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• A fiber-optic Michelson interferometric trace DMMP sensor was proposed. • The fiber-optic sensor has the structure of a Michelson interferometer. • ZIF-8@Ag/PDDA as sensing materials is used to enhance the sensitivity. • The sensor has a sensitivity of 45.71 dB/ppm for DMMP. • The sensor has good selectivity and stability. A Michelson interference dimethyl methyl phosphate (DMMP) fiber sensor with a single-mode (SMF)-dislocated single-mode (DSMF)-hollow-core fiber (HCF) structure is proposed. To fabricate the Michelson interference structure of SMF-DSMF-HCF, one end of the DSMF was misaligned and fused to the SMF, the other end was fused to the HCF to generate a reflective surface. The structure, elemental composition, and surface morphology of ZIF-8@Ag were analyzed using X-ray diffraction, X-ray photoelectron spectroscopy, and scanning electron microscopy after the ZIF-8@Ag/PDDA composite was coated on the surface of DSMF. The result shows that the sensitivity and the detection limit of the sensor are 45.71 dB/ppm and 0.1431 ppb, respectively, and the response/recovery time is around 14/12 s. The sensor also has excellent selectivity and stability. [ABSTRACT FROM AUTHOR]

Most previous visible–infrared person re-identification methods emphasized learning modality-shared features to narrow the modality differences, while neglecting the benefits of modality-specific features for feature embedding and narrowing the modality gap. To tackle this issue, our paper designs a method based on dual attention enhancement features to use shallow and deep features simultaneously. We first convert visible images into gray images to alleviate the visual difference. Then, to close the difference between modalities by learning the modality-specific features, we design a shallow feature measurement module, in which we use a class-specific maximum mean discrepancy loss to measure the distribution difference of specific features between two modalities. Finally, we design a dual attention feature enhancement module, which aims to mine more useful context information from modality-shared features to shorter the distance between classes within modalities. Specifically, our model exceeds the current SOTAs on SYSU-MM01, with 66.61% Rank-1 accuracy and 62.86% mAP. [ABSTRACT FROM AUTHOR]