Drug synthesis reactions, especially asymmetric reactions, are the key components of modern pharmaceutical chemistry. Chemists have invested a lot in manpower and resources to identify various chemical reaction patterns in order to achieve efficient synthesis and asymmetric catalysis. The latest researches of quantum mechanical computing and machine learning algorithms in this field have proved the great potential of accurate virtual screening and learning the existing drug synthesis reaction data by computers. However, the existing methods only use few single-modal data, and can only use the common machine learning methods due to the limitation of not enough data. This hinders their universal application in a wider range of scenarios. Therefore, two screening models of drug synthesis reaction integrating multimodal data were proposed for virtual screening of reaction yield and enantioselectivity. At the same time, a 3D conformation descriptor based on Boltzmann distribution was also proposed to combine the 3D spatial information of molecules with quantum mechanical properties. These two multimodal data fusion models were trained and verified in two representative organic synthesis reactions (C-N cross coupling reaction and N, S-acetal formation). The R2(R-squared) of the former is increased by more than 1 percentage point compared with those of the baseline methods in most data splitting, and the MAE(Mean Absolute Error) of the latter is decreased by more than 0.5 percentage points compared with those of the baseline methods in most data splitting. It can be seen that the models based on multimodal data fusion will bring good performance in different tasks of organic reaction screening.
In the automatic driving perception system, multi-sensor fusion is usually used to improve the reliability of the perception results. Aiming at the task of object detection in fusion perception system, a object detection method based on radar and camera fusion, namely Priori and Radar Region Proposal Network (PRRPN), was proposed,with the aim of using radar measurement and the object detection result of the previous frame to improve the generation of region proposals in the image detection network and improve the object detection performance. Firstly, the objects detected in the previous frame with the radar points in the current frame were associated to pre-classify the radar points. Then, the pre-classified radar points were projected into the image, and the corresponding prior region proposals and radar region proposals were obtained according to the distance of the radar and Radar Cross Section (RCS) information. Finally, the regression and classification of the object bounding boxes were performed according to the region proposals. In addition, PRRPN and Region Proposal Network (RPN) were fused to carry out object detection. The newly released nuScenes dataset was adopted to test and evaluate the three detection methods. Experimental results show that, compared with RPN, the proposed PRRPN can not only detect objects faster, but also increase the average detection accuracy of small objects by 2.09 percentage points. And compared with the methods by using PRRPN and RPN alone, the method by fusing the proposed PRRPN and RPN has the average detection accuracy increased by 2.54 percentage points and 0.34 percentage points respectively.
A novel scheme for information hiding based on chaos digital stream was proposed, a kind of chaos digital stream which can be used to hide various information between receiver and transmitter was constructed by means of encryption and randomness of chaotic dynamic systems, as the format of stream is open only to receiver and transmitter and chaotic systems have definite equations, the scheme is safer, having large capacity of hiding. At last, an example was given to account for the scheme.