VictorJuiz/thesis huggingface dataset:
Viscoelastic numerical modeling and deep learning for seismic reservoir characterization

In this thesis, numerical modeling and deep-learning techniques are used to tackle the complex problem of subsurface reservoir characterization using geophysical measurements. The first part of the dissertation is focused on understanding and numerically modeling rocks as viscoelastic materials. Rocks modeled using viscoelastic theory helped in understanding coupled fluid-solid interaction effects at the pore scale that cannot be modeled using elasticity theory. Modeling scale effects of heterogeneities in layered viscoelastic media confirmed the dependence of seismic velocities and intrinsic attenuation on the ratio of dominant wavelength to length spacing. A viscoelastic upscaling method was developed that helped in integration of seismic data considering viscoelastic information measured at different frequencies. The second part of the dissertation is focused on using deep learning for quantitative seismic interpretation. In this part, solutions to quantitative seismic interpretation problems were learned by a deep network directly from the data. Convolutional neural networks (CNNs) used in this work were found to capture spatial patterns and were relatively easier to design and train as compared to other deep learning architectures. The success of CNN based network architectures in solving geophysical problems were demonstrated using four different seismic reservoir characterization problems -- 1. Acoustic impedance inversion from post-stack seismic data, 2. P-wave velocity, S-wave velocity and density inversion from pre-stack seismic data, 3. Petrophysical properties (porosity and volume of clay) predictions from pre-stack seismic data, and 4. Amplitude variation with offset (AVO) classification from pre-stack seismic data. The network architectures developed in this dissertation serve as a benchmark for future deep network architectures (more sophisticated ones) that are foreseen to be developed to solve similar problems with greater accuracy.

Keywords: Geophysics.
Advisor: Mukerji Tapan
Biondi Biondo
Mavko Gary
Visual design tools in support of novice creativity

Today, there is a proliferation of tools to meet the growing and evolving needs of professional designers. However, professional designers are not the only people participating in design. Novice designers are also increasingly taking part by creating flyers, websites, and presentations, but are forced to do so using either professional design tools or simplistic template-based tools. There exists an opportunity with these users to create design tools that cater to their specific abilities and tasks. In this dissertation, I describe some fundamental work I undertook to understand novices' ability to recognize visual appeal. I then discuss four different design tools I developed to assist novices in their creative tasks. The first of these tools, Poirot, a web inspector tool, was built to help designers make changes to websites as they complete their work as novice end-user programmers. In an evaluation of Poirot compared to Chrome DevTools, Poirot led to higher task completion rates, faster task completion times, a lower perceived cognitive load, and was preferred by designers. The three remaining tools are a series of user-steered, generative design tools, a new category of visual design tools that attempt to combine the natural abilities of novice designers with the power of computational design. The purpose of these tools is to allow novice designers to take greater part in the design process, experience an increase in their perceived creativity, and produce unique, high-quality designs all without increasing the burden of using the design tool. We formally evaluated the final of these design tools, DesignQ, a user-steered, generative design tool for creating flyers. Compared to Canva—a popular novice flyer design tool— novice designers felt better supported in their creative flyer design task and their perceived cognitive load was lower when using DesignQ, while there was no significant difference in the quality of the designs produced.

Advisor: Landay James A.
Bernstein Michael S.
Follmer Sean
Vortex arrays and chaotic mixing by swimming invertebrate larvae

The first animals evolved in the oceans, which formed the context for evolutionary innovations spanning from the first specialized locomotory appendages, to the first nervous systems. Fluid dynamical constraints thus provide insight into biomechanical adaptations in early animals, illustrating how physical forces shaped the diversity of early body plans and life history strategies. This thesis seeks to bridge the existing body of work on the locomotion of microorganisms at low Reynolds numbers, with broader questions related to the origin of multicellularity and the invertebrate nervous systems. First, I review the existing literature on the biophysics of cilia and flagella, the fundamental cellular organelles that enable locomotion at low Reynolds numbers. This review specially focuses on the different modeling paradigms that apply across scales---from singularity-type hydrodynamic models describing single flagella, to topological models describing large ensembles of interacting cilia. Next, I describe a new type of cilia-driven swimming behavior that we recently observed in starfish larvae. This swimming gait involves the formation of dense vortex arrays around the swimming animal, which we find entrain and capture edible algae, the animals' primary food source. I describe how this adaptation is novel among known low-Reynolds number swimmers, and how it is enabled by the larvae's unique ability to control its cilia using its nervous system. Next, I discuss more broadly the appearance and role of neuronally-controlled ciliary bands in the swimming behavior of invertebrates. I highlight an unusual behavioral phenomenon occurring in many invertebrates, wherein the animal periodically and rapidly modulates its global ciliary beat pattern, producing substantial changes in the the larval flow field. Using a combination of hydrodynamic experiments and theoretical models, I show that these modulations are sufficient to induce strong chaotic mixing of the local flow field, further enhancing the organism's feeding rate.

