VictorJuiz/thesis huggingface dataset:
Coding for computation : learning with limited information

The emerging paradigm of ubiquitous low-cost sensors with wireless connectivity and data processing in the cloud promises to be an enabling technology for applications such as smart cities, industrial automation, intelligent energy or traffic management. However, the addition of billions of these low cost sensors can considerably increase wireless network capacity requirements, which are already strained with current demands. In order to minimize communication across a network, we explore the problem of compressing or coding data from distributed sensors for transmission over a noisy communication channel, and study algorithms for performing computation with this partial information. We first look at low-complexity randomized approaches such as sampling or compressive coding of transmissions to reduce the communication requirements of transmissions. In the scenario, where sensors feed measurements from an underlying unknown time-series to a fusion center, communication bottlenecks preclude the collection of all such measurements. To address this challenge we develop algorithms to learn the parameters of the time-series from randomized coding comprised of subsamples or compressed sampling of measurements transmitted by the sensors. We do this by first estimating the covariance of the measurements from the partial information received and using that to estimate the time-series parameters. Non-asymptotic convergence bounds for these algorithms are derived. Moreover, we show that modifications to the algorithm allow for tighter convergence bounds given priors such as low rankness and sparsity. This procedure is applied for the estimation and control of linear dynamical systems. We also consider a decentralized learning paradigm where nodes on a network cooperate to solve a global optimization problem using their local information. Current techniques to solve this problem such as distributed gradient methods assume that nodes can transmit arbitrarily large messages at each time instant. We use randomized compression approaches mentioned earlier to reduce the size of the messages transmitted and adapt these distributed optimization algorithms for networks with limited communication capabilities. We show an inverse relationship between compression and accuracy of the distributed optimization algorithm. The algorithm is also robust to the use of stochastic gradients, noisy communication links, or time varying networks. Finally, we introduce an approach where coding of structured data such as text or audio is done over its learned representations produced by neural-network based autoencoders. For the transmission of text using limited length encoding, we show how this approach retains more of the semantic content of sentences compared to traditional source and channel coding techniques.

Keywords: Electrical Engineering.
Advisor: Goldsmith Andrea
Duchi John 
Weissman Tsachy
Coherent feedback for wavelength transfer of atomic-optical nonlinearity

Light has for decades proven extremely valuable for high-bandwidth communications over long distances, and is becoming increasingly important at shorter and shorter length scales as demand for bandwidth rises. Much of the enabling optical technology, which includes optical fibers and fiber amplifiers, relies on materials compatible with the telecom band of wavelengths (roughly 1.2-1.6 μm). Additionally, there has been much interest in expanding the reach of optics into switching, logic, and memory, but such applications typically require nonlinear input-output behaviors not easily found in optical systems. The strongest optical nonlinearities in nature are generally resonant (and therefore narrow-band) and don't necessarily fall within the telecom range. A technique to bridge resonant optical nonlinearities to the telecom band would therefore be quite valuable. Such a method would be yet more valuable if it supported tunable signal wavelengths—this would enable transfer of a fixed-wavelength nonlinearity to an array of telecom wavelengths for wavelength division multiplexing (WDM), or allow optical coupling of heterogeneous nonlinearities via a single shared wavelength. In this thesis I'll describe a technique which pairs a below-threshold optical parametric oscillator (OPO) with coherent optical feedback to achieve tunable-wavelength nonlinearity transfer, and present results from a corresponding proof-of-principle implementation which demonstrates the transfer of a sharply resonant atomic-optical nonlinearity.

Keywords: Applied Physics.
Advisor: Mabuchi Hideo 
Hogan Jason
Safavi-Naeini Amir H.
Community-scale and research-based integrated education experience

The building industry is moving towards increased use of data-driven methods for designing, building, and operating sustainable, energy-efficient buildings. The adoption of these methods requires technologically skilled employees and education programs for these skills. The purpose of this thesis is to show that by aligning community, education, and industry stakeholders, a data-driven, project-based education platform can be developed that creates a pathway for generating such employees and a platform for investigating building energy efficiency and sustainability. The methodology for developing this project-based education platform hinges on partnering with stakeholders from community organizations, education, building operations, building energy efficiency, and construction. I approached the development of the education platform from my standing as researcher in civil and environmental engineering. In order to integrate industry and academia, I partnered with stakeholders who represent construction trades and K-12 education. Together we laid the groundwork for leveraging school district green energy facility projects funded by California Proposition 39 to build an education platform that follows the facilities project process. Through the last two decades of work from Stanford University's Project Based Learning (PBL) Lab, adoption of PBL created an environment for learning-by-doing in which students from different schools and countries successfully apply classroom knowledge to solve real world design problems, become student mentors, and interact with industry. Additionally, the K-12 collaborators to this thesis project have started using PBL. I have planned course modules leveraging Project Based Learning (PBL) to enable multiple levels of education to collaborate within one course. Then, I began partnering with local K-12 teachers to develop the core of the education platform: a building energy efficiency and sustainability course module. These partnerships with the construction trades, K-12 educators, and affiliated education programs are presently ongoing. Overall, this thesis presents an education platform that (1) uses a real building to teach energy efficient building design and operation and (2) partners students with industry. The presented platform integrates students from the secondary, apprentice, undergraduate, and graduate levels. This platform targets underrepresented communities where environmental justice is an everyday reality, college is often an unrealized path, and opportunities in the construction industry are a pathway to financial security.

