Innermost stable circular orbit of spinning particle in charged spinning black hole background

In this paper we investigate the innermost stable circular orbit (ISCO) for a classical spinning test particle in the background of Kerr-Newman black hole. It is shown that the orbit of the spinning particle is related to the spin of the test particle. The motion of the spinning test particle will be superluminal if its spin is too large. We give an additional condition by considering the superluminal constraint for the ISCO in the black hole backgrounds. We obtain numerically the relations between the ISCO and the properties of the black holes and the test particle. It is found that the radius of the ISCO for a spinning test particle is smaller than that of a non-spinning test particle in the black hole backgrounds.Comment: 9 pages, 9 figure

Gauge Independence of Magnetic Moment and Vanishing Charge of Dirac Neutrinos: an Exact One-loop Demonstration

The magnetic moment and vanishing charge of a Dirac neutrino are physically observable quantities and must not depend on the choice of gauge in a consistent quantum field theory. We verify this statement explicitly at the one loop level in both R_xi and unitary gauges of the minimally extended standard model. We accomplish this by manipulating directly the integrands of loop integrals and employing simple algebraic identities and integral relations. Our result generally applies for any masses of the relevant particles and unitary neutrino mixing.Comment: 14 pages, 2 figure

Thick brane in mimetic f(T)f(T) gravity

We apply the mimetic $f(T)$ theory into the thick brane model. We take the Lagrange multiplier formulation of the action and get the corresponding field equations of motion. We find solutions for different kinds of $f(T)$. Besides, we investigate the stability of the mimetic $f(T)$ brane by considering the tensor perturbations of the vielbein. Localization problem is also studied and it is shown that the four-dimensional gravity can be recovered for all the solutions. The effects of the torsion show that for the polynomial form of $f(T)$, the zero mode has a split compared with that of $f(T)=T$, but the situations for the exponential form of $f(T)$ are similar to that of $f(T)=T$.Comment: 7 pages, 6 figure

A multiple-relaxation-time lattice Boltzmann model for convection heat transfer in porous media

In this paper, a two-dimensional (2D) multiple-relaxation-time (MRT) lattice Boltzmann (LB) model is developed for simulating convection heat transfer in porous media at the representative elementary volume scale. In the model, a MRT-LB equation is used to simulate the flow field, while another MRT-LB equation is employed to simulate the temperature field. The effect of the porous media is considered by introducing the porosity into the equilibrium moments, and adding a forcing term to the MRT-LB equation of the flow field in the moment space. The present MRT-LB model is validated by numerical simulations of several 2D convection problems in porous media. The numerical results are in good agreement with the well-documented data reported in the literature.Comment: 37 pages, 7 figure

Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples

Image compression-based approaches for defending against the adversarial-example attacks, which threaten the safety use of deep neural networks (DNN), have been investigated recently. However, prior works mainly rely on directly tuning parameters like compression rate, to blindly reduce image features, thereby lacking guarantee on both defense efficiency (i.e. accuracy of polluted images) and classification accuracy of benign images, after applying defense methods. To overcome these limitations, we propose a JPEG-based defensive compression framework, namely "feature distillation", to effectively rectify adversarial examples without impacting classification accuracy on benign data. Our framework significantly escalates the defense efficiency with marginal accuracy reduction using a two-step method: First, we maximize malicious features filtering of adversarial input perturbations by developing defensive quantization in frequency domain of JPEG compression or decompression, guided by a semi-analytical method; Second, we suppress the distortions of benign features to restore classification accuracy through a DNN-oriented quantization refine process. Our experimental results show that proposed "feature distillation" can significantly surpass the latest input-transformation based mitigations such as Quilting and TV Minimization in three aspects, including defense efficiency (improve classification accuracy from $\sim20\%$ to $\sim90\%$ on adversarial examples), accuracy of benign images after defense ($\le1\%$ accuracy degradation), and processing time per image ($\sim259\times$ Speedup). Moreover, our solution can also provide the best defense efficiency ($\sim60\%$ accuracy) against the recent adaptive attack with least accuracy reduction ($\sim1\%$) on benign images when compared with other input-transformation based defense methods.Comment: 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019

Orthogonal Deep Neural Networks

In this paper, we introduce the algorithms of Orthogonal Deep Neural Networks (OrthDNNs) to connect with recent interest of spectrally regularized deep learning methods. OrthDNNs are theoretically motivated by generalization analysis of modern DNNs, with the aim to find solution properties of network weights that guarantee better generalization. To this end, we first prove that DNNs are of local isometry on data distributions of practical interest; by using a new covering of the sample space and introducing the local isometry property of DNNs into generalization analysis, we establish a new generalization error bound that is both scale- and range-sensitive to singular value spectrum of each of networks' weight matrices. We prove that the optimal bound w.r.t. the degree of isometry is attained when each weight matrix has a spectrum of equal singular values, among which orthogonal weight matrix or a non-square one with orthonormal rows or columns is the most straightforward choice, suggesting the algorithms of OrthDNNs. We present both algorithms of strict and approximate OrthDNNs, and for the later ones we propose a simple yet effective algorithm called Singular Value Bounding (SVB), which performs as well as strict OrthDNNs, but at a much lower computational cost. We also propose Bounded Batch Normalization (BBN) to make compatible use of batch normalization with OrthDNNs. We conduct extensive comparative studies by using modern architectures on benchmark image classification. Experiments show the efficacy of OrthDNNs.Comment: To Appear in IEEE Transactions on Pattern Analysis and Machine Intelligenc

