2,411 research outputs found
Statistics of Certain Models of Evolution
In a recent paper, Newman surveys the literature on power law spectra in
evolution, self-organised criticality and presents a model of his own to arrive
at a conclusion that self-organised criticality is not necessary for evolution.
Not only did he miss a key model (Ecolab) that has a clear self-organised
critical mechanism, but also Newman's model exhibits the same mechanism that
gives rise to power law behaviour as does Ecolab. Newman's model is, in fact, a
``mean field'' approximation of a self-organised critical system. In this
paper, I have also implemented Newman's model using the Ecolab software,
removing the restriction that the number of species remains constant. It turns
out that the requirement of constant species number is non-trivial, leading to
a global coupling between species that is similar in effect to the species
interactions seen in Ecolab. In fact, the model must self-organise to a state
where the long time average of speciations balances that of the extinctions,
otherwise the system either collapses or explodes. In view of this, Newman's
model does not provide the hoped-for counter example to the presence of
self-organised criticality in evolution, but does provide a simple, almost
analytic model that can used to understand more intricate models such as
Ecolab.Comment: accepted in Phys Rev E.; RevTeX; See
http://parallel.hpc.unsw.edu.au/rks/ecolab.html for more informatio
Shift of percolation thresholds for epidemic spread between static and dynamic small-world networks
The aim of the study was to compare the epidemic spread on static and dynamic
small-world networks. The network was constructed as a 2-dimensional
Watts-Strogatz model (500x500 square lattice with additional shortcuts), and
the dynamics involved rewiring shortcuts in every time step of the epidemic
spread. The model of the epidemic is SIR with latency time of 3 time steps. The
behaviour of the epidemic was checked over the range of shortcut probability
per underlying bond 0-0.5. The quantity of interest was percolation threshold
for the epidemic spread, for which numerical results were checked against an
approximate analytical model. We find a significant lowering of percolation
thresholds for the dynamic network in the parameter range given. The result
shows that the behaviour of the epidemic on dynamic network is that of a static
small world with the number of shortcuts increased by 20.7 +/- 1.4%, while the
overall qualitative behaviour stays the same. We derive corrections to the
analytical model which account for the effect. For both dynamic and static
small-world we observe suppression of the average epidemic size dependence on
network size in comparison with finite-size scaling known for regular lattice.
We also study the effect of dynamics for several rewiring rates relative to
latency time of the disease.Comment: 13 pages, 6 figure
Edge-Based Compartmental Modeling for Infectious Disease Spread Part III: Disease and Population Structure
We consider the edge-based compartmental models for infectious disease spread
introduced in Part I. These models allow us to consider standard SIR diseases
spreading in random populations. In this paper we show how to handle deviations
of the disease or population from the simplistic assumptions of Part I. We
allow the population to have structure due to effects such as demographic
detail or multiple types of risk behavior the disease to have more complicated
natural history. We introduce these modifications in the static network
context, though it is straightforward to incorporate them into dynamic
networks. We also consider serosorting, which requires using the dynamic
network models. The basic methods we use to derive these generalizations are
widely applicable, and so it is straightforward to introduce many other
generalizations not considered here
Cross-over behaviour in a communication network
We address the problem of message transfer in a communication network. The
network consists of nodes and links, with the nodes lying on a two dimensional
lattice. Each node has connections with its nearest neighbours, whereas some
special nodes, which are designated as hubs, have connections to all the sites
within a certain area of influence. The degree distribution for this network is
bimodal in nature and has finite variance. The distribution of travel times
between two sites situated at a fixed distance on this lattice shows fat
fractal behaviour as a function of hub-density. If extra assortative
connections are now introduced between the hubs so that each hub is connected
to two or three other hubs, the distribution crosses over to power-law
behaviour. Cross-over behaviour is also seen if end-to-end short cuts are
introduced between hubs whose areas of influence overlap, but this is much
milder in nature. In yet another information transmission process, namely, the
spread of infection on the network with assortative connections, we again
observed cross-over behaviour of another type, viz. from one power-law to
another for the threshold values of disease transmission probability. Our
results are relevant for the understanding of the role of network topology in
information spread processes.Comment: 12 figure
From Network Structure to Dynamics and Back Again: Relating dynamical stability and connection topology in biological complex systems
The recent discovery of universal principles underlying many complex networks
occurring across a wide range of length scales in the biological world has
spurred physicists in trying to understand such features using techniques from
statistical physics and non-linear dynamics. In this paper, we look at a few
examples of biological networks to see how similar questions can come up in
very different contexts. We review some of our recent work that looks at how
network structure (e.g., its connection topology) can dictate the nature of its
dynamics, and conversely, how dynamical considerations constrain the network
structure. We also see how networks occurring in nature can evolve to modular
configurations as a result of simultaneously trying to satisfy multiple
structural and dynamical constraints. The resulting optimal networks possess
hubs and have heterogeneous degree distribution similar to those seen in
