By Katherine Tanney
Meet Meredith Herman, a fourteen-year-old specialist witness to the sluggish unraveling of her parents' marriage amid the lunacy of l. a., 1978, a global of bell-bottoms, grapefruit diets, and cosmetic surgery. Meredith is a woman of a particular time and position tackling the common demanding situations of boys, tuition, and fogeys. Her mom, Leigh, is a housewife anguish an excruciating and infrequently hilarious midlife discontent, a malaise that leaves Meredith's father, Robert, really baffled. As Leigh makes an attempt to reinvent herself as a liberated girl - whole with assertiveness-training periods and a dalliance with an unique artist - Robert runs for defense right into a hasty moment marriage. via all of it, Meredith and Leigh fight in a combative mother-daughter courting as splendidly genuine as any in modern fiction.
Tanney's debut glints with pitch-perfect discussion and an astonishingly exact experience of position. This novel will take readers on ajourney of stomach laughs and heartbreak. The Herman family's tale will appeal and captivate you lengthy after you've became the final page.
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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 .