The political situation between the two #Sudans should be the priority for AU mediators

In the second part of his reflections on South Sudan’s first year as an independent nation, LSE’s Matthew LeRiche says unresolved political issues between Sudan and South Sudan, rather than oil, is the reason for the continuing conflict between the two countries

LSE academic describes Sudanese attack on South Sudan town

LSE’s Matthew Le Riche is in Rubkona in South Sudan, which was under attack by Sudanese forces on Monday. He sent this report of the situation in the town. You may find some of the photos accompanying the report distressing

Internal conflict within South #Sudan is as much as a challenge as that with their northern neighbour

In the first of a four-part series looking at South Sudan one year after independence, LSE’s Matthew Le Riche assesses the challenges and successes after the first year of the world’s youngest nation

OMD : Optimisation MultiDisciplinaire

http://www.emse.fr/~leriche/rapport_final_rntl_omd_public.pdfProgramme RNTL 2005 de l'Agence Nationale de la Recherch

Protection for Employee Whistleblowers under the Fair Labor Standards Act and Missouri\u27s Public Policy Exception: What Happens If the Employee Never Whistled

The Fair Labor Standards Act ( FLSA ) was implemented by Congress in 1938 in an attempt to assure most workers of an adequate minimum wage and payment for overtime work. FLSA § 15(a)(3) was enacted to protect employees from retaliatory discharges based upon the reporting of violations of the substantive FLSA provisions In Saffels v. Rice, the Court of Appeals for the Eighth Circuit extended protection to employees who were dismissed based on the employer\u27s mistaken belief the employee reported violations of the law to the authorities based on both FLSA § 15(a)(3) and Missouri\u27s commonlaw public policy exception to the at-will employment doctrine. The court, by extending protection to employees who have taken no protected action, removed analysis under FLSA § 15(a)(3) from the plain language of the statute.\u2

Robust optimization of a 2D air conditioning duct using kriging

The design of systems involving fluid flows is typically based on computationally intensive Computational Fluid Dynamics (CFD) simulations. Kriging based optimization methods, especially the Efficient Global Optimization (EGO) algorithm, are now often used to solve deterministic optimization problems involving such expensive models. When the design accounts for uncertainties, the optimization is usually based on double loop approaches where the uncertainty propagation (e.g., Monte Carlo simulations, reliability index calculation) is recursively performed inside the optimization iterations. We have proposed in a previous work a single loop kriging based method for minimizing the mean of an objective function: simulations points are calculated in order to simultaneously propagate uncertainties, i.e., estimate the mean objective function, and optimize this mean. In this report this method has been applied to the shape optimization of a 2D air conditioning duct. For comparison purposes, deterministic designs were first obtained by the EGO algorithm. Very high performance designs were obtained, but they are also very sensitive to numerical model parameters such as mesh size, which suggests a bad consistency between the physics and the numerical model. The 2D duct test case has then been reformulated by introducing shape uncertainties. The mean of the duct performance criteria with respect to shape uncertainties has been maximized with the simultaneous optimization and sampling method. The solutions found were not only robust to shape uncertainties but also to the CFD model numerical parameters. These designs show that the method is of practical interest in engineering tasks

Description de l'architecture Scilab pour le projet RNTL/OMD

http://www.emse.fr/~leriche/OMD_spec_scilab_march2008.pdfLes algorithmes d'optimisation classiques sont impl'ement'es selon un paradigme fonctionnel qui place la m'ethode d'optimisation au sommet de la hi'erarchie. Par exemple, avec Scilab on 'ecrira la commande [f,xopt]=optim(costf,x0). Cette instruction se chargera de tout le processus d'optimisation, sans contrˆole possible de l'utilisateur. Ce type d'impl'ementation est tr'es contraignant si l'on souhaite mettre en oeuvre des strat'egies d'optimisation plus souples, par exemple, pouvoir changer d'optimiseur en cours d'optimisation, estimer un ou plusieurs m'eta-mod'eles, etc.. . . Nous proposons ici une organisation logicielle selon un paradigme objet o'u les optimiseurs, les simulateurs et les m'eta-mod'eles sont des objets de mˆeme niveau hi'erarchique. L'utilisateur peut alors "jongler" avec ces diff'erents objets pour se contruire des strat'egie d'optimisation personnalis'ees

An analytic comparison of regularization methods for Gaussian Processes

Gaussian Processes (GPs) are a popular approach to predict the output of a parameterized experiment. They have many applications in the field of Computer Experiments, in particular to perform sensitivity analysis, adaptive design of experiments and global optimization. Nearly all of the applications of GPs require the inversion of a covariance matrix that, in practice, is often ill-conditioned. Regularization methodologies are then employed with consequences on the GPs that need to be better understood.The two principal methods to deal with ill-conditioned covariance matrices are i) pseudoinverse and ii) adding a positive constant to the diagonal (the so-called nugget regularization).The first part of this paper provides an algebraic comparison of PI and nugget regularizations. Redundant points, responsible for covariance matrix singularity, are defined. It is proven that pseudoinverse regularization, contrarily to nugget regularization, averages the output values and makes the variance zero at redundant points. However, pseudoinverse and nugget regularizations become equivalent as the nugget value vanishes. A measure for data-model discrepancy is proposed which serves for choosing a regularization technique.In the second part of the paper, a distribution-wise GP is introduced that interpolates Gaussian distributions instead of data points. Distribution-wise GP can be seen as an improved regularization method for GPs

Articulation des rationalités cartésienne et complexe dans les projets associant plusieurs disciplines

International audienceNous sommes formatés dans notre sphère occidentale par la rationalité cartésienne, qui tire son origine de la logique aristotélicienne. La pensée contemporaine voit les succès, mais aussi les limites, de cette rationalité, et cherche à voir comment la composer avec une rationalité que nous appellerons complexe, en référence à Edgar Morin (cette dernière s'exprime déjà dans une certaine mesure dans d'autres cultures et d'autres langues). L'exposé cherchera d'abord à présenter ces deux modes de rationalité : la rationalité cartésienne sait en particulier distinguer ses objets des relations qu'ils ont entre eux (pensée substantielle) ; la rationalité complexe ne fait pas cette distinction de façon tranchée et définit ses objets par les relations qu'ils entretiennent entre eux. Nous monterons ensuite plus spécialement pourquoi, pour des problèmes qui demandent l'intégration de plusieurs disciplines (exemple de la conception d'un avion, problèmes d'environnement), la rationalité complexe peut nous guider et nous éclairer dans la façon de comprendre les choses. Suivant la façon dont les disciplines sont reliées ou non les unes aux autres, les projets associant plusieurs disciplines impliquent différentes articulations entre rationalités cartésienne et complexe qui peuvent être qualifiées d'interdisciplinaires, de pluridisciplinaires, de métadisciplinaires ou de transdisciplinaires. L'exposé ne prétendra pas présenter une pensée originale sur ces divers sujets mais se voudra un moment didactique et une invitation au dialogue