Transport properties of dense deuterium-tritium plasmas

Consistent descriptions of the equation of states, and information about transport coefficients of deuterium-tritium mixture are demonstrated through quantum molecular dynamic (QMD) simulations (up to a density of 600 g/cm$^{3}$ and a temperature of $10^{4}$ eV). Diffusion coefficients and viscosity are compared with one component plasma model in different regimes from the strong coupled to the kinetic one. Electronic and radiative transport coefficients, which are compared with models currently used in hydrodynamic simulations of inertial confinement fusion, are evaluated up to 800 eV. The Lorentz number is also discussed from the highly degenerate to the intermediate region.Comment: 4 pages, 3 figure

Excited Dirac stars with higher azimuthal harmonic index

In this paper, we investigate the properties of the first excited state Dirac stars (DSs) with higher azimuthal harmonic index (specifically, the azimuthal harmonic indexes $m_D$ = $3/2$, $5/2$, $7/2$), as well as the relationship between the ADM mass and angular momentum of Dirac stars with respect to frequency. Moreover, We find that the ergospheres of DSs appear at lower spinor field frequencies, and both the ergospheres and the distribution of the spinor field functions are asymmetric about the equatorial plane. Furthermore, we introduce the ground state scalar field and examine its impact on this system, which is known as the multi-state Dirac-boson stars (DBSs) model. We show various types of solution families for DBSs under both synchronized frequency $\omega$ and nonsynchronized frequencies and find that similar to DSs, the spinor field and the ergospheres of DBSs are also asymmetric about the equatorial plane, but the ergospheres appear at higher spinor field frequencies.Comment: 22 pages, 8 figure

Symmetries and Lie algebra of the differential-difference Kadomstev-Petviashvili hierarchy

By introducing suitable non-isospectral flows we construct two sets of symmetries for the isospectral differential-difference Kadomstev-Petviashvili hierarchy. The symmetries form an infinite dimensional Lie algebra.Comment: 9 page

Diaqua­(5-methyl-1H-pyrazole-3-carboxyl­ato)(4-nitro­benzoato)copper(II)

In the title complex, [Cu(C7H4NO4)(C5H5N2O2)(H2O)2], the CuII ion is coordinated in a slightly distorted square-pyramidal enviroment. The basal plane is formed by an N atom and an O atom from a 5-methyl-1H-pyrazole-3-carboxyl­ate ligand and by two O atoms from two water ligands. The apical position is occupied by a carboxylate O atom from a 4-nitro­benzoate ligand. In the crystal structure, inter­molecular O—H⋯O and N—H⋯O hydrogen bonds link complex moleclues, forming extended chains parallel to the a axis

A Hessenberg-type algorithm for computing PageRank Problems

PageRank is a widespread model for analysing the relative relevance of nodes within large graphs arising in several applications. In the current paper, we present a cost-effective Hessenberg-type method built upon the Hessenberg process for the solution of difficult PageRank problems. The new method is very competitive with other popular algorithms in this field, such as Arnoldi-type methods, especially when the damping factor is close to 1 and the dimension of the search subspace is large. The convergence and the complexity of the proposed algorithm are investigated. Numerical experiments are reported to show the efficiency of the new solver for practical PageRank computations

Detecting and removing visual distractors for video aesthetic enhancement

Personal videos often contain visual distractors, which are objects that are accidentally captured that can distract viewers from focusing on the main subjects. We propose a method to automatically detect and localize these distractors through learning from a manually labeled dataset. To achieve spatially and temporally coherent detection, we propose extracting features at the Temporal-Superpixel (TSP) level using a traditional SVM-based learning framework. We also experiment with end-to-end learning using Convolutional Neural Networks (CNNs), which achieves slightly higher performance than other methods. The classification result is further refined in a post-processing step based on graph-cut optimization. Experimental results show that our method achieves an accuracy of 81% and a recall of 86%. We demonstrate several ways of removing the detected distractors to improve the video quality, including video hole filling; video frame replacement; and camera path re-planning. The user study results show that our method can significantly improve the aesthetic quality of videos