VictorJuiz/thesis huggingface dataset:
Adaptive capacity to climate change

Our climate is changing. Although the extent and rate of change are still uncertain, it is abundantly clear that the climate of the future will not resemble the climate of the past and will pose significant risks to people around the world. How well people adapt to address these risks will be determined by their adaptive capacity, their ability to instigate and implement change. Understanding and building adaptive capacity may therefore be key to reducing long-term vulnerability to global change. This dissertation clarifies the concept of adaptive capacity, synthesizes the substantial but largely unconnected body of research on adaptive capacity to date, and introduces a new methodological approach to conducting meta-analyses of adaptation science. I express a definition of adaptive capacity in mathematical terms that summarizes current theories on how adaptability is built, ties the concept to related concepts in adaptation, and poses questions about theoretical limits and thresholds for adaptation. I apply computational text analysis and network analysis tools to develop a concept model of adaptive capacity that identifies and organizes 158 determinants of adaptive capacity into 8 categories according to the functional role they play in building capacity. I propose a modular theory of adaptive capacity, in which all eight functional categories are critical but multiple pathways exist to achieve each function. This modular theory reconciles a theoretical debate in the literature and connects insights from existing theories with empirical findings from the field. I propose a new framework, the Adaptive Capacities Framework (ACF), based on the eight functional categories, that enables assessment of adaptive capacity across scales and within multi-scalar systems. Results demonstrate the fragmented nature of adaptive capacity research to date and propose new directions for future research. The dissertation also provides insights for practitioners seeking to prioritize adaptation efforts.

Advisor: Caldwell Margaret R.
Fischer Martin
Algee-Hewitt Mark 
Matson P. A. (Pamela A.)
Advances in magnetic resonance imaging near metal

It is estimated that 2.4% of the US population has an artificial hip or knee as of 2010, and the prevalence of metallic implants continues to grow. Current imaging evaluation of complications near implants relies on X-Ray, which has limited contrast in soft tissues and thus limited sensitivity for early disease. Magnetic Resonance Imaging (MRI) is a safe medical imaging modality known for its excellent soft tissue contrast, which could be a useful tool for accurate, early and non-invasive assessment of complications. However, severe magnetic field variations induced by metal often render conventional MRI techniques non-diagnostic near the implants. Multi-spectral imaging (MSI) techniques resolve metal-induced field perturbations, but they suffer from long scan times that delay their widespread clinical adoption, and residual frequency-encoding artifacts that cause resolution loss close to the metal. This dissertation focuses on techniques to improve MRI near metal in terms of scan efficiency, artifact correction and delineation of implants. First, a signal model of MSI was introduced to compactly represent the signal distribution in the spectral dimension and enable accelerations of MSI scans. The model-based reconstruction was demonstrated to provide 3-fold acceleration beyond conventional acceleration techniques including parallel imaging and partial Fourier reconstruction. Next, a deep-learning-based reconstruction was presented to reduce the reconstruction times of optimization-based reconstruction and improve the reconstructed image quality of accelerated imaging near metal. Then, the frequency-encoding artifacts induced by metal, including signal hyper-intensities, signal oscillations and resolution loss, were analyzed and alternating-gradient MSI acquisitions were introduced to correct these artifacts. Finally, a method for susceptibility mapping inside signal voids was presented to delineate the geometry and material of metallic implants.

Keywords: Electrical Engineering.
Advisor: Hargreaves Brian Andrew 
Nishimura Dwight George 
Pauly John (John M.)
Advancing ab initio electronic structure theory and computational methods for excited state molecular dynamics

Photochemistry studies chemical reactions caused by absorption of light. Developing theoretical and computational tools for photochemistry will not only help better understand photochemical processes such as photosynthesis and vision, but can also provide guidelines about how molecular photodevices can be better designed. Therefore, the goal of my graduate research is to develop a set of computational tools for studying photochemical processes. Physical systems have a hierarchical structure, i.e. basic particles like nuclei and electrons interact leading to the formation of molecules, and molecules interact and change conformations giving rise to chemical reactions. Naturally, the corresponding theoretical methods should also follow this hierarchy. At the bottom level, we need molecular integrals to describe different types of interactions between basic particles. I introduced the automated code engine (ACE) that generates optimized codes for computing integrals on the graphical processing units, and developed several variants of tensor hyper-contraction (THC) approximations. ACE reduces the computational prefactor of integral evaluations whereas THC reduces the formal scaling. On top of the integrals, we then need electronic structure methods to describe the energies and forces for a molecule at any given nuclear configuration; including electron correlation is the key to having an accurate description. Here, I first developed single reference THC-MP2 to capture the dynamic correlations, and then developed multi-reference THC-CASPT2 method to incorporate static correlations simultaneously. These methods were later generalized to THC-MSPT2 to enable descriptions for excited states and conical intersections, both are critical for photochemistry. Finally, given the electronic structure methods, we then need methods to explore the potential energy surfaces. In particular, critical point search methods locate the important configurations (e.g. Franck-Condon point, conical intersections), while molecular dynamics methods generate trajectories describing how the molecules move and interact with each other. By interfacing the electronic structure methods that I developed with the geomeTRIC geometry optimizer and G-AIMS non-adiabatic dynamics framework, a complete toolbox for understanding photochemistry is provided.

