703 research outputs found
Drinfeld Twists and Algebraic Bethe Ansatz of the Supersymmetric t-J Model
We construct the Drinfeld twists (factorizing -matrices) for the
supersymmetric t-J model. Working in the basis provided by the -matrix (i.e.
the so-called -basis), we obtain completely symmetric representations of the
monodromy matrix and the pseudo-particle creation operators of the model. These
enable us to resolve the hierarchy of the nested Bethe vectors for the
invariant t-J model.Comment: 23 pages, no figure, Latex file, minor misprints are correcte
Influenza: From zoonosis to pandemic
Global surveillance and advances in vaccine technology are essential to answer the threat of influenza pandemics http://ow.ly/Yt3e
Path finding strategies in scale-free networks
We numerically investigate the scale-free network model of Barab{\'a}si and
Albert [A. L. Barab{\'a}si and R. Albert, Science {\bf 286}, 509 (1999)]
through the use of various path finding strategies. In real networks, global
network information is not accessible to each vertex, and the actual path
connecting two vertices can sometimes be much longer than the shortest one. A
generalized diameter depending on the actual path finding strategy is
introduced, and a simple strategy, which utilizes only local information on the
connectivity, is suggested and shown to yield small-world behavior: the
diameter of the network increases logarithmically with the network size
, the same as is found with global strategy. If paths are sought at random,
is found.Comment: 4 pages, final for
Scaling exponents and clustering coefficients of a growing random network
The statistical property of a growing scale-free network is studied based on
an earlier model proposed by Krapivsky, Rodgers, and Redner [Phys. Rev. Lett.
86, 5401 (2001)], with the additional constraints of forbidden of
self-connection and multiple links of the same direction between any two nodes.
Scaling exponents in the range of 1-2 are obtained through Monte Carlo
simulations and various clustering coefficients are calculated, one of which,
, is of order , indicating the network resembles a
small-world. The out-degree distribution has an exponential cut-off for large
out-degree.Comment: six pages, including 5 figures, RevTex 4 forma
Generic scale of the "scale-free" growing networks
We show that the connectivity distributions of scale-free growing
networks ( is the network size) have the generic scale -- the cut-off at
. The scaling exponent is related to the exponent
of the connectivity distribution, . We propose the
simplest model of scale-free growing networks and obtain the exact form of its
connectivity distribution for any size of the network. We demonstrate that the
trace of the initial conditions -- a hump at --
may be found for any network size. We also show that there exists a natural
boundary for the observation of the scale-free networks and explain why so few
scale-free networks are observed in Nature.Comment: 4 pages revtex, 3 figure
Highly clustered scale-free networks
We propose a model for growing networks based on a finite memory of the
nodes. The model shows stylized features of real-world networks: power law
distribution of degree, linear preferential attachment of new links and a
negative correlation between the age of a node and its link attachment rate.
Notably, the degree distribution is conserved even though only the most
recently grown part of the network is considered. This feature is relevant
because real-world networks truncated in the same way exhibit a power-law
distribution in the degree. As the network grows, the clustering reaches an
asymptotic value larger than for regular lattices of the same average
connectivity. These high-clustering scale-free networks indicate that memory
effects could be crucial for a correct description of the dynamics of growing
networks.Comment: 6 pages, 4 figure
The impact of forest regeneration on streamflow in 12 mesoscale humid tropical catchments
Although regenerating forests make up an increasingly large portion of humid tropical landscapes, little is known of their water use and effects on streamflow (Q). Since the 1950s the island of Puerto Rico has experienced widespread abandonment of pastur
A Geometric Fractal Growth Model for Scale Free Networks
We introduce a deterministic model for scale-free networks, whose degree
distribution follows a power-law with the exponent . At each time step,
each vertex generates its offsprings, whose number is proportional to the
degree of that vertex with proportionality constant m-1 (m>1). We consider the
two cases: first, each offspring is connected to its parent vertex only,
forming a tree structure, and secondly, it is connected to both its parent and
grandparent vertices, forming a loop structure. We find that both models
exhibit power-law behaviors in their degree distributions with the exponent
. Thus, by tuning m, the degree exponent can be
adjusted in the range, . We also solve analytically a mean
shortest-path distance d between two vertices for the tree structure, showing
the small-world behavior, that is, , where N is
system size, and is the mean degree. Finally, we consider the case
that the number of offsprings is the same for all vertices, and find that the
degree distribution exhibits an exponential-decay behavior
World-Wide Web scaling exponent from Simon's 1955 model
Recently, statistical properties of the World-Wide Web have attracted
considerable attention when self-similar regimes have been observed in the
scaling of its link structure. Here we recall a classical model for general
scaling phenomena and argue that it offers an explanation for the World-Wide
Web's scaling exponent when combined with a recent measurement of internet
growth.Comment: 1 page RevTeX, no figure
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