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Sufficient conditions for comparing the convolutions of heterogeneous gamma random variables in terms of the usual stochastic order are established. Such comparisons are characterized by the Schur convexity properties of the cumulative distribution function of the convolutions. Some examples of the practical applications of our results are given. [ABSTRACT FROM AUTHOR]

Bidder asymmetries are common in certain auctions where heterogeneous bidders coexist. In this paper, we employ the tool of majorization to study the effect of bidder asymmetry on revenue in second-price auctions. As a preorder on vectors of real components, the majorization is used to rank the asymmetry of bid distributions from different auctions. Our analysis indicates that bidders' asymmetries may raise revenue under certain classes of value distributions (e.g., proportional reversed hazard rate models) and may not in others (e.g., proportional hazard rate models). These results are further validated through supportive evidence that is based on simulated three-bidder auctions. The use of majorization allows us to extend and strengthen the results obtained in the existing literature under more general conditions. This study adds to the literature a useful analytical technique for future research on asymmetric auctions. [ABSTRACT FROM AUTHOR]

Like how useful weather forecasting is, the capability of forecasting or predicting cyber threats can never be overestimated. Previous investigations show that cyber attack data exhibits interesting phenomena, such as long-range dependence and high nonlinearity, which impose a particular challenge on modeling and predicting cyber attack rates. Deviating from the statistical approach that is utilized in the literature, in this paper we develop a deep learning framework by utilizing the bi-directional recurrent neural networks with long short-term memory, dubbed BRNN-LSTM. Empirical study shows that BRNN-LSTM achieves a significantly higher prediction accuracy when compared with the statistical approach. [ABSTRACT FROM AUTHOR]