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3,336 results on '"Zhang, Libo"'

219. A Neoglycoprotein-Immobilized Fluorescent Magnetic Bead Suspension Multiplex Array for Galectin-Binding Studies

Author

Zhang, Libo, Yu, Hai, Bai, Yuanyuan, Mishra, Bijoyananda, Yang, Xiaoxiao, Wang, Jing, Yu, Evan B, Li, Riyao, and Chen, Xi

Subjects
Medicinal and Biomolecular Chemistry, Chemical Sciences, Biotechnology, Generic health relevance, Blood Proteins, Carbohydrate Conformation, Carbohydrate Sequence, Fluorescent Dyes, Galectins, Glycation End Products, Advanced, Glycoproteins, Humans, Immobilized Proteins, Magnetic Phenomena, Plant Lectins, Polysaccharides, Protein Array Analysis, Protein Binding, Recombinant Proteins, Serum Albumin, Serum Albumin, Bovine, Glycated Serum Albumin, carbohydrate-protein conjugate, galectin, glycan-binding protein, lectin, multiplex assay, Organic Chemistry, Theoretical and Computational Chemistry, Medicinal and biomolecular chemistry, Organic chemistry
Abstract

Carbohydrate-protein conjugates have diverse applications. They have been used clinically as vaccines against bacterial infection and have been developed for high-throughput assays to elucidate the ligand specificities of glycan-binding proteins (GBPs) and antibodies. Here, we report an effective process that combines highly efficient chemoenzymatic synthesis of carbohydrates, production of carbohydrate-bovine serum albumin (glycan-BSA) conjugates using a squarate linker, and convenient immobilization of the resulting neoglycoproteins on carboxylate-coated fluorescent magnetic beads for the development of a suspension multiplex array platform. A glycan-BSA-bead array containing BSA and 50 glycan-BSA conjugates with tuned glycan valency was generated. The binding profiles of six plant lectins with binding preference towards Gal and/or GalNAc, as well as human galectin-3 and galectin-8, were readily obtained. Our results provide useful information to understand the multivalent glycan-binding properties of human galectins. The neoglycoprotein-immobilized fluorescent magnetic bead suspension multiplex array is a robust and flexible platform for rapid analysis of glycan and GBP interactions and will find broad applications.

Published
2021

222. Scalable algorithm simplification using quantum AND logic

Author

Chu, Ji, He, Xiaoyu, Zhou, Yuxuan, Yuan, Jiahao, Zhang, Libo, Guo, Qihao, Hai, Yongju, Han, Zhikun, Hu, Chang-Kang, Huang, Wenhui, Jia, Hao, Jiao, Dawei, Li, Sai, Liu, Yang, Ni, Zhongchu, Nie, Lifu, Pan, Xianchuang, Qiu, Jiawei, Wei, Weiwei, Nuerbolati, Wuerkaixi, Yang, Zusheng, Zhang, Jiajian, Zhang, Zhida, Zou, Wanjing, Chen, Yuanzhen, Deng, Xiaowei, Deng, Xiuhao, Hu, Ling, Li, Jian, Liu, Song, Lu, Yao, Niu, Jingjing, Tan, Dian, Xu, Yuan, Yan, Tongxing, Zhong, Youpeng, Yan, Fei, Sun, Xiaoming, and Yu, Dapeng

Published
2023
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229. Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization

Author

Ji, Ruyi, Wen, Longyin, Zhang, Libo, Du, Dawei, Wu, Yanjun, Zhao, Chen, Liu, Xianglong, and Huang, Feiyue

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Fine-grained visual categorization (FGVC) is an important but challenging task due to high intra-class variances and low inter-class variances caused by deformation, occlusion, illumination, etc. An attention convolutional binary neural tree architecture is presented to address those problems for weakly supervised FGVC. Specifically, we incorporate convolutional operations along edges of the tree structure, and use the routing functions in each node to determine the root-to-leaf computational paths within the tree. The final decision is computed as the summation of the predictions from leaf nodes. The deep convolutional operations learn to capture the representations of objects, and the tree structure characterizes the coarse-to-fine hierarchical feature learning process. In addition, we use the attention transformer module to enforce the network to capture discriminative features. The negative log-likelihood loss is used to train the entire network in an end-to-end fashion by SGD with back-propagation. Several experiments on the CUB-200-2011, Stanford Cars and Aircraft datasets demonstrate that the proposed method performs favorably against the state-of-the-arts., Comment: accepted by CVPR 2020

