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In view of the scarcity, high cost and lack of diversity of three-dimensional (3D) face datasets, this paper designs an end-to-end self-supervised learning 3D face reconstruction network, which uses single 2D face image as input. The model bypasses the 3D face datasets and only uses the 2D face datasets for training to achieve high-precision 3D face reconstruction without any 3D face prior. First, the improved ResNet50 feature extraction module is introduced to extract and characterize the input image by deep convolutional network. Then, a lightweight convolutional block attention module is added to the face prediction subnetwork. On the one hand, channel attention extracts different information included in the image, and on the other hand spatial attention finds the location of the information. So, the serialized attention operation could accurately find the features required for different parameter predictions, further improving face reconstruction parameters’ prediction accuracy. Finally, training, ablation and comparison experiments were conducted on CelebFaces Attributes, basel face model and Photoface datasets, and the combined loss function of pixel loss and perception loss was selected. The pixel loss function was calculated at the pixel microscopic level, and the perception loss function was calculated at the image macroscopic convolution level. The combination of the two could complement each other. Compared with the historical optimal results of the same network structure, the scale-invariant depth error and mean angle deviation of the proposed algorithm are improved by 5.2% and 8.2%, respectively. Experimental results strongly prove the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The Danish fire loss dataset records commercial fire losses under three insurance coverages: building, contents, and profits. Existing research has primarily focused on the heavy-tail behaviour of the losses but ignored the relationship among different insurance coverages. In this paper, we aim to model the aggregate loss for all three coverages. To study the pairwise dependence of claims from all types of coverage, an independent model, a hierarchical model, and some copula-based models are proposed for the frequency component. Meanwhile, we applied composite distributions to capture the heavy-tailed severity component. It is shown that consideration of dependence for the multi-peril frequencies (i) significantly enhances model goodness-of-fit and (ii) provides more accurate risk measures of the aggregated losses for all types of coverage in total. [ABSTRACT FROM AUTHOR]

Gyrovirus (GyV) is small, single-stranded circular DNA viruses that has recently been assigned to the family Anelloviridae . In the last decade, many GyVs that have an apparent pan-tropism at the host level were identified by high-throughput sequencing (HTS) technology. As of now, they have achieved global distribution. Several species of GyVs have been demonstrated to be pathogenic to poultry, particularly chicken anemia virus (CAV), causing significant economic losses to the global poultry industry. Although GyVs are highly prevalent in various birds worldwide, their direct involvement in the etiology of specific diseases and the reasons for their ubiquity and host diversity are not fully understood. This review summarizes current knowledge about GyVs, with a major emphasis on their morphofunctional properties, epidemiological characteristics, genetic evolution, pathogenicity, and immunopathogenesis. Additionally, the association between GyVs and various diseases, as well as its potential impact on the poultry industry, have been discussed. Future prevention and control strategies have also been explored. These insights underscore the importance of conducting research to establish a virus culture system, optimize surveillance, and develop vaccines for GyVs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yan, Wang, Li, Zhang, Song and Cheng.)