VictorJuiz/thesis huggingface dataset:
The landscape epidemiology of zoonotic diseases [electronic resource] : rodent-borne bartonella and livestock anthrax

Zoonotic diseases, caused by pathogens that are transmitted between animals and humans, account for the majority of emerging infectious disease events. Zoonotic diseases are maintained in complex cycles that comprise the interactions among hosts, vectors, and pathogens in the environment. Though land use, an anthropogenic change, must certainly impact these diseases, we know surprisingly little about how land use influences many zoonotic disease systems. Consequently, my dissertation focuses on the environmental drivers of two zoonotic diseases in two countries at high-risk for emerging infectious diseases: a rodent-borne genus of bacteria, Bartonella, in Kenya, and a livestock-borne bacterium, Bacillus anthracis, in Bangladesh. In my first chapter, using high-resolution rodent-trapping data and environmental data, I investigated the impact of land use on rodent abundance and diversity in Laikipia, Kenya. My results indicate that coarser soil textures are associated with higher rodent abundance, which may reflect relative ease of rodent burrowing at coarser soil textures. For my second chapter, I investigate the role of land use, controlling for biophysical variables, on density of rodents infected with Bartonella and the implications for human disease risk in Laikipia, Kenya. Areas of greatest risk for increased density of infected rodents were high precipitation wildlife/ranching areas and low precipitation pasture areas. Recent anthrax outbreak investigations in Bangladesh point to soil exposure as the source of livestock anthrax. In my third chapter, using livestock case-control data, soil maps, and gridded precipitation data, I investigated the seasonality and soil characteristics of livestock anthrax outbreaks and the implications for disease control. I found novel soil parameters uncharacteristic of livestock outbreaks in the literature, and a time series analysis of monthly livestock cases and monthly rainfall suggested that outbreaks are largely seasonal—coinciding with the wet season. Further, my findings indicate that seasonal administration of livestock vaccinations is a potentially viable disease control option for livestock anthrax in Bangladesh.

Advisor: Jones James
Luby Stephen
Barry Michele
Gaffikin Lynne
Lambin Eric F. advisor. Thesis advisor
Large-scale inference with block structure [electronic resource]

The detection of weak and rare effects in large amounts of data arises in a number of modern data analysis problems. Known results show that in this situation the potential of statistical inference is severely limited by the large-scale multiple testing that is inherent in these problems. Here we show that fundamentally more powerful statistical inference is possible when there is some structure in the signal that can be exploited, e.g. if the signal is clustered in many small blocks, as is the case in relevant applications. We derive the detection boundary in such a situation where we allow both the number of blocks and the block length to grow polynomially with sample size. This result recovers as special cases the heterogeneous mixture detection problem (1) where there is no structure in the signal, as well as scan problem (2) where the signal comprises a single interval. We develop methodology that allows optimal adaptive detection in the general setting, thus exploiting the structure if it is present without incurring a penalty in the case where there is no structure. The advantage of this methodology can be considerable, as in the latter case the means need to increase at the rate of square root of the log of the problem size to ensure detection, while in the former case the means may decrease at a polynomial rate. The identification version of this problem is also considered in this thesis, where the length of the block is allowed to grow polynomially with the sample size while the number of blocks is assumed to grow at most logarithmically with the sample size. This setting greatly generalizes previous results. The multivariate version of this problem is also considered, in which we try to identify the support of the rectangular signal(s) in the hyper-rectangle. A lower bound below which the identification is impossible is presented and an asymptotically optimal and computationally efficient procedure is proposed under Gaussian white noise. This signal identification problem is shown to have the same statistical difficulty as the corresponding detection problem, in the sense that whenever we can detect the signal, we can identify the support of the signal. We also discuss about the signal identification problem under the exponential family setting and the robust identification problem where the noise distribution is unspecified.

