Computing and reducing slope complexes

In this paper we provide a new characterization of cell de- composition (called slope complex) of a given 2-dimensional continuous surface. Each patch (cell) in the decomposition must satisfy that there exists a monotonic path for any two points in the cell. We prove that any triangulation of such surface is a slope complex and explain how to obtain new slope complexes with a smaller number of slope regions decomposing the surface. We give the minimal number of slope regions by counting certain bounding edges of a triangulation of the surface obtained from its critical points.Ministerio de Economía y Competitividad MTM2015-67072-

Device-independent, real-time identification of bacterial pathogens with a metal oxide-based olfactory sensor

A novel olfactory method for bacterial species identification using an electronic nose device called the MonoNose was developed. Differential speciation of micro-organisms present in primary cultures of clinical samples could be performed by real-time identification of volatile organic compounds (VOCs) produced during microbial replication. Kinetic measurements show that the dynamic changes in headspace gas composition are orders of magnitude larger than the static differences at the end of fermentation. Eleven different, clinically relevant bacterial species were included in this study. For each of the species, two to eight different strains were used to take intra-species biodiversity into account. A total of 52 different strains were measured in an incubator at 37°C. The results show that the diagnostic specificities varied from 100% for Clostridium difficile to 67% for Enterobacter cloacae with an overall average of 87%. Pathogen identification with a MonoNose can be achieved within 6–8 h of inoculation of the culture broths. The diagnostic specificity can be improved by broth modification to improve the VOC production of the pathogens involved

Euler Well-Composedness

In this paper, we de ne a new avour of well-composedness, called Euler well-composedness, in the general setting of regular cell complexes: A regular cell complex is Euler well-composed if the Euler characteristic of the link of each boundary vertex is 1. A cell decomposi- tion of a picture I is a pair of regular cell complexes ����� K(I);K( I) such that K(I) (resp. K( I)) is a topological and geometrical model represent- ing I (resp. its complementary, I). Then, a cell decomposition of a pic- ture I is self-dual Euler well-composed if both K(I) and K( I) are Euler well-composed. We prove in this paper that, rst, self-dual Euler well- composedness is equivalent to digital well-composedness in dimension 2 and 3, and second, in dimension 4, self-dual Euler well-composedness implies digital well-composedness, though the converse is not true

Christian Moral Disengagement and Exclusion of the LGBTQ+ Community

Christian Moral Disengagement and Exclusion of the LGBTQ+ Community Despite the overwhelming presence of evil in the world, it is incredibly rare to find people who will, in the moment, look at what they are doing and think that it is evil. Humans do not like to view themselves as evil, at least in ways that matter to them. In psychology, when there is a discrepancy between the way one sees themself and the way they think they ought to be, this is called dissonance. The discrepancy between evil’s presence in the world and people’s perceptions of the moral value of their actions can be attributed to psychological defense mechanisms that humans have in place to prevent from feeling dissonance when they engage in self-serving or sinful actions

A 4D counter-example showing that DWCness does not imply CWCness in n-D

International audienceIn this paper, we prove that the two flavors of well-composedness called Continuous Well-Composedness (shortly CWCness) and Digital Well-Composedness (shortly DWCness) are not equivalent in dimension 4 thanks to an example of a configuration of 8 tesseracts (4D cubes) sharing a common corner (vertex), which is DWC but not CWC. This result is surprising since we know that CWCness and DWCness are equivalent in 2D and 3D. To prove this new result, local (and then relative) homology are used. This paper has been submitted to IWCIA

3D visual saliency and convolutional neural network for blind mesh quality assessment

International audienceA number of full reference and reduced reference methods have been proposed in order to estimate the perceived visual quality of 3D meshes. However, in most practical situations, there is a limited access to the information related to the reference and the distortion type. For these reasons, the development of a no-reference mesh visual quality (MVQ) approach is a critical issue, and more emphasis needs to be devoted to blind methods. In this work, we propose a no-reference convolutional neural network (CNN) framework to estimate the perceived visual quality of 3D meshes. The method is called SCNN-BMQA (3D visual saliency and CNN for blind mesh quality assessment). The main contribution is the usage of a CNN and 3D visual saliency to estimate the perceived visual quality of distorted meshes. To do so, the CNN architecture is fed by small patches selected carefully according to their level of saliency. First, the visual saliency of the 3D mesh is computed. Afterward, we render 2D projections from the 3D mesh and its corresponding 3D saliency map. Then the obtained views are split into 2D small patches that pass through a saliency filter in order to select the most relevant patches. Finally, a CNN is used for the feature learning and the quality score estimation. Extensive experiments are conducted on four prominent MVQ assessment databases, including several tests to study the effect of the CNN parameters, the effect of visual saliency and comparison with existing methods. Results show that the trained CNN achieves good rates in terms of correlation with human judgment and outperforms the most effective state-of-the-art methods. Keywords Mesh visual quality assessment Á Mean opinion score Á Mesh visual saliency Á Convolutional neural networ