Yi-Nan Chen | Searcharxiv

arXiv2025

The energy structure function of fast radio bursts supports a stochastic origin model

The origin of fast radio bursts (FRBs) has remained a mystery up to now. There are two kinds of process invoking neutron stars as an origin of FRBs, namely inner-driven starquakes and outer-driven collisions with interstellar objects (ISOs). The former origin should exhibit an earthquake-like statistical behavior while the latter should show a stochastic process. In this paper, we introduce a new statistical method by making use of the energy structure function of active repeating FRBs and earthquakes. We find that the energy structure function of FRBs exhibits a very different statistical behavior compared to that of earthquakes. On small time-interval scales, the energy of an earthquake show a tendency to decay with time-interval and the energy difference of a pair of events increases with time-interval. Such a behavior is not found in FRBs, whose energy function is very similar to those of a stochastic process. Our result shows that repeating FRBs may have an origin process differing from that of earthquakes, i.e., FRBs arise from a series of unrelated events such as collisions of a neutron star with ISOs.

astro-ph.HE↗

arXiv2022

Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving

Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D detection by enhancing the capability of perceiving depth with depth estimation networks, and using LiDAR-based 3D detection architectures. The advanced stereo 3D detectors can also accurately localize 3D objects. The gap in image-to-image generation for stereo views is much smaller than that in image-to-LiDAR generation. Motivated by this, we propose a Pseudo-Stereo 3D detection framework with three novel virtual view generation methods, including image-level generation, feature-level generation, and feature-clone, for detecting 3D objects from a single image. Our analysis of depth-aware learning shows that the depth loss is effective in only feature-level virtual view generation and the estimated depth map is effective in both image-level and feature-level in our framework. We propose a disparity-wise dynamic convolution with dynamic kernels sampled from the disparity feature map to filter the features adaptively from a single image for generating virtual image features, which eases the feature degradation caused by the depth estimation errors. Till submission (November 18, 2021), our Pseudo-Stereo 3D detection framework ranks 1st on car, pedestrian, and cyclist among the monocular 3D detectors with publications on the KITTI-3D benchmark. The code is released at https://github.com/revisitq/Pseudo-Stereo-3D.

cs.CV↗