VictorJuiz/thesis huggingface dataset:
Meet the Mother: Maternity, Identity, and Agency in The Tale of Genji
Keywords:  Stanford Global Studies
Premodern Japanese Literature
Heian Literature
The Tale of Genji
Motherhood
Agency
Gender
Stanford Center for East Asian Studies
Thesis

The Tale of Genji, the 11th century classic work of Japanese literature, has never ceased to be the focus of scholars’ curiosity and exploration. Despite the inclination of contemporary readers to reflect ourselves in the premodern figures who are almost a thousand years away from us—to be confounded by the notorious affairs of the protagonist and sympathize with the female characters—we witness the recent effort of researchers to detach our standard understanding of agency and gender from the study of premodern Japanese literature. This thesis follows the path and aims to complicate the previously constructed definitions of agency/passivity, freedom/restriction, and empowerment/victimization. This study mainly investigates the following three elements in the tale: the mother figures, the fluidity of roles and privileges, and the female characters’ agency. In general, the analysis shows multiple possibilities of interpreting the story.
Mesostructure design and analysis of additive manufactured polymers
Keywords: Aeronautics and Astronautics.

Additive manufacturing (AM) is revolutionizing how designers, engineers, manufacturers, and end-users create, fabricate, and interact with products. It enables the fabrication of complex parts with novel mechanical, thermal, electrical, and optical properties not found in any natural materials. In addition, AM has spurred development of novel design and analysis tools that can handle the extreme intricacy of AM parts. By combining powerful computational tools with the rapid turnaround of AM, new products can be created at breakneck speeds. However, there are still several barriers to widespread adoption of AM technologies, including a lack of reliable mechanical models. This research investigates the most widespread AM process: material extrusion of polymers. Despite its extremely low cost, material extrusion is rarely used for production-grade parts due to its high anisotropy. Being able to predict the anisotropy will lead to greater adoption. However, existing mechanical models for material extrusion parts tend to be empirical or numerical without explaining the fundamental origin of observed anisotropy, leading to results that differ from printer to printer. This work proposes a geometric explanation for the anisotropic mechanical properties found in material extrusion parts. First, this work investigates the mesostructure of material extrusion parts and provides a parameterization for the void geometry. Next, a closed-form constitutive model is developed based on the void geometry and porosity. Numerical and empirical studies conducted show the closed-form model is accurate within 10% for porosity values below 10%. Finally, the model is used to develop a hybrid stress-aligned/concentric infill scheme that accounts for optimal print path orientation as well as printability constraints. The hybrid infill scheme is evaluated against a baseline rectilinear infill scheme for the classical open hole notch test, yielding 9% higher stiffness and 8% higher strength than the baseline rectilinear infill
Methods and metrics for efficient video object detection
Keywords: Electrical Engineering.

Understanding video contents by object detection is an essential task for machine intelligence. Video is an abundant data source, yet detecting objects from videos is computationally intensive or even prohibitive on edge devices. We start with efficient video object detection methods, which require fewer average FLOPs per video frame. We also find out that current widely adopted metrics do not fully describe a video object detector; therefore, we propose new metrics that measure the swiftness of an algorithm. By combining the proposed methods and evaluating them with the new metrics, we design a video object detection system that achieves high average accuracy, low response time, and a balanced workload under a limited computation budget. Two efficient methods for video object detection described in this thesis are CaTDet, which reduces the spatial area of detection, and PatchNet, which reduces the detection frequency with little accuracy drop. Experiments on multiple datasets show that CaTDet and PatchNet can reduce computation by 3.8x-13.0x and 3.4-4.9x, respectively. Combining CaTDet and PatchNet, we design SwiftDet, which performs joint spatial-temporal reduction and reduces FLOPs by 6.0x on ImageNet for Faster R-CNN ResNet-101. Evaluation with average delay and average latency further demonstrates that SwiftDet achieves a lower response time than CaTDet or PatchNet alone
Methods for imaging brain function using BOLD and viscoelastic contrast
Keywords: Bioengineering.

