Large Graph Analysis in the GMine System

Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers and communities. These tasks are better performed in an interactive environment, where human expertise can guide the process. For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend. To cope with these problems, we propose an innovative framework suited for any kind of tree-like graph visual design. GMine integrates (a) a representation for graphs organized as hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b) a graph summarization methodology - CEPS. Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click. As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally

Two-mode squeezed vacuum and squeezed light in correlated interferometry

We study in detail a system of two interferometers aimed to the detection of extremely faint phase-fluctuations. This system can represent a breakthrough for detecting a faint correlated signal that would remain otherwise undetectable even using the most sensitive individual interferometric devices, that are limited by the shot noise. If the two interferometers experience identical phase-fluctuations, like the ones introduced by the so called "holographic noise", this signal should emerge if their output signals are correlated, while the fluctuations due to shot noise and other independent contributions will vanish. We show how the injecting quantum light in the free ports of the interferometers can reduce the photon noise of the system beyond the shot-noise, enhancing the resolution in the phase-correlation estimation. We analyze both the use of two-mode squeezed vacuum or twin-beam state (TWB) and of two independent squeezing states. Our results basically confirms the benefit of using squeezed beams together with strong coherent beams in interferometry, even in this correlated case. However, mainly we concentrate on the possible use of TWB, discovering interesting and probably unexplored areas of application of bipartite entanglement and in particular the possibility of reaching in principle surprising uncertainty reduction

Quantum state reconstruction using binary data from on/off photodetection

The knowledge of the density matrix of a quantum state plays a fundamental role in several fields ranging from quantum information processing to experiments on foundations of quantum mechanics and quantum optics. Recently, a method has been suggested and implemented in order to obtain the reconstruction of the diagonal elements of the density matrix exploiting the information achievable with realistic on/off detectors, e.g. silicon avalanche photo-diodes, only able to discriminate the presence or the absence of light. The purpose of this paper is to provide an overview of the theoretical and experimental developments of the on/off method, including its extension to the reconstruction of the whole density matrix.Comment: revised version, 11 pages, 6 figures, to appear as a review paper on Adv. Science Let

State reconstruction by on/off measurements

We demonstrate a state reconstruction technique which provides either the Wigner function or the density matrix of a field mode and requires only avalanche photodetectors, without any phase or amplitude discrimination power. It represents an alternative, of simpler implementation, to quantum homodyne tomography.Comment: 6 pages, 4 figures, revised and enlarged versio

Montando questionários para medir a satisfação do usuário: avaliação de interface de um sistema que utiliza técnica de recuperação de imagens por conteúdo.

O resumo intuitivo e atraente da interface com o usuário é essencial para o sucesso de qualquer sistema computacional. Dentro do escopo de sistemas CBIR (content-Based Image Retrieval), que são sistemas inerentemente interativos de recuperação de imagens baseadas em conteúdo, a interface do usuário ainda é um elemento constituído de pouca pesquisa e desenvolvimento. Um dos maiores obstáculos para a eficácia de design desses sistemas consiste da necessidade em prover aos usuários uma interface de alta qualidade na formulação de perguntas e exibição dos resultados. O objetivo principal deste trabalho visou analisar a interação do usuário em sistemas CBIR em relação à funcionalidade e usabilidade por meio da interação de usuários reais dentro do domínio da aplicação e propor questionário que reflita, fielmente os pontos avaliados. Para alcançar esse desafio, a interação humano-computador foi avaliada, adotando técnicas de questionamento a fim de mensurar a satisfação do usuário em relação ao uso do sistema

Combinação de técnicas de interação humano-computador para avaliação de sistemas de recuperação de imagens por conteúdo.

A interação humana pode contribuir efetivamente para avaliar o desempenho de sistemas de recuperação de imagens baseadas em conteúdo (Content-based Image Retrieval - CBIR), tanto em relação à eficiência e eficácia, quanto a satisfação do usuário. O crescimento, aprimoramento e a excelência da pesquisa em técnicas CBIR, avaliados pela sua performance na recuperação de informações, contradizem-se com a ausência de procedimentos específicos de avaliação por meio da percepção visual do usuário. Um dos desafios decorrentes desses sistemas consiste na necessidade em prover aos usuários uma interface de alta necessidade em prover aos usuários uma interface de alta qualidade na formulação de perguntas e exibição dos resultados. O objetivos deste trabalho foi avaliar a interação humano-computador para sistemas CBIR que atuam em modo compartilhado usando intranet, aplicando uma combinação de técnicas de avaliação, levando em consideração o usuário final e os fatores humanos que influenciam o trabalho com grandes volumes de dados e imagens. Tal avaliação foi baseada em um estudo de caso aplicado à medicina, visando tornar o diagnóstico médico e a evolução dos tratamentos cada vez mais precisos e eficientes.WebMedia 200

Mining relevant and extreme patterns on climate time series with CLIPSMiner.

One of the most important challenges for the researchers in the 21st Century is related to global heating and climate change that can have as consequence the intensification of natural hazards. Another problem of changes in the Earth's climate is its impact in the agriculture production. In this scenario, application of statistical models as well as development of new methods become very important to aid in the analyses of climate from ground-based stations and outputs of forecasting models. Additionally, remote sensing images have been used to improve the monitoring of crop yields. In this context we propose a new technique to identify extreme values in climate time series and to correlate climate and remote sensing data in order to improve agricultural monitoring. Accordingly, this paper presents a new unsupervised algorithm, called CLIPSMiner (CLImate PatternS Miner) that works on multiple time series of continuous data, identifying relevant patterns or extreme ones according to a relevance factor, which can be tuned by the user. Results show that CLIPSMiner detects, as expected, patterns that are known in climatology, indicating the correctness and feasibility of the proposed algorithm. Moreover, patterns detected using the highest relevance factor is coincident with extreme phenomena. Furthermore, series correlations detected by the algorithm show a relation between agroclimatic and vegetation indices, which confirms the agrometeorologists' expectations