We develop and analyze methods for computing provably optimal maximum a posteriori probability (MAP) configurations for a subclass of Markov random fields defined on graphs with cycles. By decomposing ...

As the school year comes to a close, young Canadians entering the job market are facing one of the toughest hiring seasons in ...

This letter proposes a machine learning-based linear programming model that quickly establishes the nonparametric prediction intervals of wind power by integrating extreme learning machine and ...