Existing S equential R ecommendation (SR) methods have conventionally viewed historical interactions as one-dimensional sequences, often overlooking the fact that user behaviors can be multi-faceted and uncertain. Such a straightforward perspective fails to account for varied behavior patterns embedded in the historical sequences. Moving beyond adhering to singular historical sequences, we treat augmented sequences as meaningful behavior patterns and jointly optimize all sequences (augmented and original sequences) to capture diverse patterns, intricate dependencies, and uncertainties. To acknowledge and distinguish new patterns derived from the original sequence, we develop a sequential order-enhanced method to calculate the edge weight, highlighting the unique dependency relationships inherent in each individual sequence. To prevent recommendations from becoming monotonous due to similarities in augmented sequences, we apply a repulsive mechanism to sequences with similar topics/categories, ensuring a broader spectrum of suggestions. Considering the potent expressive capability of the probabilistic model, Structured Determinantal Point Processes (SDPP), in representing structures, we perceive original and augmented sequences as such structures, leading to our generic learning framework S tructured S equential Rec ommendation (SSRec), which is theoretically proved to be a structured ranking optimization criterion . Comprehensive experiments on real-world datasets demonstrate SSRec’s distinct advantages over state-of-the-art models in terms of both diversity and accuracy.

Joint entity and relation extraction plays a crucial role in various fields, including natural language processing, knowledge graph construction, and question answering systems. Among these methods, the joint extraction approach based on table filling has become a focal point for researchers due to its excellent performance and ability to accurately extract entities and relations from intricate sentences. Despite this, there is still potential to be further explored in such methods. Currently, most methods focus on learning relational features between words but neglect the relational features between labels and between labels and words. These relations are also important because a relation triplet can only be correctly identified when all labels are predicted correctly. This some what increases the risk of missing key information and inefficient decoding in the entity and relation extraction process. To overcome this limitation, we design a fine-grained relation feature extraction module that aims to encourage the model to fully consider the associations between words and labels, as well as between labels, while focusing on the relationships between words. Specifically, we create label representations and embed them into fine-grained relation extraction, enabling it to treat labels and word representations as queries, keys, and values, and then utilize the self-attention mechanism to model the associations between them. Additionally, the table labeling strategy has a significant impact on model performance, especially in terms of decoding efficiency. We propose a vertex labeling method to label the table, enhancing the model’s accuracy in entity recognition and overall decoding efficiency. We evaluate our proposed model on three benchmark datasets. The experimental results demonstrate that our model is effective and achieves state-of-the-art performance on all these datasets.

In recent years, graph convolution networks (GCNs) have been widely used in recommender systems due to high-order node information propagation and aggregation mechanisms. However, existing GCN-based recommender systems drop sharply in performance as the depth of the network increases. This phenomenon is called over-smoothing, which refers to the fact that the embeddings of all nodes become more similar and indistinguishable. Previous works have rarely explored over-smoothing from characteristics of the recommendation field. Specifically, we found experimentally that too many layers can lead to such large loss values that they are difficult to decrease. After theoretical analysis, we can effectively solve the problem of difficulty in decreasing the loss value by adding only a hyperparameter, called "power". This hyperparameter can effectively control the smoothness and alleviate the over-smoothing problem. Experiments on four public datasets demonstrate that this hyperparameter can effectively improve performance.

Satellite imagery analysis plays a pivotal role in remote sensing; however, information loss due to cloud cover significantly impedes its application. Although existing deep cloud removal models have achieved notable outcomes, they scarcely consider contextual information. This study introduces a high-performance cloud removal architecture, termed Progressive Multi-scale Attention Autoencoder (PMAA), which concurrently harnesses global and local information to construct robust contextual dependencies using a novel Multi-scale Attention Module (MAM) and a novel Local Interaction Module (LIM). PMAA establishes long-range dependencies of multi-scale features using MAM and modulates the reconstruction of fine-grained details utilizing LIM, enabling simultaneous representation of fine- and coarse-grained features at the same level. With the help of diverse and multi-scale features, PMAA consistently outperforms the previous state-of-the-art model CTGAN on two benchmark datasets. Moreover, PMAA boasts considerable efficiency advantages, with only 0.5% and 14.6% of the parameters and computational complexity of CTGAN, respectively. These comprehensive results underscore PMAA’s potential as a lightweight cloud removal network suitable for deployment on edge devices to accomplish large-scale cloud removal tasks. Our source code and pre-trained models are available at https://github.com/XavierJiezou/PMAA.

BACKGROUND: Tree-structured neural networks have shown promise in extracting lexical representations of sentence syntactic structures, particularly in the detection of event triggers using recursive neural networks. METHODS: In this study, we introduce an attention mechanism into Child-Sum Tree-LSTMs for the detection of biomedical event triggers. We incorporate previous researches on assigning attention weights to adjacent nodes and integrate this mechanism into Child-Sum Tree-LSTMs to improve the detection of event trigger words. We also address a limitation of shallow syntactic dependencies in Child-Sum Tree-LSTMs by integrating deep syntactic dependencies to enhance the effect of the attention mechanism. RESULTS: Our proposed model, which integrates an enhanced attention mechanism into Tree-LSTM, shows the best performance for the MLEE and BioNLP'09 datasets. Moreover, our model outperforms almost all complex event categories for the BioNLP'09/11/13 test set. CONCLUSION: We evaluate the performance of our proposed model with the MLEE and BioNLP datasets and demonstrate the advantage of an enhanced attention mechanism in detecting biomedical event trigger words.