Solving loop equations by Hitchin systems via holography in large-N QCD_4

For (planar) closed self-avoiding loops we construct a "holographic" map from the loop equations of large-N QCD_4 to an effective action defined over infinite rank Hitchin bundles. The effective action is constructed densely embedding Hitchin systems into the functional integral of a partially quenched or twisted Eguchi-Kawai model, by means of the resolution of identity into the gauge orbits of the microcanonical ensemble and by changing variables from the moduli fields of Hitchin systems to the moduli of the corresponding holomorphic de Rham local systems. The key point is that the contour integral that occurs in the loop equations for the de Rham local systems can be reduced to the computation of a residue in a certain regularization. The outcome is that, for self-avoiding loops, the original loop equations are implied by the critical equation of an effective action computed in terms of the localisation determinant and of the Jacobian of the change of variables to the de Rham local systems. We check, at lowest order in powers of the moduli fields, that the localisation determinant reproduces exactly the first coefficient of the beta function.Comment: 65 pages, late

An introduction to map-making for CMB experiments

International audienceThe cosmic microwave background (CMB) anisotropies are a powerful probe of the early universe, and have largely contributed to establishing the current standard cosmological model. To extract the information encoded in those tiny variations, one must first compress the raw, time-domain data collected by a telescope into maps of the sky at the observed frequencies, in a procedure known as map-making. I provide a general introduction to this problem, and highlight a few specificities of the MAPPRAISER implementation

CSSNET: A Learning Algorithm for the Segmentation of Compressed Hyperspectral Images

International audienceThe paper presents a semantic segmentation method which is directly applicable to compressed hyperspectral images acquired with a dual-disperser CASSI instrument. It introduses an algorithm based on a shallow neural network that exploits the spectral filtering performed by the optical system and the compressed hyperspectral images measured by the detector. Encouraging results that exploit 50 to 100 less data than the whole hyperspectral datacube on PaviaU and IndianPines datasets are presented

CSSNET: A Learning Algorithm for the Segmentation of Compressed Hyperspectral Images

International audienceThe paper presents a semantic segmentation method which is directly applicable to compressed hyperspectral images acquired with a dual-disperser CASSI instrument. It introduses an algorithm based on a shallow neural network that exploits the spectral filtering performed by the optical system and the compressed hyperspectral images measured by the detector. Encouraging results that exploit 50 to 100 less data than the whole hyperspectral datacube on PaviaU and IndianPines datasets are presented

CSSNET: A Learning Algorithm for the Segmentation of Compressed Hyperspectral Images

International audienceThe paper presents a semantic segmentation method which is directly applicable to compressed hyperspectral images acquired with a dual-disperser CASSI instrument. It introduses an algorithm based on a shallow neural network that exploits the spectral filtering performed by the optical system and the compressed hyperspectral images measured by the detector. Encouraging results that exploit 50 to 100 less data than the whole hyperspectral datacube on PaviaU and IndianPines datasets are presented

CSSNET: A Learning Algorithm for the Segmentation of Compressed Hyperspectral Images

International audienceThe paper presents a semantic segmentation method which is directly applicable to compressed hyperspectral images acquired with a dual-disperser CASSI instrument. It introduses an algorithm based on a shallow neural network that exploits the spectral filtering performed by the optical system and the compressed hyperspectral images measured by the detector. Encouraging results that exploit 50 to 100 less data than the whole hyperspectral datacube on PaviaU and IndianPines datasets are presented

Algorithme d'apprentissage pour la segmentation d'images hyperspectrales compressées

International audienceCet article présente une technique d'apprentissage qui permet de segmenter une scène hyperspectrale en classes connues, sur la seule base d'une image compressée et d'une image panchromatique de la scène. Ces images sont obtenues par un capteur hyperspectral particulier, qui comprend deux lignes de dispersion spectrales séparées par une matrice de micro-miroirs qui opère une sélection spatiale. L'algorithme d'apprentissage exploite l'architecture existante de réseau de neurones DSSNet dédiée à la segmentation de cubes hyperspectraux complets, en la complétant par un bloc de convolution appliqué à la structure de données modélisant l'acquisition d'une image compressée. L'approche traite un volume d'information 50 fois inférieur à celui d'un cube hyperspectral complet, et fournit des résultats légèrement moins précis que ceux obtenus par DSSNet pour la segmentation du cube hyperspectral complet. L'article détaille la modélisation du capteur exploité, la nouvelle couche de convolution introduite, la manière dont le réseau est entraîné, et analyse les résultats obtenus

CSSNET: A Learning Algorithm for the Segmentation of Compressed Hyperspectral Images

International audienceThe paper presents a semantic segmentation method which is directly applicable to compressed hyperspectral images acquired with a dual-disperser CASSI instrument. It introduses an algorithm based on a shallow neural network that exploits the spectral filtering performed by the optical system and the compressed hyperspectral images measured by the detector. Encouraging results that exploit 50 to 100 less data than the whole hyperspectral datacube on PaviaU and IndianPines datasets are presented

Algorithme d'apprentissage pour la segmentation d'images hyperspectrales compressées

International audienceCet article présente une technique d'apprentissage qui permet de segmenter une scène hyperspectrale en classes connues, sur la seule base d'une image compressée et d'une image panchromatique de la scène. Ces images sont obtenues par un capteur hyperspectral particulier, qui comprend deux lignes de dispersion spectrales séparées par une matrice de micro-miroirs qui opère une sélection spatiale. L'algorithme d'apprentissage exploite l'architecture existante de réseau de neurones DSSNet dédiée à la segmentation de cubes hyperspectraux complets, en la complétant par un bloc de convolution appliqué à la structure de données modélisant l'acquisition d'une image compressée. L'approche traite un volume d'information 50 fois inférieur à celui d'un cube hyperspectral complet, et fournit des résultats légèrement moins précis que ceux obtenus par DSSNet pour la segmentation du cube hyperspectral complet. L'article détaille la modélisation du capteur exploité, la nouvelle couche de convolution introduite, la manière dont le réseau est entraîné, et analyse les résultats obtenus