VictorJuiz/thesis huggingface dataset:
Reliable machine learning via distributional robustness
Keywords: Management Science and Engineering.

As machine learning systems increasingly get applied in high-stake domains such as autonomous vehicles and medical diagnosis, it is imperative that they maintain good performance when deployed. Modeling assumptions rarely hold due to noisy inputs, shifts in environment, unmeasured confounders, and even adversarial attacks to the system. The standard machine learning paradigm that optimize average performance is brittle to even small amounts of noise, and exhibit poor performance on underrepresented minority groups. We study \emph{distributionally robust} learning procedures that explicitly protect against potential shifts in the data-generating distribution. Instead of doing well just on average, distributionally robust methods learn models that can do well on a range of scenarios that are different to the training distribution. In the first part of thesis, we show that robustness to small perturbations in the data allows better generalization by optimally trading between approximation and estimation error. We show that robust solutions provide asymptotically exact confidence intervals and finite-sample guarantees for stochastic optimization problems. In the second part of the thesis, we focus on notions of distributional robustness that correspond to uniform performance across different subpopulations. We build procedures that balance tail-performance alongside classical notions of average performance. To trade these multiple goals \emph{optimally}, we show fundamental trade-offs (lower bounds), and develop efficient procedures that achieve these limits (upper bounds). Then, we extend our formulation to study partial covariate shifts, where we are interested in marginal distributional shifts on a subset of the feature vector. We provide convex procedures for these robust formulations, and characterize their non-asymptotic convergence properties. In the final part of the thesis, we develop and analyze distributionally robust approaches using Wasserstein distances, which allows models to generalize to distributions that have different support than the training distribution. We show that for smooth neural networks, our robust procedure guarantees performance under imperceptible adversarial perturbations. Extending such notions to protect against distributions defined on learned feature spaces, we show these models can also improve performance across unseen domains.
Remembering Okinawa: The Himeyuri Story and Construction of Wartime Memory in Japan
Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
Japan
Okinawa
History
20th century
Battle of Okinawa
Museums
Memory
Territoriality
Peace
Thesis

The Battle of Okinawa is remembered as the bloodiest battle of the Pacific War. It involved the mobilization of locals on an unprecedented scale, leading to countless civilian casualties inflicted by both forces. Himeyuri Corps was a nursing unit during the battle that was comprised of mobilized female students in Okinawa. Since the early years of the postwar, the story about the unit has become central to how the wartime is remembered in Japan. A series of media productions popularized the story, making the monument commemorating deceased members of the unit, the Himeyuri Cenotaph, a popular tourist destination since the 1950s. However, it was not until the 1980s the survivors publicly discussed their experience through founding the Himeyuri Peace Museum. This thesis will address the following questions: how did the story become a cultural icon? What was the survivors’ role in establishing and maintaining the story? This thesis will first discuss the popularity of the story and its reflection of Japanese territoriality. It will then look into the opening process of the museum alongside its current narrative.
Representation theory, randomness, and quantum information science
Keywords: Physics.

Often, understanding physical systems or mathematical structures requires analyzing the generic behaviors instead of specific examples. In such cases, a common approach is identifying the degrees of freedom that are supposed to be generic and replacing them with entirely random values. This line of thinking has been fruitful in many areas of physics, such as nuclear physics, quantum chaos, quantum field theory, and quantum gravity. Within quantum information science, which is the primary focus of this dissertation, multiple areas including quantum entanglement, the quantum capacity of channels and quantum Shannon theory, the construction of quantum codes, and benchmarking quantum systems have heavily benefited from randomization techniques. Most of this manuscript is devoted to random constructions and relevant mathematical formalisms, such as representation theory and group theory, and their applications in quantum information science and quantum gravity. In particular, we will study the following topics: (1) Applications of representation theory and randomization in quantum error correction, including approximate versions of the Eastin-Knill theorem and the reference frame quantum error correction. (2) Schur-Weyl duality for the stabilizer formalism, with applications in quantum property testing and de Finetti theorems (3) Randomized constructions and information theoretic methods for holographic duality, including the holographic entropy cone and random tensor networks.
The response of electronic nematic order to dynamical strain : a study of the iron based superconductors
Keywords: Physics.

