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11 results on '"Yuan, Siyu"'

1. Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction

Author

Yuan, Siyu, Chen, Jiangjie, Ge, Xuyang, Xiao, Yanghua, and Yang, Deqing

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computation and Language (cs.CL)
Abstract

Analogical reasoning is essential for human cognition, allowing us to comprehend new concepts by relating them to familiar ones based on common relational structures. Previous work mainly focuses on word analogies, which do not fully represent the analogical reasoning ability of language models (LMs) aligning with humans. This paper first examines analogy prompting for large language models (LLMs) in scientific question-answering tasks. Then we discover that LLMs tend to ignore relational structures when performing word analogies, casting doubt on their utility for evaluating analogical reasoning. For better evaluation aligning with humans, we propose an analogical structure abduction task based on cognitive psychology, which aims to abduct structures between two systems to establish an analogy. Then we create a benchmark of scientific analogical reasoning with structure abduction, SCAR, consisting of 400 scientific analogies across 13 domains for this task. Empirical results reveal that LLMs struggle with this task, but the Chain-of-Thought (CoT) method with background knowledge and explanations can improve their capability.

Published
2023

2. Distilling Script Knowledge from Large Language Models for Constrained Language Planning

Author

Yuan, Siyu, Chen, Jiangjie, Fu, Ziquan, Ge, Xuyang, Shah, Soham, Jankowski, Charles Robert, Xiao, Yanghua, and Yang, Deqing

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computation and Language (cs.CL)
Abstract

In everyday life, humans often plan their actions by following step-by-step instructions in the form of goal-oriented scripts. Previous work has exploited language models (LMs) to plan for abstract goals of stereotypical activities (e.g., "make a cake"), but leaves more specific goals with multi-facet constraints understudied (e.g., "make a cake for diabetics"). In this paper, we define the task of constrained language planning for the first time. We propose an overgenerate-then-filter approach to improve large language models (LLMs) on this task, and use it to distill a novel constrained language planning dataset, CoScript, which consists of 55,000 scripts. Empirical results demonstrate that our method significantly improves the constrained language planning ability of LLMs, especially on constraint faithfulness. Furthermore, CoScript is demonstrated to be quite effective in endowing smaller LMs with constrained language planning ability., Accepted to ACL 2023

Published
2023

3. ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base

Author

Yuan, Siyu, Chen, Jiangjie, Sun, Changzhi, Liang, Jiaqing, Xiao, Yanghua, and Yang, Deqing

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computation and Language (cs.CL)
Abstract

Analogical reasoning is a fundamental cognitive ability of humans. However, current language models (LMs) still struggle to achieve human-like performance in analogical reasoning tasks due to a lack of resources for model training. In this work, we address this gap by proposing ANALOGYKB, a million-scale analogy knowledge base (KB) derived from existing knowledge graphs (KGs). ANALOGYKB identifies two types of analogies from the KGs: 1) analogies of the same relations, which can be directly extracted from the KGs, and 2) analogies of analogous relations, which are identified with a selection and filtering pipeline enabled by large LMs (InstructGPT), followed by minor human efforts for data quality control. Evaluations on a series of datasets of two analogical reasoning tasks (analogy recognition and generation) demonstrate that ANALOGYKB successfully enables LMs to achieve much better results than previous state-of-the-art methods.

Published
2023
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4. Causality-aware Concept Extraction based on Knowledge-guided Prompting

Author

Yuan, Siyu, Yang, Deqing, Liu, Jinxi, Tian, Shuyu, Liang, Jiaqing, Xiao, Yanghua, and Xie, Rui

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computation and Language (cs.CL)
Abstract

Concepts benefit natural language understanding but are far from complete in existing knowledge graphs (KGs). Recently, pre-trained language models (PLMs) have been widely used in text-based concept extraction (CE). However, PLMs tend to mine the co-occurrence associations from massive corpus as pre-trained knowledge rather than the real causal effect between tokens. As a result, the pre-trained knowledge confounds PLMs to extract biased concepts based on spurious co-occurrence correlations, inevitably resulting in low precision. In this paper, through the lens of a Structural Causal Model (SCM), we propose equipping the PLM-based extractor with a knowledge-guided prompt as an intervention to alleviate concept bias. The prompt adopts the topic of the given entity from the existing knowledge in KGs to mitigate the spurious co-occurrence correlations between entities and biased concepts. Our extensive experiments on representative multilingual KG datasets justify that our proposed prompt can effectively alleviate concept bias and improve the performance of PLM-based CE models.The code has been released on https://github.com/siyuyuan/KPCE., Comment: Accepted to ACL 2023

