No abstract

No abstract

No abstract

No abstract

No abstract

This study attempted the feasibility to determine the ratio of tea polyphenols to amino acids in green tea infusion using near infrared (NIR) spectroscopy combined with synergy interval PLS (siPLS) algorithms. First, SNV was used to preprocess the original spectra of tea infusion; then, siPLS was used to select the efficient spectra regions from the preprocessed spectra. Experimental results showed that the spectra regions [7 8 18] were selected, which were out of the strong absorption of H2O. The optimal PLS model was developed with the selected regions when 6 PCs components were contained. The RMSEP value was equal to 0.316 and the correlation coefficient (R) was equal to 0.8727 in prediction set. The results demonstrated that NIR can be successfully used to determinate the ration of tea polyphenols to amino acids in green tea infusion.

Recent studies show that hierarchical Vision Transformer with interleaved non-overlapped intra window selfattention & shifted window self-attention is able to achieve state-of-the-art performance in various visual recognition tasks and challenges CNN's dense sliding window paradigm. Most follow-up works try to replace shifted window operation with other kinds of cross window communication while treating self-attention as the de-facto standard for intra window information aggregation. In this short preprint, we question whether self-attention is the only choice for hierarchical Vision Transformer to attain strong performance, and what makes for hierarchical Vision Transformer? We replace self-attention layers in Swin Transformer and Shuffle Transformer with simple linear mapping and keep other components unchanged.The resulting architecture with 25.4M parameters and 4.2G FLOPs achieves 80.5% Top-1 accuracy, compared to 81.3% for Swin Transformer with 28.3M parameters and 4.5G FLOPs. We also experiment with other alternatives to self-attention for context aggregation inside each non-overlapped window, which all give similar competitive results under the same architecture. Our study reveals that the macro architecture of Swin model families (i.e., interleaved intra window & cross window communications), other than specific aggregation layers or specific means of cross window communication, may be more responsible for its strong performance and is the real challenger to CNN's dense sliding window paradigm.

No abstract