VictorJuiz/thesis huggingface dataset:
Incentivizing cybersecurity through liability and insurance : focusing on medical device software and healthcare organizations
Keywords: Law JSD.

The increasing use of digital technologies in economic activities, though creating significant benefits through delivering convenience and efficiency, is also leading to significant risks. As more medical devices are equipped with wireless capabilities, the threat of a cyberattack increases accordingly. The healthcare industry has faced more cyberattacks than other industries. This dissertation focuses on the problem of cyberattacks on both medical devices and hospital networks. While hackers play an obvious role in cyberattacks by discovering and exploiting the existing vulnerabilities in software, the threat of cyberattacks could be properly addressed by also regulating the behavior of two other actors—software manufacturers and software users. The current legal framework focuses on deterring potential malicious actors but it is difficult to implement the deterrence and, thus, the current approach to deter potential hackers does not sufficiently address the threat of cyberattacks. There is a need to create adequate incentives for software manufacturers to adopt improved security measures in their products and to provide software users with a mechanism that helps them understand the financial impact of potential cyberattacks accurately and better prepare for cyberattacks. This dissertation makes five contributions. First, it analyzes the current legal framework that applies to cyberattacks on medical devices and hospital networks and identifies gaps in the statutory and regulatory framework that make this framework inadequate to address the growing threat of cyberattacks. Second, it suggests product liability, especially post-sale duty to update, as a tool to give manufacturers of medical device software an incentive to secure medical device software. It also analyzes how the claims alleging a violation of a post-sale duty to update would overcome the preemption hurdle and how the recently adopted EU directives would indirectly affect the changes in the US market in the direction of recognizing a software manufacturer's post-sale duty to update. Third, it is the first research that investigates and compares two recent efforts to legislate compulsory cyber insurance—those of South Korea and in California. Fourth, different from the previous literature that mainly focused on the rosy promises of mandating cyber insurance, this dissertation analyzes not only the benefits but also the pitfalls of introducing compulsory cyber insurance and thereby identifies conditions for a comprehensive compulsory cyber insurance program to function well as a regulatory tool for software users. Finally, it recommends a two-staged approach in introducing compulsory cyber insurance, under which data breach liability insurance is mandated first and then compulsory cyber insurance would be expanded to cover both first-party losses and third-party losses when the above-mentioned circumstances are have taken root
Incomplete : risk topographies and conflict landscapes in Turkey in the age of the new gold rush
Keywords: Anthropology.

The world has witnessed an unprecedented global proliferation of mining activities over the past four decades. In line with the World Bank's structural adjustment programs, by the mid-1980s, more than 90 countries changed their mining codes within a few years to accommodate private and foreign mining activities. Many call this era the extractive moment. Gold mining constitutes the bulk of this extractive moment, attracting close to half of all the new mining investment during this era. This global expansion of gold mining generated a proliferation of socio-environmental movements by indigenous and farming communities living in the mined areas against the threats of gold mining. This dissertation follows one fundamental question: How can we theorize the politics of this juncture in the mineral age as consisting of incomplete processes shaped by risk topographies? In addressing this question, this dissertation explores the making of Turkey's first gold mine in Bergama, a farming town in Western Turkey, by focusing on the conflict that lasted for more than a decade from 1990 onward between farming communities, the gold mining company Eurogold, and state authorities centered on the technopolitics of risk. Despite rapidly growing risk scholarship and anthropology's ever-increasing interest in environmental justice and mining, the entanglement of risk and mining is largely understudied. The scrutiny of this entanglement is particularly needed considering the increased rate at which toxic mining waste dams fail. How is it that, despite all the environmental scrutiny, heightened safety measures, and a much more mature and potent risk analysis discipline, there is an alarming rate of increase in mine tailings dam failures, both in frequency and in intensity? This dissertation offers a three-layered argument: 1) gold is not discovered but assembled. In contrast to popular imagery, gold mining is less about "extraction" and more akin to chemical manufacturing. The peculiar assembly of gold, in turn, engenders socio-environmental threats and uncertainties, bringing risk to the fore of the debates; 2) farming communities are not simply risk-averse; instead, they are expert risk managers. This expertise situates them in the best position to assess the risks of gold mining. As a result, farming communities' expertise enables them to forge successful alliances with scientists in their struggle against the gold mine; 3) risk is not an objective state or condition but a performative enactment by what I call the risk dispositif. However, the performative aspect of the risk dispositif has its limits stemming from what I call its necessary incompleteness. The necessary incompleteness, in turn, makes risk dispositif counterperformative, producing a world that is ultimately not less but more risky. When intersected through the conflict, these three layers assemble a set of diverse precarities fueled by multilayered incompleteness that mark the current global extractive moment
In-database machine learning on reconfigurable dataflow accelerators
Keywords: Electrical Engineering.

