VictorJuiz/thesis huggingface dataset:
Thin film applications for energy storage

Renewable technology, a solution to many global warming problems, has been developed in recent decades and progressing rapidly; however, its global adoption rate is still limited mainly because of energy storage sources. To replace fossil fuels, we need to improve the energy and power densities of alternative storage technologies to be competitive. By employing thin film techniques, particularly based on atomic layer deposition (ALD), one can shorten the transport length for improved kinetics and better ability to withstand higher applied electric fields for energy storage devices. First, in this work, a nanoscale protective ALD coating layer for the Li-ion battery cathode to enhance battery durability is introduced. The coating needs not only protect the battery against numerous degradation mechanisms but also exhibit sufficiently high ionic and electronic conductivities. Many ALD-deposited metal oxide films, including Ti, Nb, W, V, Zr, and Li, are explored and ranked according to their conductivities, resulting in Li-V-O coating with minimal Nb-doping as the best candidate. Secondly, an energy storage device with nanoscale thin film in a heterostructure form is often subjected to high applied current or voltage, leading to high thermal loading, which can cause device failure due to thermal expansion mismatch. Therefore, we introduce atomic force microscopy with the harmonic Joules heating method to probe thermal expansion coefficients in nanoscale thin films. We apply such techniques with a varying aspect ratio pillar micropattern to reveal anisotropy in thermal expansion in poly(methyl methacrylate) for the first time. Finally, we explore a new kind of energy storage/transport device by adopting a metal-insulator-semiconductor (MIS) architecture. Guided by density functional theory, the ALD dielectric with high breakdown strength and high aspect ratio structure enable the application of high electric fields, which, in turn, may induce topological electronic states. We observed unconventional current-voltage behavior, including current spikes much larger than the capacitive current at low voltages and an inverse scaling of maximum current densities with area, indicating high energy density behavior. These effects could only be seen in MIS capacitors and not in metal-insulator-metal (normal) capacitors. In addition, simulations indicated that classical bulk and surface leakage currents, as well as tunneling currents, cannot explain the observed behavior. These efforts demonstrate how thin film applications may be crucial for future energy storage technologies

Keywords: Engineering.
Mechanical Engineering.
Advisor: Prinz F. B.
Jornada Felipe
Santiago Juan G.
Think globally - act locally : scalable networked artificial intelligence

Machine learning aims to relegate many decision-making processes to agents (machines). In applications like autonomous vehicles, communication networks, and robotics, the agents interact in a networked environment, where the decisions of one agent affect the other agents and their learning. As game theory predicts, this interaction can lead to inefficient outcomes for all agents involved. However, machines follow programmatic objectives and protocols that, unlike humans, are not limited by selfish interests but by information and resources. This modern paradigm calls for new tools to design efficient multiagent protocols for distributed and cooperative agents, which is the subject of this thesis. First, we present a distributed learning algorithm that can maximize the sum of rewards in a general game (for a concave objective), based on reward values alone (i.e., "bandit feedback") and limited communication. We then show that in special cases, we can design algorithms that use less (or no) coordination by leveraging the structure of the application. This is done by targeting the Nash Equilibrium (NE) of the game instead of the optimal actions directly. For distributed energy allocation, we show that although poor NE may exist, best-response dynamics (BRD) converge with high probability to an asymptotically optimal NE (in the size of the network). The idea is to look at a random game (i.e., network) rather than the worst-case one. When poor NE are unavoidable, we propose a manager that can learn to steer the inefficient NE toward an efficient point. This game control offers another tool to design efficient and mostly distributed multiagent systems since the manager does not need to know the game and only observes high-level feedback. Finally, we take a step towards dynamic multiagent optimization by considering the accumulated players' rewards over time instead of a static objective. With such a dynamic objective, players can learn to share resources over time, which can significantly increase their average reward

Keywords: Engineering.
Electrical Engineering.
Advisor: Bambos Nicholas
Ashlagi Itai
Van Roy Benjamin
Three essays on administrative law, artificial intelligence, and disparate impact

The rise of artificial intelligence raises many questions for lawyers and ethicists. Novel algorithmic tools create challenges that span multiple areas: in the private sphere, where companies have adopted pricing algorithms that can personalize prices for different customers; to the public sphere, where government agencies have adopted algorithmic tools that can predict regulatory violations or speed the processing of agency adjudications. Algorithmic tools hold great promise, but they also raise concerns about disparate impact, privacy, and the proper role of government regulation. This dissertation explores some topics in administrative law and artificial intelligence ethics and regulation.

Keywords: Humanities and Sciences.
Philosophy.
Advisor: Icard Thomas
Engstrom David Freeman
Ho Daniel
Satz Debra
Three essays on causal inference

This thesis describes three research projects in causal inference, all related to the problem of contrasting the average counterfactual outcomes on two sides of a binary decision. In the first project, we discuss estimation of the average causal effect in a randomized control trial. Here, we find that statisticians find themselves in a kind of statistical paradise: a simple model-based procedure delivers correct confidence intervals even if the experimental participants are not randomly sampled and mis-specified models are used. In the second project, we consider the problem of testing for a treatment effect using observational data with no hidden confounders. Conceptually, this is no different from a rather complicated RCT, and one might expect that a return to statistical paradise is possible. Unfortunately, this is not the case: we show that even intuitively reasonable uses of correct models may still yield misleading conclusions. The final project looks at observational data with unobserved confounding and gives methods for computing bounds on average causal effects. Here, we discover some never-before-seen robustness properties unique to the partially-identified setting.

Keywords: Humanities and Sciences.
Statistics.
Advisor: Rothenhäusler Dominik
Owen Art B.
Romano Joseph P.
Through thick and thin : improved transcranial focused ultrasound transmission estimates with acoustic simulation and magnetic resonance imaging

Transcranial focused ultrasound is a promising technique for non-invasively treating brain disorders. During treatments, acoustic energy is focused through the intact skull to a millimeter-sized target. This can result in a variety of effects on tissue, ranging from mechanical perturbations to necrosis, depending on the duration, pulsing scheme, and intensity of the sonications. However, the skull is a substantial obstacle to safe and effective treatments. It is highly attenuative, absorbing and scattering the majority of the transmitted ultrasound due to its strongly heterogeneous composition. The consequence of this could be an aberrated focal spot which might be shifted from the intended target. In this dissertation, we demonstrate the necessity for accurate estimates of acoustic intensity delivered into the brain for transcranial focused ultrasound treatments, such as ultrasound-mediated blood-brain barrier opening. We discuss and propose improvements to two promising approaches for estimating in situ acoustic intensity, one using magnetic resonance imaging and another using acoustic simulation. Magnetic resonance acoustic radiation force imaging is a phase-contrast imaging technique where motion-encoding gradients are applied to encode tissue displacement induced by the acoustic radiation force in the phase of the image, enabling localization of the focal spot. As the acoustic radiation force is proportional to the applied acoustic intensity, measured displacements could potentially be used to estimate the acoustic intensity at the target. However, variable brain stiffness remains an obstacle. We demonstrate that viscoelasticity information from magnetic resonance elastography could be used in combination with displacement estimates to more accurately predict the delivered acoustic intensity. Finally, another approach for estimating acoustic transmission through the skull is by simulating acoustic propagation using the hybrid angular spectrum method. While this method is computationally efficient, its assumptions of normal incidence and weak heterogeneity in the medium may not be suitable for transcranial applications. We demonstrate the limitations of this method using a set of numerical benchmarks, and we propose adapting it toward transcranial applications by extending the implementation of acoustic attenuation into the spatial-frequency domain. The improvements presented here have the potential to enable clinicians and researchers to more accurately determine how much acoustic energy is being delivered to which regions of the brain, allowing them to focus on exploring the treatment parameter space rather than being uncertain about whether ultrasound is even reaching the intended target at power levels relevant for therapy. We hope that this work moves the field towards safer and more effective transcranial focused ultrasound therapies

Keywords: Engineering.
Electrical Engineering.
Advisor: Pauly Kim Butts (Kim Rosemary Butts)
Dahl Jeremy J.
Nishimura Dwight George
Pauly John (John M.)
TinyML computer vision using coarsely-quantized log-gradient input images

This thesis studies the merits of applying log-gradient input images to convolutional neural networks (CNNs) for tinyML computer vision (CV). We show that log gradients enable: (i) aggressive 1-bit quantization of first-layer inputs, (ii) potential CNN resource reductions, (iii) inherent insensitivity to illumination changes (1.7% accuracy loss across 2^{-5}...2^3 brightness variation vs. up to 10% for JPEG), (iv) robustness to adversarial attacks (> 10% higher accuracy than JPEG-trained models), and (v) and insensitivity thresholds for quantization that confirms hardware implementability. We establish these results using the PASCAL RAW image data set and through a combination of experiments using quantization threshold search, neural architecture search, and a fixed three-layer network. The latter reveal that training on log-gradient images leads to higher filter similarity, making the CNN more prunable. The combined benefits of aggressive first-layer quantization, CNN resource reductions, and operation without tight exposure control and image signal processing (ISP) are helpful for pushing tinyML CV toward its ultimate efficiency limits. Dataset related issues currently prevailing in machine-centered vision tasks are reviewed and analyzed, with a focus on RAW image datasets. By approximating RAW data, our proposed method shows potential to generalize to larger datasets. Taken together, we demonstrate an energy efficient and robust tinyML computer vision system approach using coarsely-quantized log-gradient input images

