Faraday instability on a network

in Chaos (2010)

L'hiver, une force majeure?

Article for general public (2010)

Assister n’est pas assurer Les enjeux d’une mutation anthropologique au cœur des politiques sociales

in SociologieS (2010)

Dans une récente réédition de l’un de ses ouvrages, Michel Messu revient sur l’enquête qu’il avait menée au cours des années 1980 auprès des assistés sociaux et prolonge son analyse par une réflexion ... [more ▼]

Dans une récente réédition de l’un de ses ouvrages, Michel Messu revient sur l’enquête qu’il avait menée au cours des années 1980 auprès des assistés sociaux et prolonge son analyse par une réflexion originale sur les évolutions de la protection sociale actuellement en cours dans la France du XXIe siècle, que l’on pourrait résumer ainsi : la protection sociale se présenterait désormais sous le visage d’une d’assurance d’assistance, garantissant à chaque citoyen une protection qu’il recevra tantôt sous forme d’assurance, tantôt sous forme d’assistance, souvent dans une combinatoire des deux. Cette thèse permet d’insister sur les glissements de registre, voire de régime, d’une protection sociale devenant de plus en plus une protection par intervention sur autrui. Mais, pour l’auteur, elle semble néanmoins pécher par un excès de banalisation du phénomène. S’appuyant sur ses propres analyses, celui-ci propose une vision assez différente de la protection afin d’alimenter le débat [less ▲]

Dorothée Goffin à la recherche du bien-être intestinal

in EOS le magazine des sciences (2010), 30(janvier/février 2011), 70-71

Article présentant les recherches effectuées par le Dr Dorothée Goffin dans le domaine des prébiotiques et en particulier des isomaltooligosaccharides

PRESSE: Article sur le Site de vulgarisation scientifique de l'ULg: "Réflexions" (voir le lien: http://reflexions.ulg.ac.be/cms/c_30598/working-is-good-for-the-health) - "Travailler, c'est bon pour la santé"

Diverse speeche and writing (2010)

Supervised Learning for Sequential and Uncertain Decision Making Problems - Application to Short-Term Electric Power Generation Scheduling

Doctoral thesis (2010)

Our work is driven by a class of practical problems of sequential decision making in the context of electric power generation under uncertainties. These problems are usually treated as receding horizon ... [more ▼]