Stagnant CPU performance is driving an explosion in domain-specific architectures to supplement CPUs for data-intensive workloads in data centers. These accelerators sacrifice generality and programmability beyond their target domain in exchange for higher-performance. However, accelerator deployment in data centers remains limited outside all but the most ubiquitous of application domains like machine learning. High-performance systems require large dice in advanced process nodes with many supporting resources like high-bandwidth memory. As a result, to justify the price that comes with high-performance IC design, designers building non-ML accelerators are left with a choice: either piggyback on ML accelerators---giving up some efficiency but taking advantage of supporting hardware resources---or build more-efficient but less-advanced bespoke hardware. In this work, we show that reconfigurable dataflow accelerators (RDAs) are a practical alternative to building fixed-function designs per application domain. We extend Plasticine---a previously proposed ML-focused RDA--- with low-overhead, micro-architectural extensions to support analytic database queries. These extensions increase area by just 4%, and the unified accelerator outperforms a multi-core software baseline by 1500x. Finally, we show how to re-purpose these extensions to implement data structures like trees and hash tables that are critical to asymptotically optimal query plans. We introduce a threading model for vector dataflow accelerators that extracts parallelism from data structures with irregular control flow using fine-grained thread scheduling---outperforming a GPU by 8x
India's moment in the sun : inside the making of the International Solar Alliance
Keywords: Law JSD.

In 2015, leading up to the Paris Climate Conference, India faced intense scrutiny over its role in either securing or scuttling a global climate deal. On the first day of the climate talks India and France jointly announced the International Solar Alliance (ISA), and the two weeks of hectic negotiations culminated in the adoption of the Paris Agreement. In less than two years, even as multilateral climate negotiations were weakening with the United States announcing its withdrawal from the Paris Agreement, the ISA -- led by India and backed primarily by developing countries -- became a legal entity. This dissertation presents a case study of the creation of the ISA as a treaty-based international organization. Drawing on the political economy approach in the study of international law, this case study identifies the politics, players and process behind the making of the ISA. It uses mixed methods to analyze the politico-legal issues over the need for a new treaty-based international organization. The dissertations finds that the changing political leadership in India -- Narendra Modi became Prime Minister in 2014 -- marked a shift in domestic climate politics, particularly around solar energy. Modi's vision for a new, India-led global effort on solar energy was the primary driver behind the creation of the ISA. In the backdrop of multilateral climate negotiations, the political leadership empowered India's new international rulemaking stance. This dissertation presents an in-depth account of the treaty-making process to argue that it marks an innovation in the structure of international organizations. The ISA is best described as 'soft law in a hard shell', that is it uses the legal infrastructure of a treaty while relying on the social structure of participating actors for its future implementation. Empirical evidence suggests that three factors explain the treaty structure of the ISA: India's leadership role in the treaty-making process, the early involvement of non-state actors, and the preference of developing countries for legal form. Ultimately, the case illustrates a new kind of Indian economic diplomacy, making the ISA the first deliberate instrument of India's foreign policy on climate change and energy
Inferring RNA structure and stability via high-throughput experiment
Keywords: Chemistry.

The computer-aided study and design of RNA molecules is increasingly prevalent across a range of disciplines, yet advancing RNA design will require quantitative improvements in predicting RNA structure. The first part of this dissertation describes contributions to advancing RNA thermodynamics predictions, a highlight of which is the development of a novel multitask-learning-based model that links the training of an RNA thermodynamic model to the statistical mechanics of several prediction tasks. We trained this framework using large datasets of diverse synthetic constructs obtained from the crowdsourced RNA design project, Eterna. The resulting algorithm, EternaFold, demonstrated improved performance on diverse independent datasets, including complete viral genomes probed in virion, human mRNAs probed in vivo, and synthetic designs modeling mRNA vaccines. This work establishes an extensive benchmark for evaluating RNA secondary structure ensembles through several types of experiment, and a general statistical mechanical framework for inferring energetic parameters from equilibrium experimental observables. The second part of this dissertation describes advances in a pressing application of RNA design: creating more thermostable RNA vaccines. Vaccines based on messenger RNA (mRNA) emerged as forerunners in the current COVID-19 pandemic and show promise as a novel therapeutic platform, yet their inherent chemical instability sets a fundamental limit on the stability of mRNA vaccines. Predictions from our developed biophysical models indicated that the half-life of any mRNA could be immediately increased at least two-fold through sequence design, predictions validated in vitro and in vivo. We anticipate this work will guide future therapeutic and vaccine development in potency and stability
The influence of chemical kinetics on detonation structure and propagation
Keywords: Mechanical Engineering.

