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Novitas-ROYAL , 2008,
Abstract: Following the linguistic theory of Systemic Functional Linguistics (SFL), for this paper, I will describe the interpersonal meanings expressed in the lyrics of political rap and gangsta rap. From SFL, I will apply Appraisal to a small corpus of 10 rap songs, comparing 5 political rap songs with 5 gangsta rap songs. Appraisal is a linguistic analytical framework designed to identify evaluation in language. Ultimately, I aim to apply Appraisal so as to describe the ways in which both political rap and gangsta rap actually 'promote' their respective themes, and in turn, hypothesise why it is that a white, suburban, middle-class youth audience seeks to affiliate with gangsta rap rather than political rap.
Hombres y mujeres Tyler  [cached]
Vallvey Arévalo, ángela
Arbor : Ciencia, Pensamiento y Cultura , 2009,
Abstract: Este artículo es una breve mirada sobre los personajes masculinos y femeninos de la escritora Anne Tyler, cuyo indiscutible talento para los caracteres la ha hecho mundialmente reconocida. La autora tiene una inmensa capacidad para bosquejar personajes, sobre todo masculinos, de gran fuerza y originalidad. Es una magnífica retratista de las miserias y alegrías de la clase media occidental.
Convergence and Fluctuations of Regularized Tyler Estimators  [PDF]
Abla Kammoun,Romain Couillet,Frederic Pascal,Mohamed-Slim Alouini
Mathematics , 2015,
Abstract: This article studies the behavior of regularized Tyler estimators (RTEs) of scatter matrices. The key advantages of these estimators are twofold. First, they guarantee by construction a good conditioning of the estimate and second, being a derivative of robust Tyler estimators, they inherit their robustness properties, notably their resilience to the presence of outliers. Nevertheless, one major problem that poses the use of RTEs in practice is represented by the question of setting the regularization parameter $\rho$. While a high value of $\rho$ is likely to push all the eigenvalues away from zero, it comes at the cost of a larger bias with respect to the population covariance matrix. A deep understanding of the statistics of RTEs is essential to come up with appropriate choices for the regularization parameter. This is not an easy task and might be out of reach, unless one considers asymptotic regimes wherein the number of observations $n$ and/or their size $N$ increase together. First asymptotic results have recently been obtained under the assumption that $N$ and $n$ are large and commensurable. Interestingly, no results concerning the regime of $n$ going to infinity with $N$ fixed exist, even though the investigation of this assumption has usually predated the analysis of the most difficult $N$ and $n$ large case. This motivates our work. In particular, we prove in the present paper that the RTEs converge to a deterministic matrix when $n\to\infty$ with $N$ fixed, which is expressed as a function of the theoretical covariance matrix. We also derive the fluctuations of the RTEs around this deterministic matrix and establish that these fluctuations converge in distribution to a multivariate Gaussian distribution with zero mean and a covariance depending on the population covariance and the parameter $\rho$.
Nicholas Tyler; Natural limitation of the abundance of the high arctic Svalbard reindeer
Sven Skjenneberg (ed.)
Rangifer , 1987,
Abstract: Nicholas Tyler successfully defended his PhD thesis at Cambridge University on 21 May 1987. Nicholas Tyler was born in Oxford, educated at Cambridge and now works at the University of Troms , Department of Arctic Biology.
Semicircle Law for Tyler's M-Estimator of Scatter  [PDF]
Gabriel Frahm,Konstantin Glombek
Statistics , 2010,
Abstract: We show convergence in probability of the spectral distribution of Tyler's M-estimator for scatter to the semicircle law.
