Automatic segmentation of lesions in medical images is a popular and challenging task. CNN-based methods are limited by local receptive fields and struggle to capture longrange spatial dependencies. In contrast, Transformers can model global information; however, their attention mechanism exhibits high computational complexity and low efficiency when processing high-resolution 3D medical images. Therefore, this paper proposes MatFuseU-Net for lesion segmentation. Built on the U-Net architecture, this model converts Non-negative Matrix Factorization (NMF) into a differentiable computational unit and constructs an adaptive module with linear computational complexity to replace the self-attention mechanism. Different from traditional NMF module designs, this paper innovatively introduces a dynamic-rank NMF module based on local feature complexity, which adaptively adjusts the rank of NMF according to the local complexity of the input feature map, thereby enhancing the model's expressive capability. The loss function of MatFuseU-Net combines Dice loss and cross-entropy loss, and the training process is optimized through a deep supervision strategy. In the experiments, the BraTS 2021 and ISLES 2022 datasets were used to evaluate the model's performance. The model achieved promising results in two metrics: average Dice score and 95% Hausdorff Distance, demonstrating its excellent performance.

As Named Entity Recognition (NER) tasks have evolved, artificial intelligence has been widely applied in this field. However, most benchmarks are limited to English, making it challenging to replicate successful experiences in other languages. To expand NER to informal and diverse Chinese text scenarios, we have proposed a new large-scale Chinese NER dataset, OmniNER2025. This dataset, obtained from user posts on a popular Chinese social media platform Xiaohongshu, contains 195,568 samples and 89 categories, all manually annotated. To our knowledge, it is currently the largest Chinese open-source NER dataset in terms of sample size, category diversity, and domain coverage. This dataset is more challenging than existing Chinese NER datasets and better reflects real-world applications. The large sample size and diverse entity types provide valuable research resources. Additionally, we introduced the ERRTA tool for error analysis and teacher model guidance, significantly reducing model errors and improving performance. In the future, we will refine the ERRTA framework and explore optimization strategies to enhance the practical value of NER models. By releasing the OmniNER2025 dataset and introducing the ERRTA tool, we have advanced fine-grained NER research and improved model performance, promoting its application and development in real-world scenarios.

The automatic diagnosis of chest diseases is a popular and challenging task. Most current methods are based on convolutional neural networks (CNNs), which focus on local features while neglecting global features. Recently, self-attention mechanisms have been introduced into the field of computer vision, demonstrating superior performance. Therefore, this paper proposes an effective model, CheX-DS, for classifying long-tail multi-label data in the medical field of chest X-rays. The model is based on the excellent CNN model DenseNet for medical imaging and the newly popular Swin Transformer model, utilizing ensemble deep learning techniques to combine the two models and leverage the advantages of both CNNs and Transformers. The loss function of CheX-DS combines weighted binary cross-entropy loss with asymmetric loss, effectively addressing the issue of data imbalance. The NIH ChestX-ray14 dataset is selected to evaluate the model’s effectiveness. The model outperforms previous studies with an excellent average AUC score of 83.76%, demonstrating its superior performance.

Aspect Sentiment Triple Extraction (ASTE) is an emerging task in fine-grained sentiment analysis. Recent studies have employed Graph Neural Networks (GNN) to model the syntax-semantic relationships inherent in triplet elements. However, they have yet to fully tap into the vast potential of syntactic and semantic information within the ASTE task. In this work, we propose a Dual Encoder: Exploiting the potential of Syntactic and Semantic model (D2E2S), which maximizes the syntactic and semantic relationships among words. Specifically, our model utilizes a dual-channel encoder with a BERT channel to capture semantic information, and an enhanced LSTM channel for comprehensive syntactic information capture. Subsequently, we introduce the heterogeneous feature interaction module to capture intricate interactions between dependency syntax and attention semantics, and to dynamically select vital nodes. We leverage the synergy of these modules to harness the significant potential of syntactic and semantic information in ASTE tasks. Testing on public benchmarks, our D2E2S model surpasses the current state-of-the-art(SOTA), demonstrating its effectiveness.

