Delicate attention of the discriminative regions plays a critical role in Fine-Grained Visual Categorization (FGVC). Unfortunately, most of the existing attention models perform poorly in FGVC, due to the pivotal limitations in discriminative regions proposing and region-based feature learning. 1) The discriminative regions are predominantly located based on the filter responses over the images, which can not be directly optimized with a performance metric. 2) Existing methods train the region-based feature extractor as a one-hot classification task individually, while neglecting the knowledge from the entire object. To address the above issues, in this paper, we propose a novel “Filtration and Distillation Learning” (FDL) model to enhance the region attention of discriminate parts for FGVC. Firstly, a Filtration Learning (FL) method is put forward for discriminative part regions proposing based on the matchability between proposing and predicting. Specifically, we utilize the proposing-predicting matchability as the performance metric of Region Proposal Network (RPN), thus enable a direct optimization of RPN to filtrate most discriminative regions. Go in detail, the object-based feature learning and region-based feature learning are formulated as “teacher” and “student”, which can furnish better supervision for region-based feature learning. Accordingly, our FDL can enhance the region attention effectively, and the overall framework can be trained end-to-end without neither object nor parts annotations. Extensive experiments verify that FDL yields state-of-the-art performance under the same backbone with the most competitive approaches on several FGVC tasks.
This study focused on the effect of hemicellulose and lignin on enzymatic hydrolysis of dairy manure and hydrolysis process optimization to improve sugar yield. It was found that hemicellulose and lignin in dairy manure, similar to their role in other lignocellulosic material, were major resistive factors to enzymatic hydrolysis and that the removal of either of them, or for best performance, both of them, improved the enzymatic hydrolysis of manure cellulose. This result combined with scanning electron microscope (SEM) pictures further proved that the accessibility of cellulose to cellulase was the most important feature to the hydrolysis. Quantitatively, fed-batch enzymatic hydrolysis of fiber without lignin and hemicellulose had a high glucose yield of 52% with respect to the glucose concentration of 17 g/L at a total enzyme loading of 1300 FPU/L and reaction time of 160 h, which was better than corresponding batch enzymatic hydrolysis.
Heat stress (HS) threatens the worldwide dairy industry by decreasing animal production performance and health. Holstein cows and dairy buffaloes are the most important dairy animals, but their differences in the metabolic mechanism of thermotolerance remain elusive. In this study, we used serum metabolomics to evaluate the differences in thermotolerance between Holstein cows and crossbred dairy buffaloes under chronic heat stress (HS) and thermal-neutral conditions. In response to HS, the body temperatures and respiratory rates were increased more for Holstein cows than for dairy buffaloes (38.78 vs 38.24 °C, p < 0.001; 43.6 vs 32.5 breaths/min, p < 0.001). HS greatly affected serum metabolites associated with amino acids, fatty acids, and bile acids. The enriched metabolic pathways of these serum metabolites are closely related to HS. We demonstrated that buffaloes adapt to HS by adopting a metabolism of branched-chain amino acids and ketogenic amino acids and gluconeogenesis, but Holstein cows decrease the effect of HS with citrulline and proline metabolism. Both physiological parameters and serum metabolic profiles indicate that dairy buffaloes are more thermotolerant than Holstein cows, providing the feasibility to vigorously develop the buffalo dairy industry in tropical and subtropical regions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.