Mutual Information Rate and Bounds for It

The amount of information exchanged per unit of time between two nodes in a dynamical network or between two data sets is a powerful concept for analysing complex systems. This quantity, known as the mutual information rate (MIR), is calculated from the mutual information, which is rigorously defined only for random systems. Moreover, the definition of mutual information is based on probabilities of significant events. This work offers a simple alternative way to calculate the MIR in dynamical (deterministic) networks or between two time series (not fully deterministic), and to calculate its upper and lower bounds without having to calculate probabilities, but rather in terms of well known and well defined quantities in dynamical systems. As possible applications of our bounds, we study the relationship between synchronisation and the exchange of information in a system of two coupled maps and in experimental networks of coupled oscillators.

Universal behavior of the Shannon mutual information of critical quantum chains

We consider the Shannon mutual information of subsystems of critical quantum chains in their ground states. Our results indicate a universal leading behavior for large subsystem sizes. Moreover, as happens with the entanglement entropy, its finite-size behavior yields the conformal anomaly c of the underlying conformal field theory governing the long-distance physics of the quantum chain. We study analytically a chain of coupled harmonic oscillators and numerically the Q-state Potts models (Q = 2, 3, and 4), the XXZ quantum chain, and the spin-1 Fateev-Zamolodchikov model. The Shannon mutual information is a quantity easily computed, and our results indicate that for relatively small lattice sizes, its finite-size behavior already detects the universality class of quantum critical behavior.

On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model (Hosmer & Lemeshow, 1989) and a logistic regression with state-dependent sample selection model (Cramer, 2004) applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. (C) 2012 Elsevier Ltd. All rights reserved.

Gram-negative osteomyelitis: clinical and microbiological profile

Introduction: Despite the growing interest in the study of Gram-negative bacilli (GNB) infections, very little information on osteomyelitis caused by GNB is available in the medical literature. Objectives and methods: To assess clinical and microbiological features of 101 cases of osteomyelitis caused by GNB alone, between January 2007 and January 2009, in a reference center for the treatment of high complexity traumas in the city of Sao Paulo. Results: Most patients were men (63%), with median age of 42 years, affected by chronic osteomyelitis (43%) or acute osteomyelitis associated to open fractures (32%), the majority on the lower limbs (71%). The patients were treated with antibiotics as inpatients for 40 days (median) and for 99 days (median) in outpatient settings. After 6 months follow-up, the clinical remission rate was around 60%, relapse 19%, amputation 7%, and death 5%. Nine percent of cases were lost to follow-up. A total of 121 GNB was isolated from 101 clinical samples. The most frequently isolated pathogens were Enterobacter sp. (25%), Acinetobacter baumannii (21%) e Pseudomonas aeruginosa (20%). Susceptibility to carbapenems was about 100% for Enterobacter sp., 75% for Pseudomonas aeruginosa and 60% for Acinetobacter baumannii. Conclusion: Osteomyelitis caused by GNB remains a serious therapeutic challenge, especially when associated to nonfermenting bacteria. We emphasize the need to consider these agents in diagnosed cases of osteomyelitis, so that an ideal antimicrobial treatment can be administered since the very beginning of the therapy. (C) 2012 Elsevier Editora Ltda. All rights reserved.