Face Image Recognition Based on Convolutional Neural Network

With the continuous progress of The Times and the development of technology,the rise of network social media has also brought the“explosive”growth of image data.As one of the main ways of People’s Daily communication,image is widely used as a carrier of communication because of its rich content,intuitive and other advantages.Image recognition based on convolution neural network is the first application in the field of image recognition.A series of algorithm operations such as image eigenvalue extraction,recognition and convolution are used to identify and analyze different images.The rapid development of artificial intelligence makes machine learning more and more important in its research field.Use algorithms to learn each piece of data and predict the outcome.This has become an important key to open the door of artificial intelligence.In machine vision,image recognition is the foundation,but how to associate the low-level information in the image with the high-level image semantics becomes the key problem of image recognition.Predecessors have provided many model algorithms,which have laid a solid foundation for the development of artificial intelligence and image recognition.The multi-level information fusion model based on the VGG16 model is an improvement on the fully connected neural network.Different from full connection network,convolutional neural network does not use full connection method in each layer of neurons of neural network,but USES some nodes for connection.Although this method reduces the computation time,due to the fact that the convolutional neural network model will lose some useful feature information in the process of propagation and calculation,this paper improves the model to be a multi-level information fusion of the convolution calculation method,and further recovers the discarded feature information,so as to improve the recognition rate of the image.VGG divides the network into five groups(mimicking the five layers of AlexNet),yet it USES 3*3 filters and combines them as a convolution sequence.Network deeper DCNN,channel number is bigger.The recognition rate of the model was verified by 0RL Face Database,BioID Face Database and CASIA Face Image Database.

Immunotherapy of Epstein-Barr Virus Associated Malignancies Using Mycobacterial HSP70 and LMP2A356-364 Epitope Fusion Protein

Epstein-Barr virus infection is strongly associated with a number of malignancies.The EBV latent membrane protein 2A has been implicated as one of the most attractive candidates for immunotherapy of related malignancies.In previous studies,the T cell epitopes of LMP2A have been identified systematically.However,the epitope-based vaccine generally meets inefficient immunogenicity when used in vivo directly,which could be overcome by combination with appropriate adjuvants.Heat shock protein is a natural chaperon,which is able to activate the classical major histocompatibility complex class I antigen-processing pathway(cross-presentation).In this study,a minigene encoding LMP2A356-364(FLYALALLL)was genetically fused to the carboxy-terminal of mycobacterial heat shock protein 70.The epitope fusion protein was expressed and purified,and the cross-presentation of LMP2A_(356-364) by monocyte-derived dendritic cells pulsed with the epitope fusion protein was evaluated.Results showed that the epitope fusion protein-pulsed mDCs were much more efficient than the single peptide-pulsed mDCs on CTL activation.Immunization of HLA-A2.1 transgenic mice with MtHsp70-LMP2A_(356-364) generated peptide specific CTL more effectively than a single peptide plus incomplete Freund's adjuvant(IFA).Growth of LMP2A expressing B16 melanoma tumor cells was suppressed in the vaccinated groups.Our results suggested that MtHsp70-LMP2A_(356-364) fusion protein was more effective than the CD8^(+)T cell epitope alone on anti-tumor immunity.As a result,the MtHsp70-LMP2A_(356-364) fusion protein is considered to be a promising candidate vaccine for EBV related malignancies.

Contrastive learning for a single historical painting’s blind super-resolution

Most of the existing blind super-resolution(SR)methods explicitly estimate the kernel in pixel space,which usually has a large deviation and results in poor SR performance.As a seminal work,DASR learns abstract representations to distinguish various degradations in the feature space,which effectively reduces degradation estimation bias.Therefore,we also employ the feature space to extract degradation representations for an ancient painting.However,most of the blind SR mehods,including DASR,are committed to removing degradations introduced by kernels,downsampling and additive noise.Among them,downsampling degradation is often accompanied by unpleasant artifacts.To address this issue,the paper designs a high-resolution(HR)representation encoder EHR based on contrastive learning to distinguish artifacts introduced by downsampling.Moreover,to optimize the illposed nature of blind SR,we propose a contrastive regularization(CR)to minimize the contrastive loss based on VGG-19.With the help of CR,the SR images are pulled closer to the HR images and pushed far away from bicubic LR observations.Benefiting from these improvements,our method consistently achieves higher quantitative performance and better visual quality with more natural textures than state-of-the-art approaches on a specialized painting dataset.©2021 The Authors.