VictorJuiz/thesis huggingface dataset:
Moments of the Magnetic and Hamiltonian Variety – New Perspectives on Historic Approaches to the Electronic Correlation Energy and its Properties

Much progress in the theoretical sciences is founded on the application of insight from modern developments and techniques to approaches previously-considered unattractive. This dissertation is concerned with the many-body problem inherent to descriptions of the electronic wavefunction for molecular systems. We begin by developing the intractable exact solution, describe realizable approximations from the many-body to a single-body picture, and cover a range of approaches in recovering the accurate description of the energy from the approximate solution: the quantity of most interest in the chemical sciences. Special considerations for the problem when the bodies are under the effect of a static external applied field are taken into consideration for the same approximations./nWe first focus on the description of this problem via high-order cumulants of the Hamiltonian. The use of cumulants is traditionally plagued by the infeasibility of evaluating even low-order powers of the Hamiltonian, especially in comparison to techniques developed in the perturbative description of the energies. Considering the success of selective summation of infinite perturbative terms with added low-order contributions, which today constitute the ‘gold standard’ of our field, we introduce similar techniques to the span of the moments themselves for use in their respective methodologies. Harkening back to the simplest approximation of electron fluctuations through the doubly-excited manifold, we develop the Double Connected Moments (DCM(N)) approximation and evaluate its use in the Connected Moments Expansion (CMX), a realizable approach to the Horn-Weinstein Theorem./nMoving beyond the energy, we then seek to obtain properties of significant pertinence to the physical sciences: the magnetic magnetizabilities and chemical shieldings used to probe problems of all scales. General solutions for the energy of a molecular system in the presence of an externally applied and internally induced magnetic field are presented for arbitrary electronic wavefunctions by explicit consideration of the magnetically dependent Hamiltonian and naturally complexified wavefunction. While the analytic approach has advantages in computational tractability, the availability of their use for modern methods becomes an increasingly opaque procedure that may be ultimately fruitless in their descriptions. The general method allows the piloting across a swath of methodologies; towards this, we implement the general method and test the magnetic properties of the regularized orbital-optimized family of solutions in perturbation theory.

Advisor: Head-Gordon, Martin
'Ghost of the Empire': Church, Law, and the Public Sphere, 1350-1650

This dissertation examines how lawyers wrote about the Church as a legal and political actor intheir commentaries on the Spanish Siete Partidas, the feudal law, canon law, Roman civil law, and city statutes. I show that lawyers strategically rejected the vocabulary of ‘statehood’ and ‘sovereignty’ for the Church, but equally allowed it to claim many of the rights and powers of ‘states’ or ‘sovereigns’. The project argues that the Church occupied a middle political space between temporal and ecclesiastical authorities, in which they could take advantage of their influence and exercise power and jurisdiction without the responsibilities of temporal sovereignty. However, this project also argues that the current frameworks for analyzing this peculiar role of the Church—one that approaches the Church from the standpoint of the State and the secular—is insufficient. While acting, administrating, litigating, and even governing, the Church (and jurists) created models for arguments and institutions which would be adapted, adopted, and implemented by other corporate bodies, including the “Modern State”. It was the Church itself which often developed the bureaucratic machinery which would be adopted and copied by late renaissance and early modern states. There is, in other words, a latent medievalism within Early Modern thought and the development of democracy: ghosts abound. Only by returning to the Church and its legal self-understanding of its actions can we hope to understand both medieval and modern politics.

