New directions for development banking in the Caribbean: financing to take advantage of unlimited supplies of labour skills and entrepreneurship

Includes bibliography

A new approach to development banking in Jamaica

Includes bibliography

Competition, oligopoly and technological change in the construction industry: the Argentine case

Includes bibliography

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.

The modernization of bank supervision

Includes bibliography

Globalization and loss of autonomy by the fiscal, banking and monetary authorities

Includes bibliography

Convertibility and the banking system in Argentina

Includes bibliography

MERCOSUR: its challenges to small and medium-sized industrial enterprises in terms of competition

Includes bibliography

Globalization and tax competition: implications for developing countries

Includes bibliography

The banking supervision agenda in Latin America

Includes bibliography

The expansion model of the major Chilean retail chains

Includes bibliography

The relation between foreign exchange and banking crises in emerging countries: information and expectations problems

Includes bibliography

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.

Indices of competition and bio-agroeconomic efficiency of lettuce and tomato intercrops in greenhouses

Intercropping is a cropping system for the production of greenhouse vegetables. It uses space more efficiently, thus reducing the cost of production. Intercropping tomato and lettuce has not been studied, but knowledge of the competitive and agroeconomic indices of these vegetables can help in the management of the intercropping system. The objectives of this study were to assess, through biological and agroeconomic indices, the competition between species and the profitability of intercropping tomato and lettuce at different times of transplantation over two growing seasons (autumn-winter and summer-winter) in greenhouse conditions. In autumn-winter, two experiments were conducted with a randomised complete-block design and five replicates. Tomato and lettuce were the main crops in the individual experiments. Treatments were arranged in a factorial of two cropping systems (intercropping and individual crops) with four transplants of the secondary crop (0, 10, 20 and 30 days after) plus an additional treatment (individual main crop). These two experiments were repeated in summer-winter. Tomato was the dominant crop regardless of transplant order. Intercropping systems established with transplants of both species on the same day had higher values of indices of competition and bio-agroeconomic efficiency than systems with longer periods of transplants between main and secondary crops. The intercropping of lettuce and tomato in greenhouses, regardless of transplant time or order, had bio-agroeconomic advantages over individual crops. The transplantation of tomato after lettuce is recommended for greater profitability.