999 resultados para cooperation


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The objective of this paper is to argue that the dissemination of competition laws is positive for the world economy. However, the benefits derived depend crucially on adequate enforcement and institutional building in the various jurisdictions for which international cooperation within WTO and in other fora is crucial. The paper addresses two issues. Section 1 underlines a few aspects of the increasing importance of competition policy in the developing world. The second section provides a few suggestions for the international cooperation agenda.

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How to deal with the impacts of the exchange rate on the trade balance of Brazil? There is not a single answer to such question. In order to find out some legal approaches for this matter, this paper aims to describe and analyze the role of the IMF, WTO and the governments of Brazil and the United States on the currency misalignments, especially the extraterritorial effects of such misalignment on the Brazil’s bilateral trade with the United States. The article concludes that the Currency Swap Agreements and other bilateral solutions may minimize the distortions that the Brazilian balance of payment against the USA is carrying, due to the lack of legal solutions for the problem of the exchange rate misalignments that Brazil is facing.

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This article examines the arising cross-border dispute resolution models (Cooperation and Competition among national Courts) from a critical perspective. Although they have been conceived to surpass the ordinary solution of a Modern paradigm (exclusive jurisdiction, choice of court, lis pendens, forum non conveniens, among others), they are insufficient to deal with problems raised with present globalization, as they do not abandon aspects of that paradigm, namely, (i) statebased Law; and (ii) standardization of cultural issues.

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

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

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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.

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Includes bibliography

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