We present a spatially resolved stellar population analysis of 61 jellyfish galaxies and 47 control galaxies observed with ESO/MUSE attempting to understand the general trends of the stellar populations as a function of the stripping intensity and mass. This is the public sample from the GASP programme, with 0.01 < z < 0.15 and 8.9 < log (M⋆/M⊙) < 12.0. We apply the spectral population synthesis code fado to fit self-consistently both the stellar and nebular contributions to the spectra of the sources. We present 2D morphological maps for mean stellar ages, metallicities, gas-phase oxygen abundances, and star formation rates for the galaxies with Integrated Nested Laplace Approximation (inla), which is efficient in reconstructing spatial data of extended sources. We find that “extreme stripping” and “stripping” galaxies are typically younger than the other types. Regarding stellar and nebular metallicities, the “stripping” and “control passive” galaxies are the most metal-poor. Based on the phase space for jellyfish cluster members we find trends in ages, metallicities, and abundances with different regions of the diagram. We also compute radial profiles for the same quantities. We find that both the stripping and the stellar masses seem to influence the profiles, and we see differences between various groups and distinct mass bins. The radial profiles for different mass bins present relations already shown in the literature for undisturbed galaxies, i.e., profiles of ages and metallicities tend to increase with mass. However, beyond ∼0.75 effective radius, the ages of the most massive galaxies become similar to or lower than the ages of the lower mass ones.
With the convenient availability of remote sensing data, how to make models to interpret complex remote sensing data attracts wide attention. In remote sensing data, hyperspectral images contain spectral information and LiDAR contains elevation information. Hence, more explorations are warranted to better fuse the features of different source data. In this paper, we introduce semantic understanding to dynamically fuse data from two different sources, extract features of HSI and LiDAR through different capsule network branches and improve self-supervised loss and random rigid rotation in canonical capsule to a high-dimensional situation. Canonical capsule computes the capsule decomposition of objects by permutation-equivariant attention and the process is self-supervised by training pairs of randomly rotated objects. After fusing the features of HSI and LiDAR with semantic understanding, the unsupervised extraction of spectral-spatial-elevation fusion features is achieved. With two real-world examples of HSI and LiDAR fused, the experimental results show that the proposed multi-branch high-dimensional canonical capsule algorithm can be effective for semantic understanding of HSI and LiDAR. It indicates that the model can extract HSI and LiDAR data features effectively as opposed to existing models for unsupervised extraction of multi-source RS data.
Novel coronavirus is a serious disease-causing virus which spreads through the air, such a highly contagious virus will cause great harm to the body after disease. After the Novel coronavirus infects someone, viruses hidden in the body will spread rapidly and widely in the population as the carrier moves, that cause catastrophic consequences. Therefore, how to quickly detect the infection of novel coronary pneumonia has become an urgent issue. Analysing the lung image of Computed Tomography (CT) is an important method to accurately detect whether people is infected by novel coronavirus in medical practice. In this paper, firstly, we use the binarized features of the novel coronary pneumonia image, and then use the features of histogram and mask as additional features, finally we design an improved network based on Efficient-Net. Through comparative experiments with other mainstream Convolutional Neural Network(CNN) networks, it is found that the model proposed in this paper reduces the parameters of the model and improves the detection accuracy.
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