Measurement of associated production of vector bosons and top quark-antiquark pairs in pp collisions at √s=7 TeV

The first measurement of vector-boson production associated with a top quark-antiquark pair in proton-proton collisions at √s=7 TeV is presented. The results are based on a data set corresponding to an integrated luminosity of 5.0 fb-1, recorded by the CMS detector at the LHC in 2011. The measurement is performed in two independent channels through a trilepton analysis of tt̄Z events and a same-sign dilepton analysis of tt̄V (V=W or Z) events. In the trilepton channel a direct measurement of the tt̄Z cross section σtt̄Z=0.28-0.11+0.14 (stat)-0.03+0.06 (syst) pb is obtained. In the dilepton channel a measurement of the tt̄V cross section yields σtt̄V=0.43-0.15+0.17 (stat)-0.07+0.09 (syst) pb. These measurements have a significance, respectively, of 3.3 and 3.0 standard deviations from the background hypotheses and are compatible, within uncertainties, with the corresponding next-to-leading order predictions of 0.137-0.016+0.012 and 0.306-0.053+0.031 pb. © 2013 CERN. Published by the American Physical Society.

Inclusive search for a vector-like T quark with charge 2/3 in pp collisions at root s=8 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Fractional fermion charges induced by axial-vector and vector gauge potentials and parity anomaly in planar graphenelike structures

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Impact of insecticide resistance on the field control of Aedes aegypti in the State of São Paulo

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Search for invisible decays of Higgs bosons in the vector boson fusion and associated ZH production modes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Study of vector boson scattering and search for new physics in events with two same-sign leptons and two jets

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Electromagnetic gauge field interpolation between the instant form and the front form of the Hamiltonian dynamics

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mobile vector soliton in a spin-orbit coupled spin-1 condensate

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Vector solitons in a spin-orbit-coupled spin-2 Bose-Einstein condensate

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Search for vector-like T quarks decaying to top quarks and Higgs bosons in the all-hadronic channel using jet substructure

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Learning kernels for support vector machines with polynomial powers of sigmoid

In the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into a high dimensional space using some kernel functions where linear separation is more likely. However, there are some computational drawbacks associated to SVM. One of them is the computational burden required to find out the more adequate parameters for the kernel mapping considering each non-linearly separable input data space, which reflects the performance of SVM. This paper introduces the Polynomial Powers of Sigmoid for SVM kernel mapping, and it shows their advantages over well-known kernel functions using real and synthetic datasets.

Amazonian malaria: Asymptomatic human reservoirs, diagnostic challenges, environmentally driven changes in mosquito vector populations, and the mandate for sustainable control strategies

Across the Americas and the Caribbean, nearly 561,000 slide-confirmed malaria infections were reported officially in 2008. The nine Amazonian countries accounted for 89% of these infections; Brazil and Peru alone contributed 56% and 7% of them, respectively. Local populations of the relatively neglected parasite Plasmodium vivax, which currently accounts for 77% of the regional malaria burden, are extremely diverse genetically and geographically structured. At a time when malaria elimination is placed on the public health agenda of several endemic countries, it remains unclear why malaria proved so difficult to control in areas of relatively low levels of transmission such as the Amazon Basin. We hypothesize that asymptomatic parasite carriage and massive environmental changes that affect vector abundance and behavior are major contributors to malaria transmission in epidemiologically diverse areas across the Amazon Basin. Here we review available data supporting this hypothesis and discuss their implications for current and future malaria intervention policies in the region. Given that locally generated scientific evidence is urgently required to support malaria control interventions in Amazonia, we briefly describe the aims of our current field-oriented malaria research in rural villages and gold-mining enclaves in Peru and a recently opened agricultural settlement in Brazil. (C) 2011 Elsevier B.V. All rights reserved.

Evaluation of a viral vector system, based on a defective interfering RNA of tomato bushy stunt virus, for protein expression and the induction of gene silencing

A viral vector system was developed based on a DI-RNA, a sub-viral particle derived from TBSV-BS3-statice. This newly designed vector system was tested for its applicability in protein expression and induction of gene silencing. Two strategies were pursued. The first strategy was replication of the DI-RNA by a transgenically expressed TBSV replicase and the second was the replication by a so called helper virus. It could be demonstrated by northern blot analysis that the replicase, expressed by the transgenic N. benthamiana plant line TR4 or supplied by the helper virus, is able to replicate DI-RNA introduced into the plant cells. Various genes were inserted into different DI constructs in order to study the vector system with regard to protein expression. However, independent of how the replicase was provided no detectable amounts of protein were produced in the plants. Possible reasons for this failure are identified: the lack of systemic movement of the DI-RNA in the transgenic TR4 plants and the occurrence of deletions in the inserted genes in both systems. As a consequence the two strategies were considered unsuitable for protein expression. The DI-RNA vector system was able to induce silencing of transgenes as well as endogenous genes. Several different p19 deficient helper virus constructs were made to evaluate their silencing efficiency in combination with our DI-RNA constructs. However, it was found that our vector system can not compete with other existing VIGS (virus induced gene silencing) systems in this field. Finally, the influence of DI sequences on mRNA stability on transient GUS expression experiments in GUS silenced plants was evaluated. The GUS reporter gene system was found to be unsuitable for distinguishing between expression levels of wild type plants and GUS silenced transgenic plants. The results indicate a positive effect of the DI sequences on the level of protein expression and therefore further research into this area is recommended.