Published
2019

230. Guided Attention Network for Object Detection and Counting on Drones

Author

Cai, Yuanqiang, Du, Dawei, Zhang, Libo, Wen, Longyin, Wang, Weiqiang, Wu, Yanjun, and Lyu, Siwei

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Object detection and counting are related but challenging problems, especially for drone based scenes with small objects and cluttered background. In this paper, we propose a new Guided Attention Network (GANet) to deal with both object detection and counting tasks based on the feature pyramid. Different from the previous methods relying on unsupervised attention modules, we fuse different scales of feature maps by using the proposed weakly-supervised Background Attention (BA) between the background and objects for more semantic feature representation. Then, the Foreground Attention (FA) module is developed to consider both global and local appearance of the object to facilitate accurate localization. Moreover, the new data argumentation strategy is designed to train a robust model in various complex scenes. Extensive experiments on three challenging benchmarks (i.e., UAVDT, CARPK and PUCPR+) show the state-of-the-art detection and counting performance of the proposed method compared with existing methods.

Published
2019

231. Computer Assisted Composition in Continuous Time

Author

Koneputugodage, Chamin Hewa, Healy, Rhys, Lamont, Sean, Mallett, Ian, Brown, Matt, Walters, Matt, Attanayake, Ushini, Zhang, Libo, Dean, Roger T., Hunter, Alexander, Gretton, Charles, and Walder, Christian

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning
Abstract

We address the problem of combining sequence models of symbolic music with user defined constraints. For typical models this is non-trivial as only the conditional distribution of each symbol given the earlier symbols is available, while the constraints correspond to arbitrary times. Previously this has been addressed by assuming a discrete time model of fixed rhythm. We generalise to continuous time and arbitrary rhythm by introducing a simple, novel, and efficient particle filter scheme, applicable to general continuous time point processes. Extensive experimental evaluations demonstrate that in comparison with a more traditional beam search baseline, the particle filter exhibits superior statistical properties and yields more agreeable results in an extensive human listening test experiment.

Published
2019

232. Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently

Author

Liu, Dan, Du, Dawei, Zhang, Libo, Luo, Tiejian, Wu, Yanjun, Huang, Feiyue, and Lyu, Siwei

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Existing hand detection methods usually follow the pipeline of multiple stages with high computation cost, i.e., feature extraction, region proposal, bounding box regression, and additional layers for rotated region detection. In this paper, we propose a new Scale Invariant Fully Convolutional Network (SIFCN) trained in an end-to-end fashion to detect hands efficiently. Specifically, we merge the feature maps from high to low layers in an iterative way, which handles different scales of hands better with less time overhead comparing to concatenating them simply. Moreover, we develop the Complementary Weighted Fusion (CWF) block to make full use of the distinctive features among multiple layers to achieve scale invariance. To deal with rotated hand detection, we present the rotation map to get rid of complex rotation and derotation layers. Besides, we design the multi-scale loss scheme to accelerate the training process significantly by adding supervision to the intermediate layers of the network. Compared with the state-of-the-art methods, our algorithm shows comparable accuracy and runs a 4.23 times faster speed on the VIVA dataset and achieves better average precision on Oxford hand detection dataset at a speed of 62.5 fps., Comment: Accepted to AAAI2019

Published
2019
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235. Long-Term Exposure to Fine Particulate Matter and Incidence of Esophageal Cancer: A Prospective Study of 0.5 Million Chinese Adults

