Dirichlet-Neumann and Neumann-Neumann Waveform Relaxation for the Wave Equation

We present a Waveform Relaxation (WR) version of the Dirichlet-Neumann and Neumann-Neumann algorithms for the wave equation in space time. Each method is based on a non-overlapping spatial domain decomposition, and the iteration involves subdomain solves in space time with corresponding interface condition, followed by a correction step. Using a Laplace transform argument, for a particular relaxation parameter, we prove convergence of both algorithms in a finite number of steps for finite time intervals. The number of steps depends on the size of the subdomains and the time window length on which the algorithms are employed. We illustrate the performance of the algorithms with numerical results, and also show a comparison with classical and optimized Schwarz WR methods.Comment: 8 pages, 6 figures, presented in 22nd International conference on Domain Decomposition Methods, to appear in Domain Decomposition in Science and Engineering XXII, LNCSE, Springer-Verlag 201

Modified HLLC-VOF solver for incompressible two-phase fluid flows

A modified HLLC-type contact preserving Riemann solver for incompressible two-phase flows using the artificial compressibility formulation is presented. Here, the density is omitted from the pressure evolution equation. Also, while calculating the eigenvalues and eigenvectors, the variations of the volume fraction is taken into account. Hence, the equations for the intermediate states and the intermediate wave speed are different from the previous HLLC-VOF formulation [Bhat S P and Mandal J C, J. Comput. Phys. 379 (2019), pp. 173-191]. Additionally, an interface compression algorithm is used in tandem to ensure sharp interfaces. The modified Riemann solver is found to be robust compared to the previous HLLC-VOF solver, and the results produced are superior compared to non-contact preserving solver. Several test problems in two- and three-dimensions are solved to evaluate the efficacy of the solver on structured and unstructured meshes

Distinguishing Posed and Spontaneous Smiles by Facial Dynamics

Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either `spontaneous' or `posed' categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial Behavior Analysi

Chiral Properties of QCD Vacuum in Magnetars- A Nambu-Jona-Lasinio Model with Semi-Classical Approximation

The breaking of chiral symmetry of light quarks at zero temperature in presence of strong quantizing magnetic fiels is studied using Nambu-Jona-Lasinio (NJL) model with Thomas-Fermi type semi-classical formalism. It is found that the dynamically generated light quark mass can never become zero if the Landau levels are populated and the mass increases with the increase of magnetic field strength.Comment: REVTEX 11 Pages, One .eps figure (included