43 research outputs found
Synthesizing and tuning chemical reaction networks with specified behaviours
We consider how to generate chemical reaction networks (CRNs) from functional
specifications. We propose a two-stage approach that combines synthesis by
satisfiability modulo theories and Markov chain Monte Carlo based optimisation.
First, we identify candidate CRNs that have the possibility to produce correct
computations for a given finite set of inputs. We then optimise the reaction
rates of each CRN using a combination of stochastic search techniques applied
to the chemical master equation, simultaneously improving the of correct
behaviour and ruling out spurious solutions. In addition, we use techniques
from continuous time Markov chain theory to study the expected termination time
for each CRN. We illustrate our approach by identifying CRNs for majority
decision-making and division computation, which includes the identification of
both known and unknown networks.Comment: 17 pages, 6 figures, appeared the proceedings of the 21st conference
on DNA Computing and Molecular Programming, 201
The Computational Power of Beeps
In this paper, we study the quantity of computational resources (state
machine states and/or probabilistic transition precision) needed to solve
specific problems in a single hop network where nodes communicate using only
beeps. We begin by focusing on randomized leader election. We prove a lower
bound on the states required to solve this problem with a given error bound,
probability precision, and (when relevant) network size lower bound. We then
show the bound tight with a matching upper bound. Noting that our optimal upper
bound is slow, we describe two faster algorithms that trade some state
optimality to gain efficiency. We then turn our attention to more general
classes of problems by proving that once you have enough states to solve leader
election with a given error bound, you have (within constant factors) enough
states to simulate correctly, with this same error bound, a logspace TM with a
constant number of unary input tapes: allowing you to solve a large and
expressive set of problems. These results identify a key simplicity threshold
beyond which useful distributed computation is possible in the beeping model.Comment: Extended abstract to appear in the Proceedings of the International
Symposium on Distributed Computing (DISC 2015
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
Stable Leader Election in Population Protocols Requires Linear Time
A population protocol *stably elects a leader* if, for all , starting from
an initial configuration with agents each in an identical state, with
probability 1 it reaches a configuration that is correct (exactly
one agent is in a special leader state ) and stable (every configuration
reachable from also has a single agent in state ). We show
that any population protocol that stably elects a leader requires
expected "parallel time" --- expected total pairwise interactions
--- to reach such a stable configuration. Our result also informs the
understanding of the time complexity of chemical self-organization by showing
an essential difficulty in generating exact quantities of molecular species
quickly.Comment: accepted to Distributed Computing special issue of invited papers
from DISC 2015; significantly revised proof structure and intuitive
explanation