No abstract

The nationwide lockdown inflicted by the global COVID-19 disease epidemic and imposed during 57 days in France was not immune to fluctuations in atmospheric pollutant concentrations. A whole range of human activities has been suspended Monday 17 March 2020 in all French regions. Since then experiments are progressing to reflect the effectiveness of reduced emissions. In this paper, we looked at variations of pollutants prior to, during and after containment period. In a first step, we proved through experiments on eight air pollutants, how all daily maximum pollutants concentration have decreased during containment phase, apart from the ozone pollutant O 3. This Ozone pollutant has indeed increased by 27.19% during lockdown period and kept growing by 21.35% as well right after deconfinement. Indeed, the maximum daily concentrations detected in different regions of France, have decreased by 18.18%, 37.14%, 20.36%, 9.28%, 44.38%, 5.1% and 44.38%, respectively, for the pollutants SO 2 , NO 2 , CO, C 6 H 6 , NOX, PM 2.5 and PM 10. Declining levels of other pollutants, however, were not sustained after deconfinement for NO 2 , NOX and PM 10. We have reinforced these findings by classifying each pollutant according to the ATMO and AQI indexes, to better visualize their criticality throughout the three lockdown phases (Pre/During/Post). The family of air pollutant variables with their associated geographical sources was thereafter exploited to justify their approximate contribution to the daily mortality rates associated to COVID-19 across all French regions. However, more thorough study is still in progress to validate this finding. Finally, coming up to the abrupt changes in airborne pollutants experienced in this period, a question about future climate crisis was raised again. Whereby a weighting study has shown the current and very shortterm French scenario (Status-Quo) in view of its current environmental path, the political responses made towards future climate change crisis and French investments done in this sense.

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Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

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Digital images can be analyzed at wide range of levels going from pixel arrangement to semantics. As a consequence, finding a visual complexity estimator is a difficult task. In this article we propose a definition of attention based perceptual complexity. We study the performance of human eye movements and of different models of computational attention against a ground-truth of image complexity based on the observation time of an image description task. The results obtained show that besides its lack of semantic processing, attentional behavior is a good estimator of image complexity.

No abstract

In the field of scene analysis for computer vision, a trade-off must be found between the quality of the results expected and the amount of computer resources allocated for each task. Using an adaptive vision system provides a more flexible solution as its analysis strategy can be changed according to the information available concerning the execution context. The authors describe how to create and evaluate a visual attention system tailored for interacting with a computer vision system so that it adapts its processing according to the interest (or salience) of each element of the scene. The authors propose a new set of constraints called ‘PAIRED’ to evaluate the adequacy of a model with respect to its different applications. The authors then justify why dynamical systems are a good choice for visual attention simulation, and we show that predator-prey models provide good properties for simulating the dynamic competition between different kinds of information. They present different results (cross-correlation, Kullback-Leibler divergence, normalized scanpath salience) that demonstrate that, in spite of being fast and highly configurable, their results are as plausible as existing models designed for high biological fidelity.