Computational Discovery of A New Rhombohedral Diamond Phase

We identify by first-principles calculations a new diamond phase in R¯3c (D63d) symmetry, which has a 16-atom rhombohedral primitive cell, thus termed R16 carbon. This rhombohedral diamond comprises a characteristic all-sp3 six-membered-ring bonding network, and it is energetically more stable than previously identified diamondlike six-membered-ring bonded BC8 and BC12 carbon phases. A phonon mode analysis verifies the dynamic structural stability of R16 carbon, and electronic band calculations reveal that it is an insulator with a direct band gap of 4.45 eV. Simulated x-ray diffraction patterns provide an excellent match to recently reported distinct diffraction peaks found in milled fullerene soot, suggesting a viable experimental synthesis route. These findings pave the way for further exploration of this new diamond phase and its outstanding properties

Chaos for endomorphisms of completely metrizable groups and linear operators on Fr\'echet spaces

Using the techniques in topological dynamics, we give a uniform treatment of Li-Yorke chaos, mean Li-Yorke chaos and distributional chaos for continuous endomorphisms of completely metrizable groups, and characterize three kinds of chaos (resp. extreme chaos) in term of the existence of corresponding semi-irregular points (resp. irregular points). We also apply the results to the chaos theory of continuous linear operators on Fr\'echet spaces, which improve some results in the literature.Comment: 50 page

Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

Online Multi-Object Tracking (MOT) from videos is a challenging computer vision task which has been extensively studied for decades. Most of the existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm combined with popular machine learning approaches which largely reduce the human effort to tune algorithm parameters. However, the commonly used supervised learning approaches require the labeled data (e.g., bounding boxes), which is expensive for videos. Also, the TBD framework is usually suboptimal since it is not end-to-end, i.e., it considers the task as detection and tracking, but not jointly. To achieve both label-free and end-to-end learning of MOT, we propose a Tracking-by-Animation framework, where a differentiable neural model first tracks objects from input frames and then animates these objects into reconstructed frames. Learning is then driven by the reconstruction error through backpropagation. We further propose a Reprioritized Attentive Tracking to improve the robustness of data association. Experiments conducted on both synthetic and real video datasets show the potential of the proposed model. Our project page is publicly available at: https://github.com/zhen-he/tracking-by-animationComment: CVPR 201