Coupling multiple views of relations for recommendation

© Springer International Publishing Switzerland 2015. Learning user/item relation is a key issue in recommender system, and existing methods mostly measure the user/item relation from one particular aspect, e.g., historical ratings, etc. However, the relations between users/items could be influenced by multifaceted factors, so any single type of measure could get only a partial view of them. Thus it is more advisable to integrate measures from different aspects to estimate the underlying user/item relation. Furthermore, the estimation of underlying user/item relation should be optimal for current task. To this end, we propose a novel model to couple multiple relations measured on different aspects, and determine the optimal user/item relations via learning the optimal way of integrating these relation measures. Specifically, matrix factorization model is extended in this paper by considering the relations between latent factors of different users/items. Experiments are conducted and our method shows good performance and outperforms other baseline methods

Disentangling the surface and bulk electronic structures of LaOFeAs

We performed a comprehensive angle-resolved photoemission spectroscopy study of the electronic band structure of LaOFeAs single crystals. We found that samples cleaved at low temperature show an unstable and highly complicated band structure, whereas samples cleaved at high temperature exhibit a stable and clearer electronic structure. Using \emph{in-situ} surface doping with K and supported by first-principles calculations, we identify both surface and bulk bands. Our assignments are confirmed by the difference in the temperature dependence of the bulk and surface states.Comment: 5 pages, 5 figure

Observation of momentum-confined in-gap impurity state in Ba0.6_{0.6}K0.4_{0.4}Fe2_2As2_2: evidence for anti-phase s±s_{\pm} pairing

We report the observation by angle-resolved photoemission spectroscopy of an impurity state located inside the superconducting gap of Ba$_{0.6}$K$_{0.4}$Fe$_2$As$_2$ and vanishing above the superconducting critical temperature, for which the spectral weight is confined in momentum space near the Fermi wave vector positions. We demonstrate, supported by theoretical simulations, that this in-gap state originates from weak non-magnetic scattering between bands with opposite sign of the superconducting gap phase. This weak scattering, likely due to off-plane Ba/K disorders, occurs mostly among neighboring Fermi surfaces, suggesting that the superconducting gap phase changes sign within holelike (and electronlike) bands. Our results impose severe restrictions on the models promoted to explain high-temperature superconductivity in these materials.Comment: 8 pages, 5 figures. Accepted for publication in Physical Review

Broad-spectrum antimicrobial activity and improved stability of a D-Amino acid enantiomer of DMPC-10A, the designed derivative of dermaseptin truncates

DMPC-10A (ALWKKLLKK-Cha-NH2) is a 10-mer peptide derivative from the N-terminal domain of Dermaseptin-PC which has shown broad-spectrum antimicrobial activity as well as a considerable hemolytic effect. In order to reduce hemolytic activity and improve stability to endogenous enzymes, a D-amino acid enantiomer (DMPC-10B) was designed by substituting all L-Lys and L-Leu with their respective D-form amino acid residues, while the Ala1 and Trp3 remained unchanged. The D-amino acid enantiomer exhibited similar antimicrobial potency to the parent peptide but exerted lower cytotoxicity and hemolytic activity. Meanwhile, DMPC-10B exhibited remarkable resistance to hydrolysis by trypsin and chymotrypsin. In addition to these advantages, DMPC-10B exhibited an outstanding antibacterial effect against Methicillin-resistant Staphylococcus aureus (MRSA) and Klebsiella pneumoniae using the Galleria mellonella larva model and displayed synergistic activities with gentamicin against carbapenem-resistant K. pneumoniae strains. This indicates that DMPC-10B would be a promising alternative for treating antibiotic-resistant pathogens

Alfvenic Ion Temperature Gradient Activities in a Weak Magnetic Shear Plasma

We report the first experimental evidence of Alfvenic ion temperature gradient (AITG) modes in HL-2A Ohmic plasmas. A group of oscillations with $f=15-40$ kHz and $n=3-6$ is detected by various diagnostics in high-density Ohmic regimes. They appear in the plasmas with peaked density profiles and weak magnetic shear, which indicates that corresponding instabilities are excited by pressure gradients. The time trace of the fluctuation spectrogram can be either a frequency staircase, with different modes excited at different times or multiple modes may simultaneously coexist. Theoretical analyses by the extended generalized fishbone-like dispersion relation (GFLDR-E) reveal that mode frequencies scale with ion diamagnetic drift frequency and $\eta_i$, and they lie in KBM-AITG-BAE frequency ranges. AITG modes are most unstable when the magnetic shear is small in low pressure gradient regions. Numerical solutions of the AITG/KBM equation also illuminate why AITG modes can be unstable for weak shear and low pressure gradients. It is worth emphasizing that these instabilities may be linked to the internal transport barrier (ITB) and H-mode pedestal physics for weak magnetic shear.Comment: 9 pages, 7 figure

Polarization of M 2 line emitted from highly-charged beryllium-like ions following electron-impact excitation

The contributions of the Breit interaction to the linear polarization of the 1s2s 2 2p 3/2 J =2 → 1s 2 2s 2 J =0 magnetic quadrupole (M2) line following electron-impact excitation have been investigated systematically for the beryllium isoelectronic sequence with 42 ≤ Z ≤ 92. It is found that the Breit interaction depolarizes significantly the linear polarization of the M 2 fluorescence radiation and that these depolarization effects increase as the incident electron energy and/or the atomic number is enlarged

Prediction of alloy addition in ladle furnace (LF) based on LWOA-SCN

The amount of alloy added during the LF refining process affects the hit rate of steel composition control. Therefore, improving the accuracy of the alloy addition amount can help improve efficiency and reduce production costs. To address the existing problem of inaccurate alloy addition in the refining process, the group established an alloy addition prediction model based on an improved whale swarm optimization algorithm and stochastic configuration network (LWOA-SCN) with the historical smelting data of a steel mill. The model can effectively improve the prediction accuracy and convergence speed of the model. The research results show that the model is more advantageous in improving the hit rate of alloy addition, which provides theoretical guidance for practical production