VictorJuiz/thesis huggingface dataset:
Speaker and group specificity in spoken word recognition

Spoken words vary phonetically along a number of dimensions, such as duration, pitch, and vowel quality. Much of this variation is associated with social factors like the dialect, age, or gender of the speaker -- a type of variation termed 'socio-indexical'. Traditional theories of speech perception have seen this socio-indexical variation as a source of noise that listeners must 'filter out', in order to match the noisy speech signal to abstract mental representations of words or phonemes. But more recent theories propose that socially salient variation can actually make speech perception easier for listeners, as long as the variation is congruent with their experience (exemplar theory: Goldinger, 1996; Johnson, 2006), because listeners maintain specific detailed memories of instances of language that they have experienced. Exemplar models make fairly narrow predictions about the interactions between specific memories and socio-indexical information -- specific memories are represented only at the surface phonetic level, so they should not affect deeper linguistic systems like semantics; and socio-indexical group recognition is a byproduct of word recognition, happening serially after words are recognized. Recent evidence, however, raises significant questions about these predictions. In this dissertation, I argue that while socio-indexical variation does facilitate word recognition, this facilitation is more widespread than expected. I begin by using several large collections of spoken conversations to determine which words are used more often by women and which more often by men. I then use laboratory experiments to establish that listeners' sensitivity to these group-specific word frequencies interacts with both phonology and semantics beyond the predictions of exemplar theory, suggesting the need for more complex spoken word recognition mechanisms

Keywords: Linguistics.
Advisor: Sumner Meghan
Jurafsky Dan
Podesva Robert
Stabilizing anderson mixing for accelerated optimization

The acceleration of existing algorithms has been a continual effort in the field of optimization, in which Nesterov's acceleration and (quasi-)Newton methods are two of the most representative examples. However, Nesterov's acceleration is specific to gradient methods. And while (quasi-)Newton methods have been commonly adopted to accelerate general iterative algorithms by viewing them as fixed-point iterations, they are typically memory consuming and require sophisticated and potentially expensive line-search procedures. To design widely applicable schemes for black-box, memory-efficient and line-search free acceleration, we resort to Anderson acceleration (AA), which dates back to the 1960s. AA belongs to the family of sequence acceleration methods, which achieve faster convergence through certain sequence transformations. Despite its elegance in implementation, popularity in chemistry and physics, and success in specific optimization problems, a systematic treatment of AA in optimization-related applications was lacking. In addition, although AA has been demonstrated to work well on a wide spectrum of (unaccelerated) algorithms with a small memory (typically 5--10) and essentially no line search in many cases, it also suffers from severe instability issues in general. Moreover, on the theory side, the global convergence theory was missing for AA. This dissertation fills the aforementioned gaps by taking a unified view of AA as either an extrapolation method or a generalized quasi-Newton method, and showing that unlike classical quasi-Newton methods, it is effective without a line search and requires less computation and memory per iteration so long as certain stabilization measures are adopted. Our discussions cover both convex and nonconvex settings, as well as both solvable and pathological scenarios

Keywords: Computational and Mathematical Engineering.
Advisor: Boyd Stephen A.
Kochenderfer Mykel J.
Saunders Michael
Stanford circuit debugging simulator : a new tool for teaching

This thesis details the creation of a virtual circuit debugging simulator for use in an introductory circuit class. The simulator was designed to create an authentic debugging experience for students. Students could virtually take measurements on a breadboard circuit with a digital multimeter. The circuit debugging simulator allowed researchers to study debugging in a controlled setting. The simulator also shows potential as a curricular intervention to help students practice debugging

Keywords: Electrical Engineering.
Advisor: Horowitz Mark (Mark Alan)
Howe Roger Thomas
Wieman C. E. (Carl Edwin)
Statistical inference for safe and continuous navigation in the presence of GNSS spoofing

