Henrique Ferraz de Arruda

Efficient parallelization on GPU of an image smoothing method based on a variational model

Medical imaging is fundamental for improvements in diagnostic accuracy. However, noise frequently corrupts the images acquired, and this can lead to erroneous diagnoses. Fortunately, image preprocessing algorithms can enhance corrupted images, particularly in noise smoothing and removal. In the medical field, time is always a very critical factor, and so there is a need for implementations which are fast and, if possible, in real time. This study presents and discusses an implementation of a highly efficient algorithm for image noise smoothing based on general purpose computing on graphics processing units techniques. The use of these techniques facilitates the quick and efficient smoothing of images corrupted by noise, even when performed on large-dimensional data sets. This is particularly relevant since GPU cards are becoming more affordable, powerful and common in medical environments.

Using complex networks for text classification: Discriminating informative and imaginative documents

Statistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, which encompasses machine translation, automatic summarization and document classification. In the latter, many approaches have emphasized the semantical content of texts, as it is the case of bag-of-word language models. This approach has certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only on a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterizing texts.Statistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, such as machine translation and document classification. In the latter, many approaches have emphasised the semantical content of texts, as is the case of bag-of-word language models. These approaches have certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only in a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterising texts.

Knowledge acquisition: A Complex networks approach

Abstract Complex networks have been found to provide a good representation of knowledge. In this context, the discovery process can be modeled in terms of a dynamic process such as agents moving in a knowledge space. Recent studies proposed more realistic dynamics which can be influenced by the visibility of the agents, or by their memory. However, rather than dealing with these two concepts separately, in this study we propose a multi-agent random walk model for knowledge acquisition that integrates both these aspects. More specifically, we employed the true self avoiding walk modified to incorporate a type of stochastic flight. Such flights depend on fields of visibility emanating from the various agents, in an attempt to model the influence between researchers. The proposed framework has been illustrated considering a set of network models and two real-world networks, one generated from Wikipedia (articles from biology and mathematics) and another from the Web of Science comprising only the area of complex networks. The results were analyzed globally and by regions. In the global analysis, we found that most of the dynamics parameters do not affect significantly the discovery process. Yet, the local analysis revealed a substantial difference in performance, depending on the local topology. In particular, dynamics taking place at the core of the networks tended to enhance knowledge acquisition. The choice of the parameters controlling the dynamics were found to have little impact on the performance for the considered knowledge networks, even at the local scale.

Topic segmentation via community detection in complex networks

Many real systems have been modeled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting effects, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such a representation fails in capturing other textual features, such as the organization in topics or subjects. We propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favor the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study the semantical features of texts.

Minimal paths between communities induced by geographical networks

In this work we investigate the betweenness centrality in geographical networks and its relationship with network communities. We show that vertices with large betweenness define what we call characteristic betweenness paths in both modeled and real-world geographical networks. We define a geographical network model that possess a simple topology while still being able to present such betweenness paths. Using this model, we show that such paths represent pathways between entry and exit points of highly connected regions, or communities, of geographical networks. By defining a new network, containing information about community adjacencies in the original network, we describe a means to characterize the mesoscale connectivity provided by such characteristic betweenness paths.

On the "Calligraphy" of Books.

Authorship attribution is a natural language processing task that has been widely studied, often by considering small order statistics. In this paper, we explore a complex network approach to assign the authorship of texts based on their mesoscopic representation, in an attempt to capture the flow of the narrative. Indeed, as reported in this work, such an approach allowed the identification of the dominant narrative structure of the studied authors. This has been achieved due to the ability of the mesoscopic approach to take into account relationships between different, not necessarily adjacent, parts of the text, which is able to capture the story flow. The potential of the proposed approach has been illustrated through principal component analysis, a comparison with the chance baseline method, and network visualization. Such visualizations reveal individual characteristics of the authors, which can be understood as a kind of calligraphy.

A framework for analyzing the relationship between gene expression and morphological, topological, and dynamical patterns in neuronal networks

BACKGROUND A key point in developmental biology is to understand how gene expression influences the morphological and dynamical patterns that are observed in living beings. NEW METHOD In this work we propose a methodology capable of addressing this problem that is based on estimating the mutual information and Pearson correlation between the intensity of gene expression and measurements of several morphological properties of the cells. A similar approach is applied in order to identify effects of gene expression over the system dynamics. Neuronal networks were artificially grown over a lattice by considering a reference model used to generate artificial neurons. The input parameters of the artificial neurons were determined according to two distinct patterns of gene expression and the dynamical response was assessed by considering the integrate-and-fire model. RESULTS As far as single gene dependence is concerned, we found that the interaction between the gene expression and the network topology, as well as between the former and the dynamics response, is strongly affected by the gene expression pattern. In addition, we observed a high correlation between the gene expression and some topological measurements of the neuronal network for particular patterns of gene expression. COMPARISON WITH EXISTING METHODS To our best understanding, there are no similar analyses to compare with. CONCLUSIONS A proper understanding of gene expression influence requires jointly studying the morphology, topology, and dynamics of neurons. The proposed framework represents a first step towards predicting gene expression patterns from morphology and connectivity.

Connecting network science and information theory

Abstract A framework integrating information theory and network science is proposed. By incorporating and integrating concepts such as complexity, coding, topological projections and network dynamics, the proposed network-based framework paves the way not only to extending traditional information science, but also to modeling, characterizing and analyzing a broad class of real-world problems, from language communication to DNA coding. Basically, an original network is supposed to be transmitted, with or without compaction, through a sequence of symbols or time-series obtained by sampling its topology by some network dynamics, such as random walks. We show that the degree of compression is ultimately related to the ability to predict the frequency of symbols based on the topology of the original network and the adopted dynamics. The potential of the proposed approach is illustrated with respect to the efficiency of transmitting several types of topologies by using a variety of random walks. Several interesting results are obtained, including the behavior of the Barabasi–Albert model oscillating between high and low performance depending on the considered dynamics, and the distinct performances obtained for two geographical models.

How integrated are theoretical and applied physics

How well integrated are theoretically and application oriented works in Physics currently? This interesting question, which has several relevant implications, has been approached mostly in a more subjective way. Recent concepts and methods from network science are used in the current work in order to develop a more principled, quantitative and objective approach to quantifying the integration and centrality of more theoretical/applied journals within the APS journals database, represented as a directed and undirected citation network. The results suggest a level of integration between more theoretical and applied journals, which are also characterized by remarkably similar centralities in the network.

An image analysis approach to text analytics based on complex networks

Text network analysis has received increasing attention as a consequence of its wide range of applications. In this work, we extend a previous work founded on the study of topological features of mesoscopic networks. Here, the geometrical properties of visualized networks are quantified in terms of several image analysis techniques and used as subsidies for authorship attribution. It was found that the visual features account for performance similar to that achieved by using topological measurements. In addition, the combination of these two types of features improved the performance.