VictorJuiz/thesis huggingface dataset:
Network clustering and graph cluster randomization

Networks are a fundamental tool to model complex systems from diverse domains. Clustering is a prominent attribute of real-world networks, which underpins many ideas in the theory and methodology research as well as applications. This dissertation consists three recent works in this direction. For theoretical understanding, we propose new metrics to quantify the level of network clustering, and develop new theory on the existence of network community structure from the local triadic closure phenomenon. For methodology, we propose the first motif-aware local graph clustering algorithm to detect the community structure on a given network. This algorithm enjoys both the improved performance of motif-based clustering methods and scalability of local clustering algorithms. In application, we propose a new framework for the causal inference of A/B testing in the presence of network interference, which utilizes randomized graph clustering algorithms. We show that, using random graph clusterings in the design and analysis of experiments can significantly reduce the variance in estimating the global average treatment effect

Advisor: Lai T. L.
Benson Austin 
Ugander Johan
Neural circuitry of self-self and self-non-self interaction

Humans typically experience life as a single self that is tightly associated with a body, conscious awareness, sense of agency, and presence in reality. Indeed, self is an ubiquitous form of human mental experience, and the recognition of a single self within each person influences the way we interact with each other, participate in political, legal, medical, cultural systems, raise children, and relate to death. In this dissertation, I describe how early insights during my first rotation project in the peripheral nervous system inspired experiments of a complex condition called dissociation in which the self is perceived to be disconnected from one's self. Appreciating that humans and social animals brains not only generate self and self-self interaction, but also model or and attend to non-self selfs, I present studies of innate visual animacy attention in rodents. Together, these three efforts investigate neural circuitry of self-self and self-non-self interaction

Keywords: Bioengineering.
Advisor: Deisseroth Karl
Luo Liqun
Malenka Robert C.
Nuyujukian Paul Herag
Neural dynamics in the rodent prefrontal cortex underlying behavioral flexibility

Substantial evidence indicates that the prefrontal cortex (PFC) plays a crucial role during flexible behavior, such as shifts in cognitive strategy. Studies of the brain have also increasingly drawn on ideas from reinforcement learning to provide a framework with which to study the neural basis of decision making. In the work described in this thesis, we merge these two lines of thought, applying a reinforcement learning framework to a rule switching task to understand how neural dynamics of PFC might underlie the brain's representation of, and switches between, different action-reward contingencies. We used a miniature, head-mounted fluorescence microscope to image the calcium dynamics of hundreds of individual neurons in the PFC of mice that switch between two different reward contingencies to earn a water reward. We established the necessity of mouse PFC for switching between rules in this task, and identified robust, stable representations of reinforcement learning variables. We then used optogenetic manipulation to investigate the role of those representations in the mouse's ability to switch between rules. Finally, we combined this technique with simultaneous calcium imaging to determine how those signals influence subsequent coding in prefrontal cortex. Overall, this work demonstrates a powerful approach to study cognitive flexibility in the PFC and supports the idea that the PFC applies reinforcement learning dynamics to solve a rule switching task

Advisor: Schnitzer Mark Jacob
Druckmann Shaul
Ganguli Surya
Newsome William T. 
Wagner Anthony David
A new genetic model organism for primate-specific cardiac function and disease

Due to differences in cardiac structure and function, it has become increasingly clear that many aspects of cardiovascular anatomy, physiology, biochemistry, and disease are not well modeled in mice. This has spurred a search for new model organisms with the practical advantages of mice but that more closely mimic human biology and disease. This study examines mouse lemur (Microcebus species) -- the world's smallest, most prolific, and among the most abundant non-human primates -- and the cheapest and easiest to maintain, as such a model. Little is known of lemur cardiovascular physiology, cell types, or pathology. This thesis describes the characterization of normal cardiac anatomy and histology, and baseline values for heart rate, cholesterol/lipids, and cardiac waveform by electrocardiography (ECG). It also describes the development of a single cell RNA-sequencing and analysis pipeline for Microcebus murinus that provided genome-wide expression profiles of thousands of cardiac cells. Computational clustering of these transcriptomic profiles, and analysis of cluster-specific expression patterns of mouse lemur orthologues of canonical markers of human and mouse cardiac cell types, identified all major and many minor cardiac cell types in mouse lemur, including several exceedingly rare cell types. To begin to identify cardiac diseases in mouse lemur, a portable 3-lead ECG monitoring device was used to screen ~300 laboratory Microcebus murinus mouse lemurs and ~100 wild Microcebus rufus and a new species of mouse lemurs in Ranomafana National Park in Madagascar. This identified 20 naturally-occurring ECG variants in 170 affected lab and wild animals, with all identified variants resembling known human ECG variants including 8 major cardiac arrhythmias and pathologies plus 7 additional potentially pathological variants. The screen uncovered the first cases of heart disease in lemurs. One is atrial fibrillation, the most common arrhythmia in humans. Another is mouse lemur sick sinus syndrome (SSS), with episodic bradycardia resembling human SSS, whose familial forms arise from mutations in cardiac ion channel genes HCN4 or SCN5A. Both of these mouse lemur pathologic arrythmias show familial clustering, consistent with a genetic origin. The family pedigrees can now be used to determine the inheritance pattern of these diseases, map the underlying disease genes, and sequence the disease loci to identify the causative mutation. The results establish the mouse lemur as the first systematic, high-throughput primate genetic model for cardiovascular physiology and disease, and reveal human-like cardiac cell types and diseases. The approach uncovered naturally-occurring cardiac diseases that have not been found in mice. Furthermore, the strategy outlined here serves as a paradigm for identifying and studying physiological and behavioral traits and diseases beyond the cardiovascular system in this new primate genetic model organism