Keywords: Applied Physics.
Advisor: Prakash Manu 
Dabiri John O. (John Oluseun)
Greenleaf William James
War Power Moves: Executive Incentives for Unilateral Action

Mounting anxieties about executive overreach in the Global War on Terrorism have reinvigorated interest in the study of presidential war powers. In historical perspective, presidents have frequently directed the use of military force without explicit permission from Congress. Yet, presidents still court legislative approval on select occasions. Why do presidents sometimes seek congressional authorization and other times -- indeed, most of the time -- do not? To investigate the puzzle, this thesis develops a two-actor game with two decision nodes; analyzes historical data of U.S. force deployments from 1798 to 2000; and, leveraging archival sources, conducts case studies of the Korean War, Vietnam War, and Persian Gulf War. The research finds that a president’s decision to take unilateral military action rests along three dimensions: credibility, legislative limits, and military capabilities. In general, as the costs along these dimensions decrease, the president is more inclined to use military force without congressional approval. Overall, however, the principal issue the president faces when assessing the costs of unilateral action is how he expects the pivotal member of Congress to behave.

Keywords: Center for International Security and Cooperation
Presidency
U.S. Foreign Policy
War Powers
Thesis
Advisor: Rice Condoleezza
Schultz Kenneth A.
Rakove Robert B.
Waves and oscillations in a coupled conduit-crack system, applications in volcanoes and hydrocarbon reservoirs

Coupled conduit-crack systems are ubiquitous beneath active volcanoes and in hydrocarbon reservoirs. Understanding the geometry and fluid properties in such systems is critical to evaluating volcanic hazards and optimizing hydrocarbon recovery. However, geological barriers prevent direct observation of subsurface structures. In the oil industry, man-made sources are placed inside wells with known trajectories to excite tube waves or guided waves along a fluid-filled conduit, which are sensitive to properties of the surrounding formation and fractures intersecting the well. Resonance of guided waves along hydraulic fractures, known as Krauklis waves or crack waves, occurs at specific frequencies; these frequencies and associated attenuation of the resonant modes can be used to constrain fracture geometry. Direct measurements of pressures are available along the well but not possible inside the fractures. Therefore, rigorous treatment of fluid-filled cracks and the interactions between waves in the crack and along the conduit are necessary to better exploit the downhole measurements. At active volcanoes, perturbations, such as rockfalls, earthquakes, and explosions, excite very long period (VLP) (longer than 2 s) oscillations in the plumbing system captured by indirect measurements, such as ground deformation, seismic, and infrasound signals. However, a quantitative understanding of wave propagation and resonance in a coupled conduit-crack system is required to interpret observations. In this thesis, I investigate waves and oscillations in a coupled conduit-crack system and aspire to address challenges faced by both volcanology and oil industry. I perform numerical simulations in 2D and 3D to capture rich resonant modes in such systems, understand the oscillation physics of distinct modes, and identify general controls on their periods and decay characteristics, such as finite crack length, width, crack non-planarity, conduit geometry, fluid viscosity, buoyancy, etc. I establish the link between physical parameters in the coupled system with observable measure- ments, such as downhole pressure data and broadband seismic data on Earth's surface, and develop inversion procedure to obtain the optimal parameters that best explain observations.

Keywords: Geophysics.
Advisor: Dunham Eric 
Mavko Gary
Segall Paul
We here, we bin doing dis, & we een gern nowhere: Expressions of Caribbean Identity Through Twitter Aggregator Accounts

People across the Caribbean diaspora have utilized Twitter to build community across locations, build a collective sense of identity across rich debate on topics of interest, and innovate by adapting and appropriating the affordances of Twitter and other social media platforms. Through vernacular engagements in the digital space Caribbean people express their worldview, and assert themselves in ways that mark the digital space an important site for studying Caribbean identity expression. As an entryway into the use of these tools for identity expression, this work centers aggregator accounts, which produce and collect Caribbean content and channel them into one place, the feed, for consumption by a large number of Caribbean people. This thesis asks: How do aggregator accounts draw upon the technical affordances of the platform, the social culture of the digital age, and Caribbean culture to facilitate Caribbean identity expression? Aggregator accounts are found to both use Twitter sociocultural and Caribbean cultural affordances to express a Caribbean ethos, and to facilitate the use of these tools for users to do the same. Dead simple, all I really talkin’ bout is how we is express ourselves, how accounts like wimatchapp is express Caribbean identity, and how dey is help us express ourselves. Cause if we don’t study us and all the ways we is be dead funny, dead creative, dead expressive who gah study it for us?

