Frequency dependence of pulsar radiation patterns

We report on new results from simultaneous, dual frequency, single pulse observation of PSR B0329+54 using the Giant Metrewave Radio Telescope. We find that the longitude separation of subpulses at two different frequencies (238 and 612 MHz) is less than that for the corresponding components in the average profile. A similar behaviour has been noticed before in a number of pulsars. We argue that subpulses are emitted within narrow flux tubes of the dipolar field lines and that the mean pulsar beam has a conal structure. In such a model the longitudes of profile components are determined by the intersection of the line of sight trajectory with subpulse-associated emission beams. Thus, we show that the difference in the frequency dependence of subpulse and profile component longitudes is a natural property of the conal model of pulsar emission beam. We support our conclusions by numerical modelling of pulsar emission, using the known parameters for this pulsar, which produce results that agree very well with our dual frequency observations.Comment: 24 pages, 8 figures. Accepted for publication in Ap

A Very Brief Introduction to Machine Learning With Applications to Communication Systems

Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack

Multiscale likelihood analysis and complexity penalized estimation

We describe here a framework for a certain class of multiscale likelihood factorizations wherein, in analogy to a wavelet decomposition of an L^2 function, a given likelihood function has an alternative representation as a product of conditional densities reflecting information in both the data and the parameter vector localized in position and scale. The framework is developed as a set of sufficient conditions for the existence of such factorizations, formulated in analogy to those underlying a standard multiresolution analysis for wavelets, and hence can be viewed as a multiresolution analysis for likelihoods. We then consider the use of these factorizations in the task of nonparametric, complexity penalized likelihood estimation. We study the risk properties of certain thresholding and partitioning estimators, and demonstrate their adaptivity and near-optimality, in a minimax sense over a broad range of function spaces, based on squared Hellinger distance as a loss function. In particular, our results provide an illustration of how properties of classical wavelet-based estimators can be obtained in a single, unified framework that includes models for continuous, count and categorical data types

Continuum modeling of active nematics via data-driven equation discovery

Data-driven modeling seeks to extract a parsimonious model for a physical system directly from measurement data. One of the most interpretable of these methods is Sparse Identification of Nonlinear Dynamics (SINDy), which selects a relatively sparse linear combination of model terms from a large set of (possibly nonlinear) candidates via optimization. This technique has shown promise for synthetic data generated by numerical simulations but the application of the techniques to real data is less developed. This dissertation applies SINDy to video data from a bio-inspired system of mictrotubule-motor protein assemblies, an example of nonequilibrium dynamics that has posed a significant modelling challenge for more than a decade. In particular, we constrain SINDy to discover a partial differential equation (PDE) model that approximates the time evolution of microtubule orientation. The discovered model is relatively simple but reproduces many of the characteristics of the experimental data. The properties of the discovered PDE model are explored through stability analysis and numerical simulation; it is then compared to previously proposed models in the literature. Chapter 1 provides an introduction and motivation for pursuing a data driven modeling approach for active nematic systems by introducing the Sparse Identification of Nonlinear Dynamics (SINDy) modeling procedure and active nematic systems. Chapter 2 lays the foundation for modeling of active nematics to better understand the model space that is searched. Chapter 3 gives some preliminary considerations for using the SINDy algorithm and proposes several approaches to mitigate common errors. Chapter 4 treats the example problem of rediscovering a governing partial differential equation for active nematics from simulated data including some of the specific challenges that arise for discovery even in the absence of noise. Chapter 5 details the procedure for extracting data from experimental observations for use with the SINDy procedure and details tests to validate the accuracy of the extracted data. Chapter 6 presents the active nematic model extracted from experimental data via SINDy, compares its properties with previously proposed models, and provides numerical results of its simulation. Finally, Chapter 7 presents conclusions from the work and provides future directions for both active nematic systems and data-driven modeling in related systems

Evolutionary lessons from drosophila melanogaster for colonization : how do history, selection and effective population size shape evolution?

Tese de doutoramento, Biologia (Biologia Evolutiva), Universidade de Lisboa, Faculdade de Ciências, 2018Understanding the factors that constrain adaptation, namely in a colonization scenario, has been a major topic in evolutionary biology and was the chief focus of this thesis. Using a highly-replicated experimental evolution design with well-characterized Drosophila melanogaster populations, we aimed to respond to several evolutionary questions relevant for the colonization of a new habitat. First, we showed that reduced effective population size (1) impaired the responses to directional selection, (2) increased between-population differentiation, and (3) shaped the signatures of history and chance, which were overrun by selection in larger populations. Second, we saw that interpopulation hybridization can have strong effects on a population’s subsequent evolution, especially under a sustained bottleneck. Most importantly, the outcome of hybridization is unpredictable, due to the complex genetic architecture of fitness-related traits and the multitude of interfering factors. This calls for caution on the use of hybridization in conservation management, especially in small, endangered populations. Third, we showed that evolutionary history is very important for a population’s subsequent evolution and fate, namely in a reverse colonization scenario. We additionally showed that the evolutionary patterns during reverse evolution are contingent to the trait under study. Finally, we presented the first, while crude, experimental test of the Hamiltonian wave of adaptation. We found that (1) small changes in diet can have significant effects on age-specific mortality but could not determine whether adaptation to a novel diet was greater at earlier than later ages, and (2) the age-specific decrease in differentiation between adapted and non-adapted populations, predicted by the Hamiltonian hypothesis, was not verified in our system. Despite the high replication and complex design of our experiments, many questions remain unanswered. Other studies involving genomic analysis of our populations, other traits, and diets will shed light on how history, selection, and effective population size shape evolution during colonization.A compreensão dos factores que limitam a adaptação, nomeadamente durante a colonização, é um tema importante em evolução, sendo o objectivo principal desta tese. Utilizando populações de Drosophila melanogaster bem caracterizadas, num estudo de evolução experimental altamente replicado, procurámos responder a questões evolutivas relevantes para a colonização de um novo habitat. Primeiro, mostrámos que a redução do efectivo populacional (1) diminuiu as respostas à selecção direccional, (2) aumentou a diferenciação interpopulacional e (3) modelou as assinaturas da história e do acaso, rapidamente superadas pela selecção em populações grandes. Segundo, vimos que a hibridação interpopulacional pode ter fortes efeitos na evolução das populações, especialmente sob Ne reduzido. Sobretudo, verificámos que as consequências da hibridação são imprevisíveis, pela complexa arquitectura genética das características da história da vida e multiplicidade de factores que intervêm na sua evolução. Como tal, alertamos para o uso da hibridização em programas de conservação, especialmente em populações pequenas e ameaçadas. Terceiro, mostrámos que a história evolutiva é fundamental para a subsequente evolução da população, nomeadamente num cenário de colonização reversa, e que os padrões evolutivos durante a evolução reversa são contingentes às características analisadas. Finalmente, apresentámos o primeiro, apesar de rudimentar, teste experimental da onda Hamiltoniana da adaptação. Vimos que (1) alterações pequenas na dieta das populações podem ter efeitos significativos na mortalidade específica de cada idade, mas não pudemos determinar se a adaptação à nova dieta era maior em idades mais precoces e (2) a diminuição da idade-específica na diferenciação entre populações adaptadas e não adaptadas, previstas pela hipótese Hamiltoniana, não foi verificada. Apesar da elevada replicação e do complexo design destas experiências, muitas questões permanecem sem resposta. Outros estudos envolvendo análise genómica, outras características e dietas, dar-nos-ão uma melhor compreensão de como a história, a selecção e o efectivo populacional modelam a evolução durante a colonização

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure