Interview with Dr. Liu Qingquan: Traditional Chinese Medicine for COVID-19 Diagnosis and Treatment

To many of us,2020 was a remarkably difficult and challenging year.The whole world experienced a sudden,unexpected outbreak of COVID-19.According to the Zhong Guo Yi Bing Shi Jian(《中国疫病史鉴》History of Epidemics in China),there have been a total of 321 pandemics throughout the history of China ever since the Western Han dynasty(202 BCE–8 CE).

A Method for Trust Management in Cloud Computing: Data Coloring by Cloud Watermarking

With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtuMized resources are provided as services. With virtualization technology, cloud computing offers diverse services （such as virtual computing, virtual storage, virtual bandwidth, etc.） for the public by means of multi-tenancy mode. Although users are enjoying the capabilities of super-computing and mass storage supplied by cloud computing, cloud security still remains as a hot spot problem, which is in essence the trust management between data owners and storage service providers. In this paper, we propose a data coloring method based on cloud watermarking to recognize and ensure mutual reputations. The experimental results show that the robustness of reverse cloud generator can guarantee users＇ embedded social reputation identifications. Hence, our work provides a reference solution to the critical problem of cloud security.

Magnetic properties optimization of nanocomposite Nd_9Fe_(85)B_6 magnets by controlling microstructure of as-quenched ribbons

To optimize the magnetic properties of nanocomposite Nd9Fe85B6 magnets, the as-quenched ribbons with different microstructures were prepared at six wheel velocities from 10 to 30 m s^-1through rapid quenching,followed by a series of annealing treatments at 550-800 °C for 5-10 min. It is found that both the large initial grains at low cooling rate and high content of amorphous phase at high cooling rate cause a-Fe grains coarsening, which leads to a decline in the strength of exchange coupling interaction and the deterioration of magnetic properties. In order to optimize the magnetic properties, the as-quenched ribbons should be chosen with relatively small initial grains as well as a small amount of amorphous phase. For nanocomposite Nd9Fe85B6 materials, the optimized magnetic properties of Hcj= 446 kA m^-1, Br= 0.86 T,（BH）max=80 kJ m^-3are obtained for ribbons prepared at 18 m s-1after annealing at 620 °C for 5 min.

Enhancing recommender systems by incorporating social information

Although recommendation techniques have achieved distinct developments over the decades,the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality.Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings.How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge.In this paper,based on a factor graph model,we formalize the problem in a semi-supervised probabilistic model,which can incorporate different user information,user relationships,and user-item ratings for learning to predict the unknown ratings.We evaluate the method in two different genres of datasets,Douban and Last.fm.Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms.Furthermore,a distributed learning algorithm is developed to scale up the approach to real large datasets.