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Compared with the previous generation of High Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) introduces a quadtree and multi-type tree (QTMT) partition structure with nested multi-class trees so that the coding unit (CU) partition can better match the video texture features. This partition structure makes the compression efficiency of VVC significantly improved, but the computational complexity is also significantly increased, resulting in an increase in encoding time. Therefore, we propose a fast CU partition decision algorithm based on DenseNet network and decision tree (DT) classifier to reduce the coding complexity of VVC and save more coding time. We extract spatial feature vectors based on the DenseNet network model. Spatial feature vectors are constructed by predicting the boundary probabilities of 4 × 4 blocks in 64 × 64 coding units. Then, using the spatial features as the input of the DT classifier, through the classification function of the DT classifier model, the top N division modes with higher prediction probability are selected, and other division modes are skipped to reduce the computational complexity. Finally, the optimal partition mode is selected by comparing the RD cost. Our proposed algorithm achieves 47.6% encoding time savings on VTM10.0, while BDBR only increases by 0.91%. [ABSTRACT FROM AUTHOR]

The RIIβ subunit of cAMP‐dependent protein kinase A (PKA) is expressed in the brain and adipose tissue. RIIβ‐knockout mice show leanness and increased UCP1 in brown adipose tissue. The authors have previously reported that RIIβ reexpression in hypothalamic GABAergic neurons rescues the leanness. However, whether white adipose tissue (WAT) browning contributes to the leanness and whether RIIβ‐PKA in these neurons governs WAT browning are unknown. Here, this work reports that RIIβ‐KO mice exhibit a robust WAT browning. RIIβ reexpression in dorsal median hypothalamic GABAergic neurons (DMH GABAergic neurons) abrogates WAT browning. Single‐cell sequencing, transcriptome sequencing, and electrophysiological studies show increased GABAergic activity in DMH GABAergic neurons of RIIβ‐KO mice. Activation of DMH GABAergic neurons or inhibition of PKA in these neurons elicits WAT browning and thus lowers body weight. These findings reveal that RIIβ‐PKA in DMH GABAergic neurons regulates WAT browning. Targeting RIIβ‐PKA in DMH GABAergic neurons may offer a clinically useful way to promote WAT browning for treating obesity and other metabolic disorders. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low accuracy of traditional prediction methods of anti-electricity stealing, a prediction method of anti-electricity stealing based on big data of electric power was proposed. This method firstly constructed samples of electricity stealing data according to abnormal rules, and introduced the growth rate of line loss rate constraint conditions. Then it used four machine learning classification algorithms to build a prediction model on the voltage, current and power factor data sets respectively, combined the users with abnormal data output and users with abnormal line loss to output a list of users suspected of stealing electricity. Experimental results showed that the prediction accuracy of the method was satisfactory, and it was efficient and feasible in identifying users suspected of stealing electricity. [ABSTRACT FROM AUTHOR]

The current situation of fruit recognition based on machine vision was reviewed from fruit recognition based on color threshold, shape and texture, three-dimensional fruit shape recognition, nocturnal fruit recognition, fruit recognition based on machine learning, fruit recognition under the influence of shadow and occlusion. It's thought that the algorithms involved in the above research were more complicated and features were very powerful. However, in view of the limitations of visual theory, image processing technology and hardware conditions, as well as the complex and varied environment of apple garden, there was no more ideal technology for machine vision-based fruit recognition, and it needed to be improved. Future research focuses include: 1) Strengthening more effective algorithms for image enhancement, image segmentation, and feature extraction to effectively address the effects of fruit overlap, occlusion, color, and light changes; and improving the identification algorithms for day and night orchard field operations for the construction of an all-weather operation picking robot. 2) Strengthening the research on fruit recognition based on self-supervised learning to increase the feedback information received by the model and the complex applicable task types of model representation, reduce the proportion of human manual labor involved in the task, and improve the degree of automation. 3) Strengthening the research of automatic image acquisition and fruit recognition, combined with computer vision and near-infrared, laser radar and other detection technologies, integrating multi-modal image and non-image information for fruit recognition, improving processing speed and real-time, and identifing accuracy and system robustness to provide reference for apple's automatic picking and precise management of orchard. [ABSTRACT FROM AUTHOR]