5-{(2S,3R,4S,5S,6R)-3,4-Dihydr­oxy-6-hydroxy­meth­yl-3-[(2S,3R,4R,5R,6S)-3,4,5-trihydr­oxy-6-methyl­tetra­hydro­pyran-2-yloxy]tetra­hydro­pyran-2-yloxy}­-7-hydr­oxy-2-(4-hydroxy­phen­yl)chromen-4-one monohydrate

In the title compound, C27H30O14·H2O, the hydroxy­phenyl ring makes a dihedral angle of 20.05 (11)° with the chromenone ring system. The crystal structure is stabilized by intra- and inter­molecular O—H⋯O hydrogen bonds. The absolute configuration was assigned on the basis of an analagous structure

Social4Rec: Distilling User Preference from Social Graph for Video Recommendation in Tencent

Despite recommender systems play a key role in network content platforms, mining the user's interests is still a significant challenge. Existing works predict the user interest by utilizing user behaviors, i.e., clicks, views, etc., but current solutions are ineffective when users perform unsettled activities. The latter ones involve new users, which have few activities of any kind, and sparse users who have low-frequency behaviors. We uniformly describe both these user-types as "cold users", which are very common but often neglected in network content platforms. To address this issue, we enhance the representation of the user interest by combining his social interest, e.g., friendship, following bloggers, interest groups, etc., with the activity behaviors. Thus, in this work, we present a novel algorithm entitled SocialNet, which adopts a two-stage method to progressively extract the coarse-grained and fine-grained social interest. Our technique then concatenates SocialNet's output with the original user representation to get the final user representation that combines behavior interests and social interests. Offline experiments on Tencent video's recommender system demonstrate the superiority over the baseline behavior-based model. The online experiment also shows a significant performance improvement in clicks and view time in the real-world recommendation system. The source code is available at https://github.com/Social4Rec/SocialNet

Thermally enhanced photoluminescence and temperature sensing properties of Sc2_2W3_3O12_{12}:Eu3+^{3+} phosphors

Currently,lanthanide ions doped luminescence materials applying as optical thermometers have arose much concern. Basing on the different responses of two emissions to temperature, the fluorescence intensity ratio (FIR) technique can be executed and further estimate the sensitivities to assess the optical thermometry performances. In this study, we introduce different doping concentrations of Eu$^{3+}$ ions into negative expansion material Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$, accessing to the thermal enhanced luminescence from 373 to 548 K, and investigate the temperature sensing properties in detail. All samples exhibit good thermally enhanced luminescence behavior. The emission intensity of Sc$_2$W$_3$O$_{12}$: 6 mol% Eu$^{3+}$ phosphors reaches at 147.81% of initial intensity at 473 K. As the Eu doping concentration increases, the resistance of the samples to thermal quenching decreases. The FIR technique based on the transitions 5D0-7F1 (592 nm) and 5D0-7F2 (613 nm) of Eu$^{3+}$ ions demonstrate a maximum relative temperature sensitivity of 3.063% K-1 at 298 K for Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$: 6 mol% Eu$^{3+}$ phosphors. The sensitivity of sample decreases with the increase of Eu$^{3+}$ concentration. Benefiting from the thermal enhanced luminescence performance and good temperature sensing properties, the Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$: Eu$^{3+}$ phosphors can be applies as optical thermometers

A Method for Rapid Screening of Anilide-Containing AMPK Modulators Based on Computational Docking and Biological Validation

