Emotion recognition based on multimodal physiological signals is playing an increasingly important role in areas such as human-computer interaction and disease diagnosis, attracting growing attention from the research community. Current studies primarily focus on emotion recognition under unified data collection paradigms, overlooking the prevalent issue of imperfect modality matching in real-world scenarios. In particular, existing methods fail to effectively utilize these mismatched modalities, leading to incomplete emotional representations. This limits the model's ability to accurately capture the multidimensional semantic features of emotions, thereby constraining its effectiveness and applicability in practical settings. To address this challenge, we propose MoCERNet. At the modality level, it first reduces the domain gap among matched modalities and then aligns mismatched modalities in a semantics-aware manner, guided by the matched ones. At the decision level, it further mitigates the global distribution discrepancies to achieve a more complete emotional representation. In addition, we design a Nervous System Functional Structure Transformer (NFSformer) that enables the model to focus on the correlation between different brain regions and peripheral physiological signals under various emotional states, thereby enhancing its capacity to model complex emotional processes. Experiments on three multimodal emotion datasets demonstrate that MoCERNet outperforms state-of-the-art baselines under imperfect modality matching scenarios.

Medical time-series analysis differs fundamentally from general ones by requiring specialized domain knowledge to interpret complex signals and clinical context. Large language models (LLMs) hold great promise for augmenting medical time-series analysis by complementing raw series with rich contextual knowledge drawn from biomedical literature and clinical guidelines. However, realizing this potential depends on precise and meaningful prompts that guide the LLM to key information. Yet, determining what constitutes effective prompt content remains non-trivial—especially in medical settings where signal interpretation often hinges on subtle, expert-defined decision-making indicators. To this end, we propose InDiGO, a knowledge-aware evolutionary learning framework that integrates clinical signals and decision-making indicators through iterative optimization. Across four medical benchmarks, InDiGO consistently outperforms prior methods. The code is available at: https://github.com/jinxyBJTU/InDiGO.

Self-supervised contrastive learning (SCL) has driven significant advancements in time-series representation learning. While recent studies built upon the information noise contrastive estimation (InfoNCE) loss framework focus on constructing appropriate positives and negatives, we theoretically analyze and identify two overlooked issues inherent in this approach: false negatives and class imbalance. To address these challenges, we propose a simple yet effective modification based on the SimCLR framework, integrating a multi-instance discrimination task to mitigate false negatives. Additionally, we introduce a graph-based interactive projection head and semantic consistency regularization, which enhances minority-class representations with minimal annotation cost. Extensive experiments on six real-world time-series datasets demonstrate that our approach consistently outperforms state-of-the-art methods, achieving up to 3.96% higher accuracy and 10.73% improvement in $F1$ -score, particularly benefiting imbalanced data scenarios.

Contrastive learning has successfully addressed the scarcity of large-scale labeled datasets, especially in the physiological time series field. Existing methods construct easy positive pairs as substitutes for ground truth based on temporal dynamics or instance consistency. Despite the potential of hard positive samples to provide richer gradient information and facilitate the acquisition of more discriminative representations, they are frequently overlooked in sampling strategies, thus constraining the classification capacity of models. In this paper, we focus on multi-view physiological signals and propose a novel hard positive sampling strategy based on the view consistency. Multi-view signals are recorded from sensors attached to different organs of human body. Additionally, we propose a Multi-View Consistency Contrastive (MVCC) learning framework to jointly extract intra-view temporal dynamics and inter-view consistency features. Experiments have been carried out on two public datasets and our method demonstrates state-of-the-art performance, achieving 83.25% and 73.37% accuracy on SleepEDF and ISRUC, respectively.

Recent works demonstrate that capturing correlations between road network nodes is crucial to improving traffic flow forecasting accuracy. In general, there are spatial, temporal, and joint spatial-temporal correlations between two nodes, whose strength is related to spatial and temporal position factors. For example, traffic congestion that occurs at a traffic hub has a wider and stronger impact than that at a branch road. Moreover, the above impacts can vary with temporal position. Although spatial-temporal graph convolution networks have become a popular paradigm for modeling those correlations, there are still three problems with existing models: (i) failing to effectively model joint spatial-temporal correlations; (ii) ignoring spatial and temporal position factors when modeling the aforementioned correlations; and (iii) failing to capture distinct spatial-temporal patterns of each node. To cope with the above issues, this paper proposes a novelSpatial-TemporalPosition-awareGraphConvolutionNetwork (STPGCN) for traffic flow forecasting. Specifically, a trainable embedding module is constructed to represent the spatial and temporal positions of the nodes. Subsequently, a spatial-temporal position-aware relation inference module is proposed to adaptively infer the correlation weights of the three important spatial-temporal relations. Based on this, the generated spatial-temporal relations are integrated into a graph convolution layer for aggregating and updating node features. Finally, we design a spatial-temporal position-aware gated activation unit in the graph convolution, to capture the node-specific pattern features under the guidance of position embedding. Extensive experiments on six real-world datasets demonstrate the superiority of our model in terms of prediction performance and computational efficiency.