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46 results on '"Li, Minxian"'

1. RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation

Author

Zhu, Yazhou, Li, Minxian, Ye, Qiaolin, Wang, Shidong, Xin, Tong, and Zhang, Haofeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Few-shot medical image segmentation (FSMIS) aims to perform the limited annotated data learning in the medical image analysis scope. Despite the progress has been achieved, current FSMIS models are all trained and deployed on the same data domain, as is not consistent with the clinical reality that medical imaging data is always across different data domains (e.g. imaging modalities, institutions and equipment sequences). How to enhance the FSMIS models to generalize well across the different specific medical imaging domains? In this paper, we focus on the matching mechanism of the few-shot semantic segmentation models and introduce an Earth Mover's Distance (EMD) calculation based domain robust matching mechanism for the cross-domain scenario. Specifically, we formulate the EMD transportation process between the foreground support-query features, the texture structure aware weights generation method, which proposes to perform the sobel based image gradient calculation over the nodes, is introduced in the EMD matching flow to restrain the domain relevant nodes. Besides, the point set level distance measurement metric is introduced to calculated the cost for the transportation from support set nodes to query set nodes. To evaluate the performance of our model, we conduct experiments on three scenarios (i.e., cross-modal, cross-sequence and cross-institution), which includes eight medical datasets and involves three body regions, and the results demonstrate that our model achieves the SoTA performance against the compared models.

Published
2024

2. Towards Fewer Labels: Support Pair Active Learning for Person Re-identification

Author

Jin, Dapeng and Li, Minxian

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Supervised-learning based person re-identification (re-id) require a large amount of manual labeled data, which is not applicable in practical re-id deployment. In this work, we propose a Support Pair Active Learning (SPAL) framework to lower the manual labeling cost for large-scale person reidentification. The support pairs can provide the most informative relationships and support the discriminative feature learning. Specifically, we firstly design a dual uncertainty selection strategy to iteratively discover support pairs and require human annotations. Afterwards, we introduce a constrained clustering algorithm to propagate the relationships of labeled support pairs to other unlabeled samples. Moreover, a hybrid learning strategy consisting of an unsupervised contrastive loss and a supervised support pair loss is proposed to learn the discriminative re-id feature representation. The proposed overall framework can effectively lower the labeling cost by mining and leveraging the critical support pairs. Extensive experiments demonstrate the superiority of the proposed method over state-of-the-art active learning methods on large-scale person re-id benchmarks.

Published
2022

6. Unsupervised Clustering Active Learning for Person Re-identification

Author

Gao, Wenjing and Li, Minxian

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Supervised person re-identification (re-id) approaches require a large amount of pairwise manual labeled data, which is not applicable in most real-world scenarios for re-id deployment. On the other hand, unsupervised re-id methods rely on unlabeled data to train models but performs poorly compared with supervised re-id methods. In this work, we aim to combine unsupervised re-id learning with a small number of human annotations to achieve a competitive performance. Towards this goal, we present a Unsupervised Clustering Active Learning (UCAL) re-id deep learning approach. It is capable of incrementally discovering the representative centroid-pairs and requiring human annotate them. These few labeled representative pairwise data can improve the unsupervised representation learning model with other large amounts of unlabeled data. More importantly, because the representative centroid-pairs are selected for annotation, UCAL can work with very low-cost human effort. Extensive experiments demonstrate the superiority of the proposed model over state-of-the-art active learning methods on three re-id benchmark datasets., Comment: This work was submitted to BMVC2021

Published
2021

8. Unsupervised Noisy Tracklet Person Re-identification

Author

Li, Minxian, Zhu, Xiatian, and Gong, Shaogang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Existing person re-identification (re-id) methods mostly rely on supervised model learning from a large set of person identity labelled training data per domain. This limits their scalability and usability in large scale deployments. In this work, we present a novel selective tracklet learning (STL) approach that can train discriminative person re-id models from unlabelled tracklet data in an unsupervised manner. This avoids the tedious and costly process of exhaustively labelling person image/tracklet true matching pairs across camera views. Importantly, our method is particularly more robust against arbitrary noisy data of raw tracklets therefore scalable to learning discriminative models from unconstrained tracking data. This differs from a handful of existing alternative methods that often assume the existence of true matches and balanced tracklet samples per identity class. This is achieved by formulating a data adaptive image-to-tracklet selective matching loss function explored in a multi-camera multi-task deep learning model structure. Extensive comparative experiments demonstrate that the proposed STL model surpasses significantly the state-of-the-art unsupervised learning and one-shot learning re-id methods on three large tracklet person re-id benchmarks., Comment: was submitted to ICCV2019

Published
2021

9. Spatial-Temporal Adaptive Graph Convolutional Network for Skeleton-Based Action Recognition

Author

Hang, Rui, Li, MinXian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Lei, editor, Gall, Juergen, editor, Chin, Tat-Jun, editor, Sato, Imari, editor, and Chellappa, Rama, editor

Published
2023
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10. Intra-Camera Supervised Person Re-Identification

Author

Zhu, Xiangping, Zhu, Xiatian, Li, Minxian, Morerio, Pietro, Murino, Vittorio, and Gong, Shaogang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data. This requires a tedious data collection and annotation process, leading to poor scalability in practical re-id applications. On the other hand unsupervised re-id methods do not need identity label information, but they usually suffer from much inferior and insufficient model performance. To overcome these fundamental limitations, we propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation. This eliminates the most time-consuming and tedious inter-camera identity labelling process, significantly reducing the amount of human annotation efforts. Consequently, it gives rise to a more scalable and more feasible setting, which we call Intra-Camera Supervised (ICS) person re-id, for which we formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method. Specifically, MATE is designed for self-discovering the cross-camera identity correspondence in a per-camera multi-task inference framework. Extensive experiments demonstrate the cost-effectiveness superiority of our method over the alternative approaches on three large person re-id datasets. For example, MATE yields 88.7% rank-1 score on Market-1501 in the proposed ICS person re-id setting, significantly outperforming unsupervised learning models and closely approaching conventional fully supervised learning competitors., Comment: Accepted to IJCV

Published
2020

11. Part-based Multi-stream Model for Vehicle Searching

Author

Sun, Ya, Li, Minxian, and Lu, Jianfeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Due to the enormous requirement in public security and intelligent transportation system, searching an identical vehicle has become more and more important. Current studies usually treat vehicle as an integral object and then train a distance metric to measure the similarity among vehicles. However, these raw images may be exactly similar to ones with different identification and include some pixels in background that may disturb the distance metric learning. In this paper, we propose a novel and useful method to segment an original vehicle image into several discriminative foreground parts, and these parts consist of some fine grained regions that are named discriminative patches. After that, these parts combined with the raw image are fed into the proposed deep learning network. We can easily measure the similarity of two vehicle images by computing the Euclidean distance of the features from FC layer. Two main contributions of this paper are as follows. Firstly, a method is proposed to estimate if a patch in a raw vehicle image is discriminative or not. Secondly, a new Part-based Multi-Stream Model (PMSM) is designed and optimized for vehicle retrieval and re-identification tasks. We evaluate the proposed method on the VehicleID dataset, and the experimental results show that our method can outperform the baseline., Comment: Published in International Conference on Pattern Recognition 2018

Published
2019
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12. Intra-Camera Supervised Person Re-Identification: A New Benchmark

