382,991 research outputs found

    The group of causal automorphisms

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    The group of causal automorphisms on Minkowski space-time is given and its structure is analyzed

    Particle Correlation Results from the ALICE Experiment at LHC

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    Measurements of two-particle correlations of inclusive and identified charged particles performed with the ALICE detector in Pb--Pb collisions at sNN=2.76TeV\sqrt{s_{NN}} = 2.76 TeV are presented. The near-side jet shape is analyzed in the low pTp_{\rm T} regions (1<1<p_{\rm T}<8GeV/c<8GeV/c). While the RMS of the peak in Δφ\Delta\varphi-direction is independent of centrality within uncertainties, we find significant broadening in Δη\Delta\eta-direction from peripheral to central collisions. The near-side p/πp/\pi ratio of particles associated to a trigger particle from jet fragmentation in the central Pb--Pb collisions is consistent with vacuum fragmentation in the measured momentum region (1.5<1.5<p_{\rm T}<4.5GeV\c).Comment: 8 pages, 6 figures, proceeding for Physics at LHC 2012, vancouver, Canad

    The q-component static model : modeling social networks

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    We generalize the static model by assigning a q-component weight on each vertex. We first choose a component (μ)(\mu) among the q components at random and a pair of vertices is linked with a color μ\mu according to their weights of the component (μ)(\mu) as in the static model. A (1-f) fraction of the entire edges is connected following this way. The remaining fraction f is added with (q+1)-th color as in the static model but using the maximum weights among the q components each individual has. This model is motivated by social networks. It exhibits similar topological features to real social networks in that: (i) the degree distribution has a highly skewed form, (ii) the diameter is as small as and (iii) the assortativity coefficient r is as positive and large as those in real social networks with r reaching a maximum around f0.2f \approx 0.2.Comment: 5 pages, 6 figure

    Disassortativity of random critical branching trees

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    Random critical branching trees (CBTs) are generated by the multiplicative branching process, where the branching number is determined stochastically, independent of the degree of their ancestor. Here we show analytically that despite this stochastic independence, there exists the degree-degree correlation (DDC) in the CBT and it is disassortative. Moreover, the skeletons of fractal networks, the maximum spanning trees formed by the edge betweenness centrality, behave similarly to the CBT in the DDC. This analytic solution and observation support the argument that the fractal scaling in complex networks originates from the disassortativity in the DDC.Comment: 3 pages, 2 figure
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