Redes acadêmicas para pesquisa e capacitação em meio ambiente e desenvolvimento sustentável: uma revisão crítica

Tornou-se lugar comum, sobretudo em correntes ligadas aos movimentos sociais mais amplos e às questões ambientais, a crítica ao reducionismo da ciência clássica, à dinâmica de trabalho individualizado, à desconexão e à falta de integração com os problemas reais. Como resultado, cresce o número de formadores de opinião em favor de uma ciência mais integrada aos problemas reais e de um conhecimento como processo coletivo. O Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) desenvolveu um Diretório dos Grupos de Pesquisa existentes no Brasil, alguns destes grupos possuem modelo de rede. Com o cenário apresentado, a reflexão sobre a importância de avaliar o potencial destes grupos foi incentivada para influenciar a formulação e a implantação de políticas públicas ambientais. Foi possível identificar fatores chave para o fortalecimento da influência nas políticas públicas, destacando-se dentre estes, avanços em abordagens interdisciplinares. Porém, os grupos possuem dificuldades na utilização de ferramentas de comunicação mais eficientes para o trabalho em rede e para atingir os tomadores de decisões; no acesso a fundos e nos critérios para integrar os grupos estudados.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Scientific research output evaluation of professors of Sao Paulo State University, Marilia/SP

In this work, an analysis of scientific bibliographic productivity was made using the Faculdade de Filosofia e Ciencias, Universidade Estadual Paulista (FFC-UNESP) as example. It is composed by nine departments which offer altogether nine undergraduate courses: 1) Archival, 2) Library, 3) Speech Therapy, 4) Pedagogy, 5) International Relations, 6) Physiotherapy, 7) Occupational Therapy, 8) Philosophy, 9) Social Sciences and six graduate programs leading to M. S. and Ph.D. degrees. Moreover, when analyzing the different courses of FFC-UNESP, they represent typical academic organization in Brazil and Latin America and could be taken as a model for analyzing other Brazilian research institutions. Using data retrieved from the Lattes Plataform database (Curriculum Lattes) we have quantitatively the scientific productivity percentage of professors at UNESP. We observed that bibliometric evaluations using the Curriculum Lattes (CL) showed that the professors published papers in journal are not indexed by ISI and SCOPUS. This analysis was made using: 1) the total number of papers (indexed in Curriculum Lattes database), 2) the number of papers indexed by Thomson ISI Web of Science database and SCOPUS database, and 3) the Hirsch (h-index) by ISI and SCOPUS. Bibliometric evaluations of departments showed a better performance of Political Science and Economics Department when compared to others departments, in relation total number of papers (indexed in Curriculum Lattes database). We also analyzed the academic advisory (Master's Thesis and Ph. D. Thesis) by nine departments of FFC/UNESP. The Administration and School Supervision Department presented a higher academic advisory (concluded and current) when compared to the others departments.

Collective Almost Synchronisation in Complex Networks

This work introduces the phenomenon of Collective Almost Synchronisation (CAS), which describes a universal way of how patterns can appear in complex networks for small coupling strengths. The CAS phenomenon appears due to the existence of an approximately constant local mean field and is characterised by having nodes with trajectories evolving around periodic stable orbits. Common notion based on statistical knowledge would lead one to interpret the appearance of a local constant mean field as a consequence of the fact that the behaviour of each node is not correlated to the behaviours of the others. Contrary to this common notion, we show that various well known weaker forms of synchronisation (almost, time-lag, phase synchronisation, and generalised synchronisation) appear as a result of the onset of an almost constant local mean field. If the memory is formed in a brain by minimising the coupling strength among neurons and maximising the number of possible patterns, then the CAS phenomenon is a plausible explanation for it.

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

STATE INSTITUTIONS, POWER, AND SOCIAL NETWORKS IN BRAZILIAN URBAN POLICIES

A large historiographic tradition has studied the Brazilian state, yet we know relatively little about its internal dynamics and particularities. The role of informal, personal, and unintentional ties has remained underexplored in most policy network studies, mainly because of the pluralist origin of that tradition. It is possible to use network analysis to expand this knowledge by developing mesolevel analysis of those processes. This article proposes an analytical framework for studying networks inside policy communities. This framework considers the stable and resilient patterns that characterize state institutions, especially in contexts of low institutionalization, particularly those found in Latin America and Brazil. The article builds on research on urban policies in Brazil to suggest that networks made of institutional and personal ties structure state organizations internally and insert them,into broader political scenarios. These networks, which I call state fabric, frame politics, influence public policies, and introduce more stability and predictability than the majority of the literature usually considers. They also form a specific power resource-positional power, associated with the positions that political actors occupy-that influences politics inside and around the state.

