The Inversion of the Real Kinematic Properties of Coronal Mass Ejections by Forward Modeling

The kinematic properties of coronal mass ejections (CMEs) suffer from the projection effects, and it is expected that the real velocity should be larger and the real angular width should be smaller than the apparent values. Several attempts have been tried to correct the projection effects, which however led to a too large average velocity probably due to the biased choice of the CME events. In order to estimate the overall influence of the projection effects on the kinematic properties of the CMEs, we perform a forward modeling of the real distributions of the CME properties, such as the velocity, the angular width, and the latitude, by requiring their projected distributions to best match the observations. Such a matching is conducted by Monte Carlo simulations. According to the derived real distributions, it is found that (1) the average real velocity of all non-full-halo CMEs is about 514 km s$^{-1}$, and the average real angular width is about 33$^\circ$, in contrast to the corresponding apparent values of 418 km s$^{-1}$ and 42.7$^\circ$ in observations; (2) For the CMEs with the angular width in the range of $20^\circ- 120^\circ$, the average real velocity is 510 km s$^{-1}$ and the average real angular width is 43.4$^\circ$, in contrast to the corresponding apparent values of 392 km s$^{-1}$ and 52$^\circ$ in observations.Comment: 8 pages, 4 figures, to be published in Res. Astron. Astrophys. (RAA

Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis

Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge. However, existing methods cannot effectively learn a promising synthesis model for high-dimensional DT from a small number of training data. In this paper, we propose a novel DT synthesis method, which makes full use of similarity prior knowledge to address this issue. Our method bases on the proposed kernel similarity embedding, which not only can mitigate the high-dimensionality and small sample issues, but also has the advantage of modeling nonlinear feature relationship. Specifically, we first raise two hypotheses that are essential for DT model to generate new frames using similarity correlation. Then, we integrate kernel learning and extreme learning machine into a unified synthesis model to learn kernel similarity embedding for representing DT. Extensive experiments on DT videos collected from the internet and two benchmark datasets, i.e., Gatech Graphcut Textures and Dyntex, demonstrate that the learned kernel similarity embedding can effectively exhibit the discriminative representation for DT. Accordingly, our method is capable of preserving the long-term temporal continuity of the synthesized DT sequences with excellent sustainability and generalization. Meanwhile, it effectively generates realistic DT videos with fast speed and low computation, compared with the state-of-the-art methods. The code and more synthesis videos are available at our project page https://shiming-chen.github.io/Similarity-page/Similarit.html.Comment: 13 pages, 12 figures, 2 table