Spatio-Temporal Usage Patterns of Dockless Bike-Sharing Service Linking to a Metro Station: A Case Study in Shanghai, China

The dockless bike-sharing (DLBS) system serves as a link between metro stations and travelers\u27 destinations (or originations). This paper aims to uncover spatio-temporal usage patterns of dockless bike-sharing service linking to metro stations for supporting scientific planning and management of the dockless bike-sharing system. A powerful visualization tool was used to analyze the differences in usage patterns in workdays and weekends. The travel distance distributions of using dockless bike-sharing near metro stations were investigated to shed light on the service area of the dockless bike-sharing system. Agglomerative hierarchical clustering was applied to analyze differences in usage patterns of metro stations located in different areas. The results show that the usage patterns of dockless bike-sharing on weekends are different from those on workdays. The average travel distance using the dockless bike-sharing system at weekends is significantly larger than that of workdays. The travel distance distribution could be nicely fitted by the Frechet distribution of the Generalized Extreme Value (GEV) distribution family. The usage characteristics of shared bikes are correlated with land use and population density around metro stations. No matter in urban or suburban areas, there is a great demand for bike-sharing in densely populated areas with intensive land development, such as university towns in suburban areas. This study improves the understandings regarding the usage patterns of the DLBS system serving as a link between the final destinations (or originations) and metro stations. The results can be helpful to the operation and demand management of DLBS. \ua9 2020 by the authors

Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks

Ship detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection and the redundancy of detection region. In order to solve such problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ship in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving the problem resulted from the narrow width of the ship. Compared with previous multi-scale detectors such as Feature Pyramid Network (FPN), DFPN builds the high-level semantic feature-maps for all scales by means of dense connections, through which enhances the feature propagation and encourages the feature reuse. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multi-scale ROI Align for the purpose of maintaining the completeness of semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has a state-of-the-art performance.Comment: 14 pages, 11 figure

CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer

Reconstructing personalized animatable head avatars has significant implications in the fields of AR/VR. Existing methods for achieving explicit face control of 3D Morphable Models (3DMM) typically rely on multi-view images or videos of a single subject, making the reconstruction process complex. Additionally, the traditional rendering pipeline is time-consuming, limiting real-time animation possibilities. In this paper, we introduce CVTHead, a novel approach that generates controllable neural head avatars from a single reference image using point-based neural rendering. CVTHead considers the sparse vertices of mesh as the point set and employs the proposed Vertex-feature Transformer to learn local feature descriptors for each vertex. This enables the modeling of long-range dependencies among all the vertices. Experimental results on the VoxCeleb dataset demonstrate that CVTHead achieves comparable performance to state-of-the-art graphics-based methods. Moreover, it enables efficient rendering of novel human heads with various expressions, head poses, and camera views. These attributes can be explicitly controlled using the coefficients of 3DMMs, facilitating versatile and realistic animation in real-time scenarios.Comment: WACV202

Anti-inflammatory effects of NaB and NaPc in Acinetobacter baumannii-stimulated THP-1 cells via TLR-2/NF-κB/ROS/NLRP3 pathway

This study evaluated the anti-inflammation effect of the three main short-chain fatty acids (SCFAs) on Acinetobacter baumannii-induced THP-1 cells. The three main SCFAs could inhibit A. baumannii-stimulated THP-1 cell NF-κB pathway activity and the expressions of NLRP3 inflammasome and GSDMD, and increase autophagy. The three main SCFAs, especially the sodium butyrate (NaB), had the effect of down-regulation of ROS and TLR-2 expression in THP-1 cells. NaB and sodium propionate (NaPc), but not sodium acetate (NaAc), dramatically suppressed IL-1β and IFN-γ expression. The results indicated that NaB and NaPc could significantly inhibit the inflammation of THP-1 cells induced by A. baumannii, and the inhibitory effect was in the order of NaB＞NaPc＞NaAC. NaB and NaPc may inhibit inflammation through TLR-2/NF-κB/ROS/NLRP3 signaling pathway