2,020 research outputs found

    Learning Social Image Embedding with Deep Multimodal Attention Networks

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    Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visual content), simply employing the embedding learnt from network structure or data content results in sub-optimal social image representation. In this paper, we propose a novel social image embedding approach called Deep Multimodal Attention Networks (DMAN), which employs a deep model to jointly embed multimodal contents and link information. Specifically, to effectively capture the correlations between multimodal contents, we propose a multimodal attention network to encode the fine-granularity relation between image regions and textual words. To leverage the network structure for embedding learning, a novel Siamese-Triplet neural network is proposed to model the links among images. With the joint deep model, the learnt embedding can capture both the multimodal contents and the nonlinear network information. Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search. Compared to state-of-the-art image embeddings, our proposed DMAN achieves significant improvement in the tasks of multi-label classification and cross-modal search

    Enhanced bias stress stability of a-InGaZnO thin film transistors by inserting an ultra-thin interfacial InGaZnO:N layer

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    Amorphous indium-gallium-zinc oxide (a-IGZO) thin film transistors (TFTs) having an ultra-thin nitrogenated a-IGZO (a-IGZO:N) layer sandwiched at the channel/gate dielectric interface are fabricated. It is found that the device shows enhanced bias stress stability with significantly reduced threshold voltage drift under positive gate bias stress. Based on x-ray photoelectron spectroscopy measurement, the concentration of oxygen vacancies within the a-IGZO:N layer is suppressed due to the formation of N-Ga bonds. Meanwhile, low frequency noise analysis indicates that the average trap density near the channel/dielectric interface continuously drops as the nitrogen content within the a-IGZO:N layer increases. The improved interface quality upon nitrogen doping agrees with the enhanced bias stress stability of the a-IGZO TFTs.This work was supported in part by the State Key Program for Basic Research of China under Grant Nos. 2010CB327504, 2011CB922100, and 2011CB301900; in part by the National Natural Science Foundation of China under Grant Nos. 60936004 and 11104130; in part by the Natural Science Foundation of Jiangsu Province under Grant Nos. BK2011556 and BK2011050; and in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions
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