A computer vision approach for weeds identification through Support Vector Machines

This paper outlines an automatic computervision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the SupportVectorMachines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the SupportVectorMachines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies.

Implementación de una Support Vector Machine en RVC – CAL para imágenes hiperespectrales

El análisis de imágenes hiperespectrales permite obtener información con una gran resolución espectral: cientos de bandas repartidas desde el espectro infrarrojo hasta el ultravioleta. El uso de dichas imágenes está teniendo un gran impacto en el campo de la medicina y, en concreto, destaca su utilización en la detección de distintos tipos de cáncer. Dentro de este campo, uno de los principales problemas que existen actualmente es el análisis de dichas imágenes en tiempo real ya que, debido al gran volumen de datos que componen estas imágenes, la capacidad de cómputo requerida es muy elevada. Una de las principales líneas de investigación acerca de la reducción de dicho tiempo de procesado se basa en la idea de repartir su análisis en diversos núcleos trabajando en paralelo. En relación a esta línea de investigación, en el presente trabajo se desarrolla una librería para el lenguaje RVC – CAL – lenguaje que está especialmente pensado para aplicaciones multimedia y que permite realizar la paralelización de una manera intuitiva – donde se recogen las funciones necesarias para implementar el clasificador conocido como Support Vector Machine – SVM. Cabe mencionar que este trabajo complementa el realizado en [1] y [2] donde se desarrollaron las funciones necesarias para implementar una cadena de procesado que utiliza el método unmixing para procesar la imagen hiperespectral. En concreto, este trabajo se encuentra dividido en varias partes. La primera de ellas expone razonadamente los motivos que han llevado a comenzar este Trabajo de Investigación y los objetivos que se pretenden conseguir con él. Tras esto, se hace un amplio estudio del estado del arte actual y, en él, se explican tanto las imágenes hiperespectrales como sus métodos de procesado y, en concreto, se detallará el método que utiliza el clasificador SVM. Una vez expuesta la base teórica, nos centraremos en la explicación del método seguido para convertir una versión en Matlab del clasificador SVM optimizado para analizar imágenes hiperespectrales; un punto importante en este apartado es que se desarrolla la versión secuencial del algoritmo y se asientan las bases para una futura paralelización del clasificador. Tras explicar el método utilizado, se exponen los resultados obtenidos primero comparando ambas versiones y, posteriormente, analizando por etapas la versión adaptada al lenguaje RVC – CAL. Por último, se aportan una serie de conclusiones obtenidas tras analizar las dos versiones del clasificador SVM en cuanto a bondad de resultados y tiempos de procesado y se proponen una serie de posibles líneas de actuación futuras relacionadas con dichos resultados. ABSTRACT. Hyperspectral imaging allows us to collect high resolution spectral information: hundred of bands covering from infrared to ultraviolet spectrum. These images have had strong repercussions in the medical field; in particular, we must highlight its use in cancer detection. In this field, the main problem we have to deal with is the real time analysis, because these images have a great data volume and they require a high computational power. One of the main research lines that deals with this problem is related with the analysis of these images using several cores working at the same time. According to this investigation line, this document describes the development of a RVC – CAL library – this language has been widely used for working with multimedia applications and allows an optimized system parallelization –, which joins all the functions needed to implement the Support Vector Machine – SVM - classifier. This research complements the research conducted in [1] and [2] where the necessary functions to implement the unmixing method to analyze hyperspectral images were developed. The document is divided in several chapters. The first of them introduces the motivation of the Master Thesis and the main objectives to achieve. After that, we study the state of the art of some technologies related with this work, like hyperspectral images, their processing methods and, concretely, the SVM classifier. Once we have exposed the theoretical bases, we will explain the followed methodology to translate a Matlab version of the SVM classifier optimized to process an hyperspectral image to RVC – CAL language; one of the most important issues in this chapter is that a sequential implementation is developed and the bases of a future parallelization of the SVM classifier are set. At this point, we will expose the results obtained in the comparative between versions and then, the results of the different steps that compose the SVM in its RVC – CAL version. Finally, we will extract some conclusions related with algorithm behavior and time processing. In the same way, we propose some future research lines according to the results obtained in this document.