A crucial issue in multimodal language processing is representation learning. Previous works joint training the multimodal and unimodal tasks to learn the consistency and difference of modality representations. However, due to the lack of cross-modal interaction, the extraction of complementary features between modalities is not sufficient. Moreover, during multimodal fusion, the generated multimodal embeddings may be redundant, and unimodal representations also contain noise information, which negatively influence the final sentiment prediction. To this end, we construct a Modality-Complementary and Eliminating-Redundancy multi-task learning model (MCER), and additionally add a cross-modal task to learn complementary features between two modal pairs through gated transformer. Then use two label generation modules to learn modality-specific and modality-complementary representations. Additionally, we introduce the multimodal information bottleneck (MIB) in both multimodal and unimodal tasks to filter out noise information in unimodal representations as well as learn powerful and sufficient multimodal embeddings that is free of redundancy. Last, we conduct extensive experiments on two popular sentiment analysis benchmarks, MOSI and MOSEI. Experimental results demonstrate that our model significantly outperforms the current strong baselines.

Ellipsis phenomenon is prevalent, particularly in languages like Chinese, and can be observed across various domains, including everyday conversations, literary works, and product evaluations. Such ellipsis poses challenges for machines, affecting the performance of natural language processing tasks such as machine translation and comprehension. Current approaches to tackle ellipsis primarily involve fine-tuning pretrained language models like BERT(Bidirectional Encoder Representations from Transformers). This approach encompasses two subtasks: 1) detecting ellipsis positions, i.e., identifying locations where ellipsis occurs within a sentence, and 2) completing the elliptical content by predicting the missing information based on the detected positions. This paper proposes a novel model that combines fine-tuned BERT with Generative Adversarial Networks (GANs), incorporating a sampler between the generator and discriminator. The proposed method simultaneously utilizes the generator, discriminator, and sampler to locate and complete ellipsis. Experimental results demonstrate that our approach achieves an F1 score improvement of 0.03 in ellipsis position detection and an EM (Exact Match) improvement of 0.1 in ellipsis content completion compared to the baseline method.

With the increasing number of IoT devices, human activities tend to create dynamic multi-hotspot regions, which cannot be adapted to such highly dynamic scenarios due to the immobility of base station edge servers. UAVs serve as an effective solution but have limited resources to carry, thus, solving the dynamic scheduling and collaboration of UAVs in multi-hotspot regions is an important problem. In this paper, we propose a scheme called Dynamic Scheduling based on Graph Reinforcement Learning (DSGR), which aims to maximize the long-term energy efficiency of UAVs by optimizing their flight trajectories and charging timing. First, we abstract the dynamic scheduling problem of UAVs among multiple hotspots as a dynamic topological graph and use graph convolution to learn the temporal and spatial relationships among hotspots. Then, the UAV flight strategies are learned using the neighbor vectors of the graph nodes as action masks as well as the temporal and spatial relationships of the hotspots modeled as inputs to the PPO network. Compared with the benchmark algorithm, our proposed DSGR algorithm achieves the best performance in terms of UAV energy efficiency.

Most of the classification methods of urban functional areas nowadays are only based on single source data analysis and modeling, which can not make full use of the multi-scale and multi-source data that is easy to obtain. Therefore, this paper proposed a classification model of urban functional areas based on multi-modal machine learning, by analyzing regional remote sensing images and behavior data of visitors in the area, using the combination of supervised methods extracted the deep-seated features and relationships of kinds of data, filtered and merged the overall and local features of the data. The model used dual branch neural network combining SE-ResNeXt and Dual Path Network (DPN) to automatically mined and fused the overall characteristics of multi-source data, and used the designed feature engineering to deeply mine the behavior data of users to obtain more association information, then combined the algorithm based on Gradient Boosting Decision Tree to learn the characteristics of different levels and obtained the classification probability for different levels of features. Finally, we continued to use the algorithm based on the Gradient Boosting Decision Tree to learn the probability distribution of different levels of features to obtain the final prediction results of urban functional area classification. Through the analysis and experimental verification of real data sets, the results showed that MM-UrbanFAC model can effectively integrate the features of multi-modal data. Compared with a single classifier, the integration framework based on gradient lifting tree improved the prediction performance, this method can effectively integrate the results of multiple models and accurately classify urban functional areas, and the model can provide reference for tourism recommendation, urban land planning and urban construction.