The past decades have been a success story of Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). Around the world, they have become a fundamental source of position, velocity and time in countless applications such as aircraft, ships, smartphones, power grids and the financial markets. The current rapid advances in automation will likely increase this reliance on satellite navigation, with increased numbers of autonomous systems requiring precise navigation information. However, the accuracy, availability and integrity that has made GNSS a trusted cornerstone of navigation is nowadays challenged by increasing levels of interference. The most dangerous type of interference is spoofing, an event during which the receiver's navigation solution is compromised and provides erroneous information without warning. This can have dire consequences from mission failure to the loss of a vehicle. The purpose of this thesis is to develop statistical algorithms and methods for a GNSS receiver to detect and mitigate spoofing. Specifically, in this thesis I make three main contributions. I develop a highly sensitive, broadly applicable implementation of Direction of Arrival (DoA) based spoofing detection. The method shows two to ten times fewer missed detections than other results published to date. The algorithm and considerations are validated against flight data and live spoofing data. Many more detection metrics apart from DoA exist, each with strengths and weaknesses. I present a general framework for an optimal detection based on an arbitrary number of metrics. The framework results in more than two times fewer missed detections under the most challenging conditions with a guaranteed low number of false alerts. Leveraging the framework, I further demonstrate that the ideal defense design depends on the expected attack mode. Detection of a spoofing event generally results in discontinued use of GNSS for a given time or until having left a particular area. In absence of a comparable alternative to GNSS, the navigation error and uncertainty grows over time. I present an approach enabling the continued use of authentic GNSS signals despite an ongoing attack, while breaking with major assumptions in the literature. I further provide for the first time in the open literature integrity error bounds similar to those provided by Receiver Autonomous Integrity Monitoring (RAIM) throughout the duration of the attack. The results are validated using driving data and simulated spoofing attacks

Keywords: Aeronautics and Astronautics.
Advisor: Walter Todd
Gao Grace X. (Grace Xingxin)
Powell J. David
Statistical learning for large-scale survival data

The constantly growing population biobanks have provided scientists and researchers unprecedented opportunities to understand human diseases genetics. Survival analysis gives insights on the association between the predictors and time-to-event responses and is particularly suitable for such data. On the other hand, millions of genetic variants sequenced from hundreds of thousands of individuals also pose computational challenges. Chapter 1 and Chapter 3 of this dissertation present three methods that reduce the memory requirement and improve the computational speed in analyzing such data. The first method is a variable screening procedure that exploits the sparsity structure on the association between the predictors and the response in high-dimensional datasets, which reduces the frequency of expensive I/O operations for larger-than-RAM data. The second method utilizes a 2-bits-per-entry compact representation specifically for genetic matrices, which further reduces memory requirement and makes our bandwidth bound optimization algorithm scalable to more CPU cores. The third method combines the compact representation for genetic variants and a simplified version of the compressed sparse block format to represent genetic data with a large number of rare variants. The prediction performance of survival models suffers when the number of censored survival time is large. This could happen If we define the survival time as the age of onset of a rare disease. In Chapter 2, I will provide a group-sparse regression-based algorithm to boost the prediction performance on such data. This method is applicable when there are other survival responses with a large number of observed events and are associated with the same predictors as the rare event response. Finally, Chapter 4 provides a baseline-adjusted concordance index as a stable evaluation metric of survival models. This metric is particularly useful in evaluating stratified Cox models, as well as in model selection using cross validation

Advisor: Rivas Manuel (Manuel A.) 
Tibshirani Robert
Taylor Jonathan E.
Statistical learning under resource constraints

Statistical learning algorithms are an increasingly prevalent component of modern software systems. As such, the design of learning algorithms themselves must take into account the constraints imposed by resource-constrained applications. This dissertation explores resource-constrained learning from two distinct perspectives: learning with limited memory and learning with limited labeled data. In Part I, we consider the challenge of learning with limited memory, a constraint that frequently arises in the context of learning on mobile or embedded devices. First, we describe a randomized sketching algorithm that learns a linear classifier in a compressed, space-efficient form---i.e., by storing far fewer parameters than the dimension of the input features. Unlike typical feature hashing approaches, our method allows for the efficient recovery of the largest magnitude weights in the learned classifier, thus facilitating model interpretation and enabling several memory-efficient stream processing applications. Next, we shift our focus to unsupervised learning, where we study low-rank matrix and tensor factorization on compressed data. In this setting, we establish conditions under which a factorization computed on compressed data can be used to provably recover factors in the original, high-dimensional space. In Part II, we study the statistical constraint of learning with limited labeled data. We first present Equivariant Transformer layers, a family of differentiable image-to-image mappings that improve sample efficiency by directly incorporating prior knowledge on transformation invariances into their architecture. We then discuss a self-training algorithm for semi-supervised learning, where a small number of labeled examples is supplemented by a large collection of unlabeled data. Our method reinterprets the semi-supervised label assignment process as an optimal transportation problem between examples and classes, the solution to which can be efficiently approximated via Sinkhorn iteration. This formulation subsumes several commonly used label assignment heuristics within a single principled optimization framework

