Reproductive biology of an endemic Physalaemus of the Brazilian Atlantic forest, and the trade-of between clutch and egg size in terrestrial breeders of the P. signifer group

Physalaemus crombiei is a small foam-nesting frog endemic to the Atlantic forest. It is a member of the P. signifer group known only from its type locality in Santa Teresa, State of Espírito Santo, and from another locality in the State of Bahia, Brazil. Most Physalaemus species are aquatic breeders, and species in the P. signifer group are the only ones exhibiting a tendency toward terrestrial reproduction in the genus. Here we describe the reproductive period, breeding site and reproductive modes of P. crombiei from a third population in the Atlantic forest, southeastern Brazil. We also investigated reproductive effort and size-fecundity relationships in females. Reproductive traits were compared to other species in the genus Physalaemus, especially those included in the P. signifer group. Physalaemus crombiei is a prolonged breeder, reproducing throughout the year with a peak of activity during the most rainy months (October-March). Males called from the humid forest foor and eggs embedded in foam nests were deposited in the water as well as on the humid foor amidst the leaf litter, or inside fallen leaves or tree holes containing rainwater on the forest foor. As expected, P. crombiei exhibited three alternative reproductive modes, as described for other species of the P. signifer group. The number of eggs produced per female varied from 91 to 250. Female body size is positively correlated both with ovary mass and clutch size (number of eggs per clutch). Variation in the number and size of eggs observed in Physalaemus species may be explained not only by female size, but also by the terrestrial reproductive mode exhibited by the species in the P. signifer group.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

Optimizing optimum-path forest classification for huge datasets

Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain. © 2010 IEEE.

Parkinson's disease identification through Optimum-Path Forest

Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification. © 2010 IEEE.