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In this paper, we study how to utilize forecasts to design online electrical vehicle (EV) charging algorithms that can attain strong performance guarantees. We consider the scenario of an aggregator serving a large number of EVs together with its background load, using both its own renewable energy (for free) and the energy procured from the external grid. The goal of the aggregator is to minimize...
In this paper, we propose a novel joint topical n-gram language model that combines the semantic topic information with local constraints in the training procedure. Instead of training the n-gram language model and topic model independently, we estimate the joint probability of latent semantic topic and n-gram directly. In this procedure Latent Dirichlet allocation (LDA) is employed to compute latent...
In this paper, we characterize the performance of an important class of scheduling schemes, called greedy maximal scheduling (GMS), for multi-hop wireless networks. While a lower bound on the throughput performance of GMS is relatively well-known in the simple node-exclusive interference model, it has not been thoroughly explored in the more general K-hop interference model. Moreover, empirical observations...
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