Author

Chen, Junshi, Chen, Zhengming, Clarke, Robert, Collins, Rory, Guo, Yu, Li, Liming, Peto, Richard, Walters, Robin, Avery, Daniel, Bennett, Derrick, Boxall, Ruth, Burgess, Sue, Chan, Ka Hung, Chang, Yumei, Chen, Yiping, Clarke, Johnathan, Du, Huaidong, Mohamed, Ahmed Edris, Fairhurst-Hunter, Zammy, Fry, Hannah, Gilbert, Simon, Hacker, Alex, Hill, Mike, Holmes, Michael, Im, Pek Kei, Iona, Andri, Kakkoura, Maria, Kartsonaki, Christiana, Kerosi, Rene, Lin, Kuang, Mazidi, Mohsen, Millwood, Iona, Morris, Sam, Nie, Qunhua, Pozarickij, Alfred, Ryder, Paul, Said, Saredo, Sansome, Sam, Schmidt, Dan, Sherliker, Paul, Sohoni, Rajani, Stevens, Becky, Turnbull, Iain, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Yao, Pang, Han, Xiao, Hou, Can, Lv, Jun, Pei, Pei, Liu, Chao, Yu, Canqing, Xia, Qingmei, Pang, Zengchang, Gao, Ruqin, Li, Shanpeng, Duan, Haiping, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Zhai, Yaoming, Ning, Feng, Sun, Xiaohui, Li, Feifei, Lv, Silu, Wang, Junzheng, Hou, Wei, Sun, Wei, Yan, Shichun, Cui, Xiaoming, Wang, Chi, Wu, Zhenyuan, Li, Yanjie, Kang, Quan, Luo, Huiming, Ou, Tingting, Zheng, Xiangyang, Guo, Zhendong, Wu, Shukuan, Li, Yilei, Li, Huimei, Wu, Ming, Zhou, Yonglin, Zhou, Jinyi, Tao, Ran, Yang, Jie, Su, Jian, Liu, Fang, Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Lin, Mei, Lu, Zhenzhen, Zhou, Lifang, Xie, Changping, Lan, Jian, Zhu, Tingping, Liu, Yun, Wei, Liuping, Zhou, Liyuan, Chen, Ningyu, Qin, Yulu, Wang, Sisi, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Chang, Xiaoyu, Yuan, Mingqiang, Wu, Xia, Jiang, Wei, Liu, Jiaqiu, Sun, Qiang, Chen, Faqing, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, Zhang, Xi, Kang, Kai, Feng, Shixian, Tian, Huizi, Fan, Lei, Li, XiaoLin, Sun, Huarong, He, Pan, Zhang, Xukui, Yu, Min, Hu, Ruying, Wang, Hao, Zhang, Xiaoyi, Cao, Yuan, Xie, Kaixu, Chen, Lingli, Shen, Dun, Li, Xiaojun, Jin, Donghui, Yin, Li, Liu, Huilin, Fu, Zhongxi, Xu, Xin, Zhang, Hao, Chen, Jianwei, Peng, Yuan, Zhang, Libo, Qu, Chan, Sun, Dong, Liu, Cong, Zhu, Yunqing, Sun, Dianjianyi, Pang, Yuanjie, Meng, Xia, Liu, Yang, and Kan, Haidong

Published
2023
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236. Data Priming Network for Automatic Check-Out

Author

Li, Congcong, Du, Dawei, Zhang, Libo, Luo, Tiejian, Wu, Yanjun, Tian, Qi, Wen, Longyin, and Lyu, Siwei

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Automatic Check-Out (ACO) receives increased interests in recent years. An important component of the ACO system is the visual item counting, which recognizes the categories and counts of the items chosen by the customers. However, the training of such a system is challenged by the domain adaptation problem, in which the training data are images from isolated items while the testing images are for collections of items. Existing methods solve this problem with data augmentation using synthesized images, but the image synthesis leads to unreal images that affect the training process. In this paper, we propose a new data priming method to solve the domain adaptation problem. Specifically, we first use pre-augmentation data priming, in which we remove distracting background from the training images using the coarse-to-fine strategy and select images with realistic view angles by the pose pruning method. In the post-augmentation step, we train a data priming network using detection and counting collaborative learning, and select more reliable images from testing data to fine-tune the final visual item tallying network. Experiments on the large scale Retail Product Checkout (RPC) dataset demonstrate the superiority of the proposed method, i.e., we achieve 80.51% checkout accuracy compared with 56.68% of the baseline methods. The source codes can be found in https://isrc.iscas.ac.cn/gitlab/research/acm-mm-2019-ACO., Comment: Accepted to ACM MM 2019

Published
2019

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