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Clinical named entity recognition (CNER) is a fundamental and crucial task for clinical and translation research. In recent years, deep learning methods have achieved significant success in CNER tasks. However, these methods depend greatly on recurrent neural networks (RNNs), which maintain a vector of hidden activations that are propagated through time, thus causing too much time to train models. In this paper, we propose a residual dilated convolutional neural network with the conditional random field (RD-CNN-CRF) for the Chinese CNER, which makes the model asynchronous in computation and thus speeding up the training period dramatically. To be more specific, Chinese characters and dictionary features are first projected into dense vector representations, then they are fed into the residual dilated convolutional neural network to capture contextual features. Finally, a conditional random field is employed to capture dependencies between neighboring tags and obtain the optimal tag sequence for the entire sequence. Computational results on the CCKS-2017 Task 2 benchmark dataset show that our proposed RD-CNN-CRF method competes favorably with state-of-the-art RNN-based methods both in terms of computational performance and training time. [ABSTRACT FROM AUTHOR]

This paper presents a novel and practical study on the position-based radio propagation channel for High-Speed Railway by performing extensive measurements at 2.35 GHz in China. The specification on the path loss model is developed. In particular, small scale fading properties such as K-factor, Doppler frequency feature and time delay spread are parameterized, which show dynamic variances depending on the train location and the transceiver separation. Finally, the statistical position-based channel models are firstly established to characterize the High-Speed Railway channel, which significantly promotes the evaluation and verification of wireless communications in relative scenarios. [ABSTRACT FROM PUBLISHER]