Author

Zhu, Xiangping, Zhu, Xiatian, Li, Minxian, Murino, Vittorio, and Gong, Shaogang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Existing person re-identification (re-id) methods rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation process therefore leading to poor scalability in practical re-id applications. To overcome this fundamental limitation, we consider person re-identification without inter-camera identity association but only with identity labels independently annotated within each individual camera-view. This eliminates the most time-consuming and tedious inter-camera identity labelling process in order to significantly reduce the amount of human efforts required during annotation. It hence gives rise to a more scalable and more feasible learning scenario, which we call Intra-Camera Supervised (ICS) person re-id. Under this ICS setting with weaker label supervision, we formulate a Multi-Task Multi-Label (MTML) deep learning method. Given no inter-camera association, MTML is specially designed for self-discovering the inter-camera identity correspondence. This is achieved by inter-camera multi-label learning under a joint multi-task inference framework. In addition, MTML can also efficiently learn the discriminative re-id feature representations by fully using the available identity labels within each camera-view. Extensive experiments demonstrate the performance superiority of our MTML model over the state-of-the-art alternative methods on three large-scale person re-id datasets in the proposed intra-camera supervised learning setting., Comment: 9 pages, 3 figures, accepted by ICCV Workshop on Real-World Recognition from Low-Quality Images and Videos, 2019

Published
2019

13. Unsupervised Tracklet Person Re-Identification

Author

Li, Minxian, Zhu, Xiatian, and Gong, Shaogang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Most existing person re-identification (re-id) methods rely on supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability in a practical re-id deployment, due to the lack of exhaustive identity labelling of positive and negative image pairs for every camera-pair. In this work, we present an unsupervised re-id deep learning approach. It is capable of incrementally discovering and exploiting the underlying re-id discriminative information from automatically generated person tracklet data end-to-end. We formulate an Unsupervised Tracklet Association Learning (UTAL) framework. This is by jointly learning within-camera tracklet discrimination and cross-camera tracklet association in order to maximise the discovery of tracklet identity matching both within and across camera views. Extensive experiments demonstrate the superiority of the proposed model over the state-of-the-art unsupervised learning and domain adaptation person re-id methods on eight benchmarking datasets., Comment: Accepted to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence. The new dataset is publicly available at https://github.com/liminxian/DukeMTMC-SI-Tracklet

Published
2019

15. Unsupervised Person Re-identification by Deep Learning Tracklet Association

Author

Li, Minxian, Zhu, Xiatian, and Gong, Shaogang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Mostexistingpersonre-identification(re-id)methods relyon supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability in practical re-id deployment due to the lack of exhaustive identity labelling of image positive and negative pairs for every camera pair. In this work, we address this problem by proposing an unsupervised re-id deep learning approach capable of incrementally discovering and exploiting the underlying re-id discriminative information from automatically generated person tracklet data from videos in an end-to-end model optimisation. We formulate a Tracklet Association Unsupervised Deep Learning (TAUDL) framework characterised by jointly learning per-camera (within-camera) tracklet association (labelling) and cross-camera tracklet correlation by maximising the discovery of most likely tracklet relationships across camera views. Extensive experiments demonstrate the superiority of the proposed TAUDL model over the state-of-the-art unsupervised and domain adaptation re- id methods using six person re-id benchmarking datasets., Comment: ECCV 2018 Oral

