University of Luxembourg

About: University of Luxembourg is a education organization based out in Luxembourg, Luxembourg. It is known for research contribution in the topics: Population & European union. The organization has 4744 authors who have published 22175 publications receiving 381824 citations.

Bilingualism Enriches the Poor: Enhanced Cognitive Control in Low-Income Minority Children

Abstract: This study explores whether the cognitive advantage associated with bilingualism in executive functioning extends to young immigrant children challenged by poverty and, if it does, which specific processes are most affected. In the study reported here, 40 Portuguese-Luxembourgish bilingual children from low-income immigrant families in Luxembourg and 40 matched monolingual children from Portugal completed visuospatial tests of working memory, abstract reasoning, selective attention, and interference suppression. Two broad cognitive factors of executive functioning-representation (abstract reasoning and working memory) and control (selective attention and interference suppression)-emerged from principal component analysis. Whereas there were no group differences in representation, the bilinguals performed significantly better than did the monolinguals in control. These results demonstrate, first, that the bilingual advantage is neither confounded with nor limited by socioeconomic and cultural factors and, second, that separable aspects of executive functioning are differentially affected by bilingualism. The bilingual advantage lies in control but not in visuospatial representational processes.

DENS: Data Center Energy-Efficient Network-Aware Scheduling

Abstract: In modern data centers, energy consumption accounts for a considerably large slice of operational expenses. The state of the art in data center energy optimization is focusing only on job distribution between computing servers based on workload or thermal profiles. This paper underlines the role of communication fabric in data center energy consumption and presents a scheduling approach that combines energy efficiency and network awareness, termed DENS. The DENS methodology balances the energy consumption of a data center, individual job performance, and traffic demands. The proposed approach optimizes the tradeoff between job consolidation (to minimize the amount of computing servers) and distribution of traffic patterns (to avoid hotspots in the data center network).

A Practitioners’ Guide to Gravity Models of International Migration

Abstract: The use of bilateral data for the analysis of international migration is at the same time a blessing and a curse. It is a blessing since the dyadic dimension of the data allows researchers to analyze many previously unaddressed questions in the literature. This paper reviews some of the recent studies using this type of data in a gravity framework in order to identify important factors a ecting international migration ows. Our review demonstrates that considerable e orts have been conducted by many scholars and that overall we have a much better knowledge of the relevant determinants. Still, the use of bilateral data is also a curse. The methodological challenges that are implied by the use of this type of data are numerous and our paper covers some of the most signi cant ones. These include sound theoretical foundations, accounting for multilateral resistance to migration as well the choice of appropriate estimation techniques dealing with the nature of the migration data and with endogeneity concerns

Longitudinal molecular trajectories of diffuse glioma in adults

Abstract: The evolutionary processes that drive universal therapeutic resistance in adult patients with diffuse glioma remain unclear1,2. Here we analysed temporally separated DNA-sequencing data and matched clinical annotation from 222 adult patients with glioma. By analysing mutations and copy numbers across the three major subtypes of diffuse glioma, we found that driver genes detected at the initial stage of disease were retained at recurrence, whereas there was little evidence of recurrence-specific gene alterations. Treatment with alkylating agents resulted in a hypermutator phenotype at different rates across the glioma subtypes, and hypermutation was not associated with differences in overall survival. Acquired aneuploidy was frequently detected in recurrent gliomas and was characterized by IDH mutation but without co-deletion of chromosome arms 1p/19q, and further converged with acquired alterations in the cell cycle and poor outcomes. The clonal architecture of each tumour remained similar over time, but the presence of subclonal selection was associated with decreased survival. Finally, there were no differences in the levels of immunoediting between initial and recurrent gliomas. Collectively, our results suggest that the strongest selective pressures occur during early glioma development and that current therapies shape this evolution in a largely stochastic manner.

Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring

Abstract: Today's smartphones and mobile devices typically embed advanced motion sensors. Due to their increasing market penetration, there is a potential for the development of distributed sensing platforms. In particular, over the last few years there has been an increasing interest in monitoring vehicles and driving data, aiming to identify risky driving maneuvers and to improve driver efficiency. Such a driver profiling system can be useful in fleet management, insurance premium adjustment, fuel consumption optimization or CO2 emission reduction. In this paper, we analyze how smartphone sensors can be used to identify driving maneuvers and propose SenseFleet, a driver profile platform that is able to detect risky driving events independently from the mobile device and vehicle. A fuzzy system is used to compute a score for the different drivers using real-time context information like route topology or weather conditions. To validate our platform, we present an evaluation study considering multiple drivers along a predefined path. The results show that our platform is able to accurately detect risky driving events and provide a representative score for each individual driver.