The appearance of antibodies in blood is a critical signal to suggest the infection. A rapid and accurate detection method for the antibody is significant to the disease diagnosis, especially for the epidemic. To this end, a highly sensitive whispering‐gallery‐mode (WGM) optical testing kit is designed and fabricated for detecting the specific immunoglobulin antibodies. The key component of the kit is a silica self‐assembled microsphere decorated with the nucleocapsid proteins (N‐proteins) of the SARS‐CoV‐2 virus. After the N‐protein antibody immunoglobulin G (N‐IgG) and immunoglobulin M (N‐IgM) solutions being injected into the kit, the WGM red‐shifts due to the antigen–antibody reaction. The wavelength displacement rates are proportional to the concentrations of these two antibodies from 1 to 100 μg/mL. A good specificity of the kit is demonstrated by the nonspecific human immunoglobulin G (H‐IgG) and immunoglobulin M (H‐IgM).
Personalized image caption, a natural extension of the standard image caption task, requires to generate brief image descriptions tailored for users' writing style and traits, and is more practical to meet users' real demands. Only a few recent studies shed light on this crucial task and learn static user representations to capture their long-term literal-preference. However, it is insufficient to achieve satisfactory performance due to the intrinsic existence of not only long-term user literal-preference, but also short-term literal-preference which is associated with users' recent states. To bridge this gap, we develop a novel multimodal hierarchical transformer network (MHTN) for personalized image caption in this paper. It learns short-term user literal-preference based on users' recent captions through a short-term user encoder at the low level. And at the high level, the multimodal encoder integrates target image representations with short-term literal-preference, as well as long-term literal-preference learned from user IDs. These two encoders enjoy the advantages of the powerful transformer networks. Extensive experiments on two real datasets show the effectiveness of considering two types of user literal-preference simultaneously and better performance over the state-of-the-art models.
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