13,982 research outputs found
Traffic congestion in interconnected complex networks
Traffic congestion in isolated complex networks has been investigated
extensively over the last decade. Coupled network models have recently been
developed to facilitate further understanding of real complex systems. Analysis
of traffic congestion in coupled complex networks, however, is still relatively
unexplored. In this paper, we try to explore the effect of interconnections on
traffic congestion in interconnected BA scale-free networks. We find that
assortative coupling can alleviate traffic congestion more readily than
disassortative and random coupling when the node processing capacity is
allocated based on node usage probability. Furthermore, the optimal coupling
probability can be found for assortative coupling. However, three types of
coupling preferences achieve similar traffic performance if all nodes share the
same processing capacity. We analyze interconnected Internet AS-level graphs of
South Korea and Japan and obtain similar results. Some practical suggestions
are presented to optimize such real-world interconnected networks accordingly.Comment: 8 page
Variational Autoencoders for Deforming 3D Mesh Models
3D geometric contents are becoming increasingly popular. In this paper, we
study the problem of analyzing deforming 3D meshes using deep neural networks.
Deforming 3D meshes are flexible to represent 3D animation sequences as well as
collections of objects of the same category, allowing diverse shapes with
large-scale non-linear deformations. We propose a novel framework which we call
mesh variational autoencoders (mesh VAE), to explore the probabilistic latent
space of 3D surfaces. The framework is easy to train, and requires very few
training examples. We also propose an extended model which allows flexibly
adjusting the significance of different latent variables by altering the prior
distribution. Extensive experiments demonstrate that our general framework is
able to learn a reasonable representation for a collection of deformable
shapes, and produce competitive results for a variety of applications,
including shape generation, shape interpolation, shape space embedding and
shape exploration, outperforming state-of-the-art methods.Comment: CVPR 201
Mesh-based Autoencoders for Localized Deformation Component Analysis
Spatially localized deformation components are very useful for shape analysis
and synthesis in 3D geometry processing. Several methods have recently been
developed, with an aim to extract intuitive and interpretable deformation
components. However, these techniques suffer from fundamental limitations
especially for meshes with noise or large-scale deformations, and may not
always be able to identify important deformation components. In this paper we
propose a novel mesh-based autoencoder architecture that is able to cope with
meshes with irregular topology. We introduce sparse regularization in this
framework, which along with convolutional operations, helps localize
deformations. Our framework is capable of extracting localized deformation
components from mesh data sets with large-scale deformations and is robust to
noise. It also provides a nonlinear approach to reconstruction of meshes using
the extracted basis, which is more effective than the current linear
combination approach. Extensive experiments show that our method outperforms
state-of-the-art methods in both qualitative and quantitative evaluations
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