Knowledge Graph Enhanced Transformers for Diagnosis Generation of Chinese Medicine.

Chinese medicine (CM) diagnosis intellectualization is one of the hotspots in the research of CM modernization. The traditional CM intelligent diagnosis models transform the CM diagnosis issues into classification issues, however, it is difficult to solve the problems such as excessive or similar categories. With the development of natural language processing techniques, text generation technique has become increasingly mature. In this study, we aimed to establish the CM diagnosis generation model by transforming the CM diagnosis issues into text generation issues. The semantic context characteristic learning capacity was enhanced referring to Bidirectional Long Short-Term Memory (BILSTM) with Transformer as the backbone network. Meanwhile, the CM diagnosis generation model Knowledge Graph Enhanced Transformer (KGET) was established by introducing the knowledge in medical field to enhance the inferential capability. The KGET model was established based on 566 CM case texts, and was compared with the classic text generation models including Long Short-Term Memory sequence-to-sequence (LSTM-seq2seq), Bidirectional and Auto-Regression Transformer (BART), and Chinese Pre-trained Unbalanced Transformer (CPT), so as to analyze the model manifestations. Finally, the ablation experiments were performed to explore the influence of the optimized part on the KGET model. The results of Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1), ROUGE2 and Edit distance of KGET model were 45.85, 73.93, 54.59 and 7.12, respectively in this study. Compared with LSTM-seq2seq, BART and CPT models, the KGET model was higher in BLEU, ROUGE1 and ROUGE2 by 6.00-17.09, 1.65-9.39 and 0.51-17.62, respectively, and lower in Edit distance by 0.47-3.21. The ablation experiment results revealed that introduction of BILSTM model and prior knowledge could significantly increase the model performance. Additionally, the manual assessment indicated that the CM diagnosis results of the KGET model used in this study were highly consistent with the practical diagnosis results. In conclusion, text generation technology can be effectively applied to CM diagnostic modeling. It can effectively avoid the problem of poor diagnostic performance caused by excessive and similar categories in traditional CM diagnostic classification models. CM diagnostic text generation technology has broad application prospects in the future.

Ground-state cooling of mechanical oscillator via quadratic optomechanical coupling with two coupled optical cavities.

We present a scheme for the electromagnetically induced transparency (EIT)-like nonlinear ground-state cooling in a double-cavity optomechanical system in which an optical cavity mode is coupled parametrically to the square of the position of a mechanical oscillator, an additional auxiliary cavity is coupled to the optomechanical cavity. The optimum cooling conditions is derived, based on which the heating process can be well suppressed and the mechanical resonator can be cooled with an optimal effect to near its ground state through EIT-like cooling mechanism even in unresolved sideband regime. It is demonstrated by numerical simulations that not only the average phonon number of steady state is lower than that of single-cavity optomechanical system, but also the cooling rate is greatly faster than that of the linear optomechanical coupling due to the two-phonon cooling process in the quadratic coupling. Also, the ground-state cooling is achievable even with a relatively weak quadratic coupling strengthby tunning the coupling between two cavities to reach the optimum cooling conditions, thus provides an solution for overcoming the limitations of weak quadratic coupling rate in experiments. The proposed approach provides a platform for quantum manipulation of macroscopic mechanical devices beyond the resolved sideband limit and linear coupling regime.