Keywords: Chemistry.
Advisor: Martinez Todd J. (Todd Joseph)
Markland Thomas E.
Solomon Edward I.
Advancing utilization of positron emission tomography for quantifying fluid transport and CO₂ trapping in geologic porous media

Accurate observations and descriptions of the role of heterogeneity on water and CO2 transport and immobilization in porous and fractured geologic media are important for understanding and modeling multiphase conditions present in geologic carbon storage reservoirs. The growth in in-situ imaging has lead to remarkable advancements in understanding and quantification of this complex fluid transport behavior. Despite these advancements, commonly used imaging and experimental methods such as clinical computed tomography, micro computed tomography, nuclear magnetic resonance, and optical imaged micromodels each have limitations. Each modality faces individual challenges with observing fluid advection, dispersion, and diffusion, in 3D geologic porous media. In this work, micro-positron emission tomography (micro-PET), in combination with other imaging methods, is utilized to study a number of challenging transport problems in earth science.

Keywords: Energy Resources Engineering.
Advisor: Benson Sally 
Horne Roland N. 
Kovscek Anthony R. (Anthony Robert)
Advancing x-ray diffuse scattering to probe protein dynamics

Proteins are frequently characterized as molecular machines, with atomic-level motions driving biological function. Past decades have witnessed a dramatic increase in the tools available to probe these dynamics, but few methods enable us to resolve these collective motions with high spatial resolution. This dissertation investigates the potential of x-ray diffuse scattering from protein crystals to meet this critical need. Specifically, I review the models of correlated disorder that have previously been suggested to account for this signal and describe algorithms for processing the diffuse scattering in experimental diffraction data. These models and algorithms are applied to dissect the physical origins of the diffuse scattering observed from three protein crystals. Though considerable progress is still required for the analysis of diffuse scattering to become a routine biophysical method for studying protein dynamics, the framework and findings described in this dissertation make concrete steps toward that end.

Keywords: Biochemistry.
Advisor: Herschlag Daniel 
Pande Vijay 
Das Rhiju
Harbury Pehr
Aerodynamic interactions in arrays of vertical-axis wind turbines

Understanding the aerodynamic interactions between turbines in a wind farm is essential for maximizing power generation. In contrast to horizontal-axis wind turbines (HAWTs), for which wake interactions between turbines in arrays must be minimized to prevent performance losses, vertical-axis wind turbines (VAWTs) in arrays have demonstrated beneficial interactions that can result in net power output greater than that of turbines in isolation. These synergistic interactions have been observed in previous numerical simulations, laboratory experiments, and field work. This dissertation builds on previous work by identifying the aerodynamic mechanisms that result in beneficial turbine-turbine interactions and providing insights into potential wind farm optimization. The experimental data presented indicates increased power production of downstream VAWTs when positioned offset from the wake of upstream turbines. Comparison with three-dimensional, three-component flow measurements demonstrates that this enhancement is due to flow acceleration adjacent to the upstream turbine, which increase the incident freestream velocity on appropriately positioned downstream turbines. A low-order model combining potential flow and actuator disk theory accurately captures this effect. Laboratory and field experiments were used to validate the model's predictive capabilities, and an evolutionary algorithm was deployed to investigate array optimization. Furthermore, changes in upstream turbine performance are related to variations in the surrounding flow field due to the presence of the downstream rotor. Finally, three-dimensional vortex interactions behind pairs of VAWTs are observed to replenish momentum in the array's wake. These effects are described along with their implications for wind farm design.