Probing cosmic anisotropy with gravitational waves as standard sirens

The gravitational wave (GW) as a standard siren directly determines the luminosity distance from the gravitational waveform without reference to the specific cosmological model, of which the redshift can be obtained separately by means of the electromagnetic counterpart like GW events from binary neutron stars and massive black hole binaries (MBHBs). To see to what extent the standard siren can reproduce the presumed dipole anisotropy written in the simulated data of standard siren events from typical configurations of GW detectors, we find that (1) for the Laser Interferometer Space Antenna with different MBHB models during five-year observations, the cosmic isotropy can be ruled out at $3\sigma$ confidence level (C.L.) and the dipole direction can be constrained roughly around $20\%$ at $2\sigma$ C.L., as long as the dipole amplitude is larger than $0.03$, $0.06$ and $0.025$ for MBHB models Q3d, pop III and Q3nod with increasing constraining ability, respectively; (2) for Einstein Telescope with no less than $200$ standard siren events, the cosmic isotropy can be ruled out at $3\sigma$ C.L. if the dipole amplitude is larger than $0.06$, and the dipole direction can be constrained within $20\%$ at $3\sigma$ C.L. if the dipole amplitude is near $0.1$; (3) for the Deci-Hertz Interferometer Gravitational wave Observatory with no less than $100$ standard siren events, the cosmic isotropy can be ruled out at $3\sigma$ C.L. for dipole amplitude larger than $0.03$ , and the dipole direction can even be constrained within $10\%$ at $3\sigma$ C.L. if the dipole amplitude is larger than $0.07$. Our work manifests the promising perspective of the constraint ability on the cosmic anisotropy from the standard siren approach.Comment: v1, 10 pages, 4 figures, two columns; v2, 10 pages, 4 figures, Phys.Rev.D accepted, to match the published version, added discussion on the effect of detectors' rotations for LIS

Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification

In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e.g. color, texture and shape). Currently available tools ignore either the label relationship or the view complementary. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multi-label structure in the output space, we introduce multi-view vector-valued manifold regularization (MV$\mathbf{^3}$MR) to integrate multiple features. MV$\mathbf{^3}$MR exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conducted extensive experiments on two challenging, but popular datasets, PASCAL VOC' 07 (VOC) and MIR Flickr (MIR), and validated the effectiveness of the proposed MV$\mathbf{^3}$MR for image classification

An End-to-End Compression Framework Based on Convolutional Neural Networks

Deep learning, e.g., convolutional neural networks (CNNs), has achieved great success in image processing and computer vision especially in high level vision applications such as recognition and understanding. However, it is rarely used to solve low-level vision problems such as image compression studied in this paper. Here, we move forward a step and propose a novel compression framework based on CNNs. To achieve high-quality image compression at low bit rates, two CNNs are seamlessly integrated into an end-to-end compression framework. The first CNN, named compact convolutional neural network (ComCNN), learns an optimal compact representation from an input image, which preserves the structural information and is then encoded using an image codec (e.g., JPEG, JPEG2000 or BPG). The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end. To make two CNNs effectively collaborate, we develop a unified end-to-end learning algorithm to simultaneously learn ComCNN and RecCNN, which facilitates the accurate reconstruction of the decoded image using RecCNN. Such a design also makes the proposed compression framework compatible with existing image coding standards. Experimental results validate that the proposed compression framework greatly outperforms several compression frameworks that use existing image coding standards with state-of-the-art deblocking or denoising post-processing methods.Comment: Submitted to IEEE Transactions on Circuits and Systems for Video Technolog

Evidence of Spontaneous Vortex Ground State in An Iron-Based Ferromagnetic Superconductor

Spontaneous vortex phase (SVP) is an exotic quantum matter in which quantized superconducting vortices form in the absence of external magnetic field. Although being predicted theoretically nearly 40 years ago, its rigorous experimental verification still appears to be lacking. Here we present low-field magnetic measurements on single crystals of the iron-based ferromagnetic superconductor Eu(Fe$_{0.91}$Rh$_{0.09}$)$_{2}$As$_{2}$ which undergoes a superconducting transition at $T_\mathrm{sc}$ = 19.6 K followed by a magnetic transition at $T_\mathrm{m}$ = 16.8 K. We observe a characteristic first-order transition from a Meissner state within $T_\mathrm{m}<T<T_\mathrm{sc}$ to an SVP below $T_\mathrm{m}$, under a magnetic field approaching zero. Additional isothermal magnetization and ac magnetization measurements at $T\ll T_\mathrm{sc}$ confirm that the system is intrinsically in a spontaneous-vortex ground state. The unambiguous demonstration of SVP in the title material lays a solid foundation for future imaging and spectroscopic studies on this intriguing quantum matter.Comment: 7 pages 5 figure