The nonlinear behavior with unpredicted property in engineering has received widespread attention. To explore more unpredicted nonlinear behavior, this paper focuses on the function projective lag synchronization (FPLS) of complex-valued chaotic systems. Most existing control schemes primarily address system stability, lack of consideration in convergence improvement. Zeroing neural dynamics (ZND) has significant results in the convergence performance, yet achieving a balance between model structure and performance remains a challenging task. In light of this, we propose a general time base generator (TBG), and adopt the linear ZND with the simplest structure, which has relatively weak convergence but devoid of nonlinear components, to develop a TBG based neural dynamics controller (TBGBNDC) in the FPLS of complex-valued chaotic systems. The theories illustrate that the system error under the TBGBNDC has predefined-time asymptotic convergence, which is verified in both homogeneous and heterogeneous complex-valued chaotic systems. Compared with other controllers, the TBGBNDC exhibits better performance in terms of convergence, energy and time consumption. Furthermore, the higher-quality random sequences are acquired through the FPLS between these chaotic systems, showing the significant potential application of the research topic.

Multi-view clustering (MVC) based on anchor learning has been proven to be effective in improving clustering accuracy and efficiency. Existing MVC methods are mainly based on single-granularity anchor learning, that is, the number of anchors corresponding to different views is constant and consistent, which will lead to information redundancy or insufficient mining. In addition, aggregating anchors of varying scales from all views to obtain multi-view shared clustering results remains a problem to be further explored. To address the above problems, a novel MVC method named View-adaptive Multi-granularity Anchor Learning (VMAL) is proposed in this paper, where view-adaptive anchor pruning and view-shared sample clustering are jointly optimized. On the one hand, VMAL can dynamically adjust the optimal number of anchors for each view during optimization by exploiting the reconstruction error of samples. On the other hand, an intuitive and effective mapping-aggregation message passing strategy is cleverly designed, which first maps the anchor representations of different views to the cluster space and then transfers the obtained cluster information of anchors to the sample space through an aggregation matrix. As a byproduct, VMAL can directly obtain the discrete cluster distribution of samples without additional partitioning. Finally, an iterative optimization algorithm is developed to solve the proposed VMAL method. Experimental results on multiple datasets have demonstrated the superiority of VMAL in terms of clustering results when compared with other state-of-the-art methods.

Graph Neural Networks (GNNs), due to their advanced capability in extracting high-order neighbor relationships, have become essential in multimodal recommendation tasks. However, augmenting the number of propagation layers in GNNs can result in feature redundancy, which may degrade the final recommendation performance. In addition, the existing recommendation task method directly maps the preprocessed multimodal features to the low-dimensional space, which will bring the noise unrelated to user preference, thus affecting the representation ability of the model. To tackle the aforementioned challenges, we propose Multimodal Graph Neural Network (MGNM) for Recommendation with Dynamic De-Redundancy (DDR) and Modality-Guided Feature De-Noisy, which is divided into local and global interaction. Initially, in the local interaction process, we integrate a DDR loss function which is achieved by utilizing the product of the feature coefficient matrix and the feature matrix as a penalization factor. It reduces the feature redundancy effects of multimodal and behavioral features caused by the stacking of multiple GNN layers. Subsequently, in the global interaction process, we developed modality-guided global feature purifiers for each modality to alleviate the impact of modality noise. It is a two-fold guiding mechanism eliminating modality features that are irrelevant to user preferences and captures complex relationships within the modality. Experimental results demonstrate that MGNM achieves superior performance on multimodal information denoising and removal of redundant information compared to the state-of-the-art methods.

Spread through air spaces (STAS) represents a newly identified aggressive pattern in lung cancer, which is known to be associated with adverse prognostic factors and complex pathological features. Pathologists currently rely on time-consuming manual assessments, which are highly subjective and prone to variation. This highlights the urgent need for automated and precise diagnostic solutions. 2,970 lung cancer tissue slides are comprised from multiple centers, re-diagnosed them, and constructed and publicly released three lung cancer STAS datasets: STAS-CSU (hospital), STAS-TCGA, and STAS-CPTAC. All STAS datasets provide corresponding pathological feature diagnoses and related clinical data. To address the bias, sparse and heterogeneous nature of STAS, we propose an scale-aware multiple instance learning(SMILE) method for STAS diagnosis of lung cancer. By introducing a scale-adaptive attention mechanism, the SMILE can adaptively adjust high-attention instances, reducing over-reliance on local regions and promoting consistent detection of STAS lesions. Extensive experiments show that SMILE achieved competitive diagnostic results on STAS-CSU, diagnosing 251 and 319 STAS samples in CPTAC and TCGA, respectively, surpassing clinical average AUC. The 11 open baseline results are the first to be established for STAS research, laying the foundation for the future expansion, interpretability, and clinical integration of computational pathology technologies. The datasets and code are available at https://github.com/panliangrui/IJCAI25.

