The spin-polarized ν=0\nu=0 state of graphene: a spin superconductor

We study the spin-polarized $\nu=0$ Landau-level state of graphene. Due to the electron-hole attractive interaction, electrons and holes can bound into pairs. These pairs can then condense into a spin-triplet superfluid ground state: a spin superconductor state. In this state, a gap opens up in the edge bands as well as in the bulk bands, thus it is a charge insulator, but it can carry the spin current without dissipation. These results can well explain the insulating behavior of the spin-polarized $\nu=0$ state in the recent experiments.Comment: 6 pages, 4 figure

Spin-current diode with a ferromagnetic semiconductor

Diode is a key device in electronics: the charge current can flow through the device under a forward bias, while almost no current flows under a reverse bias. Here we propose a corresponding device in spintronics: the spin-current diode, in which the forward spin current is large but the reversed one is negligible. We show that the lead/ferromagnetic quantum dot/lead system and the lead/ferromagnetic semiconductor/lead junction can work as spin-current diodes. The spin-current diode, a low dissipation device, may have important applications in spintronics, as the conventional charge-current diode does in electronics.Comment: 5 pages, 3 figure

Surface plasmon polaritons in topological insulator

We study surface plasmon polaritons on topological insulator-vacuum interface. When the time-reversal symmetry is broken due to ferromagnetic coupling, the surface states exhibit magneto-optical Kerr effect. This effect gives rise to a novel transverse type surface plasmon polariton, besides the longitudinal type. In specific, these two types contain three different channels, corresponding to the pole of determinant of Fresnel reflection matrix. All three channels of surface plasmon polaritons display tight confinement, long lifetime and show strong light-matter coupling with a dipole emitter.Comment: 6 pages, 4 figure

The scaling feature of the magnetic field induced Kondo-peak splittings

By using the full density matrix approach to spectral functions within the numerical renormalization group method, we present a detailed study of the magnetic field induced splittings in the spin-resolved and the total spectral densities of a Kondo correlated quantum dot described by the single level Anderson impurity model. The universal scaling of the splittings with magnetic field is examined by varying the Kondo scale either by a change of local level position at a fixed tunnel coupling or by a change of the tunnel coupling at a fixed level position. We find that the Kondo-peak splitting $\Delta/T_K$ in the spin-resolved spectral function always scales perfectly for magnetic fields $B<8T_K$ in either of the two $T_K$-adjusted paths. Scaling is destroyed for fields $B>10T_K$. On the other hand, the Kondo peak splitting $\delta/T_K$ in the total spectral function does slightly deviate from the conventional scaling theory in whole magnetic field window along the coupling-varying path. Furthermore, we show the scaling analysis suitable for all field windows within the Kondo regime and two specific fitting scaling curves are given from which certain detailed features at low field are derived. In addition, the scaling dimensionless quantity $\Delta/2B$ and $\delta/2B$ are also studied and they can reach and exceed 1 in the large magnetic field region, in agreement with a recent experiment [T.M. Liu, et al., Phys. Rev. Lett. 103, 026803 (2009)].Comment: 8 pages, 5 figure

Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases

This paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is introduced to assess the models. The two-stage hybrid algorithms adopt Support Vector Machines (SVM) as a classification tool, and the extended Sequential Forward Search (SFS), Sequential Forward Floating Search (SFFS), and Sequential Backward Floating Search (SBFS), respectively, as search strategies, and the generalized F-score (GF) to evaluate the importance of each feature. The new accuracy measure is used as the criterion to evaluated the performance of a temporary SVM to direct the feature selection algorithms. These hybrid methods combine the advantages of filters and wrappers to select the optimal feature subset from the original feature set to build the stable and efficient classifiers. To get the stable, statistical and optimal classifiers, we conduct 10-fold cross validation experiments in the first stage; then we merge the 10 selected feature subsets of the 10-cross validation experiments, respectively, as the new full feature set to do feature selection in the second stage for each algorithm. We repeat the each hybrid feature selection algorithm in the second stage on the one fold that has got the best result in the first stage. Experimental results show that our proposed two-stage hybrid feature selection algorithms can construct efficient diagnostic models which have got better accuracy than that built by the corresponding hybrid feature selection algorithms without the second stage feature selection procedures. Furthermore our methods have got better classification accuracy when compared with the available algorithms for diagnosing erythemato-squamous diseases