Charge asymmetry dependence of the elliptic flow splitting in relativistic heavy-ion collisions

The elliptic flow splitting $\Delta v_2$ between $\bar{u}$ and $u$ quarks as well as between $\pi^-$ and $\pi^+$ in midcentral Au+Au collisions at $\sqrt{s_{NN}}=200$ GeV has been studied, based on the framework of an extended multiphase transport model with the partonic evolution described by the chiral kinetic equations of motion. Within the available statistics, the slope of $\Delta v_2$ between $\bar{u}$ and $u$ quarks with respect to the electric charge asymmetry $A_{ch}$ from the linear fit is found to be negative, due to the correlation between the velocity and the coordinate in the initial parton phase-space distribution. Simulations with the magnetic field in QGP overestimate the splitting of the spin polarization between $\Lambda$ and $\bar{\Lambda}$ observed experimentally, with the latter more consistent with results under the magnetic field in vacuum. Considering the uncertainties from the magnetic field, the quark-antiquark vector interaction, and the hadronization, as well as the hadronic evolution, our study shows that the experimentally observed positive slope of $\Delta v_2$ with respect to $A_{ch}$ is not likely due to the chiral magnetic wave.Comment: 9 pages, 7 figures, a few typos corrected. arXiv admin note: text overlap with arXiv:1707.0726

Kinetic modeling of detonation and effects of negative temperature coefficient

The kinetic modeling and simulation of reactive flows, especially for those with detonation, are further investigated. From the theoretical side, a new set of hydrodynamic equations are deduced, where the viscous stress tensor and heat flux are replaced by two non-equilibrium quantities that have been defined in our previous work. The two non-equilibrium quantities are referred to as NonOrganized Momentum Flux (NOMF) and Non-Organized Energy Flux (NOEF), respectively, here. The numerical results of viscous stress (heat flux) have a good agreement with those of NOMF (NOEF) near equilibrium state. Around sharp interfaces, the values of NOMF (NOEF) deviate reasonably from those of viscous stress (heat flux). Based on this hydrodynamic model, the relations between the two non-equilibrium quantities and entropy productions are established. Based on the discrete Boltzmann model, four kinds of detonation phenomena with different reaction rates, including Negative Temperature Coefficient (NTC) regime, are simulated and investigated. The differences of the four kinds of detonations are studied from three aspects: hydrodynamic quantities, non-equilibrium quantities and entropy productions.Comment: 30 pages, 12 figure

Coupling of evanescent waves into propagation channels within two-dimensional random waveguides

The transformation from evanescent waves to propagation waves is the key mechanism for the realization of some super-resolution imaging methods. By using the recursive Green function and scattering-matrix theory, we investigated in details on the transport of evanescent waves through a random medium and analyzed quantitatively the coupling of evanescent channels to propagation channels. By numerical calculations, we found that the transmission for the incident evanescent channel is determined by both the eigenvalues of the scattering matrix and the coupling strength to the corresponding propagation channels in random medium, and the disorder strength of the random medium influences both of them.Comment: 15 pages, 7 figure

C3AE: Exploring the Limits of Compact Model for Age Estimation

Age estimation is a classic learning problem in computer vision. Many larger and deeper CNNs have been proposed with promising performance, such as AlexNet, VggNet, GoogLeNet and ResNet. However, these models are not practical for the embedded/mobile devices. Recently, MobileNets and ShuffleNets have been proposed to reduce the number of parameters, yielding lightweight models. However, their representation has been weakened because of the adoption of depth-wise separable convolution. In this work, we investigate the limits of compact model for small-scale image and propose an extremely Compact yet efficient Cascade Context-based Age Estimation model(C3AE). This model possesses only 1/9 and 1/2000 parameters compared with MobileNets/ShuffleNets and VggNet, while achieves competitive performance. In particular, we re-define age estimation problem by two-points representation, which is implemented by a cascade model. Moreover, to fully utilize the facial context information, multi-branch CNN network is proposed to aggregate multi-scale context. Experiments are carried out on three age estimation datasets. The state-of-the-art performance on compact model has been achieved with a relatively large margin.Comment: accepted by cvpr201

