Lepton flavor violation decays of vector mesons in unparticle physics

We investigate the lepton flavor violation decays of vector mesons in the scenario of the unparticle physics by considering the constraint from $\mu-e$ conversion. In unparticle physics, the predictions of LFV decays of vector mesons depend strongly on the scale dimension $d_{\mathcal{U}}$. The predictions of LFV decays of vector mesons can reach the detective sensitivity in experiment in region of $3\le d_{\mathcal{U}}\le 4$, while the prediction of $\mu-e$ conversion rate can meet the experimental upper limit. For the searching of the lepton flavor violation processes of charged lepton sector in experiment, the process $\Upsilon\rightarrow e\mu$ may be a promising one to be observed.Comment: 10 pages, 3 figure

Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure