Fast online computation of the Qn estimator with applications to the detection of outliers in data streams

We present FQN (Fast Qn), a novel algorithm for online computation of the Qn scale estimator. The algorithm works in the sliding window model, cleverly computing the Qn scale estimator in the current window. We thoroughly compare our algorithm for online Qn with the state of the art competing algorithm by Nunkesser et al., and show that FQN (i) is faster, requiring only O(s) time in the worst case where s is the length of the window (ii) its computational complexity does not depend on the input distribution and (iii) it requires less space. To the best of our knowledge, our algorithm is the first that allows online computation of the Qn scale estimator in worst case time linear in the size of the window. As an example of a possible application, besides its use as a robust measure of statistical dispersion, we show how to use the Qn estimator for fast detection of outliers in data streams. Extensive experimental results on both synthetic and real datasets confirm the validity of our approach

AFQN: approximate Qn estimation in data streams

We present afqn (Approximate Fast Qn), a novel algorithm for approximate computation of the Qn scale estimator in a streaming setting, in the sliding window model. It is well-known that computing the Qn estimator exactly may be too costly for some applications, and the problem is a fortiori exacerbated in the streaming setting, in which the time available to process incoming data stream items is short. In this paper we show how to efficiently and accurately approximate the Qn estimator. As an application, we show the use of afqn for fast detection of outliers in data streams. In particular, the outliers are detected in the sliding window model, with a simple check based on the Qn scale estimator. Extensive experimental results on synthetic and real datasets confirm the validity of our approach by showing up to three times faster updates per second. Our contributions are the following ones: (i) to the best of our knowledge, we present the first approximation algorithm for online computation of the Qn scale estimator in a streaming setting and in the sliding window model; (ii) we show how to take advantage of our UDDSketch algorithm for quantile estimation in order to quickly compute the Qn scale estimator; (iii) as an example of a possible application of the Qn scale estimator, we discuss how to detect outliers in an input data stream

Diameter selective characterization of single-wall carbon nanotubes

A novel method is presented which allows the characterization of diameter selective phenomena in SWCNTs. It is based on the transformation of fullerene peapod materials into double-wall carbon nanotubes and studying the diameter distribution of the latter. The method is demonstrated for the diameter selective healing of nanotube defects and yield from C$_{70}$ peapod samples. Openings on small diameter nanotubes are closed first. The yield of very small diameter inner nanotubes from C$_{70}$ peapods is demonstrated. This challenges the theoretical models of inner nanotube formation. An anomalous absence of mid-diameter inner tubes is observed and explained by the suppressed amount of C$_{70}$ peapods due to the competition of the two almost equally stable standing and lying C$_{70}$ peapod configurations

Algorithmic Interpretations of Fractal Dimension

We study algorithmic problems on subsets of Euclidean space of low fractal dimension. These spaces are the subject of intensive study in various branches of mathematics, including geometry, topology, and measure theory. There are several well-studied notions of fractal dimension for sets and measures in Euclidean space. We consider a definition of fractal dimension for finite metric spaces which agrees with standard notions used to empirically estimate the fractal dimension of various sets. We define the fractal dimension of some metric space to be the infimum delta>0, such that for any eps>0, for any ball B of radius r >= 2eps, and for any eps-net N, we have |B cap N|=O((r/eps)^delta). Using this definition we obtain faster algorithms for a plethora of classical problems on sets of low fractal dimension in Euclidean space. Our results apply to exact and fixed-parameter algorithms, approximation schemes, and spanner constructions. Interestingly, the dependence of the performance of these algorithms on the fractal dimension nearly matches the currently best-known dependence on the standard Euclidean dimension. Thus, when the fractal dimension is strictly smaller than the ambient dimension, our results yield improved solutions in all of these settings. We remark that our definition of fractal definition is equivalent up to constant factors to the well-studied notion of doubling dimension. However, in the problems that we consider, the dimension appears in the exponent of the running time, and doubling dimension is not precise enough for capturing the best possible such exponent for subsets of Euclidean space. Thus our work is orthogonal to previous results on spaces of low doubling dimension; while algorithms on spaces of low doubling dimension seek to extend results from the case of low dimensional Euclidean spaces to more general metric spaces, our goal is to obtain faster algorithms for special pointsets in Euclidean space

O combate à desertificação nas cumeadas do baixo Guadiana

Dissertação de mestrado, Gestão Sustentável em Espaços Rurais, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015A cultura rural e agrícola passou, em Portugal, por uma enorme evolução, principalmente a partir dos anos sessenta do século passado. O presente estudo começa por abordar essa evolução, tentando perceber de que forma a vida rural evoluiu, quais os fatores humanos e físicos que intervieram sobre a mesma – o êxodo rural progressivo e a evolução da tecnologia – e quais as consequências que os mesmos trouxeram ao desenvolvimento do território. Uma vez contextualizada a realidade que afeta as regiões rurais, este estudo irá debruçar-se sobre uma delas - a zona da Serra do Caldeirão, onde está inserido o caso de estudo. Procurar-se-á identificar as transformações físicas e culturais que a mesma sofreu, as suas condições edafo-climáticas e os aspetos socioeconómicos, que conduziram a um processo de desertificação que tornou esta região uma região “altamente suscetível à desertificação”. Tentar-se-á portanto perceber, numa primeira fase, quais as razões que levaram ao processo da desertificação e quais as consequências que a mesma provocou essencialmente nos meios rurais. Seguidamente, numa segunda fase, que será o foco desta pesquisa, iremos procurar conhecer que ações de combate à desertificação foram levadas a cabo, pelos privados e pelas entidades oficiais, e de que modo as recomendações do Plano de Ação Nacional de Combate à Desertificação (PANCD) foram tidas em conta na elaboração de projetos ou em ações-piloto desenvolvidas, designadamente na Área Piloto da Serra do Baixo Guadiana, pelo setor público ou por agentes privados

Parameterized Complexity of Asynchronous Border Minimization

Microarrays are research tools used in gene discovery as well as disease and cancer diagnostics. Two prominent but challenging problems related to microarrays are the Border Minimization Problem (BMP) and the Border Minimization Problem with given placement (P-BMP). In this paper we investigate the parameterized complexity of natural variants of BMP and P-BMP under several natural parameters. We show that BMP and P-BMP are in FPT under the following two combinations of parameters: 1) the size of the alphabet (c), the maximum length of a sequence (string) in the input (l) and the number of rows of the microarray (r); and, 2) the size of the alphabet and the size of the border length (o). Furthermore, P-BMP is in FPT when parameterized by c and l. We complement our tractability results with corresponding hardness results

Identification of sex hormone-binding globulin in the human hypothalamus

Gonadal steroids are known to influence hypothalamic functions through both genomic and non-genomic pathways. Sex hormone-binding globulin ( SHBG) may act by a non-genomic mechanism independent of classical steroid receptors. Here we describe the immunocytochemical mapping of SHBG-containing neurons and nerve fibers in the human hypothalamus and infundibulum. Mass spectrometry and Western blot analysis were also used to characterize the biochemical characteristics of SHBG in the hypothalamus and cerebrospinal fluid (CSF) of humans. SHBG-immunoreactive neurons were observed in the supraoptic nucleus, the suprachiasmatic nucleus, the bed nucleus of the stria terminalis, paraventricular nucleus, arcuate nucleus, the perifornical region and the medial preoptic area in human brains. There were SHBG-immunoreactive axons in the median eminence and the infundibulum. A partial colocalization with oxytocin could be observed in the posterior pituitary lobe in consecutive semithin sections. We also found strong immunoreactivity for SHBG in epithelial cells of the choroid plexus and in a portion of the ependymal cells lining the third ventricle. Mass spectrometry showed that affinity-purified SHBG from the hypothalamus and choroid plexus is structurally similar to the SHBG identified in the CSF. The multiple localizations of SHBG suggest neurohypophyseal and neuroendocrine functions. The biochemical data suggest that CSF SHBG is of brain rather than blood origin. Copyright (c) 2005 S. Karger AG, Base

Predictive and Prognostic Molecular Factors in Diffuse Large B-Cell Lymphomas

Diffuse large B-cell lymphoma (DLBCL) is the commonest form of lymphoid malignancy, with a prevalence of about 40% worldwide. Its classification encompasses a common form, also termed as "not otherwise specified" (NOS), and a series of variants, which are rare and at least in part related to viral agents. Over the last two decades, DLBCL-NOS, which accounts for more than 80% of the neoplasms included in the DLBCL chapter, has been the object of an increasing number of molecular studies which have led to the identification of prognostic/predictive factors that are increasingly entering daily practice. In this review, the main achievements obtained by gene expression profiling (with respect to both neoplastic cells and the microenvironment) and next-generation sequencing will be discussed and compared. Only the amalgamation of molecular attributes will lead to the achievement of the long-term goal of using tailored therapies and possibly chemotherapy-free protocols capable of curing most (if not all) patients with minimal or no toxic effects

Bragg Grating Corrosion Sensor

Historically, corrosion has not been included in the calculation of the life expectancy of aircraft. It is well known how stress-corrosion cracking and corrosion fatigue can significantly reduce the life expectancy of structures. Therefore, it can be correctly assumed that some aircraft flying near their expected life might actually be flying well beyond their “safe life”. Furthermore, due to DoD present tight budget requirements, its is expected that some defense aircraft might not be retired at their original expected life but will be reconditioned to fly beyond that time. All of these considerations indicate that early detection, quantification and prevention of corrosion is of critical importance for military aircraft. This is particularly true for Navy aircraft which fly in the most corrosive environment of all services