbed for SNF Controller Development. This Digital Twin with SNF Capabilty is run as an OPC-UA server and client written almost entirely in Rust using Free and Open Source (FOSS) code. We then validate the Digital Twin with experimental data in literature. We also verify the transfer function simulation and Proportional, Integral and Derivative (PID) controllers written in Rust using Scilab. Moreover, we demonstrate use of data driven surrogate models (transfer functions) to construct SNF controllers in contrast to using the traditional Point Reactor Kinetics Equations (PRKE) models with the hope that they can account for the effect of spatially dependent neutron flux on reactor feedback. To construct the first surrogate models in this work, we use transient data from a representative arbitrary Fluoride Salt Cooled High Temperature Reactor (FHR) model constructed using OpenMC and GeN-Foam. Using the Digital Twin as a testbed, two design iterations of the SNF controller were developed using the data driven surrogate model. Compared to the potential development time taken in using physical experiments, using the digital twin testbed for SNF controller development resulted in a significant time saving. We hope that the approaches used in this dissertation can expedite testing and reduce expenditure for licensing novel Gen IV nuclear reactor designs.

The Legal Nature of the Legislative Process

• Barroso da Graca, Luis Otavio
• Advisor(s): Gould, Jonathan S.

In this text, I argue that the legislative process has a legal nature, as opposed to its more apparent political facet, and that breaches of procedural lawmaking rules are incompatible with such a characterization. To defend such a viewpoint, I approach the topic in three parts.The first part addresses legislatures’ procedural rules’ force of law, navigating through U.S. and Brazilian cases. Against views that take legislative procedural rules as non-mandatory and merely coordinating tools, I develop my argument upon Hans Kelsen’s and H.L.A. Hart’s theorizations and state that these provisions belong to (hard) law. Hence, though legal interpretation challenges may blur the distinction between the political and legal facets, I affirm that legislative procedures have the force of law and, as such, are binding. The second part deals with justification and overseeing mechanisms. I argue that there are several reasons why lawmakers should abide by the legislative procedural rules. First, it is a matter of the rule of law, meaning that the participants in the lawmaking process have the right to play according to the pertinent provisions. Second, compliance with the established procedures safeguards participation and the flow of diverse opinions and, thus, democratic representativeness. Third, rules’ observance fosters transparency, shedding light on a bill and its motives. Finally, I state that compliance with procedural rules should result from enforcing tools managed by legislators and third parties, such as non-partisan officers in legislatures and, under some restraints, the judiciary. The third part addresses a specific situation: the enactment of executive decrees, provisional measures, directives, or anything similar, with the force of law, to address emergencies. I defend that the misuse or abuse of these expedited lawmaking instruments is incompatible with the legal nature of the legislative process. First, I analyze the ancient Roman Republic’s approach to the circumvention of serious menaces and the theories of John Locke, Carl Schmitt, and Santi Romano in this regard. Then, I assess how governments in Brazil, Italy, and the United States usually take advantage of those instruments not to address threats but to bypass the burdens of ordinary legislative procedures. To avoid such an outcome, I argue that legislatures should enhance their oversight capacity under emergencies or pressing situations while simultaneously providing the judiciary with more specific reviewing standards.

Hydride-Supported Actinide–Transition Metal Complexes

• Ye, Christopher
• Advisor(s): Arnold, John

Chapter 1. The field of f-block–transition metal hydride chemistry is introduced and summarized. Key properties of these compounds such as small molecule activation chemistry and H2 uptake and release are outlined. The dearth of actinide–transition metal species despite their potential for fundamental bonding insight and novel reactivity is highlighted, and the motivations for studying these compounds are stated.

Chapter 2. Reaction of K[Cp*IrH3] with actinide halides led to multimetallic actinide–transition metal hydrides U{(μ-H)3IrCp*}4 and Th{[(μ-H)2(H)IrCp*]2[(μ-H)3IrCp*]2}, respectively. These complexes feature an unexpected, significant discrepancy in hydride bonding modes; the uranium species contains twelve bridging hydrides while the thorium complex contains ten bridging hydrides and two terminal, Ir-bound hydrides. Use of a U(III) starting material with the same potassium iridate resulted in the octanuclear complex {U[(μ2-H)3IrCp*]2[(μ3-H)2IrCp*]}2. Computational studies indicate significant bonding character between U/Th and Ir in the tetrairidate compounds, the first reported evidence of actinide-iridium covalency. In addition, these studies attribute the variation in hydride bonding between the tetrairidate complexes to differences in dispersion effects. This work establishes a novel route to synthesizing actinide–transition metal polyhydrides with close metal–metal contacts.

Chapter 3. Conversion of Cp*OsH5 to K[Cp*OsH4] with KBn, followed by reaction with tetravalent actinide halides results in the synthesis of uranium– and thorium–osmium heterometallic polyhydride complexes. Through these species, An–Os bonding and the reactivity of An–Os interactions are studied. These complexes are formally sixteen-coordinate, the highest observed coordination number for uranium and thorium. Computational studies suggest the presence of a significant bonding interaction between the actinide center and the four coordinated osmium centers, the first report of this behavior between osmium and an actinide. Upon photolysis, these complexes underwent intramolecular C–H activation with the formation of an Os–Os bond, while the thorium complex was able to activate an additional C–H bond of the benzene solvent, resulting in a μ-η1,η1 phenyl ligand across one Th–Os interaction. These results highlight the unique reactivity that can arise from actinide and transition metal centers in proximity, and expand the scope of actinide photolysis reactivity.

Chapter 4. The third Cp*-supported transition metal polyhydride – Cp*ReH6 – was shown to be a competent partner to actinide hydrides. The synthesis of actinide tetrarhenate complexes completed a series of iridate, osmate, and rhenate polyhydrides, allowing for structural and bonding comparisons. Computational studies examine the bonding interactions, particularly between metals, in these complexes. Several factors affect metal–metal distances and covalency for the actinide tetrametallates, including metal oxidation state, coordination number, and dispersion effects. The osmium and rhenium octametallic U2M6 clusters are reported as well, with similar analysis of structure and electronics.

Chapter 5. Reaction of the potassium iridate K[Cp*IrH3] with a bulky uranium(III) metallocene yielded a heterobimetallic U(III)–Ir species. Reactivity of this complex with CS2 is described, resulting in the novel ethanetetrathiolate fragment, as produced via hydride insertion and C–C coupling. This demonstrates the ability to combine the hydride insertion chemistry of transition metal hydrides with C–C coupling observed in U(III) compounds by bringing both metal centers in close proximity.

Safe and Trustworthy Decision Making through Reinforcement Learning

• Li, Jinning
• Advisor(s): Tomizuka, Masayoshi

The advent of advanced computational technologies and artificial intelligence has ushered in a new era of complex systems and applications, notably in the realms of autonomous vehicles (AVs) and robotics. These systems are increasingly required to make decisions autonomously in dynamic and uncertain environments. Reinforcement Learning (RL) has emerged as a pivotal technique in this context, offering a framework for learning optimal decision-making strategies through interactions with the environment. However, ensuring safety and trustworthiness in these decisions remains a critical challenge, especially in safety-critical applications such as autonomous driving.

This dissertation addresses the aforementioned challenge by proposing innovative RL-based approaches, and is structured into three distinct but interconnected parts, each focusing on a unique aspect of RL in the context of safe and trustworthy decision-making.The thread of this dissertation is based on the exploration and advancement of RL techniques to ensure safety and reliability in autonomous decision-making systems, particularly in complex, dynamic environments.

We first establish the foundational aspects of RL in decision-making, particularly in uncertain and dynamic environments. The focus here is on enhancing RL to deal with real-world complexities, such as interacting with unpredictable agents, e.g., human drivers in AV scenarios, and handling distributional shifts in offline RL settings. This sets the stage for understanding and improving the decision-making capabilities of autonomous systems under uncertainty.

Building on the first part, we then explore the integration of hierarchical planning with RL. The emphasis is on creating frameworks that combine different levels of decision-making, balancing immediate, low-level safety concerns with high-level strategic objectives. The approach aims to address the limitations of traditional RL in complex, multi-agent environments and long-duration tasks, demonstrating improved adaptability and efficiency in real-time decision-making.

The final part represents a forward-looking approach to RL, focusing on the integration of offline and online learning methodologies. This part addresses the challenge of training RL agents in a manner that is both safe and effective, particularly in contexts where exploration can be costly or dangerous. By combining the strengths of large-scale offline data (expert demonstrations) with online learning, we present a novel framework for enhancing the safety and performance of RL agents in practical, real-world applications.