Keywords: Statistics.
Advisor: Walther Guenther primary advisor. Thesis advisor
Lai T. L. advisor. Thesis advisor
Siegmund David
Learning and incentives in computer science [electronic resource]

In this thesis we look at topics in algorithmic game theory, with influences from learning theory. We use tools and insights from game theory to look at areas in computer science, such as machine learning and the bitcoin blockchain, that have been developed incognizant of incentives for selfish behavior. Here we 1) show that there are incentives to behave in a way that's harmful for the system, 2) give mechanisms where the incentives for the individual and group are aligned, and 3) measure how harmful (having to account for) selfish behavior is. In particular we study the problem of online prediction with expert advice, where we show that when experts are selfish and care about their reputation, the design of incentive compatible algorithms is tightly coupled to strictly proper scoring rules. We give algorithms that have good regret guarantees, and we prove that it is not possible to match regret guarantees for honest experts. For Bitcoin, we show that the way rewards are shared in mining pools can lead to miners strategically hiding work to improve their payout. This holds true even in the absence of outside options for miners, such as other pools or solo mining. We give a novel reward sharing function that does not have perverse incentives, and analyze its performance analytically and through simulations. On the other hand we look at how data can be used in a variety of game theory applications. We ask the questions 1) how can we use data to replace standard informational assumptions in auction theory, 2) how much data do we need for good results in this area, 3) how can we use data to learn about the utilities of agents when we observe behavior, and finally (as misrecorded data may lead algorithms astray) 4) how can we find anomalies in a data set in an unsupervised manner? For the first two questions we look at position auction environments where we give the first computationally efficient algorithm for i.i.d.~bidders with irregular value distributions, that achieves revenue arbitrarily close to optimal using polynomial samples. Due to the low sample complexity, our approach leads to a no-regret algorithm in the online setting. To address the third question, we give a computationally efficient algorithm that computes, given a correlated equilibrium (which may be a pure or mixed Nash equilibrium), the set of utilities that are consistent with it. Finally, we give an unsupervised anomaly detection algorithm that runs in a stream, and we show its performance through experiments on real and synthetic data.

Advisor: Roughgarden Tim primary advisor. Thesis advisor
Boneh Dan
Goel Ashish advisor. Thesis advisor
Lambert Nicolas
Learning the effects of genetic variation on gene regulation across human tissues [electronic resource]

Genome-wide studies have identified tens of thousands of associations between genetic variants and human phenotypes, but few trait associations have been linked to causal mechanisms. Many associated variants reside outside coding regions, suggesting that an appreciable proportion of associations may be due to altered gene expression. Most work on gene regulation has been in single tissues, but this limited setting does not provide a complete picture because gene regulation varies between tissues. In this dissertation, I present my work developing and applying methods to learn how genetic variation alters gene expression across human tissues. First, I analyzed the small RNA of cells and exosomes from large family to answer questions about the impact of genetic variation beyond individual cell types. Exosomes are small extracellular vesicles that can shuttle material, including small RNA, between cells. To understand how cells can influence their neighbors, I characterized the regulation of small RNA cargo in exosomes. I conducted the first association study of microRNA expression in exosomes and detected evidence that genetically controlled expression variation within a cell can carry forward to the exosomes it releases. Second, I evaluated a simple metric, the median Z-score, for identifying multi- tissue gene expression outliers. These are individuals with extreme expression of a particular gene across tissues compared with the rest of the population. Using v6 data from the Genotype-Tissue Expression Project (GTEx), I have shown that expression outliers enable us to discover rare variants with large effects on expression and have characterized the functional properties of these rare variants. Third, I describe a new method for discovering multi-tissue gene expression outliers while modeling the correlation structure between tissues. I present findings from applying this method to v7 GTEx data, which include over 400 more individuals with whole genome sequencing than v6, in addition to the multi-tissue RNA-seq measurements. This new approach identifies expression outliers with complementary properties to those found using the median Z-score.

