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Sequential recommendation (SR) provides personalized contents based on the user's historical interactions. Previous SR methods focus on introducing temporal signals of interaction sequence into their sequence encoders without exploring the effect of temporal density information on user preferences. To bridge this gap, we propose the Temporal Density-aware Sequential Recommendation Networks with Contrastive Learning (TDSRec). The specifics of our research mainly consist of two parts. First, we integrate temporal density information into sequential recommendation when capturing user preferences. In detail, through our proposed Temporal KDE Module, we map timestamps into temporal density vectors aiming at improving the recommendation performance. Second, we introduce contrastive learning into TDSRec to alleviate the lack of supervised learning signals as a result of sparse user-item interactions. In detail, instead of directly editing the raw sequence, we leverage two base sequence encoders to derive self-supervision signals. In this way, our approach can naturally eliminate the interference of hand-crafted data augmentation strategies on the raw sequence data. Through extensive experiments on five test datasets, we find that our proposed TDSRec outperforms state-of-the-art baselines. NDCG@10 has been improved by 5.1%, 2.9%, 2.1%, 1.9%, and 1.4% on MovieLens, Beauty, Video Games, CDs&Vinyl, and Movies&TV, respectively. • Integrating temporal kernel density information into sequential recommendation. • Capturing long and short-term user preference via attention and convolution encoders. • Deriving auxiliary self-supervision signals without manual data augmentation. • Extensive experiments with several popular baselines on five real-world datasets. [ABSTRACT FROM AUTHOR]

Medication recommendation is a hot topic in the research of applying neural networks to the healthcare area. Although extensive progressions have been made, current researches still face the following challenges: (i). Existing methods are poor at efficiently capturing and leveraging local and global dependency information from patient visit records. (ii). Current time-aware models based on irregularly interval medical records tend to ignore periodic variability in patient conditions, which limits the representational learning capability of these models. Therefore, we propose a Dynamic Time-aware Hierarchical Dependency Network (TAHDNet) for the medication recommendation task to address these challenges. Firstly, we use a Transformer-based model to learn the global information of the whole patient record through a self-supervised pre-training process. Secondly, a 1D-CNN model is used to learn the local dependencies on visitation level. Thirdly, we propose a dynamic time-aware module with a fused temporal decay function to assign different weights among different time intervals dynamically through a key-value attention mechanism. Experimental results on real-world datasets demonstrate the effectiveness of the model proposed in this paper. [ABSTRACT FROM AUTHOR]

• A cucumber canopy vine top recognition algorithm based on YOLOv5 is proposed. • The cucumber canopy vine top recognition performance of improved algorithm is analyzed and compared. • The recognition performance of the improved algorithm is verified via instance recognition. • The improved algorithm has good recognition performance for the cucumber canopy vine tops. In order to solve the problem of low recognition accuracy of cucumber canopy vine tops image caused by the high density and intermingling of the cucumber canopy vine, an image recognition model of cucumber canopy tops based on the deep learning model of improved YOLOv5 is proposed in this paper. A combination of online and offline augmentation is used to amplify the original data to ensure a balanced number of images in different types of datasets during training and testing. Considering the small leaves characteristics of the cucumber canopy vine tops, the CA mechanism module (YOLOv5-CA) is introduced into the backbone network of the YOLOv5 model to improve the recognition accuracy of the small leaf target. On this basis, the regression Loss GIOU of the target position was changed to EIOU and the cross-entropy function of confidence loss was changed to Focal Loss function (YOLOv5-CA-LS) to reduce the influence of the imbalance of difficult and easy samples on the detection results. The models before and after optimization were compared. The results show that compared with the YOLOv5 model, the accuracy, recall rate, mAP@0.5 and mAP@0.5:0.95 of the YOLOv5-CA-LS model is improved by 4.4%, 5.0%, 1.3% and 1.8% respectively. And average recognition accuracy value of the instance of the cucumber canopy tops adopted the YOLOv5-CA-LS model is 97.1% under different angle and cloudy conditions, which can meet the recognition requirement of high density and intermingling of the cucumber canopy vine tops. [ABSTRACT FROM AUTHOR]