Update on the status of hadronic squeezed correlations at RHIC energies

In high energy heavy ion collisions a hot and dense medium is formed, where the hadronic masses may be shifted from their asymptotic values. If this mass modification occurs, squeezed back-to-back correlations (BBC) of particle-antiparticle pairs are predicted to appear, both in the femionic (fBBC) and in the bosonic (bBBC) sectors. Although they have unlimited intensity even for finite-size expanding systems, these hadronic squeezed correlations are very sensitive to their time emission distribution. Here we discuss results in case this time emission is parameterized by a Lévy-type distribution, showing that it reduces the signal even more dramatically than a Lorentzian distribution, which already reduces the intensity of the effect by orders of magnitude, as compared to the sudden emission. However, we show that the signal could still survive if the duration of the process is short, and if the effect is searched for lighter mesons, such as kaons. We compare some of our results to recent PHENIX preliminary data on squeezed correlations of K +K - pairs. © 2011 Pleiades Publishing, Ltd.

Classificação de sinais acústicos utilizando a transformada wavelet discreta e a decomposição de modo empírico: aplicações na área de alimentos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modos deslizantes discretos em sistemas incertos com atraso na computação do sinal de controle

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Técnicas de ovariectomias por videolaparoscopia em ovelhas da raça Santa Inês

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Algoritmos de alinhamento múltiplo e técnicas de otimização para esses algoritmos utilizando Ant Colony

Pós-graduação em Ciência da Computação - IBILCE

Sequenciamento de lotes em prensas de alta capacidade com tempo de setup dependente da sequência

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Algoritmo para prospecção multirrelacional de dados espaciais

Pós-graduação em Ciência da Computação - IBILCE

Comparação dos parâmetros espaçotemporais da marcha durante a execução do TUG associado à dupla tarefa em adultos jovens

Introduction: Biomechanical analysis of gait can be used effectively to identify changes in movement patterns and functional decline. Objective: To analyze the effect of dual task on gait spatio-temporal variables. Methods: The sample was made up of 32 subjects of both genders aged between 18 and 25 years. The test Timed Up and Go was performed under two conditions: original form and associated with a cognitive task (verbalize backwards the months of the year). We evaluated the total execution time, number of steps, cadence, time spent to lift, average speed and variability of the step time. Results: Significant changes were observed with the addition of the cognitive task in many gait spatio-temporal variables analyzed. Conclusion: The tests showed that the increase of cognitive tasks during walking may lead to changes in the performance of this task.

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

MR-Radix: a multi-relational data mining algorithm

Background: Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods: Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results: This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion: The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

Incerteza intervalar em otimização e controle

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

Modulação autonômica, pressão arterial, aptidão funcional e qualidade de vida em mulheres com hipertensão arterial submetidas a um programa de Hatha Yoga

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Simulação sequencial na interpolação dos dados de entrada ou saída do modelo de lixiviação do software Araquá

Pós-graduação em Agronomia (Irrigação e Drenagem) - FCA

A graphical tool for the tomographic characterisation of microstructural features on metal matrix composites

Research on the micro-structural characterization of metal-matrix composites uses X-ray computed tomography to collect information about the interior features of the samples, in order to elucidate their exhibited properties. The tomographic raw data needs several steps of computational processing in order to eliminate noise and interference. Our experience with a program (Tritom) that handles these questions has shown that in some cases the processing steps take a very long time and that it is not easy for a Materials Science specialist to interact with Tritom in order to define the most adequate parameter values and the proper sequence of the available processing steps. For easing the use of Tritom, a system was built which addresses the aspects described before and that is based on the OpenDX visualization system. OpenDX visualization facilities constitute a great benefit to Tritom. The visual programming environment of OpenDX allows an easy definition of a sequence of processing steps thus fulfilling the requirement of an easy use by non-specialists on Computer Science. Also the possibility of incorporating external modules in a visual OpenDX program allows the researchers to tackle the aspect of reducing the long execution time of some processing steps. The longer processing steps of Tritom have been parallelized in two different types of hardware architectures (message-passing and shared-memory); the corresponding parallel programs can be easily incorporated in a sequence of processing steps defined in an OpenDX program. The benefits of our system are illustrated through an example where the tool is applied in the study of the sensitivity to crushing – and the implications thereof – of the reinforcements used in a functionally graded syntactic metallic foam.

Preliminary diagnosis of ophtalmological diseases through machine learning techniques

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