182,490 research outputs found
Nonlinear ring waves in a two-layer fluid
Surface and interfacial weakly-nonlinear ring waves in a two-layer fluid are
modelled numerically, within the framework of the recently derived
2+1-dimensional cKdV-type equation. In a case study, we consider concentric
waves from a localised initial condition and waves in a 2D version of the
dam-break problem, as well as discussing the effect of a piecewise-constant
shear flow. The modelling shows, in particular, the formation of 2D dispersive
shock waves (DSWs) and oscillatory wave trains. The surface and interfacial
DSWs generated in our numerical experiments look distinctively different.Comment: 16 pages, 21 figure
A Comment on "Memory Effects in an Interacting Magnetic Nanoparticle System"
Recently, Sun et al reported that striking memory effects had been clearly
observed in their new experiments on an interacting nanoparticle system [1].
They claimed that the phenomena evidenced the existence of a spin-glass-like
phase and supported the hierarchical model. No doubt that a particle system may
display spin-glass-like behaviors [2]. However, in our opinion, the experiments
in Ref. [1] cannot evidence the existence of spin-glass-like phase at all. We
will demonstrate below that all the phenomena in Ref. [1] can be observed in a
non-interacting particle system with a size distribution. Numerical simulations
of our experiments also display the same features.Comment: A comment on "Phys. Rev. Lett. 91, 167206
Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge
This paper describes our submission to the 1st 3D Face Alignment in the Wild
(3DFAW) Challenge. Our method builds upon the idea of convolutional part
heatmap regression [1], extending it for 3D face alignment. Our method
decomposes the problem into two parts: (a) X,Y (2D) estimation and (b) Z
(depth) estimation. At the first stage, our method estimates the X,Y
coordinates of the facial landmarks by producing a set of 2D heatmaps, one for
each landmark, using convolutional part heatmap regression. Then, these
heatmaps, alongside the input RGB image, are used as input to a very deep
subnetwork trained via residual learning for regressing the Z coordinate. Our
method ranked 1st in the 3DFAW Challenge, surpassing the second best result by
more than 22%.Comment: Winner of 3D Face Alignment in the Wild (3DFAW) Challenge, ECCV 201
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