Advisor: Montgomery Stephen
Bustamante Carlos (Carlos D.) advisor. Thesis advisor
Kundaje Anshul
Level Up Languages: Contextualizing Foreign Language Learning Through Text-Based Adventure Games

Outside of formal language classrooms, the resources to learn a foreign language are limited. Many people traveling abroad turn to technology for help, and take advantage of free language learning apps like Duolingo. However, users of these programs often feel like they can’t transfer what they’ve learned from the app to a real-world interaction where they want to accomplish a specific communicative goal. Research has pointed to contextualized practice to help with transfer, and the pedagogical approach of Task-Based Language Teaching (TBLT) lends itself well to this practice. However, the most popular language learning apps do not use TBLT in their approach to language teaching because of the difficulty of incorporating such a complex instructional system into their simplified instructional mechanics. One area of technology that can easily incorporate TBLT into its instructional approach is video games. Research supports the benefits of video games for learning as well as the relevance of TBLT in gaming. Several language games currently use TLBT as their pedagogical model; however, of those that are available to the public, none incorporate contextualized tasks into a larger cohesive game narrative. To address this gap, I propose to incorporate authentic, contextualized, and relevant tasks for the motivated traveler within the engaging narrative of a text-based adventure computer game. My solution focuses on 1) addressing essential language for travel and 2) increasing learner engagement by contextualizing learning through story narrative.

Keywords: Language Learning
Task-Based Language Teaching
Gaming
Computer Assisted Language Learning
Digital Game-Based Language Learning
Thesis
Leveraging similarity in statistical learning [electronic resource]

Machine learning literature has provided a toolbox of techniques for general use by the modern applied statistician. For some data analysis, these methods can be improved to yield better results based on the properties of the data in question. In this manuscript, we develop methodology for three different problems in which, in some sense, similarity can be leveraged to make better predictions, as demonstrated in baseball and epidemiology applications. First, we propose adding a penalty on the nuclear norm of the regression coefficient matrix in multinomial regression to learn which outcomes are similar. Second, we propose adding a clustering step to l1 penalized regression, to build customized training sets of observations that are similar to the test set. Third, we propose and evaluate several approaches to mining electronic medical records to support physician decision-making with data on patients similar to a patient in question. This last problem is especially challenging because of the observational and high-dimensional nature of the data.

Keywords: Statistics.
Advisor: Tibshirani Robert primary advisor. Thesis advisor
Friedman Jerome
Hastie Trevor advisor. Thesis advisor
Lightweight statistical learning [electronic resource] : accelerating and avoiding empirical risk minimization

In statistical machine learning, the goal is to train a model that, once deployed in the world, continues to predict accurately on fresh data. A unifying training principle is empirical risk minimization (ERM): globally minimizing the average prediction error incurred on a representative training set. Essentially, ERM prescribes optimization as a proxy for statistical estimation. Learning tasks for which ERM is computationally tractable call for optimization algorithms that scale as efficiently as possible as training set dimensions grow. Other tasks---namely, neural network learning and learning with indirect supervision---elude a general, tractable ERM algorithm in theory, motivating a reformulation of the training problem altogether. In this thesis, we first focus on efficient algorithms for empirical risk minimization in the overdetermined, convex setting with fully-observed data, where ERM is tractable and the aim is optimal computational efficiency. We improve the guaranteed running time for a broad range of problems (including basic regression problems) in terms of the condition number induced by the underlying training set. Atop these methods, we develop an algorithm for principal component regression. Next, we move to settings where general tractability of ERM is not guaranteed, but still guides algorithm design in practice. Specifically, we study two learning frameworks: convolutional neural network learning, and learning conditional random fields from indirect supervision. In either context, the prevalent replacement to global ERM is gradient descent. Since descent could converge to arbitrarily bad local optima, it renders initialization more relevant. In each setting, we develop lightweight training algorithms based on convex procedures. For neural networks, we consider replacing optimization of hidden layers with randomization. For indirect supervision, we reduce estimation to solving a linear system followed by a convex maximum-likelihood step. Via the role of initialization, both algorithms imply conditions under which local descent ought to fare at least as well as they do.

