Accurate traffic flow prediction is the precondition for many applications in Intelligent Transportation Systems, such as traffic control and route guidance. Traditional data driven traffic flow prediction models tend to ignore traffic self-features (e.g., periodicities), and commonly suffer from the shifts brought by various complex factors (e.g., weather and holidays). These would reduce the precision and robustness of the prediction models. To tackle this problem, in this paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts network-scale traffic flow with multiple spatiotemporal features and external factors (weather and holidays). Specifically, we classify traffic self-features into temporal continuity as short-term feature, daily periodicity and weekly periodicity as long-term features, then map them to three two-dimensional spaces, which each one is composed of time and space, represented by two-dimensional matrices. The high-level spatiotemporal features learned by CNNs from the matrices with different time lags are further fused with external factors by a logistic regression layer to derive the final prediction. Experimental results indicate that the MF-CNN model considering multi-features improves the predictive performance compared to five baseline models, and achieves the trade-off between accuracy and efficiency.
Zebrafish are an important animal model, whose structure and function information can be used to study development, pathologic changes and genetic mutations. However, limited by the penetration depth, the available optical methods are difficult to image the whole-body zebrafish in juvenile and adult stages. Based on a home-made high-resolution polarization-sensitive optical coherence tomography (PS-OCT) system, we finished in vivo volumetric imaging for zebrafish, and various muscles can be clearly discerned by scanning from dorsal, ventral, and lateral directions. Besides structure information, polarization properties extracted from PS-OCT images provide abundant function information to distinguish different muscles. Furthermore, we found local retardation and local optic axis of zebrafish muscle are related to their composition and fiber orientation. We think high-resolution PS-OCT will be a promising tool in studying myopathy models of zebrafish.
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