Anti-Insulin monoclonal antibody, clone L37bD11

With the prosperity of the location-based social networks, next point-of-interest (POI) recommendation has become an increasingly significant requirement since it can benefit both users and business. Obtaining insight into user mobility for the next POI recommendations is a vital yet challenging task. Existing approaches to understanding user mobility mainly gloss over the check-in sequence, making it fail to explicitly capture the subtle POI-POI interactions across the entire user check-in history and distinguish relevant check-ins from the irrelevant. In this paper, we proposed a novel recommendation approach, namely geographical-temporal awareness hierarchical attention network (GT-HAN) to resolve those issues. We first establish a geographical-temporal attention network to simultaneously uncover the overall sequence dependence and the subtle POI-POI relationships. Then, a context-specific co-attention network was designed to learn to change user preferences by adaptively selecting relevant check-in activities from check-in histories, which enabled GT-HAN to distinguish degrees of user preference for different checkins. Finally, we make a POI recommendation using a conditional probability distribution function. Experimental results on real world datasets (obtained from Foursquare and Gowalla) show that the GT-HAN model significantly outperforms current state-of-the-art approaches, and demonstrating the benefits produced by new technologies incorporated into GT-HAN. (C) 2020 Published by Elsevier B.V.

Because of low accuracy and density of crop point clouds obtained by the Unmanned Aerial Vehicle (UAV)-borne Light Detection and Ranging (LiDAR) scanning system of UAV, an integrated navigation and positioning optimization method based on the grasshopper optimization algorithm (GOA) and a point cloud density enhancement method were proposed. Firstly, a global positioning system (GPS)/inertial navigation system (INS) integrated navigation and positioning information fusion method based on a Kalman filter was constructed. Then, the GOA was employed to find the optimal solution by iterating the system noise variance matrix Q and measurement noise variance matrix R of Kalman filter. By feeding the optimal solution into the Kalman filter, the error variances of longitude were reduced to 0.00046 from 0.0091, and the error variances of latitude were reduced to 0.00034 from 0.0047. Based on the integrated navigation, an UAV-borne LiDAR scanning system was built for obtaining the crop point. During offline processing, the crop point cloud was filtered and transformed into WGS-84, the density clustering algorithm improved by the particle swarm optimization (PSO) algorithm was employed to the clustering segment. After the clustering segment, the pre-trained Point Cloud Up-Sampling Network (PU-net) was used for density enhancement of point cloud data and to carry out three-dimensional reconstruction. The features of the crop point cloud were kept under the processing of reconstruction model; meanwhile, the density of the crop point cloud was quadrupled.