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<p>Utilizing user-item interaction dynamics is crucial for providing efficient and precise sequential recommendations (SR). Besides earlier works based on Matrix factorization, Markov Chains, and RNNs, GCNs taking advantage of attention mechanism and Knowledge-enhanced NNs digging heterogenous auxiliary information recently made improvements. However, there still exist some problems: 1) existing works mainly focus on capturing user preference without considering the importance of item popularity; 2) introducing too much auxiliary information denies the independence of interaction behavior; 3) favoring high-order chronological history with heavy memory while ranking score referring different bias is unappreciated. To tackle these problems, we proposed DraG4Rec (Dual rating enhanced attention-based GCN for Recommendation), which makes symmetry of user preference and item popularity manifest great value. Specifically, we first merge ranking score, time, and position encoding for user/item representation in a bipartite graph. Then, an edge-view message-passing mechanism is imported into the attention-based GCN to use implicit 2-order historical information with less memory. Finally, user preference and item popularity are learned jointly. Extensive experiments on dense and sparse real-world datasets demonstrate the superior performance of our framework over state-of-the-art baselines regarding two commonly-used metrics: i.e., Recall and NDCG. The ablation study illustrates the boosting effect of different components. </p>

Oral squamous cell carcinoma (OSCC) is prevalent around the world and is associated with poor prognosis. OSCC is typically diagnosed from tissue biopsy sections by pathologists who rely on their empirical experience. Deep learning models may improve the accuracy and speed of image classification, thus reducing human error and workload. Here we developed a custom-made deep learning model to assist pathologists in detecting OSCC from histopathology images. We collected and analyzed a total of 2,025 images, among which 1,925 images were included in the training set and 100 images were included in the testing set. Our model was able to automatically evaluate these images and arrive at a diagnosis with a sensitivity of 0.98, specificity of 0.92, positive predictive value of 0.924, negative predictive value of 0.978, and F1 score of 0.951. Using a subset of 100 images, we examined whether our model could improve the diagnostic performance of junior and senior pathologists. We found that junior pathologists were able to delineate OSCC in these images 6.26 min faster when assisted by the model than when working alone. When the clinicians were assisted by the model, their average F1 score improved from 0.9221 to 0.9566 in the case of junior pathologists and from 0.9361 to 0.9463 in the case of senior pathologists. Our findings indicate that deep learning can improve the accuracy and speed of OSCC diagnosis from histopathology images.

Adaptive cross-region-based guided image filtering (ACR-GIF) is a commonly used cost aggregation method. However, the weights of points in the adaptive cross-region (ACR) are generally not considered, which affects the accuracy of disparity results. In this study, we propose an improved cost aggregation method to address this issue. First, the orthogonal weight is proposed according to the structural feature of the ACR, and then the orthogonal weight of each point in the ACR is computed. Second, the matching cost volume is filtered using ACR-GIF with orthogonal weights (ACR-GIF-OW). In order to reduce the computing time of the proposed method, an efficient weighted aggregation computing method based on orthogonal weights is proposed. Additionally, by combining ACR-GIF-OW with our recently proposed matching cost computation method and disparity refinement method, a local stereo matching algorithm is proposed as well. The results of Middlebury evaluation platform show that, compared with ACR-GIF, the proposed cost aggregation method can significantly improve the disparity accuracy with less additional time overhead, and the performance of the proposed stereo matching algorithm outperforms other state-of-the-art local and nonlocal algorithms.