Exploiting Electron Magnetron Motion in a Penning-Malmberg Trap to Measure Patch Potentials, Misalignment, and Magnetic Fields

• Christensen, Andrew Jordan
• Advisor(s): Fajans, Joel

A sequence of electron clouds is extracted from an electron plasma reservoir. These clouds are highly reproducible and their E x B drift motion is nearly identical to that of a single particle, making them useful for measurements of electric and magnetic fields. First, by weakening the trapping potential confining the clouds we observe that they move off-axis, and we use this to measure the electric field due to patch potentials. Next, we measure the total charge of these clouds using small shifts in their magnetron frequencies. The misalignment between the trap electrodes and the external magnet is measured by imaging the clouds from different axial locations in the trap. By combining electron cyclotron resonance with the patch potential measurement procedure, we can measure the magnetic field strength up to a millimeter away from the trap axis. Finally, a new magnetometry technique called electron magnetron phase imaging (EMPI) is used to measure the rapidly changing magnetic field involved in observing the effect of gravity on antihydrogen. In EMPI, the magnetron frequency is measured precisely, and then we observe small changes to the magnetron frequency as the magnetic field decreases. In the process of analyzing the experimental data from each of these measurements, subtleties in the motion of electron clouds are revealed. Some of these measurement techniques help us to understand systematic errors in the ALPHA collaboration's test of the weak equivalence principle. Other techniques are used to inform experimental procedures and help explain the behavior of ALPHA's Penning-Malmberg traps. Most of these ideas could be applied to many Penning-Malmberg traps, provided that they have the ability to image charged particles. Unknown magnetic fields, patch potentials, and misalignment pose difficulties for many experiments, so implementing these cloud-based measurements could benefit other research groups.

Distortions in Wearable Optics: Comfort, Perception, and Adaptation

• McLean, Iona
• Advisor(s): Cooper, Emily

Many people have had the experience of viewing the world through optics, such as when wearing corrective spectacles or using augmented and virtual reality devices (AR/VR). The purpose of the optical lenses present in spectacles and devices is to bring images into focus, but they also produce unwanted distortions such as magnification and minification that change the retinal image size or shape of an object. Surprisingly, small changes in retinal image size or shape can have substantial perceptual and physical consequences. While spectacles have been around for centuries, there remains a large gap in the literature on how optical distortions affect the viewer. This dissertation contains experimental investigations related to how optical distortions affect perception and comfort, and how these effects change over time. Chapter 1 establishes a fundamental understanding of the onset of perceptual and physical symptoms produced by optical minification. Chapter 2 investigates how people adapt over time to a specific type of monocular distortion that alters depth and shape perception. Chapter 3 investigates how the visual system interprets the geometry of objects when faced with perceptual disruptions caused by optical distortions. Together this research provides a much-needed foundational understanding of optical distortions from multiple domains.

Risk-Aware Algorithms for Learning-Based Control With Applications to Energy and Mechatronic Systems

• Kandel, Aaron Isaac
• Advisor(s): Moura, Scott J

This dissertation leverages and develops the powerful out-of-sample safety certificates of Wasserstein ambiguity sets to create a suite of data-driven control algorithms that help solve safety-critical industrial problems. This work is motivated by the ongoing relevance of robustness and safety when applying data-driven decision making in the real world. For example, lithium-ion batteries are driving transitions to renewable energy sources. Optimizing their performance and longevity is of the utmost importance, but highly difficult due to their complex, nonlinear, and safety-critical electrochemical dynamics. While data-driven control can dramatically improve the performance of systems like lithium-ion batteries, certifying system safety remains an open challenge. This dissertation explores certifying learning-based controllers via distributionally robust optimization (DRO). We focus on Wasserstein ambiguity sets, DRO methods that draw worst-case realizations of random variables under relatively permissive assumptions. This makes them ideal for learning-based control, where data can be highly limited and the controller is likely encounter new experience unaccounted for in its training data.

In Chapter 2, we begin by presenting simple mathematical arguments that extend an existing reformulation of Wasserstein DRO to cases where dependence on decision variables x and random variables R can be nonconvex as long as x and R are separable. By cleverly modeling stochasticity in model uncertainty, we augment nonconvex optimal control problems with Wasserstein ambiguity sets to obtain idealized probabilistic safety certificates.

The remaining chapters extend this theoretical result across the range of model-based and model-free reinforcement learning. Chapter 2 explores offline model-based reinforcement learning within a latent state-space, with application to real-time fast-charging of li-ion batteries using electrochemical information. By leveraging the results of Chapter 2, we can hedge against model and data errors to probabilistically guarantee safe distributional data-driven control.

Chapter 4 presents an end-to-end framework for safe learning-based control using nonlinear stochastic MPC. We focus on scenarios where the controller is applied directly to a system of which it has highly limited experience, toward safety during tabula-rasa learning-based control as a challenging case for validation. We validate findings with case studies of extreme lithium-ion battery fast charging and autonomous vehicle obstacle avoidance using a basic perception system.