Published
2018

22. Correction: CEPC Technical Design Report: Accelerator

Author

Abdallah, Waleed, de Freitas, Tiago CarlosAdorno, Afanaciev, Konstantin, Ahmad, Shakeel, Ahmed, Ijaz, Ai, Xiaocong, Aleem, Abid, Altmannshofer, Wolfgang, Alves, Fabio, An, Weiming, An, Rui, Anderle, Daniele Paolo, Antusch, Stefan, Arai, Yasuo, Arbuzov, Andrej, Arhrib, Abdesslam, Ashry, Mustafa, Bai, Sha, Bai, Yu, Bai, Yang, Bairathi, Vipul, Balazs, Csaba, Bambade, Philip, Ban, Yong, Bandyopadhyay, Triparno, Bao, Shou-Shan, Barber, Desmond P., Bat, Ays¸e, Batozskaya, Varvara, Behera, Subash Chandra, Belyaev, Alexander, Bertucci, Michele, Bi, Xiao-Jun, Bi, Yuanjie, Bian, Tianjian, Bianchi, Fabrizio, Bieko¨tter, Thomas, Biglietti, Michela, Bilanishvili, Shalva, Binglin, Deng, Bodrov, Denis, Bogomyagkov, Anton, Bondarenko, Serge, Boogert, Stewart, Boonekamp, Maarten, Borri, Marcello, Bosotti, Angelo, Boudry, Vincent, Boukidi, Mohammed, Boyko, Igor, Bozovic, Ivanka, Bozzi, Giuseppe, Brient, Jean-Claude, Budzinskaya, Anastasiia, Bukhari, Masroor, Bytev, Vladimir, Cacciapaglia, Giacomo, Cai, Hua, Cai, Wenyong, Cai, Wujun, Cai, Yijian, Cai, Yizhou, Cai, Yuchen, Cai, Haiying, Cai, Huacheng, Calibbi, Lorenzo, Cang, Junsong, Cao, Guofu, Cao, Jianshe, Chance, Antoine, Chang, Xuejun, Chang, Yue, Chang, Zhe, Chang, Xinyuan, Chao, Wei, Chatrabhuti, Auttakit, Che, Yimin, Che, Yuzhi, Chen, Bin, Chen, Danping, Chen, Fuqing, Chen, Fusan, Chen, Gang, Chen, Guoming, Chen, Hua-Xing, Chen, Huirun, Chen, Jinhui, Chen, Ji-Yuan, Chen, Kai, Chen, Mali, Chen, Mingjun, Chen, Mingshui, Chen, Ning, Chen, Shanhong, Chen, Shanzhen, Chen, Shao-Long, Chen, Shaomin, Chen, Shiqiang, Chen, Tianlu, Chen, Wei, Chen, Xiang, Chen, Xiaoyu, Chen, Xin, Chen, Xun, Chen, Xurong, Chen, Ye, Chen, Ying, Chen, Yukai, Chen, Zelin, Chen, Zilin, Chen, Gang, Chen, Boping, Chen, Chunhui, Cheng, Hok Chuen, Cheng, Huajie, Cheng, Shan, Cheng, Tongguang, Chi, Yunlong, Chimenti, Pietro, Chiu, Wen Han, Cho, Guk, Chu, Ming-Chung, Chu, Xiaotong, Chu, Ziliang, Coloretti, Guglielmo, Crivellin, Andreas, Cui, Hanhua, Cui, Xiaohao, Cui, Zhaoyuan, D’Anzi, Brunella, Dai, Ling-Yun, Dai, Xinchen, Dai, Xuwen, De Maria, Antonio, De Filippis, Nicola, De La Taille, Christophe, De Mori, Francesca, De Sio, Chiara, Del Core, Elisa, Deng, Shuangxue, Deng, Wei-Tian, Deng, Zhi, Deng, Ziyan, Dev, Bhupal, Dewen, Tang, Di Micco, Biagio, Ding, Ran, Ding, Siqin, Ding, Yadong, Dong, Haiyi, Dong, Jianing, Dong, Jing, Dong, Lan, Dong, Mingyi, Dong, Xu, Dong, Yipei, Dong, Yubing, Dordevic, Milos, Drewes, Marco, Du, Mingxuan, Du, Mingxuan, Du, Qianqian, Du, Xiaokang, Du, Yanyan, Du, Yong, Du, Yunfei, Duan, Chun-Gui, Duan, Zhe, Dydyshka, Yahor, Egede, Ulrik, Elmetenawee, Walaa, Eo, Yun, Fan, Ka Yan, Fan, Kuanjun, Fan, Yunyun, Fang, Bo, Fang, Shuangshi, Fang, Yuquan, Farilla, Ada, Farinelli, Riccardo, Farooq, Muhammad, Golfe, Angeles Faus, Fazliakhmetov, Almaz, Fei, Rujun, Feng, Bo, Feng, Chong, Feng, Junhua, Feng, Xu, Feng, Zhuoran, ZhuoranFeng, Castillo, Luis Roberto Flores, Forest, Etienne, Fowlie, Andrew, Fox, Harald, Fu, Hai-Bing, Fu, Jinyu, Fuks, Benjamin, Funakoshi, Yoshihiro, Gabrielli, Emidio, Gan, Nan, Gang, Li, Gao, Jie, Gao, Meisen, Gao, Wenbin, Gao, Wenchun, Gao, Yu, Gao, Yuanning, Gao, Zhanxiang, Gao, Yanyan, Ge, Kun, Ge, Shao-Feng, Ge, Zhenwu, Geng, Li-Sheng, Geng, Qinglin, Geng, Chao-Qiang, Ghosh, Swagata, Gioiosa, Antonio, Gladilin, Leonid, Gong, Ti, Gori, Stefania, Gou, Quanbu, Grinstein, Sebastian, Gu, Chenxi, Guillermo, Gerardo, da Costa, Joao Guimaraes, Guo, Dizhou, Guo, Fangyi, Guo, Jiacheng, Guo, Jun, Guo, Lei, Guo, Lei, Guo, Xia, Guo, Xin-Heng, Guo, Xinyang, Guo, Yun, Guo, Yunqiang, Guo, Yuping, Guo, Zhi-Hui, Gutie´rrez-Rodríguez, Alejandro, Ha, Seungkyu, Habib, Noman, Hajer, Jan, Hammer, Francois, Han, Chengcheng, Han, Huayong, Han, Jifeng, Han, Liang, Han, Liangliang, Han, Ruixiong, Han, Yang, Han, Yezi, Han, Yuanying, Han, Tao, Hao, Jiankui, Hao, Xiqing, XiqingHao, He, Chuanqi, He, Dayong, He, Dongbing, He, Guangyuan, He, Hong-Jian, He, Jibo, He, Jun, He, Longyan, He, Xiang, He, Xiao-Gang, He, Zhenqiang, Heinemann, Klaus, Heinemeyer, Sven, Heng, Yuekun, Herna´ndez-Ruíz, María A., Hong, Jiamin, Hor, Yuenkeung, Hou, George W. S., Hou, Xiantao, Hou, Xiaonan, Hou, Zhilong, Hou, Suen, Hu, Caishi, Hu, Chen, Hu, Dake, Hu, Haiming, Hu, Jiagen, Hu, Jun, Hu, Kun, Hu, Shouyang, Hu, Yongcai, Hu, Yu, Hu, Zhen, Hua, Zhehao, Hua, Jianfei, Huang, Chao-Shang, Huang, Fa Peng, Huang, Guangshun, Huang, Jinshu, Huang, Ke, Huang, Liangsheng, Huang, Shuhui, Huang, Xingtao, Huang, Xu-Guang, Huang, Yanping, Huang, Yonggang, Huang, Yongsheng, Huang, Zimiao, Huanyuan, Chen, Huh, Changgi, Hui, Jiaqi, Huo, Lihua, Hussain, Talab, Hwang, Kyuyeong, Ioannisian, Ara, Iqbal, Munawar, Jackson, Paul, Jafarzade, Shahriyar, Jang, Haeun, Jang, Seoyun, Ji, Daheng, Ji, Qingping, Ji, Quan, Ji, Xiaolu, Jia, Jingguang, Jia, Jinsheng, Jia, Xuewei, Jia, Zihang, Jiang, Cailian, Jiang, Han Ren, Jiang, Houbing, Jiang, Jun, Jiang, Xiaowei, Jiang, Xin, Jiang, Xuhui, Jiang, Yongcheng, Jiang, Zhongjian, Jiang, Cheng, Jiao, Ruiqi, Jin, Dapeng, Jin, Shan, Jin, Song, Jin, Yi, Jis, Junji, Jung, Sunghoon, Kacarevic, Goran, Kajfasz, Eric, Kalinovskaya, Lidia, Kampf, Aleksei, Kang, Wen, Kang, Xian-Wei, Kang, Xiaolin, Karmakar, Biswajit, Ke, Zhiyong, Keloth, Rijeesh, Khan, Alamgir, Khanpour, Hamzeh, Khosonthongkee, Khanchai, KhanchaiKhosonthongkee, Kim, Bobae, Kim, Dongwoon, Kim, Mi Ran, Kim, Minsuk, Kim, Sungwon, Kim, On, Klasen, Michael, Ko, Sanghyun, Koop, Ivan, Kornienko, Vitaliy, Kortman, Bryan, Kozlov, Gennady, Kuang, Shiqing, Kumar, Mukesh, Kuo, Chia Ming, Kwok, Tsz Hong, Lagarde, Franc¸ois Sylvain Ren, Lai, Pei-Zhu, Laktineh, Imad, Lan, Xiaofei, Lan, Zuxiu, Lavezzi, Lia, Lee, Justin, Lee, Junghyun, Lee, Sehwook, Lei, Ge, Lemmon, Roy, Leng, Yongxiang, Leung, Sze Ching, Li, Hai Tao, Li, Bingzhi, Li, Bo, Li, Bo, Li, Changhong, Li, Chao, Li, Cheng, Li, Cheng, Li, Chunhua, Li, Cui, Li, Dazhang, Li, Dikai, Li, Fei, Li, Gang, Li, Gang, Li, Gang, Li, Gaosong, Li, Haibo, Li, Haifeng, Li, Hai-Jun, Li, Haotian, Li, Hengne, Li, Honglei, Li, Huijing, Li, Jialin, Li, Jingyi, Li, Jinmian, Li, Jun, Li, Leyi, Li, Liang, Li, Ling, Li, Mei, Li, Meng, Li, Minxian, Li, Pei-Rong, Li, Qiang, Li, Shaopeng, Li, Shenghe, Li, Shu, Li, Shuo, Li, Teng, Li, Tiange, Li, Tong, Li, Weichang, Li, Weidong, Li, Wenjun, Li, Xiaoling, Li, Xiaomei, Li, Xiaonan, Li, Xiaoping, Li, Xiaoting, Li, Xin, Li, Xinqiang, Li, Xuekang, Li, Yang, Li, Yanwei, Li, Yiming, Li, Ying, Li, Ying-Ying, Li, Yonggang, Li, Yonglin, Li, Yufeng, Li, Yuhui, Li, Zhan, Li, Zhao, Li, Zhiji, Li, Tong, Li, Lingfeng, Li, Fei, Liang, Jing, Liang, Jinhan, Liang, Zhijun, Liao, Guangrui, Liao, Hean, Liao, Jiajun, Liao, Libo, Liao, Longzhou, Liao, Yi, Liao, Yipu, Limphirat, Ayut, AyutLimphirat, Lin, Tao, Lin, Weiping, Lin, Yufu, Lin, Yugen, Liu, Beijiang, Liu, Bo, Liu, Danning, Liu, Dong, Liu, Fu-Hu, Liu, Hongbang, Liu, Huangcheng, Liu, Hui, Liu, Huiling, Liu, Jia, Liu, Jia, Liu, Jiaming, Liu, Jianbei, Liu, Jianyi, Liu, Jingdong, Liu, Jinhua, Liu, Kai, Liu, Kang, Liu, Kun, Liu, Mengyao, Liu, Peng, Liu, Pengcheng, Liu, Qibin, Liu, Shan, Liu, Shidong, Liu, Shuang, Liu, Shubin, Liu, Tao, Liu, Tao, Liu, Tong, Liu, Wei, Liu, Xiang, Liu, Xiao-Hai, Liu, Xiaohui, Liu, Xiaoyu, Liu, Xin, Liu, Xinglin, Liu, Xingquan, Liu, Yang, Liu, Yanlin, Liu, Yao-Bei, Liu, Yi, Liu, Yiming, Liu, Yong, Liu, Yonglu, Liu, Yu, Liu, Yubin, Liu, Yudong, Liu, Yulong, Liu, Zhaofeng, Liu, Zhen, Liu, Zhenchao, Liu, Zhi, Liu, Zhi-Feng, Liu, Zhiqing, Liu, Zhongfu, Liu, Zuowei, Liu, Mia, Liu, Zhen, Liu, Xiaoyang, Lou, Xinchou, Lu, Cai-Dian, Lu, Jun-Xu, Lu, Qiu