Magnetic structure of superconducting Eu(Fe0.82Co0.18)2As2 as revealed by single-crystal neutron diffraction

The magnetic structure of superconducting Eu(Fe0.82Co0.18)2As2 is unambiguously determined by single-crystal neutron diffraction. A long-range ferromagnetic order of the Eu2+ moments along the c-direction is revealed below the magnetic phase transition temperature Tc = 17 K. In addition, the antiferromagnetism of the Fe2+ moments still survives and the tetragonal-to-orthorhombic structural phase transition is also observed, although the transition temperatures of the Fe-spin density wave (SDW) order and the structural phase transition are significantly suppressed to Tn = 70 K and Ts = 90 K, respectively, compared to the parent compound EuFe2As2.We present the microscopic evidences for the coexistence of the Eu-ferromagnetism (FM) and the Fe-SDW in the superconducting crystal. The superconductivity (SC) competes with the Fe-SDW in Eu(Fe0.82Co0.18)2As2.Moreover, the comparison between Eu(Fe1-xCox)2As2 and Ba(Fe1-xCox)2As2 indicates a considerable influence of the rare-earth element Eu on the magnetism of the Fe sublattice.Comment: 7 pages, 7 figures, accepted for publication in Physical Review

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations.Comment: MICCAI 2017 Camera Read

Preparation of poly(ethylene terephthalate)/layered double hydroxide nanocomposites by in-situ polymerization and their thermal property

Terephthalate (TA) intercalated layered double hydroxides (LDHs) were synthesized using hydroxides as raw materials, and poly(ethylene terephthalate) (PET)/LDH nanocomposites with different contents of TA intercalated LDHs were prepared by in-situ polymerization. The structure, morphology and thermal property of PET/LDH nanocomposites were investigated. The TA intercalated LDHs were partially exfoliated and well dispersed in PET matrix. The PET/LDH nanocomposites exhibit enhanced thermal stability relative to pure PET, confirmed by the thermogravimetric analysis results. The results of differential scanning calorimetry suggest that LDH nanoparticles could effectively promote the nucleation and crystallization of PET