Advisor: Fischer Martin
Compact and Consensus: American Foreign Policy and the Partisan Tide at the Water's Edge

Since World War II, it has been conventional to claim that the United States maintains a bipartisan foreign policy based on Vandenberg’s famous maxim that “politics stop at the water’s edge.” However, the extent to which the tradition of bipartisanship in foreign affairs holds true today, amidst rampant political polarization, has become the subject of controversy. This thesis introduces new insights from the growing literature on political polarization to bear on a historical analysis of American foreign policy-making, considering the separate impacts of both substantive, ideological polarization and affective, social polarization. In doing so, this thesis makes the case that scholars should pay attention not only to the substantive ideological consensus that developed between both major political parties in the wake of World War II, due to the external pressure of the Cold War, but also recognize the importance of the affective political compact consummated between them that enabled their cooperation in the foreign-policymaking process. Tracing the history of both compact (cooperation) and consensus (agreement) in American foreign policy from their origins through today, this thesis argues that bipartisan compact collapsed as a result of a breakdown in the collaborative process during the Vietnam War, while bipartisan consensus continued to last through the end of the Cold War, after which it gradually eroded in the absence of a clear and significant external threat to compel agreement. As such, American foreign policy has experienced both substantive and affective polarization, resulting in a present period of neither compact nor consensus.

Keywords:  Center for International Security and Cooperation
Center for International Security and Cooperation
Stanford
Honors Program in International Security
Political Science
History
political polarization
U.S. foreign policy
liberal internationalism
bipartisanship
partisanship
World War II
Cold War
Vietnam War
Congress
international institutions
multilateralism
alliances
free trade
foreign aid
military spending
military intervention
substantive polarization
affective polarization
Thesis
Advisor: Kahl Colin
Comparative analysis of ABA-mediated stress regulatory networks across the Brassicaceae

Plants are exposed to a wide range of abiotic stresses in the environment including drought, freezing, and soil salinity. Over evolutionary time, plants have acquired adaptive strategies to survive these stressors. A common type of stress-tolerant plant is a halophyte, which lives along ocean shorelines or near hypersaline lakes. Currently, the gene regulatory networks that underlie a halophyte's adaptation to stress are unknown. Studies in stress-sensitive plants like domesticated crops and Arabidopsis have identified the plant hormone ABA (abscisic acid) as a major regulator of several plant stress responses. Upon the onset of stress, transcription factors downstream of ABA signaling, known as ABRE binding factors (ABFs), activate stress-response genes by binding to cis-regulatory elements known as ABA response elements (ABREs). While components of the ABA-mediated gene-regulatory network have been established in stress-sensitive plants, the regulatory map in the halophytes may have a distinct architecture that underlies adaptive traits. Using root growth assays, I found that halophytes are less sensitive to the growth inhibitory effects of ABA than glycophytes. To identify molecular level differences that mediate growth regulation under stress, I used RNA-Seq and DNA affinity purification (DAP-Seq) and found that halophytes differ in the spatial and temporal expression of genes upon ABA treatment. Some of the halophyte specific genes are associated with previously identified stress tolerance pathways in glycophytes, such as those for cell wall modification and for toxin breakdown. The expression pattern differences are partially mediated by changes in the ABF binding landscape, determined using DAP-Seq. These findings suggest a transcriptional rewiring of ABA-mediated gene-regulatory networks in the halophytes that may contribute to their enhanced tolerance to environmental stress.