Advisor: Liang Percy primary advisor. Thesis advisor
Duchi John advisor. Thesis advisor
Valiant Gregory advisor. Thesis advisor
Literature and existentialism in Italy [electronic resource] : history and interpretation

Questo lavoro è uno studio (preparatorio) dei rapporti tra esistenzialismo e letteratura italiana nel Novecento secondo un approccio geo-critico, che mira a indagare i rapporti genetici tra letteratura e filosofia. Nel primo capitolo delineo la metodologia critica adottata nel lavoro e i problemi storico-interpretativi che fino ad oggi hanno impedito una lettura di questo genere, attraverso la costruzione di un modello spaziale che tocca quattro città italiane e i rispettivi autori: Milano (Antonia Pozzi e Vittorio Sereni); Firenze (Mario Luzi, Piero Bigongiari, Alessandro Parronchi); Torino (Cesare Pavese, Italo Calvino, Beppe Fenoglio); Padova (Andrea Zanzotto). Il secondo capitolo, che costituisce il cuore della tesi, è dedicato alla poesia di Pozzi e Sereni, e di come Antonio Banfi, Enzo Paci -- e dunque Husserl, Heidegger e Jaspers -- abbiano formato e influenzato la loro poetica. Attraverso un'analisi testuale, ho cercato di far emergere gli elementi di continuità, discontinuità e di novità tra i poeti e filosofi presi in esame, e quali sono le conseguenze sul piano estetico dell'incontro tra poesia, esistenzialismo, fenomenologia e relazionismo.

Keywords: French & Italian.
Advisor: Harrison Robert
Wittman Laura primary advisor. Thesis advisor
Gumbrecht Hans Ulrich advisor. Thesis advisor
Harrison Thomas J.
Local control of magnetic anisotropy [electronic resource]

Investigating physical mechanisms to manipulate magnetic anisotropy is important for technological applications. We use a scanning superconducting quantum interference device (SQUID) microscope to image the response of micron-scale magnetism to externally controlled currents, voltages and strains. The first part of this thesis (chapters 2 and 3) focus on the physics behind devices and materials that could replace silicon transistors. Chapter 2 focuses on a proposal for a new magnetic memory device using spin transfer torque from the spin Hall effect to manipulate ferromagnetic insulators. Ultimately, this proposal did not prove to be fruitful because of diverging ferromagnetic damping at low temperatures and lacking studies on the spin transparency between the spin Hall metal and the magnetic insulator. Chapter 3 is about SQUID characterization of a composite multiferroic, which can be the basis for another magnetic memory proposal. These results proved to be inconclusive, and modeling these systems proved to be difficult because of the multi-domain behavior of these films. The second part of this thesis (chapters 4 and 5) focuses on how a common substrate, SrTiO$_3$ (STO) can profoundly affect electronic properties including transport and magnetism. Chapter 4 describes experiments on the conducting interface between band insulators LaAlO$_3$/STO. We examined magnetism in related compounds LaGaO$_3$/STO and NdGaO$_3$/STO, and found that ferromagnetic patches in those systems, like in LaAlO$_3$/STO was very sparse and nearly non-existent. In addition, we measured how electronic transport on a local scale changed with strain, and our results were inconclusive. Finally, Chapter 5 describes how magnetism in EuS, a ferromagnetic insulator that could be used to induce exotic phases like Majorana bound states or chiral edge states, is strongly coupled to the STO substrate. We explore how strain from the STO substrate could affect magnetic anisotropy. This thesis highlights the importance of temperature on controlling magnetic anisotropy for switching experiments and for constructing devices that require homogeneous magnetic exchange.