Modern functional neuroimaging techniques have greatly advanced the field of neuroscience, with different modalities targeting distinctive aspects of brain physiology, functional architecture, and dynamics. Specifically, functional magnetic resonance imaging (fMRI) uses blood-oxygen-level dependent (BOLD) contrast to study the underlying neurovascular coupling. One of the limitations of fMRI is its low temporal resolution for collecting the time series data, which has been partially addressed in human brain imaging with the development of parallel imaging. However, parallel imaging is not attainable in some cases due to infeasibility of high-density phased-array coils. To overcome this, we developed an approach to accelerate fMRI acquisition without parallel imaging by adapting phase-offset multiplanar (POMP) imaging to echo-planar imaging (EPI), using gradient blips inspired by CAIPI to shift each of the simultaneously excited slices into different regions of an extended field-of-view, such that there is no aliasing of the simultaneously excited slices. Since fMRI only provides an indirect measure of neuronal activity, the neuroimaging community has been searching for different contrast mechanisms that can further elucidate the process of neuronal signaling. A new and emerging functional neuroimaging technique, functional magnetic resonance elastography (fMRE), measures changes in brain stiffness due to neural activity. We introduce a novel multi-modal method, fMRI-fMRE, to observe this neuromechanical coupling mechanism in the human brain. The novelty in our approach comes from utilizing a time series acquisition, similar in concept to that employed in fMRI, where the magnitude and phase information from the time series are processed and analyzed separately to arrive at both fMRI and fMRE activation maps. Since our simultaneous fMRI-fMRE method is both multi-modal and concurrent, it allows for comparison of the spatially localized BOLD and stiffness changes within the same scan, removing confounds inherent in separately acquired scans. Motivated by early reports that fMRE could provide an order of magnitude increase in temporal resolution, we initially tested this method with partial-brain scans, demonstrating stiffness increases in the visual cortex in response to visual stimuli. Subsequently, we extended the fMRI-fMRE method to whole-brain imaging to explore global brain stiffness dynamics in response to both visual and motor-planning tasks. The end of my thesis journey coincided with the COVID-19 pandemic, which led to widespread incidence of infection. To diminish the risk of virus transmission, many research MRI scanning facilities started to require continuous facial covering by scan subjects. Since mask-wearing can slightly change the carbon dioxide concentration in inspired air, and carbon dioxide is a potent vasodilator, the final project in this thesis investigates the effect of facial covering on fMRI BOLD signal
Methods for leveraging social media data to quantify and improve pharmacovigilance efforts
Keywords: Medicine. Department of Biomedical Data Science

Adverse drug reactions impact the health of 100,000s of individuals annually in the United States with associated costs in the hundreds of billions. Pharmacovigilance seeks to improve drug safety and limit patient potential for adverse drug events. Social media has been identified as a promising potential source of information for pharmacovigilance. The adoption of social media data as a component of pharmacovigilance efforts has been hindered by the massive and noisy nature of the data. I present work seeking to (1) identify social media data discussions of drug and drug-related terms, (2) use word embeddings derived from social media data to create quantitative severity scores for adverse drug reactions, and (3) create digital cohorts of social media users and monitor them for changes in prevalence of drug discussion rates in the general US potential, specifically with regards opioids. I present novel methods that enabled these efforts, as well as the results and findings of these efforts. I believe leveraging social media data to enhance pharmacovigilance will benefit U.S. patient populations and further empower public health and regulatory efforts
Methods to visualize, dissect and simulate complex cellular behaviors
Keywords: Bioengineering.

When we consider a problem, like a disease, our instinct is to find the source of the issue and fix it. For some diseases we encounter this is possible because we can identify a single trackable cause of the issue. For example, in the mid-1800's surgeons realized that they could prevent bacterial infections and death in patients by simply washing their hands and wearing gloves during surgery. But for other diseases, like diabetes or schizophrenia the 'cause' is not so evident, and work over time has taught us that these diseases are systemic. It's only through fully understanding the underlying system that we can tackle the disease. Systems large and small have been found to underlie all of biology, not just complex diseases. It's not enough to understand how proteins or pathways or cells work in isolation. If we really want to understand them, we need to piece together how they work as a whole -- as a system. My thesis work is a story of systems on different scales: from a pathway, to a cell, to a population of cells. I will first discuss work we did to understand how increasing stimulus into a pathway is able to produce non-monotonic outputs. Next, I report efforts to experimentally validate a central prediction that arose out of our lab's recent Escherichia coli whole-cell modeling project: sub-generational gene expression. I will then discuss how revelations from this work led to my most recent work, adding operon structure into the whole-cell model. And finally, I will discuss plans we have to experimentally determine the import of this architecture inensuring population survival, and perspectives on the future of model-driven discovery. Although these systems all seem different, they connect through common themes including the presence of emergent phenomena and the need for methods to visualize, dissect and simulate these complex cellular behaviors
The microbial ecology and biogeochemistry of alluvial subsurface systems
Keywords: Environmental Earth System Science.

