Multi-wavelength Emission from the Fermi Bubble III. Stochastic (Fermi) Re-Acceleration of Relativistic Electrons Emitted by SNRs

We analyse the model of stochastic re-acceleration of electrons, which are emitted by supernova remnants (SNRs) in the Galactic Disk and propagate then into the Galactic halo, in order to explain the origin on nonthermal (radio and gamma-ray) emission from the Fermi Bubbles (FB). We assume that the energy for re-acceleration in the halo is supplied by shocks generated by processes of star accretion onto the central black hole. Numerical simulations show that regions with strong turbulence (places for electron re-acceleration) are located high up in the Galactic Halo about several kpc above the disk. The energy of SNR electrons that reach these regions does not exceed several GeV because of synchrotron and inverse Compton energy losses. At appropriate parameters of re-acceleration these electrons can be re-accelerated up to the energy 10E12 eV which explains in this model the origin of the observed radio and gamma-ray emission from the FB. However although the model gamma-ray spectrum is consistent with the Fermi results, the model radio spectrum is steeper than the observed by WMAP and Planck. If adiabatic losses due to plasma outflow from the Galactic central regions are taken into account, then the re-acceleration model nicely reproduces the Planck datapoints.Comment: 33 pages, 8 figures, accepted by Ap

Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation

Scene flow estimation, which extracts point-wise motion between scenes, is becoming a crucial task in many computer vision tasks. However, all of the existing estimation methods utilize only the unidirectional features, restricting the accuracy and generality. This paper presents a novel scene flow estimation architecture using bidirectional flow embedding layers. The proposed bidirectional layer learns features along both forward and backward directions, enhancing the estimation performance. In addition, hierarchical feature extraction and warping improve the performance and reduce computational overhead. Experimental results show that the proposed architecture achieved a new state-of-the-art record by outperforming other approaches with large margin in both FlyingThings3D and KITTI benchmarks. Codes are available at https://github.com/cwc1260/BiFlow.Comment: Accepted as a conference paper at European Conference on Computer Vision (ECCV) 202

A novel fast and reduced redundancy structure for multiscale directional filter banks

2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Co-occurrence features of multi-scale directional filter bank for texture characterization

Author name used in this publication: N. F.LawAuthor name used in this publication: K. O. ChengAuthor name used in this publication: W. C. SiuRefereed conference paper2005-2006 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe