Analyzing the Stock Volatility Spillovers in Chinese Financial and Economic Sectors

By regarding the Chinese financial and economic sectors as a system, this article studies the stock volatility spillover in the system and explores its effects on the overall performance of the macroeconomy in China. The recent outbreak of COVID-19, U.S.-China trade friction, and three historical financial turbulences are involved to distinguish the changes in the spillover in these distinct crises, which has seldom been unveiled in the literature. By considering that the stock volatility spillover may vary over distinct timescales, the spillovers are disclosed through innovatively constructing the multi-scale spillover networks, followed by connectedness computation, based on variational mode decomposition (VMD) and generalized vector autoregression (GVAR) process. Our empirical analysis first demonstrates the different levels of increases in the total sectoral volatility spillover and changes in the roles of the sectors in the system under the aforementioned crises. Besides, the increases in the sectoral spillover in the long-term are verified to negatively impact the macroeconomy and can thereby act as warning signals.

[Clinical characteristics of patients infected with 2019-nCoV Omicron variants BA.2 and BA.5 in Xi'an city, China].

Objective: To analyze the epidemiological and clinical characteristics of patients infected with different subtype of 2019-nCoV Omicron variants BA.2 and BA.5 in Xi'an city. Methods: A retrospective observational study was conducted to collect data of 168 patients infected with Omicron variant admitted to the designated hospital for COVID-19 charged by Xi'an Chest Hospital during 2022. Data were collected including epidemiological, clinical features, laboratory and viral load, and the difference between BA.2 and BA.5 subtype was analyzed. Results: A total of 168 patients were admitted, including 122 cases infected with BA.2 subtype, and 46 cases infected with BA.5 subtype. Patients infected with BA.2 subtype had a higher rate of cough than BA.5 subtype (43.44%∶23.91%; P=0.021). Compared with the Omicron variant BA.2, patients infected with BA.5 subtype had a higher proportion of asymptomatic and mild infections (89.13%∶68.85%; P<0.001), higher rate of vaccination (95.66%∶68.03%; P<0.001), shorter time to nucleic acid negative conversion (8.62; P=0.047), and a higher viral load at admission (P=0.005, P=0.017). Conclusions: The Omicron variant is extremely infectious with aggregated onset, but its clinical symptoms are mild. The vaccine, especially the booster vaccination, remains effective in preventing severe stage progression and improving prognosis in patients with Omicron variant infection.

Learning from the Web: Webly Supervised Meta-Learning for Masked Face Recognition

Mask wearing has been considered as an effective measure to prevent the spread of COVID-19 during the current pandemic. However, most advanced face recognition approaches are not adequate for masked face recognition, particularly in dealing with the issue of training through the datasets covering only a limited number of images with ground-truth labels. In this work, we propose to learn from the large scale of web images and corresponding tags without any manual annotations along with limited fully annotated datasets. In particular, inspired by the recent success of webly supervised learning in deep neural networks, we capitalize on readily-available web images with noisy annotations to learn a robust representation for masked faces. Besides, except for the conventional spatial representation learning, we propose to leverage the power of frequency domain to capture the local representative information of unoccluded facial parts. This approach learns robust feature embeddings derived from our feature fusion architecture to make joint and full use of information from both spatial and frequency domains. Experimental results on seven benchmarks show that the proposed approach significantly improves the performance compared with other state-of-theart methods.

The ADAPT project: Adaptive and autonomous data performance connectivity and decentralized transport network

The ADAPT project started during the most critical phase of the COVID-19 outbreak in Europe when the demand for Personal Protective Equipment (PPE) from each country's healthcare system surpassed national stock amounts. Due to national shutdowns, reduced transport logistics, and containment measures on the federal and provincial levels, the authorities could not meet the rising demand from the health care system on the PPE equipment. Fortunately, the PPE production capacities in China have regained (and expanded) their available capacities through which Austria now can get the demand of PPE to protect its citizens. ADAPT develops an adaptive and autonomous decision-making network to support the involved stakeholders along the PPE supply chain to save and protect human lives. The ADAPT decentralized blockchain platform optimizes supply, demand, and transport capacities between China and Austria with transparent, real-time certification checks on equipment, production documentation, and intelligent decision-making capabilities at all levels of this multidimensional logistic problem. © 2021 ACM.

Digestive system manifestations and clinical significance of coronavirus disease 2019 (COVID-19): A systematic literature review

Objective: A worldwide outbreak of coronavirus disease 2019 (COVID-19) has drawn global attention. Several reports have described the gastrointestinal (GI) manifestations in the infected patients. The systematic review was designed to highlight the gaps in our knowledge about the prevalence and clinical significance of GI symptoms in patients with COVID-19. Methods: We searched PubMed database and Google articles published in both English and Chinese up to June 3, 2020, using search terms “Clinical features,” “2019 novel coronavirus,” “2019-nCoV,” “COVID-19,” or “SARS-Cov-2.” Observational studies, case reports or letters describing the clinical features or observational studies regarding the detection and/or isolation of SARS-Cov-2 viruses in stools were included. Results: A total of 22 publications were finally selected. It was reported that GI symptoms occurred in about 3%-40.7% of patients. GI manifestations included nausea, diarrhea, anorexia, vomiting, abdominal pain, belching, abdominal distension and GI haemorrhage. Diarrhea was the most common GI symptom. Infected patients had various degrees of liver dysfunction, and the severity of liver dysfunction was significantly associated with the severity of the disease. Therapy focusing on digestive system like liver supportive therapy or nutrition support or probiotics have been demonstrated to be effective interventions, which greatly improve prognosis. Fecal-oral transmission route is a potential risk for transmission. Conclusions: GI symptoms are common in COVID-19. Strengthening the recognition on abnormalities in digestive system of patients with COVID-19 is crucial for early identification and timely treatment, especially for those atypical patients. Hygiene protection and keep drainpipe free-flowing are necessary for everyone.