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Solar flares are the conversion of stored magnetic energy into particle acceleration and radiation, with potential significant detrimental effects on earth including damage to technological infrastructure. Recent work has considered methods to predict flare activity from quantitative measures of the solar magnetic field. Feature selection methods provide insight into measures which have the largest...
A new criterion is introduced for determining the order of an autoregressive model fit to time series data. The proposed technique is shown to give a consistent and asymptotically efficient order estimation. It has the benefits of the two well-known model selection techniques, the Akaike information criterion and the Bayesian information criterion. When the true order of the autoregression is relatively...
Mobile devices become more and more prevalent in recent years, especially in young groups. The rapid progress of mobile devices promotes the development of M-Commerce business. The purchase on mobile terminals accounts for a considerable percentage in the total trading volume of E-Commerce and begins to draw the attention of E-Commerce corporation. Alibaba held a Mobile Recommendation Algorithm Competition...
We proposed a deep Convolutional Neural Network (CNN) approach and a Multi-View Stacking Ensemble (MVSE) method in Ali Mobile Recommendation Algorithm competition Season 1 and Season 2, respectively. Specifically, we treat the recommendation task as a classical binary classification problem. We thereby designed a large amount of indicative features based on the logic of mobile business, and grouped...
Graphs are a fundamental model to describe complex statistical relationships over many scientific domains. In this context, graphs are commonly used to investigate relational phenomena which are not directly observable. Our work formulates a Latent Network Inference problem and develops inference methods in a common context for scientific applications where there is an absence of ground truth.
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