Recurrent neural networks (RNNs), especially long shortterm memory (LSTM) RNNs, are effective network for sequential task like speech recognition. Deeper LSTM models perform well on large vocabulary continuous speech recognition, because of their impressive learning ability. However, it is more difficult to train a deeper network. We introduce a training framework with layer-wise training and exponential moving average methods for deeper LSTM models. It is a competitive framework that LSTM models of more than 7 layers are successfully trained on Shenma voice search data in Mandarin and they outperform the deep LSTM models trained by conventional approach. Moreover, in order for online streaming speech recognition applications, the shallow model with low real time factor is distilled from the very deep model. The recognition accuracy have little loss in the distillation process. Therefore, the model trained with the proposed training framework reduces relative 14% character error rate, compared to original model which has the similar real-time capability. Furthermore, the novel transfer learning strategy with segmental Minimum Bayes-Risk is also introduced in the framework. The strategy makes it possible that training with only a small part of dataset could outperform full dataset training from the beginning.

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Aim: To evaluate the cost–effectiveness of first-line treatment for advanced renal cell carcinoma with nivolumab plus cabozantinib versus sunitinib from a US payer perspective. Methods: Economic outcomes were estimated with Markov and partitioned survival models. Efficacy, safety and other data were taken from the CheckMate 9ER trial. Costs and utilities were gathered from published sources. Sensitivity analyses addressed model uncertainties. Results: The incremental cost–effectiveness ratio of nivolumab plus cabozantinib versus sunitinib was $555,663 and $531,748 per quality-adjusted life-year in the Markov and partitioned survival models, respectively, exceeding the willingness-to-pay threshold ($150,000 per quality-adjusted life-year). Sensitivity analyses showed robust outcomes. Conclusion: From a US payer perspective, first-line nivolumab plus cabozantinib for advanced renal cell carcinoma is not cost-effective.

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