Keywords: Biology.
Advisor: Dinneny José R. 
Long Sharon R. 
Morrison Ashby J.
Wang Zhi-Yong
Comparative Study of Nationalistic Rhetoric in Chinese State Media: U.S.- China Trade War vs Huawei CFO Arrest

This research project would like to address the common state challenges in handling nationalistic sentiments under the context of the U.S.-China trade war. The main topic of interest lies in inspecting whether the government uses its monopoly over the media to sway Chinese popular opinion and hence fuel nationalism in the form of anti-American sentiments. Looking predominately at relevant publications by party-owned media during a selected period of the trade war, the thesis shall decipher key messages communicated to its readership as well as the particular use of tone and language in official discourse. Analysis of domestic media will furthermore encompass both Chinese and English language publications with the purpose of seeing whether there are any major inconsistencies when speaking to separate audiences. Lastly, coverage of the contentious Huawei CFO Meng Wanzhou arrest will be used as a point of comparison against general commentary of the trade war, without which one cannot quantity the intensity of nationalistic discourse.
The key research question becomes: How does the Chinese government seek to portray itself and regulate nationalism under the context of the U.S.-China trade war through its own media outlet and censorship tactics, and did the controversial Huawei incident change CCP strategies in governing public sentiments?

Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
International Relations
Donald Trump
Xi Jinping
Asia
Huawei
US-China Relations
Foreign Policy
Trade War
Tariffs
Media Analysis
Chinese Newspaper
Thesis
Advisor: Walder Andrew
Compliant whole-body control of humanoid robots

A major obstacle that prevents humanoid robots from accomplishing real world tasks is their inability to physically interact with, and effectively manipulate, the most common objects generally found in human environments. Even tasks that seem simple for a human remain a significant challenge for most robots. Robots generally employ precision to perform a manipulation task. Humans, in contrast, employ compliance through tactile and force feedback to overcome their imprecision, allowing them to resolve uncertainties associated with the task. The lack of compliance and force control has been indeed a major limiting factor in the ability of robots to interact and manipulate in human environments. One of the major objectives of this research is to endow humanoid robots with whole-body compliant motion abilities. With compliance, a robot overcomes position uncertainties by moving in directions that reduce contact forces, which in turn directs it towards its goal. Whole-body framework was designed to allow the robot to compliantly interact with its environment at multiple contact points. The synthesis of compliant tasks is greatly simplified by being independent of postures and constraints, which are automatically integrated in the control hierarchy. This research focuses on the development of (I) sensor-based whole-body compliant motion primitives, (II) contact sensing and contact force control, (III) whole-body multi-contact for extended support, kneeling, crawling, leaning table, and locomotion strategy to improve support in unstructured terrains, (IV) dynamic collision-free motion planning and (V) dynamic collision-free walking path planning.

Keywords: Mechanical Engineering.
Advisor: Khatib Oussama
Computational linguistic models of police-community interaction

In this dissertation, I use footage from body-worn cameras to analyze the language of police-community interactions during routine traffic stops in three studies exploring officers' words and prosody as well as the back-and-forth structure of these interactions. In the first study, I implement pragmatic theories of politeness in a computational model of officer respect, informed by a thin-slicing study of participant ratings of officer utterances. Inspection of the weights learned by the model shows the strongest influence from negative politeness speech acts like apologizing and reassurance. Using this model I find large-scale evidence that officers speak with consistently less respect towards black versus white community members, even after controlling for contextual factors like the race of the officer and the severity of the infraction. In the second study, I show an analogous racial disparity from prosody alone by obtaining human judgments on content-filtered clips of officer speech. Building a model of human judgments from simple prosodic features I find evidence that officer anxiety is foregrounded as participants judge clips with lower F0, lower intensity, and slower speech rate more highly. In the third study, I develop an ontology of dialog acts unique to the traffic stop interaction, and employ these to show that community members are overwhelmingly compliant in these interactions, but that officers respond differently to less-compliant acts (e.g. rejecting fault or deflecting questions) from black and white community members. Such disparities in common, everyday interactions between police and the communities they serve have important implications for procedural justice, the building of police-community trust, and linguistic theories of interaction. This dissertation presents the first systematic linguistic analysis of officer body-worn camera footage, demonstrating that it can be used as a rich source of data rather than merely archival evidence, and paves the way for developing powerful language-based tools for studying and potentially improving police-community relations.

Keywords: Linguistics.
Advisor: Jurafsky Dan
Eberhardt Jennifer L. (Jennifer Lynn)
Eckert Penelope 
Podesva Robert
Computational linking models of human selective visual attention

To sample the important parts of the visual world observers make saccades, moving the high-resolution and color-sensitive fovea to informative locations. Choosing to make a saccade requires sampling the periphery and identifying potentially important parts of the visual scene. This covert attention, without eye movement, is essential to selecting information in an efficient manner. At an intuitive level covert attention is a focusing on a feature or a location in the visual world and a suppression of other irrelevant features and locations. When operationalized into the laboratory, cueing an observer with covert attention can be shown to result in improved detection, smaller thresholds of discrimination, faster reaction times, and suppression of distractors. These changes are known to be in part the result of small tweaks to the representation of visual stimuli in sensory cortex, but are also the result of context-dependent selection occurring after sensory processing has gone to completion. How attention implements this balance of sensory change and selection is a central problem for the neuroscience of vision.