From Morphology to Neural Information: The Electric Sense of the Skate  [PDF]
Marcelo Camperi ,Timothy C Tricas,Brandon R Brown
PLOS Computational Biology , 2007, DOI: 10.1371/journal.pcbi.0030113
Abstract: Morphology typically enhances the fidelity of sensory systems. Sharks, skates, and rays have a well-developed electrosense that presents strikingly unique morphologies. Here, we model the dynamics of the peripheral electrosensory system of the skate, a dorsally flattened batoid, moving near an electric dipole source (e.g., a prey organism). We compute the coincident electric signals that develop across an array of the skate's electrosensors, using electrodynamics married to precise morphological measurements of sensor location, infrastructure, and vector projection. Our results demonstrate that skate morphology enhances electrosensory information. Not only could the skate locate prey using a simple population vector algorithm, but its morphology also specifically leads to quick shifts in firing rates that are well-suited to the demonstrated bandwidth of the electrosensory system. Finally, we propose electrophysiology trials to test the modeling scheme.
Performance Analysis of Tyler's Covariance Estimator  [PDF]
Ilya Soloveychik,Ami Wiesel
Statistics , 2014, DOI: 10.1109/TSP.2014.2376911
Abstract: This paper analyzes the performance of Tyler's M-estimator of the scatter matrix in elliptical populations. We focus on the non-asymptotic setting and derive the estimation error bounds depending on the number of samples n and the dimension p. We show that under quite mild conditions the squared Frobenius norm of the error of the inverse estimator decays like p^2/n with high probability.
Regularized Tyler's Scatter Estimator: Existence, Uniqueness, and Algorithms  [PDF]
Ying Sun,Prabhu Babu,Daniel P. Palomar
Statistics , 2014, DOI: 10.1109/TSP.2014.2348944
Abstract: This paper considers the regularized Tyler's scatter estimator for elliptical distributions, which has received considerable attention recently. Various types of shrinkage Tyler's estimators have been proposed in the literature and proved work effectively in the "small n large p" scenario. Nevertheless, the existence and uniqueness properties of the estimators are not thoroughly studied, and in certain cases the algorithms may fail to converge. In this work, we provide a general result that analyzes the sufficient condition for the existence of a family of shrinkage Tyler's estimators, which quantitatively shows that regularization indeed reduces the number of required samples for estimation and the convergence of the algorithms for the estimators. For two specific shrinkage Tyler's estimators, we also proved that the condition is necessary and the estimator is unique. Finally, we show that the two estimators are actually equivalent. Numerical algorithms are also derived based on the majorization-minimization framework, under which the convergence is analyzed systematically.
Aspects and challenges of mashup creator design  [PDF]
Lampros Goussis,Ioannis E. Foukarakis,Dimitrios N. Kallergis
Computer Science , 2014, DOI: 10.1109/PCI.2010.22
Abstract: With the advent of Web 2.0, an increasing number of web sites has started offering their data over the web in standard formats and exposed their functionality as APIs. A new type of applications has taken advantage of the new data and services available by mixing them, in order to generate new applications fast and efficiently, getting its name from its own architectural style: mashups. A set of applications that aims to help a user create, deploy and manage his mashups has also emerged, using various approaches. In this paper we discuss the key factors that should be taken into consideration when designing a mashup creator, along with the most important challenges that offer a field for research.
Tyler's Covariance Matrix Estimator in Elliptical Models with Convex Structure  [PDF]
Ilya Soloveychik,Ami Wiesel
Statistics , 2014,
Abstract: We address structured covariance estimation in elliptical distributions by assuming that the covariance is a priori known to belong to a given convex set, e.g., the set of Toeplitz or banded matrices. We consider the General Method of Moments (GMM) optimization applied to robust Tyler's scatter M-estimator subject to these convex constraints. Unfortunately, GMM turns out to be non-convex due to the objective. Instead, we propose a new COCA estimator - a convex relaxation which can be efficiently solved. We prove that the relaxation is tight in the unconstrained case for a finite number of samples, and in the constrained case asymptotically. We then illustrate the advantages of COCA in synthetic simulations with structured compound Gaussian distributions. In these examples, COCA outperforms competing methods such as Tyler's estimator and its projection onto the structure set.
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