Zhen, Lu, Shang, Lu, Shang, Lu, Wenxi, Lu, Xiaohan, Lu, Yunpeng, Lu, Zhiyong, Lu, Xianguo, Lu, Wei, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Lukanov, Arslan, Luo, Jinliang, Luo, Tao, Luo, xiaoan, Luo, Xiaofeng, Luo, Xiaolan, Lv, Jindong, Lyu, Feng, Lyu, Xiao-Rui, Lyu, Kun-Feng, Ma, Ande, Ma, Hong-Hao, Ma, Jun-Li, Ma, Kai, Ma, Lishuang, Ma, Na, Ma, Renjie, Ma, Weihu, Ma, Xinpeng, Ma, Yanling, Ma, Yan-Qing, Ma, Yongsheng, Ma, Zhonghui, Ma, Zhongjian, Ma, Yang, Maity, Mousam, Mao, Lining, Mao, Yanmin, Mao, Yaxian, Martens, Aure´lien, Maria, Caccia Massimo Luigi, Matsumoto, Shigeki, Mellado, Bruce, Meloni, Davide, Men, Lingling, Meng, Cai, Meng, Lingxin, Mi, Zhenghui, Miao, Yuhui, Migliorati, Mauro, Ming, Lei, Mitsou, Vasiliki A., Monaco, Laura, Moraes, Arthur, Mosala, Karabo, Moursy, Ahmad, Mu, Lichao, Mu, Zhihui, Muchnoi, Nickolai, Muenstermann, Daniel, Muenstermann, Daniel, Munbodh, Pankaj, Murray, William John, Nanni, Jérôme, Nanzanov, Dmitry, Nie, Changshan, Nikitin, Sergei, Ning, Feipeng, Ning, Guozhu, Niu, Jia-Shu, Niu, Juan-Juan, Niu, Yan, Nkadimeng, Edward Khomotso, Ohmi, Kazuhito, Oide, Katsunobu, Okawa, Hideki, Ouchemhou, Mohamed, Ouyang, Qun, Paesani, Daniele, Pagani, Carlo, Paganis, Stathes, Pakuza, Collette, Pan, Jiangyang, Pan, Juntong, Pan, Tong, Pan, Xiang, Panda, Papia, Pandey, Saraswati, Pandurovic, Mila, Paparella, Rocco, Pasechnik, Roman, Passemar, Emilie, Pei, Hua, Peng, Xiaohua, Peng, Xinye, Peng, Yuemei, Ping, Jialun, Ping, Ronggang, Adhya, Souvik Priyam, Qi, Baohua, Qi, Hang, Qi, Huirong, Qi, Ming, Qian, Sen, Qian, Zhuoni, Qiao, Congfeng, Qin, Guangyou, Qin, Jiajia, Qin, Laishun, Qin, Liqing, Qin, Qin, Qin, Xiaoshuai, Qin, Zhonghua, Qu, Guofeng, Racioppi, Antonio, Ramsey-Musolf, Michael, Raza, Shabbar, Rekovic, Vladimir, Ren, Jing, Reuter, Ju¨rgen, Robens, Tania, Rossi, Giancarlo, Ruan, Manqi, Ruan, Manqi, Rumyantsev, Leonid, Ryu, Min Sang, Sadykov, Renat, Sang, Minjing, Sanz-Cillero, Juan Jose´, Saur, Miroslav, Savla, Nishil, Schmidt, Michael A., Sertore, Daniele, Settles, Ron, Sha, Peng, Shao, Ding-Yu, Shao, Ligang, Shao, Hua-Sheng, She, Xin, Shen, Chuang, Shen, Hong-Fei, Shen, Jian-Ming, Shen, Peixun, Shen, Qiuping, Shen, Zhongtao, Sheng, Shuqi, Shi, Haoyu, Shi, Hua, Shi, Qi, Shi, Shusu, Shi, Xiaolei, Shi, Xin, Shi, Yukun, Shi, Zhan, Shipsey, Ian, Shiu, Gary, Shu, Chang, Si, Zong-Guo, Sidorenkov, Andrei, Smiljanić, Ivan, Song, Aodong, Song, Huayang, Song, Jiaojiao, Song, Jinxing, Song, Siyuan, Song, Weimin, Song, Weizheng, Song, Zhi, Sourav, Shashwat, Spruzzola, Paolo, Su, Feng, Su, Shengsen, Su, Wei, Su, Shufang, Sui, Yanfeng, Sui, Zexuan, Sullivan, Michael, Sun, Baiyang, Sun, Guoqiang, Sun, Hao, Sun, Hao-Kai, Sun, Junfeng, Sun, Liang, Sun, Mengcheng, Sun, Pengfei, Sun, Sichun, Sun, Xianjing, Sun, Xiaohu, Sun, Xilei, Sun, Xingyang, Sun, Xin-Yuan, Sun, Yanjun, Sun, Yongzhao, Sun, Yue, Sun, Zheng, Sun, Zheng, Suwonjandee, Narumon, Eldin, Elsayed Tag, Tan, Biao, Tang, Bo, Tang, Chuanxiang, Tang, Gao, Tang, Guangyi, Tang, Jian, Tang, Jingyu, Tang, Liang, Tang, Ying’Ao, Tao, Junquan, Tawfik, Abdel Nasser, Taylor, Geoffrey, Telnov, Valery, Tian, Saike, Torre, Riccardo, Trzaska, Wladyslaw Henryk, Tsybychev, Dmitri, Tu, Yanjun, Tuo, Shengquan, Tytgat, Michael, Islam, Ghalib Ul, Ushakov, Nikita, Valencia, German, Velthuis, Jaap, Vicini, Alessandro, Vickey, Trevor, Vidakovic, Ivana, Videau, Henri, Volkas, Raymond, Voronin, Dmitry, Vukasinovic, Natasa, Wan, Xia, Wan, Xuying, Wang, Xiao, Wang, Anqing, Wang, Bin, Wang, Chengtao, Wang, Chuanye, Wang, Ci, Wang, Dayong, Wang, Dou, Wang, En, Wang, Fei, Wang, Fei, Wang, Guanwen, Wang, Guo-Li, Wang, Haijing, Wang, Haolin, Wang, Jia, Wang, Jian, Wang, Jianchun, Wang, Jianli, Wang, Jiawei, Wang, Jin, Wang, Jin-Wei, Wang, Joseph, Wang, Kechen, Wang, Lechun, Wang, Lei, Wang, Liguo, Wang, Lijiao, Wang, Lu, Wang, Meng, Wang, Na, Wang, Pengcheng, Wang, Qian, Wang, Qun, Wang, Shu Lin, Wang, Shudong, Wang, Taofeng, Wang, Tianhong, Wang, Tianyang, Wang, Tong, Wang, Wei, Wang, Wei, Wang, Xiaolong, Wang, Xiaolong, Wang, Xiaoning, Wang, Xiao-Ping, Wang, Xiongfei, Wang, Xujian, Wang, Yaping, Wang, Yaqian, Wang, Yi, Wang, Yiao, Wang, Yifang, Wang, Yilun, Wang, Yiwei, Wang, You-Kai, Wang, Yuanping, Wang, Yuexin, Wang, Yuhao, Wang, Yu-Ming, Wang, Yuting, Wang, Zhen, Wang, Zhigang, Wang, Weiping, Wang, Zeren Simon, Wang, Biao, Wang, Hui, Wang, Lian-Tao, Wang, Zihui, Wang, Zirui, Wang, Jia, Wang, Tong, Wei, Daihui, Wei, Shujun, Wei, Wei, Wei, Xiaomin, Wei, Yuanyuan, Wei, Yingjie, Wen, Liangjian, Wen, Xuejun, Wen, Yufeng, White, Martin, Williams, Peter, Wolffs, Zef, Womersley, William John, Wu, Baona, Wu, Bobing, Wu, Guanjian, Wu, Jinfei, Wu, Lei, Wu, Lina, Wu, Linghui, Wu, Minlin, Wu, Peiwen, Wu, Qi, Wu, Qun, Wu, Tianya, Wu, Xiang, Wu, Xiaohong, Wu, Xing-Gang, Wu, Xuehui, Wu, Yaru, Wu, Yongcheng, Wu, Yuwen, Wu, Zhi, Wu, Xin, Xia, Lei, Xia, Ligang, Xia, Shang, Xiang, Benhou, Xiang, Dao, Xiang, Zhiyu, Xiao, Bo-Wen, Xiao, Chu-Wen, Xiao, Dong, Xiao, Guangyan, Xiao, Han, Xiao, Meng, Xiao, Ouzheng, Xiao, Rui-Qing, Xiao, Xiang, Xiao, Yichen, Xiao, Ying, Xiao, Yu, Xiao, Yunlong, Xiao, Zhenjun, Xiao, Hengyuan, Xie, Nian, Xie, Yuehong, Xin, Tianmu, Xing, Ye, Xing, Zhizhong, Xu, Da, Xu, Fang, Xu, Fanrong, Xu, Haisheng, Xu, Haocheng, Xu, Ji, Xu, Miaofu, Xu, Qingjin, Xu, Qingnian, Xu, Wei, Xu, Wei, Xu, Weixi, Xu, Xinping, Xu, Zhen, Xu, Zijun, Xu, Zehua, Xu, Yaoyuan, Xue, Feifei, Yan, Baojun, Yan, Bin, Yan, Fen, Yan, Fucheng, Yan, Jiaming, Yan, Liang, Yan, Luping, Yan, Qi-Shu, Yan, Wenbiao, Yan, Yupeng, Yan, Luping, Yan, Haoyue, Yang, Dong, Yang, Fengying, Yang, Guicheng, Yang, Haijun, Yang, Jin Min, Yang, Jing, Yang, Lan, Yang, Li, Yang, Li Lin, Yang, Lili, Yang, Litao, Yang, Mei, Yang, Qiaoli, Yang, Tiansen, Yang, Xiaochen, Yang, Yingjun, Yang, Yueling, Yang, Zhengyong, Yang, Zhenwei, Yang, Youhua, Yang, Xiancong, Yao, De-Liang, Yao, Shi, Ye, Lei, Ye, Lingxi, Ye, Mei, Ye, Rui, Ye, Rui, Ye, Yecheng, Yermolchyk, Vitaly, Yi, Kai, Yi, Li, Yi, Yang, Yin, Di, Yin, Peng-Fei, Yin, Shenghua, Yin, Ze, Yin, Zhongbao, Yinhong, Zhang, Yoo, Hwi Dong, You, Zhengyun, Young, Charles, Yu, Boxiang, Yu, Chenghui, Yu, Fusheng, Yu, Jie-Sheng, Yu, Jinqing, Yu, Lingda, Yu, Zhao-Huan, Yu, Felix, Yu, Bingrong, Yuan, Changzheng, Yuan, Li, Yuan, Xing-Bo, Yuan, Youjin, Yue, Junhui, Yue, Qian, Yue, Baobiao, Zaib, Un Nisa, Zanzottera, Riccardo, Zeng, Hao, Zeng, Ming, Zhai, Jian, Zhai, Jiyuan, Zhai, Xin Zhe, Zhan, Xi-Jie, Zhang, Ben-Wei, Zhang, Bolun, Zhang, Di, Zhang, Guangyi, Zhang, Hao, Zhang, Hong-Hao, Zhang, Huaqiao, Zhang, Hui, Zhang, Jialiang, Zhang, Jianyu, Zhang, Jianzhong, Zhang, Jiehao, Zhang, Jielei, Zhang, Jingru, Zhang, Jinxian, Zhang, Junsong, Zhang, Junxing, Zhang, Lei, Zhang, Lei, Zhang, Liang, Zhang, Licheng, Zhang, Liming, Zhang, Linhao, Zhang, Luyan, Zhang, Mengchao, Zhang, Rao, Zhang, Shulei, Zhang, Wan, Zhang, Wenchao, Zhang, Xiangzhen, Zhang, Xiaomei, Zhang, Xiaoming, Zhang, Xiaoxu, Zhang, Xiaoyu, Zhang, Xuantong, Zhang, Xueyao, Zhang, Yang, Zhang, Yang, Zhang, Yanxi, Zhang, Yao, Zhang, Ying, Zhang, Yixiang, Zhang, Yizhou, Zhang, Yongchao, Zhang, Yu, Zhang, Yuan, Zhang, Yujie, Zhang, Yulei, Zhang, Yumei, Zhang, Yunlong, Zhang, Zhandong, Zhang, Zhaoru, Zhang, Zhen-Hua, Zhang, Zhenyu, Zhang, Zhichao, Zhang, Zhi-Qing, Zhang, Zhuo, Zhang, Zhiqing, Zhang, Cong, Zhang, Tianliang, Zhang, Luyan, Zhao, Guang, Zhao, Hongyun, Zhao, Jie, Zhao, Jingxia, Zhao, Jingyi, Zhao, Ling, Zhao, Luyang, Zhao, Mei, Zhao, Minggang, Zhao, Mingrui, Zhao, Qiang, Zhao, Ruiguang, Zhao, Tongxian, Zhao, Yaliang, Zhao, Ying, Zhao, Yue, Zhao, Zhiyu, Zhao, Zhuo, Zhemchugov, Alexey, Zheng, Hongjuan, Zheng, Jinchao, Zheng, Liang, Zheng, Ran, zheng, shanxi, Zheng, Xu-Chang, Zhile, Wang, Zhong, Weicai, Zhong, Yi-Ming, Zhou, Chen, Zhou, Daicui, Zhou, Jianxin, Zhou, Jing, Zhou, Jing, Zhou, Ning, Zhou, Qi-Dong, Zhou, Shiyu, Zhou, Shun, Zhou, Sihong, Zhou, Xiang, Zhou, Xingyu, Zhou, Yang, Zhou, Yong, Zhou, Yu-Feng, Zhou, Zusheng, Zhou, Demin, Zhu, Dechong, Zhu, Hongbo, Zhu, Huaxing, Zhu, Jingya, Zhu, Kai, Zhu, Pengxuan, Zhu, Ruilin, Zhu, Xianglei, Zhu, Yingshun, Zhu, Yongfeng, Zhuang, Xiao, Zhuang, Xuai, Zobov, Mikhail, Zong, Zhanguo, Zou, Cong, and Zou, Hongying