Society's need to reduce anthropogenic CO2 emissions motivates engine efficiency research in the growing natural gas power generation and aviation sectors. One promising technology for these sectors is the detonation engine, which offers thermodynamic efficiency advantages over the conventional approaches to energy conversion. The detonation engine, however, suffers from difficulty stabilizing a detonation wave within engine geometries. This dissertation focuses on understanding, predicting, and extending the stable limits of detonation to alleviate this challenge. The effect of dopant concentrations of ozone (up to 3000 PPMv) added to detonable mixtures was evaluated through both experiments and numerical simulations. Ozone is predicted to act as an ignition promoter, reducing the time to ignition in detonation structure. Results show that ozone can reduce the size of detonation cellular structure proportionally to the associated reduction of the induction length (upwards of 70% in conditions evaluated in this dissertation). Experiments validate that ozone can then extend the detonation stable limits. This suggests that ozone can be used in engines to extend the operating space. Ozone is also a unique scientific tool to control the ignition delay time, and therefore cell size, in isolation from any other detonation property (i.e. CJ speed, post-shock and equilibrium states). The principles developed by studying ozonated detonation were used to study the differences in detonation properties between methane and natural gas (whose main component is methane). Experiments and simulations show that natural gas has a wider range of detonable conditions as compared to methane, indicating that, for an engine, it is a better fuel choice. This is true in spite of the composition variability inherent to natural gas, which is predicted to have a relatively minor impact on propagation behavior. These results further suggest that in safety studies, using methane, which is sometimes chosen as a surrogate for natural gas, is a highly-non-conservative choice. A theory was developed to predict detonation cellular stability. The theory is capable of predicting cell size without empirical correlations, and explains the difference in cell structure associated with different mixtures, including why ozone is so effective in reducing cell size. The theory is based on a negative feedback control on the size of blast kernels, which are the root of detonation cells. We developed the theory using 1D transient simulations, and tested it using 2D transient simulations. The cell stabilization theory is extended to produce a model which can predict multi-dimensional detonation cellular propagation with arbitrary boundary conditions and mixtures. The resulting model is computationally efficient, allowing rapid parametric studies of engineering-relevant geometries and thermodynamic conditions
Informality and macroeconomic policy in emerging markets
Keywords: Economics.

This doctoral thesis is a collection of three independent articles in the field of Macroeconomics written during my period as PhD student at Stanford University. They have the common characteristic of being motivated by questions that, in my view, pertain mostly to emerging market economies. The first two articles were written in close collaboration with my friend Gustavo Pereira, who is currently a fellow PhD student at Columbia University. These two chapters study different aspects of informal labor hiring, or informality, which is common and widespread in developing economies, but which also affects - perhaps in a distinct format - advanced economies. The third article, written entirely by myself, studies a medium-scale, New-Keynesian model in the presence of fiscal dominance. The main goal is to study the properties of deficit financing in emerging markets. In particular, the extent to which news about public finances change the perceived value of public debt and, therefore, inflation
Infrared nanophotonics with carbon nanotube metamaterials
Keywords: Applied Physics.

Assembly of nanomaterials into highly-ordered macroscopic structures holds potential for new optical materials and devices. This thesis introduces self-assembled films of carbon nanotubes as optical metamaterials and demonstrates their use in infrared nanophotonic resonators and electrically-driven thermal emitters. In the first part of this work, we study the effective optical constants of large-area films of globally-aligned and densely-packed single-walled carbon nanotubes prepared using a vacuum filtration process. The effective optical constants indicate that across a broad range of wavelengths in the mid-infrared these films have hyperbolic dispersion. The hyperbolic wavelength range can be tuned through reversible doping of the nanotubes. We show that ribbon structures patterned from the nanotube films function as hyperbolic resonators capable of localizing light at deeply subwavelength scales. A model for hyperbolic waveguide modes in the nanotube film explains the ribbon resonance and agrees with the results of a far-field spectroscopic study of many resonator arrays. In the second part of this work, we observe multiple resonances at different frequencies in large ribbon resonators and confirm that they are higher-order hyperbolic resonances. Combining the coexistence of multiple resonances with the doping tunability of the nanotube film, we demonstrate broadband high extinction with reversible switching in the mid-wave infrared. In the third part of this work we create electrically-driven thermal emitters in which arrays of nanotube metamaterial ribbons function as both resistive heaters and hyperbolic resonators. When the devices are electrically biased we observe quasi-coherent thermal radiation at the hyperbolic resonance frequency. Devices patterned on one chip from the same nanotube film emit at different wavelengths depending on the geometric parameters of the ribbon pattern, and the thermal radiation can be modulated up to 1 MHz because of the short thermal time constant of the heated resonator. This work establishes aligned carbon nanotubes as a tunable hyperbolic metamaterial in the mid-infrared with potential for use in actively tuned resonators and electrically-driven thermal emitters
Insights into chemical reactions at interfaces from enhanced sampling and global optimization algorithms
Keywords: Chemical Engineering.

Many important technologies and current scientific challenges involve chemical reactions that occur in complex environments, such as surfaces/interfaces or condensed phases which have complex structures and dynamics under reaction conditions. Engineering catalysts and processes for these reactions depends on developing an understanding of the mechanisms that determine the rates of these reactions. Applying computational modeling to catalytic reactions is challenging for several reasons; my work demonstrates several strategies for modeling increasingly complex chemical reactions. Accurate theoretical study of catalytic interfaces and solvent effects has been an active area of research for decades, as it requires simultaneously including detailed electronic structure and chemisorption effects from the catalyst and the many intermolecular interactions and long-range electrostatic interactions from the solvent, electrolyte or other surrounding environment, all of which must be sampled over an ensemble of configurations. I address this challenge by systematically applying global optimization algorithms to models of solvent-metal interfaces. I explain trends in adsorbate-electrolyte interactions and relate these to the adsorbate dipole moment and hydrogen bonding affinity, and shed new light on models of ion promotion effects. And I extend these methods to study the interfaces of nonaqueous solvents with transition metals, uncovering several new insights into the effects of solvent chemisorption on a variety of metal surfaces. The effect of temperature on the reaction free energies and barriers of elementary chemical reactions on surfaces is often calculated using the harmonic approximation. This is due to the computational expense of first principles calculations and the simplicity of the harmonic approximation. Often these methods rely on system-specific intuition and assumptions regarding which configurations and types of anharmonicities may be important. More rigorous, scalable and efficient free energy and enhanced sampling methods would be useful in addressing this challenge. In this thesis I describe applying a state-of-the art machine learning based enhanced sampling method to a density functional theory (DFT) model of a prototypical surface reaction. This method calculates the free energy profile of the reaction more efficiently (fewer calculations required) than other currently widely used free energy methods, while exploring and identifying the critical states in the reaction mechanism. I also apply metadyamics to study nitrogen dissociation in lithium, demonstrating the use of an enhanced sampling algorithm for the exploration of reaction mechanisms and calculation of rate constants of a surface reaction. This work is a step toward systematic, generalizable methods for the computational study of chemical reactions in complex environments
Insights into metabolism and signaling at small scale
Keywords: Chemistry.

Mass spectrometry is a powerful technique for the study of biological systems because it enables untargeted analysis of endogenous molecules, giving us insight into metabolism and signaling. Here we focus on ambient ionization methods for sampling, primarily desorption electrospray ionization imaging (DESI-MSI). We describe the adaptation of DESI-MSI for the imaging of small tissues, in particular murine adrenal glands under 2 mm in diameter and maize root primary tips less than 1 mm in diameter. We discuss the elements of probe construction, set-up, and image acquisition that influence the quality of measured DESI images. This contextualizes the modifications to the DESI probe and method that were made in order to resolve fine sub-tissue regions in these small tissues. The lateral spatial resolutions were also retroactively calculated for all imaged tissue sets in order to assess the influence of probe, step size, and sample material on spatial resolution. This work then describes two applications of DESI-MSI to small tissues. One is for targeted detection of succinate accumulation in the adrenal medulla in the development of a mouse model for succinate dehydrogenase (SDHx)-deficient pheochromocytoma. In this project, DESI-MSI detects a metabolic change that results from epigenetic silencing of genes which were not shown to be mutated by a genetic screen, suggesting further clinical applications of this technique. The second application of DESI-MSI is to the imaging of maize root cryosections, demonstrating this technique's potential for identifying small molecule regulators of root development. DESI imaging reveals not only metabolite gradients along the developmental axis, but also unique spatial patterning that correlates to known tissue sub-structures, including the inner vasculature and endodermis