5,497 research outputs found
On the Convergence of Population Protocols When Population Goes to Infinity
Population protocols have been introduced as a model of sensor networks
consisting of very limited mobile agents with no control over their own
movement. A population protocol corresponds to a collection of anonymous
agents, modeled by finite automata, that interact with one another to carry out
computations, by updating their states, using some rules. Their computational
power has been investigated under several hypotheses but always when restricted
to finite size populations. In particular, predicates stably computable in the
original model have been characterized as those definable in Presburger
arithmetic. We study mathematically the convergence of population protocols
when the size of the population goes to infinity. We do so by giving general
results, that we illustrate through the example of a particular population
protocol for which we even obtain an asymptotic development. This example shows
in particular that these protocols seem to have a rather different
computational power when a huge population hypothesis is considered.Comment: Submitted to Applied Mathematics and Computation. 200
On the algebraic numbers computable by some generalized Ehrenfest urns
This article deals with some stochastic population protocols, motivated by
theoretical aspects of distributed computing. We modelize the problem by a
large urn of black and white balls from which at every time unit a fixed number
of balls are drawn and their colors are changed according to the number of
black balls among them. When the time and the number of balls both tend to
infinity the proportion of black balls converges to an algebraic number. We
prove that, surprisingly enough, not every algebraic number can be "computed"
this way
Generalized Communicating P Systems Working in Fair Sequential Model
In this article we consider a new derivation mode for generalized
communicating P systems (GCPS) corresponding to the functioning of population
protocols (PP) and based on the sequential derivation mode and a fairness
condition. We show that PP can be seen as a particular variant of GCPS. We also
consider a particular stochastic evolution satisfying the fairness condition
and obtain that it corresponds to the run of a Gillespie's SSA. This permits to
further describe the dynamics of GCPS by a system of ODEs when the population
size goes to the infinity.Comment: Presented at MeCBIC 201
Passively Mobile Communicating Logarithmic Space Machines
We propose a new theoretical model for passively mobile Wireless Sensor
Networks. We call it the PALOMA model, standing for PAssively mobile
LOgarithmic space MAchines. The main modification w.r.t. the Population
Protocol model is that agents now, instead of being automata, are Turing
Machines whose memory is logarithmic in the population size n. Note that the
new model is still easily implementable with current technology. We focus on
complete communication graphs. We define the complexity class PLM, consisting
of all symmetric predicates on input assignments that are stably computable by
the PALOMA model. We assume that the agents are initially identical.
Surprisingly, it turns out that the PALOMA model can assign unique consecutive
ids to the agents and inform them of the population size! This allows us to
give a direct simulation of a Deterministic Turing Machine of O(nlogn) space,
thus, establishing that any symmetric predicate in SPACE(nlogn) also belongs to
PLM. We next prove that the PALOMA model can simulate the Community Protocol
model, thus, improving the previous lower bound to all symmetric predicates in
NSPACE(nlogn). Going one step further, we generalize the simulation of the
deterministic TM to prove that the PALOMA model can simulate a Nondeterministic
TM of O(nlogn) space. Although providing the same lower bound, the important
remark here is that the bound is now obtained in a direct manner, in the sense
that it does not depend on the simulation of a TM by a Pointer Machine.
Finally, by showing that a Nondeterministic TM of O(nlogn) space decides any
language stably computable by the PALOMA model, we end up with an exact
characterization for PLM: it is precisely the class of all symmetric predicates
in NSPACE(nlogn).Comment: 22 page
Branching Feller diffusion for cell division with parasite infection
We describe the evolution of the quantity of parasites in a population of
cells which divide in continuous-time. The quantity of parasites in a cell
follows a Feller diffusion, which is splitted randomly between the two daughter
cells when a division occurs. The cell division rate may depend on the quantity
of parasites inside the cell and we are interested in the cases of constant or
monotone division rate. We first determine the asymptotic behavior of the
quantity of parasites in a cell line, which follows a Feller diffusion with
multiplicative jumps. We then consider the evolution of the infection of the
cell population and give criteria to determine whether the proportion of
infected cells goes to zero (recovery) or if a positive proportion of cells
becomes largely infected (proliferation of parasites inside the cells)
Some stochastic models for structured populations : scaling limits and long time behavior
The first chapter concerns monotype population models. We first study general
birth and death processes and we give non-explosion and extinction criteria,
moment computations and a pathwise representation. We then show how different
scales may lead to different qualitative approximations, either ODEs or SDEs.
The prototypes of these equations are the logistic (deterministic) equation and
the logistic Feller diffusion process. The convergence in law of the sequence
of processes is proved by tightness-uniqueness argument. In these large
population approximations, the competition between individuals leads to
nonlinear drift terms. We then focus on models without interaction but
including exceptional events due either to demographic stochasticity or to
environmental stochasticity. In the first case, an individual may have a large
number of offspring and we introduce the class of continuous state branching
processes. In the second case, catastrophes may occur and kill a random
fraction of the population and the process enjoys a quenched branching
property. We emphasize on the study of the Laplace transform, which allows us
to classify the long time behavior of these processes. In the second chapter,
we model structured populations by measure-valued stochastic differential
equations. Our approach is based on the individual dynamics. The individuals
are characterized by parameters which have an influence on their survival or
reproduction ability. Some of these parameters can be genetic and are
inheritable except when mutations occur, but they can also be a space location
or a quantity of parasites. The individuals compete for resources or other
environmental constraints. We describe the population by a point measure-valued
Markov process. We study macroscopic approximations of this process depending
on the interplay between different scalings and obtain in the limit either
integro-differential equations or reaction-diffusion equations or nonlinear
super-processes. In each case, we insist on the specific techniques for the
proof of convergence and for the study of the limiting model. The limiting
processes offer different models of mutation-selection dynamics. Then, we study
two-level models motivated by cell division dynamics, where the cell population
is discrete and characterized by a trait, which may be continuous. In 1
particular, we finely study a process for parasite infection and the trait is
the parasite load. The latter grows following a Feller diffusion and is
randomly shared in the two daughter cells when the cell divides. Finally, we
focus on the neutral case when the rate of division of cells is constant but
the trait evolves following a general Markov process and may split in a random
number of cells. The long time behavior of the structured population is then
linked and derived from the behavior a well chosen SDE (monotype population)
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