Published
2024
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29. Design and Progress of Mechanical Support in HEPS

Author

Li, Chunhua, Jiao, Yi, Li, Minxian, Wang, Haijing, Wang, Zihao, Wu, Lei, and Zhou, Ningchuang

Subjects
MC7: Accelerator Technology, Physics::Accelerator Physics, Accelerator Physics
Abstract

HEPS is a new generation synchrotron facility on construction with very low emittance. Stringent requirements are proposed to the design of mechanical support. The alignment error between girders should be less than 50μm. Based on that, the adjusting resolution of the girder are required to be less than 5μm in both transverse and vertical directions. Besides, the Eigen frequency of magnet girder assembly should be higher than 54Hz to avoid the amplification of ground vibrations. To fulfill these requirements, studies on mechanical support design is now being carried out in HEPS. This paper will describe the design and progress of those work., Proceedings of the 10th Int. Particle Accelerator Conf., IPAC2019, Melbourne, Australia

Published
2019
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30. Experimental research on gassing characteristics of transformer oils under low thermal decomposition

Author

Li Minxian, Gao Kai, Pan Ying, Xu Peng, Deng Xianqin, Xiong Mingxiang, Peng Wei, and Xingde Huang

Subjects
chemistry.chemical_classification, Materials science, Transformer oil, Dissolved gas analysis, Thermal decomposition, Pulp and paper industry, law.invention, Hydrocarbon, Operating temperature, chemistry, law, Transformer, Distillation, Chemical composition
Abstract

The gas production of transformer oil A (Shell Diala BG) and transformer oil B (NYNAS 10XN) under normal operating temperature (70–140C) were tested by dissolved gas analysis (DGA). By analyzing the influence of different heating time and different temperature on gas production and gassing rate, the similarities and differences of gassing characteristics of transformer oil A and transformer oil B were summarized. Distillation range and hydrocarbon composition were detected to analyze the reasons for these differences of gassing characteristics. The results showed that chemical composition leads to the differences in thermal stability between transformers oil A and transformers oil B. Then the difference of gassing characteristics between transformer oil A and transformer oil B at 70–140C was obvious.

