In the last decade, X-ray fluorescence holography has been developed for the study of 3D atomic arrangements in solids. However, it encounters the twin image problem which may disturb the reconstructed atomic images. In this paper, the formation of twin image is discussed and we propose a modified two-energy algorithm to remove the twin image. The simulation shows that the method is valid and more efficient than the multiple-energy algorithm proposed by Barton.
For task-oriented dialogue systems, Natural Language Generation (NLG) is the last and vital step which aims at generating an appropriate response according to the dialogue act (DA). While end-to-end neural networks have achieved promising performances on this task, the existing models still struggle to avoid slot mistakes. To address this challenge, we propose a novel segmented generation approach in this paper. The proposed method operates by progressively generating text for the span between two adjacent keywords (act type and slots) in semantically ordered DA. This procedure is recursively applied from left to right until a response is completed. Besides, a retrieval mechanism is utilized to better match the diversity and fluency in human language. Experimental results on four datasets demonstrate that our model achieves state-of-the-art slot error rate and also gets competitive performance on BLEU score with all strong baselines.
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