Are dentists enough aware of infectious risk associated with dental unit waterlines?

Environmental conditions in DU encourage biofilm development. This biofilm may represent a risk for patients and dental staff exposed to water and aerosols generated during dental cares, particularly for immunocompromised persons. A survey was conducted on the 175 dental surgeons of the department of Vienne (France) to investigate the motivations of dental practitioners to renew their DU, their awareness levels with respect to infectious risks related to water circulating within DU, and methods used for the maintenance of DU waterlines. These dentists were only partially aware of the need for maintaining DU waterlines. For this maintaining, chemical treatments and purges of pipes were carried out by 88% and 91.5% of dentists respectively ; chemical treatments were usually on a continous mode and dentists seemed to have complete confidence in their DU supplier regarding the choice and the use of chemical treatments. Flushes were performed only once per day in most cases (63%). This survey also highlighted that dentists were not enough aware of water related infectous risk, even though 68% estimated that the development of a biofilm within DU waterlines was an actual risk. Finally, very positively, dentists strongly indicated their wish to be more informed regarding all these risks. Although these results are based on a relatively small sample, corresponding to dentists of a French department, they clearly suggest that awareness of dental surgeons is still insufficient and must be performed to permit an effective prevention of infectious risk related to DU waterlines

Bitmap or Vector? A study on sketch representations for deep stroke segmentation

National audienceDeep learning achieves impressive performances on image segmentation, which has motivated the recent developmentof deep neural networks for the related task of sketch segmentation, where the goal is to assign labels to thedifferent strokes that compose a line drawing. However, while natural images are well represented as bitmaps, linedrawings can also be represented as vector graphics, such as point sequences and point clouds. In addition to offeringdifferent trade-offs on resolution and storage, vector representations often come with additional information,such as stroke ordering and speed. In this paper, we evaluate three crucial design choices for sketch segmentationusing deep-learning : which sketch representation to use, which information to encode in this representation,and which loss function to optimize. Our findings suggest that point clouds represent a competitive alternative tobitmaps for sketch segmentation, and that providing extra-geometric information improves performance

On the log-local principle for the toric boundary

Let $X$ be a smooth projective complex variety and let $D=D_1+\cdots+D_l$ be a reduced normal crossing divisor on $X$ with each component $D_j$ smooth, irreducible, and nef. The log-local principle of van Garrel-Graber-Ruddat conjectures that the genus 0 log Gromov-Witten theory of maximal tangency of $(X,D)$ is equivalent to the genus 0 local Gromov-Witten theory of $X$ twisted by $\bigoplus_{j=1}^l\mathcal{O}(-D_j)$. We prove that an extension of the log-local principle holds for $X$ a (not necessarily smooth) $\mathbb{Q}$-factorial projective toric variety, $D$ the toric boundary, and descendent point insertions.Comment: 19 page

Unifying Color and Texture Transfer for Predictive Appearance Manipulation

International audienceRecent color transfer methods use local information to learn the transformation from a source to an exemplar image, and then transfer this appearance change to a target image. These solutions achieve very successful results for general mood changes, e.g., changing the appearance of an image from ``sunny'' to ``overcast''. However, such methods have a hard time creating new image content, such as leaves on a bare tree. Texture transfer, on the other hand, can synthesize such content but tends to destroy image structure. We propose the first algorithm that unifies color and texture transfer, outperforming both by leveraging their respective strengths. A key novelty in our approach resides in teasing apart appearance changes that can be modeled simply as changes in color versus those that require new image content to be generated. Our method starts with an analysis phase which evaluates the success of color transfer by comparing the exemplar with the source. This analysis then drives a selective, iterative texture transfer algorithm that simultaneously predicts the success of color transfer on the target and synthesizes new content where needed. We demonstrate our unified algorithm by transferring large temporal changes between photographs, such as change of season -- e.g., leaves on bare trees or piles of snow on a street -- and flooding