Published
2018
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31. Superconducting 16-Pole Wiggler for Beijing Electron-Positron Collider II

Author

Li, Minxian, Bian, Xiaojuan, Chen, Fu-San, Chen, Wan, Sun, Xianjing, Wang, Haijing, Wang, Jianli, Wang, Na, Xu, Miaofu, and Yang, Xiangchen

Subjects
07 Accelerator Technology, Physics::Accelerator Physics, T10 Superconducting Magnets, Accelerator Physics
Abstract

A superconducting 16-pole 2.6T wiggler with period 170mm of The High-Energy Photon Source and the Test Facility Project (HEPS-TF) designed and fabricating in the Institute of High Energy Physics (IHEP) in China is described. This wiggler will be installed in Beijing Electron-Positron Collider II (BEPCII). The main parameters and structure of the wiggler are presented. Besides, some vertical testing results are involved., Proceedings of the 9th Int. Particle Accelerator Conf., IPAC2018, Vancouver, BC, Canada

Published
2018
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38. Design and Thermal Analysis of ADS Beam Stop

Author

Li, Minxian, Wang, Jianli, and Wu, Lei

Subjects
Physics::Accelerator Physics, 7: Accelerator Technology, Accelerator Physics
Abstract

ADS beam stop is an important device which required for the commissioning and accelerator tests of Accelerator Driven Sub-critical System (ADS), it is used to stop the beam which power is about 100kW and consume energy of the beam. This paper will present a triangular prism structure of the ADS beam stop, its mechanical design is described in detail, and there are numerous grooves and ribs in the cooling plates which is the core component of the beam stop. The thermal analysis is performed and its result proves that the triangular prism structure meet the design requirement. Key words：Beam stop, ADS, thermal analysis, triangular prism structure., Proceedings of the 6th Int. Particle Accelerator Conf., IPAC2015, Richmond, VA, USA

Published
2015
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43. CEPC-SPPC Preliminary Conceptual Design Report. 1. Physics and Detector