Keywords: Biochemistry.
Advisor: Harbury Pehr
Krasnow Mark
Quertermous Thomas
Wu Joseph Ching-Ming
New mechanisms of ionizing radiation detection for time-of-flight positron emission tomography (ToF-PET) : modulation of optical properties and Cherenkov luminescence

Positron emission tomography (PET) is a non-invasive imaging technology used every day throughout the world that enables visualization and quantification of the molecular signatures of disease in living subjects in the clinic as well as in biological research. Photon time-of-flight (ToF) capability is a critical and highly desirable feature for PET since it enables substantial signal amplification that enhances signal-to-noise ratio (SNR) in reconstructed images, which can be exploited for better lesion visualization and quantification, or to reduce patient injected dose and/or shorten PET scan duration. This ToF enhancement is determined by the PET system's coincidence time resolution (CTR). With a goal to drastically improve CTR over the conventional scintillation-based detection approach used in all existing PET systems, I have explored a new mechanism of ionizing radiation detection using ionization-induced optical property modulations. I have also studied the Cherenkov luminescence emission in novel materials. I have performed theoretical simulations showing that pulse-counting annihilation photon detection as required in PET is feasible by detecting ionization-induced optical property modulations. The lower limit on the achievable CTR for this technique is estimated to be a few picoseconds. I have developed the first proof-of-concept experimental arrangement that shows that the mechanism of optical property modulation (e.g., small changes in refractive index) can be used to detect the effects of ionizing photons. With efforts to amplify the ionization-induced optical property modulation signal and improve the detection sensitivity of experimental setups, I have shown that individual ionizing particle detection can be achieved. I have also performed time correlation experiments and described a promising spectral encoding readout method, which is not limited by electronic readout jitter, and demonstrates the potential for optical readout of ionizing photon interactions to achieve even femtosecond scale timing resolution. In addition, I have studied the timing capability of detecting Cherenkov luminescence in new perovskite bulk crystals as potential ionizing radiation detectors for PET. With outstanding energy resolution, relatively low cost, and an easy crystal growth process in addition to excellent CTR, these perovskite crystals are promising candidates for PET detectors

Keywords: Electrical Engineering.
Advisor: Levin Craig 
Fejer Martin M. (Martin Michael)
Harris J. S. (James Stewart)
Miller D. A. B.
New methods for variable importance testing with applications to genetic studies

The objective of this thesis is to develop new practical and principled statistical methodology for the analysis of genome-wide association data, in order to identify, as precisely as possible, the genetic variants that affect complex phenotypes. This problem can be stated as one of testing multiple hypotheses of conditional independence between many possible explanatory variables and a response of interest, within a high-dimensional non-parametric regression setting. This dissertation builds upon previous work on knockoffs, which provides a general framework for addressing such variable importance testing problems. In particular, we study how to generate valid knockoffs for genetic variants, while taking into account the particular structure of these data and the hidden Markov models developed by geneticists to describe their distribution. As a result, we can obtain an effective and statistically rigorous tool for genetic mapping that controls the false discovery rate under minimal assumptions, while overcoming many of the limitations of the existing state-of-the-art methods. Extensive numerical experiments with genetic data confirm the empirical validity and effectiveness of our method, while applications to the analysis of large data sets lead to many new discoveries

Keywords: Statistics.
Advisor: Candès Emmanuel J. (Emmanuel Jean)
Sabatti Chiara
Tibshirani Robert
New problems and perspectives on learning with limited memory

