Correcting agglomeration economies: How air pollution matters

The aim of the paper is to correct standard measures of agglomeration economies in order to account for air pollution generated by commuting. This paper examines the impact of nitrogen oxide (NOX) on worker productivity. NOX emissions are primarily released by the transportation sector. Literature on agglomeration economies is abundant and highlights the positive role of density on productivity. Nevertheless, this literature does not take into account the environmental impact generated by a better accessibility, namely commuting. We rst develop a general framework to estimate the agglomeration economies for the 304 French employment areas. In line with the literature, we nd an estimate of 0.05 for the elasticity coe cient of productivity with respect to density. Then, we introduce NOX emissions. The estimates suggest that emissions reduce the positive e ect of density on productivity by more 13%. The model con rms that air pollution matters. Agglomeration economies should be corrected by the environmental impacts associated with the enhancement of accessibility such as the implementation of a new transport infrastructure or policy

The Impacts of public transport policies on a non mobility area: French study case in the north of France

Many regeneration projects aim at producing a benefit of the population around them. However, it may happen that the socio-economic problems and needs of the territory involved are not completely understood. This paper reports on a case study of the northern part of France, namely Bassin-Minier, which used to be a coal mining area. It is a partic ular metropolitan area with a specific geography and spatial distribution, which faces different social problems, along with a limited public transportation system. Focus groups were used as a methodology to disentangle the transport behaviour, situation and needs of the people living or working in this specific territory due to the limited availability of other recent sources of information. We have chosen to focus specifically on groups with limited availability of transportation or in risk of social exclusion. This research also aims at contrasting different future and recent transport and regeneration projects with the population under study, to understand whether these projects are found beneficial by them

Scalable k-Means Clustering via Lightweight Coresets

Coresets are compact representations of data sets such that models trained on a coreset are provably competitive with models trained on the full data set. As such, they have been successfully used to scale up clustering models to massive data sets. While existing approaches generally only allow for multiplicative approximation errors, we propose a novel notion of lightweight coresets that allows for both multiplicative and additive errors. We provide a single algorithm to construct lightweight coresets for k-means clustering as well as soft and hard Bregman clustering. The algorithm is substantially faster than existing constructions, embarrassingly parallel, and the resulting coresets are smaller. We further show that the proposed approach naturally generalizes to statistical k-means clustering and that, compared to existing results, it can be used to compute smaller summaries for empirical risk minimization. In extensive experiments, we demonstrate that the proposed algorithm outperforms existing data summarization strategies in practice.Comment: To appear in the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD

Adenovirus-Mediated Sensitization to the Cytotoxic Drugs Docetaxel and Mitoxantrone Is Dependent on Regulatory Domains in the E1ACR1 Gene-Region

Oncolytic adenoviruses have shown promising efficacy in clinical trials targeting prostate cancers that frequently develop resistance to all current therapies. The replication-selective mutants AdΔΔ and dl922–947, defective in pRb-binding, have been demonstrated to synergise with the current standard of care, mitoxantrone and docetaxel, in prostate cancer models. While expression of the early viral E1A gene is essential for the enhanced cell killing, the specific E1A-regions required for the effects are unknown. Here, we demonstrate that replicating mutants deleted in small E1A-domains, binding pRb (dl1108), p300/CBP (dl1104) and p400/TRRAP or p21 (dl1102) sensitize human prostate cancer cells (PC-3, DU145, 22Rv1) to mitoxantrone and docetaxel. Through generation of non-replicating mutants, we demonstrate that the small E1A12S protein is sufficient to potently sensitize all prostate cancer cells to the drugs even in the absence of viral replication and the E1A transactivating domain, conserved region (CR) 3. Furthermore, the p300/CBP-binding domain in E1ACR1 is essential for drug-sensitisation in the absence (AdE1A1104) but not in the presence of the E1ACR3 (dl1104) domain. AdE1A1104 also failed to increase apoptosis and accumulation of cells in G2/M. All E1AΔCR2 mutants (AdE1A1108, dl922–947) and AdE1A1102 or dl1102 enhance cell killing to the same degree as wild type virus. In PC-3 xenografts in vivo the dl1102 mutant significantly prolongs time to tumor progression that is further enhanced in combination with docetaxel. Neither dl1102 nor dl1104 replicates in normal human epithelial cells (NHBE). These findings suggest that additional E1A-deletions might be included when developing more potent replication-selective oncolytic viruses, such as the AdΔCR2-mutants, to further enhance potency through synergistic cell killing in combination with current chemotherapeutics

Transcriptomic analyses reveal that the cellular Gem protein promotes HTLV-1 infected cell migration and viral transmission

International audienc

Large-Scale Cover Song Recognition Using the 2D Fourier Transform Magnitude

Large-scale cover song recognition involves calculating item-to-item similarities that can accommodate differences in timing and tempo, rendering simple Euclidean measures unsuitable. Expensive solutions such as dynamic time warping do not scale to million of instances, making them inappropriate for commercial-scale applications. In this work, we transform a beat-synchronous chroma matrix with a 2D Fourier transform and show that the resulting representation has properties that fit the cover song recognition task. We can also apply PCA to efficiently scale comparisons. We report the best results to date on the largest available dataset of around 18,000 cover songs amid one million tracks, giving a mean average precision of 3.0%