316 research outputs found

    Invertible orientation bundles on 2D scalar images

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    Ionization of pyridine: interplay of orbital relaxation and electron correlation

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    The valence shell ionization spectrum of pyridine was studied using the third-order algebraic-diagrammatic construction approximation scheme for the one-particle Green’s function and the outer-valence Green’s function method. The results were used to interpret angle resolved photoelectron spectra recorded with synchrotron radiation in the photon energy range of 17–120 eV. The lowest four states of the pyridine radical cation, namely, 2A2 (1a 2 −1 1a2−1 ), 2A1(7a 1 −1 7a1−1), 2B1(2b 1 −1 2b1−1), and 2B2(5b 2 −1 5b2−1), were studied in detail using various high-level electronic structure calculation methods. The vertical ionization energies were established using the equation-of-motion coupled-cluster approach with single, double, and triple excitations (EOM-IP-CCSDT) and the complete basis set extrapolation technique. Further interpretation of the electronic structure results was accomplished using Dyson orbitals, electron density difference plots, and a second-order perturbation theory treatment for the relaxation energy. Strong orbital relaxation and electron correlation effects were shown to accompany ionization of the 7a1 orbital, which formally represents the nonbonding σ-type nitrogen lone-pair (nσ) orbital. The theoretical work establishes the important roles of the π-system (π-π* excitations) in the screening of the nσ-hole and of the relaxation of the molecular orbitals in the formation of the 7a1(nσ)−1 state. Equilibrium geometric parameters were computed using the MP2 (second-order Møller-Plesset perturbation theory) and CCSD methods, and the harmonic vibrational frequencies were obtained at the MP2 level of theory for the lowest three cation states. The results were used to estimate the adiabatic 0-0 ionization energies, which were then compared to the available experimental and theoretical data. Photoelectron anisotropy parameters and photoionization partial cross sections, derived from the experimental spectra, were compared to predictions obtained with the continuum multiple scattering approach

    Nonlocal similarity image filtering

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    Abstract. We exploit the recurrence of structures at different locations, orientations and scales in an image to perform denoising. While previous methods based on “nonlocal filtering ” identify corresponding patches only up to translations, we consider more general similarity transformations. Due to the additional computational burden, we break the problem down into two steps: First, we extract similarity invariant descriptors at each pixel location; second, we search for similar patches by matching descriptors. The descriptors used are inspired by scale-invariant feature transform (SIFT), whereas the similarity search is solved via the minimization of a cost function adapted from local denoising methods. Our method compares favorably with existing denoising algorithms as tested on several datasets.

    Single-Image Depth Prediction Makes Feature Matching Easier

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    Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines. The problem is that viewing angle and distance severely impact the recognizability of a local feature. Attempts to improve appearance invariance by choosing better local feature points or by leveraging outside information, have come with pre-requisites that made some of them impractical. In this paper, we propose a surprisingly effective enhancement to local feature extraction, which improves matching. We show that CNN-based depths inferred from single RGB images are quite helpful, despite their flaws. They allow us to pre-warp images and rectify perspective distortions, to significantly enhance SIFT and BRISK features, enabling more good matches, even when cameras are looking at the same scene but in opposite directions.Comment: 14 pages, 7 figures, accepted for publication at the European conference on computer vision (ECCV) 202

    Dynamic Evolution of Pathogenicity Revealed by Sequencing and Comparative Genomics of 19 Pseudomonas syringae Isolates

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    Closely related pathogens may differ dramatically in host range, but the molecular, genetic, and evolutionary basis for these differences remains unclear. In many Gram- negative bacteria, including the phytopathogen Pseudomonas syringae, type III effectors (TTEs) are essential for pathogenicity, instrumental in structuring host range, and exhibit wide diversity between strains. To capture the dynamic nature of virulence gene repertoires across P. syringae, we screened 11 diverse strains for novel TTE families and coupled this nearly saturating screen with the sequencing and assembly of 14 phylogenetically diverse isolates from a broad collection of diseased host plants. TTE repertoires vary dramatically in size and content across all P. syringae clades; surprisingly few TTEs are conserved and present in all strains. Those that are likely provide basal requirements for pathogenicity. We demonstrate that functional divergence within one conserved locus, hopM1, leads to dramatic differences in pathogenicity, and we demonstrate that phylogenetics-informed mutagenesis can be used to identify functionally critical residues of TTEs. The dynamism of the TTE repertoire is mirrored by diversity in pathways affecting the synthesis of secreted phytotoxins, highlighting the likely role of both types of virulence factors in determination of host range. We used these 14 draft genome sequences, plus five additional genome sequences previously reported, to identify the core genome for P. syringae and we compared this core to that of two closely related non-pathogenic pseudomonad species. These data revealed the recent acquisition of a 1 Mb megaplasmid by a sub-clade of cucumber pathogens. This megaplasmid encodes a type IV secretion system and a diverse set of unknown proteins, which dramatically increases both the genomic content of these strains and the pan-genome of the species
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