The COVID-19 epidemic is considered as the global health crisis of the whole society and the greatest challenge mankind faced since World War Two. Unfortunately, the fake news about COVID-19 is spreading as fast as the virus itself. The incorrect health measurements, anxiety, and hate speeches will have bad consequences on people's physical health, as well as their mental health in the whole world. To help better combat the COVID-19 fake news, we propose a new fake news detection dataset MM-COVID 1 (Multilingual and Multidimensional COVID-19 Fake News Data Repository). This dataset provides the multilingual fake news and the relevant social context. We collect 3981 pieces of fake news content and 7192 trustworthy information from English, Spanish, Portuguese, Hindi, French and Italian, 6 different languages. We present a detailed and exploratory analysis of MM-COVID from different perspectives.

The outbreak of COVID-19 disrupts the life of many people in the world. In response to this global pandemic, various institutions across the globe had soon issued their prevention guidelines. Governments in the US had also implemented social distancing policies. However, those policies, which were designed to slow the spread of COVID-19, and its compliance, have varied across the states, which led to spatial and temporal heterogeneity in COVID-19 spread. This paper aims to propose a spatio-temporal model for quantifying compliance with the US COVID-19 mitigation policies at a regional level. To achieve this goal, a specific partial differential equation (PDE) is developed and validated with short-term predictions. The proposed model describes the combined effects of transboundary spread among state clusters in the US and human mobilities on the transmission of COVID-19. The model can help inform policymakers as they decide how to react to future outbreaks.

Deep reinforcement learning has achieved some remarkable results in self-driving. There is quite a lot of work to do in the area of autonomous driving with high real-time requirements because of the inefficiency of reinforcement learning in exploring large continuous motion spaces. A deep imitation reinforcement learning (DIRL) framework is presented to learn control policies of self-driving vehicles, which is based on a deep deterministic policy gradient algorithm (DDPG) by vision. The DIRL framework comprises two components, the perception module and the control module, using imitation learning (IL) and DDPG, respectively. The perception module employs the IL network as an encoder which processes an image into a low-dimensional feature vector. This vector is then delivered to the control module which outputs control commands. Meanwhile, the actor network of the DDPG is initialized with the trained IL network to improve exploration efficiency. In addition, a reward function for reinforcement learning is defined to improve the stability of self-driving vehicles, especially on curves. DIRL is verified by the open racing car simulator (TORCS), and the results show that the correct control strategy is learned successfully and has less training time.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Background. Idiopathic Pulmonary Fibrosis (IPF) is one of the most common idiopathic interstitial pneumonia, which can occur all over the world. The median survival time of patients is about 3-5 years, and the mortality is relatively high. Objective. To reveal the potential molecular characteristics of IPF and deepen the understanding of the molecular mechanism of IPF. In order to provide some guidance for the clinical treatment, new drug development, and prognosis judgment of IPF. Although the preliminary conclusion of this study has certain guiding significance for the treatment of IPF and so on, it needs more accurate analytical approaches and large sample clinical trials to verify. Methods. 220 patients with IPF were divided into different subgroups according to the gene expression profiles, which were obtained from the Gene Expression Omnibus (GEO) database. In addition, these subgroups present different expression forms and clinical features. Therefore, weighted gene coexpression analysis (WGCNA) was used to seek the differences between subtypes. And six subgroup-specific WGCNA modules were identified. Results. Combined with the characteristics of WGCNA and KEGG enrichment modules, the autophagic pathway was only upregulated in subgroup I and enriched significantly. The differentiation pathways of Th1 and Th2 cells were only upregulated and enriched in subgroup II. At the same time, combined with clinical information, IPF patients in subgroup II were older and more serious, which may be closely related to the differentiation of Th1 and Th2 cells. In contrast, the neuroactive ligand-receptor interaction pathway and Ca+ signaling pathway were significantly upregulated and enriched in subgroup III. Although there was no significant difference in prognosis between subgroup I and subgroup III, their intrinsic biological characteristics were very different. These results suggest that the subtypes may represent risk factors of age and intrinsic biological characteristics and may also partly reflect the severity of the disease. Conclusion. In conclusion, current studies have improved our understanding of IPF-related molecular mechanisms. At the same time, because the results show that patients from different subgroups may have their own unique gene expression patterns, it reminds us that patients in each subgroup should receive more personalized treatment.