On the construction of generalized Grassmann representatives of state vectors

Generalized $Z_k$-graded Grassmann variables are used to label coherent states related to the nilpotent representation of the q-oscillator of Biedenharn and Macfarlane when the deformation parameter is a root of unity. These states are then used to construct generalized Grassmann representatives of state vectors.Comment: 8 page

Capturing wheat phenotypes at the genome level

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence

Aircraft recognition using a statistical model and sparserepresentation

International audienceThis paper presents a novel approach for automatic targetrecognition (ATR) using inverse synthetic aperture radar(ISAR) images. This proposed approach is mainly com-posed of two steps. In the rst step, we adopt a statisti-cal method to compute a novel target template from fea-ture descriptors. The proposed template is achieved bycombining the Gamma statistical parameters of the bothdual-tree complex wavelet transform (DT-CWT) coecientsand the scale-invariant feature transform (SIFT) descrip-tor. In order to validate the proposed target template,we achieve in the second step the recognition task usinga sparse representation-based classication (SRC) method.The performance of the proposed approach has been success-fully veried using ISAR images reconstructed from anechoicchamber. The experimental results show that the proposedmethod can achieve a high average accuracy and is signi-cantly superior to the well-known SVM classier

Multivariate copula statistical model and weighted sparse classification for radar image target recognition

International audienceWe propose in this paper a new method for targets recognition from radar images. To characterize the radar images, we adopt a statistical multivariate modeling using copula in the complex wavelet domain. For the recognition step, we investigate the weighted sparse representation-based classification (WSRC) method. To build the dictionary, the estimated copula parameters are stacked together in a matrix structure. In order to include the locality information of this dictionary for each unknown radar image to recognize, we affect weights for its atoms (columns). That is done by calculating the Kullback–Leibler divergence (KLD) between the multivariate copula parameters of training and test radar images. Finally, the unknown radar image is recognized through the SRC classifier. Several empirical results carried out on the SAR (synthetic aperture radar) and ISAR (inverse synthetic aperture radar) images demonstrate that the proposed method achieves high recognition rates and outperforms the remaining methods