The synergy between foveal and peripheral processing is fundamental to the efficiency of biological vision. While hybrid Convolutional Neural Network (CNN)-Transformer architectures aim to capture both local and global features, they often rely on static, predefined structures that struggle to dynamically align information and adaptively allocate computational resources, ultimately limiting their performance. To address this limitation, we introduce the Central-Peripheral Vision Transformer (CPVT), a novel architecture that explicitly and hierarchically mimics this biological dichotomy. CPVT employs fine-grained, convolutionally modulated attention in its shallow layers to emulate foveal vision, while seamlessly transitioning to a coarse-grained, global attention mechanism in deeper layers to emulate peripheral vision. This design is enhanced by two specialized Feed-Forward Networks that facilitate synergistic information interaction. Rigorously validated on diverse medical imaging benchmarks, CPVT achieves state-of-the-art performance, attaining classification accuracies of 87.98% on the International Skin Imaging Collaboration (ISIC) 2018 challenge dataset and 90.41% on the Kvasir dataset. These results demonstrate that an adaptive, hierarchical integration of biological vision principles can significantly enhance machine perception for medical image analysis.

The Chamfer Distance (CD) is a cornerstone objective function for point cloud completion, yet its inherent symmetric weighting mechanism limits the quality of the generated results. By penalizing local detail deviations and global coverage deficiencies equally, standard CD often causes structural defects such as point aggregation and incomplete spatial structures. We introduce the Flexible-weighted Chamfer Distance (FCD), which decouples CD into local precision and global completeness sub-objectives. FCD employs an asymmetric weighting strategy that prioritizes global structural integrity, steering the optimization away from sub-optimal solutions. As a plug-and-play module with negligible overhead, extensive experiments on state-of-the-art networks demonstrate that FCD significantly enhances global distribution metrics while preserving local precision. Specifically, on the ShapeNet55 benchmark using AdaPoinTr, FCD reduces the Density-aware Chamfer Distance (DCD) by approximately 12.4% (from 0.613 to 0.537), effectively mitigating point clustering. Similarly, on the PCN dataset, the proposed method reduces the Earth Mover's Distance (EMD) from 23.79 to 21.40, demonstrating superior global uniformity compared to the standard CD baseline. Furthermore, FCD demonstrates excellent generalization. When applied to diverse tasks and datasets, including real-world scans (KITTI), industrial components (ABC), and point cloud upsampling (PU-GAN), it yields significant quantitative gains and produces visually more uniform and structurally complete point clouds. These results underscore FCD's potential as a versatile objective function for the broader point cloud generation domain.

This paper introduces a transformer-based multi-branch attention network (MBTFuse) as a solution to the challenges faced by existing image fusion methods. These methods often overlook the complementary and enhancing nature of intra-modal and inter-modal features, resulting in the loss of shared features and distortion of unique information in the fused results. To address these issues, the proposed MBTFuse network enables joint learning of inter-modal and intra-modal relationships in multi-modal image fusion. The generator network of MBTFuse comprises two information refinement branches and an attention-driven branch. The former two branches combine the convolutional neural network and the transformer architecture to extract semantic information within each modality. This integration ensures a comprehensive understanding of the modalities and their individual characteristics. The attention-driven branch compensates for specific information loss during global similarity analysis by extracting shared features between modalities. This step enhances the fusion process by preserving important shared information. After extensive experiments, results on multiple datasets show that MBTFuse produces competitive fusion results compared with existing fusion frameworks.

Event extraction (EE) is an important natural language processing task. With the passage of time, many powerful and effective models for event extraction tasks have been developed. However, there has been limited research on complex overlapping event extraction. Therefore, we propose a new cascade decoding model: A Joint Learning Framework for Cascade Decoding with Multi-Feature Fusion and Conditional Enhancement for Overlapping Event Extraction. 1) In this model, we introduce a cascade decoding mechanism with multi-feature fusion to better capture the interaction between decoding layers. 2) Additionally, we introduce an enhanced conditional layer normalization (ECLN) mechanism to enhance the interaction between subtasks. Simultaneously, the use of a cascade decoding model effectively addresses the problem of overlapping events. The model successively performs three subtasks, type detection, trigger word extraction and argument extraction. All three subtasks learned together in a framework, and a new conditional normalization mechanism is used to capture dependencies among these subtasks. The experiments are conducted using the overlapping event benchmark, FewFC dataset. The experimental evaluation demonstrates that our model achieves a higher F1 score on the overlapping event extraction task compared to the original overlapping event extraction model.

