[1-6-19, l'Américain Earl William] Eby gagnant du 400 m [championnats de l'armée américaine à Colombes] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 54199

[1-6-19, l'Américain Earl William] Eby gagnant du 400 m [championnats d'athlétisme de l'armée américaine à Colombes] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 54198

[Earl William] Eby, Américain, 2ème, [Daniel Leslie] Mason, Nouvelle-Zélande, 1er du 800 [mètres des Jeux interalliés, stade Pershing, le 4 juillet 1919] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 54702

Temporal trends in intergenerational social mobility in Switzerland: A cohort study of men and women born between 1912 and 1974

The central question addressed in this paper is to what extent the influence of social origin on life chances has changed over time for both men and women. In order to capture this change, intergenerational social mobility of eight different birth-cohorts, covering most of the entire twentieth century, is analysed using a unique collection of twelve Swiss national population sample surveys. The main results show that social mobility has remained constant across cohorts born in 1912 and those born in 1974. This suggests that unlike some other industrialised countries, inequality based on social origin is persistent in Switzerland.

Lady [Nancy] Astor qui a été élue à la Chambre des Communes, [présence d'] Isaac Foot [et] M. [William] Gay, [ses adversaires politiques] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 57100

S-system parameter estimation for noisy metabolic profiles using newton-flow analysis.

Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.