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We derived an exact solution of the spherically symmetric Hayward black hole surrounded by perfect fluid dark matter (PFDM). By applying the Newman-Janis algorithm, we generalized it to the corresponding rotating black hole. Then, we studied the shadows of rotating Hayward black hole in PFDM. The apparent shape of the shadow depends upon the black hole spin $a$, the magnetic charge $Q$ and the PFDM intensity parameter $k$ ($k<0$). The shadow is a perfect circle in the non-rotating case ($a=0$) and a deformed one in the rotating case ($a\neq{0}$). For a fixed value of $a$, the size of the shadow increases with the increasing $\vert{k}\vert$, but decreases with the increasing $Q$. We further investigated the black hole emission rate. We found that the emission rate decreases with the increasing $\vert{k}\vert$ (or $Q$) and the peak of the emission shifts to lower frequency. Finally, we discussed the observational prospects corresponding to the supermassive black hole $\mathrm{Sgr\ A^{*}}$ at the center of the Milky Way.

In this paper,we discuss optical properties of the nonlinear magnetic charged black hole surrounded by quintessence with a non-zero cosmological constant $\Lambda$. Setting the state parameter $\omega=-3/2$ , we studied the horizon, the photon region and the shadow of this black hole. It turned out that for a fixed quintessential parameter $\gamma$, in a certain range, with the increase of the rotation parameter $a$ and magnetic charge $Q$, the inner horizon radius increases while the outer horizon radius decreases. And the cosmological horizon $r_{\Lambda}$ decrease when $\gamma$ or $\Lambda$ incease and increase slightly with increasing $a$ and $Q$. The shapes of photon region were then studied and depicted through graphical illustrations. Finally, we discussed the effects of the quintessential parameter $\gamma$ and the cosmological constant $\Lambda$ on the shadow cast by this balck hole with a fixed observer position.

Relativistic massless Weyl and Dirac fermions have isotropic and linear dispersion relations to maintain Poincar\'{e} symmetry, which is the most basic symmetry in high-energy physics. The situation in condensed matter physics is less constrained; only certain subgroups of Poincar\'{e} symmetry -- the 230 space groups that exist in 3D lattices -- need be respected. Then, the free fermionic excitations that have no high energy analogues could exist in solid state systems. Here, We discovered a type of nonlinear Dirac fermion without high-energy analogue in SrAgBi and named it type-IV Dirac fermion. The type-IV Dirac fermion has a nonlinear dispersion relationship and is similar to the type-II Dirac fermion, which has electron pocket and hole pocket. The effective model for the type-IV Dirac fermion is also found. It is worth pointing out that there is a type-II Dirac fermion near this new Dirac fermion. So we used two models to describe the coexistence of these two Dirac fermions. Topological surface states of these two Dirac points are also calculated. We envision that our findings will stimulate researchers to study novel physics of type-IV Dirac fermions, as well as the interplay of type-II and type-IV Dirac fermions., Comment: 23 pages, 34 figures. arXiv admin note: text overlap with arXiv:1904.06216 by other authors

We derive an exact solution of the spherically symmetric Bardeen black hole surrounded by perfect fluid dark matter (PFDM). By treating the magnetic charge $g$ and dark matter parameter $\alpha$ as thermodynamic variables, we find that the thermodynamic first law and the corresponding Smarr formula are satisfied. The thermodynamic stability of the black hole is also studied. The result show that, there exists a critical radius $r_{+}^{C}$, where the heat capacity diverges, suggesting that the black hole is thermodynamically stable in the range $0

In recent studies, the tensor ring (TR) rank has shown high effectiveness in tensor completion due to its ability of capturing the intrinsic structure within high-order tensors. A recently proposed TR rank minimization method is based on the convex relaxation by penalizing the weighted sum of nuclear norm of TR unfolding matrices. However, this method treats each singular value equally and neglects their physical meanings, which usually leads to suboptimal solutions in practice. In this paper, we propose to use the logdet-based function as a nonconvex smooth relaxation of the TR rank for tensor completion, which can more accurately approximate the TR rank and better promote the low-rankness of the solution. To solve the proposed nonconvex model efficiently, we develop an alternating direction method of multipliers algorithm and theoretically prove that, under some mild assumptions, our algorithm converges to a stationary point. Extensive experiments on color images, multispectral images, and color videos demonstrate that the proposed method outperforms several state-of-the-art competitors in both visual and quantitative comparison. Key words: nonconvex optimization, tensor ring rank, logdet function, tensor completion, alternating direction method of multipliers.

The tensor train (TT) rank has received increasing attention in tensor completion due to its ability to capture the global correlation of high-order tensors ($\textrm{order} >3$). For third order visual data, direct TT rank minimization has not exploited the potential of TT rank for high-order tensors. The TT rank minimization accompany with \emph{ket augmentation}, which transforms a lower-order tensor (e.g., visual data) into a higher-order tensor, suffers from serious block-artifacts. To tackle this issue, we suggest the TT rank minimization with nonlocal self-similarity for tensor completion by simultaneously exploring the spatial, temporal/spectral, and nonlocal redundancy in visual data. More precisely, the TT rank minimization is performed on a formed higher-order tensor called group by stacking similar cubes, which naturally and fully takes advantage of the ability of TT rank for high-order tensors. Moreover, the perturbation analysis for the TT low-rankness of each group is established. We develop the alternating direction method of multipliers tailored for the specific structure to solve the proposed model. Extensive experiments demonstrate that the proposed method is superior to several existing state-of-the-art methods in terms of both qualitative and quantitative measures.

Thermal wave crystals based on the dual-phase-lag model are investigated in this paper by both theoretical analysis and numerical simulation to control the non-Fourier heat conduction process. The transfer matrix method is used to calculate the complex dispersion curves. The temperature field is calculated by the finite difference time domain method. The results show that thermal band-gaps exist due to the Bragg-scattering. The key parameters characterizing the band-gaps are analyzed. The thermal wave impedance and mid-gap frequencies are introduced to predict band-gaps theoretically. Our results show that the larger the difference in the thermal wave impedances is, the wider of the thermal band-gaps will be. This study demonstrates a type of the thermal metamaterials which have potential innovative applications such as thermal imagining, thermal diodes and thermal waveguides for energy transmission.

Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when it faces multiclass classification problems. In order to solve the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is proposed in this paper. The proposed classifier successfully improves the training accuracy and reduces the training time in the multiclass classification problems. Moreover, the validity of the Mexican Hat wavelet as a kernel function of ELM is rigorously proved. Experimental results on different data sets show that the performance of the proposed classifier is significantly superior to the compared classifiers., Comment: Published by Computational Intelligence and Neuroscience, 8 pages, 1 figure, 13 tables