Intrinsic Textures for Relightable Free-Viewpoint Video

This paper presents an approach to estimate the intrinsic texture properties (albedo, shading, normal) of scenes from multiple view acquisition under unknown illumination conditions. We introduce the concept of intrinsic textures, which are pixel-resolution surface textures representing the intrinsic appearance parameters of a scene. Unlike previous video relighting methods, the approach does not assume regions of uniform albedo, which makes it applicable to richly textured scenes. We show that intrinsic image methods can be used to refine an initial, low-frequency shading estimate based on a global lighting reconstruction from an original texture and coarse scene geometry in order to resolve the inherent global ambiguity in shading. The method is applied to relighting of free-viewpoint rendering from multiple view video capture. This demonstrates relighting with reproduction of fine surface detail. Quantitative evaluation on synthetic models with textured appearance shows accurate estimation of intrinsic surface reflectance properties. © 2014 Springer International Publishing

Lagrangian Neural Style Transfer for Fluids

Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production. In this paper, we present a neural style transfer approach from images to 3D fluids formulated in a Lagrangian viewpoint. Using particles for style transfer has unique benefits compared to grid-based techniques. Attributes are stored on the particles and hence are trivially transported by the particle motion. This intrinsically ensures temporal consistency of the optimized stylized structure and notably improves the resulting quality. Simultaneously, the expensive, recursive alignment of stylization velocity fields of grid approaches is unnecessary, reducing the computation time to less than an hour and rendering neural flow stylization practical in production settings. Moreover, the Lagrangian representation improves artistic control as it allows for multi-fluid stylization and consistent color transfer from images, and the generality of the method enables stylization of smoke and liquids likewise.Comment: ACM Transaction on Graphics (SIGGRAPH 2020), additional materials: http://www.byungsoo.me/project/lnst/index.htm

CO-FREE Alternative Test Products for Copper Reduction in Agriculture

The project CO-FREE (2012-2016) aimed to develop strategies to replace/reduce copper use in organic, integrated and conventional farming. CO-FREE alternative test products (CTPs) were tested and integrated together with decision support systems, disease-tolerant varieties, and innovative breeding goals (ideotypes) into improved management strategies. CO-FREE focused on apple/apple scab (Venturia inaequalis), grape/downy mildew (Plasmopara viticola), and tomato and potato/late blight (Phytophthora infestans). Starting point of the project were ten CTPs with direct or indirect modes of action including Trichoderma atroviride SC1 and protein extract SCNB, Lysobacter spp., yeast-based derivatives, Cladosporium cladosporioides H39, the oligosaccharidic complex COS-OGA, Aneurinibacillus migulanus and Xenorhabdus bovienii, sage (Salvia officinalis) extract, liquorice (Glycyrrhiza glabra) extract, PLEX- and seaweed plant extracts. As the project progressed, further promising CTPs were included by the partners. Field trials were performed in different European countries in 2012-2015 following EPPO standards. In the first years, stand-alone applications of CTPs were tested. In the following years these were integrated into complete strategies. Effects on main and further diseases, on yield and on non-target organisms were assessed. Here, field trial results with CTPs are summarized

Perceptual quality of BRDF approximations: dataset and metrics

International audienceBidirectional Reflectance Distribution Functions (BRDFs) are pivotal to the perceived realism in image synthesis. While measured BRDF datasets are available, reflectance functions are most of the time approximated by analytical formulas for storage efficiency reasons. These approximations are often obtained by minimizing metrics such as L 2 —or weighted quadratic—distances, but these metrics do not usually correlate well with perceptual quality when the BRDF is used in a rendering context, which motivates a perceptual study. The contributions of this paper are threefold. First, we perform a large-scale user study to assess the perceptual quality of 2026 BRDF approximations, resulting in 84138 judgments across 1005 unique participants. We explore this dataset and analyze perceptual scores based on material type and illumination. Second, we assess nine analytical BRDF models in their ability to approximate tabulated BRDFs. Third, we assess several image-based and BRDF-based (Lp, optimal transport and kernel distance) metrics in their ability to approximate perceptual similarity judgments