The chemical industry is faced with the urgent challenge of effectively harnessing the vast amounts of time-series data generated by thousands of sensors, which is essential for forecasting chemical states, achieving accurate real-time control of production processes. Traditional forecasting methods suffer from high computational latency and struggle with the complexity of spatiotemporal dependencies. As a result, modeling this data becomes challenging. This paper introduces a novel approach, referred to as ASTNet, designed to address these challenges. ASTNet integrates an asynchronous spatiotemporal modeling framework that combines temporal and spatial encoders, enabling concurrent learning of temporal and spatial dependencies while reducing computational latency. Additionally, it introduces a gated graph fusion mechanism that adaptively combines static (meta) and evolving (dynamic) sensor graphs, enhancing the handling of heterogeneous sensor data and spatial correlations. Extensive experiments on three real-world chemical sensor datasets demonstrate that ASTNet outperforms SOTA methods in terms of both prediction accuracy and computational efficiency, making ASTNet successfully deployed in chemical engineering industrial scenarios.

Automated seizure detection (ASD) using intracranial electroencephalography (iEEG) is critical for effective epilepsy treatment. However, the significant domain shift of iEEG signals across subjects poses a major challenge, limiting their applicability in real-world clinical scenarios. In this paper, we address this issue by analyzing the primary cause behind the failure of existing iEEG models for subject-independent seizure detection, and identify a critical universal seizure pattern: seizure events consistently exhibit higher average amplitude compared to adjacent normal events. To mitigate the domain shifts and preserve the universal seizure patterns, we propose a novel self-comparison mechanism. This mechanism effectively aligns iEEG signals across subjects and time intervals. Building upon these findings, we propose Difference Matrix-based Neural Network (DMNet), a subject-independent seizure detection model, which leverages self-comparison based on two constructed (contextual, channel-level) references to mitigate shifts of iEEG, and utilize a simple yet effective difference matrix to encode the universal seizure patterns. Extensive experiments show that DMNet significantly outperforms previous SOTAs while maintaining high efficiency on a real-world clinical dataset collected by us and two public datasets for subject-independent seizure detection. Moreover, the visualization results demonstrate that the generated difference matrix can effectively capture the seizure activity changes during the seizure evolution process. Additionally, we deploy our method in an online diagnosis system to illustrate its effectiveness in real clinical applications.

The proliferation of abundant electricity time series (ETS) data presents numerous opportunities for various applications within power systems, including demand-side management, grid stability, and consumer behavior analysis. Deep learning models have advanced ETS modeling by effectively capturing sequence dependence. However, learning a generic representation of ETS data for various applications is challenging due to the inherently complex hierarchical structure of ETS data. Moreover, ETS data exhibits intricate temporal dependencies and is susceptible to the influence of exogenous variables. Furthermore, different instances exhibit diverse electricity consumption behavior. In this paper, we propose a foundation model PowerPM for ETS data, providing a large-scale, off-the-shelf model for power systems. PowerPM consists of a temporal encoder and a hierarchical encoder. The temporal encoder captures temporal dependencies within ETS data, taking into account exogenous variables. The hierarchical encoder models correlations between different levels of hierarchy. Furthermore, PowerPM leverages a novel self-supervised pre-training framework consisting of masked ETS modeling and dual-view contrastive learning. This framework enables PowerPM to capture temporal dependency within ETS windows and aware the discrepancy across ETS windows, providing two different perspectives to learn generic representation. Our experiments span five real-world scenario datasets, including both private and public data. Through pre-training on massive ETS data, PowerPM achieves SOTA performance on diverse downstream tasks within the private dataset. Notably, when transferred to public datasets, PowerPM retains its edge, showcasing its remarkable generalization ability across various tasks and domains. Moreover, ablation studies and few-shot experiments further substantiate the effectiveness of our model.