Transition of stoichiometricSr2VO3FeAs to a superconducting state at 37.2 K

The superconductor Sr4V2O6Fe2As2 with transition temperature at 37.2 K has been fabricated. It has a layered structure with the space group of p4/nmm, and with the lattice constants a = 3.9296Aand c = 15.6732A. The observed large diamagnetization signal and zero-resistance demonstrated the bulk superconductivity. The broadening of resistive transition was measured under different magnetic fields leading to the discovery of a rather high upper critical field. The results also suggest a large vortex liquid region which reflects high anisotropy of the system. The Hall effect measurements revealed dominantly electron-like charge carriers in this material. The superconductivity in the present system may be induced by oxygen deficiency or the multiple valence states of vanadium.Comment: 5 pages, 4 figure

Superconductivity at 15.6 K in Calcium-doped Tb_{1-x}Ca_xFeAsO: the structure requirement for achieving superconductivity in the hole-doped 1111 phase

Superconductivity at about 15.6 K was achieved in Tb_{1-x}Ca_xFeAsO by partially substituting Tb^{3+} with Ca^{2+} in the nominal doping region x = 0.40 \sim 0.50. A detailed investigation was carried out in a typical sample with doping level of x = 0.44. The upper critical field of this sample was estimated to be 77 Tesla from the magnetic field dependent resistivity data. The domination of hole-like charge carriers in the low-temperature region was confirmed by Hall effect measurements. The comparison between the calcium-doped sample Pr_{1-x}Ca_xFeAsO (non-superconductive) and the Strontium-doped sample Pr_{1-x}Sr_xFeAsO (superconductive) suggests that a lager ion radius of the doped alkaline-earth element compared with that of the rare-earth element may be a necessary requirement for achieving superconductivity in the hole-doped 1111 phase.Comment: 7 pages, 7 figure

Robust Image Analysis by L1-Norm Semi-supervised Learning

This paper presents a novel L1-norm semi-supervised learning algorithm for robust image analysis by giving new L1-norm formulation of Laplacian regularization which is the key step of graph-based semi-supervised learning. Since our L1-norm Laplacian regularization is defined directly over the eigenvectors of the normalized Laplacian matrix, we successfully formulate semi-supervised learning as an L1-norm linear reconstruction problem which can be effectively solved with sparse coding. By working with only a small subset of eigenvectors, we further develop a fast sparse coding algorithm for our L1-norm semi-supervised learning. Due to the sparsity induced by sparse coding, the proposed algorithm can deal with the noise in the data to some extent and thus has important applications to robust image analysis, such as noise-robust image classification and noise reduction for visual and textual bag-of-words (BOW) models. In particular, this paper is the first attempt to obtain robust image representation by sparse co-refinement of visual and textual BOW models. The experimental results have shown the promising performance of the proposed algorithm.Comment: This is an extension of our long paper in ACM MM 201

Doping effect of Cu and Ni impurities on the Fe-based superconductor Ba0.6K0.4Fe2As2

Copper and Nickel impurities have been doped into the iron pnictide superconductor Ba0.6K0.4Fe2As2. Resistivity measurements reveal that Cu and Ni impurities suppress superconducting transition temperature T_c with rates of \Delta T_c/Cu-1% = -3.5 K and \Delta T_c/Ni-1% = -2.9 K respectively. Temperature dependence of Hall coefficient R_H of these two series of samples show that both Cu-doping and Ni-doping can introduce electrons into Ba0.6K0.4Fe2As2. With more doping, the sign of R_H gradually changes from positive to negative, while the changing rate of Cu-doped samples is much faster than that of Ni-doped ones. Combining with the results of first-principles calculations published previously and the non-monotonic evolution of the Hall coefficient in the low temperature region, we argue that when more Cu impurities were introduced into Ba0.6K0.4Fe2As2, the removal of Fermi spectral weight in the hole-like Fermi surfaces is much stronger than that in the electron-like Fermi surfaces, which is equivalent to significant electron doping effect. DC magnetization and the lattice constants analysis reveal that static magnetic moments and notable lattice compression have been formed in Cu-doped samples. It seems that the superconductivity can be suppressed by the impurities disregard whether they are magnetic or nonmagnetic in nature. This gives strong support to a pairing gap with a sign reversal, like S^\pm. However, the relatively slow suppression rates of T_c show the robustness of superconductivity of Ba0.6K0.4Fe2As2 against impurities, implying that multi-pairing channels may exist in the system.Comment: 7 pages, 7 figure