3,180 research outputs found
Finite size corrections to random Boolean networks
Since their introduction, Boolean networks have been traditionally studied in
view of their rich dynamical behavior under different update protocols and for
their qualitative analogy with cell regulatory networks. More recently, tools
borrowed from statistical physics of disordered systems and from computer
science have provided a more complete characterization of their equilibrium
behavior. However, the largest part of the results have been obtained in the
thermodynamic limit, which is often far from being reached when dealing with
realistic instances of the problem. The numerical analysis presented here aims
at comparing - for a specific family of models - the outcomes given by the
heuristic belief propagation algorithm with those given by exhaustive
enumeration. In the second part of the paper some analytical considerations on
the validity of the annealed approximation are discussed.Comment: Minor correction
Uniform test of algorithmic randomness over a general space
The algorithmic theory of randomness is well developed when the underlying
space is the set of finite or infinite sequences and the underlying probability
distribution is the uniform distribution or a computable distribution. These
restrictions seem artificial. Some progress has been made to extend the theory
to arbitrary Bernoulli distributions (by Martin-Loef), and to arbitrary
distributions (by Levin). We recall the main ideas and problems of Levin's
theory, and report further progress in the same framework.
- We allow non-compact spaces (like the space of continuous functions,
underlying the Brownian motion).
- The uniform test (deficiency of randomness) d_P(x) (depending both on the
outcome x and the measure P should be defined in a general and natural way.
- We see which of the old results survive: existence of universal tests,
conservation of randomness, expression of tests in terms of description
complexity, existence of a universal measure, expression of mutual information
as "deficiency of independence.
- The negative of the new randomness test is shown to be a generalization of
complexity in continuous spaces; we show that the addition theorem survives.
The paper's main contribution is introducing an appropriate framework for
studying these questions and related ones (like statistics for a general family
of distributions).Comment: 40 pages. Journal reference and a slight correction in the proof of
Theorem 7 adde
Renyi entropies as a measure of the complexity of counting problems
Counting problems such as determining how many bit strings satisfy a given
Boolean logic formula are notoriously hard. In many cases, even getting an
approximate count is difficult. Here we propose that entanglement, a common
concept in quantum information theory, may serve as a telltale of the
difficulty of counting exactly or approximately. We quantify entanglement by
using Renyi entropies S(q), which we define by bipartitioning the logic
variables of a generic satisfiability problem. We conjecture that
S(q\rightarrow 0) provides information about the difficulty of counting
solutions exactly, while S(q>0) indicates the possibility of doing an efficient
approximate counting. We test this conjecture by employing a matrix computing
scheme to numerically solve #2SAT problems for a large number of uniformly
distributed instances. We find that all Renyi entropies scale linearly with the
number of variables in the case of the #2SAT problem; this is consistent with
the fact that neither exact nor approximate efficient algorithms are known for
this problem. However, for the negated (disjunctive) form of the problem,
S(q\rightarrow 0) scales linearly while S(q>0) tends to zero when the number of
variables is large. These results are consistent with the existence of fully
polynomial-time randomized approximate algorithms for counting solutions of
disjunctive normal forms and suggests that efficient algorithms for the
conjunctive normal form may not exist.Comment: 13 pages, 4 figure
Attractor and Basin Entropies of Random Boolean Networks Under Asynchronous Stochastic Update
We introduce a numerical method to study random Boolean networks with
asynchronous stochas- tic update. Each node in the network of states starts
with equal occupation probability and this probability distribution then
evolves to a steady state. Nodes left with finite occupation probability
determine the attractors and the sizes of their basins. As for synchronous
update, the basin entropy grows with system size only for critical networks,
where the distribution of attractor lengths is a power law. We determine
analytically the distribution for the number of attractors and basin sizes for
frozen networks with connectivity K = 1.Comment: 5 pages, 3 figures, in submissio
A novel Boolean kernels family for categorical data
Kernel based classifiers, such as SVM, are considered state-of-the-art algorithms and are widely used on many classification tasks. However, this kind of methods are hardly interpretable and for this reason they are often considered as black-box models. In this paper, we propose a new family of Boolean kernels for categorical data where features correspond to propositional formulas applied to the input variables. The idea is to create human-readable features to ease the extraction of interpretation rules directly from the embedding space. Experiments on artificial and benchmark datasets show the effectiveness of the proposed family of kernels with respect to established ones, such as RBF, in terms of classification accuracy
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