On the Energy and Centrality Dependence of Higher Order Moments of Net-Proton Distributions in Relativistic Heavy Ion Collisions

The higher order moments of the net-baryon distributions in relativistic heavy ion collisions are useful probes for the QCD critical point and fluctuations. Within a simple model we study the colliding energy and centrality dependence of the net-proton distributions in the central rapidity region. The model is based on considering the baryon stopping and pair production effects in the processes. Based on some physical reasoning, the dependence is parameterized. Predictions for the net-proton distributions for Au+Au and Pb+Pb collisions at different centralities at $\sqrt{s_{NN}}$=39 and 2760 GeV, respectively, are presented from the parameterizations for the model parameters. A possible test of our model is proposed from investigating the net proton distributions in the non-central rapidity region for different colliding centralities and energies.Comment: 6 pages in revtex4, 8 eps figures. arXiv admin note: text overlap with arXiv:1107.474

ASAP : towards accurate, stable and accelerative penetrating-rank estimation on large graphs

Pervasive web applications increasingly require a measure of similarity among objects. Penetrating-Rank (P-Rank) has been one of the promising link-based similarity metrics as it provides a comprehensive way of jointly encoding both incoming and outgoing links into computation for emerging applications. In this paper, we investigate P-Rank efficiency problem that encompasses its accuracy, stability and computational time. (1) We provide an accuracy estimate for iteratively computing P-Rank. A symmetric problem is to find the iteration number K needed for achieving a given accuracy ε. (2) We also analyze the stability of P-Rank, by showing that small choices of the damping factors would make P-Rank more stable and well-conditioned. (3) For undirected graphs, we also explicitly characterize the P-Rank solution in terms of matrices. This results in a novel non-iterative algorithm, termed ASAP , for efficiently computing P-Rank, which improves the CPU time from O(n 4) to O( n 3 ). Using real and synthetic data, we empirically verify the effectiveness and efficiency of our approaches