1,687 research outputs found

    On the robustness of Bayesian phylogenetic gene tree estimation

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    Clustering MIC data through Bayesian mixture models: an application to detect M. Tuberculosis resistance mutations

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    Antimicrobial resistance is becoming a major threat to public health throughout the world. Researchers are attempting to contrast it by developing both new antibiotics and patient-specific treatments. In the second case, whole-genome sequencing has had a huge impact in two ways: first, it is becoming cheaper and faster to perform whole-genome sequencing, and this makes it competitive with respect to standard phenotypic tests; second, it is possible to statistically associate the phenotypic patterns of resistance to specific mutations in the genome. Therefore, it is now possible to develop catalogues of genomic variants associated with resistance to specific antibiotics, in order to improve prediction of resistance and suggest treatments. It is essential to have robust methods for identifying mutations associated to resistance and continuously updating the available catalogues. This work proposes a general method to study minimal inhibitory concentration (MIC) distributions and to identify clusters of strains showing different levels of resistance to antimicrobials. Once the clusters are identified and strains allocated to each of them, it is possible to perform regression method to identify with high statistical power the mutations associated with resistance. The method is applied to a new 96-well microtiter plate used for testing M. Tuberculosis

    Fundamental and Applied Problems of the String Theory Landscape

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    In this thesis we study quantum corrections to string-derived effective actions \textit{per se} as well as their implications for phenomenologically relevant setups like the \textit{Large Volume Scenario} (LVS) and the \textit{anti-D3-brane} uplift. In the first part of this thesis, we improve the understanding of string loop corrections on general Calabi-Yau orientifolds from an effective field theory perspective by proposing a new classification scheme for quantum corrections. Thereby, we discover new features of string loop corrections, like for instance possible logarithmic effects in the Kahler and scalar potential, which are relevant for phenomenological applications like models of inflation. In the next part of the thesis, we derive a simple and explicit formula, the \textit{LVS parametric tadpole constraint} (PTC), that ensures that the anti-D3-brane uplifted LVS dS vacuum is protected against the most dangerous higher order corrections. The main difficulty appears to be the small uplifting contribution which is necessary due to the exponentially large volume obtained via the LVS. This in turn requires a large negative contribution to the tadpole which is quantified in the PTC. As the negative contribution to the tadpole is limited in weakly coupled string theories, the PTC represents a concrete challenge for the LVS. The last part of the thesis investigates the impact of α′\alpha' corrections to the brane-flux annihilation process discovered by Kachru, Pearson, and Verlinde (KPV) on which the anti-D3-brane uplift is based. We find that α′\alpha' corrections drastically alter the KPV analysis with the result that much more flux in the Klebanov-Strassler throat is required than previously assumed in order to control the leading α′\alpha' corrections on the NS5-brane. The implication for the LVS with standard anti-D3-brane uplift can again be quantified by the PTC. Incorporating this new bound significantly increases the required negative contribution to the tadpole. In addition, we uncover a new uplifting mechanism not relying on large fluxes and hence deep warped throats, thereby sidestepping the main difficulties related to the PTC

    Development, Implementation, and Optimization of a Modern, Subsonic/Supersonic Panel Method

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    In the early stages of aircraft design, engineers consider many different design concepts, examining the trade-offs between different component arrangements and sizes, thrust and power requirements, etc. Because so many different designs are considered, it is best in the early stages of design to use simulation tools that are fast; accuracy is secondary. A common simulation tool for early design and analysis is the panel method. Panel methods were first developed in the 1950s and 1960s with the advent of modern computers. Despite being reasonably accurate and very fast, their development was abandoned in the late 1980s in favor of more complex and accurate simulation methods. The panel methods developed in the 1980s are still in use by aircraft designers today because of their accuracy and speed. However, they are cumbersome to use and limited in applicability. The purpose of this work is to reexamine panel methods in a modern context. In particular, this work focuses on the application of panel methods to supersonic aircraft (a supersonic aircraft is one that flies faster than the speed of sound). Various aspects of the panel method, including the distributions of the unknown flow variables on the surface of the aircraft and efficiently solving for these unknowns, are discussed. Trade-offs between alternative formulations are examined and recommendations given. This work also serves to bring together, clarify, and condense much of the literature previously published regarding panel methods so as to assist future developers of panel methods

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Transition Physics and Boundary-Layer Stability: Computational Modeling in Compressible Flow

