Synchronization and Stability in Noisy Population Dynamics

We study the stability and synchronization of predator-prey populations subjected to noise. The system is described by patches of local populations coupled by migration and predation over a neighborhood. When a single patch is considered, random perturbations tend to destabilize the populations, leading to extinction. If the number of patches is small, stabilization in the presence of noise is maintained at the expense of synchronization. As the number of patches increases, both the stability and the synchrony among patches increase. However, a residual asynchrony, large compared with the noise amplitude, seems to persist even in the limit of infinite number of patches. Therefore, the mechanism of stabilization by asynchrony recently proposed by R. Abta et. al., combining noise, diffusion and nonlinearities, seems to be more general than first proposed.Comment: 3 pages, 3 figures. To appear in Phys. Rev.

Topology and Evolution of Technology Innovation Networks

The web of relations linking technological innovation can be fairly described in terms of patent citations. The resulting patent citation network provides a picture of the large-scale organization of innovations and its time evolution. Here we study the patterns of change of patents registered by the US Patent and Trademark Office (USPTO). We show that the scaling behavior exhibited by this network is consistent with a preferential attachment mechanism together with a Weibull-shaped aging term. Such attachment kernel is shared by scientific citation networks, thus indicating an universal type of mechanism linking ideas and designs and their evolution. The implications for evolutionary theory of innovation are discussed.Comment: 6 pages, 5 figures, submitted to Physical Review

Exploring complex networks by walking on them

We carry out a comparative study on the problem for a walker searching on several typical complex networks. The search efficiency is evaluated for various strategies. Having no knowledge of the global properties of the underlying networks and the optimal path between any two given nodes, it is found that the best search strategy is the self-avoid random walk. The preferentially self-avoid random walk does not help in improving the search efficiency further. In return, topological information of the underlying networks may be drawn by comparing the results of the different search strategies.Comment: 5 pages, 5 figure

Non elliptic SPDEs and ambit fields: existence of densities

Relying on the method developed in [debusscheromito2014], we prove the existence of a density for two different examples of random fields indexed by (t,x)\in(0,T]\times \Rd. The first example consists of SPDEs with Lipschitz continuous coefficients driven by a Gaussian noise white in time and with a stationary spatial covariance, in the setting of [dalang1999]. The density exists on the set where the nonlinearity $\sigma$ of the noise does not vanish. This complements the results in [sanzsuess2015] where $\sigma$ is assumed to be bounded away from zero. The second example is an ambit field with a stochastic integral term having as integrator a L\'evy basis of pure-jump, stable-like type.Comment: 23 page

Scale-free Networks from Optimal Design

A large number of complex networks, both natural and artificial, share the presence of highly heterogeneous, scale-free degree distributions. A few mechanisms for the emergence of such patterns have been suggested, optimization not being one of them. In this letter we present the first evidence for the emergence of scaling (and smallworldness) in software architecture graphs from a well-defined local optimization process. Although the rules that define the strategies involved in software engineering should lead to a tree-like structure, the final net is scale-free, perhaps reflecting the presence of conflicting constraints unavoidable in a multidimensional optimization process. The consequences for other complex networks are outlined.Comment: 6 pages, 2 figures. Submitted to Europhysics Letters. Additional material is available at http://complex.upc.es/~sergi/software.ht

Topological reversibility and causality in feed-forward networks

Systems whose organization displays causal asymmetry constraints, from evolutionary trees to river basins or transport networks, can be often described in terms of directed paths (causal flows) on a discrete state space. Such a set of paths defines a feed-forward, acyclic network. A key problem associated with these systems involves characterizing their intrinsic degree of path reversibility: given an end node in the graph, what is the uncertainty of recovering the process backwards until the origin? Here we propose a novel concept, \textit{topological reversibility}, which rigorously weigths such uncertainty in path dependency quantified as the minimum amount of information required to successfully revert a causal path. Within the proposed framework we also analytically characterize limit cases for both topologically reversible and maximally entropic structures. The relevance of these measures within the context of evolutionary dynamics is highlighted.Comment: 9 pages, 3 figure

Communication in networks with hierarchical branching

We present a simple model of communication in networks with hierarchical branching. We analyze the behavior of the model from the viewpoint of critical systems under different situations. For certain values of the parameters, a continuous phase transition between a sparse and a congested regime is observed and accurately described by an order parameter and the power spectra. At the critical point the behavior of the model is totally independent of the number of hierarchical levels. Also scaling properties are observed when the size of the system varies. The presence of noise in the communication is shown to break the transition. Despite the simplicity of the model, the analytical results are a useful guide to forecast the main features of real networks.Comment: 4 pages, 3 figures. Final version accepted in PR

Logarithmic growth dynamics in software networks

In a recent paper, Krapivsky and Redner (Phys. Rev. E, 71 (2005) 036118) proposed a new growing network model with new nodes being attached to a randomly selected node, as well to all ancestors of the target node. The model leads to a sparse graph with an average degree growing logarithmically with the system size. Here we present compeling evidence for software networks being the result of a similar class of growing dynamics. The predicted pattern of network growth, as well as the stationary in- and out-degree distributions are consistent with the model. Our results confirm the view of large-scale software topology being generated through duplication-rewiring mechanisms. Implications of these findings are outlined.Comment: 7 pages, 3 figures, published in Europhysics Letters (2005

Polymorphic microsatellite loci from the endangered Giant Otter (Pteronura brasiliensis).

We describe the first microsatellite loci isolated from the giant otter (Pteronura brasiliensis), an endangered mustelid endemic to South America. Fourteen di- and trinucleotide polymorphic loci were characterised in fourteen individuals from the Pantanal wetlands, Central Brazil. Number of alleles per locus ranged from 2 to 5, and average observed heterozygosity was 0.577. Two loci were in linkage disequilibrium, and one further locus deviated from Hardy?Weinberg equilibrium, probably due to the presence of null alleles. The transferability of these markers to two other mustelids (Lontra longicaudis and Eira barbara) and to the mephitid Conepatus semistriatus was also evaluated. These loci are useful to study the ecology and evolution of these species

Diversity, competition, extinction: the ecophysics of language change

As early indicated by Charles Darwin, languages behave and change very much like living species. They display high diversity, differentiate in space and time, emerge and disappear. A large body of literature has explored the role of information exchanges and communicative constraints in groups of agents under selective scenarios. These models have been very helpful in providing a rationale on how complex forms of communication emerge under evolutionary pressures. However, other patterns of large-scale organization can be described using mathematical methods ignoring communicative traits. These approaches consider shorter time scales and have been developed by exploiting both theoretical ecology and statistical physics methods. The models are reviewed here and include extinction, invasion, origination, spatial organization, coexistence and diversity as key concepts and are very simple in their defining rules. Such simplicity is used in order to catch the most fundamental laws of organization and those universal ingredients responsible for qualitative traits. The similarities between observed and predicted patterns indicate that an ecological theory of language is emerging, supporting (on a quantitative basis) its ecological nature, although key differences are also present. Here we critically review some recent advances lying and outline their implications and limitations as well as open problems for future research.Comment: 17 Pages. A review on current models from statistical Physics and Theoretical Ecology applied to study language dynamic