The aim of steganography detection is to identify whether the multimedia data contain hidden information. Although many detection algorithms have been presented to solve tasks with inconsistent distributions between the source and target domains, effectively exploiting transferable correlation information across domains remains challenging. As a solution, we present a novel multiperspective progressive structure adaptation (MPSA) scheme based on active progressive learning (APL) for JPEG steganography detection across domains. First, the source and target data originating from unprocessed steganalysis features are clustered together to explore the structures in different domains, where the intradomain and interdomain structures can be captured to provide adequate information for cross-domain steganography detection. Second, the structure vectors containing the global and local modalities are exploited to reduce nonlinear distribution discrepancy based on APL in the latent representation space. In this way, the signal-to-noise ratio (SNR) of a weak stego signal can be improved by selecting suitable objects and adjusting the learning sequence. Third, the structure adaptation across multiple domains is achieved by the constraints for iterative optimization to promote the discrimination and transferability of structure knowledge. In addition, a unified framework for single-source domain adaptation (SSDA) and multiple-source domain adaptation (MSDA) in mismatched steganalysis can enhance the model's capability to avoid a potential negative transfer. Extensive experiments on various benchmark cross-domain steganography detection tasks show the superiority of the proposed approach over the state-of-the-art methods.This paper presents a low cost, noninvasive, clinical-grade Pulse Wave Velocity evaluation device. The proposed system relies on a simultaneous acquisition of femoral and carotid pulse waves to improve estimation accuracy and correctness. The sensors used are two high precision MEMS force sensors, encapsulated in two ergonomic probes, and connected to the main unit. Data are then wirelessly transmitted to a standard laptop, where a dedicated graphical user interface (GUI) runs for analysis and recording. Besides the interface, the Athos system provides a Matlab algorithm to process the signals quickly and achieve a reliable PWV assessment. To better compare the results at the end of each analysis, a detailed report is generated, including all the relevant examination information (subject data, mean PTT, and obtained PWV). A pre-clinical study was conducted to validate the system by realizing several Pulse Wave Velocity measurements on ten heterogeneous healthy subjects of different ages. The collected results were then compared with those measured by a well-established and largely more expensive clinical device (SphygmoCor).The 2019-nCoV coronavirus protein was confirmed to be highly susceptible to various mutations, which can trigger apparent changes of virus' transmission capacity and even the pathogenic mechanism. In this article, the binding interface was obtained by analyzing the interaction modes between 2019-nCoV coronavirus and the human specific target protein ACE2. Based on the "SIFT server" and the "bubble" identification mechanism, 9 amino acid sites were selected as potential mutation-sites from the 2019-nCoV-S1-ACE2 binding interface. Subsequently, one total number of 171 mutant systems for 9 mutation-sites were optimized for binding-pattern comparsion analysis, and 14 mutations that may improve the binding capacity of 2019-nCoV-S1 to ACE2 were selected. The Molecular Dynamic Simulations were conducted to calculate the binding free energies of all 14 mutant systems. Finally, we found that most of the 14 mutations on the 2019-nCoV-S1 protein could enhance the binding ability between the 2019-nCoV coronavirus and the human protein ACE2. Among which, the binding capacities for G446R, Y449R and F486Y mutations could be increased by 20%, and that for S494R mutant increased even by 38.98%. We hope this research could provide significant help for the future epidemic detection, drug development research, and vaccine development and administration.Point cloud upsampling is vital for the quality of the mesh in three-dimensional reconstruction. Recent research on point cloud upsampling has achieved great success due to the development of deep learning. However, the existing methods regard point cloud upsampling of different scale factors as independent tasks. Thus, the methods need to train a specific model for each scale factor, which is both inefficient and impractical for storage and computation in real applications. To address this limitation, in this work, we propose a novel method called ``Meta-PU" to firstly support point cloud upsampling of arbitrary scale factors with a single model. In the Meta-PU method, besides the backbone network consisting of residual graph convolution (RGC) blocks, a meta-subnetwork is learned to adjust the weights of the RGC blocks dynamically, and a farthest sampling block is adopted to sample different numbers of points. Together, these two blocks enable our Meta-PU to continuously upsample the point cloud with arbitrary scale factors by using only a single model.  https://www.selleckchem.com/products/pf-06650833.html  In addition, the experiments reveal that training on multiple scales simultaneously is beneficial to each other. Thus, Meta-PU even outperforms the existing methods trained for a specific scale factor only.Skeleton data have been extensively used for action recognition since they can robustly accommodate dynamic circumstances and complex backgrounds. To guarantee the action-recognition performance, we prefer to use advanced and time-consuming algorithms to get more accurate and complete skeletons from the scene. However, this may not be acceptable in time- and resource-stringent applications. In this paper, we explore the feasibility of using low-quality skeletons, which can be quickly and easily estimated from the scene, for action recognition. While the use of low-quality skeletons will surely lead to degraded action-recognition accuracy, in this paper we propose a structural knowledge distillation scheme to minimize this accuracy degradations and improve recognition model's robustness to uncontrollable skeleton corruptions. More specifically, a teacher which observes high-quality skeletons obtained from a scene is used to help train a student which only sees low-quality skeletons generated from the same scene.