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    Laminar-to-turbulent transition of boundary layers remains a critical subject of study in aerodynamics. The differences in surface friction and heating between laminar and turbulent flows can be nearly an order of magnitude. Accurate prediction of the transition region between these two regimes is essential for design applications. The objective of this work is to advance simplified approaches to representing the laminar boundary layer and perturbation dynamics that usher flows to turbulence. A versatile boundary-layer solver called DEKAF including thermochemical effects has been created, and the in-house nonlinear parabolized stability equation technique called EPIC has been advanced, including an approach to reduce divergent growth associated with the inclusion of the mean-flow distortion. The simplified approaches are then applied to advance studies in improving aircraft energy efficiency. Under the auspices of a NASA University Leadership Initiative, the transformative technology of a swept, slotted, natural-laminar-flow wing is leveraged to maintain laminar flow over large extents of the wing surface, thereby increasing energy efficiency. From an aircraft performance perspective, sweep is beneficial as it reduces the experienced wave drag. From a boundary-layer transition perspective, though, sweep introduces several physical complications, spawned by the crossflow instability mechanism. As sweep is increased, the crossflow mechanism becomes increasingly unstable, and can lead to an early transition to turbulence. The overarching goal of the present analysis then is to address the question, how much sweep can be applied to this wing while maintaining the benefits of the slotted, natural-laminar-flow design? Linear and nonlinear stability analyses will be presented to assess various pathways to turbulence. In addition, companion computations are presented to accompany the risk-reduction experiment run in the Klebanoff-Saric Wind Tunnel at Texas A&M University. Linear analyses assess a wide range of various configurations to inform experimentalists where relevant unstable content resides. A comparison between simulation and experimental measurements is presented, for which there is a good agreement

    On factor models for high-dimensional time series

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    The aim of this thesis is to develop statistical methods for use with factor models for high-dimensional time series. We consider three broad areas: estimation, changepoint detection, and determination of the number of factors. In Chapter 1, we sketch the backdrop for our thesis and review key aspects of the literature. In Chapter 2, we develop a method to estimate the factors and parameters in an approximate dynamic factor model. Specifically, we present a spectral expectation-maximisation (or \spectral EM") algorithm, whereby we derive the E and M step equations in the frequency domain. Our E step relies on the Wiener-Kolmogorov smoother, the frequency domain counterpart of the Kalman smoother, and our M step is based on maximisation of the Whittle Likelihood with respect to the parameters of the model. We initialise our procedure using dynamic principal components analysis (or \dynamic PCA"), and by leveraging results on lag-window estimators of spectral density by Wu and Zaffaroni (2018), we establish consistency-with-rates of our spectral EM estimator of the parameters and factors as both the dimension (N) and the sample size (T) go to infinity. We find rates commensurate with the literature. Finally, we conduct a simulation study to numerically validate our theoretical results. In Chapter 3, we develop a sequential procedure to detect changepoints in an approximate static factor model. Specifically, we define a ratio of eigenvalues of the covariance matrix of N observed variables. We compute this ratio each period using a rolling window of size m over time, and declare a changepoint when its value breaches an alarm threshold. We investigate the asymptotic behaviour (as N;m ! 1) of our ratio, and prove that, for specific eigenvalues, the ratio will spike upwards when a changepoint is encountered but not otherwise. We use a block-bootstrap to obtain alarm thresholds. We present simulation results and an empirical application based on Financial Times Stock Exchange 100 Index (or \FTSE 100") data. In Chapter 4, we conduct an exploratory analysis which aims to extend the randomised sequential procedure of Trapani (2018) into the frequency domain. Specifically, we aim to estimate the number of dynamically loaded factors by applying the test of Trapani (2018) to eigenvalues of the estimated spectral density matrix (as opposed to the covariance matrix) of the data

    Statistical Anomaly Discovery Through Visualization

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    Developing a deep understanding of data is a crucial part of decision-making processes. It often takes substantial time and effort to develop a solid understanding to make well-informed decisions. Data analysts often perform statistical analyses through visualization to develop such understanding. However, applicable insight can be difficult due to biases and anomalies in data. An often overlooked phenomenon is mix effects, in which subgroups of data exhibit patterns opposite to the data as a whole. This phenomenon is widespread and often leads inexperienced analysts to draw contradictory conclusions. Discovering such anomalies in data becomes challenging as data continue to grow in volume, dimensionality, and cardinality. Effectively designed data visualizations empower data analysts to reveal and understand patterns in data for studying such paradoxical anomalies. This research explores several approaches for combining statistical analysis and visualization to discover and examine anomalies in multidimensional data. It starts with an automatic anomaly detection method based on correlation comparison and experiments to determine the running time and complexity of the algorithm. Subsequently, the research investigates the design, development, and implementation of a series of visualization techniques to fulfill the needs of analysis through a variety of statistical methods. We create an interactive visual analysis system, Wiggum, for revealing various forms of mix effects. A user study to evaluate Wiggum strengthens understanding of the factors that contribute to the comprehension of statistical concepts. Furthermore, a conceptual model, visual correspondence, is presented to study how users can determine the identity of items between visual representations by interpreting the relationships between their respective visual encodings. It is practical to build visualizations with highly linked views informed by visual correspondence theory. We present a hybrid tree visualization technique, PatternTree, which applies the visual correspondence theory. PatternTree supports users to more readily discover statistical anomalies and explore their relationships. Overall, this dissertation contributes a merging of new visualization theory and designs for analysis of statistical anomalies, thereby leading the way to the creation of effective visualizations for statistical analysis

