We measure the 3D kinematic structures of the young stars within the central 0.5 pc of our Galactic Center using the 10 m telescopes of the W. M. Keck Observatory over a time span of 25 yr. Using high-precision measurements of positions on the sky and proper motions and radial velocities from new observations and the literature, we constrain the orbital parameters for each young star. Our results show two statistically significant substructures: a clockwise stellar disk with 18 candidate stars, as has been proposed before, but with an improved disk membership; and a second, almost edge-on plane of 10 candidate stars oriented east–west on the sky that includes at least one IRS 13 star. We estimate the eccentricity distribution of each substructure and find that the clockwise disk has 〈e〉 = 0.39 and the edge-on plane has 〈e〉 = 0.68. We also perform simulations of each disk/plane with incompleteness and spatially variable extinction to search for asymmetry. Our results show that the clockwise stellar disk is consistent with a uniform azimuthal distribution within the disk. The edge-on plane has an asymmetry that cannot be explained by variable extinction or incompleteness in the field. The orientation, asymmetric stellar distribution, and high eccentricity of the edge-on plane members suggest that this structure may be a stream associated with the IRS 13 group. The complex dynamical structure of the young nuclear cluster indicates that the star formation process involved complex gas structures and dynamics and is inconsistent with a single massive gaseous disk.

High-precision, high-speed detection and classification of weakly differentiated targets has always been a difficult problem in the field of image vision. In this paper, the detection of phytopathogenic Bursaphelenchus xylophilus with small size and very weak inter-species differences is taken as an example. Our work is aimed at the current problem of weakly differentiated target detection: We propose a lightweight self attention network. Experiments show that the key feature recognition areas of plant nematodes found by our Self Attention network are in good agreement with the experience and knowledge of customs experts, and the feature areas found by this method can obtain higher detection accuracy than expert knowledge; In order to optimize the computing power brought by the whole image input, we use low resolution images to quickly obtain the location coordinates of key features, and then obtain the information of high resolution feature regions based on the coordinates; The adaptive weighted multi feature joint detection method based on heat map brightness is adopted to further improve the detection accuracy; We have constructed a more complete high-resolution training data set, involving 24 species of Equisetum and other common hybrids, with a total data volume of more than 10,000. The algorithm proposed in this paper replaces the tedious extensive manual labelling in the training process, improves the average training time of the model by more than 50%, reduces the testing time of a single sample by about 27%, optimizes the model storage size by 65%, improves the detection accuracy of the ImageNet pre-trained model by 12.6%, and improves the detection accuracy of the no-ImageNet pre-trained model by more than 48%.

With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem. This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality. Neural networks are used to reshape the process of calculating information entropy, improving the speed and efficiency of the operation. Experiments conducted with this study’s image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people’s average opinion scores compared with other image quality assessment metrics, proving that RDIE can better quantify the perceived quality of images.

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