Author

Ahmad, Muhammd, Alves, Daniele, An, Haipeng, An, Qi, Arhrib, Abdesslam, Arkani-Hamed, Nima, Ahmed, Ijaz, Bai, Yu, Ferroli, Rinaldo Baldini, Ban, Yong, Bartalini, Paolo, Benbrik, Rachid, Bi, Xiaojun, Bigi, Ikaros, Bai, Yang, Bramante, Joseph, Balagura, Vladislav, Boudry, Vincent, Brient, Jean Claude, Cai, Yifu, Cai, Hao, Cao, Zhen, Cao, Junjie, Chang, Yu-Hsiang, Chang, Yuan-Hann, Chang, We-Fu, Chang, Paoti, Chang, Chiu-Ping, Chang, Ningbo, Chang, Qin, Chao, Yuan, Chen, Jiunn-Wei, Chen, Shaolong, Chen, Ye, Chen, Mingshui, Chen, Guoming, Chen, Xuewen, Chen, Shanzhen, Chen, Zhenxing, Chen, Shenjian, Chen, Xurong, Chen, Xun, Chen, Shaomin, Chen, Mingjun, Chen, Shiyong, Chen, Ning, Chen, Gang, Chen, Shi, Cheng, Hai-Yang, Cheng, Shan, Cheng, Tongguang, Chiang, Cheng-Wei, Choi, Kiwoon, Chu, Wai Cheong, Chua, Chun-Khiang, Chui, Kris, Cohen, Timothy, Craig, Nathaniel, Curtin, David, D Agnolo, Raffaele Tito, Dai, Jianping, Deng, Zhi, Deng, Weitian, Ding, Hengtong, Ding, Xuefeng, Ding, Ran, Ding, Yadong, Dong, Mingyi, Dordevic, Milos, Elsayed, Medhat, Erler, Jens, Essig, Rouven, Everett, Lisa, Fan, Jiawei, Fan, Jiji, Fan, Jingzhou, Fang, Shuangshi, Fang, Yaquan, Fang, Xin, Fang, Wenxing, Feng, Cunfeng, Fischer, Oliver, Flores Castillo, Luis Roberto, Fu, Zaiwei, Fedderke, Michael A., Galloway, Jamison, Gao, Yuanning, Gao, Xuyang, Gao, Yonggui, Gao, Jie, Ge, Shaofeng, Geng, Chaoqiang, Geng, Lisheng, Gong, Guanghua, Gong, Li, Gong, Lingling, Gori, Stefania, Goshaw, Alfred, Gou, Quanbu, Gu, Jiayin, Gu, Yikun, Gu, Peihong, Guo, Lei, Guo, Wanlei, Guo, Jun, Guo, Yan, Haddad, Yacine, Hajer, Jan, Han, Yanliang, Han, Ran, Han, Tao, Han, Liang, Han, Shuang, Han, Jinzhong, Hance, Michael, Hao, Yongliang, Hao, Xiqing, He, Hong-Jian, He, Xiaogang, Heng, Yuekun, Hinchliffe, Ian, Hong, Daojin, Hou, Wei-Shu, Hou, Zhilong, Howe, Kiel, Hsiung, Yee Bob, Hu, Yu, Hu, Qingli, Hu, Tao, Huang, Chaoshang, Huang, Jinshu, Huang, Yanping, Huang, Xingtao, Huang, Guangshun, Hook, Anson, Ismail, Ahmed, Ji, Qingping, Jia, Duojie, Jia, Junji, Jia, Xuejun, Jiang, Yun, Jiang, Lili, Jiang, Luwen, Jiang, Xingyu, Jiao, Jianbin, Jin, Shan, Jin, Hongying, Jung, Sunghoon, Jyotishmati, Susmita, Kaiser, Markus, Kang, Xiaoshen, Kang, Xiaolin, Khoze, Valentin V., Kling, Felix, Koirala, Suman, Kong, Otto, Kotwal, Ashutosh, Kuo, Chia Ming, Kwok, Kevin, Kong, Shibei, Laktineh, Imad, Larkoski, Andrew, Lei, Guo, Li, Shu, Li, Ling-Fong, Li, Qiang, Li, Ying, Li, Minxian, Li, Kang, Li, Yufeng, Li, Haibo, Li, Zhao, Li, Xiaozhou, Li, Bo, Li, Ye, Li, Haijun, Li, Liang, Li, Xinqiang, Li, Weiguo, Li, Xiaoling, Li, Wenzhao, Li, Fei, Li, Hengne, Li, Junli, Li, Tianjun, Li, Cheng, Li, Yijie, Li, Honglei, Li, Shiyuan, Li, Gang, Li, Huijing, Li, Fengyun, Li, Lei, Li, Ke, Li, Hsiangnan, Li, Guannan, Li, Qiyun, Li, Yingying, Li, Xiaomei, Li, Dikai, Liang, Zhijun, Liao, Yi, Liao, Wei, Liao, Libo, Ling, Jiajie, Liu, Hui, Liu, Bing, Liu, Chuan, Liu, Zhen, Liu, Landiao, Liu, Yanlin, Liu, Tao, Liu, Yilin, Liu, Zhongxiu, Liu, Beijiang, Liu, Hongbang, Liu, Junyu, Liu, Jia, Liu, Jinyan, Xin Liu, Liu, Xiang, Liu, Jianbei, Liu, Peilian, Liu, Yanwen, Liu, Fuhu, Liu, Zhaofeng, Liu, Hu, Liu, Ning, Liu, Rong, Liu, Qian, Lo, Cheuk Yee, Lou, Xinchou, Lou, Hou Keong, Low, Ian, Low, Matthew, Lu, Caidian, Lu, Weiguo, Lu, Yunpeng, Luo, Shu, Luo, Xiaofeng, Luo, Tao, Lyu, Lanchun, Lyu, Xiaorui, Lin, Tongyan, Ma, Guoliang, Ma, Ernest, Ma, Lianliang, Ma, Wengan, Ma, Binsong, Ma, Boqiang, Ma, Hailong, Ma, Mingming, Mao, Yajun, Mccullough, Matthew, Meade, Patrick, Meng, Xiangwei, Min, Tianjue, Mo, Xin, Murray, William, Ning, Feipeng, Padhi, Sanjay, Paganis, Stathes, Pang, Longgang, Passemar, Emilie, Peng, Cong, Peters, Klaus, Ping, Ronggang, Prokofiev, Kirill, Perez, Gilad, Qi, Huirong, Qi, Ming, Qi, Hongrong, Qian, Jianming, Qiao, Congfeng, Qin, Nian, Qin, Guangyou, Qin, Zhonghua, Qin, Liqing, Ramsey-Musolf, Michael, Reece, Matthew, Ren, Jing, Rompotis, Nikolaos, Ruan, Xifeng, Ruan, Manqi, Ruderman, Joshua, Ruiz, Richard, Sayre, Joshua, Shaheen, Sarmad Masood, Shelton, Jessie, Shen, Yuqiao, Shen, Chengping, Shiu, Jingge, Shiu, Gary, Shiu, Fu Hong John, Shu, Jing, Si, Zongguo, Song, Weimin, Song, Xinying, Song, Huichao, Su, Shufang, Sujeet, Akula, Sun, Yueling, Sun, Sichun, Sun, Yanjun, Sun, Hao, Sun, Xilei, Tandean, Jusak, Tang, Zhicheng, Tao, Junquan, Thaler, Jesse, Thalapillil, Arun, Tseng, Shihyen, Tu, Yanjun, Tweedie, Brock, Ker, Jay G. Wac, Wang, Minzu, Wang, Bin, Wang, Yong, Wang, Kechen, Wang, Tianhong, Wang, Mengmeng, Wang, Shaoming, Wang, Joseph, Wang, Zhe, Wang, Wei, Wang, Yiwei, Wang, Haiyun, Wang, Jin, Wang, Chi, Wang, Xiaoping, Wang, Feng, Wang, Liantao, Wang, Zhiyong, Wang, Yuchen, Wang, Xiaoyun, Wang, Qun, Wang, Zheng, Wang, Xinnian, Wang, Yue, Wang, Wenyu, Wang, Fei, Wang, Zongyuan, Wang, Dayong, Wang, Rongkun, Wang, Weiping, Wang, Binlong, Wang, Meng, Wang, Zirui, Wang, Chengtao, Wang, Xiongfei, Wang, Xueqiang, Wang, Xiaolong, Wang, Siguang, Wang, Meifen, Wei, Yuqian, Wen, Shuopin, Westhoff, Susanne, Wong, Sung Ching Sam, Wu, Xinggang, Wu, Xiaohong, Wu, Wenjing, Wu, Lei, Wu, Linghui, Wu, Yusheng, Xiang, Qianfei, Xianyu, Zhong-Zhi, Xiao, Zhenjun, Xiao, Ming, Xiao, Ruiqing, Xiao, Bowen, Xie, Yuehong, Xie, Yuguang, Xing, Zhi-Zhong, Xiu, Qinglei, Xu, Fanrong, Xu, Qingjun, Xu, Lailin, Xu, Da, Xu, Nu, Xu, Yin, Xu, Haojie, Xu, Xunjie, Xia, Lei, Yan, Wenbiao, Yan, Qishu, Yan, Tian, Yan, Fang, Yang, Yueling, Yang, Haijun, Yang, Xuan, Yang, Jinmin, Yang, Liu, Yang, Bingfang, Yang, Zhongjuan, Yang, Huan, Yao, Weiming, Yao, Weichao, Ye, Mei, Ye, Jingbo, Yin, Pengfei, Yin, Zhongbao, Yin, Furong, Yin, Junhao, You, Zhengyun, Yu, Fusheng, Yu, Tientien, Yu, Felix, Yu, Chunxu, Yu, Boxiang, Yu, Zhaohuan, Yuan, Li, Yuan, Changzheng, Yuan, Ye, Yue, Chongxing, Yue, Qian, Zhang, Yue, Zhang, Lei, Zhang, Jiawen, Zhang, Renyou, Zhang, Chen, Zhang, Yumei, Zhang, Yulian, Zhang, Jianhui, Zhang, Yu, Zhang, Xiaomei, Zhang, Yi, Zhang, Wenchao, Zhang, Zhiqing, Zhang, Xueyao, Zhang, Huaqiao, Zhang, Yujie, Zhang, Jianyong, Zhang, Benwei, Zhang, Hanzhong, Zhang, Zhenyu, Zhang, Yanxi, Zhang, Chi, Zhang, Jielei, Zhang, Bingxin, Zhang, Zhenghao, Zhang, Peng, Zhang, Kai, Zhang, Honghao, Zhang, Guoqing, Zhang, Hao, Zhao, Minggang, Zhao, Zhenhua, Zhao, Zhengguo, Zhao, Jie, Zhao, Ling, Zhao, Wei, Zhao, Hang, Zhen, Minshan, Zheng, Liang, Zheng, Bo, Zheng, Yajuan, Zhong, Yong, Zhou, Hang, Zhou, Bing, Zhou, Shun, Zhou, Yeling, Zhou, Siyi, Zhou, Yufeng, Zhou, Ning, Zhou, Xiang, Zhou, Xiaorong, Zhu, Zian, Zhu, Hongbo, Zhu, Kai, Zhu, Chengguang, Zhu, Ruilin, Zhu, Jianhui, Zhu, Kejun, Zhu, Junjie, Zhu, Qianwen, Zhu, Xiangrong, Zhuang, Xuai, and Zupan, Jure