At the intersection of terrestrial and aquatic environments, alluvial landscapes link up-land, riverine, and subsurface ecosystems. Floodplains and associated alluvial aquifers are hotspots of the biogeochemical processes influencing surface water and groundwater quality. The microbial communities within these zones are often drivers of these processes, and thus their structure and function may affect water quality. Currently, depth-resolved information about the structure and function of microbial communities within floodplains and their impacts on alluvial aquifers is limited. However, such information is critical for understanding biogeochemical cycles and how elemental stores are altered from perturbations initiated by the water cycle within floodplains. Surveyed from the surface to the sediment-bedrock interface, this dissertation illuminates temporal and depth-specific trends in microbial community dynamics within floodplains of the Western U.S. Here I present results from multiple field projects throughout the Western U.S. sampled under different hydrologic conditions. This work employs complementary molecular approaches in a field-based study, where the microbial ecology and geochemistry of key biogeochemical cycles (N, Fe, S, C) are explored. Through concurrent lines of geochemical and microbial evidence, spatiotemporal in-sights on subsurface microbial dynamics over a record flooding event are presented. Semiarid, intermontane cores (down to 6m depth) sampled under drought conditions in the Western U.S. and over drought-to-flood conditions on the Wind River-Little Wind River floodplain at Riverton, WY. Pairing depth-resolved high-throughput sequencing with detailed geochemical measurements for more than 250 samples from five sites, I identified hydrologic regime transitions drove the development of microbial niches and geochemical redox-stratification patterns within the vertical profiles examined. These findings suggest that transitions from unsaturated to saturated conditions coincided with major changes in microbial diversity and community stability, supporting trends in spatiotemporal succession that hold possible microbial and geochemical implications for subsurface systems and riverine water quality extending beyond this work
Microfoundations and measurement for ambiguity in communication with application to social networks
Keywords: Business.

Extant research often models ambiguity — that one object can have multiple interpretations — as "noise" or "optional" (e.g. McMahan and Evans 2018, Reagans and Zuckerman 2008). This stance is in tension with evidence that ambiguity is common, exists to varying degrees, and is consequential. Ambiguity is therefore better thought of as "more or less" rather than "yes or no". Predictably, literatures that seek to bracket ambiguity find themselves without the analytical tools to specify core theoretical claims and devise measurement strategies. This dissertation works towards taking ambiguity seriously by introducing microfoundations for its analytical specification and a measurement strategy at the level of the word. To demonstrate the benefits and broader ramifications of this orientation, these theoretical and methodological tools are applied to the "brokerage" literature (Burt 1982) to surface, clarify, and resolve a long-standing contradiction: do brokers merely transmit or actively translate?
Microlocal analysis with applications to seismic inverse problems
Keywords: Mathematics.

This thesis considers the travel time tomography problem for elastic media, particularly in the transversely isotropic setting. A pseudo-linearization argument reduces the problem to the microlocal analysis of certain operators, bringing the problem in line with certain problems in X-ray tomography and boundary rigidity studied by de Hoop, Stefanov, Uhlmann, Vasy, et al.. In this thesis, we will consider two versions of the travel time tomography problem, with the two versions using different kinds of assumptions, thus leading to the usage of different kinds of mathematical analysis. The analysis involves asking whether we can provide parametrices for certain classes of operators that are not quite elliptic. In one version of the problem, we show recovery under "global" assumptions using an operator calculus first developed by Boutet de Monvel used to provide parametrices for parabolic operators, while in the second version of the problem, we localize the problem by adding an artificial boundary; the analysis then concerns finding parametrices to certain classes of non-elliptic scattering pseudodifferential operators
Micromechanical modeling of rate-dependent multiphysics fracture propagation
Keywords: Energy Resources Engineering.

Rate-dependent behavior associated with deformation and fracturing of materials poses significant challenges for modeling. In addition to the complications of the viscoelastic response, the speed of fracture propagation reflects micromechanical mechanisms in the fracture process zone (FPZ). In order to represent these complicated behaviors, a thermodynamically consistent, rate-dependent fracture model is required. Based on rigorous thermodynamic principles, we derive a rate-dependent phase-field mechanical model coupled with single-phase fluid flow in both the matrix and the fracture. The model is guaranteed to satisfy energy conservation during fracture propagation. The model serves as a strong basis for investigating rate-dependent fracturing experiments and for making predictions of material behaviors under new conditions. In addition, we extended the model to different domains such as core-scale breakdown experiments, internal pressure-driven fractures, and multi-scale constitutive modeling. We demonstrated the accuracy of our model in different contexts by performing benchmark experiments against analytical solutions or by comparing the numerical results with experimental measurements. The multi-scale constitutive modeling framework allows us to simulate materials with microscopic fracture networks. We also presented a semi-analytical solution for fracture-matrix imbibition that does not require any tuning/matching parameters. We demonstrated that our model is capable of accurately capturing the nonlinearity of the displacement curves with very little computational cost
Mineralogy and Scaling Relationships in Shale During Acidification: Models and Experiments
Keywords:  Department of Energy Resources Engineering
School of Earth Energy &amp; Environmental Sciences
Department of Energy Resources Engineering
Stanford University
acidic reactive flow
shale-embedded microfluidic
Thesis

