By Celia Blanco
Las fantasías sexuales de las mujeres que dejaron de soñar con ser princesas.
Cuando las mujeres dejamos de soñar con ser princesas, cuando ya no nos conformamos con los cuentos que hasta entonces nos han contado, empezamos a imaginar otros cuentos, más nuestros, más íntimos, a solas con nuestros deseos. Sin prejuicios, con dos tacones.
En estas páginas sinceras y por momentos muy atrevidas, los angeles periodista Celia Blanco ha reunido las historias que le han confesado, con todo lujo de detalles, mujeres de a pie, entre 18 y seventy five años, que buscan gozar y disfrutar de sus encuentros sexuales. Orgías veraniegas, sexo con un desconocido en el vagón de un tren, amantes que solo miran, princesas que se dejan atar y desayunos clandestinos con chicos más jóvenes son solo algunos de los momentos de este libro dedicado enteramente al placer.
Fantasías sexuales de todos los colores, para todos los gustos, que nos muestran lo que son capaces de imaginar las mujeres sobre el sexo cuando no tienen que rendir cuentas a nadie.
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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 , 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 .
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 , and are of key interest to the pharmaceutical industry as approximately 50% of all existing drugs are targeting transmembrane proteins .