Keywords: Engineering.
Electrical Engineering.
Advisor: Murmann Boris
Pilanci Mert
Wetzstein Gordon
To make a christian of Andrew Jackson : religious politics at the rise of American democracy

This dissertation identifies the mid- to late-1820s as uniquely inaugurating a new and permanent kind of religious politicking at the presidential level. Whereas in almost all previous elections, religion had been a personal matter, the elections of 1824 and 1828 saw a surge in interest concerning religious matters. Competing for votes was Andrew Jackson against John Quincy Adams, and both had their theological convictions, spiritual habits, denominational preferences, and religious biographies utilized and scrutinized in ways never seen before in American politics. But it was among Jackson's supporters that a collection of energized voters and savvy political organizers saw in him not only the people's choice, but also God's choice. In speeches, newspapers, and art, these religiously motivated operatives (as well as their more cynical allies) smoothed out Jackson's rough edges, downplayed his faults, exaggerated his spiritual qualities, and exploited his Christian orthodoxy. As in so many other ways, these Jacksonians transformed presidential religious politics and, in the process, made religion an unavoidable issue in American democratic politics.

Keywords: Humanities and Sciences.
Religious Studies.
Advisor: Lum Kathryn
Campbell James
Mayse Evan
WilentzRobert S.
To rise from the ashes : frontiers in the age of civil wars

This dissertation seeks to answer how and why frontiers of empires remained an integral part of empire-building and maintenance during periods of civil wars. In doing so, it will also address the following questions: How different were frontier policies in these periods from those during times of imperial stability? How did rival factions exploit frontiers, and how did these acts of exploitation allow these factions to further their political struggles in the interior? Why were minority groups often the target of exploitation? Did these minority peoples form some ethnic consciousness (ethnogenesis), or did regionalization and profession serve as dominant forces of group cohesion instead? Finally, why was the mismanagement of frontier affairs so dangerous for these potential claimants to the throne? Using primary literary sources and archaeological evidence, this dissertation examines the military, political, and economic aspects of frontiers in the age of civil wars to fully understand the centrality of frontiers in the power of empires and emperors. Frontiers of the late Eastern Han Empire and the late Roman Empire underwent significant changes during civil wars when unified imperial authority over the empires had broken down, and rival claimants to the throne were vying to establish dynastic control over the lands. By comparing these periods of civil disorder, it is possible to look past the traditional narratives that focus heavily on political developments at the imperial center and instead understand how frontiers played a vital role in the deconstruction and reconstruction of imperial power. Even during civil wars, there was a direct symbiotic relationship between the organization of frontiers and imperial stability at the core of empires. In these times of civil conflict, imperial authority and immediate control over the lands degenerated as rival claimants to power fought for control over the empire. For these rival powers, the frontiers became sources of opportunity and threats to their political fortunes. Inspired by existing Western scholarship on the concept of ethnogenesis and regionalism, this dissertation will also examine how the movement of peoples from different parts of both empires gradually created ethnic identification, which was closely linked to their locale and their military service. As these people settled at the frontiers (voluntarily or coerced), they were exploited for their labor and military service. As a result, they took on new markers of identity

Keywords: Humanities and Sciences.
History.
Advisor: Lewis Mark
Leidwanger Justin
Potter D. S. (David Stone)
Sommer Matthew Harvey
To the Best of My Abilities and Understanding: Do Legal Credentials Affect Sentencing Decisions in South Carolina?

This thesis considers the impact of legal credentialing on judicial sentencing behavior. In particular, the paper examines whether the lawyers appointed as judges to South Carolina’s Magistrate Court make different criminal sentencing decisions than the non-lawyers who serve as judges on the same court. To answer this question, the study uses a novel data set containing all publicly available criminal sentences handed down by the Magistrate Court in 2021 and cross-referenced the sentences with a roster of magistrate judges created through formal public information requests to the South Carolina Judicial System. The analysis also controls for other explanatory variables, including race and sex of the defendant, race and sex of the judge, and the crime committed. This paper finds that lawyer and non-lawyer magistrate judges impose similar sentences in nearly all respects. However, they differ along two important dimensions: racial bias (lawyer magistrate judges give Black defendants 22% greater fines than they give White defendants, while non-lawyer magistrate judges treat the groups similarly), and observance of a “plea premium” norm (lawyer magistrate judges give defendants who plead guilty 16% lower fines, while non-lawyer magistrate judges do not).

