Continuum-continuum coupling and polarization potentials for weakly bound systems

We investigate the influence of couplings among continuum states in collisions of weakly bound nuclei. For this purpose, we compare cross sections for complete fusion, breakup, and elastic scattering evaluated by continuum discretized coupled channel (CDCC) calculations, including and not including these couplings. In our study, we discuss this influence in terms of the polarization potentials that reproduces the elastic wave function of the coupled channel method in single channel calculations. We find that the inclusion of couplings among continuum states renders the real part of the polarization potential more repulsive, whereas it leads to weaker absorption to the breakup channel. We show that the noninclusion of continuum-continuum couplings in CDCC calculations may lead to qualitative and quantitative wrong conclusions.

Deformed Gaussian-orthogonal-ensemble description of small-world networks

The study of spectral behavior of networks has gained enthusiasm over the last few years. In particular, random matrix theory (RMT) concepts have proven to be useful. In discussing transition from regular behavior to fully chaotic behavior it has been found that an extrapolation formula of the Brody type can be used. In the present paper we analyze the regular to chaotic behavior of small world (SW) networks using an extension of the Gaussian orthogonal ensemble. This RMT ensemble, coined the deformed Gaussian orthogonal ensemble (DGOE), supplies a natural foundation of the Brody formula. SW networks follow GOE statistics until a certain range of eigenvalue correlations depending upon the strength of random connections. We show that for these regimes of SW networks where spectral correlations do not follow GOE beyond a certain range, DGOE statistics models the correlations very well. The analysis performed in this paper proves the utility of the DGOE in network physics, as much as it has been useful in other physical systems.

Spin-Orbit Coupling in n-Type PbTe/PbEuTe Quantum Wells

Magnetoresistance measurements were performed on an n-type PbTe/PbEuTe quantum well and weak antilocalization effects were observed. This indicates the presence of spin orbit coupling phenomena and we showed that the Rashba effect is the main mechanism responsible for this spin orbit coupling. Using the model developed by Iordanskii et al., we fitted the experimental curves and obtained the inelastic and spin orbit scattering times. Thus we could compare the zero field energy spin-splitting predicted by the Rashba theory with the energy spin-splitting obtained from the analysis of the experimental curves. The final result confirms the theoretical prediction of strong Rashba effect on IV-VI based quantum wells.

Ferromagnetic coupling in a Co-doped graphenelike ZnO sheet

Using first-principles calculations it is demonstrated that Co doped graphenelike ZnO sheet presents ferromagnetic coupling. The Co atoms are energetically barrierless absorbed in the Zn sites, suffering a Jahn-Teller distortion. The results reveal that the origin of the ferromagnetic coupling, different from the bulk 3D ZnO stacking, is mainly guided by a direct exchange interaction without any additional defect. This ferromagnetic coupling is due to the system topology, namely, it is a direct consequence of the two-dimensional character of the ZnO monolayer within graphenelike structure. Increasing the number of ZnO layers the ferromagnetic coupling vanishes.

Observation of pi(+)pi(-)pi(+)pi(-) photoproduction in ultraperipheral heavy-ion collisions at root s(NN)=200 GeV at the STAR detector

We present a measurement of pi(+)pi(-)pi(+)pi(-) photonuclear production in ultraperipheral Au-Au collisions at root s(NN) = 200 GeV from the STAR experiment. The pi(+)pi(-)pi(+)pi(-) final states are observed at low transverse momentum and are accompanied by mutual nuclear excitation of the beam particles. The strong enhancement of the production cross section at low transverse momentum is consistent with coherent photoproduction. The pi(+)pi(-)pi(+)pi(-) invariant mass spectrum of the coherent events exhibits a broad peak around 1540 +/- 40 MeV/c(2) with a width of 570 +/- 60 MeV/c(2), in agreement with the photoproduction data for the rho(0)(1700). We do not observe a corresponding peak in the pi(+)pi(-) final state and measure an upper limit for the ratio of the branching fractions of the rho(0)(1700) to pi(+)pi(-) and pi(+)pi(-)pi(+)pi(-) of 2.5% at 90% confidence level. The ratio of rho(0)(1700) and rho(0)(770) coherent production cross sections is measured to be 13.4 +/- 0.8(stat.) +/- 4.4(syst.)%.

Breakup coupling effects on near-barrier quasi-elastic scattering of (6,7)Li on (144)Sm

Excitation functions of quasi-elastic scattering at backward angles have been measured for the (6,7)Li + (144)Sm systems at near-barrier energies, and fusion barrier distributions have been extracted from the first derivatives of the experimental cross sections with respect to the bombarding energies. The data have been analyzed in the framework of continuum discretized coupled-channel calculations, and the results have been obtained in terms of the influence exerted by the inclusion of different reaction channels, with emphasis on the role played by the projectile breakup.

Communication structure of cortical networks

Large-scale cortical networks exhibit characteristic topological properties that shape communication between brain regions and global cortical dynamics. Analysis of complex networks allows the description of connectedness, distance, clustering, and centrality that reveal different aspects of how the network's nodes communicate. Here, we focus on a novel analysis of complex walks in a series of mammalian cortical networks that model potential dynamics of information flow between individual brain regions. We introduce two new measures called absorption and driftness. Absorption is the average length of random walks between any two nodes, and takes into account all paths that may diffuse activity throughout the network. Driftness is the ratio between absorption and the corresponding shortest path length. For a given node of the network, we also define four related measurements, namely in-and out-absorption as well as in-and out-driftness, as the averages of the corresponding measures from all nodes to that node, and from that node to all nodes, respectively. We find that the cat thalamo-cortical system incorporates features of two classic network topologies, Erdos-Renyi graphs with respect to in-absorption and in-driftness, and configuration models with respect to out-absorption and out-driftness. Moreover, taken together these four measures separate the network nodes based on broad functional roles (visual, auditory, somatomotor, and frontolimbic).

