Avril 2017

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Mardi 4 Avril
Heure: 14:00 - 17:00
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: String algorithms for bioinformatics
Description: Mireille Régnier
Mardi 11 Avril
Heure: 14:00 - 17:00
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Utilisation avancée de Maple : calcul parallèle et interfaçage avec des bibliothèques externes
Description: Nicolas Gachadoit et Nicolas Cottereau Maple possède plusieurs milliers de fonctions, mais certains calculs spécifiques peuvent nécessiter l'utilisation de bibliothèques externes. Par ailleurs, les ordinateurs n'évoluent plus tellement dans le sens d'une augmentation de la fréquence des processeurs maisdans le sens d'une augmentation du nombre de c?urs de calcul.Cette présentation (par Nicolas Gachadoit) détaillera les fonctionnalités de Maple permettant de réaliser des calculs parallèles et de s'interfacer avec du code écrit dans d'autres langages.Il y aura également une présentation rapide (par Nicolas Cottereau) de quelques fonctionnalités de la plateforme Maple dédiée pour l?enseignement.
Mardi 18 Avril
Heure: 12:30 - 13:30
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Deriving differential approximation results for k-CSPs from combinatorial designs
Description: Sophie Toulouse Given two integers q, k ? 2, k-CSP?q is the unconstrained optimization problem in which variables have domain Z_q and the goal is to optimize a weighted sum of constraints, each acting on at most k of the variables. Standard inapproximability results for Max-k-CSP?q often involve balanced k-wise independent distributions over Z_q or rather equivalently, orthogonal arrays of strength k over Z_q. We here illustrate how combinatorial designs are a relevant tool in order to establish approximation results for k-CSP?q with respect to the differential approximation measure, which compares the distance between the approximate value and the worst solution value to the instance diameter. First, connecting the average differential ratio to orthogonal arrays, we deduce that this ratio is ?(1/n^(k/2)) when q = 2, ?(1/n^(k-1)) when q is a prime power and 1/q^k on (k+1)-partite instances. We also consider pairs of arrays that can be viewed as some constrained decomposition of balanced k-wise independent functions. We exhibit such pairs that allow when q > k to reduce k-CSP?q to k-CSP?k with an expansion 1/(q?k/2)^k on the approximation guarantee. This implies together with the result of [Yuri Nesterov, Semidefinite relaxation and nonconvex quadratic optimization, Optimization Methods and Software, 9 (1998), pp. 141–160] a lower approximability bound of 0.429/(q ? 1)^2 for 2-CSP?q. Similar designs also allow to establish that every Hamming ball with radius k provides a ?(1/n^k)-approximation of the instance diameter.
Heure: 14:00 - 17:00
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Holonomie et non-holonomie des marches dans le quart de plan
Description: Thomas Dreyfus
Vendredi 21 Avril
Heure: 15:00 - 16:30
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Complex network analysis in ubiquitous and social environments
Description: Martin Atzmueller In the world of today, a variety of interaction data of humans, services and systems is generated, e.g. , by sensors and social media. This enables the observation and capture of physical and social activities, and subsequent extended analysis of interactions, structures and patterns covering both online and offline contexts. This talk focuses on behavioral analytics in social media and the physical world, and presents exemplary methods and results in the context of real-world systems. Specifically, we focus on the grounding and analysis of behavior, interactions and complex structures emerging from heterogeneous data, and according modeling approaches using complex network analysis.
Mardi 25 Avril
Heure: 14:00 - 17:00
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Random graphs and average-case analysis of NP-complete problems
Description: Tom Denat
Jeudi 27 Avril
Heure: 12:30 - 13:30
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: RoSTBiDFramework: Optimised Framework based on Rough Set Theory for Big Data Pre- processing in Certain and Imprecise Contextsble
Description: Zeineb Chelly Over the last decades, the amount of data has increased in an
unprecedented rate, leading to a new terminology: "Big Data". Big data
are specified by their Volume, Variety, Velocity and by their
Veracity/Imprecision. Based on these 4V specificities, it has become
difficult to quickly acquire the most useful information from the huge
amount of data at hand. Thus, it is necessary to perform data
(pre-)processing as a first step. In spite of the existence of many
techniques for this task, most of the state-of-the-art methods require
additional information for thresholding and are neither able to deal
with the big data veracity aspect nor with their computational
requirements. This project's overarching aim is to fill these major
research gaps with an optimised framework for big data pre-processing
in certain and imprecise contexts. Our approach is based on Rough Set
Theory (RST) for data pre-processing and Randomised Search Heuristics
for optimisation and will be implemented under the Spark MapReduce
model. The project combines the expertise of the experienced
researcher Dr Zaineb Chelly Dagdia in machine learning, rough set
theory and information extraction with the knowledge in optimisation
and randomised search heuristics of the supervisor Dr Christine Zarges
at Aberystwyth University. Further expertise is provided by internal
and external collaborators from academic and non-academic
institutions, namely Lebbah and Azzag (University of Paris 13), Shen
(University of Aberystwyth), Tino (University of Birmingham), Merelo
(University of Granada) and an industrial partner from France.