Esprit de Corps: Sketches from Diplomatic Life by Lawrence Durrell

By Lawrence Durrell

The writer of sour Lemons and the acclaimed novel Justine wrote a few brief sketches as a light-hearted jeu d'esprit that have been accrued jointly below the name Esprit de Corps. listed here are leisure and rest natural and straightforward. other than that there's some extent the place laughter can develop into painful . . . 'Whatever wars there is and no matter what crises, there'll nonetheless, please heaven, be the diplomatic corps, with its protocol and formalities and a box for humour which i haven't visible greater used than in those stories.' John Betjeman, day-by-day Telegraph 'Uproariously humorous and smart . . . with the arrows of farce and of satire, he's at the objective back and again.' John Connell, night common

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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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