Emergent Phases of Nodeless and Nodal Superconductivity Separated by Antiferromagnetic Order in Iron-based Superconductor (Ca4Al2O6)Fe2(As1-xPx)2: 75As- and 31P-NMR Studies

We report $^{31}$P- and $^{75}$As-NMR studies on (Ca$_4$Al$_2$O$_{6}$)Fe$_2$(As$_{1-x}$P$_x$)$_2$ with an isovalent substitution of P for As. We present the novel evolution of emergent phases that the nodeless superconductivity (SC) in 0$\le x \le$0.4 and the nodal one around $x$=1 are intimately separated by the onset of a commensurate stripe-type antiferromagnetic (AFM) order in 0.5$\le x \le$ 0.95, as an isovalent substitution of P for As decreases a pnictogen height $h_{Pn}$ measured from the Fe plane. It is demonstrated that the AFM order takes place under a condition of 1.32\AA$\le h_{Pn} \le$1.42\AA, which is also the case for other Fe-pnictides with the Fe$^{2+}$ state in (Fe$Pn$)$^{-}$ layers. This novel phase evolution with the variation in $h_{Pn}$ points to the importance of electron correlation for the emergence of SC as well as AFM order.Comment: 5pages, 4figures; accepted for publication as a Rapid Communication in Phys. Rev.

High-Tc Nodeless s_\pm-wave Superconductivity in (Y,La)FeAsO_{1-y} with Tc=50 K: 75As-NMR Study

We report 75As-NMR study on the Fe-pnictide high-Tc superconductor Y0.95La0.05FeAsO_{1-y} (Y0.95La0.051111) with Tc=50 K that includes no magnetic rare-earth elements. The measurement of the nuclear-spin lattice-relaxation rate 75(1/T1) has revealed that the nodeless bulk superconductivity takes place at Tc=50 K while antiferromagnetic spin fluctuations (AFSFs) develop moderately in the normal state. These features are consistently described by the multiple fully-gapped s_\pm-wave model based on the Fermi-surface (FS) nesting. Incorporating the theory based on band calculations, we propose that the reason that Tc=50 K in Y0.95La0.051111 is larger than Tc=28 K in La1111 is that the FS multiplicity is maximized, and hence the FS nesting condition is better than that in La1111.Comment: 4 pages, 3 figures, accepted for publication in Phys Rev. Let

Gradient descent learning in and out of equilibrium

Relations between the off thermal equilibrium dynamical process of on-line learning and the thermally equilibrated off-line learning are studied for potential gradient descent learning. The approach of Opper to study on-line Bayesian algorithms is extended to potential based or maximum likelihood learning. We look at the on-line learning algorithm that best approximates the off-line algorithm in the sense of least Kullback-Leibler information loss. It works by updating the weights along the gradient of an effective potential different from the parent off-line potential. The interpretation of this off equilibrium dynamics holds some similarities to the cavity approach of Griniasty. We are able to analyze networks with non-smooth transfer functions and transfer the smoothness requirement to the potential.Comment: 08 pages, submitted to the Journal of Physics

Functional Optimisation of Online Algorithms in Multilayer Neural Networks

We study the online dynamics of learning in fully connected soft committee machines in the student-teacher scenario. The locally optimal modulation function, which determines the learning algorithm, is obtained from a variational argument in such a manner as to maximise the average generalisation error decay per example. Simulations results for the resulting algorithm are presented for a few cases. The symmetric phase plateaux are found to be vastly reduced in comparison to those found when online backpropagation algorithms are used. A discussion of the implementation of these ideas as practical algorithms is given

Lobby index as a network centrality measure

We study the lobby index (l-index for short) as a local node centrality measure for complex networks. The l-inde is compared with degree (a local measure), betweenness and Eigenvector centralities (two global measures) in the case of biological network (Yeast interaction protein-protein network) and a linguistic network (Moby Thesaurus II). In both networks, the l-index has poor correlation with betweenness but correlates with degree and Eigenvector. Being a local measure, one can take advantage by using the l-index because it carries more information about its neighbors when compared with degree centrality, indeed it requires less time to compute when compared with Eigenvector centrality. Results suggests that l-index produces better results than degree and Eigenvector measures for ranking purposes, becoming suitable as a tool to perform this task.Comment: 11 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:1005.480

On the robustness of scale invariance in SOC models

A random neighbor extremal stick-slip model is introduced. In the thermodynamic limit, the distribution of states has a simple analytical form and the mean avalanche size, as a function of the coupling parameter, is exactly calculable. The system is critical only at a special point Jc in the coupling parameter space. However, the critical region around this point, where approximate scale invariance holds, is very large, suggesting a mechanism for explaining the ubiquity of scale invariance in Nature.Comment: 6 pages, 4 figures; submitted to Physical Review E; http://link.aps.org/doi/10.1103/PhysRevE.59.496

On the random neighbor Olami-Feder-Christensen slip-stick model

We reconsider the treatment of Lise and Jensen (Phys. Rev. Lett. 76, 2326 (1996)) on the random neighbor Olami-Feder-Christensen stik-slip model, and examine the strong dependence of the results on the approximations used for the distribution of states p(E).Comment: 6pages, 3 figures. To be published in PRE as a brief repor