Published
2023
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5. The Contribution of Human Body Capacitance/Body-Area Electric Field To Individual and Collaborative Activity Recognition

Author

Bian, Sizhen, Rey, Vitor Fortes, Yuan, Siyu, and Lukowicz, Paul

Subjects
Computer Science - Human-Computer Interaction, Electrical Engineering and Systems Science - Signal Processing
Abstract

The current dominated wearable body motion sensor is IMU. This work presented an alternative wearable motion-sensing approach: human body capacitance (HBC, also commonly defined as body-area electric field). While being less robust in tracking the posture and trajectory, HBC has two properties that make it an attractive. First, the deployment of the sensing node on the being tracked body part is not a requirement for HBC sensing approach. Second, HBC is sensitive to the body's interaction with its surroundings, including both touching and being in the immediate proximity of people and objects. We first described the sensing principle for HBC, sensor architecture and implementation, and methods for evaluation. We then presented two case studies demonstrating the usefulness of HBC as a complement/alternative to IMUs. First, we explored the exercise recognition and repetition counting of seven machine-free leg-only exercises and eleven general gym workouts with the signal source of HBC and IMU. The HBC sensing shows significant advantages over the IMU signals in classification(0.89 vs 0.78 in F-score) and counting(0.982 vs 0.938 in accuracy) of the leg-only exercises. For the general gym workouts, HBC only shows recognition improvement for certain workouts like adductor where legs alone complete the movement. And it also supplies better results over the IMU for workouts counting(0.800 vs. 0.756 when wearing the sensors on the wrist). In the second case, we tried to recognize actions related to manipulating objects and physical collaboration between users by using a wrist-worn HBC sensing unit. We detected collaboration between the users with 0.69 F-score when receiving data from a single user and 0.78 when receiving data from both users. The capacitive sensor can improve the recognition of collaborative activities with an F-score over a single wrist accelerometer approach by 16\%., Comment: 30 Pages, 35 Figures

Published
2022

6. Digital Twins for Industry 4.0 in the 6G Era

Author

Han, Bin, Habibi, Mohammad Asif, Richerzhagen, Bjoern, Schindhelm, Kim, Zeiger, Florian, Lamberti, Fabrizio, Pratticò, Filippo Gabriele, Upadhya, Karthik, Korovesis, Charalampos, Belikaidis, Ioannis-Prodromos, Demestichas, Panagiotis, Yuan, Siyu, and Schotten, Hans D.

Subjects
FOS: Computer and information sciences, Computer Science - Computers and Society, Computers and Society (cs.CY)
Abstract

Having the Fifth Generation (5G) mobile communication system recently rolled out in many countries, the wireless community is now setting its eyes on the next era of Sixth Generation (6G). Inheriting from 5G its focus on industrial use cases, 6G is envisaged to become the infrastructural backbone of future intelligent industry. Especially, a combination of 6G and the emerging technologies of Digital Twins (DT) will give impetus to the next evolution of Industry 4.0 (I4.0) systems. This article provides a survey in the research area of 6G-empowered industrial DT system. With a novel vision of 6G industrial DT ecosystem, this survey discusses the ambitions and potential applications of industrial DT in the 6G era, and identifies the emerging challenges as well as the key enabling technologies. The introduced ecosystem is supposed to bridge the gaps between humans, machines, and the data infrastructure, and therewith enable numerous novel application scenarios., Submitted to IEEE Open Journal of the Communications Society

Published
2022

7. Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation

Author

Yi, Jingjie, Yang, Deqing, Yuan, Siyu, Cao, Caiyan, Zhang, Zhiyao, and Xiao, Yanghua

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL)
Abstract

Emotion recognition in conversation (ERC) aims to detect the emotion for each utterance in a given conversation. The newly proposed ERC models have leveraged pre-trained language models (PLMs) with the paradigm of pre-training and fine-tuning to obtain good performance. However, these models seldom exploit PLMs' advantages thoroughly, and perform poorly for the conversations lacking explicit emotional expressions. In order to fully leverage the latent knowledge related to the emotional expressions in utterances, we propose a novel ERC model CISPER with the new paradigm of prompt and language model (LM) tuning. Specifically, CISPER is equipped with the prompt blending the contextual information and commonsense related to the interlocutor's utterances, to achieve ERC more effectively. Our extensive experiments demonstrate CISPER's superior performance over the state-of-the-art ERC models, and the effectiveness of leveraging these two kinds of significant prompt information for performance gains. To reproduce our experimental results conveniently, CISPER's sourcecode and the datasets have been shared at https://github.com/DeqingYang/CISPER., Accepted by ECML-PKDD 2022

