HIV, EBV and KSHV: Viral cooperation in the pathogenesis of human malignancies

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

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

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

Desempenho escolar da criança vitimizada encaminhada ao fórum judicial

O objetivo do estudo foi caracterizar o desempenho escolar da criança vítima de violência doméstica atendida no Fórum Judicial. Participaram do estudo 20 crianças vitimizadas comparadas com seus pares da mesma sala de aula, mesmo sexo e mesma faixa etária, mas sem histórico de violência doméstica, suas respectivas mães e professoras. As crianças responderam ao Teste de Desempenho Escolar, Inventário de Estilos Parentais e ao Teste de Raven (Escala Especial) e apresentaram o caderno escolar. As mães responderam a uma entrevista e a Escala de Táticas de Conflitos Revisada (CTS-2). As professoras apresentaram sua opinião sobre o desempenho acadêmico dos participantes. Os dados obtidos mostraram que a criança vitimizada tem desempenho escolar inferior ao grupo controle. Os resultados da CTS-2 indicaram que a maioria das crianças vitimizadas estava exposta à violência conjugal. O estudo mostrou que, além da violência doméstica direta e indireta, tais crianças estavam expostas a outros fatores de risco, tais como, pobreza, baixa escolaridade materna e uso de álcool e/ou droga por familiares.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

Fuzzy community structure detection by particle competition and cooperation

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

Sociedade de economia mista: possibilidade de recuperação judicial

Pós-graduação em Direito - FCHS

Insuficiências e propostas teórico-práticas para a efetivação judicial de direitos humanos fundamentais

Pós-graduação em Direito - FCHS

O ativismo judicial em investida ao Estado democrático

Pós-graduação em Direito - FCHS

A judicialização da saúde e o controle judicial de políticas públicas: uma análise de como o poder judiciário pode contribuir para a efetivação do direito à saúde na sociedade brasileira. -

Pós-graduação em Direito - FCHS

Processo judicial previdenciário: a necessária mudança de paradigma para a efetividade da política de previdencia social no Brasil

Pós-graduação em Direito - FCHS

A aposentadoria por idade do trabalhador rural sob o enfoque constitucional: efetivação por meio da atividade judicial

Pós-graduação em Direito - FCHS

A legitimidade democrática do Supremo Tribunal Federal: contribuições para a prática do ativismo judicial

Pós-graduação em Direito - FCHS

Interpretação conforme a constituição e declaração de inconstitucionalidade sem redução de texto: problemas da aplicação judicial do direito constitucional

O texto apresenta as principais características e controvérsias jurídicas sobre a declaração de inconstitucionalidade sem redução de texto e a interpretação conforme a Constituição. São analisadas as hipóteses de cabimento, seus efeitos processuais, assim como casos de sua aplicação jurisprudencial. Em paralelo, são analisadas criticamente divergências doutrinárias sobre a natureza desses institutos e seus efeitos processuais. O texto apresenta esclarecimentos conceituais acerca dos limites impostos na aplicação dessas técnicas e indica sua relevância para o exercício do controle judicial de constitucionalidade.