This thesis explores various new questions around the role of memory in learning. These questions are motivated by the scale of modern machine learning models and datasets which have made memory a bottleneck in many practical settings, but at their core they are fundamental questions about the interaction of data, computation and learning. Following are some of the questions we consider: 1) What role does memory play in learning and optimization? Are there inherent trade-offs between the amount of memory required for learning or optimization, and the amount of data or computation required? 2) How do we learn concepts and make accurate predictions with small memory? When can we solve learning problems on high-dimensional data with memory much less than the dimensionality of the data? 3) How do we consolidate and reference memories to make predictions about future observations? When is it necessary to leverage long-term memory of observations from the distant past to make accurate predictions? In this thesis, we distill down these questions and formalize them to enable a systematic study. We undertake both a theoretical and an experimental study---showing new inherent limitations on learning with limited memory, and also developing new algorithms which can solve many problems of interest with a small memory footprint in practice. These results illuminate new perspectives on the role of memory in learning, and also suggest several directions of future research

Keywords: Electrical Engineering.
Advisor: Valiant Gregory
Duchi John
Wootters Mary
News in the Age of New Media: The Changing Meaning of News as told by the Story of CNN

This thesis examines the meaning of “news” in the age of new media. Contemporary scholarship across communication studies, political science and computer science draws attention to numerous problems impacting the state of media today: disinformation, political polarization, and declining public trust describe just a few of these conversations. At the foundation of all of these conversations lies a deceptively simple question: what does “media” mean today in the first place? To answer this question, my thesis traces the changing meaning of news in the age of new media through the story of the Cable News Network (CNN). As the first international 24-hour cable news network, CNN embodies many elements of new media. In my thesis, CNN provides a window into 1) the rapid technological, cultural and institutional developments that were foundational to emergence of new media and 2) the changing meaning of journalism in the era of new media. To contextualize the emergence of new media, I examine the technological innovation of satellite technology; the cultural shift that increased public desire for news; and the institutional changes in the news industry marked by the Federal Communications Commission’s (FCC) deregulations. I then identify the changing meaning of news in the age of new media. I find that news in the new media era is best described as a paradox that allows for far more and yet remarkably less: innovations in new media opens new frontiers for journalism, but the incredibly fast pace of change amplifies existing problems in media. News was able to develop more types of stories, but less stories focused on news. News expanded audiences internationally, but the nonstop newscycle disrupted viewer trust in media. News diversified content options, while saturated competition siloed viewers. My story of CNN from 1980 to the late 20th Century presents a narrative that contextualizes the changing meaning of news in the age of new media. Clarity on what “media” means will provide a foundation for contemporary scholarship that focuses on the causes and solutions to the problems in the state of media today.

Keywords:  Fisher Family Honors Program in Democracy
Stanford University
media
disinformation
cnn
history
Thesis
Advisor: Hamilton James
NGOs, the state, and legitimacy in contemporary China

What explains the explosive growth of the NGO sector in China in recent decades? The extant literature on civil society and the state focuses on the organizations themselves, which falls short in explaining the phenomenon. By parting away from the agency and focusing on the state incentives, this dissertation explores why the state has facilitated the growth of the sector in recent decades. An important assumption that is clarified in this dissertation is that the NGO sector in China not only comprises civilian-established civil society organizations but also government-established groups and former public agencies that have been transitioned to NGOs. When examining state incentives, two incentives emerge - one is economic, which relates to the idea of using the legal term of "NGOs" to downsize parts of the bureaucracy, and second is political, which centers on using NGOs to outsource services with the ultimate goal of appeasing the masses. The former strategy emerged as early as the early 90s and was implemented in several provinces in the early 2000s as a response to fiscal shocks stemming from the tax-for-fee reforms. The comparison of China's case with NGO policy development in other authoritarian states -- Kazakhstan, Russia, and Belarus suggests that China is unique in that its incentive for NGO sector expansion was not only economic but political. The empirical evidence is drawn from county-level statistics, rural governance data (CHIP), fieldwork, web-scraped data on NGOs, a unique survey conducted in the field, and case studies

Keywords: Political Science.
Advisor: Oi Jean C. (Jean Chun)
Laitin David D.
Rodden Jonathan
Nonadiabatic dynamics of gas phase molecules in collaboration with experiment

Photochemistry is the chemical response of molecules to light and drives many natural processes such as photosynthesis, vision, and bioluminescence. Nonradiative decay via intersystem crossing or internal conversion are mechanisms of particular interest due to the direct conversion of energy from light into mechanical motion. This presents unique difficulties to experiment due to the rapid time-scale on which these dynamics often occur. The involvement of multiple electronic states is a commensurate challenge to theoretical methods. Nonetheless, theoretical quantum chemistry approaches have had much success towards this objective and have been used in conjunction with experiment to produce powerful results. In particular, advancements such as GPU acceleration of electronic structure energy and gradient calculations have enabled on-the-fly simulation techniques such as ab initio multiple spawning (AIMS) to simulate these processes. In this dissertation, we use AIMS to produce one-to-one comparisons with experiment through direct modelling of experimental observables. We specifically highlight dynamics involved in photoisomerization of cis-stilbene and photodissociation of ortho-nitrophenol in comparison to time-resolved photoelectron spectra and ultrafast electron diffraction respectively. Additionally, we discuss enhancements of AIMS algorithms to address existing challenges in simulating excitations to higher lying electronic states

