Exploring vision transformer layer choosing for semantic segmentation

Extensive work has demonstrated the effectiveness of Vision Transformers. The plain Vision Transformer tends to obtain multi-scale features by selecting fixed layers, or the last layer of features aiming to achieve higher performance in dense prediction tasks. However, this selection is often based on manual operation. And different samples often exhibit different features at different layers (e.g., edge, structure, texture, detail, etc.). This requires us to seek a dynamic adaptive fusion method to filter different layer features. In this paper, unlike previous encoder and decoder work, we design a neck network for adaptive fusion and feature selection, called ViTController. We validate the effectiveness of our method on different datasets and models and surpass previous state-of-the-art methods. Finally, our method can also be used as a plug-in module and inserted into different networks.Comment: Accepted by IEEE ICASS

Acquisitions driven by stock overvaluation: are they good deals?

Theory and recent evidence suggest that overvalued firms can create value for shareholders if they exploit their overvaluation by using their stock as currency to purchase less overvalued firms. We challenge this idea and show that, in practice, overvalued acquirers significantly overpay for their targets. These acquisitions do not, in turn, lead to synergy gains. Moreover, these acquisitions seem to be concentrated among acquirers with the largest governance problems. CEO compensation, not shareholder value creation, appears to be the main motive behind acquisitions by overvalued acquirers

UniNeXt: Exploring A Unified Architecture for Vision Recognition

Vision Transformers have shown great potential in computer vision tasks. Most recent works have focused on elaborating the spatial token mixer for performance gains. However, we observe that a well-designed general architecture can significantly improve the performance of the entire backbone, regardless of which spatial token mixer is equipped. In this paper, we propose UniNeXt, an improved general architecture for the vision backbone. To verify its effectiveness, we instantiate the spatial token mixer with various typical and modern designs, including both convolution and attention modules. Compared with the architecture in which they are first proposed, our UniNeXt architecture can steadily boost the performance of all the spatial token mixers, and narrows the performance gap among them. Surprisingly, our UniNeXt equipped with naive local window attention even outperforms the previous state-of-the-art. Interestingly, the ranking of these spatial token mixers also changes under our UniNeXt, suggesting that an excellent spatial token mixer may be stifled due to a suboptimal general architecture, which further shows the importance of the study on the general architecture of vision backbone. All models and codes will be publicly available

AxWin Transformer: A Context-Aware Vision Transformer Backbone with Axial Windows

Recently Transformer has shown good performance in several vision tasks due to its powerful modeling capabilities. To reduce the quadratic complexity caused by the attention, some outstanding work restricts attention to local regions or extends axial interactions. However, these methos often lack the interaction of local and global information, balancing coarse and fine-grained information. To address this problem, we propose AxWin Attention, which models context information in both local windows and axial views. Based on the AxWin Attention, we develop a context-aware vision transformer backbone, named AxWin Transformer, which outperforming the state-of-the-art methods in both classification and downstream segmentation and detection tasks

Use of BRCA Mutation Test in the US, 2004-2014

Introduction BRCA mutation testing has been used for screening women at high risk of breast and ovarian cancer and for selecting the best treatment for those with breast cancer. To optimize the infrastructure and medical resources allocation for genetic testing, it is important to understand the use of BRCA mutation testing in the U.S. health system. Methods This retrospective cohort study included 53,254 adult women with insurance claims for BRCA mutation testing between 2004 and 2014 from ClinformaticsTM Data Mart Database. Data analysis was performed in 2016. This study assessed trends in the use of BRCA mutation testing in women with previously diagnosed breast or ovarian cancer and those without (unaffected women). Results Between 2004 and 2014, of those receiving BRCA testing, the proportion of BRCA tests performed in unaffected women increased significantly (p\u3c0.001), from 24.3% in 2004 to 61.5% in 2014. An increase in the proportion of BRCA tests used in unaffected women was found in each characteristic subgroup. In 2014, most subgroups had a proportion surpassing 50%, except for those aged 51–65 years and those without a family history of breast cancer. There was a much lower proportion of those aged 20–40 years among tested women with previously diagnosed breast or ovarian cancer than in unaffected women (17.6% vs 41.7%, p\u3c0.001). Conclusions During the past decade, the role of BRCA testing has gradually shifted from being used primarily in cancer patients to being used in unaffected women in the U.S