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We present a comparative study of two nearby type Ia supernovae (SNe Ia), 2018xx and 2019gbx, that exploded in NGC 4767 and MCG-02-33-017 at a distance of 48 Mpc and 60 Mpc, respectively. The B -band light curve decline rate for SN 2018xx is estimated to be 1.48 ± 0.07 mag and for SN 2019gbx it is 1.37 ± 0.07 mag. Despite the similarities in photometric evolution, quasi-bolometric luminosity, and spectroscopy between these two SNe Ia, SN 2018xx has been found to be fainter by about ∼0.38 mag in the B -band and has a lower 56 Ni yield. Their host galaxies have similar metallicities at the SN location, indicating that the differences between these two SNe Ia may be associated with the higher progenitor metallicity of SN 2018xx. Further inspection of the near-maximum-light spectra has revealed that SN 2018xx has relatively strong absorption features near 4300 Å relative to SN 2019gbx. The application of the code TARDIS fitting to the above features indicates that the absorption features near 4300 Å appear to be related to not only Fe  II /Mg  II abundance but possibly to the other element abundances as well. Moreover, SN 2018xx shows a weaker carbon absorption at earlier times, whichmore »is also consistent with higher ejecta metallicity.« less

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics.

Pinpointing the geographic location of an IP address is important for a range of location-aware applications spanning from targeted advertising to fraud prevention. The majority of traditional measurement-based and recent learning-based methods either focus on the efficient employment of topology or utilize data mining to find clues of the target IP in publicly available sources. Motivated by the limitations in existing works, we propose a novel framework named GraphGeo, which provides a complete processing methodology for street-level IP geolocation with the application of graph neural networks. It incorporates IP hosts knowledge and kinds of neighborhood relationships into the graph to infer spatial topology for high-quality geolocation prediction. We explicitly consider and alleviate the negative impact of uncertainty caused by network jitter and congestion, which are pervasive in complicated network environments. Extensive evaluations across three large-scale real-world datasets demonstrate that GraphGeo significantly reduces the geolocation errors compared to the state-of-the-art methods. Moreover, the proposed framework has been deployed on the web platform as an online service for 6 months.