biological systems.Comment: 15 pages, 6 figures, to appear in Proceedings of "Dynamics On and Of
Complex Networks", ECSS'07 Satellite Workshop, Dresden, Oct 1-5, 200
Finding and evaluating community structure in networks
We propose and study a set of algorithms for discovering community structure
in networks -- natural divisions of network nodes into densely connected
subgroups. Our algorithms all share two definitive features: first, they
involve iterative removal of edges from the network to split it into
communities, the edges removed being identified using one of a number of
possible "betweenness" measures, and second, these measures are, crucially,
recalculated after each removal. We also propose a measure for the strength of
the community structure found by our algorithms, which gives us an objective
metric for choosing the number of communities into which a network should be
divided. We demonstrate that our algorithms are highly effective at discovering
community structure in both computer-generated and real-world network data, and
show how they can be used to shed light on the sometimes dauntingly complex
structure of networked systems.Comment: 16 pages, 13 figure
Flight of the dragonflies and damselflies
This work is a synthesis of our current understanding of the mechanics, aerodynamics and visually mediated control of dragonfly and damselfly flight, with the addition of new experimental and computational data in several key areas. These are: the diversity of dragonfly wing morphologies, the aerodynamics of gliding flight, force generation in flapping flight, aerodynamic efficiency, comparative flight performance and pursuit strategies during predatory and territorial flights. New data are set in context by brief reviews covering anatomy at several scales, insect aerodynamics, neuromechanics and behaviour. We achieve a new perspective by means of a diverse range of techniques, including laser-line mapping of wing topographies, computational fluid dynamics simulations of finely detailed wing geometries, quantitative imaging using particle image velocimetry of on-wing and wake flow patterns, classical aerodynamic theory, photography in the field, infrared motion capture and multi-camera optical tracking of free flight trajectories in laboratory environments. Our comprehensive approach enables a novel synthesis of datasets and subfields that integrates many aspects of flight from the neurobiology of the compound eye, through the aeromechanical interface with the surrounding fluid, to flight performance under cruising and higher-energy behavioural modes
Punctuated equilibria and 1/f noise in a biological coevolution model with individual-based dynamics
We present a study by linear stability analysis and large-scale Monte Carlo
simulations of a simple model of biological coevolution. Selection is provided
through a reproduction probability that contains quenched, random interspecies
interactions, while genetic variation is provided through a low mutation rate.
Both selection and mutation act on individual organisms. Consistent with some
current theories of macroevolutionary dynamics, the model displays
intermittent, statistically self-similar behavior with punctuated equilibria.
The probability density for the lifetimes of ecological communities is well
approximated by a power law with exponent near -2, and the corresponding power
spectral densities show 1/f noise (flicker noise) over several decades. The
long-lived communities (quasi-steady states) consist of a relatively small
number of mutualistically interacting species, and they are surrounded by a
``protection zone'' of closely related genotypes that have a very low
probability of invading the resident community. The extent of the protection
zone affects the stability of the community in a way analogous to the height of
the free-energy barrier surrounding a metastable state in a physical system.
Measures of biological diversity are on average stationary with no discernible
trends, even over our very long simulation runs of approximately 3.4x10^7
generations.Comment: 20 pages RevTex. Minor revisions consistent with published versio
Numerical Evolution of General Relativistic Voids
In this paper, we study the evolution of a relativistic, superhorizon-sized
void embedded in a Friedmann-Robertson-Walker universe. We numerically solve
the spherically symmetric general relativistic equations in comoving,
synchronous coordinates. Initially, the fluid inside the void is taken to be
homogeneous and nonexpanding. In a radiation- dominated universe, we find that
radiation diffuses into the void at approximately the speed of light as a
strong shock---the void collapses. We also find the surprising result that the
cosmic collapse time (the -crossing time) is much smaller than
previously thought, because it depends not only on the radius of the void, but
also on the ratio of the temperature inside the void to that outside. If the
ratio of the initial void radius to the outside Hubble radius is less than the
ratio of the outside temperature to that inside, then the collapse occurs in
less than the outside Hubble time. Thus, superhorizon-sized relativistic void
may thermalize and homogenize relatively quickly. These new simulations revise
the current picture of superhorizon-sized void evolution after first-order
inflation.Comment: 37 pages plus 12 figures (upon request-- [email protected])
LaTeX, FNAL-PUB-93/005-
Class of correlated random networks with hidden variables
We study a class models of correlated random networks in which vertices are
characterized by \textit{hidden variables} controlling the establishment of
edges between pairs of vertices. We find analytical expressions for the main
topological properties of these models as a function of the distribution of
hidden variables and the probability of connecting vertices. The expressions
obtained are checked by means of numerical simulations in a particular example.
The general model is extended to describe a practical algorithm to generate
random networks with an \textit{a priori} specified correlation structure. We
also present an extension of the class, to map non-equilibrium growing networks
to networks with hidden variables that represent the time at which each vertex
was introduced in the system
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