Our work is driven by a class of practical problems of sequential decision making in the context of electric power generation under uncertainties. These problems are usually treated as receding horizon deterministic optimization problems, and/or as scenario-based stochastic programs. Stochastic programming allows to compute a first stage decision that is hedged against the possible futures and -- if a possibility of recourse exists -- this decision can then be particularized to possible future scenarios thanks to the information gathered until the recourse opportunity. Although many decomposition techniques exist, stochastic programming is currently not tractable in the context of day-ahead electric power generation and furthermore does not provide an explicit recourse strategy. The latter observation also makes this approach cumbersome when one wants to evaluate its value on independent scenarios. We propose a supervised learning methodology to learn an explicit recourse strategy for a given generation schedule, from optimal adjustments of the system under simulated perturbed conditions. This methodology may thus be complementary to a stochastic programming based approach. With respect to a receding horizon optimization, it has the advantages of transferring the heavy computation offline, while providing the ability to quickly infer decisions during online exploitation of the generation system. Furthermore the learned strategy can be validated offline on an independent set of scenarios. On a realistic instance of the intra-day electricity generation rescheduling problem, we explain how to generate disturbance scenarios, how to compute adjusted schedules, how to formulate the supervised learning problem to obtain a recourse strategy, how to restore feasibility of the predicted adjustments and how to evaluate the recourse strategy on independent scenarios. We analyze different settings, namely either to predict the detailed adjustment of all the generation units, or to predict more qualitative variables that allow to speed up the adjustment computation procedure by facilitating the ``classical'' optimization problem. Our approach is intrinsically scalable to large-scale generation management problems, and may in principle handle all kinds of uncertainties and practical constraints. Our results show the feasibility of the approach and are also promising in terms of economic efficiency of the resulting strategies. The solutions of the optimization problem of generation (re)scheduling must satisfy many constraints. However, a classical learning algorithm that is (by nature) unaware of the constraints the data is subject to may indeed successfully capture the sensitivity of the solution to the model parameters. This has nevertheless raised our attention on one particular aspect of the relation between machine learning algorithms and optimization algorithms. When we apply a supervised learning algorithm to search in a hypothesis space based on data that satisfies a known set of constraints, can we guarantee that the hypothesis that we select will make predictions that satisfy the constraints? Can we at least benefit from our knowledge of the constraints to eliminate some hypotheses while learning and thus hope that the selected hypothesis has a better generalization error? In the second part of this thesis, where we try to answer these questions, we propose a generic extension of tree-based ensemble methods that allows incorporating incomplete data but also prior knowledge about the problem. The framework is based on a convex optimization problem allowing to regularize a tree-based ensemble model by adjusting either (or both) the labels attached to the leaves of an ensemble of regression trees or the outputs of the observations of the training sample. It allows to incorporate weak additional information in the form of partial information about output labels (like in censored data or semi-supervised learning) or -- more generally -- to cope with observations of varying degree of precision, or strong priors in the form of structural knowledge about the sought model. In addition to enhancing the precision by exploiting information that cannot be used by classical supervised learning algorithms, the proposed approach may be used to produce models which naturally comply with feasibility constraints that must be satisfied in many practical decision making problems, especially in contexts where the output space is of high-dimension and/or structured by invariances, symmetries and other kinds of constraints. [less ▲]

Machine Learning Solution Methods for Multistage Stochastic Programming

Doctoral thesis (2010)

This thesis investigates the following question: Can supervised learning techniques be successfully used for finding better solutions to multistage stochastic programs? A similar question had already been ... [more ▼]

This thesis investigates the following question: Can supervised learning techniques be successfully used for finding better solutions to multistage stochastic programs? A similar question had already been posed in the context of reinforcement learning, and had led to algorithmic and conceptual advances in the field of approximate value function methods over the years. This thesis identifies several ways to exploit the combination "multistage stochastic programming/supervised learning" for sequential decision making under uncertainty. Multistage stochastic programming is essentially the extension of stochastic programming to several recourse stages. After an introduction to multistage stochastic programming and a summary of existing approximation approaches based on scenario trees, this thesis mainly focusses on the use of supervised learning for building decision policies from scenario-tree approximations. Two ways of exploiting learned policies in the context of the practical issues posed by the multistage stochastic programming framework are explored: the fast evaluation of performance guarantees for a given approximation, and the selection of good scenario trees. The computational efficiency of the approach allows novel investigations relative to the construction of scenario trees, from which novel insights, solution approaches and algorithms are derived. For instance, we generate and select scenario trees with random branching structures for problems over large planning horizons. Our experiments on the empirical performances of learned policies, compared to golden-standard policies, suggest that the combination of stochastic programming and machine learning techniques could also constitute a method per se for sequential decision making under uncertainty, inasmuch as learned policies are simple to use, and come with performance guarantees that can actually be quite good. Finally, limitations of approaches that build an explicit model to represent an optimal solution mapping are studied in a simple parametric programming setting, and various insights regarding this issue are obtained. [less ▲]

Longitudinal study of European birth cohorts on pet ownership up to age 10 years as a risk for childhood asthma - a GA²LEN project.

Conference (2010, December 20)

Cyrtospiriferid brachiopods from the mid-Late Devonian of southern Belgium (Namur-Dinant Basin)

in The Palaeontological Association. 54th Annual Meeting. Programme and Abstracts (2010, December 18)

Les débats publics sur l’intégration et la diversité en Europe

Scientific conference (2010, December 18)