VictorJuiz/thesis huggingface dataset:
Leveraging the geometric structure of robotic tasks for motion design

Robots operating in the unstructured environments of the real world must contend with at least two sources of geometric complexity: (1) the differential geometric complexity of robot configuration spaces and task spaces, which can in practice be general non-Euclidean manifolds, and (2) the complexity of the geometric shape of robot links and obstacles in the environment, which have infinite variability and are often highly nonconvex. Mature robot autonomy requires algorithms that can tackle these sources of geometric complexity with precision and at real-time control and planning speeds. This thesis focuses on bridging existing gaps in previous methods to meet these needs. In particular, to address differential geometric complexity in motion design, we first present a framework called Multi-Task Pullback Bundle Dynamical Systems (PBDS), which is a geometric control methodology for forming fast composable geometric motion policies, respecting simultaneous robotic tasks on non-Euclidean robot and task manifolds. Second, we present an embedded sequential convex programming approach which exploits differential geometric structure to eliminate explicit manifold-type constraints in trajectory optimization while still guaranteeing final satisfaction of these constraints. Together, these approaches correctly enforce the differential geometric structure of robot planning and control problems while maintaining similar or greater computational efficiency compared to past approaches. Finally, we address the shape complexity of robot links and obstacles by repurposing hardware-accelerated ray-tracing (i.e., ray-tracing cores) for rapidly forming collision avoidance constraints in trajectory optimization, particularly targeting complex robot and obstacle triangle mesh representations. This enables robot motion designers to leverage the full complexity of robot and obstacle geometries at speeds orders of magnitude faster than currently available collision-checking libraries, while leveraging recently-developed ray-tracing cores which have previously had little utility in the robot autonomy stack

Keywords: Aeronautics and Astronautics.
Advisor: Pavone Marco
Bohg Jeannette
Cutkosky Mark R.
Schwager Mac
Lifting the Digital Curtain: Utilizing Social Media to Promote Health Content and Engage with Asian Populations

As our lives become more intertwined with the digital landscape, an increasing number of individuals are relying on the internet and social media as their main sources of health information. Currently, there is a dearth of knowledge on how the Asian community interacts with digital health information, specifically in the context of social media.
In this thesis, a 10-week social media campaign was implemented with the goal of increasing the percentage of Asian participants in the Stanford Research Registry - platforms utilized include Facebook, Instagram, and Twitter through the Stanford Center for Asian Health Research and Education accounts. Participant data was disaggregated by race and ethnicity in order to better understand the diversity among Asian subgroups.
The percentage of Asian participants increased from 14.26% at baseline to 23.83% at the end of the campaign (525 Asian identifying individuals to 1,871). The greatest increase occurred during the General Outreach phase which utilized all channels of outreach available. Frequencies of Japanese, Korean, and Vietnamese ethnicities were higher in the Multi-Ethnic category compared to their corresponding monoethnic group.
Social media is a powerful tool that can be leveraged for targeted recruitment – in this study we see how it can increase diversity amongst research participants and potentially be used as an effective tool for information dissemination. This work can be expanded in the future by looking into other social media platforms that are more targeted towards Asian populations and by disaggregating the data even further to fully understand the diversity present in the Asian population.

Keywords: Asian Americans
Asians
Research
Social media
Internet
Ethnicity
Text
Thesis
"Light and Sound, Not Song and Dance": Cybernetic Subjectivity in the Environmental Art of the Pulsa Group, 1966–1973

The proliferation of computing technologies and their attendant futurisms throughout the 1960s and 1970s prompted engineers and artists alike to reckon with a world humans would share with artificially intelligent machines. However, current scholarship around this period of techno-aesthetic experimentation lacks a full-scale exegesis of the work of the Pulsa Group, an artists collective composed of seven Yale University graduates who lived communally and created programmed environments from 1967 to 1973. This thesis makes use of archival research and interviews to chart the arc of Pulsa’s career, beginning with the light and sound environments they constructed in a New Haven loft and ending with their implementation of a feedback-enabled environment wherein human movements elicited responses from a computational infrastructure. I characterize the group’s iterative work process as moving toward evolutionary environments aimed at evolving the human perceptual panoply to better cope with a dawning cybernetic age. The audience’s experience of a Pulsa environment bolsters this project in that the boundary between art and viewer or object and subject becomes blurry—the human and the machine are in fact interdependent. Eventually, Pulsa took their environments to the urban environment, where the dream of facilitating dialogue between computers and their users became an element of city planning. Finally, Pulsa’s work evolved toward mutualistic feedback between participants and environment, where input from people in the space affected the system output. Rather than attempting to control participant behavior, I argue, Pulsa’s interactive works created a networked environment in which action could occur and be fed back into the system. In the resultant social vision, control persists not in the overt control of behavior, but in the creation of seemingly endless opportunities for action within a system that learns from those choices.

