Visual attention has been successfully applied in structural prediction tasks such as visual captioning and question answering. Existing visual attention models are generally spatial, i.e., the attention is modeled as spatial probabilities that re-weight the last conv-layer feature map of a CNN encoding an input image. However, we argue that such spatial attention does not necessarily conform to the attention mechanism -a dynamic feature extractor that combines contextual fixations over time, as CNN features are naturally spatial, channel-wise and multi-layer. In this paper, we introduce a novel convolutional neural network dubbed SCA-CNN that incorporates Spatial and Channelwise Attentions in a CNN. In the task of image captioning, SCA-CNN dynamically modulates the sentence generation context in multi-layer feature maps, encoding where (i.e., attentive spatial locations at multiple layers) and what (i.e., attentive channels) the visual attention is. We evaluate the proposed SCA-CNN architecture on three benchmark image captioning datasets: Flickr8K, Flickr30K, and MSCOCO. It is consistently observed that SCA-CNN significantly outperforms state-of-the-art visual attention-based image captioning methods.

No abstract

No abstract

The rapid discovery of novel viruses using next generation sequencing (NGS) technologies including DNA-Seq and RNA-Seq, has greatly expanded our understanding of viral diversity in recent years. The timely identification of novel viruses using NGS technologies is also important for us to control emerging infectious diseases caused by novel viruses. In this study, we identified a novel duck coronavirus (CoV), distinct with chicken infectious bronchitis virus (IBV), using RNA-Seq. The novel duck-specific CoV was a potential novel species within the genus Gammacoronavirus, as indicated by sequences of three regions in the viral 1b gene. We also performed a survey of CoVs in domestic fowls in China using reverse-transcription polymerase chain reaction (RT-PCR), targeting the viral nucleocapsid (N) gene. A total of 102 CoV positives were identified through the survey. Phylogenetic analysis of the viral N sequences suggested that CoVs in domestic fowls have diverged into several region-specific or host-specific clades or subclades in the world, and IBVs can infect ducks, geese and pigeons, although they mainly circulate in chickens. Moreover, this study provided novel data supporting the notion that some host-specific CoVs other than IBVs circulate in ducks, geese and pigeons, and indicated that the novel duck-specific CoV identified through RNA-Seq in this study is genetically closer to some CoVs circulating in wild water fowls. Taken together, this study shed new insight into the diversity, distribution, evolution and control of avian CoVs.

The identification of activating mutations in NOTCH1 in 50% of T cell acute lymphoblastic leukemia has generated interest in elucidating how these mutations contribute to oncogenic Reprints and permissions information is available at www.nature.com/reprints.