Teaching Practice about Flipped Classroom on Circuit Course

In view of the problems in circuit course, flipped classroom is introduced and new teaching mode is explored. Teaching design, teaching reform suggestion, teaching effects and results are presented. It is verified that this kind of teaching mode can enhance the enthusiasm, initiative and participation of the students, teaching efficiency is also improved. It is also a good way for comprehensive practice

The Relationships Between the Level of Lignin, a Secondary Metabolite in Soybean Plant, and Aphid Resistance in Soybeans

In the present report, the relationship was discussed between the level of lignin-one of the secondary metabolites in soybean plant and the chemical defense reaction of soybean to the soybean aphid (Aphis glycines Muts). Experimental results indicated that the cultivars with higher level of lignin are more resistant to the damage of aphids than those with lower level of lignin. Lignin is one of the compounds that are responsible to the chemical defense reaction of soybean. This finding laid a foundation for the elucidation of the mechanism of aphid resistance in plants and its biochemical basis.Originating text in Chinese.Citation: Hu, Qi, Zhao, Jianwei, Cui, Jianwen. (1993). The Relationships Between the Level of Lignin, a Secondary Metabolite in Soybean Plant, and Aphid Resistance in Soybeans. Plant Protection (Institute of Plant Protection, CAAS, China), 19(1), 8-9

NegDL: Privacy-Preserving Deep Learning Based on Negative Database

In the era of big data, deep learning has become an increasingly popular topic. It has outstanding achievements in the fields of image recognition, object detection, and natural language processing et al. The first priority of deep learning is exploiting valuable information from a large amount of data, which will inevitably induce privacy issues that are worthy of attention. Presently, several privacy-preserving deep learning methods have been proposed, but most of them suffer from a non-negligible degradation of either efficiency or accuracy. Negative database (\textit{NDB}) is a new type of data representation which can protect data privacy by storing and utilizing the complementary form of original data. In this paper, we propose a privacy-preserving deep learning method named NegDL based on \textit{NDB}. Specifically, private data are first converted to \textit{NDB} as the input of deep learning models by a generation algorithm called \textit{QK}-hidden algorithm, and then the sketches of \textit{NDB} are extracted for training and inference. We demonstrate that the computational complexity of NegDL is the same as the original deep learning model without privacy protection. Experimental results on Breast Cancer, MNIST, and CIFAR-10 benchmark datasets demonstrate that the accuracy of NegDL could be comparable to the original deep learning model in most cases, and it performs better than the method based on differential privacy

CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing

Leveraging supervised information can lead to superior retrieval performance in the image hashing domain but the performance degrades significantly without enough labeled data. One effective solution to boost the performance is to employ generative models, such as Generative Adversarial Networks (GANs), to generate synthetic data in an image hashing model. However, GAN-based methods are difficult to train and suffer from mode collapse issue, which prevents the hashing approaches from jointly training the generative models and the hash functions. This limitation results in sub-optimal retrieval performance. To overcome this limitation, we propose a novel framework, the generative cooperative hashing network (CoopHash), which is based on the energy-based cooperative learning. CoopHash jointly learns a powerful generative representation of the data and a robust hash function. CoopHash has two components: a top-down contrastive pair generator that synthesizes contrastive images and a bottom-up multipurpose descriptor that simultaneously represents the images from multiple perspectives, including probability density, hash code, latent code, and category. The two components are jointly learned via a novel likelihood-based cooperative learning scheme. We conduct experiments on several real-world datasets and show that the proposed method outperforms the competing hashing supervised methods, achieving up to 10% relative improvement over the current state-of-the-art supervised hashing methods, and exhibits a significantly better performance in out-of-distribution retrieval

Single image super resolution based on multi-scale structure and non-local smoothing

In this paper, we propose a hybrid super-resolution method by combining global and local dictionary training in the sparse domain. In order to present and differentiate the feature mapping in different scales, a global dictionary set is trained in multiple structure scales, and a non-linear function is used to choose the appropriate dictionary to initially reconstruct the HR image. In addition, we introduce the Gaussian blur to the LR images to eliminate a widely used but inappropriate assumption that the low resolution (LR) images are generated by bicubic interpolation from high-resolution (HR) images. In order to deal with Gaussian blur, a local dictionary is generated and iteratively updated by K-means principal component analysis (K-PCA) and gradient decent (GD) to model the blur effect during the down-sampling. Compared with the state-of-the-art SR algorithms, the experimental results reveal that the proposed method can produce sharper boundaries and suppress undesired artifacts with the present of Gaussian blur. It implies that our method could be more effect in real applications and that the HR-LR mapping relation is more complicated than bicubic interpolation

Chiral Brønsted acid catalyzed enantioselective dehydrative Nazarov-type electrocyclization of aryl and 2-Thienyl vinyl alcohols

An efficient chiral Brønsted acid-catalyzed enantioselective dehydrative Nazarov-type electrocyclization (DNE) of electron-rich aryl- and 2-thienyl-β-amino-2-en-1-ols is described. The 4π conrotatory electrocyclization reaction affords access to a wide variety of the corresponding 1H-indenes and 4H-cyclopenta[b]thiophenes in excellent yields of up to 99% and enantiomeric excess (ee) values of up to 99%. Experimental and computational studies based on a proposed intimate contact ion-pair species that is further assisted by hydrogen bonding between the amino group of the substrate cation and chiral catalyst anion provide insight into the observed product enantioselectivities

Fracture failure analysis and bias tearing strength criterion for PVDF coated bi-axial warp knitted fabrics

This paper concerns the fracture failure and bias tearing strength criterion for a PVDF coated bi-axial warp knitted fabrics (BWKFs) widely used in air supported membrane structures (ASMSs). Central slit tearing tests were carefully conducted on bias specimens with seven off-axis angles, and the corresponding tearing properties, including failure behaviors and tearing strength criterion were discussed. Results show that coated bi-axial warp knitted fabrics are typical direction-depended materials, and their tearing characteristics vary greatly with the bias angles. Typical tearing stress-displacement curves of bias samples could exhibit four characteristic regions: a co-deformation region, a shear deformation region, a plateau region, and a post peak region. No matter what the orientation of the initial slit or the yarn is, the propagation is always parallel to the secondary yarns. For specimens with different bias angles, some obvious differences in tearing behaviors are observed in terms of maximum displacement, damage mode, curve slope, and number of stress peaks, and these differences could be attributed to the material orthotropy and different failure mechanism of constituent materials. Unlike results of tensile strength for most of woven fabrics, for the studied BWKF composite, there is a W-shaped relationship between tearing strength and bias angle, with a local strength peak at 45o angle. The new tearing strength criterion proposed in the prior research is validated due to the strong agreements between the calculated and experimental results for the BWKF