Keywords:  Program in Science
Pulsa
STS
cybernetics
art and technology
environmental art
Thesis
Advisor: Turner Fred
Limitations of the Intellectual Borders created by the Human Rights Field: A Fractal Exploration of Drummond in Chiriguaná, Colombia

Drummond is a powerful coal mining company operating in Chiriguaná, Colombia. Based in Alabama, the corporation ranked in 2018 as 165th on Forbes’ list of America’s Largest Private Companies. The coal the company mines is barely used in Colombia – the country exports 92% of the coal it produces. In the United States human rights field, Drummond is famous for allegedly hiring the AUC paramilitary to murder three of its union leaders. In the human rights reports on Drummond, this event is detailed only within the context of Colombia’s internal armed conflict. This thesis aims to widen the lens, exploring Drummond’s presence in Chiriguaná through an intersectional approach based in border theory and environmental history. In doing so, it argues that the limited perspective of human rights reports reproduces the arbitrary borders that help create the conditions for the very abuses the field denounces. The thesis concludes by imagining alternative practices for the human rights field that are based in connectedness rather than separation.

Keywords: Center for Latin American Studies
Colombia
Land use > Environmental aspects > History
Text
Thesis
Advisor: Hoyos Hectór
Linking Processing Speed And White Matter: a bridge between Education and Neuroscience research

In the first chapter of this thesis, I review the history of processing speed as a cognitive capacity since the late 19th century. In the second chapter of this thesis, I analyze the definitional and conceptual constraints surrounding the use of ‘processing speed’ in the behavioral psychology literature. Specifically, I evaluate the degree of diversity observed in operationalized definitions of processing speed advanced in recent literature, and then present three ways to reduce this diversity in order to unify scholarly conceptualizations of processing speed in the future. In the third chapter of this thesis, I assess a candidate for an anatomical/mechanistic grounding for characterizations of processing speed as a cognitive capacity. In the final chapter of this thesis, I summarize arguments presented in chapters two and three and discuss implications for future research, as well as the importance of the research paradigm more broadly.

Keywords: Processing Speed
White Matter
Educational Neuroscience
Stanford
School of Humanities and Sciences
Symbolic Systems
Thesis
Literary Embodiment: Representations of Disability in Anthony Doerr's "All the Light We Cannot See"

According to disability studies theories such as ‘narrative prosthesis,’ ‘aesthetic nervousness,’ and ‘aesthetic blindness,’ disability in literature is often metaphorized, stereotyped, or typologized. Through close readings and character studies, this thesis explores how Anthony Doerr’s “All the Light We Cannot See” resists, complicates, and reverses such concepts as a novel with a disabled cast.

Keywords:  Department of English
Stanford University
Department of English
Disability Studies
Disability Representation
Disability Theory
Blindness
Anthony Doerr
All the Light We Cannot See
Contemporary Fiction
Thesis
Advisor: Greif Mark
Vermeule Blakey
Living too long : the extant figure in American literature

My dissertation finds that nineteenth-century American literature and culture organized itself around the fear of living too long. This fear manifests most strikingly in a character type that I call the extant figure: someone who disrupts their environment by refusing to die when expected, their lifespan stretching past the limits of one historical moment and into another where they cannot fully belong. I use the word "character" to encompass not only examples from fiction (Rip Van Winkle, Natty Bumppo, Uncle Julius), but also the speakers of such lyrics as Dickinson's F764 ("My Life had stood -- a Loaded Gun"). Behind their differences of genre, mode, and period, these case studies share a preoccupation with the unsettling power of too much longevity. By tracking various permutations of this character type between 1820 and the close of the century, Living Too Long plots a new inroad into America's political and intellectual life during that span. In the extant figure's struggles we may observe a young republic anxiously predicating its continued survival on the concept of timely generational succession, and dreading the possibility of that system breaking down when one generation overextends its reach; an effort to reconcile, or else to transcend, religious and secular models of time, with their different implications for human life; and several successive generations learning gradually to conceive of their own existence as historically embedded. Reading these works against the backdrop of critical studies on race, time consciousness, and nationalism, I argue that age, not youth and innocence, was the prime mover of the period

Advisor: Jones Gavin Roger
Woloch Alex
Gigante Denise
Ruttenburg Nancy
Loan loss measurement and bank lending

I use both theoretical and empirical models to assess how alternative measurement approaches to banks' loan loss allowances affect lending when banks are subject to regulatory capital requirements. I find that: (1) the Current Expected Credit Loss (CECL) method increases loan loss reserves on average by 16% relative to the Incurred Credit Loss (ICL) method; (2) the difference between CECL loan loss allowances and ICL loan loss allowances is larger in economic downturns than upswings; (3) banks reduce lending on average by 3.15% (50 basis points) when switching from ICL to CECL; and (4) CECL results in less procyclical lending than ICL, specifically, the difference between lending in up- vs. downturns decreases by 0.8% (37 basis points) when moving from ICL to CECL

