Distributed Acoustic Sensing (DAS) systems provide rich vibration information for industrial monitoring. However, raw one-dimensional signals are often noisy and exhibit complex and heterogeneous temporal characteristics, making it difficult for traditional models to extract discriminative features and limiting classification performance and generalization. To address these challenges, we propose a DAS signal classification frame-work that integrates dynamic time-frequency representation with a learnable multi-source feature fusion network. First, a dynamic Short-Time Fourier Transform (STFT) method is introduced, which adaptively adjusts the window size and overlap ratio according to the input signal length. This design ensures consistent time-frequency resolution while avoiding distortions caused by padding or resampling, thereby enhancing spectral discriminability. Second, we design MFOFUNet, a dual-branch UNet-based architecture that separately encodes the dynamic spectrograms of intensity and phase channels and integrates them through a multi-stage fusion mechanism. This structure preserves modality-specific complementary information while enabling effective cross-modal interaction. Furthermore, a lightweight learnable fusion module is proposed to adaptively balance the contributions of different modalities at each encoding stage, achieving improved accuracy and stability with minimal additional complexity. In addition, a dual-stage data augmentation strategy is developed, combining 1D signal-level and 2D spectrogram-level enhancements to improve robustness. Extensive experiments on a challenging 10-class DAS dataset demonstrate that the proposed method achieves superior performance, reaching 89.33±4.27% accuracy, 89.02±4.50% F1-score, and 95.57±2.40% mAP under 10-fold cross-validation, outperforming existing mainstream approaches and showing strong potential for industrial sensing applications.

Multivariate time series forecasting holds significant theoretical and practical importance in various fields, including web analytics and transportation. Recently, graph neural networks and graph differential equations have shown exceptional capabilities in modeling spatio-temporal features. However, existing methods often suffer from over-smoothing, hindering real-world problem-solving. In this work, we analyze the graph propagation process as a dynamical system and propose a novel feedback mechanism to enhance representation power, adaptively adjusting the representations to align with desired performance outcomes, thereby fundamentally mitigating the issue of over-smoothing. Moreover, we introduce an effective multivariate time series forecasting model called SF-GDE, based on the proposed graph propagation with the feedback mechanism. Intensive experiments are conducted on three real-world datasets from diverse fields. Results show that SF-GDE outperforms the state of the arts, and the feedback mechanism can serve as a universal booster to improve performance for graph propagation models.

With the rapid development of mobile networks and the ubiquity of mobile devices, spatial crowdsourcing (SC), which refers to assigning spatial–temporal tasks to moving workers, has drawn increasing attention. Thus, many researchers aim at various task assignment methods in SC. However, existing works generally consider workers’ location and preference categories separately. Ignorance of the correlation between them can often lead to poor assignment results. In this article, we propose a location-and-preference joint prediction model (JPM) to predict workers’ locations and preference categories jointly at each sample timestamp. Based on the predictive location probability distribution and preference probability distribution, we elaborately design a greedy multiattribute joint task assignment algorithm (MAJA) to maximize the average number of completed tasks under constraints. Then, an overall procedure incorporating the JPM and MAJA, called the location-and-preference joint prediction-based task assignment (LPJTA), is implemented to focus on assigning tasks to workers who are near the task location and willing to perform the task based on predicting locations and preference categories. We theoretically analyze the time complexity and approximation ratio of the proposed methods and construct extensive experiments on three real datasets to empirically verify their effectiveness, comparing with the state-of-the-art baselines.

We propose a general and scalable global optimization framework directly operating on annotated graph data by introducing a Bayesian graph neural network to approximate the expensive-to-evaluate objectives. It prevents the cubical complexity of Gaussian processes and can scale linearly with the number of observations. Its parallelized variant makes it scalable. We provide strict theoretical support on its convergence. Intensive experiments conducted on both artificial and real-world problems, including molecular discovery and urban road network design, demonstrate the effectiveness of the proposed methods compared with the current state of the art.