Keywords:  The Program in Science
Caribbean
identity expression
sociocultural affordances
Caribbean cultural affordances
aggregator account
Science Technology and Society at Stanford
Thesis
Advisor: Banks Adam J.
Weak supervision from high-level abstractions

The interfaces for interacting with machine learning models are changing. Consider, for example, that while computers run on 1s and 0s, that is no longer the level of abstraction we use to program most computers. Instead, we use higher-level abstractions such as assembly language, high-level languages, or declarative languages to more efficiently convert our objectives into code. Similarly, most machine learning models are trained with "1s and 0s" (individually labeled examples), but we need not limit ourselves to interacting with them at this low level. Instead, we can use higher-level abstractions to more efficiently convert our domain knowledge into the inputs our models require. In this work, we show that weak supervision from high-level abstractions can be used to train high-performance machine learning models. At three different levels of abstraction, we describe the system we built to enable such interaction. We begin with Snorkel, which elevates label generation from a manual process to a programmatic one. With this system, domain experts encode their knowledge in potentially noisy and correlated black-box functions called labeling functions. These functions can then be automatically denoised and applied to unlabeled data to create large training sets quickly. Next, with Fonduer we enable an abstraction one step higher where advanced primitives defined over multiple modalities (visual, textual, structural, and tabular) allow users to programmatically supervise over richly formatted data (e.g., PDFs with tables and formatting). Finally, in BabbleLabble we show that we can even utilize supervision given in the form of natural language explanations, maintaining the benefits of programmatic supervision while removing the burden of writing code. For all of these systems, we demonstrate their effectiveness with empirical results and present real-world use cases where they have enabled rapid development of machine learning applications, including in bio-medicine, commerce, and defense.

Advisor: Ré Christopher 
Jurafsky Dan
Liang Percy
A web-based integration framework over heterogeneous biomedical data and knowledge sources

The biomedical research community has been one of the earliest adopters of Semantic Web technologies and Linked Data principles. Several databases and knowledge bases are published and linked on the Web using these technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. During this age of data and knowledge explosion in biomedicine, Semantic Web technologies and the LSLOD cloud may provide a unique opportunity toward the integration of disparate biomedical data and knowledge stored in isolated repositories. However, in the current state of the LSLOD cloud, it is still very difficult for most biomedical researchers to query and integrate data and knowledge from multiple sources simultaneously. The semantic heterogeneity across the LSLOD cloud makes the task of serendipitously discovering implicit associations illusive. I hypothesize that a Semantic Web pattern-based query federation framework can aid in the integration of multiple disparate, heterogeneous biomedical data and knowledge sources for discovering novel implicit associations in biomedicine serendipitously. I detect and quantify the manifestations of semantic heterogeneity across biomedical ontologies and linked data sources in the LSLOD cloud. I develop the PhLeGrA (Linked Graph Analytics in Pharmacology) framework for heterogeneous biomedical data and knowledge integration, and association discovery. I demonstrate the utility of the PhLeGrA framework to generate a systems pharmacology network composed of drugs, proteins, pathways and phenotypes. In conjunction with this systems pharmacology network, PhLeGrA mines clinical data in spontaneous reporting systems and electronic health records for detecting adverse drug reactions that manifest due to multiple drug intake, and provides explanations on the underlying biological mechanisms. The findings presented in this research will make Semantic Web developers and publishers more aware of the architectural issues associated with mining the LSLOD cloud. The methods that I have developed should enable biomedical researchers to query and integrate data and knowledge from multiple, heterogeneous LSLOD sources for solving complex biomedical problems.

Advisor: Musen Mark A. 
Altman Russ 
Leskovec Jurij 
Polleres Axel
WETTABILITY ALTERATION OF CARBONATES WITH SEAWATER SALINITY BRINES EXPLORED USING A 3D MICROMODEL

This report shows experimental evidence of wettability alteration using seawater salinity brine of an oil-wet system composed by a three-dimensional carbonate micromodel, crude oil, and connate-water brine salinity in a carbonate micromodel. We designed this procedure as a first step for evaluation of using seawater as an Improved Oil Recovery (IOR) agent. Our design combines two main experimental best practices: micromodels for repeatable experiments and X-ray computed tomography (CT) as a non-invasive technique (Akin and Kovscek, 2003) for monitoring multiphase saturation in situ. Both practices merge into a new three-dimensional micromodel set-up.
Wettability alteration plays a key role to improve oil recovery from matrix blocks surrounded by water-invaded fractures in carbonate reservoir rocks. We designed a simple and replicable experimental apparatus and procedure to quantify contact angle distributions inside of porous media with a controlled level of heterogeneity in roughness and mineralogy. This experiment consists of visualizing in-situ contact angle distribution of the aqueous phase inside a three-dimensional carbonate micromodel. Using Micro Computerized Tomography (MicroCT), we obtained three-dimensional images of fluid distribution with an approximately constant resolution of 3.8 microns.
We successfully altered wettability from more oil wet to less water wet conditions. The water contact angle of the ganglia showed a 70% contact angle reduction from an oil-wet to a water-wet system in one micromodel and an 80% contact angle reduction in another. The initial average contact angles were 122.35 and 140.39, respectively. After flooding with a synthetic seawater, the average contact angles declined to 36.24 and 43.69, respectively.

