Wavevector-dependent spin filtering and spin transport through magnetic barriers in graphene

We study the spin-resolved transport through magnetic nanostructures in monolayer and bilayer graphene. We take into account both the orbital effect of the inhomogeneous perpendicular magnetic field as well as the in-plane spin splitting due to the Zeeman interaction and to the exchange coupling possibly induced by the proximity of a ferromagnetic insulator. We find that a single barrier exhibits a wavevector-dependent spin filtering effect at energies close to the transmission threshold. This effect is significantly enhanced in a resonant double barrier configuration, where the spin polarization of the outgoing current can be increased up to 100% by increasing the distance between the barriers

Magnetic confinement of massless Dirac fermions in graphene

Due to Klein tunneling, electrostatic potentials are unable to confine Dirac electrons. We show that it is possible to confine massless Dirac fermions in a monolayer graphene sheet by inhomogeneous magnetic fields. This allows one to design mesoscopic structures in graphene by magnetic barriers, e.g. quantum dots or quantum point contacts.Comment: 4 pages, 3 figures, version to appear in PR

On the transition to efficiency in Minority Games

The existence of a phase transition with diverging susceptibility in batch Minority Games (MGs) is the mark of informationally efficient regimes and is linked to the specifics of the agents' learning rules. Here we study how the standard scenario is affected in a mixed population game in which agents with the `optimal' learning rule (i.e. the one leading to efficiency) coexist with ones whose adaptive dynamics is sub-optimal. Our generic finding is that any non-vanishing intensive fraction of optimal agents guarantees the existence of an efficient phase. Specifically, we calculate the dependence of the critical point on the fraction $q$ of `optimal' agents focusing our analysis on three cases: MGs with market impact correction, grand-canonical MGs and MGs with heterogeneous comfort levels.Comment: 12 pages, 3 figures; contribution to the special issue "Viewing the World through Spin Glasses" in honour of David Sherrington on the occasion of his 65th birthda

Recorded displacements in a landslide slope due to regional and teleseismic earthquakes

Regional and teleseismic earthquakes can induce displacements along joints in a landslideinvolved rocky slope in Central Italy. The rarity of these effects is due to specific physical properties of the seismic signals associated with: (i) the energy content, (ii) the distribution of relative energy and peak of ground acceleration related to the ground motion components and (iii) the spectral amplitude distribution in the frequency domain; these properties allow the triggering earthquakes to be distinguished from the others. The observed effects are relevant when compared to the direction of the landslide movement and the dimensions of the involved rock mass volume. The landslide movement is less constrained in the direction parallel to the dip of the slope and the landslide dimensions are associated with characteristic periods that control the landslide deformational response in relation to the spectral content of the ground motion. The earthquake-induced displacements are significant because they have the same order of magnitude as the average annual cumulative displacement based on a decade of strain measurements within the slope

Experimental evidence of laser power oscillations induced by the relative Fresnel (Goos-Haenchen) phase

The amplification of the relative Fresnel (Goos-Haenchen) phase by an appropriate number of total internal reflections and the choice of favorable incidence angles allow to observe full oscillations in the power of a DPSS laser transmitted through sequential BK7 blocks. The experimental results confirm the theoretical predictions. The optical apparatus used in this letter can be seen as a new type of two-phase ellipsometric system where the phase of the complex refractive index is replaced by the relative Fresnel (Goos-Haenchen) phase.Comment: 7 pages, 3 figures, 1 tabl

Von Neumann's expanding model on random graphs

Within the framework of Von Neumann's expanding model, we study the maximum growth rate r achievable by an autocatalytic reaction network in which reactions involve a finite (fixed or fluctuating) number D of reagents. r is calculated numerically using a variant of the Minover algorithm, and analytically via the cavity method for disordered systems. As the ratio between the number of reactions and that of reagents increases the system passes from a contracting (r1). These results extend the scenario derived in the fully connected model (D\to\infinity), with the important difference that, generically, larger growth rates are achievable in the expanding phase for finite D and in more diluted networks. Moreover, the range of attainable values of r shrinks as the connectivity increases.Comment: 20 page

Detecting the traders' strategies in Minority-Majority games and real stock-prices

Price dynamics is analyzed in terms of a model which includes the possibility of effective forces due to trend followers or trend adverse strategies. The method is tested on the data of a minority-majority model and indeed it is capable of reconstructing the prevailing traders' strategies in a given time interval. Then we also analyze real (NYSE) stock-prices dynamics and it is possible to derive an indication for the the ``sentiment'' of the market for time intervals of at least one day.Comment: 13 pages, 10 figure

Evaluation of chemical composition and meat quality of breast muscle in broilers reared under light-emitting diode

The present study was designed to investigate the role of three different light-emitting diode (LED) light color temperatures on the growth performance, carcass characteristics, and breast meat quality of broilers. In our experimental condition, 180 chicks were randomly distributed into four environmentally controlled rooms (three replicates/treatment). The experimental design consisted of four light sources: neon (Control), Neutral (Neutral LED; K = 3500–3700), Cool (Cool LED; K = 5500–6000), and Warm (Warm LED; K = 3000–2500). Upon reaching the commercial weight (3.30 ± 0.20 kg live weight), 30 birds from each group were randomly selected, and live and carcass weight were evaluated to determinate the carcass yield. Following the slaughtering, samples of hemibreast meat were collected from each group and analyzed for physical and chemical properties, fatty acids composition, and volatile compounds. Live weight and carcass weight were negatively influenced by the Warm LED; however, no significant differences were observed in carcass yield in any of the experimental conditions. Higher drip loss values were detected in breast meat samples obtained by broilers reared under Neutral and Cool LEDs. In regard to the meat fatty acids profiles, higher polyunsaturated fatty acids (PUFA) values were detected with the Warm LED; however, the ratio of PUFA/saturated fatty acids (SFA) did not change in any group. The evaluation of volatile profiles in cooked chicken meat led to the identification of 18 compounds belonging to the family of aldehydes, alcohols, ketones, and phenolic compounds, both at 0 (T0) and 7 (T7) d after the cooking. The results of the present study suggest that the LED represents an alternative technology that is cheaper and more sustainable than traditional light sources, since it allows economic savings for poultry farming without significant alterations on the production parameters or the quality of the product

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements. The choice of a distance or similarity metric is, however, not trivial and can be highly dependent on the application at hand. In this work, we propose a novel metric learning method to evaluate distance between graphs that leverages the power of convolutional neural networks, while exploiting concepts from spectral graph theory to allow these operations on irregular graphs. We demonstrate the potential of our method in the field of connectomics, where neuronal pathways or functional connections between brain regions are commonly modelled as graphs. In this problem, the definition of an appropriate graph similarity function is critical to unveil patterns of disruptions associated with certain brain disorders. Experimental results on the ABIDE dataset show that our method can learn a graph similarity metric tailored for a clinical application, improving the performance of a simple k-nn classifier by 11.9% compared to a traditional distance metric.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201