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-Line AdaTron Learning of Unlearnable Rules

We study the on-line AdaTron learning of linearly non-separable rules by a simple perceptron. Training examples are provided by a perceptron with a non-monotonic transfer function which reduces to the usual monotonic relation in a certain limit. We find that, although the on-line AdaTron learning is a powerful algorithm for the learnable rule, it does not give the best possible generalization error for unlearnable problems. Optimization of the learning rate is shown to greatly improve the performance of the AdaTron algorithm, leading to the best possible generalization error for a wide range of the parameter which controls the shape of the transfer function.)Comment: RevTeX 17 pages, 8 figures, to appear in Phys.Rev.

Antiferromagnetic Order and Superconductivity in Sr4(Mg0.5-xTi0.5+x)2O6Fe2As2 with Electron Doping: 75As-NMR Study

We report an 75As-NMR study on iron (Fe)-based superconductors with thick perovskitetype blocking layers Sr4(Mg0.5-xTi0.5+x)2O6Fe2As2 with x=0 and 0.2. We have found that antiferromagnetic (AFM) order takes place when x=0, and superconductivity (SC) emerges below Tc=36 K when x=0.2. These results reveal that the Fe-pnictides with thick perovskitetype blocks also undergo an evolution from the AFM order to the SC by doping electron carriers into FeAs planes through the chemical substitution of Ti+4 ions for Mg+2 ions, analogous to the F-substitution in LaFeAsO compound. The reason why the Tc=36 K when x=0.2 being higher than the optimally electron-doped LaFeAsO with Tc=27 K relates to the fact that the local tetrahedron structure of FeAs4 is optimized for the onset of SC.Comment: 4 pages, 3 figures, 1 tabl

75As NQR/NMR Studies on Oxygen-deficient Iron-based Oxypnictide Superconductors LaFeAsO_{1-y} (y=0,0.25,0.4) and NdFeAsO_{0.6}

We report 75As-NQR/NMR studies on the oxygen-deficient iron(Fe)-based oxypnictide superconductors LaFeAsO_{0.6} (T_c=28 K) along with the results on LaFeAsO, LaFeAsO_{0.75}(T_c=20 K) and NdFeAsO_{0.6}(T_c=53 K). Nuclear spin-lattice relaxation rate 1/T_1 of 75As NQR at zero field on LaFeAsO_{0.6} has revealed a T^3 dependence below T_c upon cooling without the coherence peak just below T_c, evidencing the unconventional superconducting state with the line-node gap. We have found an intimate relationship between the nuclear quadrupole frequencyof 75As and T_c for four samples used in this study. It implies microscopically that the local configuration of Fe and As atoms is significantly related to the T_c of the Fe-oxypnictide superconductors, namely, the T_c can be enhanced up to 50 K when the local configuration of Fe and As atoms is optimal, in which the band structure may be also optimized through the variation of hybridization between As 4p orbitals and Fe 3d orbitals.Comment: 4 pages, 5 figures, Accepted for publication in J. Phys. Soc. Jpn., vol.77, No.

Identification of Ischemic Regions in a Rat Model of Stroke

Investigations following stroke first of all require information about the spatio-temporal dimension of the ischemic core as well as of perilesional and remote affected tissue. Here we systematically evaluated regions differently impaired by focal ischemia.Wistar rats underwent a transient 30 or 120 min suture-occlusion of the middle cerebral artery (MCAO) followed by various reperfusion times (2 h, 1 d, 7 d, 30 d) or a permanent MCAO (1 d survival). Brains were characterized by TTC, thionine, and immunohistochemistry using MAP2, HSP72, and HSP27. TTC staining reliably identifies the infarct core at 1 d of reperfusion after 30 min MCAO and at all investigated times following 120 min and permanent MCAO. Nissl histology denotes the infarct core from 2 h up to 30 d after transient as well as permanent MCAO. Absent and attenuated MAP2 staining clearly identifies the infarct core and perilesional affected regions at all investigated times, respectively. HSP72 denotes perilesional areas in a limited post-ischemic time (1 d). HSP27 detects perilesional and remote impaired tissue from post-ischemic day 1 on. Furthermore a simultaneous expression of HSP72 and HSP27 in perilesional neurons was revealed.TTC and Nissl staining can be applied to designate the infarct core. MAP2, HSP72, and HSP27 are excellent markers not only to identify perilesional and remote areas but also to discriminate affected neuronal and glial populations. Moreover markers vary in their confinement to different reperfusion times. The extent and consistency of infarcts increase with prolonged occlusion of the MCA. Therefore interindividual infarct dimension should be precisely assessed by the combined use of different markers as described in this study

A framework for the local information dynamics of distributed computation in complex systems

The nature of distributed computation has often been described in terms of the component operations of universal computation: information storage, transfer and modification. We review the first complete framework that quantifies each of these individual information dynamics on a local scale within a system, and describes the manner in which they interact to create non-trivial computation where "the whole is greater than the sum of the parts". We describe the application of the framework to cellular automata, a simple yet powerful model of distributed computation. This is an important application, because the framework is the first to provide quantitative evidence for several important conjectures about distributed computation in cellular automata: that blinkers embody information storage, particles are information transfer agents, and particle collisions are information modification events. The framework is also shown to contrast the computations conducted by several well-known cellular automata, highlighting the importance of information coherence in complex computation. The results reviewed here provide important quantitative insights into the fundamental nature of distributed computation and the dynamics of complex systems, as well as impetus for the framework to be applied to the analysis and design of other systems.Comment: 44 pages, 8 figure

On-line learning with adaptive back-propagation in two-layer networks

An adaptive back-propagation algorithm parameterized by an inverse temperature 1/T is studied and compared with gradient descent (standard back-propagation) for on-line learning in two-layer neural networks with an arbitrary number of hidden units. Within a statistical mechanics framework, we analyse these learning algorithms in both the symmetric and the convergence phase for finite learning rates in the case of uncorrelated teachers of similar but arbitrary length T. These analyses show that adaptive back-propagation results generally in faster training by breaking the symmetry between hidden units more efficiently and by providing faster convergence to optimal generalization than gradient descent

Emergent complex neural dynamics

A large repertoire of spatiotemporal activity patterns in the brain is the basis for adaptive behaviour. Understanding the mechanism by which the brain's hundred billion neurons and hundred trillion synapses manage to produce such a range of cortical configurations in a flexible manner remains a fundamental problem in neuroscience. One plausible solution is the involvement of universal mechanisms of emergent complex phenomena evident in dynamical systems poised near a critical point of a second-order phase transition. We review recent theoretical and empirical results supporting the notion that the brain is naturally poised near criticality, as well as its implications for better understanding of the brain

Pairing symmetry and properties of iron-based high temperature superconductors

Pairing symmetry is important to indentify the pairing mechanism. The analysis becomes particularly timely and important for the newly discovered iron-based multi-orbital superconductors. From group theory point of view we classified all pairing matrices (in the orbital space) that carry irreducible representations of the system. The quasiparticle gap falls into three categories: full, nodal and gapless. The nodal-gap states show conventional Volovik effect even for on-site pairing. The gapless states are odd in orbital space, have a negative superfluid density and are therefore unstable. In connection to experiments we proposed possible pairing states and implications for the pairing mechanism.Comment: 4 pages, 1 table, 2 figures, polished versio