Advisor: Valiant Gregory
Zaharia Matei
Statistical theory for the detection of persistent scatterers in insar imagery

Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technique for observing subtle deformation of the Earth's surface over time through multiple observations of the same ground area. Because radar backscatter depends on wavelength-scale properties of surfaces, traditional InSAR methods can fail over naturally changing terrain. The persistent scatterer InSAR (PS-InSAR) technique is one important extension for time-series analysis which identifies and utilizes only the most reliable points in InSAR images for analysis. PS-InSAR has been successfully applied to detect mm-level deformation associated with natural hazards such as earthquakes, volcanoes, and landslides. To date, however, the implementation of PS-InSAR has not been fully optimized, which can limit its utility in challenging mixed-terrain regions. In this thesis, we show that these techniques can be further optimized by characterizing the statistics of PS and developing a statistical framework for applying PS-InSAR techniques. There are three major parts to this work. First, we analyze PS density for different terrain types and image resolution and present a model for predicting the change in PS density, which adheres to empirical results within 50% error and closer for points that form the desired network for PS. Second, we characterize the probability distribution functions (PDFs) of the backscatter from PS and non-PS (clutter) and find that both are highly non-Gaussian over a variety of bandwidths and wavelengths. Finally, we demonstrate a novel maximum likelihood PS detector based on these non-Gaussian models. We show results from the improved detector over Hawaii's Kilauea Volcano and California's Central Valley. In both areas, the non-Gaussian detector finds many more PS than in the existing detector, which leads to a more complete map of deformation. Further, we find that the retrieved deformation time-series is consistent with that measured with three other methods: the existing maximum likelihood Gaussian detector, the small baseline subset (SBAS) InSAR method, and GPS

Keywords: Electrical Engineering.
Advisor: Zebker Howard A.
Gao Grace X. (Grace Xingxin)
Nishimura Dwight George
Stochastic control and deep learning approaches to high-dimensional statistical arbitrage

The central problem of this dissertation is the mathematical study of statistical arbitrage in the case of a high-dimensional number of assets, which is analyzed from two complementary approaches. In the first part of the dissertation, we consider the problem from a stochastic control perspective that extends and combines the Avellaneda and Lee model for statistical arbitrage with the classical Merton framework for portfolio theory. In our framework, given a high-dimensional number of assets and a mean-reverting stochastic model for the dynamics of their residuals through a statistical factor model, an investor must decide how to trade the original assets to maximize the expected utility of her terminal wealth in a finite time horizon, while taking into account market frictions and common statistical arbitrage constraints like dollar neutrality. We study continuous-time and discrete-time versions of the trading problem with both exponential utility and a mean-variance objective, and we prove the existence of interpretable analytic or semi-analytic optimal trading strategies through the study of the corresponding Hamilton-Jacobi-Bellman partial differential equations. We supplement this theoretical study with extensive Monte Carlo simulations that provide further insight about the qualitative behavior of the found optimal strategies under different parameter regimes. In the second part of the dissertation, we complement the previous study with a general deep-learning framework that mitigates two limitations of the stochastic control approach: strong modeling assumptions on the residual dynamics, and solving the high-dimensional Hamilton-Jacobi-Bellman equations for more realistic objective functions, models, and constraints. To this end, we frame the residual modeling and trading problems as a double optimal control problem, that we solve numerically by restricting the controls to a series of functional classes that range from classical parametric models to the most advanced neural network architectures adapted to our problem. We test these methods by conducting an extensive out-of-sample empirical study with high-capitalization U.S. equity data over the main families of factor models, which provides a comprehensive analysis of the importance of the different elements of a statistical arbitrage strategy and the gains from machine learning methods

Keywords: Mathematics.
Advisor: Papanicolaou George
Pelger Markus
Giesecke Kay
Strategic Publicity?: Understanding US Government Cyber Attribution

When and under what circumstances does the U.S. government publicly attribute state-backed cyber intrusions? Over the past decade, the U.S. has become increasingly willing to publicly assign responsibility for cyber incidents to state adversaries. Emerging works examine theoretical motivations for states to publicly attribute, without providing a systematic, empirical study of the practice. This thesis seeks to fill this gap by compiling a data set of public attributions by the U.S. government to another state from 2010-2020, in addition to data on timing. My analysis suggests that public attribution by the U.S. government does not occur through a unified policy process, but four distinct “channels” – technical, criminal, official and unofficial policy. Bilateral relations between the U.S. and adversary state, U.S. domestic politics, and attribution by private sector entities have no systematic effect on the timing of public attribution. Moreover, two case studies of public attribution, of Iran’s Operation Ababil and Russia’s Dragonfly 2.0 campaign, demonstrate that organizational factors specific to attributing agencies influence the timing of the practice. These findings have implications for the U.S. government’s signaling and development of international norms in cyberspace.

