How Chinese provincial governments responded to the Delta and Omicron waves

How long will China continue to try to eliminate COVID? A change of strategy is not very likely, argue Hao Zha (Tsinghua), Yuxi Zhang (LSE), and Thomas Hale (Oxford) who collect and analyse China’s data for the Oxford COVID-19 Government Response Tracker

Fault Injection based Failure Analysis of three CentOS-like Operating Systems

The reliability of operating system (OS) has always been a major concern in the academia and industry. This paper studies how to perform OS failure analysis by fault injection based on the fault mode library. Firstly, we use the fault mode generation method based on Linux abstract hierarchy structure analysis to systematically define the Linux-like fault modes, construct a Linux fault mode library and develop a fault injection tool based on the fault mode library (FIFML). Then, fault injection experiments are carried out on three commercial Linux distributions, CentOS, Anolis OS and openEuler, to identify their reliability problems and give improvement suggestions. We also use the virtual file systems of these three OSs as experimental objects, to perform fault injection at levels of Light and Normal, measure the performance of 13 common file operations before and after fault injection.Comment: 9 pages, 8 figure

Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validation

Automated driving vehicles~(ADV) promise to enhance driving efficiency and safety, yet they face intricate challenges in safety-critical scenarios. As a result, validating ADV within generated safety-critical scenarios is essential for both development and performance evaluations. This paper investigates the complexities of employing two major scenario-generation solutions: data-driven and knowledge-driven methods. Data-driven methods derive scenarios from recorded datasets, efficiently generating scenarios by altering the existing behavior or trajectories of traffic participants but often falling short in considering ADV perception; knowledge-driven methods provide effective coverage through expert-designed rules, but they may lead to inefficiency in generating safety-critical scenarios within that coverage. To overcome these challenges, we introduce BridgeGen, a safety-critical scenario generation framework, designed to bridge the benefits of both methodologies. Specifically, by utilizing ontology-based techniques, BridgeGen models the five scenario layers in the operational design domain (ODD) from knowledge-driven methods, ensuring broad coverage, and incorporating data-driven strategies to efficiently generate safety-critical scenarios. An optimized scenario generation toolkit is developed within BridgeGen. This expedites the crafting of safety-critical scenarios through a combination of traditional optimization and reinforcement learning schemes. Extensive experiments conducted using Carla simulator demonstrate the effectiveness of BridgeGen in generating diverse safety-critical scenarios

CoDeF: Content Deformation Fields for Temporally Consistent Video Processing

We present the content deformation field CoDeF as a new type of video representation, which consists of a canonical content field aggregating the static contents in the entire video and a temporal deformation field recording the transformations from the canonical image (i.e., rendered from the canonical content field) to each individual frame along the time axis.Given a target video, these two fields are jointly optimized to reconstruct it through a carefully tailored rendering pipeline.We advisedly introduce some regularizations into the optimization process, urging the canonical content field to inherit semantics (e.g., the object shape) from the video.With such a design, CoDeF naturally supports lifting image algorithms for video processing, in the sense that one can apply an image algorithm to the canonical image and effortlessly propagate the outcomes to the entire video with the aid of the temporal deformation field.We experimentally show that CoDeF is able to lift image-to-image translation to video-to-video translation and lift keypoint detection to keypoint tracking without any training.More importantly, thanks to our lifting strategy that deploys the algorithms on only one image, we achieve superior cross-frame consistency in processed videos compared to existing video-to-video translation approaches, and even manage to track non-rigid objects like water and smog.Project page can be found at https://qiuyu96.github.io/CoDeF/.Comment: Project Webpage: https://qiuyu96.github.io/CoDeF/, Code: https://github.com/qiuyu96/CoDe

A POLYMER-BASED MICROFLUIDIC RESISTIVE SENSOR FOR DETECTING DISTRIBUTED LOADS

ABSTRACT This paper reports on a polymer-based microfluidic resistive sensor for detecting distributed loads. The sensor is comprised of a polymer rectangular microstructure with an embedded electrolyte-filled microchannel and an array of electrodes aligned along the microchannel length. Electrolyte solution in the microchannel serves as impedance transduction. Distributed loads acting on the polymer microstructure give rise to different deflection along the microstructure length, which is recorded as the resistance change in electrolyte solution. This sensor can detect distributed loads by monitoring the resistance change at each pair of electrodes. A sensor with an in-plane dimension of ~20mm10mm and five pairs of electrodes is fabricated using a CNC machine. 1M KCl solution is used as the electrolyte. Using a custom built electronic circuit on breadboard and a custom LabVIEW program, the static and dynamic performance of the sensor is characterized, demonstrating the feasibility of employing this sensor to detect distributed loads

Retinoic Acids Potentiate BMP9-Induced Osteogenic Differentiation of Mesenchymal Progenitor Cells

As one of the least studied bone morphogenetic proteins (BMPs), BMP9 is one of the most osteogenic BMPs. Retinoic acid (RA) signaling is known to play an important role in development, differentiation and bone metabolism. In this study, we investigate the effect of RA signaling on BMP9-induced osteogenic differentiation of mesenchymal progenitor cells (MPCs).Both primary MPCs and MPC line are used for BMP9 and RA stimulation. Recombinant adenoviruses are used to deliver BMP9, RARalpha and RXRalpha into MPCs. The in vitro osteogenic differentiation is monitored by determining the early and late osteogenic markers and matrix mineralization. Mouse perinatal limb explants and in vivo MPC implantation experiments are carried out to assess bone formation. We find that both 9CRA and ATRA effectively induce early osteogenic marker, such as alkaline phosphatase (ALP), and late osteogenic markers, such as osteopontin (OPN) and osteocalcin (OC). BMP9-induced osteogenic differentiation and mineralization is synergistically enhanced by 9CRA and ATRA in vitro. 9CRA and ATRA are shown to induce BMP9 expression and activate BMPR Smad-mediated transcription activity. Using mouse perinatal limb explants, we find that BMP9 and RAs act together to promote the expansion of hypertrophic chondrocyte zone at growth plate. Progenitor cell implantation studies reveal that co-expression of BMP9 and RXRalpha or RARalpha significantly increases trabecular bone and osteoid matrix formation.Our results strongly suggest that retinoid signaling may synergize with BMP9 activity in promoting osteogenic differentiation of MPCs. This knowledge should expand our understanding about how BMP9 cross-talks with other signaling pathways. Furthermore, a combination of BMP9 and retinoic acid (or its agonists) may be explored as effective bone regeneration therapeutics to treat large segmental bony defects, non-union fracture, and/or osteoporotic fracture