Adenosine 5′-monophsphate-activated protein kinase (AMPK) is a crucial energy sensor for maintaining cellular homeostasis. Targeting AMPK may provide an alternative approach in treatment of various diseases like cancer, diabetes, and neurodegenerations. Accordingly, novel AMPK activators are frequently identified from natural products in recent years. However, most of such AMPK activators are interacting with AMPK in an indirect manner, which may cause off-target effects. Therefore, the search of novel direct AMPK modulators is inevitable and effective screening methods are needed. In this report, a rapid and straightforward method combining the use of in silico and in vitro techniques was established for selecting and categorizing huge amount of compounds from chemical library for targeting AMPK modulators. A new class of direct AMPK modulator have been discovered which are anilides or anilide-like compounds. In total 1,360,000 compounds were virtually screened and 17 compounds were selected after biological assays. Lipinski’s rule of five assessment suggested that, 13 out of the 17 compounds are demonstrating optimal bioavailability. Proton acceptors constituting the structure of these compounds and hydrogen bonds with AMPK in the binding site appeared to be the important factors determining the efficacy of these compounds

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Multi‐scale saliency detection via inter‐regional shortest colour path

Saliency detection has attracted considerable attention, and numerous approaches aimed at locating meaningful regions in images have been presented. Nevertheless, accurate saliency detection algorithms remain in urgent demand. Many algorithms work well when dealing with simple images, but work poorly with complex images that contain small‐scale and high‐contrast structures. Moreover, most existing local and global regional saliency detection methods measure image saliency through region contrast. Such measurement is achieved by directly computing the difference between non‐adjacent regions. In this study, the authors introduce a new perspective for evaluating region contrast. We propose a novel multi‐scale saliency region detection method by optimising the shortest path of two non‐adjacent regions in the colour space and by measuring the region contrast from different scales. The final saliency maps indicate that the proposed method can work well with images containing small patches, but with high contrast. The proposed approach can also make the foreground significantly more uniform. Experimental results on three public benchmark datasets show that the proposed method achieves better precision–recall curve than some state‐of‐the‐art methods

Casual stereoscopic photo authoring

Abstract—Stereoscopic 3D displays becomemore andmore pop-ular these years. However, authoring high-quality stereoscopic 3D content remains challenging. In this paper, we present a method for easy stereoscopic photo authoring with a regular (monocular) camera. Our method takes two images or video frames using a monocular camera as input and transforms them into a stereo-scopic image pair that provides a pleasant viewing experience. The key technique of our method is a perceptual-plausible image rectification algorithm that warps the input image pairs to meet the stereoscopic geometric constraint while avoiding noticeable visual distortion. Our method uses spatially-varying mesh-based image warps. Our warping method encodes a variety of con-straints to best meet the stereoscopic geometric constraint and minimize visual distortion. Since each energy term is quadratic, our method eventually formulates the warping problem as a quadratic energy minimization which is solved efficiently using a sparse linear solver. Our method also allows both local and global adjustments of the disparities, an important property for adapting resulting stereoscopic images to different viewing con-ditions. Our experiments demonstrate that our spatially-varying warping technique can better support casual stereoscopic photo authoring than existing methods and our results and user study show that our method can effectively use casually-taken photos to create high-quality stereoscopic photos that deliver a pleasant 3D viewing experience. Index Terms—Stereoscopic photo authoring, stereoscopic pho-tography, image rectification. I

CF‐based optimisation for saliency detection

In view of the observation that saliency maps generated by saliency detection algorithms usually show similarity imperfection against the ground truth, the authors propose an optimisation algorithm based on clustering and fitting (CF) for saliency detection. The algorithm uses a fitting model to represent the quantitative relationship between ground truth and algorithm‐generated saliency maps. The authors use the K‐means method to cluster the images into k clusters according to the similarities among images. Image similarity is measured in terms of scene and colour by using the GIST and colour histogram features, after which the fitting model for each cluster is calculated. The saliency map of a new image is optimised by using one of the fitting models which correspond to the cluster to which the image belongs. Experimental results show that their CF‐based optimisation algorithm improves the performance of various single image saliency detection algorithms. Moreover, the improvement achieved by their algorithm when using both CF strategies is greater than the improvement achieved by the same algorithm when not using the clustering strategy. In addition, their proposed optimisation algorithm can also effectively optimise co‐saliency detection algorithms which already consider multiple similar images simultaneously to improve saliency of single images