Keywords: Mechanical Engineering.
Advisor: Dabiri John O. (John Oluseun)
Eaton John K. 
Ouellette Nicholas (Nicholas Testroet)
Affective influences on consumer decision making under uncertainty

This dissertation investigates various affective influences on decision making under risk and uncertainty. The first essay of the dissertation shows that the relationship between physiological arousal and risk-taking is more nuanced than previously found. The results demonstrate that lower levels of physiological arousal increase sensitivity to expected values of risky prospects, leading to more adaptive decisions, instead of increasing or decreasing risk seeking across the board. The second essay of the dissertation elucidates how anticipated guilt can lead to choices of uncertain options over certain ones, establishing how choosing uncertain outcomes can serve as a guilt reduction mechanism. Finally, the third essay investigates neural affective correlates of consumer disengagement from consumption episodes that have uncertain rewards that are known only as they unfold over time and exhibits how data from a neural focus group can improve forecasts of market-level behavior.

Keywords: Business.
Advisor: Shiv Baba
Khan Uzma Aslam
Knutson Brian
Aggregated Model Spaces

In many practical applications, machine learning is divided over multiple agents, where each agent learns a different task and/or learns from a different dataset. We present Aggregated Model Spaces (AMS), a framework for learning a global model by aggregating local learnings performed by each agent. Our approach forgoes sharing of data between agents, makes no assumptions on the distribution of data across agents, and requires minimal communication between agents. We empirically validate our techniques on MNIST experiments and discuss how AMS can generalize to a wide range of problem settings, including federated averaging and catastrophic forgetting. We believe our framework to be among the first to lay out a general methodology for “combining” distinct models.

Keywords:  Department of Computer Science
Machine Learning
Privacy
Knowledge Aggregation
Computer Science
Thesis
Advisor: Sahami Mehran
Algorithmic mirrors : an examination of how personalized recommendations can shape self-perceptions and reinforce gender stereotypes

With the growing prevalence of algorithmic decision-making, scholars have become increasingly concerned about algorithmic bias, or discriminatory differences in algorithmic decisions based one's identity. The present study examines that the extent that algorithmic bias can affect perceptions of the self, as well as the extent that one's understanding of the system moderates this effect. A total of 117 women were randomly assigned to receive a personalized recommendation for a stereotypical "feminine" (i.e., nurse, librarian) or "masculine" career (i.e., lawyer, chief executive) ostensibly based on their Facebook activity. Participants' a priori beliefs about the system's objectivity, personal data collection, and certainty about how the system worked were measured before they received their recommendation. Participant's self-perceptions of masculinity, leadership ability, and self-confidence were measured after they received their recommendation, along with their beliefs about the cause of the recommendation. Women who received a stereotypical feminine career recommendation reported lower masculinity, lower leadership ability, and lower self-confidence than women who received a stereotypical masculine career. Additionally, women who received a masculine career recommendation and believed the recommendation was based on internal characteristics (i.e., internal locus) were more likely to report greater leadership ability than women who received the same recommendation but believed the recommendation was based on external factors (i.e., external locus). The likelihood of making internal locus signal attributions for their recommendation was greater for people who more actively use Facebook and who believed the system was more objective. Together, the dissertation findings suggest that people's self-perceptions can be influenced by algorithmic recommendations, and this effect is magnified by one's understanding of the system.

Keywords: Communication.
Advisor: Hancock Jeff 
Bailenson Jeremy 
Bernstein Michael S.
Harari Gabriella
Algorithms and lower bounds for parameter-free online learning

Training a machine learning model today involves minimizing a loss function on datasets that are often gigantic, and so almost all practically relevant training algorithms operate in an online manner by reading in small chunks of the data at a time and making updates to the model on-the-fly. As a result, online learning, a popular way to analyze optimization algorithms operating on datastreams, is at the heart of modern machine learning pipelines. In order to converge to the optimal model as quickly as possible, online learning algorithms all require some user-specified parameters that reflect the shape of the loss or statistics of the input data. Examples of such parameters include the size of the gradients of the losses, the distance from some initial model to the optimal model, and the amount of variance in the data, among others. Since the true values for these parameters are often unknown, the practical implementation of online learning algorithms usually involves simply guessing (called ``tuning''), which is both inefficient and inelegant. This motivates the search for parameter-free algorithms that can adapt to these unknown values. Prior algorithms have achieved adaptivity to many different unknown parameters individually - for example one may adapt to an unknown gradient sizes given a known distance to the optimal model, or adapt to the unknown distance given a known bound on gradient size. However, no algorithm could adapt to both parameters simultaneously. This work introduces new lower bounds, algorithms, and analysis techniques for adapting to many parameters at once. We begin by proving a lower bound showing that adapting to both the size of the gradients and distance to optimal model simultaneously is fundamentally much harder than adapting to either individually, and proceed to develop the first algorithm to meet this lower bound, obtaining optimal adaptivity to both parameters at once. We then expand upon this result to design algorithms that adapt to more unknown parameters, including the variance of the data, different methods for measuring distances, and upper or lower bounds on the second derivative of the loss. We obtain these results by developing new techniques that convert non-parameter-free optimization algorithms into parameter-free algorithms. In addition to providing new and more adaptive algorithms, the relative simplicity of non-parameter-free algorithms allows these techniques to significantly reduce the complexity of many prior analyses.

