VictorJuiz/thesis huggingface dataset:
Statistical inference for safe and continuous navigation in the presence of GNSS spoofing
Keywords: Aeronautics and Astronautics.

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
Statistical theory for the detection of persistent scatterers in insar imagery
Keywords: Electrical Engineering.

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
Stochastic control and deep learning approaches to high-dimensional statistical arbitrage
Keywords: Mathematics.

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
Strategic Publicity?: Understanding US Government Cyber Attribution
Keywords:  Center for International Security and Cooperation
Center for International Security and Cooperation
cybersecurity
attribution
Thesis

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.
Striking the Balance: "Checks and Balances" in the Post-1978 Chinese System
Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
Leadership
Politics
Political System
China
Thesis

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.
Strong tails in multifield inflation
Keywords: Physics.

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
The structure and growth of arborescent lycopsids
Keywords: Geological Sciences.

The biology of arborescent lycopsids has been debated for over a century, however basic aspects of their structure and growth have remained unknown. In this dissertation I apply statistical analyses of the fossil record and describe new fossil specimens to elucidate how arborescent lycopsids produced their bark, secondary vasculature, and rooting organs. Contrary to previous understanding, their bark production would have been limited, their wood production was relatively complex, they produced secondary phloem, and their rooting organs experienced aboveground lateral growth early in ontogeny. Together these findings motivate development of a new establishment model likely involving sequential production of progressively larger stigmarian axes through upright growth
Structure and mechanism of the blood-brain barrier lipid transporter MFSD2A
Keywords: Molecular and Cellular Physiology.

Ion channels and transporters carry out a variety of critical physiological processes, being pivotal in signal transduction, metabolism, and cellular homeostasis. The Major Facilitator Superfamily (MFS) represents the largest group of secondary active transporters identified to date, responsible for translocating numerous diverse substrates. Due to their diverse array of substrates, the MFS proteins represent attractive targets for drug discovery and mechanistic studies. Major facilitator superfamily containing domain 2a (Mfsd2a) is a novel lysolipid transporter within MFS, transporting docosahexanoic acid (DHA) conjugated to lysophosphatidylcholine (LPC), which is crucial for proper brain development. Mfsd2a's lipid transport function has also been found to be key for inhibiting transcytosis at the blood-brain barrier (BBB), ultimately regulating BBB permeability. An exciting possibility raised by this physiological function is modulating Mfsd2a to manipulate BBB permeability for therapeutic treatments. However, a lack of structural information impedes a mechanistic understanding of this key transporter. Herein, I detail the work to determine the cryo-EM structure of Mfsd2a. Based on the Mfsd2a structure, key residues important for activity have also been mapped out in order to improve our understanding of Mfsd2a transport mechanism. The insights into Mfsd2a lipid transport can be leveraged as a foundation to guide future therapeutic development
Structure engineering and electrochemical applications of metal-organic framework-based materials
Keywords: Materials Science and Engineering.

Metal-organic frameworks (MOFs) are a class of crystalline porous solids consisting of metal ions or clusters coordinated to organic ligands. Because of their appealing features such as regular and adjustable pore size, large specific surface area, and diverse topologies and morphologies, MOFs are traditionally used in many applications including gas/vapor separation, gas storage, and catalysis. In the past decade, MOFs have been identified as a promising platform in electrochemical applications. The very recent advances in electrically conductive MOFs further enhanced the prospect of electrochemical applications for MOFs. Despite the increasing interest of applying MOF-based materials in many electrochemical applications, there is still much to be investigated, including the stability, particle size engineering, electronic property, and novel structure development for the next generation of MOF-based materials to promote their electrochemical applications. In this work, I explore the possibility of employing metal-organic framework (MOF)-based materials in electrochemical applications. Chapter 1 introduces the background and motivation of this thesis. Chapter 2 demonstrates the efforts to employ an Aluminum porphyrinic MOF for oxygen reduction reaction. Chapter 3 focuses on particle size study in oxygen electroreduction done by a Zirconium porphyrinic MOF with impressive stability in acidic electrolyte, which provides general guidelines for utilizing non-conductive MOF materials in electrocatalysis. Chapter 4 presents tuning the electronic properties of a series of conductive MOFs comprised of otcaaminophthalocynine ligands and divalent transition metals (Co, Ni and Cu) and then studied their electrocatalytic performances in oxygen reduction under basic condition. The remainder of the dissertation focuses on the design and synthesis of a 3D conductive MOF and its valence-dependent conductivity. These findings provide general guidelines for utilizing MOF materials in electrochemical applications
Stuck in a Bubble: Examining Kindergarten Teachers' Approaches to Social Justice Education in Private Bay Area Classrooms
Keywords:  Graduate School of Education
social justice education
early-childhood
private school
kindergarten
teacher identity
bias
Thesis

The current study aims to highlight the voices of teachers that identify as committed to bringing social justice approaches and curriculums to private early-childhood settings. Particularly, this research attempts to explore the question of how a selection of Bay Area private school kindergarten teachers utilize their own identities and understandings of social justice-oriented approaches to education to affirm diverse identities and embolden students to reflect on and take action toward greater social justice. A series of semi-structured interviews with 11 teachers, representing multiple gender and racial identities, were conducted, transcribed, and coded using an iterative and inductive process. Teachers name personal reflection and continual education as necessary tools they use to help address their own identities and biases, how they interact with their understandings of social justice, and their teaching role. Teachers emphasize how they try to empower students to participate in similar self-reflection and understanding throughout several pedagogical approaches. This study highlights the powerful role early childhood educators view themselves to have in helping students understand and reflect on their own identities, biases, and understandings of social justice. This study also points to the need for further research, structure, support, and evidence-based approaches to aid teachers in successfully teaching and implementing social justice-oriented approaches to education in private early childhood settings.
Studies of neural circuit modulation
Keywords: Biology.

