6,069 research outputs found
Eisenstein Series and String Thresholds
We investigate the relevance of Eisenstein series for representing certain
-invariant string theory amplitudes which receive corrections from BPS
states only. may stand for any of the mapping class, T-duality and
U-duality groups , or respectively.
Using -invariant mass formulae, we construct invariant modular functions
on the symmetric space of non-compact type, with the
maximal compact subgroup of , that generalize the standard
non-holomorphic Eisenstein series arising in harmonic analysis on the
fundamental domain of the Poincar\'e upper half-plane. Comparing the
asymptotics and eigenvalues of the Eisenstein series under second order
differential operators with quantities arising in one- and -loop string
amplitudes, we obtain a manifestly T-duality invariant representation of the
latter, conjecture their non-perturbative U-duality invariant extension, and
analyze the resulting non-perturbative effects. This includes the and
couplings in toroidal compactifications of M-theory to any
dimension and respectively.Comment: Latex2e, 60 pages; v2: Appendix A.4 extended, 2 refs added, thms
renumbered, plus minor corrections; v3: relation (1.7) to math Eis series
clarified, eq (3.3) and minor typos corrected, final version to appear in
Comm. Math. Phys; v4: misprints and Eq C.13,C.24 corrected, see note adde
Scale-invariant statistics of period in directed earthquake network
A new law regarding structure of the earthquake networks is found. The
seismic data taken in California is mapped to a growing directed network. Then,
statistics of period in the network, which implies that after how many
earthquakes an earthquake returns to the initial location, is studied. It is
found that the period distribution obeys a power law, showing the fundamental
difficulty of statistical estimate of period.Comment: 11 pages including 3 figure
Emergence of robustness against noise: A structural phase transition in evolved models of gene regulatory networks
We investigate the evolution of Boolean networks subject to a selective
pressure which favors robustness against noise, as a model of evolved genetic
regulatory systems. By mapping the evolutionary process into a statistical
ensemble and minimizing its associated free energy, we find the structural
properties which emerge as the selective pressure is increased and identify a
phase transition from a random topology to a "segregated core" structure, where
a smaller and more densely connected subset of the nodes is responsible for
most of the regulation in the network. This segregated structure is very
similar qualitatively to what is found in gene regulatory networks, where only
a much smaller subset of genes --- those responsible for transcription factors
--- is responsible for global regulation. We obtain the full phase diagram of
the evolutionary process as a function of selective pressure and the average
number of inputs per node. We compare the theoretical predictions with Monte
Carlo simulations of evolved networks and with empirical data for Saccharomyces
cerevisiae and Escherichia coli.Comment: 12 pages, 10 figure
Boolean networks with reliable dynamics
We investigated the properties of Boolean networks that follow a given
reliable trajectory in state space. A reliable trajectory is defined as a
sequence of states which is independent of the order in which the nodes are
updated. We explored numerically the topology, the update functions, and the
state space structure of these networks, which we constructed using a minimum
number of links and the simplest update functions. We found that the clustering
coefficient is larger than in random networks, and that the probability
distribution of three-node motifs is similar to that found in gene regulation
networks. Among the update functions, only a subset of all possible functions
occur, and they can be classified according to their probability. More
homogeneous functions occur more often, leading to a dominance of canalyzing
functions. Finally, we studied the entire state space of the networks. We
observed that with increasing systems size, fixed points become more dominant,
moving the networks close to the frozen phase.Comment: 11 Pages, 15 figure
Network of recurrent events for the Olami-Feder-Christensen model
We numerically study the dynamics of a discrete spring-block model introduced
by Olami, Feder and Christensen (OFC) to mimic earthquakes and investigate to
which extent this simple model is able to reproduce the observed spatiotemporal
clustering of seismicty. Following a recently proposed method to characterize
such clustering by networks of recurrent events [Geophys. Res. Lett. {\bf 33},
L1304, 2006], we find that for synthetic catalogs generated by the OFC model
these networks have many non-trivial statistical properties. This includes
characteristic degree distributions -- very similar to what has been observed
for real seismicity. There are, however, also significant differences between
the OFC model and earthquake catalogs indicating that this simple model is
insufficient to account for certain aspects of the spatiotemporal clustering of
seismicity.Comment: 11 pages, 16 figure
Dynamical evolution of clustering in complex network of earthquakes
The network approach plays a distinguished role in contemporary science of
complex systems/phenomena. Such an approach has been introduced into seismology
in a recent work [S. Abe and N. Suzuki, Europhys. Lett. 65, 581 (2004)]. Here,
we discuss the dynamical property of the earthquake network constructed in
California and report the discovery that the values of the clustering
coefficient remain stationary before main shocks, suddenly jump up at the main
shocks, and then slowly decay following a power law to become stationary again.
Thus, the network approach is found to characterize main shocks in a peculiar
manner.Comment: 10 pages, 3 figures, 1 tabl
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