31,255 research outputs found
Causal graph dynamics
We extend the theory of Cellular Automata to arbitrary, time-varying graphs.
In other words we formalize, and prove theorems about, the intuitive idea of a
labelled graph which evolves in time - but under the natural constraint that
information can only ever be transmitted at a bounded speed, with respect to
the distance given by the graph. The notion of translation-invariance is also
generalized. The definition we provide for these "causal graph dynamics" is
simple and axiomatic. The theorems we provide also show that it is robust. For
instance, causal graph dynamics are stable under composition and under
restriction to radius one. In the finite case some fundamental facts of
Cellular Automata theory carry through: causal graph dynamics admit a
characterization as continuous functions, and they are stable under inversion.
The provided examples suggest a wide range of applications of this mathematical
object, from complex systems science to theoretical physics. KEYWORDS:
Dynamical networks, Boolean networks, Generative networks automata, Cayley
cellular automata, Graph Automata, Graph rewriting automata, Parallel graph
transformations, Amalgamated graph transformations, Time-varying graphs, Regge
calculus, Local, No-signalling.Comment: 25 pages, 9 figures, LaTeX, v2: Minor presentation improvements, v3:
Typos corrected, figure adde
A transfer principle and applications to eigenvalue estimates for graphs
In this paper, we prove a variant of the Burger-Brooks transfer principle
which, combined with recent eigenvalue bounds for surfaces, allows to obtain
upper bounds on the eigenvalues of graphs as a function of their genus. More
precisely, we show the existence of a universal constants such that the
-th eigenvalue of the normalized Laplacian of a graph
of (geometric) genus on vertices satisfies where denotes the maximum valence of
vertices of the graph. This result is tight up to a change in the value of the
constant , and improves recent results of Kelner, Lee, Price and Teng on
bounded genus graphs.
To show that the transfer theorem might be of independent interest, we relate
eigenvalues of the Laplacian on a metric graph to the eigenvalues of its simple
graph models, and discuss an application to the mesh partitioning problem,
extending pioneering results of Miller-Teng-Thurston-Vavasis and Spielman-Tang
to arbitrary meshes.Comment: Major revision, 16 page
Reliability of Erasure Coded Storage Systems: A Geometric Approach
We consider the probability of data loss, or equivalently, the reliability
function for an erasure coded distributed data storage system under worst case
conditions. Data loss in an erasure coded system depends on probability
distributions for the disk repair duration and the disk failure duration. In
previous works, the data loss probability of such systems has been studied
under the assumption of exponentially distributed disk failure and disk repair
durations, using well-known analytic methods from the theory of Markov
processes. These methods lead to an estimate of the integral of the reliability
function.
Here, we address the problem of directly calculating the data loss
probability for general repair and failure duration distributions. A closed
limiting form is developed for the probability of data loss and it is shown
that the probability of the event that a repair duration exceeds a failure
duration is sufficient for characterizing the data loss probability.
For the case of constant repair duration, we develop an expression for the
conditional data loss probability given the number of failures experienced by a
each node in a given time window. We do so by developing a geometric approach
that relies on the computation of volumes of a family of polytopes that are
related to the code. An exact calculation is provided and an upper bound on the
data loss probability is obtained by posing the problem as a set avoidance
problem. Theoretical calculations are compared to simulation results.Comment: 28 pages. 8 figures. Presented in part at IEEE International
Conference on BigData 2013, Santa Clara, CA, Oct. 2013 and to be presented in
part at 2014 IEEE Information Theory Workshop, Tasmania, Australia, Nov.
2014. New analysis added May 2015. Further Update Aug. 201
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