Finally, in Chapter 5, we apply the same DRO architecture to value-based RL. We describe a structure for deep Q-learning within the framework of constrained Markov decision processes (CMDPs). By characterizing the uncertainty of constraint cost functions based on their temporal-difference errors, we augment relevant constraints with tightening offset variables based on DRO theory of Chapter 2.

In our concluding remarks, we discuss the broader relevance of our findings and map directions for future work.

The Method of Distributions for Random Ordinary Differential Equations

• Maltba, Tyler Evan
• Advisor(s): Evans, Steven N ;
• DeWeese, Michael R

Random ordinary differential equations (RODEs) describe numerous physical and biological systems whose dynamics contain some level of inherent randomness. These sources of uncertainty enter into dynamics in two forms: (a) externally imposed or internally generated random excitations, i.e., noise, and/or (b) probabilistic representations of uncertain coefficients and initial/boundary data. Such systems admit a distribution of solutions, which is (partially) characterized by the single-time joint probability density function (PDF) of system states. If the random excitations correspond to Gaussian white noise, it is relatively straightforward to derive a closed-form deterministic partial differential equation (PDE) known as the Fokker-Planck (or Kolmogorov’s forward) equation, which governs the evolution of the joint PDF. However, most plausible noise sources are correlated (colored). In this case, the resulting PDF equations require a closure approximation. Via the method of distributions, we propose two methods for closing such equations: (a) modified large-eddy-diffusivity closures, and (b) a data-driven closure relying on sparse regression to learn relevant features. In the realms of nonequilibrium statistical mechanics and computational neuroscience, the closures are tested in a head-to-head comparison against Monte Carlo simulations for colored-noise sources such as Ornstein-Uhlenbeck and sine-Wiener processes. Additionally, the approaches’ algorithmic complexities are thoroughly discussed.

Implementing the method of distributions for high-dimensional systems of RODEs is challenging due to the computational burden of solving the high-dimensional PDE associated with the joint PDF of states. Although recent advancements in numerical integration techniques for high-dimensional PDEs have been made, they are often tailored to specific applications and lack generality for large numbers of states/dimensions. However, for many applications, only a low-dimensional quantity of interest (QoI) from the underlying high-dimensional system is desired. In these cases, it is sufficient to study a reduced-order PDF (RO-PDF) equation, i.e., a low-dimensional PDE for the QoI’s PDF, allowing classical integration techniques to be employed. Moreover, unclosed coefficients in the RO-PDF equations can be rewritten as conditional expectations, which we directly estimate from data via nonparametric regression. When the RODE exhibits strong nonlinearities and/or stiffness, it is usually necessary to supplement the learned reduced-order PDE with a data assimilation method to account for model misspecification that may occur from regression discrepancies. We propose nudging (a.k.a., Newtonian relaxation) and deep neural networks for this task, which are successfully tested for uncertainty quantification of stochastically forced oscillators and transmission failures in electrical power grids.

Essays on the Responses to Taxation by US Firms

• Love, Michael
• Advisor(s): Auerbach, Alan

Business taxation, by affecting the costs of certain behaviors of firms, owners, or their counterparties, can trigger potentially substantial changes in real activity, such as changes in inputs or production processes. But it can also prompt avoidance responses---such as legal restructuring or changes in tax reporting---that may have important effects on efficiency and distribution. Understanding such responses is thus critical for enacting efficient and well-informed tax policy.

In this dissertation I investigate the real and avoidance responses at the intersection of several important topics in businesses taxation, namely capital taxation, taxation of passthrough entities, international taxation, and corporate taxation. My research sheds new light on our understanding of US business taxation by employing a variety of empirical methods to (1) develop new explanations for persistent puzzles in the literature, (2) fill knowledge gaps in the current body of business tax research, and (3) draw attention to new issues that have so far received little attention by public finance economists.

In Chapter 1, I investigate financing and investment responses by corporations to a change in capital taxation, presenting results that help resolve an existing conflict among empirical findings in the public finance literature. I estimate that dividend taxes, by impacting the cost of equity financing, have large effects on the financing, investment, and real outcomes of many US public firms. But---in contrast with economists' longstanding focus on capital investment outcomes---I find these responses are mostly from smaller, cash-constrained firms through “non-capital” investment channels: R&D and operating expenditures. Exploiting a quasi-experiment that tracks financing and expenditure responses to the 2003 dividend tax cut, I estimate a large, immediate, and sustained increase in average equity financing (+86% ± 11%) by these firms, reflecting a high elasticity to the cost of capital. Responsive firms put the cash substantially toward operating expenditures and R&D, rather than tangible investment. I also find higher job growth and long-run sales among the responsive firms. These results make sense, reconciling mixed evidence in recent research: because dividend taxes affect the cost of equity financing, the firms impacted most are those that actually rely on equity financing---smaller, often unprofitable, less capital-intensive firms who invest heavily in "non-capital" pathways.

