A Jangada de Pedra by José Saramago

By José Saramago

«Em A Jangada de Pedra (...) o escritor recorre a um estratagema típico. Uma série de acontecimentos sobrenaturais culmina na separação da Península Ibérica que começa a vogar no Atlântico, inicialmente em direcção aos Açores. A situação criada por Saramago dá-lhe um sem-número de oportunidades para, no seu estilo muito pessoal, tecer comentários sobre as grandezas e pequenezas da vida, ironizar sobre as autoridades e os políticos e, talvez muito especialmente, com os actores dos jogos de poder na alta política. O engenho de Saramago está ao serviço da sabedoria.» (Real Academia Sueca, eight de Outubro de 1998)

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Com Maximum Margin Matrix Factorization (MMMF) has been proposed as a learning approach to the task of collaborative filtering with promising results. In our recent paper [2], we proposed to extend the general MMMF framework to allow for structured (ranking) losses in addition to the squared error loss. In this paper, we introduce a novel algorithm to compute the ordinal regression ranking loss which is significantly faster than the state of the art. In addition, we propose severals extensions to the MMMF model: We introduce offset terms to cater for user and item biases.

We give algorithms for the problems and study the performance of the algorithms via extensive experimental evaluation. Our results show that, despite the high theoretical complexity of the problems, even simple algorithms can perform well with both synthetic and real-world data. References 1. : The Boolean column and column-row matrix decompositions. Data Mining and Knowledge Discovery 17(1), 39–56 (August 2008) This is an extended abstract of an article published in the Data Mining and Knowledge Discovery journal [1].

The contribution of this work is the development of a prediction approach that first uses a binary SVM classifier to predict the helix residues and then it employs a pair of HMM models that incorporate the SVM predictions and hydropathy-based features to identify the entire transmembrane helix segments by capturing the structural characteristics of these proteins. TOPTMH outperforms state-of-the-art prediction methods and achieves the best performance on an independent static benchmark. 1 Introduction Transmembrane helical (TMH) proteins play a crucial role in several cellular functions, such as cell-to-cell communication, cell signaling, and transportation of ions and small molecules [3], and are of key interest to the pharmaceutical industry as approximately 50% of all existing drugs are targeting transmembrane proteins [15].

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