Eyes: Novellas and Stories by William H. Gass

By William H. Gass

Enter the chic, upside-down / inside-out international of William H. Gass . . . as a consequence the place the  Eyes  have it each which means, together with up . . . in a stunning new number of novellas and tales (six in all) from probably the most respected writers of our time, writer of 16 books, between them, the universally acclaimed The Tunnel ("An striking achievement"--Michael Dirda, The Washington Post); Middle C("Exhilaratingly ingenious"--Cynthia Ozick, The long island instances publication Review cover); and Life Sentences ("A literary miracle"--The big apple Observer).

This spell binding, Gassian trip starts off with "In Camera," an research into what's more likely to enhance while a almost certainly illicit choice of photos turns into the thing of a grasping salesman's loving eyes . . . In "Charity," a tender attorney, whose enterprise it truly is to maintain health center gear truthfully produced, deals an easy present and is dropped at the ambiguous center of charity itself. "Don't Even test, Sam" tells of the battered, outdated piano Dooley Wilson performs in Casablanca as it complains in an interview of its remedy through the making of the image. "Soliloquy for a Chair" is simply that, a rumination by way of a folding chair in a barber store that's eventually bombed . . . and in "The Toy Chest," Disneylike creatures tackle human roles and concerns and dwell in an environment of a child's imagination.

A excellent fantasia; each one, quintessentially Gass; every one, a virtuoso delight.

From the Hardcover edition.

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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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