9,026 research outputs found
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- 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
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