Research on the infiltration of environmental protection consciousness in the teaching of chemistry teachers' specialty

As a chemistry teacher, this paper studies the recycling of experimental drugs and the treatment of three wastes in the middle school chemical demonstration experiment from the perspective of environmental protection. The concept of experimental environment-friendly was emphasized, and the concept of environmentally friendly chemistry and green chemistry education was established, and this concept into the future chemistry teaching of middle school was put. In this way, it achieves the training goal of chemists and environmental education functions, and also achieves the penetration of environmental protection awareness in middle school chemistry teaching. Keywords Environmental protection awareness; chemistry teacher major; environmentally sound chemical education; three waste treatment DOI: 10.7176/JEP/10-6-1

Influence of Fermion Velocity Renormalization on Dynamical Mass Generation in QED3_3

We study dynamical fermion mass generation in (2+1)-dimensional quantum electrodynamics with a gauge field coupling to massless Dirac fermions and non-relativistic scalar bosons. We calculate the fermion velocity renormalization and then examine its influence on dynamical mass generation by using the Dyson-Schwinger equation. It is found that dynamical mass generation takes place even after including the scalar bosons as long as the bosonic compressibility parameter $\xi$ is sufficiently small. In addition, the fermion velocity renormalization enhances the dynamically generated mass.Comment: 6 pages, 3 figures, Chinese Physics Letter, Vol 29, page 057401(2012

When Sparse Neural Network Meets Label Noise Learning: A Multistage Learning Framework

Recent methods in network pruning have indicated that a dense neural network involves a sparse subnetwork (called a winning ticket), which can achieve similar test accuracy to its dense counterpart with much fewer network parameters. Generally, these methods search for the winning tickets on well-labeled data. Unfortunately, in many real-world applications, the training data are unavoidably contaminated with noisy labels, thereby leading to performance deterioration of these methods. To address the above-mentioned problem, we propose a novel two-stream sample selection network (TS 3 -Net), which consists of a sparse subnetwork and a dense subnetwork, to effectively identify the winning ticket with noisy labels. The training of TS 3 -Net contains an iterative procedure that switches between training both subnetworks and pruning the smallest magnitude weights of the sparse subnetwork. In particular, we develop a multistage learning framework including a warm-up stage, a semisupervised alternate learning stage, and a label refinement stage, to progressively train the two subnetworks. In this way, the classification capability of the sparse subnetwork can be gradually improved at a high sparsity level. Extensive experimental results on both synthetic and real-world noisy datasets (including MNIST, CIFAR-10, CIFAR-100, ANIMAL-10N, Clothing1M, and WebVision) demonstrate that our proposed method achieves state-of-the-art performance with very small memory consumption for label noise learning. Code is available at https://github.com/Runqing-forMost/TS3-Net/tree/master