Anisotropic Confinement, Electronic Coupling and Strain Induced Effects Detected by Valence-Band Anisotropy in Self-Assembled Quantum Dots

A method to determine the effects of the geometry and lateral ordering on the electronic properties of an array of one-dimensional self-assembled quantum dots is discussed. A model that takes into account the valence-band anisotropic effective masses and strain effects must be used to describe the behavior of the photoluminescence emission, proposed as a clean tool for the characterization of dot anisotropy and/or inter-dot coupling. Under special growth conditions, such as substrate temperature and Arsenic background, 1D chains of In(0.4)Ga(0.6) As quantum dots were grown by molecular beam epitaxy. Grazing-incidence X-ray diffraction measurements directly evidence the strong strain anisotropy due to the formation of quantum dot chains, probed by polarization-resolved low-temperature photoluminescence. The results are in fair good agreement with the proposed model.

Exotic phases induced by strong spin-orbit coupling in ordered double perovskites

We construct and analyze a microscopic model for insulating rocksalt ordered double perovskites, with the chemical formula A(2)BB'O(6), where the B' atom has a 4d(1) or 5d(1) electronic configuration and forms a face-centered-cubic lattice. The combination of the triply degenerate t(2g) orbital and strong spin-orbit coupling forms local quadruplets with an effective spin moment j=3/2. Moreover, due to strongly orbital-dependent exchange, the effective spins have substantial biquadratic and bicubic interactions (fourth and sixth order in the spins, respectively). This leads, at the mean-field level, to three main phases: an unusual antiferromagnet with dominant octupolar order, a ferromagnetic phase with magnetization along the [110] direction, and a nonmagnetic but quadrupolar ordered phase, which is stabilized by thermal fluctuations and intermediate temperatures. All these phases have a two-sublattice structure described by the ordering wave vector Q=2 pi(001). We consider quantum fluctuations and argue that in the regime of dominant antiferromagnetic exchange, a nonmagnetic valence-bond solid or quantum-spin-liquid state may be favored instead. Candidate quantum-spin-liquid states and their basic properties are described. We also address the effect of single-site anisotropy driven by lattice distortions. Existing and possible future experiments are discussed in light of these results.

Spin-charge coupling in quantum wires at zero magnetic field

We discuss an approximation for the dynamic charge response of nonlinear spin-1/2 Luttinger liquids in the limit of small momentum. Besides accounting for the broadening of the charge peak due to two-holon excitations, the nonlinearity of the dispersion gives rise to a two-spinon peak, which at zero temperature has an asymmetric line shape. At finite temperature the spin peak is broadened by diffusion. As an application, we discuss the density and temperature dependence of the Coulomb drag resistivity due to long-wavelength scattering between quantum wires.

Knowledge acquisition by networks of interacting agents in the presence of observation errors

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Renyi or Barabasi-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.

Connectivity and dynamics of neuronal networks as defined by the shape of individual neurons

Biological neuronal networks constitute a special class of dynamical systems, as they are formed by individual geometrical components, namely the neurons. In the existing literature, relatively little attention has been given to the influence of neuron shape on the overall connectivity and dynamics of the emerging networks. The current work addresses this issue by considering simplified neuronal shapes consisting of circular regions (soma/axons) with spokes (dendrites). Networks are grown by placing these patterns randomly in the two-dimensional (2D) plane and establishing connections whenever a piece of dendrite falls inside an axon. Several topological and dynamical properties of the resulting graph are measured, including the degree distribution, clustering coefficients, symmetry of connections, size of the largest connected component, as well as three hierarchical measurements of the local topology. By varying the number of processes of the individual basic patterns, we can quantify relationships between the individual neuronal shape and the topological and dynamical features of the networks. Integrate-and-fire dynamics on these networks is also investigated with respect to transient activation from a source node, indicating that long-range connections play an important role in the propagation of avalanches.

Border detection in complex networks

One important issue implied by the finite nature of real-world networks regards the identification of their more external (border) and internal nodes. The present work proposes a formal and objective definition of these properties, founded on the recently introduced concept of node diversity. It is shown that this feature does not exhibit any relevant correlation with several well-established complex networks measurements. A methodology for the identification of the borders of complex networks is described and illustrated with respect to theoretical (geographical and knitted networks) as well as real-world networks (urban and word association networks), yielding interesting results and insights in both cases.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Construct and Compare Gene Coexpression Networks with DAPfinder and DAPview

Background: DAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes. Results: Each significant difference in gene-gene association represents a Differentially Associated Pair (DAP). Our tools include several choices of filtering methods, gene-gene association metrics, statistical testing methods and multiple comparison adjustments. Network results are easily displayed in Cytoscape. Analyses of glioma experiments and microarray simulations demonstrate the utility of these tools. Conclusions: DAPfinder is a new friendly-user tool for reconstruction and comparison of biological networks.