Fast Parallel SVM using Data Augmentation

As one of the most popular classifiers, linear SVMs still have challenges in dealing with very large-scale problems, even though linear or sub-linear algorithms have been developed recently on single machines. Parallel computing methods have been developed for learning large-scale SVMs. However, existing methods rely on solving local sub-optimization problems. In this paper, we develop a novel parallel algorithm for learning large-scale linear SVM. Our approach is based on a data augmentation equivalent formulation, which casts the problem of learning SVM as a Bayesian inference problem, for which we can develop very efficient parallel sampling methods. We provide empirical results for this parallel sampling SVM, and provide extensions for SVR, non-linear kernels, and provide a parallel implementation of the Crammer and Singer model. This approach is very promising in its own right, and further is a very useful technique to parallelize a broader family of general maximum-margin models

Vector Approximate Message Passing Algorithm for Structured Perturbed Sensing Matrix

In this paper, we consider a general form of noisy compressive sensing (CS) where the sensing matrix is not precisely known. Such cases exist when there are imperfections or unknown calibration parameters during the measurement process. Particularly, the sensing matrix may have some structure, which makes the perturbation follow a fixed pattern. While previous work has focused on extending the approximate message passing (AMP) and LASSO algorithm to deal with the independent and identically distributed (i.i.d.) perturbation, we propose the robust variant vector approximate message passing (VAMP) algorithm with the perturbation being structured, based on the recent VAMP algorithm. The performance of the robust version of VAMP is demonstrated numerically.Comment: 6 pages, 3 figure

Triple Generative Adversarial Nets

Generative Adversarial Nets (GANs) have shown promise in image generation and semi-supervised learning (SSL). However, existing GANs in SSL have two problems: (1) the generator and the discriminator (i.e. the classifier) may not be optimal at the same time; and (2) the generator cannot control the semantics of the generated samples. The problems essentially arise from the two-player formulation, where a single discriminator shares incompatible roles of identifying fake samples and predicting labels and it only estimates the data without considering the labels. To address the problems, we present triple generative adversarial net (Triple-GAN), which consists of three players---a generator, a discriminator and a classifier. The generator and the classifier characterize the conditional distributions between images and labels, and the discriminator solely focuses on identifying fake image-label pairs. We design compatible utilities to ensure that the distributions characterized by the classifier and the generator both converge to the data distribution. Our results on various datasets demonstrate that Triple-GAN as a unified model can simultaneously (1) achieve the state-of-the-art classification results among deep generative models, and (2) disentangle the classes and styles of the input and transfer smoothly in the data space via interpolation in the latent space class-conditionally

Regularity of powers of edge ideals of vertex-weighted oriented unicyclic graphs

In this paper we provide some exact formulas for the regularity of powers of edge ideals of vertex-weighted oriented cycles and vertex-weighted unicyclic graphs. These formulas are functions of the weight of vertices and the number of edges. We also give some examples to show that these formulas are related to direction selection and the weight of vertices

Projective dimension and regularity of edge ideals of some vertex-weighted oriented mm-partite graphs

In this paper we provide some exact formulas for the projective dimension and the regularity of edge ideals associated to three special types of vertex-weighted oriented $m$-partite graphs. These formulas are functions of the weight and number of vertices. We also give some examples to show that these formulas are related to direction selection and the weight of vertices.Comment: arXiv admin note: substantial text overlap with arXiv:1904.03019, arXiv:1904.02305, arXiv:1802.0631

Light transport behaviours in quasi-1D disordered waveguides composed of random photonic lattices

We present a numerical study on the light transport properties which are modulated by the disorder strength in quasi-one-dimensional disordered waveguide which consists of periodically arranged scatterers with random dielectric constant. The transport mean free path is found to stay inversely proportional to the square of the relative fluctuation of the dielectric constant as in the 1D and 2D cases but with . The transport properties of light through a sample with a fixed size can be modulated from ballistic to localized regime as well, and a generalized scaling function is defined to determine the light transport status in such a sample. The calculation of the diffusion coefficient and the energy density profile of the most transmitted eigenchannel clearly exhibits the transition of transport behaviour from diffusion to localization.Comment: 13 pages, 6 figure