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The classification of frequently occurring terrestrial-origin transient signals, called glitches, in the time series data from gravitational wave detectors is important for mitigating their adverse effects on searches for rare and valuable astrophysical signals. While formally a time series classification problem, recent successes in glitch classification have all come from using their time-frequency image representations. Using transfer learning with the VGG16 deep convolutional neural network for image classification, we compare the efficacy of different types of image representations for classifying simulated glitches. We find the novel result that training the network with 2D plots of the noisy glitch time series provides better classification accuracy than their time-frequency images. 

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality. While tremendous progress has been made to model high-level spatio-temporal regularities with deep learning, most, if not all of the existing methods are neither aware of the dynamic interactions among multiple transport modes nor adaptive to unprecedented volatility brought by potential societal events. In this paper, we are therefore motivated to improve the canonical spatio-temporal network (ST-Net) from two perspectives: (1) design a heterogeneous mobility information network (HMIN) to explicitly represent intermodality in multimodal mobility; (2) propose a memory-augmented dynamic filter generator (MDFG) to generate sequence-specific parameters in an on-the-fly fashion for various scenarios. The enhanced event-aware spatio-temporal network, namely EAST-Net, is evaluated on several real-world datasets with a wide variety and coverage of societal events. Both quantitative and qualitative experimental results verify the superiority of our approach compared with the state-of-the-art baselines. Code and data are published on https://github.com/underdoc-wang/EAST-Net.