The embedding of Knowledge Graphs (KGs) in hyperbolic space has recently received great attention in the field of deep learning because it can provide more accurate and concise representations of hierarchical structures compared to Euclidean spaces and complex spaces. Although hyperbolic space embeddings have shown significant improvements over Euclidean spaces and complex space embeddings in handling the task of KG embedding, they still face challenges related to the uneven distribution and insufficient alignment of high-dimensional sparse data. To address this issue, we propose the CONHyperKGE model, which leverages contrastive learning to optimize the embedding distribution in hyperbolic space. This approach enables better capture of hierarchical structures, improved handling of symmetry, and enhanced treatment of sparse matrices. Our proposed method is evaluated on four standard KG Embedding (KGE) datasets: WN18RR, FB15k-237, Kinship, and UMLS. After extensive experimental verification, our method has improved its performance on all four datasets. Notably, on the low-dimensional Kinship dataset, our method achieves an average Mean Reciprocal Rank (MRR) improvement of 2% over the original method, while on the high-dimensional WN18RR dataset, an average MRR improvement of 1% is observed compared to the original method.

In this paper, we study multimodal metaphor detection to obtain real semantic meaning from multiple heterogeneous information sources . The existing approaches mainly suffer from two drawbacks. (1) They focus on textual aspects, overlooking the characteristics of visual metaphor information. (2) Efficient methods for fusing multimodal metaphor features are lacking. To address the first issue, we propose a visual information enhancement method based on dual-granularity visual feature fusion , obtaining complete metaphorical visual features. To achieve bidirectional interaction among multimodal metaphor features, we further develop a multi-interactive crossmodal residual network (MCRN) that fuses the consistent and complementary information between different modalities and design a progressive fusion strategy to enhance the iterative fusion ability of the model. We extensively evaluate the proposed method on the popular Met-meme metaphor detection benchmark, outperforming the existing state-of-the-art methods by a large margins; i.e., we achieve F1 score improvements ranging from 1.47% to 2.55% under different languages. In addition, we further extend the evaluation to the Sarcasm dataset to validate the ability of the model to perceive semantic contrasts and meaning transformations, and the experimental results are superior to those of a strong baseline model .

Abstract Few-shot Event Detection (FSED) is a sub-task of Event Detection that aims to accurately identify event types with limited training instances and enable smooth transfer to newly-emerged event types. Recently, the dominant works have used the prototypical network to accomplish this task and employ contrastive learning to alleviate the issue of semantically-close categories. Nevertheless, these methods still suffer from two serious problems: (1) inadequate learning of prototype representations resulting from limited training data; (2) hard-easy sample imbalance and categories imbalance caused by the large number of non-trigger word("O" tags) in the token-level classification task. To address the problems, this paper proposes the Multi-channels Prototype and Contrastive learning method with Conditional Adversarial attack, which introduces the improved multi-channels prototype and contrastive networks to alleviate the categories and hard-easy samples imbalance. Moreover, we devise a constrained adversarial attack to improve the problem of limited training data. Extensive experimental results show that our model performs better than other FSED methods. All the code and data will be available for online public access.

