Paris Dauphine University

About: Paris Dauphine University is a education organization based out in Paris, France. It is known for research contribution in the topics: Population & Approximation algorithm. The organization has 1766 authors who have published 6909 publications receiving 162747 citations. The organization is also known as: Paris Dauphine & Dauphine.

RDF analytics: lenses over semantic graphs

Abstract: The development of Semantic Web (RDF) brings new requirements for data analytics tools and methods, going beyond querying to semantics-rich analytics through warehouse-style tools. In this work, we fully redesign, from the bottom up, core data analytics concepts and tools in the context of RDF data, leading to the first complete formal framework for warehouse-style RDF analytics. Notably, we define i) analytical schemas tailored to heterogeneous, semantics-rich RDF graph, ii) analytical queries which (beyond relational cubes) allow flexible querying of the data and the schema as well as powerful aggregation and iii) OLAP-style operations. Experiments on a fully-implemented platform demonstrate the practical interest of our approach.

Wasserstein active contours

Abstract: In this paper, we propose a novel and rigorous framework for region-based active contours that combines the Wasserstein distance between statistical distributions in arbitrary dimension and shape derivative tools. To speed-up the computation and be able to handle high-dimensional features and large-scale data, we introduce an approximation of the differential of the Wasserstein distance between histograms. The framework is flexible enough to allow either minimization of the Wasserstein distance to prior distributions, or maximization of the distance between the distributions of the regions to be segmented (i.e. region competition). Numerical results reported demonstrate the advantages of the proposed optimal transport distance with respect to point-wise metrics.

Simple and fast reoptimizations for the Steiner tree problem

Abstract: We address reoptimization issues for the Steiner tree problem. We assume that an optimal solution is given for some instance of the problem and the objective is to maintain a good solution when the instance is subject to minor modifications, the simplest such modifications being vertex insertions and deletions. We propose fast reoptimization strategies for the case of vertex insertions and we show that maintenance of a good solution for the “shrunk” instance, without ex nihilo computation, is impossible when vertex deletions occur. We also provide lower bounds for the approximation ratios of the reoptimization strategies studied.

Relational risk perception and alliance management in French biotechnology SMEs

Abstract: Purpose – Strategic alliances are often described as risky, dangerous, and instable. When firms adopt these strategies, they are confronted with a relational risk. Nevertheless, little empirical work has been down on relational risk in alliances. For this reason, this research is founded and constructed on two principal questions: what is relational risk? And how is this risk to be managed?Design/methodology/approach – From a methodological point of view, neither one paradigm nor the other concerning previous research was favoured. The process of the empirical research is based on an inductive non‐demonstrative step. It was carried out in two phases. Firstly, exploratory research was aimed at complementing previous research and formulating hypotheses. These hypotheses were tested with survey data on 87 partnerships of French biotechnology firms.Findings – The results demonstrate the multidimensional character of relational risk and the duality of relational control. Relational control includes autonomous ...

Parallel Nested Monte-Carlo search

Abstract: We address the parallelization of a Monte-Carlo search algorithm. On a cluster of 64 cores we obtain a speedup of 56 for the parallelization of Morpion Solitaire. An algorithm that behaves better than a naive one on heterogeneous clusters is also detailed.