Study of the long-term evolution of the accretion dynamics of GX 339-4

We study the dynamical behaviour of the galactic black hole source GX 339-4 during 2002-2011 outbursts using RXTE, Swift(XRT), XMM-Newton(PN) archival data. We present the spectral evolution of the source using four outbursts data and discuss their similarities/differences between outbursts. We infer that the second peak in 2002/03 and 2004/05 outbursts can be due to a second instant of triggered instability in the accretion disc due to irradiation from the central X-ray source after peak-I. This propagates in viscous time scale and takes ~80-90 days after peak-I to produce peak-II. This unifies all four outbursts having a long rising time of ~90 days. The dynamical evolution of accretion parameters have been studied by modeling the individual observed spectrum with two-component accretion disc model where a Keplerian accretion disc produces the soft photons and the hard part of the spectrum originates from a hot sub-Keplerian central corona. A generic mathematical model has been proposed to understand the evolution of accretion parameters for sources like GX 339-4 which have longer rising time. Also, the possible differences of physical scenario for outbursts with shorter rising time are also discussed.Comment: Accepted for publication in mnra

Maxwell's Refrigerator: An Exactly Solvable Model

We describe a simple and solvable model of a device that -- like the "neat-fingered being" in Maxwell's famous thought experiment -- transfers energy from a cold system to a hot system by rectifying thermal fluctuations. In order to accomplish this task, our device requires a memory register to which it can write information: the increase in the Shannon entropy of the memory compensates the decrease in the thermodynamic entropy arising from the flow of heat against a thermal gradient. We construct the nonequilibrium phase diagram for this device, and find that it can alternatively act as an eraser of information. We discuss our model in the context of the second law of thermodynamics.Comment: 9 pages (Main Text + Supplemental Material), 3 figures, to appear in Physical Review Letter

Anti-chiral edge states in an exciton polariton strip

We present a scheme to obtain anti-chiral edge states in an exciton-polariton honeycomb lattice with strip geometry, where the modes corresponding to both edges propagate in the same direction. Under resonant pumping the effect of a polariton condensate with nonzero velocity in one linear polarization is predicted to tilt the dispersion of polaritons in the other, which results in an energy shift between two Dirac cones and the otherwise flat edge states become tilted. Our simulations show that due to the spatial separation from the bulk modes the edge modes are robust against disorder.Comment: 6 pages, 5 figure

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

An analysis of the Bayesian track labelling problem

In multi-target tracking (MTT), the problem of assigning labels to tracks (track labelling) is vastly covered in literature, but its exact mathematical formulation, in terms of Bayesian statistics, has not been yet looked at in detail. Doing so, however, may help us to understand how Bayes-optimal track labelling should be performed or numerically approximated. Moreover, it can help us to better understand and tackle some practical difficulties associated with the MTT problem, in particular the so-called ``mixed labelling'' phenomenon that has been observed in MTT algorithms. In this memorandum, we rigorously formulate the optimal track labelling problem using Finite Set Statistics (FISST), and look in detail at the mixed labeling phenomenon. As practical contributions of the memorandum, we derive a new track extraction formulation with some nice properties and a statistic associated with track labelling with clear physical meaning. Additionally, we show how to calculate this statistic for two well-known MTT algorithms