# Prof. Dr. Osvaldo Rosso ministrará palestra no IC

**Title: GENERALIZED STATISTICAL COMPLEXITY MEASURE: A NEW TOOL FOR DYNAMICAL SYSTEMS.****Abstract:** López-Ruiz, Mancini and Calbet have proposed a statistical complexity measure, based on the notion of “disequilibrium”, as a quantifier of the degree of physical structure in a time series Given a probability distribution P and its associate information measure I[P], an amount of “disorder” associated to the state of a system, the LMC-statistical complexity, is the product of a normalized entropy H (normalized Shannon-entropy) times the disequilibrium Q, given by the Euclidean “distance” from P to the uniform distribution Pe. The statistical complexity vanishes both for a totally random process and for a purely periodic one. Martín, Plastino and Rosso improved on this measure by suitably modifying the distance-component (in the concomitant probability space). The obtained MPR-statistical complexity is (i) able to grasp essential details of the dynamics, (ii) an intensive quantity, and (iii) capable of discerning among different degrees of periodicity and chaos. The MPR-statistical complexity can be viewed as a functional that characterizes the probability distribution P associated to the time series generated by the dynamical system under study. It quantifies not only randomness but also the presence of correlational structures. In this seminar, selection of the information measure I and generalized disorder, as well as, election of distance D and generalized disequilibrium are reviewed. Evaluation of the probability distribution P associated to a dynamical system or time series under study is a physical problem. Additional improvements can be expected if the underlying probability distribution is “extracted” by more appropriate consideration regarding causal effects in the system’s dynamics. Several well-known model-generated time series, usually regarded as being of either stochastic or chaotic nature, are analyzed. The main achievement of this approach is the possibility of clearly distinguishing between them in the H x MPR-Complexity representation space, something that is rather difficult otherwise. In addition, recent applications to time series from biological (epileptic EEG records, deformation of red blood cells, cancer progression, etc.) and physics systems (stochastic resonance, econophysics, literature, ENSO/El Niño, etc.) will be reviewer.** Short-bio:** Osvaldo A. Rosso was born in Rojas, Argentina, on October 1954. He received the M.S. and Ph.D. degree in Physics from National University of La Plata, Argentina, in 1978 an 1984 respectively. He held a post-doc position at Forschungzentrum Jülich GmBH (KFA), Jülich, Germany (1988-1990). Visiting Researcher at Istituto Lamel, Sezione di Cinematografia Scientifica, CNR, Bologna, Italy (1990-1992). Research Academic, School of Electrical Engineering and Computer Science, The University of Newcastle, Newcastle, Australia (2007-2009). Actually is Professor Visitante do Exterior (PVE), category Doutor Senior, CAPES, Brazil (2010). Since March 1998, he has held a permanent research position at the Argentinean Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). He has authored or co-authored over 150 publications including about 95 journal papers. His main research interests include time series analysis, non-linear dynamics, Information Theory, Time-Frequency Analysis and, their applications to physics, engineering, biological and medical sciences.