We study the connection establishment of label switched paths (LSPs), and compare the LSP blocking performance of the overlay and peer models in IP/MPLS over optical networks. We consider two dynamic routing algorithms for the establishment of LSP connections, of which one is for the overlay model and the other is for the peer model. Our investigations on two typical network topologies, namely NSFNET and ARPA2 networks, show that the number of add/drop ports (or transceivers) on optical crossconnects (OXCs) has a significant impact on the LSP blocking performance for both models. We show by computer simulation that in each case, there is a threshold value for the add/drop ratio, which can achieve almost the best blocking performance. This threshold value remains virtually unchanged as the traffic load varies, but it does depend on the network topology and the number of wavelengths per fiber. This will then indicate the number of add/drop ports to be used so that one can achieve a near optimal blocking performance without incurring unnecessarily excessive network costs. Our investigations reveal that the peer model achieves a much better blocking performance than the overlay model when the number of add/drop ports is relatively high, but that this is not always true when the number of add/drop ports is small.

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Multi-task learning has an ability to share the knowledge among related tasks and implicitly increase the training data. However, it has long been frustrated by the interference among tasks. This paper investigates the performance of capsule network for text, and proposes a capsule-based multi-task learning architecture, which is unified, simple and effective. With the advantages of capsules for feature clustering, proposed task routing algorithm can cluster the features for each task in the network, which helps reduce the interference among tasks. Experiments on six text classification datasets demonstrate the effectiveness of our models and their characteristics for feature clustering.

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