Keywords: Business.
Advisor: Beyer Anne
Barth Mary E.
Reiss Peter C. (Peter Clemens)
Local and average structural evolution of halide perovskites upon compression

Halide perovskites have emerged over the past decade as promising materials for photovoltaic solar cells. Pressure can be a powerful parameter for tuning the structures of these materials which in turn can dramatically influence their optoelectronic properties; however, previous research has only looked at the average structural evolution of various halide perovskites upon compression. In this dissertation, I describe our work studying the local structural evolution of halide perovskites upon compression using X-ray total scattering and pair distribution function analysis. First, I explore the double halide perovskite Cs2AgBiBr6. On average, this material has a cubic crystal structure, but analysis of the pair distribution function reveals local Br distortion and Cs off-centering, which are enhanced upon compression. Second, I look at the local and average structural evolution of 3-D halide perovskites, including our use of reverse Monte Carlo simulations to gain insight into the complex structural evolution of CsPbBr3. These studies highlight the importance of considering both the local and average behavior to build a more accurate understanding of halide perovskite structures, a key building block toward designing new materials with improved performance

Keywords: Applied Physics.
Advisor: Mao Wendy (Wendy Li-wen)
Reis David A.
Dionne Jennifer Anne
Locality in quantum many-body systems

How can we understand complex quantum many-body systems without finding exact solutions? Recent developments have shown that first principle constraints can provide satisfactory or even accurate estimates for quantum observables in interacting many-body systems. This dissertation summarizes a series of results centered on implications of locality in quantum dynamics and thermodynamics. We first discuss microscopic locality bounds on quantum transport, chaos and ground states in open and closed lattice models. Such locality bounds provide useful and rigorous information in strongly interacting systems. Then we consider the problem of emergent locality in holographic theories, with analytical and numerical examples in holographic tensor networks and matrix quantum mechanics. Emergent locality and causality are detected and analyzed, augmenting our microscopic understanding of holography

Keywords: Physics.
Advisor: Hartnoll Sean
Qi Xiaoliang
Shenker Stephen Hart
Long-time asymptotics for reaction-diffusion and stochastic Burgers equations

This dissertation consists of two parts. In the first, we study the long-time behavior of various reaction-diffusion equations. We begin by describing a complete asymptotic expansion for solutions to the classical Fisher—KPP equation in the long-time limit. We then take up a version of Fisher—KPP with nonlocal diffusion. We show that typical nonlocal equations resemble the local model at long times; however, strongly asymmetric nonlocal diffusion can disrupt this behavior. Next, we use a connection between the Fisher—KPP equation and branching Brownian motion (BBM) to study the probability that the leading two particles in BBM are unusually far apart. This is joint work with J. Berestycki, Brunet, Mytnik, Roquejoffre, and Ryzhik. Finally, in collaboration with H. Berestycki, we study steady states, propagation, and traveling waves for a much wider class of reaction-diffusion equations in half-spaces. The second part of this dissertation explores the long-time behavior of the stochastic Burgers equation; it consists of joint work with Dunlap and Ryzhik. We draw on the theory of parabolic partial differential equations to construct stationary solutions to the stochastic Burgers equation on the line. Moreover, we show that the extremal stationary solutions are parameterized by their means and arise as stable long-time limits of constant initial data

Keywords: Mathematics.
Advisor: Ryzhik Leonid
Papanicolaou George
Ying Lexing
Long-time behavior of the international trade equation and average gromov hyperbolicity

In this thesis, we will describe results that hail from two very different probabilistic models. First, we will discuss an equation that models the distribution of productivity within a global economy that allows for knowledge sharing. We will show that the equation has traveling wave solutions, and that time-dependent solutions with compact initial data converge to traveling fronts at the minimal speed with a Bramson delay. In the second chapter, we will prove that probability spaces which obey an average notion of ultrametricity have a tree-like structure, a result that has applications in many spin-glass models

Keywords: Mathematics.
Advisor: Ryzhik Leonid
Chatterjee Sourav
Papanicolaou George
Looking East: Evaluating Russia's Pivot to Asia

At the September 2012 Asia-Pacific Economic Cooperation Forum, Russian President Vladimir Putin launched his “Pivot to Asia”, ostensibly launching a reorientation of Russia’s foreign policy. Yet, has Russia truly pivoted to Asia? If so, why? Data on Russia’s economic and security ties with major Asian countries illustrate that Russia’s shift to Asia is real, especially towards China. Such a shift has been motivated by the Kremlin’s desire to reduce Russia’s economic reliance on Europe in light of Western sanctions as well as shared regional security interests and elite diplomacy between Putin and other heads of state. While Putin and the Kremlin have taken deliberate actions to support a pivot east, broader geopolitical and economic trends have been at least as consequential in pushing Russia east, such as Asia’s growing economic power. Far from starting in 2012 with Putin’s pronouncement, Moscow continually looking east is part of a historical pattern dating back several hundred years. As the global balance of power shifts towards Asia, U.S. policymakers must be cognizant of the second and third order effects U.S. foreign policy can have on the Kremlin’s ties with major powers in Asia.