Recently, three-dimensional (3D) point-cloud analysis has been extensively utilized in the domain of machine vision, encompassing tasks include shape classification and segmentation. However the inherent disorder in point clouds poses a challenge in capturing relationships among points, particularly when dealing with mutilated and occluded data. To this end, We propose the Point Geometry Transformation (PointGT) method for 3D point-cloud classification and part segmentation, by exploring the underlying geometric structure in the local and global of points. Specifically, the efficacy of PointGT arises from the integration of a local abstraction (LA) module and an optimization strategy. The LA module is tailored to address the localized features inherent to point clouds. This module encapsulates the multidimensional attributes of local edge and inside points. The bi-directional cross-attention mechanism amalgamates these two constituents into the native channel with the primary objective of optimizing the exploitation of edge and inside delineations, thereby judiciously mitigating noise artifacts. Ultimately, the channel residual connections disseminate the postdownsampling point attributes, thereby inheriting the edge and inside delineations gleaned via post bi-directional attention. The effectiveness of the proposed method was verified through the validation of point-cloud classification and segmentation datasets. The empirical findings confirmed the efficacy of PointGT; accuracies of 93.2% and 87.8% were achieved for the ModelNet40 and ScanObjectNN datasets, respectively.

Studies have shown that IncRNA-miRNA interactions can affect cellular expression at the level of gene molecules through a variety of regulatory mechanisms and have important effects on the biological activities of living organisms. Several biomolecular network-based approaches have been proposed to accelerate the identification of lncRNA-miRNA interactions. However, most of the methods cannot fully utilize the structural and topological information of the lncRNA-miRNA interaction network. In this article, we proposed a new method, ISLMI, a prediction model based on information injection and second order graph convolution network(SOGCN). The model calculated the sequence similarity and Gaussian interaction profile kernel similarity between lncRNA and miRNA, fused them to enhance the intrinsic interaction between the nodes, using SOGCN to learn second-order representations of similarity matrix information. At the same time, multiple feature representations obtain using different graph embedding methods were also injected into the second-order graph representation. Finally, matrix complementation was used to increase the model accuracy. The model combined the advantages of different methods and achieved reliable performance in 5-fold cross-validation, significantly improved the performance of predicting lncRNA-miRNA interactions. In addition, our model successfully confirmed the superiority of ISLMI by comparing it with several other model algorithm.

The lightweight MLP-based decoder has become increasingly promising for semantic segmentation. However, the channel-wise MLP cannot expand the receptive fields, lacking the context modeling capacity, which is critical to semantic segmentation. In this paper, we propose a parametric-free patch rotate operation to reorganize the pixels spatially. It first divides the feature map into multiple groups and then rotates the patches within each group. Based on the proposed patch rotate operation, we design a novel segmentation network, named PRSeg, which includes an off-the-shelf backbone and a lightweight Patch Rotate MLP decoder containing multiple Dynamic Patch Rotate Blocks (DPR-Blocks). In each DPR-Block, the fully connected layer is performed following a Patch Rotate Module (PRM) to exchange spatial information between pixels. Specifically, in PRM, the feature map is first split into the reserved part and rotated part along the channel dimension according to the predicted probability of the Dynamic Channel Selection Module (DCSM), and our proposed patch rotate operation is only performed on the rotated part. Extensive experiments on ADE20K, Cityscapes and COCO-Stuff 10K datasets prove the effectiveness of our approach. We expect that our PRSeg can promote the development of MLP-based decoder in semantic segmentation.

Long non-coding RNA(lncRNA) can interact with microRNA(miRNA) and play an important role in inhibiting or activating the expression of target genes and the occurrence and development of tumors. Accumulating studies focus on the prediction of miRNA-lncRNA interaction, and mostly are concerned with biological experiments and machine learning methods. These methods are found with long cycles, high costs, and requiring over much human intervention. In this paper, a data-driven hierarchical deep learning framework was proposed, which was composed of a capsule network, an independent recurrent neural network with attention mechanism and bi-directional long short-term memory network. This framework combines the advantages of different networks, uses multiple sequence-derived features of the original sequence and features of secondary structure to mine the dependency between features, and devotes to obtain better results. In the experiment, five-fold cross-validation was used to evaluate the performance of the model, and the zea mays data set was compared with the different model to obtain better classification effect. In addition, sorghum, brachypodium distachyon and bryophyte data sets were used to test the model, and the accuracy reached 0.9850, 0.9859 and 0.9777, respectively, which verified the model's good generalization ability.

