Paris Dauphine University

About: Paris Dauphine University is a education organization based out in Paris, France. It is known for research contribution in the topics: Context (language use) & Population. The organization has 1766 authors who have published 6909 publications receiving 162747 citations. The organization is also known as: Paris Dauphine & Dauphine.

La transgression : une dimension oubliée de l’organisation. Les pratiques transgressives comme médiatrices des contradictions organisationnelles

Abstract: Cet article theorique cherche a rehabiliter le role de la transgression des regles dans l'organisation. Critiquant la condamnation systematique de tout phenomene transgressif par les sciences de gestion, nous cherchons a montrer qu'une partie du phenomene ne doit pas etre compris comme une pathologie de l'organisation, mais bien comme un element de son fonctionnement normal. Nous mobilisons les cadres theoriques de l'analyse strategique (Crozier et Friedberg) et de la theorie de la regulation conjointe (Reynaud) pour montrer combien l'acte transgressif peut avoir une dimension positive dans le fonctionnement organisationnel. Il est possible de mettre en evidence en particulier le fait que la transgression joue dans l'organisation le role de conciliateur des contraintes contradictoires des organisations.

Tackling Whiteness in organizations and management

Abstract: Purpose – The purpose of this paper is to offer a theoretical and methodological framework to overcome knowledge gaps on Whiteness in organizational and management studies. Design/methodology/approach – The paper draws on a brief review of literature on ethnic privileges. Findings – The authors propose a relational approach to tackling ethnic privileges in organizations and management research. Research limitations/implications – The framework contributes to a better understanding and deconstruction of ethnic privileges at work. Originality/value – The paper proposes a theoretical and a methodological framework for tackling Whiteness in organizational and management studies. By doing so, it elucidates the topic of Whiteness, bringing new insights from an interdisciplinary perspective.

High-availability LH* schemes with mirroring

Abstract: Mirroring is a popular technique for enhancing file availability. The authors incorporate this technique into the LH* algorithms for scalable distributed linear hash files. Several schemes for mirroring LH* files are presented in this paper. The schemes increase the availability of LH* files in the presence of node failures. Every record remains accessible in the presence of a single node failure, and usually in the presence of multiple-node failures. The price is, as usual, twice as much storage for data, and an increase in the number of messages. The different schemes are characterized by different trade-offs, and they accommodate diverse application requirements. The additional messaging cost per insert is about the same for all the schemes, and is roughly only one message. The cost of a bucket recovery may in contrast vary greatly, from one message for one type of scheme, to a few for another, and many for yet another.

When lockdown policies amplify social inequalities in COVID-19 infections: evidence from a cross-sectional population-based survey in France.

Abstract: Significant differences in COVID-19 incidence by gender, class and race/ethnicity are recorded in many countries in the world. Lockdown measures, shown to be effective in reducing the number of new cases, may not have been effective in the same way for all, failing to protect the most vulnerable populations. This survey aims to assess social inequalities in the trends in COVID-19 infections following lockdown. A cross-sectional survey conducted among the general population in France in April 2020, during COVID-19 lockdown. Ten thousand one hundred one participants aged 18–64, from a national cohort who lived in the three metropolitan French regions most affected by the first wave of COVID-19. The main outcome was occurrence of possible COVID-19 symptoms, defined as the occurrence of sudden onset of cough, fever, dyspnea, ageusia and/or anosmia, that lasted more than 3 days in the 15 days before the survey. We used multinomial regression models to identify social and health factors related to possible COVID-19 before and during the lockdown. In all, 1304 (13.0%; 95% CI: 12.0–14.0%) reported cases of possible COVID-19. The effect of lockdown on the occurrence of possible COVID-19 was different across social hierarchies. The most privileged class individuals saw a significant decline in possible COVID-19 infections between the period prior to lockdown and during the lockdown (from 8.8 to 4.3%, P = 0.0001) while the decline was less pronounced among working class individuals (6.9% before lockdown and 5.5% during lockdown, P = 0.03). This differential effect of lockdown remained significant after adjusting for other factors including history of chronic disease. The odds of being infected during lockdown as opposed to the prior period increased by 57% among working class individuals (OR = 1.57; 95% CI: 1.00–2.48). The same was true for those engaged in in-person professional activities during lockdown (OR = 1.53; 95% CI: 1.03–2.29). Lockdown was associated with social inequalities in the decline in COVID-19 infections, calling for the adoption of preventive policies to account for living and working conditions. Such adoptions are critical to reduce social inequalities related to COVID-19, as working-class individuals also have the highest COVID-19 related mortality, due to higher prevalence of comorbidities.

Variational Texture Synthesis with Sparsity and Spectrum Constraints

Abstract: This paper introduces a new approach for texture synthesis. We propose a unified framework that both imposes first order statistical constraints on the use of atoms from an adaptive dictionary, as well as second order constraints on pixel values. This is achieved thanks to a variational approach, the minimization of which yields local extrema, each one being a possible texture synthesis. On the one hand, the adaptive dictionary is created using a sparse image representation rationale, and a global constraint is imposed on the maximal number of use of each atom from this dictionary. On the other hand, a constraint on second order pixel statistics is achieved through the power spectrum of images. An advantage of the proposed method is its ability to truly synthesize textures, without verbatim copy of small pieces from the exemplar. In an extensive experimental section, we show that the resulting synthesis achieves state of the art results, both for structured and small scale textures.