Over the last decade, there has been increasing evidence that electronic nematic states, which break discrete rotational symmetry in crystalline solids, and their associated critical points are important constituents in the phase diagrams of several widely studied families of materials. Electronic nematic states are evident in the iron-based superconductors, and there are mounting indications of their presence in the cuprate and heavy fermion superconductors. This work discusses new techniques to investigate signatures of electronic nematicity, and more broadly addresses how dynamical mechanical deformation can be used to control and study materials. This thesis begins with the development and characterization of an improved technique for measuring the resistance response of materials to strain, using AC strain at frequencies between 10mHz and 10kHz. By using strain of the same symmetry as the electronic-nematic order parameter in Ba(Fe0.975Co0.025)2As2, this thesis demonstrate a novel and effective way to measure the dynamical nematic susceptibility, which has not been studied previously, and highlight potential future use cases in disordered media or high magnetic fields. Next, using dynamical strain of a different symmetry class than the nematic and Neel order parameters I show new anomalies in the elastoresistivity and elastocaloric effect of Ba(Fe0.975Co0.025)2As2, which extends the well-known Fisher-Langer theory of critical phenomena. Lastly, I study how the resistance response to an external mechanical perturbation is influenced by and encodes information about the thermal activated relaxation of domain walls in response to external stresses.
Retail to E-tail: Understanding how ecommerce has reshaped the retail industry
Keywords:  Department of Communication
Stanford Communication Department
Stanford University
Retail Industry
Retail
Ecommerce
Etail
Amazon.com
Amazon Effect
Retail Apocalypse
Thesis

This thesis further explores and analyzes the changing landscape of the retail industry with the advent and growth of ecommerce. First, I provide background information of traditional brick and mortar retail, which I argue was largely defined by its cost structures associated with dependence on physical space. I begin my transition to understanding etail by following historical patterns of how retail changed alongside technological advancements. However, the internet and its capabilities quickly proved to be revolutionary and the traditional brick and mortar operation confined by traditional cost structures and physical spaces could not adapt adequately. Analysis of the evolving new consumer and rebalance of power dynamics reveals how the industry has begun to transform. This work concludes with three case studies to illustrate the complexities of this transformation and operations in the new e-tail heavy industry.
Rethinking single-cell RNA-Seq analysis
Keywords: Electrical Engineering.

Since the Human Genome Project was completed in 2003, scientists have developed technologies for measuring the RNA content of a single cell. In the last decade, the number of individual cells profiled per study has grown exponentially to over 1,000,000 cells. In this thesis, I will discuss some of the computational and statistical challenges associated with the analysis of such large single-cell datasets. After introducing background information, the thesis covers three main works. The first work introduces a novel, interpretable framework with the biologist end user in mind. The framework also addresses the clustering subjectivity issue by justifying its results based on a rigorous definition of cell type. This allows us to cluster using feature selection to uncover multiple levels of biologically meaningful populations in the data. The second work considers a novel approach for representing single-cell RNA-Seq data. We argue that gene or transcript expression vectors, while intuitive, are not the most optimal way for representing single cell genomic profiles. Rather than counting the number of reads that comes from each transcript, which requires resolving the ambiguity associated with read multimapping, we decide to count the number of reads that comes from each transcript set. We show that these new representations are both more computationally efficient to obtain and more information-rich. The third and perhaps most interesting work first observes a post-selection inference problem in standard single-cell computational pipelines. Standard pipelines perform differential analysis after clustering on the same dataset, and this reusing of the same dataset generates artificially low p-values and hence false discoveries. We introduce a valid post-clustering differential analysis framework which corrects for this problem. In summary, we discuss multiple works for drawing key insights from single-cell RNA-Seq data: a clustering method that emphasizes interpretability of results, a representation of single cells that retains more information from read data, and a framework for correcting the selection bias from standard analysis pipelines.
Revealing the small scale structure of dark matter with gravitational lensing
Keywords: Physics.

The standard model for dark matter, the Cold Dark Matter model, which accurately predicts the formation and evolution of large scale structure in the universe, appears to be in disagreement with observations of structure on much smaller scales. It is, as of yet, unclear if this is due to astrophysical processes causing a reduction in the efficiency of star formation in low mass galaxies, or due to the suppression of low mass structure formation altogether caused by exciting new dark matter microphysics. Gravitational lensing offers a unique and luminosity-independent means of distinguishing between these possibilities. In this work, I present tools to simulate realistic ALMA observations of strong gravitational lenses, and to model the small scale distribution of dark matter using interferometric observations of strong lenses. We applied this technique to observations of the gravitational lens SDP 81, and identified a low mass perturbing object, and placed new constraints on the abundance of low mass dark matter halos. I also present several techniques to automate the inference of the smooth distribution of matter in these objects, which allow inference to proceed 1.6 billion times faster, and which strongly reduce the need for human supervision of the modeling process. With these techniques in hand, we are poised to extract important information about the elusive dark matter.
RF-penetrable PET insert integrated with a phased-array RF receiver coil for simultaneous PET/MRI
Keywords: Mechanical Engineering.

