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Le Capitaine H.,CNRS Nantes Atlantic Computer Science Laboratory | Le Capitaine H.,University of La Rochelle
IEEE Transactions on Fuzzy Systems | Year: 2012

Matching pairs of objects is a fundamental operation in data analysis. However, it requires the definition of a similarity measure between objects that are to be matched. The similarity measure may not be adapted to the various properties of each object. Consequently, designing a method to learn a measure of similarity between pairs of objects is an important generic problem in machine learning. In this paper, a general framework of fuzzy logical-based similarity measures based on T-equalities that are derived from residual implication functions is proposed. Then, a model that allows us to learn the parametric similarity measures is introduced. This is achieved by an online learning algorithm with an efficient implication-based loss function. Experiments on real datasets show that the learned measures are efficient at a wide range of scales and achieve better results than existing fuzzy similarity measures. Moreover, the learning algorithm is fast so that it can be used in real-world applications, where computation times are a key feature when one chooses an inference system. © 2012 IEEE. Source


Dittami S.M.,Paris-Sorbonne University | Dittami S.M.,CNRS Integrative Biology of Marine Models | Eveillard D.,CNRS Nantes Atlantic Computer Science Laboratory | Tonon T.,Paris-Sorbonne University | Tonon T.,CNRS Integrative Biology of Marine Models
Molecular Ecology | Year: 2014

Increasing evidence exists that bacterial communities interact with and shape the biology of algae and that their evolutionary histories are connected. Despite these findings, physiological studies were and still are generally carried out with axenic or at least antibiotic-treated cultures. Here, we argue that considering interactions between algae and associated bacteria is key to understanding their biology and evolution. To deal with the complexity of the resulting 'holobiont' system, a metabolism-centred approach that uses combined metabolic models for algae and associated bacteria is proposed. We believe that these models will be valuable tools both to study algal-bacterial interactions and to elucidate processes important for the acclimation of the holobiont to environmental changes. © 2014 John Wiley & Sons Ltd. Source


Rusu I.,CNRS Nantes Atlantic Computer Science Laboratory
Theoretical Computer Science | Year: 2014

Common intervals of K permutations over the same set of n elements were firstly investigated by T. Uno and M. Yagiura (2000) [23], who proposed an efficient algorithm to find common intervals when K= 2. Several particular classes of intervals have been defined since then, e.g. conserved intervals and nested common intervals, with applications mainly in genome comparison. Each such class, including common intervals, led to the development of a specific algorithmic approach for K = 2, and - except for nested common intervals - for its extension to an arbitrary K. In this paper, we propose a common and efficient algorithmic framework for finding different types of common intervals in a set P of K permutations, with arbitrary K. Our generic algorithm is based on a global representation of the information stored in P, called the MinMax-profile of P, and an efficient data structure, called an LR-stack, that we introduce here. We show that common intervals (and their subclasses of irreducible common intervals and same-sign common intervals), nested common intervals (and their subclass of maximal nested common intervals) as well as conserved intervals (and their subclass of irreducible conserved intervals) may be obtained by appropriately setting the parameters of our algorithm in each case. All the resulting algorithms run in O( Kn+ N) time and need O( n) additional space, where N is the number of solutions. The algorithms for nested common intervals and maximal nested common intervals are new for K> 2, in the sense that no other algorithm has been given so far to solve the problem with the same complexity, or better. The other algorithms are as efficient as the best known algorithms. © 2014 Elsevier B.V. Source


Rusu I.,CNRS Nantes Atlantic Computer Science Laboratory
Journal of Discrete Algorithms | Year: 2012

In this paper, we address two different problems related to conserved regions in K≥2 genomes represented as permutations. The first one requires to enumerate the conserved regions, whereas the second one asks only to count them. We show that the generator-based technique, introduced by Bergeron et al. to enumerate common intervals of K genomes represented as permutations, may be extended following two directions. Firstly, it may be applied to signed permutations, yielding (1) a method to enumerate in O(Kn+N) time the N conserved intervals of K such permutations on n elements, and (2) a method to enumerate in O(Kn) time the irreducible conserved intervals of those K permutations. Secondly, it may be used to solve in O(Kn) time the counting problem, for every class of intervals which admits a so-called canonical generator. Both common and conserved intervals of K (signed) permutations admit such a generator. Although some (not all) of the above running times have already been reached by previous algorithms, it is the first time that the features shared by common and conserved intervals are exploited under a common efficient framework. © 2011 Elsevier B.V. All rights reserved. Source


Goldsztejn A.,CNRS Nantes Atlantic Computer Science Laboratory
Reliable Computing | Year: 2012

In Modal Intervals Revisited Part 1, new extensions to generalized intervals (intervals whose bounds are not constrained to be ordered), called AE-extensions, have been defined. They provide the same interpretations as modal intervals and therefore enhance the interpretations of classical interval extensions (for example, both inner and outer approximations of function ranges are in the scope of AE-extensions). The construction of AE-extensions is similar to the cnstruction of classical interval extensions. In particular, a natural AE-extension has been dened from Kaucher arithmetic which simplied some central results of modal interval theory. Starting from this framework, the mean-value AE-extension is now defined. It represents a new way to linearize a real function, which is compatible with both inner and outer approximations of its range. With a quadratic order of convergence for real-valued functions, it allows one to overcome some dificulties which were encountered using a preconditioning process together with the natural AE-extensions. Some application examples are finally presented, displaying the application potential of the mean-value AE-extension. Source

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