Catalogue de tableaux et dessins de l'école moderne provenant de la collection de M. B... / [expert] Francis Petit

[Vente. Art. 1860-12-22. Paris]

Processus primaire ou possession moderne ?

Les concepts de processus primaire chez Freud et du Réel chez Lacan sont des notions difficiles à appréhender. La difficulté devient même insurmontable lorsqu'on cherche à les relier aux phénomènes cliniques qu'ils sont censés représenter. Rebondissant sur des articles d'un numéro récent de Psychothérapies, il sera question d'apporter un éclairage différent sur ces notions, en les rapprochant des analyses de M. de Certeau sur un phénomène de possession au XVIIe siècle. Cette échappée historique permettrait également de réinterroger une clinique difficile, celle du traumatisme.

Salon d'art décoratif moderne : [Catalogue] / [Couv.ill. par Henry de Waroquier]

[Exposition internationale. Gand. 1913]

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

Quasi-Nonparametric Blind Inversion of Wiener Systems

An e cient procedure for the blind inversion of a nonlinear Wiener system is proposed. We proved that the problem can be expressed as a problem of blind source separation in nonlinear mixtures, for which a solution has been recently proposed. Based on a quasi-nonparametric relative gradient descent, the proposed algorithm can perform e ciently even in the presence of hard distortions.

Initialisation of Nonlinearities for PNL and Wiener systems Inversion

This paper proposes a very fast method for blindly initial- izing a nonlinear mapping which transforms a sum of random variables. The method provides a surprisingly good approximation even when the basic assumption is not fully satis¯ed. The method can been used success- fully for initializing nonlinearity in post-nonlinear mixtures or in Wiener system inversion, for improving algorithm speed and convergence.

Blind Inversion of Wiener Systems

A system in which a linear dynamic part is followed by a non linear memoryless distortion a Wiener system is blindly inverted This kind of systems can be modelised as a postnonlinear mixture and using some results about these mixtures an e cient algorithm is proposed Results in a hard situation are presented and illustrate the e ciency of this algorithm