Published
2022

8. Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension

Author

Yuan, Siyu, Yang, Deqing, Liang, Jiaqing, Sun, Jilun, Huang, Jingyue, Cao, Kaiyan, Xiao, Yanghua, and Xie, Rui

Subjects
FOS: Computer and information sciences, Information Retrieval (cs.IR), Computer Science - Information Retrieval
Abstract

The concepts in knowledge graphs (KGs) enable machines to understand natural language, and thus play an indispensable role in many applications. However, existing KGs have the poor coverage of concepts, especially fine-grained concepts. In order to supply existing KGs with more fine-grained and new concepts, we propose a novel concept extraction framework, namely MRC-CE, to extract large-scale multi-granular concepts from the descriptive texts of entities. Specifically, MRC-CE is built with a machine reading comprehension model based on BERT, which can extract more fine-grained concepts with a pointer network. Furthermore, a random forest and rule-based pruning are also adopted to enhance MRC-CE's precision and recall simultaneously. Our experiments evaluated upon multilingual KGs, i.e., English Probase and Chinese CN-DBpedia, justify MRC-CE's superiority over the state-of-the-art extraction models in KG completion. Particularly, after running MRC-CE for each entity in CN-DBpedia, more than 7,053,900 new concepts (instanceOf relations) are supplied into the KG. The code and datasets have been released at https://github.com/fcihraeipnusnacwh/MRC-CE

Published
2022
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9. Generative Entity Typing with Curriculum Learning

Author

Yuan, Siyu, Yang, Deqing, Liang, Jiaqing, Li, Zhixu, Liu, Jinxi, Huang, Jingyue, and Xiao, Yanghua

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computation and Language (cs.CL)
Abstract

Entity typing aims to assign types to the entity mentions in given texts. The traditional classification-based entity typing paradigm has two unignorable drawbacks: 1) it fails to assign an entity to the types beyond the predefined type set, and 2) it can hardly handle few-shot and zero-shot situations where many long-tail types only have few or even no training instances. To overcome these drawbacks, we propose a novel generative entity typing (GET) paradigm: given a text with an entity mention, the multiple types for the role that the entity plays in the text are generated with a pre-trained language model (PLM). However, PLMs tend to generate coarse-grained types after fine-tuning upon the entity typing dataset. Besides, we only have heterogeneous training data consisting of a small portion of human-annotated data and a large portion of auto-generated but low-quality data. To tackle these problems, we employ curriculum learning (CL) to train our GET model upon the heterogeneous data, where the curriculum could be self-adjusted with the self-paced learning according to its comprehension of the type granularity and data heterogeneity. Our extensive experiments upon the datasets of different languages and downstream tasks justify the superiority of our GET model over the state-of-the-art entity typing models. The code has been released on https://github.com/siyuyuan/GET., Comment: Accepted to EMNLP 2022

Published
2022
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10. Study on the Temperature Changing Rules of U75V Rail in the Cooling Process

Author

Tong Shanhu, Jia Hao, Song Hua, Yuan Siyu, Jiang Juanjuan, Gao Mingxin, and Wang Zhongqiang

Subjects
Heavy rail cooling, Phase transition, Engineering, Computer simulation, Bending (metalworking), business.industry, Process (computing), Mechanical engineering, General Medicine, Deformation (meteorology), Temperature field, Volume (thermodynamics), Latent heat, Head (vessel), business, Engineering(all)
Abstract

Caused by the differences in volume and heat dissipation area of rail head and bottom, latent heat of phase transition releasing and other reasons, the complicated temperature changes occurs in rail head and bottom between during the cooling process, especially in solid state phase transition. In the paper, the ANSYS heat-stress couple module is adopted to carry on numerical simulation on the cooling process of 60 kg/m U75 V heavy rail. Though the post-processing result analyzed by using the post-processor module, we got the temperature changing rules for heavy rail cooling process. This paper is of great reference value for the bending deformation study in the hundred-meter heavy rail cooling processing.