Keywords: Applied Physics.
Advisor: Moler Kathryn A. primary advisor. Thesis advisor
Goldhaber-Gordon David
Suzuki Yuri (Applied physicist) advisor. Thesis advisor
Long-term decrease in plate coupling prior to the 2011 Tohoku, Japan earthquake [electronic resource] : insights from GPS and seismicity data and physics-based models

Until the turn of the century, studies of crustal deformation had shown that strain around active fault zones, as measured by geodetic techniques, accumulates at a constant rate between large earthquakes. Over the past fifteen years, however, thanks to the rapid growth of continuous geodetic data, mainly using the Global Positioning System (GPS), a variety of transient deformation signals have been discovered. Mostly occurring in subduction zones, these transient events last for several days to a few years and manifest as anomalous deviations from the interseismic strain rate. In this thesis, I present evidence of a much longer-term deformation transient in northern Japan, over the 15-year period from 1996 to 2011, prior to the M 9 Tohoku-Oki earthquake. GPS position time series in the south-central Tohoku region exhibit nonlinear trends from 1996 to 2011, but in the period 2003--2011 the time series are heavily influenced from postseismic transients due to several M ∼ 6--7 earthquakes. In the first part of the thesis, I correct for these effects and quantify the residual change in interseismic strain rate. I find spatially coherent and statistically significant accelerations in the position time series at numerous stations throughout the Tohoku region. These accelerations can be explained by accelerating aseismic creep on the Japan trench plate interface in the decades before the 2011 Tohoku-Oki earthquake. In the second part of the thesis, I show that changes in the recurrence intervals of repeating earthquakes on the megathrust in the period 1996 to 2011 are consistent with accelerating slip preceding the Tohoku-Oki earthquake. All sequences of repeating earthquakes with statistically significant trends in recurrence interval (at 95% confidence) offshore south-central Tohoku occurred at an accelerating rate. Furthermore, estimates of the magnitude of slip acceleration from repeating earthquakes are consistent with the completely independent geodetic estimates. From joint modeling of the GPS and seismicity data, I infer that a substantial portion of the megathrust experienced accelerating slip, partly surrounding the eventual rupture zone of the M 9 earthquake. The inference of long-term accelerating creep challenges the conventional "asperity model" for the Japan Trench megathrust which, before 2011, posited that the fault was partitioned into fixed velocity-weakening patches (asperities) that are locked interseismically and velocity-strengthening regions that creep stably without accumulating stress. In the third part of the thesis, I use physics-based simulations of earthquake cycles with thermal pressurization to develop a physical model for shrinking asperities. This model may provide a viable physical mechanism for this surprising transient.

Keywords: Geophysics.
Advisor: Segall Paul
Beroza Gregory C. (Gregory Christian) advisor. Thesis advisor
Dunham Eric advisor. Thesis advisor
Loop equations and string dualities in lattice gauge theories [electronic resource]

The purpose of this dissertation is to explore loop equations and string dualities in lattice gauge theories. A lattice gauge theory involves a lattice, a compact Lie group, a matrix representation of the group and a parameter which is called an inverse coupling strength. The main objects of interest in the lattice gauge theories are the Wilson loop variables. A loop equation refers to expressing the expectation of a Wilson loop variable in terms of the expectations of Wilson loop sequences obtained from the loop by various loop operations. These loop equations can be used to establish the 1/N expansion of Wilson loop expectations in a strongly coupled regime. The coefficients of this expansion are represented as absolutely convergent sums over trajectories in a string theory on the lattice, establishing one kind of gauge-string duality. Finally, we will present several applications of this expansion such as the Wilson area law upper bound, the factorization property, and the correspondence of SO(N) and SU(N) Wilson loop expectations in the large N limit.

Keywords: Mathematics.
Advisor: Chatterjee Sourav primary advisor. Thesis advisor
Diaconis Persi advisor. Thesis advisor
Ying Lexing advisor. Thesis advisor