Advisor: Lee, Daniel
Associations between plant hosts and the pathogen X. fastidiosa

Xylella fastidiosa was the first bacterial plant pathogen to have its whole genome sequenced. Nonetheless, the scientific community remains uncertain about the mechanism by which this pathogen causes disease. The determinants of its host range are largely unknown. While as a species, X. fastidiosa can infect hundreds of plants, any individual strain’s potential hosts cannot be predicted. This dissertation is an interrogation of host specificity at three vastly different scales. The research is ordered from broad to narrow, beginning with a global analysis. I computationally modeled pathogen host range using 23 plant genera. Through this breadth, I was able to find correlations between hosts and genome content, as well as make estimates for likely ancestral hosts at internal phylogenetic nodes. My second chapter compares strains from Central America with strains introduced into the United States. I show that the introduced pathogen population is poor at infecting an ancestral host. I also found a suite of genes in the introduced population associated with the host jump. Finally, I dive into disease progression in one host species grown in Northern California, Vitis vinifera. Over the course of three years, I documented how disease development can vary among grapevine cultivars. This final chapter provides suggestions for disease management through more accurate detection of X. fastidiosa in an agricultural setting. The overarching goal of these works is to improve our understanding of the determinants of host specificity and disease progression of the economically and environmentally important pathogen species Xylella fastidiosa.

Advisor: Almeida, Rodrigo PP
Data-driven Planning For Food and Forest Supply Chains under Disruptions

Global disruptions such as the Covid-19 pandemic emphasize the fragile nature of connected supply chains and the impact of these interruptions on our daily lives. The alarming growth in the type and number of disruptions complicates planning efforts and will lead to increased losses in the absence of efficient systems that reduce complexity for policy makers. We investigate food and forestry supply chains, key systems for human survival. In the context of food and agricultural supply chains, disruptions have not only caused bottlenecks in different stages of food supply chains but also exposed the need for holistic solutions to food supply and allocation. To tackle this challenge, we model a two-stage stochastic resource allocation problem with non-linear connectivity costs to capture trade dynamics between countries. We compare model recommendations to historical trade flow data including coffee trade between countries, unveiling the value of centralized planning under potential disruption scenarios against the current practices. In another chapter, we design and incrementally update a data-driven network risk measure that focuses on including the downstream impact of nodes. starting with a hand crafted intuitive risk measure then the Downstream Protection Value (DPV) and finally proposing the Downstream Supply Risk Measure (DSRM). The consistency in results between DPV and DSRM measures is investigated and their limitations are discussed./nAdditionally, the existence of a large variety of food products that are exchanged within and across countries renders the creation of separate policies for each product impractical. To aid this problem, we develop a framework to systematically group different food product supply chains by identifying their structure using world bank trade data between countries for $53$ different products over a period of $25$ years. Decision makers can thus create policies for food networks at the group level instead of individual policies for the large variety of food and agricultural products. In the context of Forestry systems, we extend an existing fuel treatment allocation model to include socio-environmental costs to the objective function. We investigate how to develop efficient action to mitigate future fires (fuel treatment plans) so as to mitigate risk in Forestry supply chains. In the long run, developing an understanding of the impacts of risk and how they can be mitigated will be essential in resolving their lasting and usually devastating impacts to the world ecosystem.

Advisor: Shen, Zuo-Jun (Max)
Towards Robust and Scalable Large Language Models

This dissertation addresses two significant challenges of large language models (LLMs): robustness and scalability. Firstly, we focus on improving large language model robustness through the lens of learning code representations. I highlight our work on ContraCode which learns representations of code that are robust to label-preserving edits. Secondly, we tackle scalability challenges from a systems perspective. We present Checkmate, a system to support training models beyond GPU memory capacity limits through optimal rematerialization. Furthermore, Skyplane, a system that optimizes bulk data transfers between cloud object stores, enables training models on larger pre-training datasets in the cloud. Together, these contributions present a roadmap for enhancing the robustness and scalability of large language models.