Keywords: Chemistry.
Advisor: Martinez Todd J. (Todd Joseph)
Fayer Michael D.
Gaffney Kelly
Nonlinear angles-only orbit estimation for autonomous distributed space systems

There is a growing interest in future space mission concepts which involve the interaction of multiple satellites, including on-orbit servicing and debris mitigation, space situational awareness, and swarm-based sensing. These so-called distributed space systems place strict requirements on spaceborne relative navigation accuracy and robustness, autonomy in early phases of the mission, scalability to multiple agents, and resource efficiency in order to meet mission specifications. This research rises to these demands by focusing on a mid- to far-range estimation method called angles-only navigation, wherein observer satellites use bearing angles obtained from onboard monocular camera imagery of target space objects to infer the target orbital trajectories. In this context, vision-based estimation is chosen because it provides a passive and high-dynamic-range navigation technology that uses simple, flight-proven, and miniaturization-friendly hardware. However, since a target appears as only a cluster of pixels in the images, estimating its orbital motion with respect to the observer is fundamentally constrained due to a lack of range information and generally results in poor (or even divergent) navigation performance. To overcome this limitation, a novel angles-only navigation architecture is developed which leverages a deep insight into the relative motion dynamics and advanced filtering techniques to capture key nonlinearities in the dynamics and measurement modeling that lead to range disambiguation. Whereas current approaches require repetition of complex maneuver profiles to gain new vantage points on the target for range rectification, the new methods posed here are completely maneuver-free. To address further deficiencies in the current state of the art, the proposed framework generalizes the applicability of angles-only navigation to new domains beyond low Earth orbit, to situations where no prior state knowledge is available for estimator initialization, and to new scenarios involving multiple observers and/or targets. The functionality and performance of the proposed navigation architecture are verified using rigorous software-based and hardware-in-the-loop simulation. Finally, the algorithms developed in this work have led to the systematic design of the Starling Formation-flying Optical eXperiment (StarFOX), which will test angles-only navigation for future deep-space swarms while flying on board NASA's Starling1 technology demonstration formation in 2021

Keywords: Aeronautics and Astronautics.
Advisor: D'Amico Simone
Rock Stephen M.
Schwager Mac
Nonlinear stability analysis of acoustic streaming using the energy method

Acoustic streaming is recently used in lithium metal batteries to inhibit the formation of dendrites. We review the concepts of hydrodynamic stability and its necessity for finding critical parameters. The energy method is further explored to study the nonlinear stability of outer Rayleigh streaming in a channel. Dimensionless analysis yields a critical parameter in the form of an acoustic Reynolds number and the resulting Euler-Lagrange equations contain nonlinear eigenvalues. The eigenvalue problem is solved numerically and an optimization algorithm is introduced to deal with the nonlinearity. Discontinuities in the domain were handled by solving and matching two models, and energy stability curves were generated for different wave amplitude ratios and channel thicknesses. The obtained critical numbers showed that monotonic stability is maintained longer in thinner channels due to smaller velocities in the base flow. Instabilities are guaranteed to be absent for acoustic Reynolds number less than 259 for thin channels and 0.011 for thick channels. The results proved to be in-line with experimental observations of acoustic flows in batteries. With the provided nonlinear stability thresholds, minimum requirements for instabilities in a channel are better understood.

Keywords:  Energy Resources Engineering
School of Earth Energy &amp; Environmental Sciences
stability analysis
acoustic streaming
energy method
Thesis
Advisor: Tartakovsky Daniel
A nonparametric measure of conditional dependence

There are numerous problems where one needs to quantify the dependence between two random variables and how this dependence changes by conditioning on a third random variable. Correlated random variables might become independent when we observe a third random variable or two independent random variables might become dependent after conditioning on the third one. Thanks to the wide potential application range e.g., bioinformatics, economics, psychology, etc, finding efficient measures of conditional dependence has been an active area of research in many subareas of statistics and machine learning. However, the literature on measures of conditional dependence is not so large, especially in the non-parametric setting. We introduce two novel measures of conditional dependence, and propose estimators based on i.i.d. samples. Using these statistics, we devise a new variable selection algorithm, called Feature Ordering by Conditional Independence (FOCI). FOCI is model-free with no tuning parameters and is provably consistent under sparsity assumptions. We provide a number of example application analyses to both synthetic and real datasets

Keywords: Statistics.
Advisor: Chatterjee Sourav
Bayati Mohsen 
Taylor Jonathan E.