Elliptic flow at high transverse momentum in Pb-Pb collisions at sNN=2.76\sqrt{s_{NN}} = 2.76 TeV with the ALICE experiment

An observable that can be used to better constrain the mechanism responsible for the parton energy loss is the elliptic azimuthal event anisotropy, $v_2$. We report on measurements of $v_2$ for inclusive and identified charged particles in Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV recorded by the ALICE experiment at the LHC. $v_2$ is presented for a wide range of particle transverse momentum up to $p_T=20$ GeV/c. The particle $v_2$ is finite, positive and approximately constant for $p_T>8$ GeV/c. The proton $v_2$ is higher than that of the pion up to about $p_T=8$ GeV/c. The results are compared to the measurements at lower energy reported by RHIC experiments.Comment: 4 pages, QM 2011 proceeding

Phase Transition in a Self-repairing Random Network

We consider a network, bonds of which are being sequentially removed; that is done at random, but conditioned on the system remaining connected (Self-Repairing Bond Percolation SRBP). This model is the simplest representative of a class of random systems for which forming of isolated clusters is forbidden. It qualitatively describes the process of fabrication of artificial porous materials and degradation of strained polymers. We find a phase transition at a finite concentration of bonds $p=p_c$, at which the backbone of the system vanishes; for all $p<p_c$ the network is a dense fractal.Comment: 4 pages, 4 figure

The topological relationship between the large-scale attributes and local interaction patterns of complex networks

Recent evidence indicates that the abundance of recurring elementary interaction patterns in complex networks, often called subgraphs or motifs, carry significant information about their function and overall organization. Yet, the underlying reasons for the variable quantity of different subgraph types, their propensity to form clusters, and their relationship with the networks' global organization remain poorly understood. Here we show that a network's large-scale topological organization and its local subgraph structure mutually define and predict each other, as confirmed by direct measurements in five well studied cellular networks. We also demonstrate the inherent existence of two distinct classes of subgraphs, and show that, in contrast to the low-density type II subgraphs, the highly abundant type I subgraphs cannot exist in isolation but must naturally aggregate into subgraph clusters. The identified topological framework may have important implications for our understanding of the origin and function of subgraphs in all complex networks.Comment: pape

Minimum spanning trees on random networks

We show that the geometry of minimum spanning trees (MST) on random graphs is universal. Due to this geometric universality, we are able to characterise the energy of MST using a scaling distribution ($P(\epsilon)$) found using uniform disorder. We show that the MST energy for other disorder distributions is simply related to $P(\epsilon)$. We discuss the relationship to invasion percolation (IP), to the directed polymer in a random media (DPRM) and the implications for the broader issue of universality in disordered systems.Comment: 4 pages, 3 figure

Random-field Ising model on complete graphs and trees

We present exact results for the critical behavior of the RFIM on complete graphs and trees, both at equilibrium and away from equilibrium, i.e., models for hysteresis and Barkhausen noise. We show that for stretched exponential and power law distributions of random fields the behavior on complete graphs is non-universal, while the behavior on Cayley trees is universal even in the limit of large co-ordination.Comment: 4 pages, 4 figure

Coarse-Graining and Self-Dissimilarity of Complex Networks

Can complex engineered and biological networks be coarse-grained into smaller and more understandable versions in which each node represents an entire pattern in the original network? To address this, we define coarse-graining units (CGU) as connectivity patterns which can serve as the nodes of a coarse-grained network, and present algorithms to detect them. We use this approach to systematically reverse-engineer electronic circuits, forming understandable high-level maps from incomprehensible transistor wiring: first, a coarse-grained version in which each node is a gate made of several transistors is established. Then, the coarse-grained network is itself coarse-grained, resulting in a high-level blueprint in which each node is a circuit-module made of multiple gates. We apply our approach also to a mammalian protein-signaling network, to find a simplified coarse-grained network with three main signaling channels that correspond to cross-interacting MAP-kinase cascades. We find that both biological and electronic networks are 'self-dissimilar', with different network motifs found at each level. The present approach can be used to simplify a wide variety of directed and nondirected, natural and designed networks.Comment: 11 pages, 11 figure

Using graph concepts to understand the organization of complex systems

Complex networks are universal, arising in fields as disparate as sociology, physics, and biology. In the past decade, extensive research into the properties and behaviors of complex systems has uncovered surprising commonalities among the topologies of different systems. Attempts to explain these similarities have led to the ongoing development and refinement of network models and graph-theoretical analysis techniques with which to characterize and understand complexity. In this tutorial, we demonstrate through illustrative examples, how network measures and models have contributed to the elucidation of the organization of complex systems.Comment: v(1) 38 pages, 7 figures, to appear in the International Journal of Bifurcation and Chaos v(2) Line spacing changed; now 23 pages, 7 figures, to appear in the Special Issue "Complex Networks' Structure and Dynamics'' of the International Journal of Bifurcation and Chaos (Volume 17, Issue 7, July 2007) edited by S. Boccaletti and V. Lator

Long-lived stops in MSSM scenarios with a neutralino LSP

This work investigates the possibility of a long-lived stop squark in supersymmetric models with the neutralino as the lightest supersymmetric particle (LSP). We study the implications of meta-stable stops on the sparticle mass spectra and the dark matter density. We find that in order to obtain a sufficiently long stop lifetime so as to be observable as a stable R-hadron at an LHC experiment, we need to fine tune the mass degeneracy between the stop and the LSP considerably. This increases the stop-neutralino coanihilation cross section, leaving the neutralino relic density lower than what is expected from the WMAP results for stop masses ~1.5 TeV/c^2. However, if such scenarios are realised in nature we demonstrate that the long-lived stops will be produced at the LHC and that stop-based R-hadrons with masses up to 1 TeV/c^2 can be detected after one year of running at design luminosity