Intelligent Systems Group

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Intelligent Systems Group

New impact factor publication -- Transportmetrica A: Transport Science


Our group members Usue Mori, Alexander Mendiburu and Jose Antonio Lozano, together with Maite Alvarez, a researcher of the Tecnalia corporation, recently completed the following publication:

"A Review of Travel Time Estimation and Forecasting for Advanced Traveller Information Systems". Transportmetrica A: Transport Science


Due to the increase in vehicle transit and congestion in road networks, providing information about the state of the traffic to commuters has become a critical issue for Advanced Traveller Information Systems. These systems should assist users in making pre-trip and en-route decisions and, for this purpose, delivering travel time information is very useful because it is very intuitive and easily understood by all travellers.

The aim of this paper is to present a global view of the literature on the modelling of travel time, introducing essential concepts and giving a thorough classification of the existing techniques. Most of the attention will focus on travel time estimation and travel time prediction, which are two of the most relevant challenges in travel time modelling. The definition and goals of these two modelling tasks along with the methodologies used to carry them out will be further explored and categorized.


New impact factor publication -- Information Sciences


Our group member Alexander Mendiburu, together with a group of colleagues of the Polytechnic University of Madrid, recently completed the following publication:

"Distributed estimation of distribution algorithms for continuous optimization: how does the exchanged information influence their behavior". Information Sciences.


One of the most promising areas in which probabilistic graphical models have shown an incipient activity is the field of heuristic optimization and, in particular, in Estimation of Distribution Algorithms. Due to their inherent parallelism, different research lines have been studied trying to improve Estimation of Distribution Algorithms from the point of view of execution time and/or accuracy. Among these proposals, we focus on the so-called distributed or island-based models. This approach defines several islands (algorithms instances) running independently and exchanging information with a given frequency. The information sent by the islands can be either a set of individuals or a probabilistic model. This paper presents a comparative study for a distributed univariate Estimation of Distribution Algorithm and a multivariate version, paying special attention to the comparison of two alternative methods for exchanging information, over a wide set of parameters and problems – the standard benchmark developed for the IEEE Workshop on Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems of the ISDA 2009 Conference. Several analyses from different points of view have been conducted to analyze both the influence of the parameters and the relationships between them including a characterization of the configurations according to their behavior on the proposed benchmark.

New impact factor publication -- IEEE Transactions on Parallel and Distributed Systems


Our group members Unai Lopez-Novoa, Alexander Mendiburu and Jose Miguel-Alonso, recently completed the following publication:

"A survey of performance modeling and simulation techniques for accelerator-based computing". IEEE Transactions on Parallel and Distributed Systems.


The high performance computing landscape is shifting from collections of homogeneous nodes towards heterogeneous systems, in which nodes consist of a combination of traditional out-of-order execution cores and accelerator devices. Accelerators, built around GPUs, many-core chips, FPGAs or DSPs, are used to offload compute-intensive tasks. The advent of this type of systems has brought about a wide and diverse ecosystem of development platforms, optimization tools and performance analysis frameworks. This is a review of the state-of-the-art in performance tools for heterogeneous computing, focusing on the most popular families of accelerators: GPUs and Intel’s Xeon Phi. We describe current heterogeneous systems and the development frameworks and tools that can be used for developing for them. The core of this survey is a review of the performance models and tools, including simulators, proposed in the literature for these platforms.

Big data and data mining -- Radio interview


Our group member Iñaki Inza was interviewed around the Big Data and Data Mining areas in the "Faktoria" program of "Euskadi Irratia" (basque public radio). The interview can be found in the following link of the radio program: the interview starts around 1h45min of the associated mp3 podcast


Pedro Larrañaga -- 2013 Spanish Prize on Computer Science


Our group founder Pedro Larrañaga, currently enroled in the Polytechnic University of Madrid, has received the "2013 Spanish Prize on Computer Science" ("Premio Nacional de Informática"). Specifically, the "Aritmel Prize" for researchers who has performed outstanding scientific contributions. The Jury has also emphasized his capability to form research groups: initially as founder of our "Intelligent Systems Group" in San Sebastian, and after 2007 of the "Computational Intelligence Group" in Madrid.

Several links to the new can be found here: link1, link2, link3.

Zorionak Pedro!!!!


New impact factor publication -- Frontiers in Neural Circuits


Our group member Roberto Santana, together with our former group member Pedro Larrañaga and other co-authors, have recently completed the following publication:

"Classification of neocortical interneurons using affinity propagation". Frontiers in Neural Circuits.

doi: 10.3389/fncir.2013.00185

Abstract: In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. Neuronal classification has been a difficult problem because it is unclear what a neuronal cell class actually is and what are the best characteristics are to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological or molecular characteristics, when applied to selected datasets, have provided quantitative and unbiased identification of distinct neuronal subtypes. However, better and more robust classification methods are needed for increasingly complex and larger datasets. We explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. In fact, using a combined anatomical/physiological dataset, our algorithm differentiated parvalbumin from somatostatin interneurons in 49 out of 50 cases. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

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