No abstract

An efficient I2 (20 mol %)/TBPB mediated oxidative formal [4 + 1] cycloaddition of N-tosylhydrazones with anilines via C-N/N-N bond formation and S-N cleavage has been developed. This protocol represents a simple, general, and efficient approach for the construction of 1,2,3-triazoles under metal-free and azide-free conditions by utilizing a catalytic amount of I2.

Multi-modal hashing methods could support efficient multimedia retrieval by combining multi-modal features for binary hash learning at the both offline training and online query stages. However, existing multi-modal methods cannot binarize the queries, when only one or part of modalities are provided. In this article, we propose a novel Flexible Multi-modal Hashing (FMH) method to address this problem. FMH learns multiple modality-specific hash codes and multi-modal collaborative hash codes simultaneously within a single model. The hash codes are flexibly generated according to the newly coming queries, which provide any one or combination of modality features. Besides, the hashing learning procedure is efficiently supervised by the pair-wise semantic matrix to enhance the discriminative capability. It could successfully avoid the challenging symmetric semantic matrix factorization and O ( n 2 ) storage cost of semantic matrix. Finally, we design a fast discrete optimization to learn hash codes directly with simple operations. Experiments validate the superiority of the proposed approach.

Flat lenses, including metalens and diffractive lens, have attracted increasing attention due to their ability to miniaturize the imaging devices. However, realizing a large scale achromatic flat lens with high performance still remains a big challenge. Here, we developed a new framework in designing achromatic multi-level diffractive lenses by light coherence optimization, which enables the implementation of large-scale flat lenses under non-ideal conditions. As results, a series achromatic polymer lenses with diameter from 1 to 10 mm are successfully designed and fabricated. The subsequent optical characterizations substantially validate our theoretical framework and show relatively good performance of the centimeter-scale achromatic multi-level diffractive lenses with a super broad bandwidth in optical wavelengths (400–1100 nm). After comparing with conventional refractive lens, this achromatic lens shows significant advantages in white-light imaging performance, implying a new strategy in developing practical planar optical devices.

High-resolution remote sensing images are abundant in texture information, and the detection method of the change of pixel-level mainly analyzes the spectral information of the image, which has certain limitations. In this paper, a high-resolution remote sensing image change detection method combining pixel and object levels is proposed to solve the problem that many pepper and salt phenomenon and false detection in the change detection of pixel-level and object-level change detection method are cumbersome for image segmentation process. We integrate the multi-dimensional features of high-resolution remote sensing images and use random forest classifiers to classify to obtain the pixel-level change detection results. Then, we use the improved U-net network to semantically segment the post-phase remote sensing image to obtain the image object segmentation result. Finally, the consequences of pixel-level change detection and image object segmentation result are fused to obtain the image changing area and the unchanging area. The experimental results demonstrate that the algorithm has a higher accuracy rate and detection precision.