Titlebook: Innovation Networks; New Approaches in Mo Andreas Pyka,Andrea Scharnhorst Book 2009 Springer-Verlag Berlin Heidelberg 2009 Evolution.Innova

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g behaviors. However, most of existing works focus on devising an effective prediction method based on social network data, and few research studies explore the data characteristic of retweeting behaviors which is typical binary discrete distribution and sparse data. To this end, we propose two nove

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Pier Paolo Saviottites the communication in distributed SGD. One promising technique is using the gradient compression to reduce the communication cost. Recently, many approaches have been developed for the deep neural networks. However, they still suffer from the high memory cost, slow convergence and serious stalene

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Frank Beckenbach,Ramón Briegel,Maria Daskalakisarrives very fast, requires that database systems have to provide efficient solutions to process continuously-arriving queries. However, traditional disk-based DBMSs have a large overhead in maintaining database consistency. This is mainly due to the logging, locking, and latching mechanisms inside

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Robin Cowan,Nicolas Jonardarrives very fast, requires that database systems have to provide efficient solutions to process continuously-arriving queries. However, traditional disk-based DBMSs have a large overhead in maintaining database consistency. This is mainly due to the logging, locking, and latching mechanisms inside

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Jürg Reichardt,Stefan Bornholdteeding. In 2013, China Judgments Online system was launched officially for record keeping and notification, up to now, over 23 million electronic judgment documents are recorded. The huge amount of judgment documents has witnessed the improvement of judicial justice and openness. Document categoriza

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Michael D. König,Stefano Battiston,Frank Schweitzersian network (BN) structure from big datasets is becoming more and more valuable for modeling and reasoning under uncertainties in many areas. Facing big data, most of the current BN structure learning algorithms have limitations. First, learning BNs structure from big datasets is an expensive proce

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