Intelligent transportation systems (ITS) have been developed rapidly over the last few decades because of global urbanization and industrialization. ITS involve a wide range of different technologies and applications such as automatic road enforcement, dynamic traffic light sequence, and as a result, a significant number of scientific papers have been published in the field of ITS. In this paper, we present a useful insight into the development of ITS area by systematically analyzing the publications over the period of 20 years. First, we identify the most cited papers and most impactful authors in the field. Second, in the aspect of topic analysis, we identify some active keywords. To do so, we develop a keyword co-occurrence network to find topics in the ITS field. Finally, for the collaboration pattern analysis, we construct two networks to interpret collaboration patterns, including a co-authorship network, and an author co-keyword network to show the development and research tendency of ITS. Some most interesting findings from our investigation include the following: 1) Besides the USA, China and Europe have begun to play an increasingly significant role in this field and 2)GPS,traffic control, androad safetyshow an upward trend from the analysis of the evolution of ITS research topics, given the rise of new research areas such as autonomous vehicles. Our first-hand investigation and analysis of the literature provides a valuable reference to research activities in the development of ITS field and presents worthy insights on the current status and future technical trends.

Intelligent transportation systems (ITS) has been one of the most active research fields in recent years. This paper identifies most productive authors, institutions, and countries/regions inIEEE Transactions on Intelligent Transportation Systemsfrom 2000 to 2015. The results of bibliographic analysis show that the USA is the most influential country in that it not only has the most papers but also has six out of the ten most-cited papers. Meanwhile, researchers from China and Europe have published nearly half of the papers in this field. In addition, we generate three networks (including coauthorship network, keyword co-occurrence network, and author co-keyword network) to analyze collaboration patterns among authors in the field of ITS. The active keywords are investigated, and the top three arevehicles,road vehicles, androad traffic. Finally, visual pictures are presented to show topological interactions of authors' collaboration.

Recommender systems are constructed to search the content of interest from overloaded information by acquiring useful knowledge from massive and complex data. Since the amount of information and the complexity of the data structure grow, it has become a more interesting and challenging topic to find an efficient way to process, model, and analyze the information. Due to the Global Positioning System (GPS) data recording the taxi's driving time and location, the GPS-equipped taxi can be regarded as the detector of an urban transport system. This paper proposes a Taxi-hunting Recommendation System (Taxi-RS) processing the large-scale taxi trajectory data, in order to provide passengers with a waiting time to get a taxi ride in a particular location. We formulated the data offline processing system based on HotSpotScan and Preference Trajectory Scan algorithms. We also proposed a new data structure for frequent trajectory graph. Finally, we provided an optimized online querying subsystem to calculate the probability and the waiting time of getting a taxi. Taxi-RS is built based on the real-world trajectory data set generated by 12 000 taxis in one month. Under the condition of guaranteeing the accuracy, the experimental results show that our system can provide more accurate waiting time in a given location compared with a naïve algorithm.

In the dynamic social network, how to use data mining tools to find the hidden dynamic knowledge in the social network has become the focus of the study. It can be applied to a wide range of areas with good practical value and application significance. We propose a novel algorithm called iDBMM based on the improvement of DBMM algorithm. At first, iDBMM algorithm classifies the training set to obtain the basic characteristics of each role. Then it scores the test set relative to each role and distribute the role of the highest score to the corresponding node. Finally, the transition model is obtained by the statistical method. Experimental results show that new method determines the distribution of the roles of the nodes effectively to make up for the shortcoming of non-negative matrix factorization and improve the prediction accuracy.

Cooperative phenomenon is widely researched within the fields of computational genomics, artificial intelligence, machine learning and data mining technologies. A key idea behind complex system constructed by intelligent agents is to establish cooperation between different agents so as to solve problems more effectively and efficiently than a single agent can do. Understanding the evolutionary mechanisms that promote and maintain cooperative behavior is recognized as a major theoretical problem where the intricacy increases with the complexity of the participating individuals. We presents current research on the effect of the "probability of learning" (Pl) and the "probability of reconnecting" (Pr) in NIPD game in evolution experiment based on complex network system, static and dynamic. We show that the "probability of learning" (Pl) is not the decisive factor on static network but it plays an important role on dynamic network where the "probability of reconnecting" (Pr) is not zero.

Cooperation within selfish individuals can be promoted by natural selection only in the presence of an additional mechanism. In this paper, we focus on an indirect reciprocity mechanism in dynamical structured populations. In social networks rational individuals update their strategies and adjust their social relationships. We propose a three-strategy prisoner's dilemma game model to investigate the evolution of cooperation on dynamical networks. In the coevolution of state and structure process, reciprocators adapt their behaviors and switch their partners based on reputation. Simulation results show that the dynamics of strategies and links can promote cooperation provided the partners switch proceeds much faster than the strategy updating.