Over the past few years, integrated positron emission tomography/magnetic resolution imaging (PET/MRI) systems emerged with great potential by providing added value that exceeds the sum of each system. However, the system's overall cost has impeded the widespread adoption of PET/MRI and, furthermore, limited research opportunities for finding more clinical applications. To enhance the dissemination of PET/MRI, a brain-dedicated PET insert that can be inserted into any existing MRI for simultaneous PET/MRI was developed in our lab. This cost-effective PET insert with a better imaging performance could improve the adoption of PET/MR and allow more clinical and research opportunities in the Neurology field. This thesis focuses on the development of an RF-penetrable brain-dedicated PET insert; (a) design and performance evaluation of the 1st-generation PET insert prototype are described, (b) its limitations and strategies for design improvements are explained, and (c) design and preliminary performance evaluation of the upgraded 2nd-generation PET insert prototype are presented. The 1st-generation RF-penetrable PET insert prototype that allows the RF field from the MRI body coil to penetrate through small inter-module gaps showed promising results indicating that we can achieve simultaneous PET/MRI without modifying the existing MRI hardware. Nonetheless, some minor issues including the gradient-induced eddy current and low SNR MR image from receive attenuation were observed. These issues could be solved by (a) replacing the solid copper shield with a phosphor bronze mesh shield which has high RF shielding effectiveness and negligible induced eddy current and (b) placing a phased-array RX-only RF coil inside the PET insert for high SNR MR images while preserving the PET performance. With these modifications, the 2nd-generation RF-penetrable PET insert has been designed with a longer scintillation crystal axial FOV (16 cm for a full brain) for better sensitivity that enables it to be practical for clinical brain imaging. Preliminary results of one detector module have been collected and analyzed and overall performance of the MR system and the PET detector module was preserved.
Ribosomes : stability, instability, and cellular elasticity
Keywords: Chemical and Systems Biology.

The ribosome is usually defined by what it does, what it's made of, or what it looks like. This thesis explores these definitions with the intention of forming a unified appreciation for how the composition of the protein biosynthesis apparatus relates to what it does in a cell. Understanding the compositional nature of ribosomes first requires defining them, and I do this by discussing the stability of the ribosome particle and the instability of associated factors that control the translation mechanism. This framework is critical for appreciating the first major direction of my dissertation research that is described in the first three chapters of this thesis. In these sections, I will describe several approaches to dissect eukaryotic translation at the single-molecule level that established the groundwork of a system to study for many years to come. In recent years, the stability of the mature ribosomal particle has been questioned, raising the possibility that such irregularities might relate to what it does via a ribosome "code". As I hope to demonstrate in the second major direction of my dissertation, there is an enormous gap between the "what it's made of" and "what it does" perspectives on the ribosome. Recognizing the centrality of the ribosome for cellular protein homeostasis is crucial to this end and sometimes necessitates thinking off the ribosome. This thesis will conclude with a discussion of broad implications and future directions that new researchers in the field may pursue.
Risk below the surface: Concussions in artistic swimming
Keywords:  Department of Communication and Journalism
Communication
Journalism
Stanford University
Master of Arts
artistic swimming
synchronized swimming
concussions
sports
Thesis

This thesis is a reported piece of journalism. It includes data that I gathered through a survey sent out to artistic swimmers in the United States. My analysis of this data showed that 25 percent of artistic swimmers sustained at least one concussion during their careers and that 15 percent might have sustained one or more, documenting the prevalence of concussions in artistic swimming. The article features four former elite artistic swimmers who sustained concussions and photographs of an award-winning team.
In addition to the article and photographs, there is a 360-degree video experience that showcases the risks below the surface. The video is available at this link: https://youtu.be/EhnrB26rRFg
Rites of revolution : Chinese revolutionary aesthetics as ritual practice
Keywords: Comparative Literature.

Rites of Revolution: Chinese Revolutionary Aesthetics as Ritual Practice argues that revolutionary literature and performance should be approached as forms of secular religious ritual. Placing the Cultural Revolution within a longer revolutionary history, and working across media (including the novel, film, opera, and ballet) as well as with diaries and later memoirs, my project demonstrates the centrality of narrative to historical conceptions of Chinese modernity. I show how a socialist philosophy of history emerged from an earlier Chinese discourse of modernity that was explicitly explored in literary texts. This teleological worldview of progressive history came to be gradually encoded into a masterplot that crossed genres and media as it developed in the mid-twentieth century. Informed by work on Soviet aesthetics and in conversation with work on the Cultural Revolution in literature, history, anthropology, and religious studies, I demonstrate that the ritual forms of revolutionary works sought to symbolically reconstruct the world in order to transform it. Engaging a larger question of the role and nature of literature within society, my interpretation of cultural production as a staging of quasi-religious performance addresses the tension between art's aesthetic value and its social utility. By placing literary texts at the intersection of competing philosophies of history and subjectivity, my research provides a particular praxis for the discussion of politics formed through the aesthetics of charismatic rule and aesthetics as a political method for the conditioning of individual consciousness. The role of narrative as both metaphor (for life) and metonym (for a theory of history) joins the interpretive lines of politics and aesthetics and forms the foundation of my argument for the particularly literary roots of Maoist cultural politics.
Robust and accurate shock-capturing in discontinuous Galerkin discretizations
Keywords: Aeronautics and Astronautics.