44. CEPC Conceptual Design Report: Volume 2 - Physics & Detector

Author

Guimarães Da Costa, João Barreiro, Gao, Yuanning, Jin, Shan, Qian, Jianming, Tully, Christopher G., Young, Charles, Wang, Lian-Tao, Ruan, Manqi, Zhu, Hongbo, Dong, Mingyi, Ouyang, Qun, Wu, Zhigang, Deng, Zhi, Li, Yulan, Qi, Huirong, Wang, Meng, Fu, Chengdong, Yao, Wei-Ming, Grancagnolo, Franco, Liu, Jianbei, Hu, Tao, Yang, Haijun, Bedeschi, Franco, Ferrari, Roberto, Zhao, Wei, Zhu, Zian, Giacomelli, Paolo, Li, Liang, Li, Fei, Liu, Zhenan, Zhu, Kejun, Hou, Suen, Bozovic Jelisavcic, Ivanka, Li, Gang, Fang, Yaquan, Li, Qiang, Boonekamp, Maarten, Liang, Zhijun, Piccinini, Fulvio, Shi, Xin, Abbrescia, Marcello, Ahmad, Muhammad, Ai, Xiaocong, Albergo, Sebastiano, Aleem, Muhammad Abid, Alexeev, Maxim, Aliev, Malik, Altmannshofer, Wolfgang, Alves, Fábio, An, Fenfen, An, Guangpeng, An, Haipeng, An, Qi, An, Rui, Andreazza, Attilio, Anduze, Marc, Antonello, Massimiliano, Garcia Pascual, Juan Antonio, Antusch, Stefan, Arhrib, Abdesslam, Arkani-Hamed, Nima, Arndt, Kirk, Azzi, Patrizia, Azzurri, Paolo, Palmer, Robert B., Bai, Bowen, Bai, Sha, Bai, Yang, Bai, Yu, Balagura, Vladislav, Balossino, Ilaria, Ban, Yong, Barger, Vernon, Barklow, Timothy, Bartalini, Paolo, Becattini, Francesco, Bellagamba, Lorenzo, Belloni, Alberto, Bencivenni, Giovanni, Berg, J. Scott, Bernius, Catrin, Bertani, Monica, Bertella, Claudia, Bertucci, Michele, Bi, Xiaojun, Bi, Yuanjie, Bian, Ligong, Bian, Tianjian, Bianchi, Fabrizio, Bigi, Ikaros I., Biglietti, Michela, Bignami, Andrea, Bilei, Gianmario, Borgonovi, Lisa, Bortoletto, Daniela, Bortone, Alberto, Boscherini, Davide, Bosotti, Angelo, Boudry, Vincent, Braibant, Sylvie, Bramante, Joseph, Branchini, Paolo, Brient, Jean-Claude, Caccia, Massimo, Cai, Chengfeng, Cai, Hao, Cai, Wenyong, Cai, Xiao, Cai, Yiming, Cai, Yuchen, Cai, Yunhai, Cai, Zhiqiang, Cano Bret, Marc, Cao, Bo, Cao, Dewen, Cao, Jianshe, Cao, Junjie, Cao, Qing-Hong, Carloni Calame, Carlo Michel, Casanova, Raimon, Cavasinni, Vincenzo, Chai, Junying, Chai, Weiping, Chang, Ningbo, Chang, Qin, Chang, Spencer, Chang, We-Fu, Chang, Xuejun, Chang, Yuan-Hann, Chekanov, Sergei, Chen, Bin, Chen, Chunhui, Chen, Fusan, Chen, Gang, Chen, Guoming, Chen, Huan, Chen, Huirun, Chen, Lei, Chen, Liejian, Chen, Mingjun, Chen, Mingshui, Chen, Nian, Chen, Ning, Chen, Shanhong, Chen, Shanzhen, Chen, Shao-Long, Chen, Shaomin, Chen, Shenjian, Chen, Shi, Chen, Wei, Chen, Wen, Chen, Xin, Chen, Xun, Chen, Xurong, Chen, Ye, Chen, Yu, Chen, Yuan, Chen, Yuanbai, Chen, Yukai, Chen, Zhenxing, Cheng, Hao, Cheng, Hsin-Chia, Cheng, Huajie, Cheng, Jian, Cheng, Shan, Cheng, Tongguang, Cheng, Weishuai, Cheong, Sanha, Chi, Yunlong, Chiarello, Gianluigi, Chiesa, Mauro, Chiu, Wen Han, Chou, Weiren, Chu, Chungming Paul, Chu, Ming-Chung, Chu, Xiaotong, Chu, Zhaolin, Chua, Chun-Khiang, Cibinetto, Gianluigi, Ciuchini, Marco, Cobal, Marina, Cochran, James, Coelho Lopes Sa, Rafael, Cossio, Fabio, Craig, Nathaniel, Cui, Han, Cui, Hanhua, Cui, Xiaohao, Cui, Zhaoyuan, Curtin, David, D Agnolo, Raffaele Tito, Dai, Jian-Ping, Dai, Jianping, Dai, Jin, Dai, Lei, Dai, Wei, Dai, Xuwen, Curtis, Stefania, Filippis, Nicola, Lucia, Erika, Mori, Francesca, Delgado, Antonio, Demarteau, Marcel, Deng, Changdong, Deng, Wei-Tian, Destefanis, Marco, Dev, P. S. 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Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut de Physique Nucléaire de Lyon (IPNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CEPC Study Group, Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Sud - Paris 11 (UP11), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), and HEP, INSPIRE

Subjects
detector: technology, Higgs particle: particle source, electron positron: storage ring, [PHYS.HEXP] Physics [physics]/High Energy Physics - Experiment [hep-ex], engineering, FOS: Physical sciences, threshold: production, electron positron: annihilation, B-factory, High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex), High Energy Physics - Phenomenology (hep-ph), [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], accelerator: technology, detector: design, Particle Physics - Phenomenology, new physics, hep-ex, Z0: decay, Higgs-factory, High Energy Physics::Phenomenology, Z0: particle source, hep-ph, electron positron: colliding beams, sensitivity, CEPC, High Energy Physics - Phenomenology, tau: particle source, 240 GeV-cms, vertex: primary, Higgs particle: electroproduction, charm: particle source, High Energy Physics::Experiment, proposed experiment, numerical calculations: Monte Carlo, Z0-factory, performance, Particle Physics - Experiment
Abstract

The Circular Electron Positron Collider (CEPC) is a large international scientific facility proposed by the Chinese particle physics community to explore the Higgs boson and provide critical tests of the underlying fundamental physics principles of the Standard Model that might reveal new physics. The CEPC, to be hosted in China in a circular underground tunnel of approximately 100 km in circumference, is designed to operate as a Higgs factory producing electron-positron collisions with a center-of-mass energy of 240 GeV. The collider will also operate at around 91.2 GeV, as a Z factory, and at the WW production threshold (around 160 GeV). The CEPC will produce close to one trillion Z bosons, 100 million W bosons and over one million Higgs bosons. The vast amount of bottom quarks, charm quarks and tau-leptons produced in the decays of the Z bosons also makes the CEPC an effective B-factory and tau-charm factory. The CEPC will have two interaction points where two large detectors will be located. This document is the second volume of the CEPC Conceptual Design Report (CDR). It presents the physics case for the CEPC, describes conceptual designs of possible detectors and their technological options, highlights the expected detector and physics performance, and discusses future plans for detector R&D and physics investigations. The final CEPC detectors will be proposed and built by international collaborations but they are likely to be composed of the detector technologies included in the conceptual designs described in this document. A separate volume, Volume I, recently released, describes the design of the CEPC accelerator complex, its associated civil engineering, and strategic alternative scenarios., 424 pages

46. Position-Regulated Electrostatic Interactions for Single Amino Acid Revealed by Aspartic Acid-Scanning Mutagenesis.

Author

Chen M, Ma L, Li M, Fang X, Yang Y, and Wang C

Abstract

We have examined in this contribution the electrostatic interactions between single arginine and aspartic acid by analyzing the peptide-peptide binding characteristics involving arginine-aspartic acid, arginine-glycine, arginine-tryptophan and tryptophan-glycine interactions. The results of aspartic acid mutagenesis revealed that the interactions between arginine and aspartic acid have significant dependence on the position and composition of amino acids. While the primary interaction can be attributed to arginine-tryptophan contacts originated from the indole moieties with the main chains of 14-mers containing N-H and C=O moieties, pronounced enhancement could be identified in association with the electrostatic side-chain-side-chain interactions between arginine and aspartic acid. An optimal separation of 2~4 amino acids between two adjacent aspartic acid and tryptophan binding sites can be identified to achieve maximal enhancement of binding interactions. Such observed separation dependence may be utilized to unravel cooperative effects in heterogeneous interactions between single pair of amino acids., (© 2024 Wiley‐VCH GmbH.)

Published
2024
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