Advisor: Stoica, Ion
Gonzalez, Joseph E
Essays on Urban Economics

This dissertation studies the role of urban policies in shaping local housing markets, local labor markets, and transportation decisions. Chapter 1 uses tax records to document the first available evidence on the short-run response of financial capital to the Opportunity Zone (OZ) program, a federal place-based policy that provides tax incentives for capital investments in low-income neighborhoods. My coauthor and I document how OZ investments are spatially concentrated, flow towards areas with improving trends, and are primarily claimed by individuals at the 99th percentile of the national income distribution. In Chapter 2, I explore how real estate investment and home values respond to OZ tax credits. I find that targeted neighborhoods see a significant increase in new development and that local home values appreciate. Nearby areas see an increase in investment as well. Through a spatial-equilibrium model, I find that while the program as implemented had net benefits in terms of home value appreciation, alternative designations of neighborhoods for the tax credit could have substantially improved the program's equity and efficiency. In Chapter 3, my coauthor and I investigate the long-run effects of converting buildings to high-end condominiums on local home prices, demographics, and new business entry. We find that owners who are able to change their building’s legal status to condominium tend to see their property values increase and are more likely to invest in renovations and rent out their units before selling them. Following these conversions, the values of adjacent buildings increase and the surrounding neighborhood gentrifies. Finally, in Chapter 4, my coauthors and I study whether ride-hailing complements or substitutes public transportation by examining the response of Uber ridership to rail expansions. We find that a new station significantly increases Uber ridership nearby, and that this effect decays to zero farther away. In studying the economic effects of urban policies, the chapters of my dissertation point to the value of a layered approach: using granular spatial data; adjusting analyses for neighborhood particularities to estimate the causal impacts of a policy and to account for its indirect effects on nearby areas; and delineating the manner in which public policy effects vary with neighborhood characteristics.

Advisor: Moretti, Enrico
Application of InSAR Analysis to Earthquake-Induced Liquefaction Observations

Liquefaction mapping currently relies on visual analysis of satellite and aerial images that covers the area of interest. Sand boils, lateral spreading and ground settlement are common evidences that researchers look for when identifying liquefaction damaged areas after an earthquake. However, this method can be unreliable. For example, irrelevant objects on images like burnt rice stalks in agricultural field can be misclassified as sand boils. Interferometric Synthetic Aperture Radar (InSAR), as a remote sensing technique, is widely used for characterizing ground displacement with high resolution and accuracy. With the increasing availability of free InSAR data sets and the short return period of modern satellites, co-seismic InSAR analysis becomes possible shortly after the occurrence of an earthquake. Therefore, InSAR is well suited for analyzing lateral and vertical displacement caused by liquefaction and works as a reference for field reconnaissance teams. In this thesis three earthquake case histories with significant liquefaction damage, specifically the 2010-2011 Canterbury Earthquake Sequence, including 2010 Darfield Earthquake and 2011 Christchurch Earthquake, the 2016 Kumamoto Earthquake, and the 2018 Hokkaido Eastern Iburi Earthquake are analyzed with four different InSAR methods: differential InSAR analysis, persistent scatter time series analysis, coherence-based method and pixel (dense) offset analysis. The co-seismic displacement results are converted into vertical and horizontal displacements to compare with field observations from reconnaissances right after these earthquakes. After comparing the effectiveness of the different methods and different satellites at each site, an InSAR workflow for analysing earthquake-induced liquefaction observations is presented.

Advisor: Kayen, Robert
Sitar, Nicholas
Mechanisms of Resilience to Megafire in Californian Wildlife Communities