Signal propagation in Bayesian networks and its relationship with intrinsically multivariate predictive variables

A set of predictor variables is said to be intrinsically multivariate predictive (IMP) for a target variable if all properly contained subsets of the predictor set are poor predictors of the. target but the full set predicts the target with great accuracy. In a previous article, the main properties of IMP Boolean variables have been analytically described, including the introduction of the IMP score, a metric based on the coefficient of determination (CoD) as a measure of predictiveness with respect to the target variable. It was shown that the IMP score depends on four main properties: logic of connection, predictive power, covariance between predictors and marginal predictor probabilities (biases). This paper extends that work to a broader context, in an attempt to characterize properties of discrete Bayesian networks that contribute to the presence of variables (network nodes) with high IMP scores. We have found that there is a relationship between the IMP score of a node and its territory size, i.e., its position along a pathway with one source: nodes far from the source display larger IMP scores than those closer to the source, and longer pathways display larger maximum IMP scores. This appears to be a consequence of the fact that nodes with small territory have larger probability of having highly covariate predictors, which leads to smaller IMP scores. In addition, a larger number of XOR and NXOR predictive logic relationships has positive influence over the maximum IMP score found in the pathway. This work presents analytical results based on a simple structure network and an analysis involving random networks constructed by computational simulations. Finally, results from a real Bayesian network application are provided. (C) 2012 Elsevier Inc. All rights reserved.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Social Networks, Segregation and Poverty in Sao Paulo

The associations between segregation and urban poverty have been intensely scrutinized by the sociology and urban studies literatures. More recently, several studies have emphasized the importance of social networks for living conditions. Yet relatively few studies have tested the precise effects of social networks, and fewer still have focused on the joint effects of residential segregation and social networks on living conditions. This article explores the associations between networks, segregation and some of the most important dimensions of access to goods and services obtained in markets: escaping from social precariousness and obtaining monetary income. It is based on a study of the personal networks of 209 individuals living in situations of poverty in seven locales in the metropolitan area of Sao Paulo. Using network analysis and multivariate techniques, I show that relational settings strongly influence the access individuals have to markets, leading some individuals into worse living conditions and poverty. At the same time, although segregation plays an important role in poverty, its effects tend to be mediated by the networks in which individuals are embedded. Networks in this sense may enhance or mitigate the effects of isolation produced by space.

PERFORMANCE EVALUATION OF RESOURCE-AWARE BUSINESS PROCESSES USING STOCHASTIC AUTOMATA NETWORKS

In this work, we study the performance evaluation of resource-aware business process models. We define a new framework that allows the generation of analytical models for performance evaluation from business process models annotated with resource management information. This framework is composed of a new notation that allows the specification of resource management constraints and a method to convert a business process specification and its resource constraints into Stochastic Automata Networks (SANs). We show that the analysis of the generated SAN model provides several performance indices, such as average throughput of the system, average waiting time, average queues size, and utilization rate of resources. Using the BP2SAN tool - our implementation of the proposed framework - and a SAN solver (such as the PEPS tool) we show through a simple use-case how a business specialist with no skills in stochastic modeling can easily obtain performance indices that, in turn, can help to identify bottlenecks on the model, to perform workload characterization, to define the provisioning of resources, and to study other performance related aspects of the business process.

Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.

Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool

Fraud is a global problem that has required more attention due to an accentuated expansion of modern technology and communication. When statistical techniques are used to detect fraud, whether a fraud detection model is accurate enough in order to provide correct classification of the case as a fraudulent or legitimate is a critical factor. In this context, the concept of bootstrap aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the adjusted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper, for the first time, we aim to present a pioneer study of the performance of the discrete and continuous k-dependence probabilistic networks within the context of bagging predictors classification. Via a large simulation study and various real datasets, we discovered that the probabilistic networks are a strong modeling option with high predictive capacity and with a high increment using the bagging procedure when compared to traditional techniques. (C) 2012 Elsevier Ltd. All rights reserved.

A Java-based simulation environment for networks of simplified neuron models

ACR is supported by a research grant from CNPq.

The implementation of the Medical Regulation Office and Mobile Emergency Attendance System and its impact on the gravity profile of non-traumatic afflictions treated in a University Hospital: a research study

Abstract Background The public health system of Brazil is structured by a network of increasing complexity, but the low resolution of emergency care at pre-hospital units and the lack of organization of patient flow overloaded the hospitals, mainly the ones of higher complexity. The knowledge of this phenomenon induced Ribeirão Preto to implement the Medical Regulation Office and the Mobile Emergency Attendance System. The objective of this study was to analyze the impact of these services on the gravity profile of non-traumatic afflictions in a University Hospital. Methods The study conducted a retrospective analysis of the medical records of 906 patients older than 13 years of age who entered the Emergency Care Unit of the Hospital of the University of São Paulo School of Medicine at Ribeirão Preto. All presented acute non-traumatic afflictions and were admitted to the Internal Medicine, Surgery or Neurology Departments during two study periods: May 1996 (prior to) and May 2001 (after the implementation of the Medical Regulation Office and Mobile Emergency Attendance System). Demographics and mortality risk levels calculated by Acute Physiology and Chronic Health Evaluation II (APACHE II) were determined. Results From 1996 to 2001, the mean age increased from 49 ± 0.9 to 52 ± 0.9 (P = 0.021), as did the percentage of co-morbidities, from 66.6 to 77.0 (P = 0.0001), the number of in-hospital complications from 260 to 284 (P = 0.0001), the mean calculated APACHE II mortality risk increased from 12.0 ± 0.5 to 14.8 ± 0.6 (P = 0.0008) and mortality rate from 6.1 to 12.2 (P = 0.002). The differences were more significant for patients admitted to the Internal Medicine Department. Conclusion The implementation of the Medical Regulation and Mobile Emergency Attendance System contributed to directing patients with higher gravity scores to the Emergency Care Unit, demonstrating the potential of these services for hierarchical structuring of pre-hospital networks and referrals.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.