Keywords:  Energy Resources Engineering
School of Earth Energy &amp; Environmental Sciences
Energy Resources Engineering
MicroCT
X-rays
3D segmentation
wettability
seawater
carbonate
offshore
clean energy
micromodel
salinity
petroleum
EOR
wettability ateration
contact angle
pore
porous
Thesis
Advisor: Kovscek Anthony R.
What counts as science writing and who gets to be "good" at it? : pedagogy, ideology, and inequality in high school biology

Writing matters in science education. It can provide opportunities not only for assessment or developing writing skills, but also for learning content and developing students' identities as producers of scientific knowledge. New research and educational standards suggest these goals can be achieved through a variety of activities, including writing activities with real-world audiences and few linguistic constraints. However, history also suggests that pedagogical reforms can have unintended consequences, and that systemic racism and sexism can shape the outcomes of reforms in unanticipated and inequitable ways. To provide all students with equitable opportunities to learn science through writing, researchers must proactively investigate problems that may arise during the implementation of new writing activities. This dissertation uses survey and experimental methods to examine how teachers of biology (the most common science course in U.S. secondary schools) implement, evaluate, provide feedback on, and formulate expectations for student writing. Findings suggest: (1) participants' stated purposes for student writing do not always align with enacted writing pedagogy; (2) participants provided longer feedback on student writing attributed to a non-Hispanic White male compared with the same writing attributed to Hispanic or Latina female; and (3) participants who worked in schools that served primarily White and Asian American students expressed higher expectations for student science writing than colleagues in schools serving primarily Latinx or African American students. Practical and theoretical implications for science educators, researchers, and policymakers are discussed.

Keywords: Education.
Advisor: Ball Arnetha F.
Brown Bryan Anthony 
Martínez Ramón
Rickford John R.
When do gradient methods work well in non-convex learning problems?

Classical learning theory advocates the use of convex losses in machine learning due to its guaranteed computational efficiency, yet recent success in deep learning suggests that highly non-convex losses can often be efficiently minimized by simple gradient-based algorithms, a phenomenon not well explained by existing theory. Towards closing this gap, this thesis proposes and instantiates landscape analyses, a theoretical framework for studying the optimization of non-convex learning problems via analyzing optimization-aware geometric properties of the empirical risk. In the first part of the thesis, we establish a generic connection between the optimization landscape of smooth non-convex empirical risks, namely, that their gradients and Hessians concentrate uniformly around those of the population risk with statistical rate. Consequently, benign landscape properties on the population risk can be carried onto the empirical risk as well when the sample size is sufficiently large. We apply this principle to showing that gradient descent succeeds in solving several non-convex empirical risk minimization problems, including non-convex binary classification (and its sparse version), robust linear regression, and Gaussian mixture estimation. In the second part, we adopt landscape analysis to solving the orthogonal dictionary learning problem via minimizing a non-convex non-smooth L1 risk on the sphere, which was shown to work well empirically but not well understood in theory. Standard uniform convergence analysis fails on this problem as the subgradients are set-valued and non-Lipschitz. We develop tools, building on Hausdorff distance between sets and a novel sign-aware metric customized for the dictionary learning problem, to handle the above issues and establish the uniform convergence of subgradients. We show that the landscape of the non-smooth empirical risk is benign and subgradient descent succeeds in recovering the dictionary with required sample complexity lower than existing approaches based on non-convex optimization

Keywords: Statistics.
Advisor: Duchi John
Candès Emmanuel J. (Emmanuel Jean)
Montanari Andrea
When people's voices matter : examining mini-public deliberation and digital crowdsourcing with machine learning tools

Governments across the world have been increasingly using a variety of forms of public consultation to inform governance and strengthen legitimacy. In some public consultation, people's voices are thoughtful and consequential for policymaking while in other forms of public consultation, people's voices are more likely to be distorted by political interests. This dissertation aims to answer a question (challenge) posed by the latest development in the field deliberative democracy: what institutional designs can make a deliberative system function well and/or badly? To answer this question, I examine in depth, cases of mini-public deliberation and digital crowdsourcing. My dissertation provides building blocks regarding the elements of design for an effective deliberative system and offers suggestive lessons for facilitating a healthier and more impactful public dialogues in both democracies and authoritarianisms. My dissertation also demonstrates how we could leverage machine learning tools to systematically analyze the impact and quality of political dialogues.

Keywords: Communication.
Advisor: Fishkin James S. 
Goel Ashish 
Iyengar Shanto 
Pan Jennifer