Acidic reactive flow and the dissolution of carbonate minerals in porous media is important in many subsurface activities, such as hydraulic fracturing, carbon geological sequestration and wastewater disposal. The mineralogy of the rock and their evolution in pore structure during acidization are critical factors for predicting subsurface flows, such as leakage, injectability and fluid production. In this work, we studied the interplay between flow, reaction and boundary motion of real rock samples incorporated in microfluidic chips. In particular, we examined how the mineralogy of shale, a fine-grained sedimentary rock, controlled the evolution of its interface, surface area and porosity during acidification, including the scaling between such quantities. The two shale samples used in this study were Marcellus (MCL) and Wolfcamp 2H5 (WFC). The MCL sample is a homogeneously carbonate-rich shale, while the WFC sample is characterized by its grain-like distributions of carbonate. Each shale sample was polished, fractured and positioned inside a fabricated microfluidic chip. Once the system reached steady-state flow conditions, 1% hydrochloric acid was injected into the inlet of the shale embedded microfluidic chip at a flow rate of 1000μL/hour for 3 minutes. Preferential dissolution of carbonate over inert minerals led to the creation of channels and cavities and an increased fracture roughness in the WFC sample. Meanwhile, uniform dissolution into the matrix had smoothened the surface of the MCL sample. Video segmentation and image analyses were performed to quantify the changes in porosity and interfacial length. The results show that the evolution of surface geometry depended strongly on the carbonate component of the shale and their spatial distributions, with a positive correlation between the porosity and interfacial length in the WFC sample and a negative correlation in the MCL sample. In addition, a novel approach was devised to model the observed correlation between mineralogy and changes in surface geometry from acidization mathematically. The devised model captured the scale of the changes in porosity and interfacial length effectively. However, a fitting parameter was necessary to shift the intercept of the model results to align with the experimental results. The evolution of shale interface and porosity depends in a complex way on the carbonate minerals and their spatial distribution. The scale of these changes due to the mineral composition and patterning can be captured through a simplified model.
Minimax regret bounds for stochastic linear bandit algorithms
Keywords: Statistics.

The multi-armed bandit problem is a popular framework to model online experiments and the ideas in this domain are intended to help making better decisions as more data becomes available to a learner. Stochastic linear bandits are an important subclass of this general problem that have many applications in areas such as dynamic asset pricing and healthcare. In this dissertation, we study this problem and provide an analysis that produces many existing results as well as some new bounds. Our analysis also naturally leads to a new algorithm, called Sieved-Greedy (SG), that reduces the exploration while maintaining strong theoretical guarantees. Furthermore, we show that all of these bounds can be improved significantly under additional gap assumptions. In particular, we show that the regret of Optimism in Face of Uncertainty Linear bandit (OFUL) and Thompson Sampling (TS), two popular algorithms in this domain, scale poly-logarithmically under these assumptions (rather than as $\sqrt T$ ). Next, we focus on TS and prove that, although it achieves near optimal Bayesian regret bounds, it may perform poorly under mild distributional mismatches. Finally, we introduce a data-driven version of TS and prove that, under mild conditions, it achieves minimax optimal regret even in the worst case. An important property of both of our proposed algorithms (SG and improved TS) is that they use the observed data to adjust the rate of exploration
Minimum-distortion embedding
Keywords: Electrical Engineering.

We consider the vector embedding problem. We are given a finite set of items, with the goal of assigning a representative vector to each one, possibly under some constraints (such as the collection of vectors being standardized, i.e., having zero mean and unit covariance). We are given data indicating that some pairs of items are similar, and optionally, some other pairs are dissimilar. For pairs of similar items, we want the corresponding vectors to be near each other, and for dissimilar pairs, we want the corresponding vectors to not be near each other, measured in Euclidean distance. We formalize this by introducing distortion functions, defined for some pairs of the items. Our goal is to choose an embedding that minimizes the total distortion, subject to the constraints. We call this the minimum-distortion embedding (MDE) problem. This monograph is accompanied by an open-source Python package, PyMDE, for approximately solving MDE problems. Users can select from a library of distortion functions and constraints or specify custom ones, making it easy to rapidly experiment with different embeddings. Because our algorithm is scalable, and because PyMDE can exploit GPUs, our software scales to data sets with millions of items and tens of millions of distortion functions. Additionally, PyMDE is competitive in runtime with specialized implementations of specific embedding methods. To demonstrate our method, we compute embeddings for several real-world data sets, including images, an academic co-author network, US county demographic data, and single-cell mRNA transcriptomes