Abstract:
It is suggested that the degree distribution for networks of the cell-metabolism for simple organisms reflects a ubiquitous randomness. This implies that natural selection has exerted no or very little pressure on the network degree distribution during evolution. The corresponding random network, here termed the blind watchmaker network has a power-law degree distribution with an exponent γ≥2. It is random with respect to a complete set of network states characterized by a description of which links are attached to a node as well as a time-ordering of these links. No a priory assumption of any growth mechanism or evolution process is made. It is found that the degree distribution of the blind watchmaker network agrees very precisely with that of the metabolic networks. This implies that the evolutionary pathway of the cell-metabolism, when projected onto a metabolic network representation, has remained statistically random with respect to a complete set of network states. This suggests that even a biological system, which due to natural selection has developed an enormous specificity like the cellular metabolism, nevertheless can, at the same time, display well defined characteristics emanating from the ubiquitous inherent random element of Darwinian evolution. The fact that also completely random networks may have scale-free node distributions gives a new perspective on the origin of scale-free networks in general.

Abstract:
It has recently been suggested from scaling arguments that the non-linear IV-exponent a, for a two-dimensional superconductor is different from the exponent originally suggested by Ambegaokar et al. The relation between the new and the old exponent is a=a_AHNS-3. The new scaling behaviour is linked to the logarithmic vortex interaction and the long range time tail which this gives rise to. Consequently one may expect that the scaling behavior is generic for models which have these basic features. The simplest model of this type is the two-dimensional Coulomb gas model with Langevin dynamics. We here explicitly verify, through computer simulations, that the IV-characteristics of this model indeed scales according to the new scaling exponent a. Keywords: vortex, Coulomb gas, IV-exponent, Simulations, Langevin, 2D superconductor, thin films.

Abstract:
The dynamics of two dimensional (2D) vortex fluctuations are investigated through simulations of the 2D Coulomb gas model in which vortices are represented by soft disks with logarithmic interactions. The simulations trongly support a recent suggestion that 2D vortex fluctuations obey an intrinsic anomalous dynamics manifested in a long range 1/t-tail in the vortex correlations. A new non-linear IV-exponent a, which is different from the commonly used AHNS exponent, a_AHNS and is given by a = 2a_AHNS - 3, is confirmed by the simulations. The results are discussed in the context of earlier simulations, experiments and a phenomenological description.

Abstract:
Complex networks are mapped to a model of boxes and balls where the balls are distinguishable. It is shown that the scale-free size distribution of boxes maximizes the information associated with the boxes provided configurations including boxes containing a finite fraction of the total amount of balls are excluded. It is conjectured that for a connected network with only links between different nodes, the nodes with a finite fraction of links are effectively suppressed. It is hence suggested that for such networks the scale-free node-size distribution maximizes the information encoded on the nodes. The noise associated with the size distributions is also obtained from a maximum entropy principle. Finally explicit predictions from our least bias approach are found to be born out by metabolic networks.

Abstract:
We extend the merging model for undirected networks by Kim et al. [Eur. Phys. J. B 43, 369 (2004)] to directed networks and investigate the emerging scale-free networks. Two versions of the directed merging model, friendly and hostile merging, give rise to two distinct network types. We uncover that some non-trivial features of these two network types resemble two levels of a certain randomization/non-specificity in the link reshuffling during network evolution. Furthermore the same features show up, respectively, in metabolic networks and transcriptional networks. We introduce measures that single out the distinguishing features between the two prototype networks, as well as point out features which are beyond the prototypes.

Abstract:
A random null model termed the Blind Watchmaker network (BW) has been shown to reproduce the degree distribution found in metabolic networks. This might suggest that natural selection has had little influence on this particular network property. We here investigate to what extent other structural network properties have evolved under selective pressure from the corresponding ones of the random null model: The clustering coefficient and the assortativity measures are chosen and it is found that these measures for the metabolic network structure are close enough to the BW-network so as to fit inside its reachable random phase space. It is furthermore shown that the use of this null model indicates an evolutionary pressure towards low assortativity and that this pressure is stronger for larger networks. It is also shown that selecting for BW networks with low assortativity causes the BW degree distribution to slightly deviate from its power-law shape in the same way as the metabolic networks. This implies that an equilibrium model with fluctuating degree distribution is more suitable as a null model, when identifying selective pressures, than a randomized counterpart with fixed degree sequence, since the overall degree sequence itself can change under selective pressure on other global network properties.

Abstract:
The two-dimensional random gauge \xy model, where the quenched random variables are magnetic bond angles uniformly distributed within $[-r\pi, r\pi]$ ($0 \leq r \leq 1$), is studied via Monte Carlo simulations. We investigate the phase diagram in the plane of the temperature $T$ and the disorder strength $r$, and infer, in contrast to a prevailing conclusion in many earlier studies, that the system is superconducting at any disorder strength $r$ for sufficiently low $T$. It is also argued that the superconducting to normal transition has different nature at weak disorder and strong disorder: termed Kosterlitz-Thouless (KT) type and non-KT type, respectively. The results are compared to earlier works.

Abstract:
We investigate a social system of agents faced with a binary choice. We assume there is a correct, or beneficial, outcome of this choice. Furthermore, we assume agents are influenced by others in making their decision, and that the agents can obtain information that may guide them towards making a correct decision. The dynamic model we propose is of nonequilibrium type, converging to a final decision. We run it on random graphs and scale-free networks. On random graphs, we find two distinct regions in terms of the "finalizing time" -- the time until all agents have finalized their decisions. On scale-free networks on the other hand, there does not seem to be any such distinct scaling regions.

Abstract:
It is numerically shown that the discontinuous character of the helicity modulus of the two-dimensional XY model at the Kosterlitz-Thouless (KT) transition can be directly related to a higher order derivative of the free energy without presuming any {\it a priori} knowledge of the nature of the transition. It is also suggested that this higher order derivative is of intrinsic interest in that it gives an additional characteristics of the KT transition which might be associated with a universal number akin to the universal value of the helicity modulus at the critical temperature.

Abstract:
Percolation for a planar lattice has a single percolation threshold, whereas percolation for a negatively curved lattice displays two separate thresholds. The enhanced binary tree (EBT) can be viewed as a prototype model displaying two separate percolation thresholds. We present an analytic result for the EBT model which gives two critical percolation threshold probabilities, $p_{c1}=1/2\sqrt{13}-3/2$ and $p_{c2}=1/2$, and yields a size-scaling exponent $\Phi =\ln [\frac{p(1+p)}{1-p(1-p)}]/\ln 2$. It is inferred that the two threshold values give exact upper limits and that $p_{c1}$ is furthermore exact. In addition, we argue that $p_{c2}$ is also exact. The physics of the model and the results are described within the midpoint-percolation concept: Monte Carlo simulations are presented for the number of boundary points which are reached from the midpoint, and the results are compared to the number of routes from the midpoint to the boundary given by the analytic solution. These comparisons provide a more precise physical picture of what happens at the transitions. Finally, the results are compared to related works, in particular, the Cayley tree and Monte Carlo results for hyperbolic lattices as well as earlier results for the EBT model. It disproves a conjecture that the EBT has an exact relation to the thresholds of its dual lattice.