In Chapter 2, I describe and estimate tax avoidance behavior that uses complex entity structures involving partnerships and tax havens to exploit discrepancies in tax treatment of capital income across jurisdictions. I also address a significant missing piece of knowledge in the public finance literature: where partnership income goes. Partnerships are the fastest growing class of business entity in the United States and represent over one third of reported business income, but due to their legal complexity, data quality, and opaque nature economists have not yet been able to identify where a sizeable portion of this income goes. In this paper, I use US federal tax records from 2005-2019 to compile a comprehensive analysis covering 99% of the income flowing to the owners of partnerships. I find that a much larger portion goes to foreign owners than previously thought, and that most of this amount goes to tax havens---over $1 trillion since 2011. The majority of these flows likely face zero tax in either the US or in the tax haven. The evidence I present suggests a prevalent use of entity arrangements by investment firms that shield investors from tax and reporting through "blocker structures," predominantly in the Cayman Islands. Evidence also suggests a substantial increase in income reported after the enactment of Foreign Account Tax Compliance Act (FATCA).

In Chapter 3, I investigate the degree to which corporations can manipulate their accounting of expenses to avoid taxes, and what effects this has on the corporate tax base. The investigation exploits a unique corporate tax reform in Texas that replaced a 4.5% profits tax with a broader 1% gross revenue tax and that eliminated almost all deductions, but still permitted corporations to deduct one of two categories of expenses: cost of goods sold (COGS) or total worker compensation. Data from federal corporate income tax returns makes it possible to estimate the effects of the reform, as data are consistent across years and harmonized across states. Strong evidence reveals a very large avoidance response for COGS but not for compensation: corporations reduced the tax base roughly 4% by reclassifying non-deductible expenses into COGS (with a large elasticity of roughly -5 ± 1), but there is little reclassification into compensation. These findings reveal the potentially very large but also highly context-specific nature of accounting reclassification responses. Given that numerous states have some form of gross receipts tax and that there is currently wide discussion of measures to broaden corporate tax bases by incorporating accounting measures, these findings offer important considerations for policymakers and tax authorities when designing, scoring, and enforcing corporate tax changes.

More than Mere Deadweight: The Variety of Regulatory Imaginaries that Shape How Regulators, Innovators, and Entrepreneurs Coproduce Disruptive Technological Innovation

• Posch, Konrad Edward Ian
• Advisor(s): Ansell, Christopher K.

