15,412 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
Wormholes and the Thermodynamic Arrow of Time
In classical thermodynamics, heat cannot spontaneously pass from a colder
system to a hotter system, which is called the thermodynamic arrow of time.
However, if the initial states are entangled, the direction of the
thermodynamic arrow of time may not be guaranteed. Here we take the thermofield
double state at dimension as the initial state and assume its gravity
duality to be the eternal black hole in AdS space. We make the temperature
difference between the two sides by changing the Hamiltonian. We turn on proper
interaction between the two sides and calculate the changes in energy and
entropy. The energy transfer, as well as the thermodynamic arrow of time, are
mainly determined by the competition between two channels: thermal diffusion
and anomalous heat flow. The former is not related to the wormhole and obeys
the thermodynamic arrow of time; the latter is related to the wormhole and
reverses the thermodynamic arrow of time, i.e. transfer energy from the colder
side to the hotter side at the cost of entanglement consumption. Finally, we
find that the thermal diffusion wins the competition, and the whole
thermodynamic arrow of time has not been reversed.Comment: 37 pages, 21 figures; v2: minor corrections and updated figure
Testing the number of common factors by bootstrapped sample covariance matrix in high-dimensional factor models
This paper studies the impact of bootstrap procedure on the eigenvalue
distributions of the sample covariance matrix under the high-dimensional factor
structure. We provide asymptotic distributions for the top eigenvalues of
bootstrapped sample covariance matrix under mild conditions. After bootstrap,
the spiked eigenvalues which are driven by common factors will converge weakly
to Gaussian limits via proper scaling and centralization. However, the largest
non-spiked eigenvalue is mainly determined by order statistics of bootstrap
resampling weights, and follows extreme value distribution. Based on the
disparate behavior of the spiked and non-spiked eigenvalues, we propose
innovative methods to test the number of common factors. According to the
simulations and a real data example, the proposed methods are the only ones
performing reliably and convincingly under the existence of both weak factors
and cross-sectionally correlated errors. Our technical details contribute to
random matrix theory on spiked covariance model with convexly decaying density
and unbounded support, or with general elliptical distributions.Comment: 95 pages, 9 figures, 4 table
GG: A domain involved in phage LTF apparatus and implicated in human MEB and non-syndromic hearing loss diseases
AbstractHere, we report the identification of a novel domain – GG (domain in KIAA1199, FAM3, POMGnT1 and Tmem2 proteins, with two well-conserved glycine residues), present in eukaryotic FAM3 superfamily (FAM3A, FAM3B, FAM3C and FAM3D), POMGnT1 (protein O-linked mannose β-1,2-N-acetylglucosaminyltransferase), TEM2 proteins as well as phage gp35 proteins. GG domain has been revealed to be implicated in muscle–eye–brain disease and non-syndromic hearing loss. The presence of GG domain in Bacteriophage gp35 hinge connector of long tail fiber might reflect the horizontal gene transfer from organisms. And we proposed that GG domain might function as important structural element in phage LTF
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