Keywords:  Center for International Security and Cooperation
Center for International Security and Cooperation
cybersecurity
attribution
Thesis
Advisor: Grotto Andrew
Zegart Amy
Strategies for analyzing spatial single cell data

There are multiple levels of heterogeneity within the tumor microenvironment from molecular heterogeneity, to heterogeneity in cellular populations. This heterogeneity is a major factor in differentiating tumors that progress from those that will remain indolent. One critically understudied aspect of heterogeneity in the tumor microenvironment is spatial context. Specifically, leveraging where cells are located and what is surrounding them in the tissue, to better understand tumor progression. While the number of studies profiling cancer through the lens of spatially constrained phenomena is growing, there is a lack of structure to which analyses adhere. As a result, assumptions made are often not clearly detailed, it is difficult to compare and contrast between study methodologies, and important tumoral contexts are often not considered. To this end I have 1) identified and defined four paradigms for the analysis of spatial single cell data; 2) framed spatial interactions in a tumor ecology context; 3) developed an algorithm to identify tumor level contexts; and 4) applied this algorithm to identify differential tumor responses to a hypoxic context and developed a novel spatial biomarker. This work helps to frame cancers as systems and tumoral phenomena as spatially-constrained contexts to better leverage spatial information to discriminate between tumors that will progress from those that remain indolent

Advisor: Altman Russ 
Mallick Parag
Gentles Andrew J.
West Robert (Data scientist)
Striking the Balance: "Checks and Balances" in the Post-1978 Chinese System

For centuries, political theorists have wrestled with the question of how to strike the right balance between granting political leaders authority sufficient to accomplish the functions of government and giving them too much power and thus the capacity to act as unconstrained dictators. This thesis looks at the ways the question was perceived by the American constitutional framers during the Philadelphia Convention in the late 1780s and how they conceived of possible solutions to managing the problem for ideas and illustrations concerning the creation of a viable executive branch. It aims to explore the structural mechanisms that function as the “checks and balances” in the post-Mao Chinese system. Analyzing the writings and statements of Chinese officials, drawn from published Party documents and the scholarly literature, this thesis argues that the political devices implemented by the Chinese leaders in the reform era include the following mechanisms: the system of collective leadership plus division of policy labor with fixed individual responsibilities, institutional norms and disciplinary regulations, and procedures for regulating leadership retirement. The findings of this thesis indicate that attempts to tackle this issue in question in different countries evince a degree of similarity in spite of their different polities. It sheds light on the reason why the United States has relied heavily on structures and divisions of power to constrain the authority of the chief executive while China has focused much more on internal norms and disciplinary rules to regulate the top leader’s behavior. This paper also offers a limited outlook on the expectations of the future development of the Chinese system, which shows how the distribution of power within the top leadership level will evolve based on the political situation at hand.

Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
Leadership
Politics
Political System
China
Thesis
Advisor: Fingar Thomas
Strong privacy for communication, browsing, and storage

As news of large-scale privacy compromises becomes increasingly frequent, there is a need for new tools to protect user privacy across all the technologies we use in our daily lives. This thesis develops new approaches to protecting user data as it is typed into a web browser, when it is transmitted in messaging conversations, and when it resides in cloud storage. The techniques used will vary from new system designs using hardware enclaves to novel applications of zero-knowledge proof systems, but for each application the thesis will show how to achieve order of magnitude performance improvements that enable previously out of reach use cases

Advisor: Boneh Dan
Durumeric Zakir
Zaharia Matei
Strong tails in multifield inflation

We investigate the tail of the distribution of cosmological perturbations in multifield inflation. We show that it can be quite generally parametrically non-Gaussian and heavy. This leads us to a new primordial black hole (PBH) production mechanism, that does not violate the current constraints from the CMB. We then study the connected N-point correlation functions of these theories and find that they have a general N!-enhancement. Finally, we present a new mechanism for inflation which exhibits a speed limit on scalar motion, generating accelerated expansion even on a steep potential, by explicitly integrating out the short modes of the additional fields coupled to the inflaton

Keywords: Physics.
Advisor: Silverstein Eva
Kallosh Renata
Linde A. D.