    An Efficient Federated Learning Method Enables Larger Local Intervals

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    Federated learning is an emerging distributed machine learning framework that jointly trains a global model via a large number of local devices with data privacy protections. Its performance suffers from the non-vanishing biases introduced by the local inconsistent optimal and the rugged client-drifts by the local over-fitting. In this thesis, we propose two novel and practical methods, FedSpeed and its variant FedSpeed-Ing, to alleviate the negative impacts posed by these problems. Concretely, FedSpeed applies the prox-correction term on the current local updates to efficiently reduce the biases introduced by the prox-term, a necessary regularizer to maintain strong local consistency. Furthermore, FedSpeed merges the vanilla stochastic gradient with a perturbation computed from an extra gradient ascent step in the neighborhood, thereby alleviating the issue of local over-fitting. Then, we introduce two inertial momenta on the global update as the FedSpeed-Ing method, which could further improve the optimization speed. Our theoretical analysis indicates that the convergence rate is related to both the communication rounds T and local intervals K with an upper bound O(1/T) if setting a proper local interval. Moreover, we conduct extensive experiments on the real-world dataset to demonstrate the efficiency of the proposed FedSpeed, which performs significantly faster and achieves the state-of-the-art (SOTA) performance on the general FL experimental settings than several baselines including FedAvg, FedProx, FedCM, FedAdam, SCAFFOLD, FedDyn, FedADMM, etc

    Multi-Scale Fluctuations in Non-Equilibrium Systems: Statistical Physics and Biological Application

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    Understanding how fluctuations continuously propagate across spatial scales is fundamental for our understanding of inanimate matter. This is exemplified by self-similar fluctuations in critical phenomena and the propagation of energy fluctuations described by the Kolmogorov-Law in turbulence. Our understanding is based on powerful theoretical frameworks that integrate fluctuations on intermediary scales, as in renormalisation group or coupled mode theory. In striking contrast to typical inanimate systems, living matter is typically organised into a hierarchy of processes on a discrete set of spatial scales: from biochemical processes embedded in dynamic subcellular compartments to cells giving rise to tissues. Therefore, the understanding of living matter requires novel theories that predict the interplay of fluctuations on multiple scales of biological organisation and the ensuing emergent degrees of freedom. In this thesis, we derive a general theory of the multi-scale propagation of fluctuations in non-equilibrium systems and show that such processes underlie the regulation of cellular behaviour. Specifically, we draw on paradigmatic systems comprising stochastic many-particle systems undergoing dynamic compartmentalisation. We first derive a theory for emergent degrees of freedom in open systems, where the total mass is not conserved. We show that the compartment dynamics give rise to the localisation of probability densities in phase space resembling quasi-particle behaviour. This emergent quasi-particle exhibits fundamentally different response kinetics and steady states compared to systems lacking compartment dynamics. In order to investigate a potential biological function of such quasi-particle dynamics, we then apply this theory to the regulation of cell death. We derive a model describing the subcellular processes that regulate cell death and show that the quasi-particle dynamics gives rise to a kinetic low-pass filter which suppresses the response of the cell to fast fluituations in cellular stress signals. We test our predictions experimentally by quantifying cell death in cell cultures subject to stress stimuli varying in strength and duration. In closed systems, where the total mass is conserved, the effect of dynamic compartmentalisation depends on details of the kinetics on the scale of the stochastic many-particle dynamics. Using a second quantisation approach, we derive a commutator relation between the kinetic operators and the change in total entropy. Drawing on this, we show that the compartment dynamics alters the total entropy if the kinetics of the stochastic many-particle dynamics violate detailed balance. We apply this mechanism to the activation of cellular immune responses to RNA-virus infections. We show that dynamic compartmentalisation in closed systems gives rise to giant density fluctuations. This facilitates the emergence of gelation under conditions that violate theoretical gelation criteria in the absence of compartment dynamics. We show that such multi-scale gelation of protein complexes on the membranes of dynamic mitochondria governs the innate immune response. Taken together, we provide a general theory describing the multi-scale propagation of fluctuations in biological systems. Our work pioneers the development of a statistical physics of such systems and highlights emergent degrees of freedom spanning different scales of biological organisation. By demonstrating that cells manipulate how fluctuations propagate across these scales, our work motivates a rethinking of how the behaviour of cells is regulated
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