1,994 research outputs found
Temporal Gillespie algorithm: Fast simulation of contagion processes on time-varying networks
Stochastic simulations are one of the cornerstones of the analysis of
dynamical processes on complex networks, and are often the only accessible way
to explore their behavior. The development of fast algorithms is paramount to
allow large-scale simulations. The Gillespie algorithm can be used for fast
simulation of stochastic processes, and variants of it have been applied to
simulate dynamical processes on static networks. However, its adaptation to
temporal networks remains non-trivial. We here present a temporal Gillespie
algorithm that solves this problem. Our method is applicable to general Poisson
(constant-rate) processes on temporal networks, stochastically exact, and up to
multiple orders of magnitude faster than traditional simulation schemes based
on rejection sampling. We also show how it can be extended to simulate
non-Markovian processes. The algorithm is easily applicable in practice, and as
an illustration we detail how to simulate both Poissonian and non-Markovian
models of epidemic spreading. Namely, we provide pseudocode and its
implementation in C++ for simulating the paradigmatic
Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and
a Susceptible-Infected-Recovered model with non-constant recovery rates. For
empirical networks, the temporal Gillespie algorithm is here typically from 10
to 100 times faster than rejection sampling.Comment: Minor changes and updates to reference
Equational Reasonings in Wireless Network Gossip Protocols
Gossip protocols have been proposed as a robust and efficient method for
disseminating information throughout large-scale networks. In this paper, we
propose a compositional analysis technique to study formal probabilistic models
of gossip protocols expressed in a simple probabilistic timed process calculus
for wireless sensor networks. We equip the calculus with a simulation theory to
compare probabilistic protocols that have similar behaviour up to a certain
tolerance. The theory is used to prove a number of algebraic laws which
revealed to be very effective to estimate the performances of gossip networks,
with and without communication collisions, and randomised gossip networks. Our
simulation theory is an asymmetric variant of the weak bisimulation metric that
maintains most of the properties of the original definition. However, our
asymmetric version is particularly suitable to reason on protocols in which the
systems under consideration are not approximately equivalent, as in the case of
gossip protocols
Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation
This paper is part of the ENHAnCE ITN project (https://www.h2020-enhanceitn.eu/) funded by the European Union's Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement No. 859957. The authors would like to thank the Lloyd's Register Foundation (LRF), a charitable foundation in the U.K. helping to protect life and property by supporting engineeringrelated education, public engagement, and the application of research. The authors gratefully acknowledge the support of these organizations which have enabled the research reported in this paper.The accurate modeling of engineering systems and processes using Petri nets often results in complex graph
representations that are computationally intensive, limiting the potential of this modeling tool in real life
applications. This paper presents a methodology to properly define the optimal structure and properties of
a reduced Petri net that mimic the output of a reference Petri net model. The methodology is based on
Approximate Bayesian Computation to infer the plausible values of the model parameters of the reduced model
in a rigorous probabilistic way. Also, the method provides a numerical measure of the level of approximation
of the reduced model structure, thus allowing the selection of the optimal reduced structure among a set
of potential candidates. The suitability of the proposed methodology is illustrated using a simple illustrative
example and a system reliability engineering case study, showing satisfactory results. The results also show
that the method allows flexible reduction of the structure of the complex Petri net model taken as reference,
and provides numerical justification for the choice of the reduced model structure.European Commission 859957Lloyd's Register Foundation (LRF), a charitable foundation in the U.K
Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems
Recent results in telecardiology show that compressed sensing (CS) is a
promising tool to lower energy consumption in wireless body area networks for
electrocardiogram (ECG) monitoring. However, the performance of current
CS-based algorithms, in terms of compression rate and reconstruction quality of
the ECG, still falls short of the performance attained by state-of-the-art
wavelet based algorithms. In this paper, we propose to exploit the structure of
the wavelet representation of the ECG signal to boost the performance of
CS-based methods for compression and reconstruction of ECG signals. More
precisely, we incorporate prior information about the wavelet dependencies
across scales into the reconstruction algorithms and exploit the high fraction
of common support of the wavelet coefficients of consecutive ECG segments.
Experimental results utilizing the MIT-BIH Arrhythmia Database show that
significant performance gains, in terms of compression rate and reconstruction
quality, can be obtained by the proposed algorithms compared to current
CS-based methods.Comment: Accepted for publication at IEEE Journal of Biomedical and Health
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