Keywords:  Center for International Security and Cooperation
Center for International Security and Cooperation
Russia
China
pivot
Russian foreign policy
international security
Thesis
Advisor: McFaul Michael
Naimark Norman M.
Low power audio feature extraction for machine learning applications

Always-on sound classification is a desirable but power-intensive function for a variety of emerging applications such as wearables and IoT devices. The hardware energy consumption of sound classifiers is typically driven by signal digitization, feature extraction, classification model storage, and execution. Yet, the complexity of the signal of interest is often much lower than that of the raw signal acquired from the microphone and processed by a machine learning engine. For instance, semi-stationary sounds (e.g., engine noise, baby cry, running water, human chatter, etc.) are signals with lower information content than more complex sounds such as music or speech. In this dissertation, I will present the benefits of leveraging an engineered feature set to efficiently classify semi-stationary sounds. This approach requires one to three orders of magnitude fewer parameters and can be therefore trained over ten times faster than competitive deep learning models. I will also describe a circuit topology and system architecture that can be used to extract both engineered features as well as more general purpose ones. Our work resulted in a 32-channel analog filterbank IC for audio front-end signal processing. It employs a passive N-path switched capacitor topology to achieve high power efficiency and reconfigurability. The circuit's unwanted harmonic mixing products are absorbed by the machine learning model during training. To enable a systematic pre-silicon study of this effect, we develop a computationally efficient circuit model that can process large machine learning datasets in practical run-times. Measured results using a 130 nm CMOS prototype IC indicate competitive classification accuracy on datasets for baby cry detection (93.7% AUC) and voice commands (92.4% average precision), while lowering the feature extraction energy compared to digital realizations by approximately 2x and 10x, respectively. The 1.44 mm2 chip consumes 800 nW, which corresponds to the lowest normalized power per simultaneously sampled channel in recent literature

Keywords: Electrical Engineering.
Advisor: Murmann Boris
Raina Priyanka (Assistant Professor of Electrical Engineering)
Rivas-Davila Juan
The LZ dark matter experiment : from detector development to early data

Due to a compelling body of astrophysical and cosmological evidence, dark matter has come to be accepted as a crucial ingredient of modern cosmology, yet its physical nature remains one of the most pressing questions in the field of physics. One historically favored model of dark matter is weakly interacting massive particles, or WIMPs. LUX-ZEPLIN (LZ) is a next-generation dark matter detector designed to achieve field-leading sensitivity to much of the remaining accessible parameter space within the WIMP dark matter paradigm. To help realize the full-scale LZ detector, the System Test R&D platform was constructed at SLAC National Accelerator Laboratory to validate the performance of critical LZ subsystems at scales approaching or comparable to the LZ design. In this dissertation, I present results showing that the passivation of the high voltage electrodes in citric acid leads to a significant reduction in spontaneous emission of single electrons, potentially limiting a major instrumental background by up to several orders of magnitude and enabling a more sensitive dark matter search. The LZ detector has now been assembled at the Sanford Underground Research Facility (SURF) in Lead, South Dakota and is taking early data. I also give a first look at commissioning data that captured the first light from electrons in the LZ detector, and share methods to validate the in situ performance of the high voltage electrodes

Keywords: Physics.
Advisor: Akerib Daniel S.
Shutt Thomas (Thomas Alan)
Tanaka Hirohisa 
Toro Natalia
Machine learning based wavefront estimation for the Rubin observatory

We present a new conceptual framework for wavefront sensing for wide-field telescopes. The framework divides the problem into two subproblems that are highly amenable to machine learning and optimization. The first involves making local wavefront estimates with a convolutional neural network. The second involves interpolating the optics wavefront from all the local estimates with least squares. In this thesis, we develop simulated observations and images from the upcoming Rubin Observatory to develop, refine and assess this new algorithm. In a realistic Rubin mini-survey, the algorithm reduces the total magnitude of the optics wavefront by 66%, the optics PSF FWHM by 27%, and increases the Strehl ratio by a factor of 6. Ultimately, this algorithm has the potential to improve image quality on Rubin and multiple current and upcoming wide-field telescopes, and to boost the scientific returns for astrophysics and cosmology

Advisor: Kahn Steven M. (Steven Michael)
Boyd Stephen P. 
Burchat P. (Patricia)