Existing tree decoder is only suitable for solving single variable problems， but has no good effect of solving multivariate problems. At the same time， most mathematical solvers select truth expression wrongly， which leads to learning deviation occurred in training. Aiming at the above problems， a Graph to Equation Tree （GET） model based on expression level-by-level aggregation and dynamic selection was proposed. Firstly， text semantics was learned through the graph encoder. Then， subexpressions were obtained by aggregating quantities and unknown variables iteratively from bottom of the equation tree layer by layer. Finally， combined with the longest prefix of output expression， truth expression was selected dynamically to minimize the deviation. Experimental results show that the precision of proposed model reaches 83.10% on Math23K dataset， which is 5.70 percentage points higher than that of Graph to Tree （Graph2Tree） model. Therefore， the proposed model can be applied to solution of complex multivariate mathematical problems， and can reduce influence of learning deviation on experimental results.

In recent years， Generative Adversarial Network （GAN） has become a new research hotspot in Low-Dose Computed Tomography （LDCT） artifact suppression because of its performance advantages. Due to irregular distribution and strong relevance to the normal tissues of artifacts， denoising performance of the existing GAN-based denoising networks is limited. Aiming at this problem， a LDCT denoising algorithm based on artifacts sensing GAN was proposed. Firstly， an artifacts direction sensing generator was designed. In this generator， on the basis of U-residual encoding and decoding structure， an Artifacts Direction Sensing Sub-module （ADSS） was added to improve the generator’s sensitivity to artifacts direction features. Secondly， the Attention Discriminator （AttD） was designed to improve the ability of distinguishing noise and artifacts. Finally， the loss functions corresponding to the network functions were designed. Through the cooperation of multiple loss functions， the denoising performance of network was improved. Experimental results show that compared to the High-Frequency Sensitive GAN （HFSGAN）， the proposed denoising algorithm has the average Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） improved by 4.9% and 2.8% respectively， and has good artifact suppression effect.