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