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click on any audio book you want to download , then observe the wall of text , inside the link.. with information of what the torrent have inside , and about the end ,before the comments section in the end ,below ,if will show you the info hash code .. is a big number of like 20 numbers.. like this. .
copy /paste the big number above , and test that one , is a collection of many books ,best of 2019 from science fiction and fantasy.. according to the author of the post. copy the info wash , grab it with the mouse and copy , then open any torrent program of your choice... in the menu , go to [add torrent] button , then it will open a window so you find the torrent tab ,of what you want to download.. but don't use tab , use the info hash code above and paste that info ,in the section to manually enter the hash code.. and this way you can manually download torrents and no subscription need , no advertisement .fully free. remember the 3 ways to download torrent.. one is downloading the torrent link tag, the other is the magnet link ,and the last one is the info hash number.. that almost every torrent website always provide somewhere of the software ,game,music or video or audio book you want to download.
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"Recent advances in high-throughput technologies have unleashed a torrent of data with a large number of dimensions. Examples include gene expression pattern images, microarray gene expression data, and neuroimages. Variable selection is crucial for the analysis of these data. In this talk, we consider the structured sparse learning for variable selection where the structure over the features can be represented as a hierarchical tree, an undirected graph, or a collection of disjoint or overlapping groups. We show that the proximal operator associated with these structures can be computed efficiently, thus accelerated gradient techniques can be applied to scale structured sparse learning to large-size problems. We demonstrate the efficiency and effectiveness of the presented algorithms using synthetic and real data."
Probabilistic matrix factorization (PMF) is a powerful method for modeling data associated with pairwise relationships. PMF is used in collaborative filtering, computational biology, and document analysis among other application areas. In many of these domains, additional information is available that might assist in the modeling of the pairwise interactions. For example, when modeling movie ratings, we might know when the rating occurred, where the user lives, or what actors appear in the movie. It has been difficult, however, to incorporate this kind of side information into the probabilistic matrix factorization model. I will discuss some recent work to develop a nonparametric Bayesian framework for incorporating side information by coupling together multiple PMF problems via Gaussian process priors. I will show how we have used this model to successfully model the outcomes of NBA basketball games, allowing for the attributes of the teams to vary over time and to include home-team advantage. This is joint work with George Dahl and Iain Murray.
Maximum margin clustering extends the theory of support vector machine to unsupervised learning, and has shown promising performance in recent studies. However, it has three major problems that question its application of real-world applications: (1) it is computationally expensive and difficult to scale to large-scale datasets; (2) it requires data preprocessing to ensure the clustering boundary to pass through the origins, which makes it unsuitable for clustering unbalanced dataset; and (3) its performance is sensitive to the choice of kernel functions. In this paper, we propose the "Generalized Maximum Margin Clustering" framework that addresses the above three problems simultaneously. The new framework generalizes the maximum margin clustering algorithm in that (1) it allows any clustering boundaries including those not passing through the origins; (2) it significantly improves the computational efficiency by reducing the number of parameters; and (3) it automatically determines the appropriate kernel matrix without any labeled data. Our empirical studies demonstrate the efficiency and the effectiveness of the generalized maximum margin clustering algorithm. Furthermore, in this talk, I will show the theoretical connection among the spectral clustering, the maximum margin clustering and the generalized maximum margin clustering.
We present a question answering (QA) system which learns how to detect and rank answer passages by analyzing questions and their answers (QA pairs) provided as training data. Our key technique is to recover, from the question, fragments of what might have been posed as a structured query, had a suitable schema been available. One fragment comprises _selectors_: tokens that are likely to appear (almost) unchanged in an answer passage. The other fragment contains question tokens which give clues about the _answer_type_, and are expected to be _replaced_ in the answer passage by tokens which _specialize_ or _instantiate_ the desired answer type. Selectors are like constants in where-clauses in relational queries, and answer types are like column names. We propose a simple conditional exponential model over a pair of feature vectors, one derived from the question and the other derived from the a candidate passage. Features are extracted using a lexical network and surface context as in named entity extraction, except that there is no direct supervision available in the form of fixed entity types and their examples. We do not need any manually-designed question type system or supervised question classification. Using the exponential model, we filter candidate passages and see substantial improvement in the mean rank at which the first answer passage is found. With TREC QA data, our system achieves mean reciprocal rank (MRR) that compares favorably with the best scores in recent years, and generalizes from one corpus to another. 2ff7e9595c
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