Non-parallel story author-style transfer is an important but challenging task in natural language process, which requires transferring an input story into another author-style while maintaining source semantics. Despite recent progress, current text style transfer systems still face the challenges of low robustness of the model and low quality of the generated stories. To address these challenges, we propose an end-to-end framework incorporating dual encoder components and a fusion mechanism, which can achieve explicit style-content disentanglement and effectively fusing source-domain content with target-domain stylistic features. First, we extract text from source stories containing content information using empirical extraction rules and prompt engineering. And then, we propose a novel generation model which achieves story-style transfer through capturing source content features and target style features and then fusing them. We use two additional training objectives to learn high-level discourse representations. Moreover, we have constructed a new dataset for this task. Extensive experiments based on automatic and human evaluation show that our model significantly outperforms state-of-the-art baselines, achieving approximately 8.5% average improvement in comprehensive performance metrics, demonstrating the effectiveness of our model in story-style transfer.

Based on the SEIR model, which takes into account prevention and control measures, prevention and control awareness, and economic level and medical level indicators, this paper proposes an infectious disease model of “susceptible‐exposed‐infected‐removed‐asymptomatic‐isolated” (short for SEIR‐AQ) to assess and predict the development of the COVID‐19 pandemic with different prevention and control strategies. The kinetic parameters of the SEIR‐AQ model were obtained by fitting, and the parameters of the SEIR‐AQ model were solved through the Euler method. Furthermore, the effects of different countries’ prevention and control strategies on the number of infections, the proportion of isolation, the number of deaths, and the number of recoveries were also simulated. The theoretical analysis showed that measures such as isolation for prevention and control and medical tracking isolation had a significant inhibitory effect on the development of the COVID‐19 pandemic, among which stratified treatment and enhanced awareness played a key role in the rapid regression of the peak of COVID‐19‐infected patients. Conclusion of the Simulation. The SEIR‐AQ model can be used to evaluate the development status of the COVID‐19 epidemic and has some theoretical value for the prediction of COVID‐19.

Given a set $\cal P$ of points in the Euclidean plane and two triangulations of $\cal P$, the flip distance between these two triangulations is the minimum number of flips required to transform one triangulation into the other. Parameterized Flip Distance problem is to decide if the flip distance between two given triangulations is equal to a given integer $k$. The previous best FPT algorithm runs in time $O^{*}(k\cdot c^{k})$ ($c\leq 2\times 14^{11}$), where each step has fourteen possible choices, and the length of the action sequence is bounded by $11k$. By applying the backtracking strategy and analyzing the underlying property of the flip sequence, each step of our algorithm has only five possible choices. Based on an auxiliary graph $G$, we prove that the length of the action sequence for our algorithm is bounded by $2|G|$. As a result, we present an FPT algorithm running in time $O^{*}(k\cdot 32^{k})$.