Disruptive technological innovation is the contemporary face of innovation and a dominant force in society. Change is occurring faster and upsetting existing scientific and technical policy systems. Entrepreneurs and innovators, drawing on a folk economic model of regulation, often believe that regulation cannot keep up with the pace of change and therefore policy makers should stay out of their way. Like many folk models, this perception of regulation-as-intrinsic-impediment-to-innovation may sometimes be true but it is not always true. Worse yet, this folk perception of regulators-as-impediment leads entrepreneurs and innovators to ignore opportunities to co-create beneficial regulations and instead create their own bad outcomes by prompting regulators to craft draconian regulations in response to entrepreneurs’ malicious non-compliance.Innovators thus oppose regulation not because they’ve had bad experiences but because they think they will in the future. A popular version of this folk economic model of regulation brandishes the word “disrupt” while storming the halls of stodgy industries and regulatory agencies. Despite this contemporary disruptive innovation narrative, substantial technological change is not a recent invention (though it may be accelerating). The reified economic rhetoric of the folk economic model has convinced disruptive entrepreneurs that regulation is a dirty word synonymous with state inadequacy. Although never perfect and sometimes inadequate, regulators have invariably adapted to technological change. This project explains how regulators have before, are now, and can again become allies of innovators when entrepreneurs look past limiting preconceptions. Regulatory scholars who study actually-existing regulation will recognize the folk economic model as an extreme version of “capture” within “command and control” regulation (c.f. Carpenter and Moss 2014b; Slayton and Clark-Ginsberg 2018). They have repeatedly demonstrated the deceptive inadequacy of totalizing catch-all models of regulation. Nevertheless, scholars who do not study actually-existing regulation often use this folk economic capture baseline to judge all work on regulation which hinders scholarly understanding of relationships between regulation and innovation (c.f. Dal Bó 2006; Carrigan and Coglianese 2015). With these scholarly limitations, lay entrepreneurs’ misperceptions are no surprise. Contrary to the folk model, I argue regulators have been, are now, and can again be so much more than merely a deadweight loss to innovation if only innovators and entrepreneurs can be guided past self-limiting imaginaries such as the folk economic model of disruptive innovation. To develop this argument, I derive a deductive typology of regulatory imaginaries and discuss how we can use this typology to understand the variety of relationships between regulators, entrepreneurs, and innovators that can lead to better or worse effects on innovation. I then specify my novel methodological approach of Bayesian Type Validation (BayesTV) which combines deductive typological theory with logical Bayesian analysis. Finally, I employ BayesTV to inductively verify my typology using three technological cases in the United States and European Union: autonomous vehicles (AVs), gene editing (GE), and electronic health records (EHR). The Folk Economic Model imaginary is but one of seven possible regulatory imaginaries of the proper relationship between regulators, entrepreneurs, and innovators. Regulatory imaginaries, based on the concept of sociotechnical imaginaries (Jasanoff 2015a), are collectively held, publicly performed conceptions of desirable relationships between regulation and technological innovation which actors believe are (or should be) institutionalized within regulatory agencies. Where the Folk Economic Model imaginary sees regulation as only an impediment to be minimized, the other six imaginaries see other potential effects such as moderation, constraint, and catalyst. Critically, my deductively derived and empirically validated typology also demonstrates that regulatory imaginaries are plural, diverse, and malleable. In presenting three empirical chapters covering multiple imaginaries, I demonstrate that there are plural actually-existing imaginaries around well know technologies. In presenting both similarities and differences in the US and EU implementations of regulation for each disruptive technology, I demonstrate that there is meaningful diversity among regulatory imaginaries in conceptual derivation, expected effect on innovation, and empirical implementation. Finally, in the application of BayesTV to the empirical cases, I demonstrate that regulatory imaginaries are malleable through policy. This project focuses on regulatory imaginaries because they shape the perceptions of what is possible and desirable about the relationship between regulators, entrepreneurs, and innovators around disruptive innovation. While future studies should build on this focus on imaginaries by exploring their origins and how contending imaginaries shape the outcomes of the policies that are built around them, this project focuses on the imaginaries themselves in order to demonstrate that we need not limit ourselves to the Folk Economic Model which sees regulation, as a rule, as merely deadweight.

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Home » For Authors & Researchers » Open Access Theses & Dissertations

Open Access Theses & Dissertations

Theses and dissertations produced by students as part of the completion of their degree requirements often represent unique and interesting scholarship. Universities are increasingly making this work available online, and UC is no exception. Find information related to open access theses and dissertations below.

UC has an open access policy for theses and dissertations, but procedures and specifics vary by campus

Several UC campuses have established policies requiring open access to the electronic theses and dissertations (ETDs) written by their graduate students. As of March 25, 2020, there is now a systemwide Policy on Open Access for Theses and Dissertations , indicating that UC “requires theses or dissertations prepared at the University to be (1) deposited into an open access repository, and (2) freely and openly available to the public, subject to a requested delay of access (’embargo’) obtained by the student.”

In accordance with these policies, campuses must ensure that student ETDs are available open access via eScholarship (UC’s open access repository and publishing platform), at no cost to students. By contrast, ProQuest, the world’s largest commercial publisher of ETDs, charges a $95 fee to make an ETD open access. Institutions worldwide have moved toward open access ETD publication because it dramatically increases the visibility and reach of their graduate research.

Policies and procedures for ETD filing, including how to delay public release of an ETD and how long such a delay can last, vary by campus. Learn more about the requirements and procedures for ETDs at each UC campus:

• UC Berkeley: Dissertation Filing Guidelines (for Doctoral Students) and Thesis Filing Guidelines (for Master’s Students)
• UC Davis: Preparing and Filing Your Thesis or Dissertation
• UC Irvine: Thesis/Dissertation Electronic Submission
• UCLA: File Your Thesis or Dissertation
• UC Merced: Dissertation/Thesis Submission
• UC Riverside: Dissertation and Thesis Submission
• UC San Diego:  Preparing to Graduate
• UCSF: Dissertation and Thesis Guidelines
• UC Santa Barbara:  Filing Your Thesis, Dissertation, or DMA Supporting Document
• UC Santa Cruz: Dissertation and Thesis Guidelines (PDF) from the Graduate Division’s Accessing Forms Online page

Open access can be delayed in certain circumstances

Some campuses allow students to elect an embargo period before the public release of their thesis/dissertation; others require approval from graduate advisors or administrators. Visit your local graduate division’s website (linked above) for more information.