Advisor: Boahen Kwabena (Kwabena Adu) 
Liang Percy 
Boneh Dan
Duchi John 
Sidford Aaron
Algorithms for real-time time-dependent density functional theory and calculation of phase diagrams for two-dimensional phase-change materials

Two-dimensional materials have found use in a wide range of engineering applications, owing in part to their unique properties not found in the bulk and their potential to enable fabrication of devices that approach atomic sizes. The inherently quantum mechanical nature of 2D materials can be treated using density functional theory (DFT)-based methods, with some limitations that can in principle be addressed with the time-dependent formulation of DFT, known as time-dependent density functional theory (TDDFT). Here, we explore the use of DFT and TDDFT for applications of 2D materials, with two primary focuses: (1) the implementation and analysis of numerical integration schemes for time-domain TDDFT and associated applications to 2D and bulk materials properties and (2) the application of ground-state DFT to the study of thermodynamic properties of 2D materials that undergo structural phase transformations under electrostatic gating. Towards focus 1, we have implemented a variety of implicit and explicit integration schemes in the context of plane-wave real-time TDDFT and assessed the accuracy, stability, and computational cost of these methods. We find that for plane-waves, high-order explicit multi-step methods, including Adams-Bashforth and Adams-Bashforth-Moulton methods, outperform commonly-used methods including Crank-Nicolson and Runge-Kutta. We have developed a code, called rt-tddft, as an extension to the popular Quantum ESPRESSO code, which will enable researchers performing ground-state plane-wave DFT calculations to immediately benefit from our time-domain integration schemes. The code is written in object-oriented Fortran and is designed using object-oriented design patterns and built-in testing. We also apply the code to a new time-domain formulation of the adiabatic connection fluctuation-dissipation theorem (ACFDT) to compute electron correlation energies, an application especially relevant to van der Waals interactions in 2D materials systems. Towards focus 2, we have developed modeling methods to predict the phase transformation properties and phase diagrams of electrostatically-gated 2D materials. Using DFT-based calculations, we are able to predict the critical charge and critical voltage required to induce a structural phase transformation in a variety of 2D materials, and also determine how these critical values change with temperature. Using these methods, we are able to map out detailed phase diagrams for different 2D materials systems. In addition, we study the potential to use the electrostatic gating mechanism to absorb heat from the surroundings, an effect we term the electrostaticaloric effect. The methods we develop are useful for a variety of realistic engineering applications, including the use of 2D materials for applications in phase-change memory, controllable atomic-scale heat absorbers, and atomic sensors.

Keywords: Mechanical Engineering.
Advisor: Cai Wei
Reed Evan J. 
Lindenberg Aaron Michael 
Pop Eric 
Salleo Alberto
American-Russian Cultural Encounter in Siberia (1918-1920)

From August 1918 to April 1920, over 9,500 American soldiers and aid workers were in Siberia as part of the Allied intervention in the Russian Civil War. Half of these soldiers came from the Phillippines, and half from the temporary Army base at Camp Fremont, CA in present day Menlo Park and Stanford University. The soldiers in Siberia had a neutral military mission, guarded the Trans-Siberian railroad, and lived in close proximity to local Russians. Consequently, there was an American-Russian cultural encounter, recorded in dozens articles, diaries, letters, and notes of Americans who were in Siberia.
Through everyday close contact, Americans evidenced more understanding of Russians. They remembered the generosity and hospitality of their hosts, the humbling experience of communicating in another language, and the differences and similarities in rituals like Easter celebrations, weddings, and going to the banya. American society plunged into the depths of the first Red Scare and America and Russia grew further apart. However, in contrast to their society back home, American soldiers and volunteer workers in Siberia bridged the gap in cultural understanding through their experiences, resulting in a shift in their perceptions of Russian identities, in communicating and building relationships with Russians, and in observing and participating in Russian rituals. In contrast to what has been called a civilizational or imperial lens, and a dialogue of superiority and inferiority, Americans displayed increasingly positive impressions of Russians the closer they were to them, while negatively perceived differences were greatest when encounters were brief or at a distance.

Keywords:  Stanford Global Studies
Stanford University
Stanford Global Studies
Center for Russian
East European
and Eurasian Studies
Thesis
Advisor: Knezevic Jovana
Patenaude Bertrand