Keywords: Psychology.
Advisor: Gardner Justin
Grill-Spector Kalanit 
Norcia Anthony Matthew
Computational methods to study mechanical instabilities in soft and multi-physics media

Predictive modeling of complex materials is gaining more and more attention each day as the complexity of problems increases rapidly with innovations in fabrication and monitoring technologies. At the same time, these innovations are revealing unsolved fundamental problems both in nature and engineered systems, mainly because of high nonlinearities in material and geometry, multiple physics, and multiple length and time scale behavior. In this thesis, we focus on developing computational tools to model soft and multiphysics materials as well as to capture geometrical and material instabilities observed in these complex materials. We show that the developed computational schemes successfully pinpoint the onset and simulate the evolution of instabilities in soft materials under large deformations, extending the fundamental understanding of the complex bifurcation response of bilayer materials and commonly observed instability modes of buckling, wrinkling, period-doubling, and creasing. Through fundamental studies on the transient nature of poroelastic instabilities, we address the influence of solvent diffusion on instabilities for stimuli-responsive materials such as hydrogels. Particularly, we discuss the numerical modeling aspects of hydrogels along with dissipative fluid transport phenomena through developing new numerically stable mixed isogeometric hydrogel models. We also introduce a new structural stability criteria for hydrogels with a saddle-point formulation, enabling computational studies designed to elucidate the diffusion-driven swelling-induced instabilities of hydrogels. Equipped with the developed accurate and efficient methods, we draw stability diagrams highly relevant to designing functional and tunable soft hydrogel devices over a wide range of length scales.

Advisor: Linder Christian (Engineering professor) 
Borja Ronaldo I. (Ronaldo Israel)
Kuhl Ellen
Computational modeling of CO₂ electrocatalysis on surfaces and interfaces towards C₂ products

Atmospheric carbon dioxide (CO2) concentrations have continually increased to levels that far exceed pre-industrialization, largely due to our dependence on fossil fuels as a source of energy. This demands a renewable and clean source of energy for energy production; the sun is the largest resource at our disposal. The cost of electricity from renewable sources has seen a decline in recent years, leaving an open question of energy storage. To that end, we would like to take nature's design and use this renewable energy to produce solar fuels, similar to the natural process of photosynthesis. Ideally, we would like to reduce CO2 to valuable hydrocarbon products, especially to C2 compounds, which are more energy dense. Decades of research on the electrochemical CO2 reduction reaction (CO2RR) has showed that Cu is the only metal catalyst that can produce a variety of hydrocarbon products but at a low efficiency. Since these early findings, there have been many experimental and theoretical studies to both understand the catalysis on simple transition metal (TM) surfaces and design improved catalysts through a variety of techniques. This thesis is composed of 7 chapters. Chapter 1 is an introduction and details the current energy problem our planet is facing, a potential solution to this problem in the CO2RR, and prior research in CO2RR. Chapter 2 is the methodology and details the methods employed in this work. This includes density functional theory (DFT) to obtain energies of various adsorbed species during catalysis and analysis of this data using methods such as scaling relations and the computational hydrogen electrode. Chapter 3 involves calculations of C-C coupling barriers on various Cu facets at the electrochemical interface, including effects of electric fields, coverage effects, and strain effects. Chapter 4 uses some of these calculations as well as free energy diagrams to elucidate C-C coupling pathways on three facets of Cu: (100), (111), and (211). From our calculations, we advised our experimental collaborators to preferentially expose the (100) facet of Cu nanocubes, which showed an enhanced activity and selectivity towards C2 products. Chapter 5 includes some of these calculations as well as other barriers in the pathways to C2 compounds. These calculations are combined into a microkinetic model to predict reaction rates and compare well with experimental results. After gaining insight into C-C coupling on Cu, the best-known transition metal catalyst, chapter 6 then explores the metal-oxide etal interface for CO hydrogenation, an important step in CO2RR. Metal supported TiO nanostripes are investigated to both gain a fundamental understanding of this complex interface and attempt to break scaling to discover more active and selective catalysts towards CO2RR. Chapter 7 details conclusions drawn from each chapter of the thesis.

Keywords: Chemistry.
Advisor: Martinez Todd J. (Todd Joseph)
Noerskov Jens
Cargnello Matteo