Keywords: Crime
Criminal Justice
Judicial Reform
Law and Economics
Legal Credentialing
Magistrate Court
Occupational Licensing
Race
South Carolina
Text
Thesis
Advisor: Donohue John
Tobacco cultivation and production in twentieth-century Palestine/Israel

Tobacco cultivation began in Palestine in the late nineteenth century and lasted until the late 1980s. It existed under repeated monopolizations of the market: First by the Ottoman Regie company; then by the British American Tobacco Co. during the British Mandate; and finally, by Dubek Co. in Israel. Dubek's hold on cigarette production in Israel, cemented in 1971, marked the beginning of the end of Palestinian tobacco cultivation in Israel. Palestinian cigarette production, on the other hand, disappeared earlier, in 1966. That was the year in which the Arab Cigarette & Tombac Co., the only factory which survived the Nakba [Ar. Catastrophe] of 1948, finally closed. Tobacco was the main cash crop fellahin [peasants] could grow in the hilly hinterlands. Their family households, and sometimes entire villages, depended on it for an inflow of cash. Cash was desperately needed in a time of monetization and colonial development, which rarely benefitted the agrarian population. Colonial officers were prejudiced toward fellahin and deemed them incapable of adequately growing or marketing their tobacco. However, as I demonstrate using oral history interviews, and state and local archives in Israel, Turkey, and Britain, they were highly savvy about the methods of cultivation, the market, and the industry. Through the Arab Cigarette & Tombac Co., established in Nazareth in 1933 as a nationalist economic Palestinian venture, fellahin and Bedouin bought shares and took part in the entire production cycle of cigarettes. They also protested the three regimes' ban on the independent sale of tobacco, and while resisting the label "smuggling, " proceeded to sell their crop informally, risking fines and imprisonment. They continuously called for a free market, agricultural instruction, and export markets. As colonial policies deprived them of their lands and access to water and turned them into a minority in their country, the fellahin who remained and became citizens slowly gave up and pursued work outside agriculture. The history of tobacco in Palestine ultimately exemplifies some of the main processes through which Palestinians became depeasantized and excluded from the agrarian economy, culture, and landscape

Keywords: Humanities and Sciences.
History.
Advisor: Barakat Nora Elizabeth
Beinin Joel
Seikaly Sherene
Yaycioglu Ali
Topics in econometrics and optimization

This thesis investigates the interesting connections and interactions between some classic topics in econometrics and optimization. We show that insights and results from one area can help solve hard problems in the other, and that there is value in connecting seemingly unrelated disciplines and problems. We will focus on two important models of discrete choice: probit and logit models. Probit models are important tools for modeling discrete responses economics, finance, statistics, and epidemiology. However, the latent nature of probit coupled with identification constraints pose significant computational challenges for its estimation, especially when the dimension of the response variable is large. We propose a computationally efficient EM algorithm for estimating large multi-dimensional probit models. We use expectation propagation (EP) instead of simulation methods to approximate moments of the truncated multivariate normal (TMVN) in the E (expectation) step. In addition, we transform the constrained optimization problem in the M (maximization) step into a one-dimensional problem, which is solved directly using Newton's algorithm. Our method enables the analysis of choice data in the presence of many alternatives and features common in modern applications but has been difficult in practice with probit models. In the second part of the thesis, we show, for a broad class of choice and ranking models based on Luce's choice axiom, that the associated maximum likelihood estimation problem is equivalent to a classic matrix balancing problem with target row and column sums. We draw inspirations from these connections and resolve important open problems on the study of Sinkhorn's algorithm. We first prove the global linear convergence of Sinkhorn's algorithm for non-negative matrices whenever finite solutions to the balancing problem exist. We characterize this global rate of convergence in terms of the algebraic connectivity of the bipartite graph constructed from data. Next, we also derive the sharp asymptotic rate of linear convergence, which generalizes a classic result of Knight (2008). To our knowledge, these are the first quantitative linear convergence results for Sinkhorn's algorithm for general non-negative matrices and positive marginals.

Keywords: Humanities and Sciences.
Economics.
Advisor: Imbens Guido
Hong Han
Ugander Johan
Topics in statistical prediction and inference

In the era of big data with increasingly complex learning systems, reliable and valid statistical inference is crucial for extracting meaningful insights and ensuring the integrity of data analysis. For example, in error estimation and model evaluation, statistical inference provides principled methods to estimate and quantify uncertainty in prediction errors. In causal inference, randomized experiments and hypothesis testing are widely used to detect significant average treatment effect, etc. However, larger datasets and more complex settings can lead to misuse of existing inference tools in situations beyond their assumptions. This thesis explores the following challenges faced by modern statistical inference for the targets of prediction error and average treatment effect. For inference of prediction error, covariate shift may occur and time correlation could result in failure of CLT-based naive confidence intervals. For Inference of average treatment effect, "hunting" for significance can inflate type-I errors and false discovery rates in large-scale sequential experiments, and interference poses challenges to valid inference of exposure contrasts and optimal experimental design. In this thesis, we will examine particular settings with the above challenges and build upon existing literature to discuss principle-inspired methodologies to tackle them.

Keywords: Humanities and Sciences.
Statistics.
Advisor: Tibshirani Robert
Hastie Trevor
Owen Art