Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Spatio-Temporal Self-Supervised Learning (ST-SSL) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. Specifically, our ST-SSL is built over an integrated module with temporal and spatial convolutions for encoding the information across space and time. To achieve the adaptive spatio-temporal self-supervised learning, our ST-SSL first performs the adaptive augmentation over the traffic flow graph data at both attribute- and structure-levels. On top of the augmented traffic graph, two SSL auxiliary tasks are constructed to supplement the main traffic prediction task with spatial and temporal heterogeneity-aware augmentation. Experiments on four benchmark datasets demonstrate that ST-SSL consistently outperforms various state-of-the-art baselines. Since spatio-temporal heterogeneity widely exists in practical datasets, the proposed framework may also cast light on other spatial-temporal applications. Model implementation is available at https://github.com/Echo-Ji/ST-SSL.

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With the increasing demand for high precision micro parts in machining field, the development of micro hole drilling technology, especially the finite element simulation technology is rapidly promoted. However, there are many limitations in the simulation of micro-drilling, such as element size, computational efficiency, chip forming, etc. In the present work, a finite element software, Abaqus has been used to simulate the thermal-mechanical coupling micro-drilling process of titanium alloy material, and the key technologies, such as twist bit modeling, material constitutive model, chip separation criterion and element division, were investigated. Through simulation, it was found that with the increase of rotational speed and the decrease of feed speed, chip shape gradually fragmented, in addition, thrust force and torque diminished. Since chip shape, thrust force and torque are important factors affecting drilling quality and tool life, the work could offer important guiding significance for cutting parameter optimization.