IPO underpricing and mutual fund allocation: New evidence from registration system

We study the effect of mutual fund allocation on China's IPO market under the new registration system. The introduction of mutual fund bids significantly increases the IPO offer price, resulting in a low initial short-term return and suppressed IPO underpricing. Those newly listed stocks witness lower volatility in the following weeks due to preferential allocation to the mutual fund at the primary market. Further analysis suggests that large investors' net purchase strengthens IPO after-market return and volatility. Besides, the effect of mutual fund participation on IPOs is stronger in places where the COVID-19 outbreak. This new evidence suggests that mutual fund allocation plays a critical role in IPO price discovery and decreases investor lottery trading.

CSES Satellite Observations of 50 Hz Power Line Radiation Over Mainland China and Its Response to COVID‐19

We present a low‐altitude satellite survey of power line harmonic radiation (PLHR) at 50 Hz over Mainland China. We analyzed the month‐to‐month variation pattern in PLHR occurrence rate and further analyzed its correlation with some influencing factors (i.e., solar radiation, lightning flashes, and electricity consumption) using CSES satellite electric field data from 2019 to 2021. We also investigate the response of PLHR occurrence rate to COVID‐19. The statistical results show the dayside PLHR occurrence rate decreasing from winter to summer solstice and increasing from summer to winter solstice, which indicates it is controlled by the solar radiation. The nightside variation is more complex, which may be due to many sources that could influence the nightside lower ionosphere. The PLHR occurrence rate significantly decreased over Mainland China in February 2020, which is because of the significant decrease in electricity consumption due to the suspension of industrial production caused by COVID‐19.Alternate :Plain Language SummaryPower line harmonic radiation (PLHR) is the electromagnetic waves radiated by electric power systems at harmonic frequencies of 50 or 60 Hz, depending on the frequency of the system on the ground. Previous research mainly focuses on identification of individual PLHR events and their subsequent analysis. However, the number of base‐frequency PLHR signal events is the most abundant, which is suitable for the statistical study of PLHR occurrence rate and its variation pattern, and further study of the factors affecting its variation pattern. In this paper, we use 3 years of electric field data from the China Seismo‐Electromagnetic Satellite (CSES) which is an LEO satellite launched into orbit in February 2018 to investigate the month‐to‐month variation pattern of PLHR occurrence rate over Mainland China and its correlation with the influencing factors. The response of PLHR occurrence rate to COVID‐19 are also investigated.

Dysregulation of Innate and Adaptive Immune Responses in Asymptomatic SARS-CoV-2 Infection with Delayed Viral Clearance.

Asymptomatic infection with SARS-CoV-2 is a major concern in the control of the COVID-19 pandemic. Many questions concerning asymptomatic infection remain to be answered, for example, what are the differences in infectivity and the immune response between asymptomatic and symptomatic infections? In this study, based on a cohort established by the Wuchang District Health Bureau of Wuhan in the early stage of the COVID-19 pandemic in Wuhan in 2019, we conducted a comprehensive analysis of the clinical, virological, immunological, and epidemiological data of asymptomatic infections. The major findings of this study included: 1) the asymptomatic cohort enrolled this study exhibited low-grade but recurrent activity of viral replication; 2) despite a lack of overt clinical symptoms, asymptomatic infections exhibited ongoing innate and adaptive immune responses; 3) however, the immune response from asymptomatic infections was not activated adequately, which may lead to delayed viral clearance. Given the fragile equilibrium between viral infection and host immunity, and the delayed viral clearance in asymptomatic individuals, close viral monitoring should be scheduled, and therapeutic intervention may be needed.

Mixed-Reality-Enhanced Human–Robot Interaction with an Imitation-Based Mapping Approach for Intuitive Teleoperation of a Robotic Arm-Hand System

This paper presents an integrated mapping of motion and visualization scheme based on a Mixed Reality (MR) subspace approach for the intuitive and immersive telemanipulation of robotic arm-hand systems. The effectiveness of different control-feedback methods for the teleoperation system is validated and compared. The robotic arm-hand system consists of a 6 Degrees-of-Freedom (DOF) industrial manipulator and a low-cost 2-finger gripper, which can be manipulated in a natural manner by novice users physically distant from the working site. By incorporating MR technology, the user is fully immersed in a virtual operating space augmented by real-time 3D visual feedback from the robot working site. Imitation-based velocity-centric motion mapping is implemented via the MR subspace to accurately track operator hand movements for robot motion control and enables spatial velocity-based control of the robot Tool Center Point (TCP). The user control space and robot working space are overlaid through the MR subspace, and the local user and a digital twin of the remote robot share the same environment in the MR subspace. The MR-based motion and visualization mapping scheme for telerobotics is compared to conventional 2D Baseline and MR tele-control paradigms over two tabletop object manipulation experiments. A user survey of 24 participants was conducted to demonstrate the effectiveness and performance enhancements enabled by the proposed system. The MR-subspace-integrated 3D mapping of motion and visualization scheme reduced the aggregate task completion time by 48% compared to the 2D Baseline module and 29%, compared to the MR SpaceMouse module. The perceived workload decreased by 32% and 22%, compared to the 2D Baseline and MR SpaceMouse approaches.

Let AI Perform Better Next Time—A Systematic Review of Medical Imaging-Based Automated Diagnosis of COVID-19: 2020–2022

The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming characteristic of the Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted to confirm positive COVID-19 RT-PCR tests. Since the very beginning of the pandemic, researchers in the artificial intelligence area have proposed a large number of automatic diagnosing models, hoping to assist radiologists and improve the diagnosing accuracy. However, after two years of development, there are still few models that can actually be applied in real-world scenarios. Numerous problems have emerged in the research of the automated diagnosis of COVID-19. In this paper, we present a systematic review of these diagnosing models. A total of 179 proposed models are involved. First, we compare the medical image modalities (CT or X-ray) for COVID-19 diagnosis from both the clinical perspective and the artificial intelligence perspective. Then, we classify existing methods into two types—image-level diagnosis (i.e., classification-based methods) and pixel-level diagnosis (i.e., segmentation-based models). For both types of methods, we define universal model pipelines and analyze the techniques that have been applied in each step of the pipeline in detail. In addition, we also review some commonly adopted public COVID-19 datasets. More importantly, we present an in-depth discussion of the existing automated diagnosis models and note a total of three significant problems: biased model performance evaluation;inappropriate implementation details;and a low reproducibility, reliability and explainability. For each point, we give corresponding recommendations on how we can avoid making the same mistakes and let AI perform better in the next pandemic.