Interaction effects on 1D fermionic symmetry protected topological phases

In free fermion systems with given symmetry and dimension, the possible topological phases are labeled by elements of only three types of Abelian groups, Z_1, Z_2, or Z. For example non-interacting 1D fermionic superconducting phases with S_z spin rotation and time-reversal symmetries are classified by Z. We show that with weak interactions, this classification reduces to Z_4. Using group cohomology, one can additionally show that there are only four distinct phases for such 1D superconductors even with strong interactions. Comparing their projective representations, we find all these four symmetry protected topological phases can be realized with free fermions. Further, we show that 1D fermionic superconducting phases with Z_n discrete S_z spin rotation and time-reversal symmetries are classified by Z_4 when n=even and Z_2 when n=odd; again, all these strongly interacting topological phases can be realized by non-interacting fermions. Our approach can be applied to systems with other symmetries to see which 1D topological phases can be realized by free fermions

Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking

Multi-person human pose estimation and tracking in the wild is important and challenging. For training a powerful model, large-scale training data are crucial. While there are several datasets for human pose estimation, the best practice for training on multi-dataset has not been investigated. In this paper, we present a simple network called Multi-Domain Pose Network (MDPN) to address this problem. By treating the task as multi-domain learning, our methods can learn a better representation for pose prediction. Together with prediction heads fine-tuning and multi-branch combination, it shows significant improvement over baselines and achieves the best performance on PoseTrack ECCV 2018 Challenge without additional datasets other than MPII and COCO.Comment: Extended abstract for the ECCV 2018 PoseTrack Worksho