Most current metaphor detection methods use restricted context, such as modeling the context of a single sentence. Considering the language environment of metaphors, we argue that combining broader discourse features has a greater impact on the improvement of metaphor detection performance. We propose a metaphor detection method based on fine-grained discourse, which embeds the current sentence and surrounding context in a weighted manner. With the help of fine-grained discourse, our model learns local and remote information as a reference for decision-making, and provides an efficient and natural method for metaphor detection tasks. Experimental results on VU Amsterdam Metaphor Corpus show that our technique surpasses the state-of-the-art models.

The traditional method of extracting entities and relations not only disregards the dependency between the two subtasks of entities and relations but also facilitates the cumulative propagation of errors. To solve these problems, a method – called MSBD – of joint extraction of entities and relations based on the Character Graph Convolutional Network (CGCN) and Multi-Head Self-Attention Mechanism (MS) is proposed. First, a new tagging scheme is used to tag the positions of entities and relations in the text. Second, to depict the hierarchical structure information between the entities and the relations in the text and the internal structure information of the entity, the CGCN is designed to obtain the character vector of the text. MS is introduced into the coding framework of Bidirectional Long Short-Term Memory (BiLSTM) to capture the relative position information between two entities in the text and represent the subspace information. The Dense Connected Convolutional Network (Dense Net) is embedded in the decoding framework to enhance the reuse and transmission of key information and achieve joint extraction of entities and relations in the text. The experimental results show that the P, R and F values are significantly improved for extracting entities and relations.

Deep neural network models for Uyghur personal pronoun resolution learn semantic information for personal pronoun and antecedents, but tend to be short-sighted—they ignore the importance of each feature. In this article, we propose a Uyghur personal pronoun resolution model based on Attention mechanism, Convolutional neural networks and Gated recurrent unit (ATCG). Our model studies the grammatical structure and semantic features of Uyghur, and extracts 11 key features for Uyghur resolution task. Attention mechanism can focus on the importance of words in sentences. Gated Recurrent Unit (GRU) is applied in this model to achieve the interdependent features with long distance. The ATCG model effectively makes up for the shortcomings of relying only on the features of the content level and achieves better classification performance. Experimental results on Uyghur resolution dataset show that our model surpasses the state-of-the-art models.

In the traditional remote sensing image recognition, the traditional features (e.g., color features and texture features) cannot fully describe complex images, and the relationships between image pixels cannot be captured well. Using a single model or a traditional sequential joint model, it is easy to lose deep features during feature mining. This article proposes a new feature extraction method that uses the word embedding method from natural language processing to generate bidirectional real dense vectors to reflect the contextual relationships between the pixels. A bidirectional independent recurrent neural network (BiIndRNN) is combined with a convolutional neural network (CNN) to improve the sliced recurrent neural network (SRNN) algorithm model, which is then constructed in parallel with graph convolutional networks (GCNs) under an attention mechanism to fully exploit the deep features of images and to capture the semantic information of the context. This model is collectively named an improved SRNN and attention-treated GCN-based parallel (SAGP) model. Experiments conducted on Populus euphratica forests demonstrate that the proposed method outperforms traditional methods in terms of recognition accuracy. The validation done on public data set also proved it.

To solve the problems from using single-layer features in traditional water body identification models, such as the lack of local descriptors, large quantization errors, and the lack of semantic information descriptions, a multimodal model is proposed based on the different levels of feature knowledge. First, based on the multidescriptor hierarchical feature, the middle-level local feature extraction of the water body is achieved, and, combined with the convolutional neural network, the high-order global features of the water body are extracted. Then, the image features are hierarchically normalized, and multimodal RBM self-encoding is used for fusion to reduce the quantization error of each layer feature in the encoding process. Finally, the generative model of the Multimodal Model is used to expand the data and filter the multilayered features after fusion. In addition, the semantic information of a water body is further discovered by using the encoder and decoder of the discriminant model and is classified by employing SoftMax. The results show that compared with the traditional water body identification methods, the proposed method improves the recognition accuracy and image description ability.

