Abstract
EpiGauss is a method that combines single dipole model with dipole clustering to characterize active brain generators in space and time related to EEG events. EpiGauss was applied to study epileptogenic activity in 4 patients suffering of hypothalamic hamartoma related epilepsy, a rare syndrome with a unique epileptogenic source - the hamartoma lesion - and natural propagation hypothesis - from hamartoma to the surface EEG focus. The results are compared to Rap-MUSIC and Single Moving Dipole methods over the same patients. © Springer-Verlag Berlin Heidelberg 2006.

Abstract
Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or 'ligands'). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to "align" the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or 'conformations') arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is "active"; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning.

Abstract
Multimedia retrieval brings new challenges, mainly derived from the mismatch between the level of the user interaction-high-level concepts, and that of the automatically processed descriptors-low-level features. The effective use of the low-level descriptors is therefore mandatory. Many data structures have been proposed for managing the representation of multidimensional descriptors, each geared toward efficiency in some set of basic operations. The paper introduces a highly parametrizable structure called the BitMatrix, along with its search algorithms. The BitMatrix is compared with existing methods, all implemented in a common framework. The tests have been performed on two datasets, with parameters covering significant ranges of values. The BitMatrix has proved to be a robust and flexible structure that can compete with other methods for multidimensional descriptor indexing.

Abstract
Java applets run on a Virtual Machine that checks code integrity and correctness before execution using a module called the Bytecode Verifier. Java Card technology allows Java applets to run on smart cards. The large memory requirements of the verification process do not allow the implementation of an embedded Bytecode Verifier in the Java Card Virtual Machine. To address this problem, we propose a verification algorithm that optimizes the use of system memory by imposing an ordering on the verification of the instructions. This algorithm is based on control flow dependencies and immediate postdominators in control flow graphs. © 2006 Oxford University Press.

Abstract
This chapter reviews a dependability model for evaluation of the behavior of a CAN network in situations of transient faults, which affect the data communications. The fault occurrence can be modeled by a Markov Modulated Poisson Process (MMPP), which is capable of describing the typical behavior of the electromagnetic interferences (EMI) that occur in the industrial environments. An accurate and efficient representation of the network behavior is achieved by adopting a set of assumptions that reduces the pessimism level and are closer to the real operating conditions. The model used for analysing the dependability evaluation is based on the Stochastic Petri Nets, which are a high-level modeling formalism able to produce very compact and efficient models, supporting both the analytical and simulation solutions. Dependability measures are established from the achievement of the real-time constraints (deadlines) defined on the messages exchanged among the network nodes. The chapter concludes by reviewing a case study that is proposed to assess both the model performance and the network dependability.

Abstract
This paper analyzes the performance of two cooperative robot manipulators. It is studied the implementation of fractional-order algorithms in the position/force control of two robots holding an object. The experiments reveal that fractional algorithms lead to performances superior to classical integer-order controllers.