The satisfiability (SAT) problem is a core problem in computer science. Existing studies have shown that most industrial SAT instances can be effectively solved by modern SAT solvers while random SAT instances cannot. It is believed that the structural characteristics of different SAT formula classes are the reasons behind this difference. In this paper, we study the structural properties of propositional formulas in conjunctive normal form (CNF) by the principle of structural entropy of formulas. First, we used structural entropy to measure the complex structure of a formula and found that the difficulty solving the formula is related to the structural entropy of the formula. The smaller the compressing information of a formula, the more difficult it is to solve the formula. Secondly, we proposed a $\lambda$-approximation strategy to approximate the structural entropy of large formulas. The experimental results showed that the proposed strategy can effectively approximate the structural entropy of the original formula and that the approximation ratio is more than 92%. Finally, we analyzed the structural properties of a formula in the solution process and found that a local search solver tends to select variables in different communities to perform the next round of searches during a search and that the structural entropy of a variable affects the probability of the variable being flipped. By using these conclusions, we also proposed an initial candidate solution generation strategy for a local search for SAT, and the experimental results showed that this strategy effectively improves the performance of the solvers CCAsat and Sparrow2011 when incorporated into these two solvers.

The traditional view-aware based vehicle re-identification methods integrated the vehicle view information into the training process, allowing the model parameters to learn the vehicle view knowledge. So, the extracted vehicle features contained vehicle view information, and the impact of view differences on model performance can be reduced. Since ensemble training with different methods will affect each other, it is difficult for these methods to train models together with other non view-aware methods. In order to address this challenge, in this paper, we propose a view-aware-based post-processing method (VABPP), which uses vehicle view information to re-rank the re-identification results during testing. According to vehicle views, VABPP method divide the distances between vehicle id features into several groups. During testing, multiply the distances of each different group by different coefficients. So, it treats different views equally. In order to achieve better implementation of this post-processing method, in this paper, we also propose a scheme that unifies the feature distance distributions of the training set and the test set. This scheme can enable some properties of the training set to be directly used in the test set. As the properties of the training set are trained under the guidance of correctly labeled labels, which enhances the robustness of the test set properties. The mAP in VeRi-776 dataset and in the three test sets of VERI-Wild dataset are 83.7%, 88.7%, 84.6% and 78.5%, respectively. And the rank-1 in the three test sets of VehicleID dataset are 88.6%, 85.4% and 81.1%, respectively.