To improve the accuracy and automation of malware Uniform Resource Locator (URL) recognition, a joint approach of Convolutional neural network (CNN) and Long-short term memory (LSTM) based on the Attention mechanism (JCLA) is proposed to identify and detect malicious URL. Firstly, the URL features including texture information, lexical information and host information are extracted and filtered, and pre-processed with encode. Then, the feature matrix more relevant to the output are chose according to the weight of the attention mechanism and input to the constructed parallel processing model called CNN_LSTM, combinating CNN and LSTM to get local features. Next, the extracted local features are merged to calculate the global features of the URLs to be detected. Finally, the URLs are classified by the SoftMax classifier using global features, the accuracy of the model in malicious URL recgonition is 98.26%. The experimental results show that the JCLA model proposed in this paper is better than the traditional deep learning model or CNN_LSTM combined model for detecting malicious URLs.

Malaria is a kind of disease that greatly threatens human health. Nearly half of the world’s population is at risk of malaria. Anti-malarial drugs which are sought, developed and synthesized keep malaria under control, having received increasing attention in drug discovery field. Machine learning techniques have been used widely in drug research and development. On the basis of semi-supervised machine learning for molecular descriptions, this research develops a multilayer deep belief network (DBN) that can be used to identify whether compounds have the anti-malarial activity. Firstly, the influence of feature dimensions on predicting accuracy is discussed. Furthermore, the proposed model is applied to contrast shallow machine learning and supervised machine learning with the similar deep architecture. The research results show that the proposed model can predict anti-malarial activity accurately. The stable performance on the evaluation metrics confirms the practicability of our model. The proposed DBN model performs better than other shallow supervised models and deep supervised models. Moreover, it could be applied to reduce the cost and the time of drug discovery.

As a core subtask in anaphora resolution, anaphoricity determination has aroused the interest of researchers. However, in recent work, the influence caused by the deep semantic information and the context of the coreference elements have not been taken into account. In this paper, by combining the semantic feature of Uygur, we established a Convolutional Neural Network & Long Short-Term Memory (CNN_LSTM) model in determining the anaphoricity of Uygur pronoun. Firstly, the deep negative semantic feature representation is extracted via word2vec. Secondly, the shallow explicit feature representation of coreference elements is extracted by our system. Afterwards, two kinds of features are combined to recognize whether coreference element is referential or not. The results showed that the method we used can distinguish coreference element accurately, the ACC[Formula: see text] score is 90.18% and the ACC[Formula: see text] score is 89.93%, which are higher than ANN (Artificial Neural Network) and SVM (Support Vector Machine) respectively.

The cytochrome P450 (CYP) superfamily, exists in the human liver, is responsible for more than 90% of the metabolism of clinical drugs. So it is necessary to adopt a new kind of computer simulation methods that can predict the rejection capability of compounds for a concrete CYPs isoform. In this work, a model is presented for classification of CYP450 1A2 inhibitors and noninhibitors based on a multi-tiered deep belief network (DBN) on a large dataset. The dataset composed of more than 13,000 heterogeneous compounds was acquired from PubChem. Firstly, 139 2D and 53 3D descriptors are calculated and preprocessed. Then, the unsupervised learning method is used to train DBN model to automatically extract multiple levels of distributed representation from the descriptors of training set. Finally, by using testing set and external validation set, we evaluate the classified performance of DBN for the inhibition of CYP1A2. Meanwhile, the proposed model is compared with shallow machine learning models (support vector machine (SVM) and artificial neural network (ANN)). We also discussed the performance of DBN by comparing it with different features combination. The experimental results showed that DBN has a better prediction ability compared with SVM and ANN. And these models combined with the features of 2D and 3D obtain the best forecast accuracy.

Traditional machine learning methods for water body extraction need complex spectral analysis and feature selection which rely on wealth of prior knowledge. They are time-consuming and hard to satisfy our request for accuracy, automation level and a wide range of application. We present a novel deep learning framework for water body extraction from Landsat imagery considering both its spectral and spatial information. The framework is a hybrid of convolutional neural networks (CNN) and logistic regression (LR) classifier. CNN, one of the deep learning methods, has acquired great achievements on various visual-related tasks. CNN can hierarchically extract deep features from raw images directly, and distill the spectral–spatial regularities of input data, thus improving the classification performance. Experimental results based on three Landsat imagery datasets show that our proposed model achieves better performance than support vector machine (SVM) and artificial neural network (ANN).