Semantic Graph Convolutional Networks for 3D Human Pose Regression

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data. SemGCN learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph. These semantic relationships can be learned through end-to-end training from the ground truth without additional supervision or hand-crafted rules. We further investigate applying SemGCN to 3D human pose regression. Our formulation is intuitive and sufficient since both 2D and 3D human poses can be represented as a structured graph encoding the relationships between joints in the skeleton of a human body. We carry out comprehensive studies to validate our method. The results prove that SemGCN outperforms state of the art while using 90% fewer parameters.Comment: In CVPR 2019 (13 pages including supplementary material). The code can be found at https://github.com/garyzhao/SemGC

Spin current through an ESR quantum dot: A real-time study

The spin transport in a strongly interacting spin-pump nano-device is studied using the time-dependent variational-matrix-product-state (VMPS) approach. The precession magnetic field generates a dissipationless spin current through the quantum dot. We compute the real time spin current away from the equilibrium condition. Both transient and stationary states are reached in the simulation. The essentially exact results are compared with those from the Hartree-Fock approximation (HFA). It is found that correlation effect on the physical quantities at quasi-steady state are captured well by the HFA for small interaction strength. However the HFA misses many features in the real time dynamics. Results reported here may shed light on the understanding of the ultra-fast processes as well as the interplay of the non-equilibrium and strongly correlated effect in the transport properties.Comment: 5 pages, 5 figure

Constraints on a new alternative model to dark energy

The recent type Ia supernova data suggest that the universe is accelerating now and decelerated in recent past. This may provide the evidence that the standard Friedmann equation needs to be modified. We analyze in detail a new model in the context of modified Friedmann equation using the supernova data published by the High-$z$ Supernova Search Team and the Supernova Cosmology Project. The new model explains recent acceleration and past deceleration. Furthermore, the new model also gives a decelerated universe in the future.Comment: 12 pages, 5 figures, use ws-ijmpd, minor changes made. In the new version, a detailed derivation of the model is give

Object Picture of Quasinormal Modes for Stringy Black Holes

We study the quasinormal modes (QNMs) for stringy black holes. By using numerical calculation, the relations between the QNMs and the parameters of black holes are minutely shown. For (1+1)-dimensional stringy black hole, the real part of the quasinormal frequency increases and the imaginary part of the quasinormal frequency decreases as the mass of the black hole increases. Furthermore, the dependence of the QNMs on the charge of the black hole and the flatness parameter is also illustrated. For (1+3)-dimensional stringy black hole, increasing either the event horizon or the multipole index, the real part of the quasinormal frequency decreases. The imaginary part of the quasinormal frequency increases no matter whether the event horizon is increased or the multipole index is decreased.Comment: 4 pages, 5 figure