Anthropogenic pressures continue to shift the patterning and intensity of ecological disturbances globally. These changes in disturbance dynamics threaten conservation efforts of biodiversity in ecosystems around the world. One of the most conspicuous examples of this are novel, rapid changes in global fire regimes that have increased the frequency and severity of wildfires in fire-prone ecosystems. These megafires have the potential to quickly and dramatically change entire landscapes in ways that may impact the natural resilience of ecosystems and their wildlife communities. In this dissertation, I explore the potential mechanisms in which ecological resilience to severe fire may be facilitated and potentially enhanced in Californian wildlife communities, predominantly in oak woodland savannas./nThis work addresses the interaction between principles of resilience and fire disturbance at multiple ecological scales. In Chapter 1, I present an overview of the ways wildfire in California spatially overlaps areas of conservation priority as well as broad categories of land cover types. In Chapters 2-4, I then zoom into a specific case study to address how a specific megafire, the 2018 Mendocino Complex Fire, impacts the resilience of wildlife communities (Chapter 2 and Chapter 4) and the adaptive capacity and behaviors of an ecologically dominant ungulate species (Chapter 3). Specifically, in Chapter 2, I examine the impacts of this fire on the recovery and response of mammal species using an array of trail cameras before, during, and after this fire event. In Chapter 3, I focus on the specific behavioral responses of a single species (Odocoileus hemionus columbianus) to this wildfire event and discuss how these behaviors may influence interspecies interactions at broader ecological scales. Finally, in Chapter 4 I examine the impacts of this wildfire event on broad patterns of species diversity and community assemblages across bird and bat communities using a set of acoustic monitors. Understanding how these qualities of resilience are realized, and potentially enhanced by human action, will become increasingly critical as fire regimes across the state, and globally, continue to change in response to anthropogenic pressures.

Advisor: Brashares, Justin
Glaciers retreat, frogs advance: rapid adaptation, genetic drift, and infection dynamics during the climate-driven range expansion of an Andean frog

All over the world, species are shifting their geographic distributions in response to climate change. We know from studies of other range expansion and contraction scenarios—like those driven by species invasions or by the end of the last Ice Age—that they affect ecological interactions and the evolutionary trajectories of species. The impacts of range expansions and contractions driven specifically by contemporary climate change, though, are still profoundly uncertain. Understanding these ecological and evolutionary impacts will be important for biodiversity conservation and management under rapid global change. To address this critical knowledge gap, I combine fieldwork, molecular work, and analyses of population genetics and disease ecology over the course of three dissertation chapters. My research illuminates how the whole genome diversity, genetic adaptation, and infection dynamics of a host species were affected by its climate-driven range expansion. Specifically, my dissertation examines the expansion of the Marbled four-eyed frog (Pleurodema marmoratum) into upslope, mountain pass habitat that deglaciated over the last 150 years in the Cordillera Vilcanota, Peru.

Advisor: Gillespie, Rosemary G
The Method of Distributions for Random Ordinary Differential Equations

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./nImplementing 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.

Advisor: Evans, Steven N
DeWeese, Michael R
Advances in X-ray Scattering Interferometry and Liquid Chromatography Coupled Small Angle X-Ray Scattering Towards Exploring DNA-Protein Interactions and Bionanoengineering

This dissertation demonstrates the development and application of advanced X-ray scattering techniques toward the exploration of DNA-protein interactions and engineering bionanotechnology. Herein I describe the development of size exclusion chromatography-coupled small-angle X-ray scattering with in-line multi-angle light scattering (SEC-SAXS-MALS) and X-ray scattering interferometry (XSI). SEC-SAXS-MALS is a high-throughput, multimodal structural biology approach with integrated purification whereas XSI leverages the interference pattern between ordered gold nanoparticles (AuNPs) to calculate discrete inter-AuNP distances, effectively turning them into molecular rulers. I demonstrate the application of these techniques in the study of the single-strand break (SSB) DNA-damage repair meiotic recombination 11 (MRE11) nuclease activity, poly(ADP-ribose) polymerase 1 (PARP-1) damage recognition, activation, and inhibition, and the dynamics of the DNA-dependent protein kinase holoenzyme DNA-PK complex. Additionally, I show the versatility of these techniques in bionanoengineering by exploring the surface adsorbed morphology and nanosensing mechanisms of DNA‐functionalized single‐walled carbon nanotubes (ssDNA‐SWCNTs), and the innovation of RuBisCO assemblies. Finally, I show how these techniques provide high-throughput capabilities that enable rapid disaster responses by characterizing the Nsp7, Nsp8, and Nsp12 complexes of SARS-CoV-2 involved in replication, and the exploration of rigid monoclonal antibodies to improve detection of the SARS-CoV-2 nucleocapsid.

Advisor: Hura, Greg L
Landry, Markita P