Neural circuit modulations are closely related, if not causally connected, with animal behavioral changes, such as acquired behaviors during learning, periodic sexual behaviors or environment-induced behavioral adaptations. Circuit modulations can be implemented through various ways. For example, at the neuronal level, cellular excitability and excitation-inhibition input balance may be adjusted; at the synaptic transmission level, synaptic strength or numbers may be regulated; and at the population level, particular region may exhibit differential activity intensity or network pattern during the behavioral change. My graduate research involved the neural circuit modulation in the context of three different behavioral adaptations. In the first study, I focused on the modulation of striatal circuits by a neuropeptide called dynorphin. Conditional knocking out dynorphin from the striatum led to an enhanced long-term potentiation (LTP) in the striatal direct-pathway neurons, and rendered the mice to perform better during the reward-guided instrumental learning. This indicates dynorphin may suppress striatal LTP expression and downregulate instrumental learning. The second study focused on the modulation of sensory cortex by long-term environmental stress. We showed that stress led to loss of dendritic spines of layer 5 pyramidal neurons, and a deteriorated performance during whisker-dependent texture discrimination task. This might be caused by the reduced excitability of parvalbumin-expressing inhibitory interneurons (PV+ INs), which altered the excitation-inhibition input balance onto layer 5 pyramidal neurons. The third study focused on the modulation of a ventromedial hypothalamus (VMH) circuit during female sexual cycle. We reported that projections from progesterone receptor expressing neurons in the VMH (PR+ VMH) to the anteroventral periventricular (AVPV) nucleus change across the 5-day mouse estrous cycle, with ~3-fold more termini and functional connections during estrus. This cyclic increase is regulated by estrogen signaling, and essential for female sexual behaviors. This study showed the neural circuit mechanism that links female sexual behavior to the hormonal cycle
A study of the photogalvanic effect in bismuth-based cuprate superconductors
Keywords: Applied Physics.

In this thesis, we probe the fate of inversion symmetry in the pseudogap phase of cuprate high-temperature superconductors. We study the photogalvanic effect in both single and double layer bismuth-based cuprates with an emphasis on both the circular and linear photogalvanic effects. Through a detailed analysis of photogalvanic currents on the (i) polarization of excitation light, (ii) temperature, (iii) angle of incidence, and (iv) orientation of crystals, we find that the pseudogap state breaks inversion symmetry and exhibits a chiral behavior at the length scale of micrometers. We also carry out a full symmetry analysis on photogalvanic tensors, and find that the point group symmetry of the pseudogap is either mm2 (antiferromagnetic) or mm21 (paramagnetic). With domain averaging that maintains chirality, these symmetry groups are consistent with previous measurements that specifically probe rotation symmetry breaking, time-reversal symmetry breaking, or inversion symmetry breaking, and also with some models of the pseudogap order parameter. The origin of such domain averaging is yet to be understood
Style in time : online perception of sociolinguistic cues
Keywords: Linguistics.

Speakers use styles - combinations of socially meaningful sounds - to construct and project different social identities, or personae. How do listeners recognize these sounds as belonging to a particular linguistic style, spoken by a particular kind of person? Sociolinguistic work has shown that the contribution of individual speech features (or cues) is highly mutable, and context-dependent - suggesting that listeners must integrate the meaning contributions of individual cues with all the other social impressions that arise when hearing someone talk. In this dissertation, I ask how and when they do so. I present results from a series of web-based experiments that ask how two cues - -in' (vs. -ing) and HRT ('uptalk') - modulate listeners' implicit, online inferences, and explicit, offline judgments about two social personae; a Tough and a Valley Girl speaker. I first show that the contribution of these cues on explicit, offline judgments is dependent on the contextual information available to listeners: sociolinguistic cues don't vary just in the range of meanings they elicit, but also in the degree to which they give rise to specific meanings. I then show that listeners rapidly reconcile the meaning contributions of sociolinguistic cues when making inferences about a speaker's persona, and that the extent to which listeners' online and offline beliefs are modulated by a given cue is broadly proportional to the cue's socioindexical informativity. I then ask whether these results generalize to an additional set of four voices: I show that they do, and that listeners weigh the meaning contributions of a cue against their existing expectations about the speaker. Finally, I show that when faced with conflicting information about a speaker's persona, listeners will variably weight the contribution of a given cue based on voice-specific detail. Together, these results point to a probabilistic account of sociolinguistic perception in which listeners integrate various sources of contextual and linguistic information, but prioritize and place the greatest weight on the most informative aspects. This dissertation draws on insights from the cognitive sciences in systematically investigating problems and phenomena that are central to the study of sociolinguistic style. In doing so, it provides a methodological and conceptual framework for future experimentation and theorization