Common copyright concerns of students writing theses and dissertations

Students writing theses/dissertations most commonly have questions about their own copyright ownership or the use of other people’s copyrighted materials in their own work.

You automatically own the copyright in your thesis/dissertation  as soon as you create it, regardless of whether you register it or include a copyright page or copyright notice (see this FAQ from the U.S. Copyright Office for more information). Most students choose not to register their copyrights, though some choose to do so because they value having their copyright ownership officially and publicly recorded. Getting a copyright registered is required before you can sue someone for infringement.

If you decide to register your copyright, you can do so

• directly, through the Copyright Office website , for $35
• by having ProQuest/UMI contact the Copyright Office on your behalf, for $65.

It is common to incorporate 1) writing you have done for journal articles as part of your dissertation, and 2) parts of your dissertation into articles or books . See, for example, these articles from Wiley and Taylor & Francis giving authors tips on how to successfully turn dissertations into articles, or these pages at Sage , Springer , and Elsevier listing reuse in a thesis or dissertation as a common right of authors. Because this is a well-known practice, and often explicitly allowed in publishers’ contracts with authors, it rarely raises copyright concerns. eScholarship , which hosts over 55,000 UC ETDs, has never received a takedown notice from a publisher based on a complaint that the author’s ETD was too similar to the author’s published work.

Incorporating the works of others in your thesis/dissertation – such as quotations or illustrative images – is often allowed by copyright law. This is the case when the original work isn’t protected by copyright, or if the way you’re using the work would be considered fair use. In some circumstances, however, you will need permission from the copyright holder.  For more information, please consult the Berkeley Library’s guide to Copyright and Publishing Your Dissertation .

How to find UC Dissertations and Theses online

All ten UC campuses make their electronic theses and dissertations (ETDs) openly accessible to readers around the world. You can view over 55,000 UC ETDs in eScholarship , UC’s open access repository. View ETDs from each campus:

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Krste asanović.

Electronic-Photonic Co-Design of Silicon Photonic Interconnects

Eecs department, university of california, berkeley, technical report no. ucb/eecs-2017-208, december 13, 2017, http://www2.eecs.berkeley.edu/pubs/techrpts/2017/eecs-2017-208.pdf.

Silicon photonic interconnects hold great promise in meeting the high bandwidth and low energy demands of next-generation interconnects. System-level driven electronic-photonic co-design is the key to improving the bandwidth density and energy efficiency. In this study, a comprehensive co-optimization framework is developed for high-speed silicon photonic transmitters utilizing compact models and a detailed optical simulation framework. Given technology and link constraints, microring and Mach-Zehnder transmitter designs are optimized and compared based on a unified optical phase shifter model. Non-return-to-zero (NRZ) and pulse-amplitude-modulation-4 (PAM-4) modulation schemes are analyzed and compared for microring-based transmitters. Using the co-design approach, a monolithic 40Gb/s optical NRZ transmitter based on microring modulators is designed and demonstrated in zero-change 45nm CMOS SOI process. Electronic-photonic co-design with the high swing driver enables this transmitter to achieve total energy efficiency of 330fJ/b and the photonics and modulator driver area bandwidth density of 6.7 Tb/s/mm2. This dissertation also discusses the design and demonstration of the first full silicon photonic interconnect on a 3D integrated electronic-photonic platform. These results make the microring-based silicon photonic transceivers an attractive solution for the next-generation inter and intra-rack photonic interconnects. Finally, a short-reach laser-forwarding coherent link architecture is proposed to further improve the energy efficiency of silicon photonic interconnects. The key concepts of the proposed architecture are verified experimentally with microring-based silicon photonic transmitters. The architecture saves the laser power by 6-7.5x and could enable complex modulation schemes for the future short-reach optical links.