Hyperspectral image (HSI) encompasses abundant spatial and spectral details, while Light Detection and Ranging (LiDAR) delivers precise elevation data. The amalgamation of HSI and LiDAR data significantly improves the precision of image classification. However, most methods focus solely on spatial features while neglecting frequency domain information, limiting the ability of deep models to characterize land cover. Furthermore, how to establish a sufficient interaction between different modalities is also an important issue. In this paper, we propose a novel multiscale cross-domain fusion network (MCFNet) for joint classification of HSI and LiDAR data. The main idea is that the wavelet transform can provide details at different resolutions simultaneously, supplementing spatial domain information and enriching feature representation. In addition, the multimodal fusion module (MFM) guided by HSI and the cross-domain fusion module (CDFM) strategy are developed to integrate features from diverse modalities and domains, respectively. Specifically, frequency domain features are extracted by discrete wavelet transform, and spatial domain features of the image are captured through a set of convolution operations. Then interactive fusion is performed by MFM and CDFM, and finally the integrated features are categorized using a classification module. Extensive experiments on three widely-used HSI and LiDAR datasets indicate that MCFNet outperforms the SOTA methods. The code will be available at https://github.com/MSFLabX/MCFNet.

Multimodal recommendation systems integrate diverse multimodal information into the feature representations of both items and users, thereby enabling a more comprehensive modeling of user preferences. However, existing methods are hindered by data sparsity and the inherent noise within multimodal data, which impedes the accurate capture of users' interest preferences. Additionally, discrepancies in the semantic representations of items across different modalities can adversely impact the prediction accuracy of recommendation models. To address these challenges, we introduce a novel diffusion-based contrastive learning (DiffCL) framework for multimodal recommendation. DiffCL employs a diffusion model (DM) to generate contrastive views that effectively mitigate the impact of noise during the contrastive learning phase. Furthermore, it improves semantic consistency across modalities by aligning distinct visual and textual semantic information through stable ID embeddings. Finally, the introduction of the item-item graph (I-I graph) enhances multimodal feature representations, thereby alleviating the adverse effects of data sparsity on the overall system performance. We conduct extensive experiments on three public datasets, and the results demonstrate the superiority and effectiveness of the DiffCL.

The time-varying Lyapunov equation (TVLE) plays a crucial role in control design and system stability. However, there has been limited research conducted on the time-varying generalized Lyapunov equation in the quaternion field. To tackle the time-varying quaternion generalized Lyapunov equation, a nonlinear noise-resistant zeroing neural network (NNR-ZNN) model with a novel power activation function (NPAF) is devised. The issue of non-commutativity within quaternion is circumvented by utilizing the real representation. The theoretical analyses provide a sufficient explanation for the global stability, fixed-time convergence, and robustness of the NNR-ZNN model. Under several different kinds of noises, the exceptional robustness of the NNR-ZNN model is highlighted by comparison with other existing models. In the end, the successful applications of the NNR-ZNN model to color image fusion and color image denoising confirm the practical value of the NNR-ZNN model.

Multimodal recommendation systems improve the accuracy of recommendations by integrating information from different modalities to obtain potential representations of users and items. However, existing multimodal recommendation methods often use single user embedding to model users’ interests in different modalities, neglecting multimodal information. Furthermore, the semantics expressed by the same items in different modalities may be inconsistent, leading to suboptimal recommendation performance. To alleviate the impact of these issues, we propose a new multimodal recommendation framework called Learnable Prompts and ID-guided Contrastive Learning (LPIC). Specifically, we introduce a continuously learnable prompt embedding method, incorporating multimodal features of items to model users’ interests in specific modalities. Then, we propose an ID-guided contrastive learning component to enhance historical interaction features in textual, visual, and fused modalities, while aligning text, image, and fused modality to enhance semantic consistency between modalities. Finally, we conduct extensive experiments on three publicly available Amazon datasets to demonstrate the effectiveness of the LPIC framework.

Understanding and elucidating human behavior across diverse scenarios represents a pivotal research challenge in pursuing seamless human-computer interaction. However, previous research on multi-participant dialogues has mostly relied on proprietary datasets, which are not standardized and openly accessible. To propel advancements in this domain, the MultiMediate'23 Challenge presents two sub-challenges: Eye contact detection and Next speaker prediction, aiming to foster a comprehensive understanding of multi-participant behavior. To tackle these challenges, we propose a multi-scale conformer fusion network (MSCFN) for enhancing the perception of multi-participant group behaviors. The conformer block combines the strengths of transformers and convolution networks to facilitate the establishment of global and local contextual relationships between sequences. Then the output features from all Conformer blocks are concatenated to fusion multi-scale representations. Our proposed method was evaluated using the officially provided dataset, and it achieves the best and second best performance in next speaker prediction and gaze detection tasks of MultiMediate'23, respectively.

Automatic speech recognition (ASR) is the major human-machine interface in many intelligent systems, such as intelligent homes, autonomous driving, and servant robots. However, its performance usually significantly deteriorates in the presence of external noise, leading to limitations of its application scenes. The audio-visual speech recognition (AVSR) takes visual information as a complementary modality to enhance the performance of audio speech recognition effectively, particularly in noisy conditions. Recently, the transformer-based architectures have been used to model the audio and video sequences for the AVSR, which achieves a superior performance. However, its performance may be degraded in these architectures due to extracting irrelevant information while modeling long-term dependences. In addition, the motion feature is essential for capturing the spatio-temporal information within the lip region to best utilize visual sequences but has not been considered in the AVSR tasks. Therefore, we propose a multimodal sparse transformer network (MMST) in this article. The sparse self-attention mechanism can improve the concentration of attention on global information by selecting the most relevant parts wisely. Moreover, the motion features are seamlessly introduced into the MMST model. We subtly allow motion-modality information to flow into visual modality through the cross-modal attention module to enhance visual features, thereby further improving recognition performance. Extensive experiments conducted on different datasets validate that our proposed method outperforms several state-of-the-art methods in terms of the word error rate (WER).