Computational Fluid Dynamics (CFD) has become a critical component in analyzing fluid flows and designing industrial products. Among various numerical methods in CFD, second-order numerical schemes have been widely used in both industry and academia. Second-order methods are robust enough to use on complex geometries and usually provide a sufficient amount of accuracy in flow simulations. However, second-order accurate solutions may not be sufficient for many aerodynamic applications such as vortex flows, Large Eddy Simulations (LES), and aeroacoustics problems. As a consequence, researchers have sought high-order numerical methods to simulate complex flows with low dissipation over the past few decades. Many approaches have been suggested including Finite Difference (FD), Finite Volume (FV), and Finite Element (FE) frameworks for CFD. In the group of high-order methods, discontinuous Galerkin (DG) methods have become popular in academia because of their distinctive benefits. For DG methods, high-order accuracy in flow solutions can be easily achieved by just adding more degrees of freedom in each element. Furthermore, DG methods are well suited to modern computer hardware, even on GPUs, due to high arithmetic intensity and the locality of operations. Despite their numerous benefits, DG methods are not widely adopted because of some remaining challenges, especially in industry. One of these difficulties is shock-capturing. Similarly to other numerical methods in CFD, DG methods also suffer from spurious oscillations if discontinuities arise during flow simulations. The accuracy of solutions will degrade significantly, or solutions may diverge unless these discontinuities are captured appropriately. Therefore, a shock-capturing capability becomes necessary for DG methods to simulate compressible flows with shocks. In this work, robust and accurate shock-capturing approaches for DG methods will be demonstrated. To precisely capture various strengths of shocks, a simple shock-detector is first proposed for DG discretizations, which only relies on local flow information. Additionally, filtering strengths are precalculated to avoid parameter tuning procedures and are optimized to achieve maximum accuracy while capturing shocks. The proposed methods are then applied to two- and three-dimensional canonical problems to demonstrate the shock-capturing capabilities of the proposed methods.
Robust control, planning, and inference for safe robot autonomy
Keywords: Aeronautics and Astronautics.

Integrating autonomous robots into safety-critical settings requires reasoning about uncertainty at all levels of the autonomy stack. This thesis presents novel algorithmic tools for imbuing robustness within two hierarchically complementary areas, namely: motion planning and decision-making. In Part I of the thesis, by harnessing the theories of contraction and semi-infinite convex optimization and the computational tool of sum-of-squares programming, we present a unified framework for robust real-time motion planning for complex underactuated nonlinear systems. Broadly, the approach entails pairing open-loop motion planning algorithms that neglect uncertainty and are optimized for generating trajectories for simple kinodynamic models in real-time, with robust nonlinear trajectory-tracking feedback controllers. We demonstrate how to systematically synthesize these controllers and integrate them within planning to generate and execute certifiably safe trajectories that are robust to the closed-loop effects of disturbances and planning with simplified models. In Part II of the thesis, we demonstrate how to embed the control-theoretic advancements developed in Part I as constraints within a novel semi-supervised algorithm for learning dynamical systems from user demonstrations. The constraints act as a form of context-driven hypothesis pruning to yield learned models that jointly balance regression performance and stabilizability, ultimately resulting in generated trajectories for the robot that are conditioned for feedback control. Experimental results on a quadrotor testbed illustrate the efficacy of the proposed algorithms in Parts I and II of the thesis, and clear connections between theory and hardware. Finally, in Part III of the thesis, we describe a framework for lifting notions of robustness from low-level motion planning to higher-level sequential decision-making using the theory of risk measures. Leveraging a class of risk measures with favorable axiomatic foundations, we demonstrate how to formulate decision-making algorithms with tunable robustness properties. In particular, we focus on a novel application of this framework to inverse reinforcement learning where we learn predictive motion models for humans in safety-critical scenarios, and illustrate their effectiveness within a commercial driving simulator featuring humans in-the-loop. The contributions within this thesis constitute an important step towards endowing modern robotic systems with the ability to systematically and hierarchically reason about safety and efficiency in the face of uncertainty, which is crucial for safety-critical applications.