6,001 research outputs found

    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

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    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

    Transition of stoichiometricSr2VO3FeAs to a superconducting state at 37.2 K

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    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

    CANF-VC: Conditional Augmented Normalizing Flows for Video Compression

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    This paper presents an end-to-end learning-based video compression system, termed CANF-VC, based on conditional augmented normalizing flows (CANF). Most learned video compression systems adopt the same hybrid-based coding architecture as the traditional codecs. Recent research on conditional coding has shown the sub-optimality of the hybrid-based coding and opens up opportunities for deep generative models to take a key role in creating new coding frameworks. CANF-VC represents a new attempt that leverages the conditional ANF to learn a video generative model for conditional inter-frame coding. We choose ANF because it is a special type of generative model, which includes variational autoencoder as a special case and is able to achieve better expressiveness. CANF-VC also extends the idea of conditional coding to motion coding, forming a purely conditional coding framework. Extensive experimental results on commonly used datasets confirm the superiority of CANF-VC to the state-of-the-art methods. The source code of CANF-VC is available at https://github.com/NYCU-MAPL/CANF-VC

    Robust Image Analysis by L1-Norm Semi-supervised Learning

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    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
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