About the relevance of the concept of risk acceptability in the risk management process : a decisional approach in the French national context

International audienceA rapid view to the evolution of the legal context at the national European and international level shows a need to introduce both more "visibility" and more "legibility" on the way the decisions in risk analysis and risk management are taken. This can be introduced by: i) giving an image of what the scientific and the experts agree to be the technical "state of the Art" in there respective discipline to reduce and control hazardous activities; (ii) improving the way the population and the other stakeholders are involved and participate to the risk management process. In France, the Toulouse disaster has revealed a real need to improve the way decisions are taken in the risk prevention processes. In this paper, we will show how the establish concept of "risks acceptability" can induce bias on the way risk analysis are performed in the context of the hazard induced by industrial activities. We will show that there is a need to distinguish the acceptability known as "technical" from the one known as "social". We will then propose a new enlightenment on the way risk analysis are performed in Safety Studies and then discuss about the issue of land-use planning in France using "Risk prevention plans" (TRPP) around SEVESO sites

Contribution of organizational approach and decision aiding theory for assessing risks and benefits of outsourcing : implementing an action plan for contracts optimization in a company submitted to authorization

International audienceOutsourcing support activities in companies and industries is a key trend with core business strategy. This trend has increased with the hardening of the current financial crisis. The issue of outsourcing support is a major theme in the field of risk prevention. Even if, outsourcing is considered by Preventers as a key factor of risk, Managers still consider this solution as cost-effective and in some cases that subcontractors have more expertise. This paper will report on a methodology developed for studying the risks (financial, judicial, availability, safety) induced by outsourcing and the conclusion of a real case study performed in 2009 within a company submitted to authorization in order to optimize the management of outsourcing contracts. We will first analyze the state of the art about risks and benefits induced by outsourcing. A description of the company and the management's request will be described in a second time. We will then present our comprehensive methodological approach based on decision aiding framework and on the achievement of an organizational diagnosis to consider impact on working relationships and organizational conditions in managing outsourcing service delivery contracts. Finally, we will discuss the way our recommendations were taken into account by the management staff more than two years after proposing an action plan aiming at sustaining the quality and the performances of the services delivered to the Companie

Robust Methods for High-Dimensional Linear Learning

We propose statistically robust and computationally efficient linear learning methods in the high-dimensional batch setting, where the number of features $d$ may exceed the sample size $n$. We employ, in a generic learning setting, two algorithms depending on whether the considered loss function is gradient-Lipschitz or not. Then, we instantiate our framework on several applications including vanilla sparse, group-sparse and low-rank matrix recovery. This leads, for each application, to efficient and robust learning algorithms, that reach near-optimal estimation rates under heavy-tailed distributions and the presence of outliers. For vanilla $s$-sparsity, we are able to reach the $s\log (d)/n$ rate under heavy-tails and $\eta$-corruption, at a computational cost comparable to that of non-robust analogs. We provide an efficient implementation of our algorithms in an open-source $\mathtt{Python}$ library called $\mathtt{linlearn}$, by means of which we carry out numerical experiments which confirm our theoretical findings together with a comparison to other recent approaches proposed in the literature.Comment: accepted versio

Convergence and concentration properties of constant step-size SGD through Markov chains

We consider the optimization of a smooth and strongly convex objective using constant step-size stochastic gradient descent (SGD) and study its properties through the prism of Markov chains. We show that, for unbiased gradient estimates with mildly controlled variance, the iteration converges to an invariant distribution in total variation distance. We also establish this convergence in Wasserstein-2 distance in a more general setting compared to previous work. Thanks to the invariance property of the limit distribution, our analysis shows that the latter inherits sub-Gaussian or sub-exponential concentration properties when these hold true for the gradient. This allows the derivation of high-confidence bounds for the final estimate. Finally, under such conditions in the linear case, we obtain a dimension-free deviation bound for the Polyak-Ruppert average of a tail sequence. All our results are non-asymptotic and their consequences are discussed through a few applications

Solvability the telegraph equation with purely integral conditions

In this paper a numerical technique is developed for the one-dimensional telegraph equation, we prove the existence, uniqueness, and continuous dependence upon the data of solution to a telegraph equation with purely integral conditions. The proofs are based on a priori estimates and Laplace transform method. Finally, we obtain the solution by using a simple and efficient algorithm for numerical solution.Publisher's Versio

Solvability the telegraph equation with purely integral conditions

In this paper a numerical technique is developed for the one-dimensional telegraph equation. We prove the existence, uniqueness, and continuous dependence upon the data of solution to a telegraph equation with purely integral conditions. The proofs are based on a priori estimates and Laplace transform method. Finally, we obtain the solution by using a simple and efficient algorithm for numerical solution.Publisher's Versio

DC performance analysis of a 20nm gate lenght n-type silicon GAA junctionless (Si JL-GAA) transistor

With integrated circuit scales in the 22-nm regime, conventional planar MOSFETs have approached the limit of their potential performance. To overcome short channel effects 'SCEs' that appears for deeply scaled MOSFETs beyond 10nm technology node many new device structures and channel materials have been proposed. Among these devices such as Gate-all-around FET. Recentely, junctionless GAA MOSFETs JL-GAA MOSFETs have attracted much attention since the junctionless MOSFET has been presented. In this paper, DC characteristics of an n-type JL-GAA MOSFET are presented using a 3-D quantum transport model .This new generation device is conceived with the same doping concentration level in its channel source/drain allowing to reduce fabrication complexity . The performance of our 3D JL-GAA structure with a 20nm gate length and a rectangular cross section have been obtained using SILVACO TCAD tools allowing also to study short channel effects. Our device reveals a favorable on/off current ratio and better SCE characteristics compared to an inversion-mode GAA transistor. Our device reveals a threshold voltage of 0.55 V, a sub-threshold slope of 63mV / decade which approaches the ideal value, an Ion / Ioff ratio of 10e + 10 value and a drain induced barrier lowring (DIBL) value of 98mV / V