@article{b,
title = {A data compression approach to monolingual GIRT task: an agnostic point of view},
author = {Daniela Alderduccio and Luciana Bordoni and Vittorio Loreto},
url = {http://www.springerlink.com/content/rclr22d4636dgpb0/},
year = {2004},
date = {2004-01-01},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {3237},
pages = {391--400},
publisher = {C. Peters (Ed.) (Springer-Verlag)},
abstract = {In this paper we apply a data-compression IR method in the GIRT social science database, focusing on the monolingual task in German and English. For this purpose we use a recently proposed general scheme for context recognition and context classification of strings of characters (in particular texts) or other coded information. The key point of the method is the computation of a suitable measure of remoteness (or similarity) between two strings of characters. This measure of remoteness reflects the distance between the structures present in the two strings, i.e. between the two different distributions of elements of the compared sequences. The hypothesis is that the information-theory oriented measure of remoteness between two sequences could reflect their semantic distance. It is worth stressing the generality and versatility of our information-theoretic method which applies to any kind of corpora of character strings, whatever the type of coding used (i.e. language).},
keywords = {complexity, data_compression, information_theory, loreto, zippers},
pubstate = {published},
tppubtype = {article}
}
In this paper we apply a data-compression IR method in the GIRT social science database, focusing on the monolingual task in German and English. For this purpose we use a recently proposed general scheme for context recognition and context classification of strings of characters (in particular texts) or other coded information. The key point of the method is the computation of a suitable measure of remoteness (or similarity) between two strings of characters. This measure of remoteness reflects the distance between the structures present in the two strings, i.e. between the two different distributions of elements of the compared sequences. The hypothesis is that the information-theory oriented measure of remoteness between two sequences could reflect their semantic distance. It is worth stressing the generality and versatility of our information-theoretic method which applies to any kind of corpora of character strings, whatever the type of coding used (i.e. language).
@article{b,
title = {Concept of complexity in random dynamical systems},
author = {Vittorio Loreto and Giovanni Paladin and Angelo Vulpiani},
url = {http://pre.aps.org/abstract/PRE/v53/i3/p2087_1
http://samarcanda.phys.uniroma1.it/vittorioloreto/PAPERS/1996/Loreto_PhysRevE_1996.pdf},
year = {1996},
date = {1996-01-01},
journal = {PHYSICAL REVIEW E},
volume = {53},
pages = {2087--2098},
publisher = {AMERICAN PHYSICAL SOC},
abstract = {We introduce a measure of complexity in terms of the average number of bits per time unit necessary to specify the sequence generated by the system. In dynamical systems with small random perturbations, this indicator coincides with the rate K of divergence of nearby trajectories evolving under two different noise realizations. The meaning of K is discussed in the context of the information theory, and it is shown that it can be determined from real experimental data. In the presence of strong dynamical intermittency, the value of K is very different from the standard Lyapunov exponent lambda(sigma) computed considering two nearby trajectories evolving under the same realization of the randomness. However, the former is much more relevant than the latter from a physical point of view, as illustrated by some numerical computations for noisy maps and sandpile models.},
keywords = {complexity, dynamical_systems, loreto, paladin, statistical_physics, vulpiani},
pubstate = {published},
tppubtype = {article}
}
We introduce a measure of complexity in terms of the average number of bits per time unit necessary to specify the sequence generated by the system. In dynamical systems with small random perturbations, this indicator coincides with the rate K of divergence of nearby trajectories evolving under two different noise realizations. The meaning of K is discussed in the context of the information theory, and it is shown that it can be determined from real experimental data. In the presence of strong dynamical intermittency, the value of K is very different from the standard Lyapunov exponent lambda(sigma) computed considering two nearby trajectories evolving under the same realization of the randomness. However, the former is much more relevant than the latter from a physical point of view, as illustrated by some numerical computations for noisy maps and sandpile models.
