Transcatheter arterial embolization of aneurysm with metal microcoils is notoriously prone to recanalization arising from the low filling ratio due to their extreme rigidity. Smart hydrogel microcoils with tunable modulus may essentially significantly improve the therapeutic efficacy. Here, a radiopaque highly stiff body‐temperature‐triggered shape memory (SM) hydrogel is fabricated for the first time by introducing reversible hydrophobic dipole pairing microdomains in the flexibly crosslinked network, followed by BaSO4 precipitation. This radiopacification does not affect their mechanical performances as well as the SM effect. It is demonstrated that the mechanical properties of SM hydrogels are comparable to those of rubbers and can be modulated by adjusting temperature ranging from 20 to 40 °C. Benefiting from the thermoresponsive mechanical properties, the stiff radiopaque hydrogel strip can easily pass through a catheter under the protection of cool saline for delivery into pig's renal artery, and spontaneously and rapidly transformed into a microcoil upon contacting blood. Real‐time angiogram reveals that continuous delivery of several hydrogel microcoils can efficiently occlude the blood supply. The kidneys are atrophied considerably over three month postoperative follow‐up, and no recanalization occurs throughout the experimental time. These novel hydrogel microcoils are promising to be developed as novel permanent embolic agents for treating aneurysm.

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In this work, we present the Klout Score, an influence scoring system that assigns scores to 750 million users across 9 different social networks on a daily basis. We propose a hierarchical framework for generating an influence score for each user, by incorporating information for the user from multiple networks and communities. Over 3600 features that capture signals of influential interactions are aggregated across multiple dimensions for each user. The features are scalably generated by processing over 45 billion interactions from social networks every day, as well as by incorporating factors that indicate real world influence. Supervised models trained from labeled data determine the weights for features, and the final Klout Score is obtained by hierarchically combining communities and networks. We validate the correctness of the score by showing that users with higher scores are able to spread information more effectively in a network. Finally, we use several comparisons to other ranking systems to show that highly influential and recognizable users across different domains have high Klout scores.

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Depth image denoising is increasingly becoming the hot research topic nowadays, because it reflects the three-dimensional scene and can be applied in various fields of computer vision. But the depth images obtained from depth camera usually contain stains such as noise, which greatly impairs the performance of depth-related applications. In this article, considering that group-based image restoration methods are more effective in gathering the similarity among patches, a group-based nuclear norm and learning graph (GNNLG) model was proposed. For each patch, we find and group the most similar patches within a searching window. The intrinsic low-rank property of the grouped patches is exploited in our model. In addition, we studied the manifold learning method and devised an effective optimized learning strategy to obtain the graph Laplacian matrix, which reflects the topological structure of image, to further impose the smoothing priors to the denoised depth image. To achieve fast speed and high convergence, the alternating direction method of multipliers is proposed to solve our GNNLG. The experimental results show that the proposed method is superior to other current state-of-the-art denoising methods in both subjective and objective criterion.