University of Rennes

About: University of Rennes is a education organization based out in Rennes, France. It is known for research contribution in the topics: Population & Catalysis. The organization has 18404 authors who have published 40374 publications receiving 995327 citations.

Color image database TID2013: Peculiarities and preliminary results

Abstract: Visual quality of color images is an important aspect in various applications of digital image processing and multimedia. A large number of visual quality metrics (indices) has been proposed recently. In order to assess their reliability, several databases of color images with various sets of distortions have been exploited. Here we present a new database called TID2013 that contains a larger number of images. Compared to its predecessor TID2008, seven new types and one more level of distortions are included. The need for considering these new types of distortions is briefly described. Besides, preliminary results of experiments with a large number of volunteers for determining the mean opinion score (MOS) are presented. Spearman and Kendall rank order correlation factors between MOS and a set of popular metrics are calculated and presented. Their analysis shows that adequateness of the existing metrics is worth improving. Special attention is to be paid to accounting for color information and observers focus of attention to locally active areas in images.

Elastic Properties and Short-to Medium-Range Order in Glasses

Abstract: Very different materials are named “Glass,” with Young's modulus (E) and Poisson's ratio (ν) extending from 5 to 180 GPa and from 0.1 to 0.4, respectively, in the case of bulk inorganic glasses. Although glasses have in common the lack of long-range order in the atomic organization, they offer a wide range of structural features at the nanoscale and we show in this analysis that beside the essential role of elastic properties for materials selection in mechanical design, the elastic characteristics (E, ν) at the continuum scale allow to get insight into the short- and medium-range orders existing in glasses. In particular, ν, the atomic packing density (Cg) and the glass network dimensionality appear to be strongly correlated. Maximum values for ν and Cg are observed for metallic glasses (ν∼0.4 and Cg>0.7), which are based on cluster-like structural units. Atomic networks consisting primarily of chains and layers units (chalcogenides, low Si-content silicate, and phosphate glasses) correspond to ν>0.25 and Cg>0.56. On the contrary, ν<0.25 is associated with a highly cross-linked network, such as in a-SiO2, with a tri-dimensional organization resulting in a low packing density. Moreover, the temperature dependence of the elastic moduli brings a new light on the structural changes occurring above the glass transition temperature and on the depolymerization rate in the supercooled liquid. The softening rate depends on the level of cooperativity of atomic movements at the source of the deformation process, with an obvious correlation with the “fragility” of the liquid.

Integrating conflicting data: the role of source dependence

Abstract: Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sources can often provide conflicting values. To present quality data to users, it is critical that data integration systems can resolve conflicts and discover true values. Typically, we expect a true value to be provided by more sources than any particular false one, so we can take the value provided by the majority of the sources as the truth. Unfortunately, a false value can be spread through copying and that makes truth discovery extremely tricky. In this paper, we consider how to find true values from conflicting information when there are a large number of sources, among which some may copy from others.We present a novel approach that considers dependence between data sources in truth discovery. Intuitively, if two data sources provide a large number of common values and many of these values are rarely provided by other sources (e.g., particular false values), it is very likely that one copies from the other. We apply Bayesian analysis to decide dependence between sources and design an algorithm that iteratively detects dependence and discovers truth from conflicting information. We also extend our model by considering accuracy of data sources and similarity between values. Our experiments on synthetic data as well as real-world data show that our algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources.