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Abstract. The aim of this article is to present a random graph representation, that is based on 2 nd order relations between graph elements, for modeling sets of attributed graphs (AGs). We refer to these models as second-order random graphs (SORGs). The basic feature of SORGs is that they include both marginal probability functions of graph elements and 2 ndorder joint probability functions. This allows a more precise description of both the structural and semantic information contents in a set of AGs and, consequently, an expected improvement in graph matching and object recognition. The article presents a probabilistic formulation of SORGs that includes as particular cases the two previously proposed approaches based on random graphs, namely the first-order random graphs (FORGs) and the function-described graphs (FDGs). We then propose a distance measure derived from the probability of instantiating a SORG into an AG and an incremental procedure to synthesize SORGs from sequences of AGs.Finally, SORGs are shown to improve the performance of FORGs, FDGs and direct AG-to-AG matching in three experimental recognition tasks: one -2 -in which AGs are randomly generated and the other two in which AGs represent multiple views of 3D objects (either synthetic or real) that have been extracted from color images. In the last case, object learning is achieved through the synthesis of SORG models.

In this paper we will discuss the use of some graph-based representations and techniques for image processing and analysis. Instead of making an extensive review of the graph techniques in this field, we will explain how we are using these techniques in an active vision system for an autonomous mobile robot developed in the Institut de Robòtica i Informàtica Industrial within the project "Active Vision System with Automatic Learning Capacity for Industrial Applications (CICYT TAP98-0473)". Specifically we will discuss the use of graph-based representations and techniques for image segmentation, image perceptual grouping and object recognition. We first present a generalisation of a graph partitioning greedy algorithm for colour image segmentation. Next we describe a novel fusion of colour-based segmentation and depth from stereo that yields a graph representing every object in the scene. Finally we describe a new representation of a set of attributed graphs (AGs), denominated Function Described Graphs (FDGs), a distance measure for matching AGs with FDGs and some applications for robot vision.

Abstract-This study describes and evaluates two new methods for finding and following people in urban settings using a humanoid service robot: the Continuous Real-time POMCP method, and its improved extension called Adaptive Highest Belief Continuous Real-time POMCP follower. They are able to run in real-time, in large continuous environments. These methods make use of the online search algorithm Partially Observable Monte-Carlo Planning (POMCP), which in contrast to other previous approaches, can plan under uncertainty on large state spaces. We compare our new methods with a heuristic person follower and demonstrate that they obtain better results by testing them extensively in both simulated and real-life experiments. More than two hours, over 3 km, of autonomous navigation during real-life experiments have been done with a mobile humanoid robot in urban environments.

Abstract. Function-described graphs (FDGs) have b e e n i n troduced very recently as a representation of an ensemble of attributed relational graphs (ARGs) for structural pattern recognition 1, 2]. In this paper, the relationship between FDGs and Random Graphs 3] is analysed and the synthesis process of FDGs is studied, whereas the matching process between FDGs and ARGs is discussed elsewhere 4]. Two procedures are described to synthesize an FDG from a set of commonly labelled ARGs or FDGs, respectively. Then, a general incremental method to synthesize an FDG from a sequence of unlabelled ARGs is proposed. Speci c methods are obtained when an ARG-to-FDG matching algorithm is selected to nd optimal morphisms.

Abstract. G protein-coupled receptors (GPCRs) have a key function in regulating the function of cells due to their ability to transmit extracelullar signals. Given that the 3D structure and the functionality of most GPCRs is unknown, there is a need to construct robust classification models based on the analysis of their amino acid sequences for protein homology detection. In this paper, we describe the supervised classification of the different subtypes of class C GPCRs using support vector machines (SVMs). These models are built on different transformations of the amino acid sequences based on their physicochemical properties. Previous research using semi-supervised methods on the same data has shown the usefulness of such transformations. The obtained classification models show a robust performance, as their Matthews correlation coefficient is close to 0.91 and their prediction accuracy is close to 0.93.

Recently, error minimized extreme learning machines (EM-ELMs) have been proposed as a simple and efficient approach to build single-hidden-layer feedforward networks (SLFNs) sequentially. They add random hidden nodes one by one (or group by group) and update the output weights incrementally to minimize the sum-of-squares error in the training set. Other very similar methods that also construct SLFNs sequentially had been reported earlier with the main difference that their hidden-layer weights are a subset of the data instead of being random. By analogy with the concept of support vectors original of support vector machines (SVMs), these approaches can be referred to as support vector sequential feed-forward neural networks (SV-SFNNs), and they are a particular case of the Sequential Approximation with Optimal Coefficients and Interacting Frequencies (SAOCIF) method. In this paper, it is firstly shown that EM-ELMs can also be cast as a particular case of SAOCIF. In particular, EM-ELMs can easily be extended to test some number of random candidates at each step and select the best of them, as SAOCIF does. Moreover, it is demonstrated that the cost of the calculation of the optimal output-layer weights in the originally proposed EM-ELMs can be improved if it is replaced by the one included in SAOCIF. Secondly, we present the results of an experimental study on 10 benchmark classification and 10 benchmark regression data sets, comparing EM-ELMs and SV-SFNNs, that was carried out under the same conditions for the two models. Although both models have the same (efficient) computational cost, a statistically significant improvement in generalization performance of SV-SFNNs vs. EM-ELMs was found in 12 out of the 20 benchmark problems.

-The process of weaning from mechanical ventilation is one of the challenges in intensive care. 149 patients under extubation process (T-tube test) were studied: 88 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 23 patients with successful test but that had to be reintubated before 48 hours (group R). Each patient was characterized using 8 time series and 6 statistics extracted from respiratory and cardiac signals. A moving window statistical analysis was applied obtaining for each patient a sequence of patterns of 48 features. Applying a cluster analysis two groups with the majority dataset were obtained. Neural networks were applied to discriminate between patients from groups S, F and R. The best performance obtained was 84.0% of well classified patients using a linear perceptron trained with a feature selection procedure (that selected 19 of the 48 features) and taking as input the main cluster centroid. However, the classification baseline 69.8% could not be improved when using the original set of patterns instead of the centroids to classify the patients.