Reevaluation of the density dependence of nucleon radius and mass in the global color symmetry model of QCD

With the global color symmetry model (GCM) at finite chemical potential, the density dependence of the bag constant, the total energy and the radius of a nucleon in nuclear matter is investigated. A relation between the nuclear matter density and the chemical potential with the action of QCD being taken into account is obtained. A maximal nuclear matter density for the existence of the bag with three quarks confined within is given. The calculated results indicate that, before the maximal density is reached, the bag constant and the total energy of a nucleon decrease, and the radius of a nucleon increases slowly, with the increasing of the nuclear matter density. As the maximal nuclear matter density is reached, the mass of the nucleon vanishes and the radius becomes infinite suddenly. It manifests that a phase transition from nucleons to quarks takes place.Comment: 18 pages, 3 figure

Characteristics and Performance of Nanozinc Oxide/Mesoporous Silica Gel Photocatalytic Composite Prepared by a Sol-Gel Method

Nano-ZnO loaded mesoporous SiO2 was prepared by sol-gel technology as a photocatalytic composite. XRD, SEM, TEM, EDX, and N2 sorption isotherms were used to characterize the nano-ZnO/mesoporous SiO2. Acid Red 18 was used as simulated pollutant to determine the photocatalytic performance of nano-ZnO/mesoporous SiO2 under ultraviolet light and solar light. The results showed that 6.4 nm ZnO was obtained and immobilized on mesoporous SiO2. Compared to the mesoporous SiO2, the surface area and average pore width of nano-ZnO/mesoporous SiO2 were reduced by 12 m2/g and 0.7 nm, respectively. 50% ZnO content in a composite calcinated at 200∘C exhibited the best photocatalytic activity. The removal of Acid Red 18 under solar irradiation was 10% higher than ultraviolet light

TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers

CutMix is a popular augmentation technique commonly used for training modern convolutional and transformer vision networks. It was originally designed to encourage Convolution Neural Networks (CNNs) to focus more on an image's global context instead of local information, which greatly improves the performance of CNNs. However, we found it to have limited benefits for transformer-based architectures that naturally have a global receptive field. In this paper, we propose a novel data augmentation technique TokenMix to improve the performance of vision transformers. TokenMix mixes two images at token level via partitioning the mixing region into multiple separated parts. Besides, we show that the mixed learning target in CutMix, a linear combination of a pair of the ground truth labels, might be inaccurate and sometimes counter-intuitive. To obtain a more suitable target, we propose to assign the target score according to the content-based neural activation maps of the two images from a pre-trained teacher model, which does not need to have high performance. With plenty of experiments on various vision transformer architectures, we show that our proposed TokenMix helps vision transformers focus on the foreground area to infer the classes and enhances their robustness to occlusion, with consistent performance gains. Notably, we improve DeiT-T/S/B with +1% ImageNet top-1 accuracy. Besides, TokenMix enjoys longer training, which achieves 81.2% top-1 accuracy on ImageNet with DeiT-S trained for 400 epochs. Code is available at https://github.com/Sense-X/TokenMix.Comment: ECCV 2022; Code: https://github.com/Sense-X/TokenMi