Advisors: Vladimir Stojanovic

BibTeX citation:

EndNote citation:

Ming Wu Lab

• Ph.D. Theses
• Low-loss and Nonlinear Silicon-based Integrated Photonic Circuits Jean-Etienne Tremblay [2020]
• Co-planar Optoelectrowetting (OEW) Device for Droplet Manipulation Jodi Loo [2020]
• Monolayer Transition Metal Dichalcogenide NanoLEDs: Towards High Speed and High Efficiency Kevin Han [2019]
• FMCW Lidar: Scaling to the Chip-Level and Improving Phase-Noise-Limited Performance Phillip Sandborn [2017]
• Integrated Nanoscale Antenna-LED for On-Chip Optical Communication Seth Fortuna [2017]
• Highly Scalable Silicon Photonic Switches Based on Waveguide Crossbar with Movable Waveguide Couplers Sangyoon Han [2016]
• Metal Optics Based nanoLEDs: In Search of a Fast, Efficient, Nanoscale Light Emitter Michael Eggleston [2015]
• Optofluidic Devices for Droplet and Cell Manipulation Shao Ning Pei [2015]
• Ultra-low energy photoreceivers for optical interconnects Ryan Going [2015]
• Low Noise, Low Power Cavity Optomechanical Oscillators Alejandro Grine [2014]
• Optical Whispering-Gallery Mode Resonators for Applications in Optical Communication and Frequency Control Karen Grutter [2013]
• Engineering Optical Antenna for Efficient Local Field Enhancement Tae Joon Seok [2012]
• Linear, Low Noise Microwave Photonic Systems using Phase and Frequency Modulation John Wyrwas [2012]
• Metal-optic and Plasmonic Semiconductor-based Nanolasers Amit Lakhani [2012]
• Light-induced Electrokinetics: A path to a versatile micro total analysis system Justin K Valley [2011]
• MEMS Lens Scanners for Free-Space Optical Interconnects Jeffrey Chou [2011]
• Photopatterned polyacrylamide gels enable efficient microfluidic protein assays Chenlu Hou [2011]
• Optoelectronic Manipulation, Assembly, and Patterning of Nanoparticles Arash Jamshidi [2009]
• SIlicon Photonic Devices for Optoelectronic Integrated Circuits Ming-Chun Tien [2009]
• Optofluidic Devices for Cell, Microparticle, and Nanoparticle Manipulation Aaron Takami Ohta [2008]
• Tunable Optical Microresonators with Micro-Electro-Mechanical-System (MEMS) Integration Jin Yao [2007]
• High-Speed Modulation of Optical Injection-Locked Semiconductor Lasers Erwin K Lau [2006]
• Strong Optical Injection Locking of Edge-Emitting Lasers and Its Applications Hyuk-Kee Sung [2006]
• Massively Parallel Optical Manipulation of Single Cells, Micro- and Nano-Particles on Optoelectronic Devices Pei-Yu Chiou [2005]

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Cal Cybersecurity Research Fellowship Fall 2024

CLTC is now accepting applications for the next Cal Cybersecurity Research Fellowship! 

Since 2019 CLTC has provided a Cal Cybersecurity Research Fellowship, made possible through the generous support of Tim M. Mather, CLTC External Advisor and a Cal alumnus (Class of ´81). This award is typically up to $15,000, and potentially as much as $30,000, and is for UC Berkeley students or postdoctoral scholars to pursue cybersecurity research in security-related fields.

This year, we are seeking research projects that aim to advance our understanding of how AI and machine learning technologies can help automate and amplify the capabilities of cyber defenders. Advances in SOAR (security orchestration, automation and response) – software solutions that leverage AI and machine learning to enable systems to detect and respond to cybersecurity vulnerabilities, threats, and events autonomously – is one potential area of inquiry, though this RFP is not limited to SOAR techniques and we encourage applications with new ideas that meaningfully push the envelope on machine learning for security.

The award may support:

• Seed Funding: generally below $15,000. These awards could fund an exploratory study, a small pilot, a PhD dissertation project, or other means of ‘prospecting’ a problem area; or
• Discrete Projects: up to $30,000. These awards intend to fund projects that have defined boundaries with clear outcomes and impact potential. While the default will be that these grants have a one-year timeline, CLTC will entertain proposals for longer discrete projects when scientifically justified.

This award may be used only to support a current student or postdoc’s research efforts. The award funds are discretionary and can be applied to any legitimate research expenses, including but not limited to: student/postdoc salary on an official university appointment, conference registration and travel, research supplies, and other direct costs related to the project.

You can see examples of previously awarded Cal Cybersecurity Research Fellowships here .

If you would like to apply for the Research Fellowship, please submit the following information to this form by September 29th, 2024 at 11:59 PM PT :  https://forms.gle/8DxJsudB9WyVqeuk7

• Project proposal (5 pg. maximum) including a project title, abstract, previous related work, research methods, and expected outcomes and impact.
• Appendix: append a brief biography of you and your advisor(s) and a simple budget describing how you would use the award funds.

Questions can be directed to Audrie Francis at [email protected] .

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