Membrane paradigm of black holes in Chern-Simons modified gravity

The membrane paradigm of black hole is studied in the Chern-Simons modified gravity. Derived with the action principle a la Parikh-Wilczek, the stress tensor of membrane manifests a rich structure arising from the Chern-Simons term. The membrane stress tensor, if related to the bulk stress tensor in a special form, obeys the low-dimensional fluid continuity equation and the Navier-Stokes equation. This paradigm is applied to spherically symmetric static geometries, and in particular, the Schwarzschild black hole, which is a solution of a large class of dynamical Chern-Simons gravity.Comment: 16 pages, author information update

Smoothing methods comparison for CMB E- and B-mode separation

The anisotropies of the B-mode polarization in the cosmic microwave background radiation play a crucial role for the study of the very early Universe. However, in the real observation, the mixture of the E-mode and B-mode can be caused by the partial sky surveys, which must be separated before applied to the cosmological explanation. The separation method developed by Smith (\citealt{PhysRevD.74.083002}) has been widely adopted, where the edge of the top-hat mask should be smoothed to avoid the numerical errors. In this paper, we compare three different smoothing methods, and investigate the leakage residuals of the E-B mixture. We find that, if the less information loss is needed and the smaller region is smoothed in the analysis, the \textit{sin}- and \textit{cos}-smoothing methods are better. However, if we need a clean constructed B-mode map, the larger region around the mask edge should be smoothed. In this case, the \textit{Gaussian}-smoothing method becomes much better. In addition, we find that the leakage caused by the numerical errors in the \textit{Gaussian}-smoothing method mostly concentrates on two bands, which is quite easy to be reduced for the further E-B separations.Comment: 14 pages, 7 figures, RAA accepte

Effective Blog Pages Extractor for Better UGC Accessing

Blog is becoming an increasingly popular media for information publishing. Besides the main content, most of blog pages nowadays also contain noisy information such as advertisements etc. Removing these unrelated elements can improves user experience, but also can better adapt the content to various devices such as mobile phones. Though template-based extractors are highly accurate, they may incur expensive cost in that a large number of template need to be developed and they will fail once the template is updated. To address these issues, we present a novel template-independent content extractor for blog pages. First, we convert a blog page into a DOM-Tree, where all elements including the title and body blocks in a page correspond to subtrees. Then we construct subtree candidate set for the title and the body blocks respectively, and extract both spatial and content features for elements contained in the subtree. SVM classifiers for the title and the body blocks are trained using these features. Finally, the classifiers are used to extract the main content from blog pages. We test our extractor on 2,250 blog pages crawled from nine blog sites with obviously different styles and templates. Experimental results verify the effectiveness of our extractor.Comment: 2016 3rd International Conference on Information Science and Control Engineering (ICISCE

Constructing Calabi-Yau Metrics From Hyperkahler Spaces

Recently, a metric construction for the Calabi-Yau 3-folds from a four-dimensional hyperkahler space by adding a complex line bundle was proposed. We extend the construction by adding a U(1) factor to the holomorphic (3,0)-form, and obtain the explicit formalism for a generic hyperkahler base. We find that a discrete choice arises: the U(1) factor can either depend solely on the fibre coordinates or vanish. In each case, the metric is determined by one differential equation for the modified Kahler potential. As explicit examples, we obtain the generalized resolutions (up to orbifold singularity) of the cone of the Einstein-Sasaki spaces Y^{p,q}. We also obtain a large class of new singular CY3 metrics with SU(2)\times U(1) or SU(2)\times U(1)^2 isometries.Comment: 29 pages, no figures, version appeared in Classical and Quantum Gravit

Maximum mutual information regularized classification

In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reduc- ing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradi- ent descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization

Patch-based Contour Prior Image Denoising for Salt and Pepper Noise

The salt and pepper noise brings a significant challenge to image denoising technology, i.e. how to removal the noise clearly and retain the details effectively? In this paper, we propose a patch-based contour prior denoising approach for salt and pepper noise. First, noisy image is cut into patches as basic representation unit, a discrete total variation model is designed to extract contour structures; Second, a weighted Euclidean distance is designed to search the most similar patches, then, corresponding contour stencils are extracted from these similar patches; At the last, we build filter from contour stencils in the framework of regression. Numerical results illustrate that the proposed method is competitive with the state-of-the-art methods in terms of the peak signal-to-noise (PSNR) and visual effects

Normal heat conduction in lattice models with asymmetry harmonic interparticle interactions

We study the thermal conduction behaviors of one-dimensional lattice models with asymmetry harmonic interparticle interactions in this paper. Normal thermal conductivity independent of the system size is observed when the lattice chains are long enough. Because only the harmonic interactions are involved, the result confirms without ambiguous interpretation that the asymmetry plays key role in resulting in the normal thermal conduction in one-dimensional momentum conserving lattices. Both equilibrium and nonequilibrium simulations are performed to support the conclusion.Comment: 4 pages,3 figure

Deep Learning-Based Gait Recognition Using Smartphones in the Wild

Compared to other biometrics, gait is difficult to conceal and has the advantage of being unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to capture gait dynamics. These inertial sensors are commonly integrated into smartphones and are widely used by the average person, which makes gait data convenient and inexpensive to collect. In this paper, we study gait recognition using smartphones in the wild. In contrast to traditional methods, which often require a person to walk along a specified road and/or at a normal walking speed, the proposed method collects inertial gait data under unconstrained conditions without knowing when, where, and how the user walks. To obtain good person identification and authentication performance, deep-learning techniques are presented to learn and model the gait biometrics based on walking data. Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space and time domains are successively abstracted by a convolutional neural network and a recurrent neural network. In the experiments, two datasets collected by smartphones for a total of 118 subjects are used for evaluations. The experiments show that the proposed method achieves higher than 93.5\% and 93.7\% accuracies in person identification and authentication, respectively.Comment: IEEE Transactions on Information Forensics and Security, 15(1), 202

Unsupervised Learning Layers for Video Analysis

This paper presents two unsupervised learning layers (UL layers) for label-free video analysis: one for fully connected layers, and the other for convolutional ones. The proposed UL layers can play two roles: they can be the cost function layer for providing global training signal; meanwhile they can be added to any regular neural network layers for providing local training signals and combined with the training signals backpropagated from upper layers for extracting both slow and fast changing features at layers of different depths. Therefore, the UL layers can be used in either pure unsupervised or semi-supervised settings. Both a closed-form solution and an online learning algorithm for two UL layers are provided. Experiments with unlabeled synthetic and real-world videos demonstrated that the neural networks equipped with UL layers and trained with the proposed online learning algorithm can extract shape and motion information from video sequences of moving objects. The experiments demonstrated the potential applications of UL layers and online learning algorithm to head orientation estimation and moving object localization

Multi transitions in MgB2 films prepared by pulsed laser deposition

We have grown MgB2 films in a postannealing process. Precursors were prepared on Al2O3(0001) substrates by codeposition of Mg and MgB2 using pulsed laser deposition technique. Superconducting MgB2 thin films were obtained via an ex situ postannealing process, with various annealing temperatures and durations. In magnetic measurement we found more than one transitions in almost all the samples, and the temperature dependence of the resistance confirmed this phenomena. We proved this is not result of magnesium deficiency. Transportation properties of MgB2 thin films under strong magnetic fields are also studied.Comment: 17 pages, 2 tables, 7 figure