Pablo F. Verdes

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The implementation of new methods for reliable and fast identification and classification of seeds is of major technical and economical importance in the agricultural industry. As in ocular inspection, the automatic classification of seeds should be based on knowledge of seed size, shape, color and texture. In this work, we assess the discriminating power(More)
We explore the feasibility of implementing fast and reliable computer-based systems for the automatic identification of weed seeds from color and black and white images. Seeds size, shape, color and texture characteristics are obtained by standard image-processing techniques, and their discriminating power as classification features is assessed. These(More)
Ensembles of artificial neural networks show improved generalization capabilities that outperform those of single networks. However, for aggregation to be effective, the individual networks must be as accurate and diverse as possible. An important problem is, then, how to tune the aggregate members in order to have an optimal compromise between these two(More)
Earlier and more reliable detection of drug-induced kidney injury would improve clinical care and help to streamline drug-development. As the current standards to monitor renal function, such as blood urea nitrogen (BUN) or serum creatinine (SCr), are late indicators of kidney injury, we conducted ten nonclinical studies to rigorously assess the potential(More)
The Predictive Safety Testing Consortium's first regulatory submission to qualify kidney safety biomarkers revealed two deficiencies. To address the need for biomarkers that monitor recovery from agent-induced renal damage, we scored changes in the levels of urinary biomarkers in rats during recovery from renal injury induced by exposure to carbapenem A or(More)
  • P F Verdes
  • Physical review. E, Statistical, nonlinear, and…
  • 2005
In this work we propose a general nonparametric test of causality for weakly dependent time series. More precisely, we study the problem of attribution, i.e., the proper comparison of the relative influence that two or more external dynamics trigger on a given system of interest. We illustrate the possible applications of the proposed methodology in very(More)
The performance of a single regressor/classifier can be improved by combining the outputs of several predictors. This is true provided the combined predictors are accurate and diverse enough, which posses the problem of generating suitable aggregate members in order to have optimal generalization capabilities. We propose here a new method for selecting(More)
We propose a general overembedding method for modeling and prediction of nonstationary systems. It basically enlarges the standard time-delay-embedding space by inclusion of the (unknown) slow driving signal, which is estimated simultaneously with the intrinsic stationary dynamics. Our method can be implemented with any modeling tool. Using, in particular,(More)
We propose a simple method for the accurate reconstruction of slowly changing external forces acting on nonlinear dynamical systems. The method traces the evolution of the external force by locally linearizing the map dependency with the shifting parameter. Application of our algorithm to synthetic data corresponding to discrete models of evolving(More)