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

Water and nitrogen distribution in uncropped ridgetilled soil under different ridge width

Nitrate leaching to ground and surface water is an increasing concern in agriculture. A ridge-tillage configuration, with placement of nitrate nitrogen (NO3--N) or its source in the elevated portion of the ridge, can potentially isolate fertilizer from downward water flow and minimize nitrate leaching. In the experiment, the simultaneous distribution of water, nitrate, and ammonium under three ridge widths was measured using the gravimetric method. Monitoring of the water movement revealed that vertical water movement was much greater than horizontal movement. Compared with 30 and 90 cm ridge width, soil water content at 60 cm ridge width could better meet crop water requirement and relatively decreased irrigation volume. The distribution of NO3--N in the soil was similar to distribution patterns of water, while ammonium distribution measurements indicated that there existed an extremely high ammonium concentration in the furrow for ridge-furrow system. These results support the conclusion that water infiltrated in furrows and primarily moved laterally to ridge positions, minimizing downward water movement under the ridge. In this experiment, we recommend that the suitable ridge width for ridge tillage management should be within the range of 60 to 75 cm.Key words: Ridge tillage, ridge width, wetted front, soil water content, nitrate-nitrogen distribution, ammonium distribution

Magnetic ordering and structural phase transitions in strained ultrathin SrRuO3_{3}/SrTiO3_{3} superlattice

Ruthenium-based perovskite systems are attractive because their Structural, electronic and magnetic properties can be systematically engineered. SrRuO$_3$/SrTiO$_3$ superlattice, with its period consisting of one unit cell each, is very sensitive to strain change. Our first-principles simulations reveal that in the high tensile strain region, it transits from a ferromagnetic (FM) metal to an antiferromagnetic (AFM) insulator with clear tilted octahedra, while in the low strain region, it is a ferromagnetic metal without octahedra tilting. Detailed analyses of three spin-down Ru-t$_{2g}$ orbitals just below the Fermi level reveal that the splitting of these orbitals underlies these dramatic phase transitions, with the rotational force constant of RuO$_6$ octahedron high up to 16 meV/Deg$^2$, 4 times larger than that of TiO$_6$. Differently from nearly all the previous studies, these transitions can be probed optically through the diagonal and off-diagonal dielectric tensor elements. For one percent change in strain, our experimental spin moment change is -0.14$\pm$0.06 $\mu_B$, quantitatively consistent with our theoretical value of -0.1 $\mu_B$.Comment: 3 figures, 1 supplementary material, accepted by Phys. Rev. Let

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

SODE: Self-Adaptive One-Dependence Estimators for classification

© 2015 Elsevier Ltd. SuperParent-One-Dependence Estimators (SPODEs) represent a family of semi-naive Bayesian classifiers which relax the attribute independence assumption of Naive Bayes (NB) to allow each attribute to depend on a common single attribute (superparent). SPODEs can effectively handle data with attribute dependency but still inherent NB's key advantages such as computational efficiency and robustness for high dimensional data. In reality, determining an optimal superparent for SPODEs is difficult. One common approach is to use weighted combinations of multiple SPODEs, each having a different superparent with a properly assigned weight value (i.e., a weight value is assigned to each attribute). In this paper, we propose a self-adaptive SPODEs, namely SODE, which uses immunity theory in artificial immune systems to automatically and self-adaptively select the weight for each single SPODE. SODE does not need to know the importance of individual SPODE nor the relevance among SPODEs, and can flexibly and efficiently search optimal weight values for each SPODE during the learning process. Extensive experiments and comparisons on 56 benchmark data sets, and validations on image and text classification, demonstrate that SODE outperforms state-of-the-art weighted SPODE algorithms and is suitable for a wide range of learning tasks. Results also confirm that SODE provides an appropriate balance between runtime efficiency and accuracy

Towards mining trapezoidal data streams

© 2015 IEEE. We study a new problem of learning from doubly-streaming data where both data volume and feature space increase over time. We refer to the problem as mining trapezoidal data streams. The problem is challenging because both data volume and feature space are increasing, to which existing online learning, online feature selection and streaming feature selection algorithms are inapplicable. We propose a new Sparse Trapezoidal Streaming Data mining algorithm (STSD) and its two variants which combine online learning and online feature selection to enable learning trapezoidal data streams with infinite training instances and features. Specifically, when new training instances carrying new features arrive, the classifier updates the existing features by following the passive-aggressive update rule used in online learning and updates the new features with the structural risk minimization principle. Feature sparsity is also introduced using the projected truncation techniques. Extensive experiments on the demonstrated UCI data sets show the performance of the proposed algorithms