Soft chemical synthesis and luminescence properties of red long-lasting phosphors Y_2O_2S:Sm^(3+)

Sm3＋-activated Y2028 red phosphors were prepared by the combustion method and microemulsion method at the first time. X-ray characterization and electron diffraction show that, Y202S：Sm3＋, Ti4＋, Mg2＋ samples prepared by these two methods are pure hexagonal crystals in structure with a trivial change due to dopants. Scanning electron microscopy （SEM） results show that the product presents an almond-like sheet in uniform size. Under the excitation of 269 nm ultraviolet light, Y202S：Sm3＋ samples fabricated by these two methods exhibit three main groups of red emission lines located at 564, 604, and 656 nm, respectively, which are attributed to the transitions of 4G5/2 →6H5/2, 4G~/2 →6H7/2, 4G5/2 →6H9/2, respectively. The samples prepared by microemulsion are seven times higher in fluorescent emission intensity and half time longer in afterglow time than that prepared by combustion.

Deep Multimodal Reinforcement Network with Contextually GuidedRecurrent Attention for Image Question Answering

Image question answering (IQA) has emerged as a promising interdisciplinary topic in computer vision and natural language processing fields. In this paper, we propose a contextually guided recurrent attention model for solving the IQA issues. It is a deep reinforcement learning based multimodal recurrent neural network. Based on compositional contextual information, it recurrently decides where to look using reinforcement learning strategy. Different from traditional 'static' soft attention, it is deemed as a kind of 'dynamic' attention whose objective is designed based on reinforcement rewards purposefully towards IQA. The finally learned compositional information incorporates both global context and local informative details, which is demonstrated to benefit for generating answers. The proposed method is compared with several state-of-the-art methods on two public IQA datasets, including COCO-QA and VQA from dataset MS COCO. The experimental results demonstrate that our proposed model outperforms those methods and achieves better performance.

Corpus-based research on English word recognition rates in primary school and word selection strategy

Acquiring vocabulary is important when studying English, as it assists in listening, speaking, reading, and writing. In this paper, we develop an English webpage corpus(EWC) and create a word frequency list using web crawler technology. By comparing EWC word lists with the British National Corpus(BNC), we find that the BNC word frequency list possesses the feature of timeliness. We also explore primary school students' English word recognition rates by comparing the word frequency lists of several corpora, including EWC, BNC, SUBTLEX-US, and Subtitle Corpus of Children's BBC(CBBC). The results show that the word recognition rates for primary school children are relatively low in both general language and specific language register. Motivated by the experiment results, we finally propose some word-selection strategies for compiling English textbooks for Chinese primary school students.