Published
2011
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11. 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. Bhupal, Di Micco, Biagio, Ding, Ran, Ding, Xuefeng, Ding, Yadong, Da Rocha Rolo, Manuel Dionisio, Domenici, Danilo, Dong, Bingbing, Dong, Chongmin, Dong, Dong, Dong, Haiyi, Dong, Jianing, Dong, Jing, Dong, Lan, Dopke, Jens, Dordevic, Milos, Dos Santos Ramos, Tiago, Draper, Patrick, Drewes, Marco, Du, Mingxuan, Duan, Guang Hua, Eklund, Lars, Eno, Sarah, Erler, Jens, Essig, Rouven, Fan, Jiji, Fan, Shenghong, Fan, Wenjie, Fan, Xiangning, Fang, Shuangshi, Fano, Livio, Farilla, Addolarata, Farinelli, Riccardo, Faus-Golfe, Angeles, Fedderke, Michael A., Felici, Giulietto, Feng, Changqing, Feng, Cunfeng, Feng, Feng, Feng, Jianxin, Feng, Jun, Feng, Tai-Fu, Fischer, Oliver, Flores Castillo, Luis Roberto, Fontanesi, Elisa, Freitas, Ayres, Frotin, M., Frugiuele, Claudia, Fu, Jinyu, Fu, Qibin, Fu, Shinian, Fuchs, Elina, Fukuda, Shigeki, Gabrielli, Emidio, Gaido, Luciano, Gan, Pingping, Gao, Jie, Gao, Jun, Gao, Wu, Gao, Yanyan, Gao, Yu, Garzia, Isabella, Gaudio, Gabriella, Ge, Shao-Feng, Geng, Chao-Qiang, Geng, Huiping, Li-Sheng Geng, Gentile, Simonetta, Giraud, J., Giudice, Gian, Godbole, Rohini M., Gong, Dianjun, Gong, Guanghua, Gong, Hui, Gong, Li, Gong, Lingling, Gori, Stefania, Gou, Quanbu, Greco, Mario, Greco, Michela, Gribanov, Sergei S., Grinstein, Sebastian, Grondin, D., Gu, Jiayin, Gu, Limin, Gu, Pei-Hong, Guidi, Vincenzo, Guo, Fangyi, Guo, Jingyuan, Guo, Jun, Guo, Lei, Guo, Xin, Guo, Yuanyuan, Guo, Zhigang, Gupta, Ramesh, Han, Chengcheng, Han, Dejun, Han, Jinzhong, Han, Liangliang, Han, Ran, Han, Ruixiong, Han, Shuang, Han, Yanliang, Han, Yubo, Hang, Yanfeng, Hao, Jiankui, Hao, Xiqing, He, Dayong, He, Hong-Jian, He, Jibo, He, Jun, He, Min, He, Xiang, He, Xianke, He, Xiaogang, He, Yangle, He, Zhenqiang, Heinemeyer, Sven, Heng, Yuekun, Hong, Daojin, Hong, Jiangliu, Hong, Yang, Hor, Yuenkeung, Hostachy, J. -Y, Hou, Qingbo, Hou, Zhilong, Hsu, Shih-Chieh, Hu, Bitao, Hu, Jifeng, Hu, Jun, Hu, Shouyang, Hu, Shuyang, Hu, Yongcai, Hu, Yu, Hu, Zhen, Hu, Zhongjun, Huang, Chao-Shang, Huang, Fapeng, Huang, Guangshun, Huang, Guo-Yuan, Huang, Jinghui, Huang, Jinshu, Huang, Junjie, Huang, Liangsheng, Huang, Rijun, Huang, Shuhui, Huang, Tongming, Huang, Tuchen, Huang, Xiaozhong, Huang, Xingtao, Huang, Xuguang, Huang, Yanping, Huang, Yongsheng, Huang, Yuyan, Huo, Ran, Ignatov, Fedor V., Iqbal, Munawar, Jackson, Paul, Javaid, Tahir, Ji, Daheng, Ji, Qingping, Ji, Xiaoli, Jia Jia, Jia, Junji, Jia, Yu, Jia, Zihang, Jiang, Jiechen, Jiang, Yun, Jiao, Jianbin, Jin, Dapeng, Jin, Mingjie, Jin, Song, Jin, Yanli, Jin, Yi, Jing, Maoqiang, John, Jaya, Jones, Tim, Ju, Xudong, Jueid, Adil, Jung, Sunghoon, Jyotishmati, Susmita, Kacarevic, Goran, Kane, Gordon, Kang, Wen, Karagoz, Muge, Kato, Chikuma, Ke, Zhiyong, Kharzeev, Dmitri, Khoze, Valentin, Kilic, Can, Kiuchi, Ryuta, Ko, Pyungwon, Kobayashi, Tetsuya, Kong, Panyu, Kong, Shibei, Kopp, Joachim, Kotwal, Ashutosh, Kozaczuk, Jonathan, Kozyrev, Evgeny A., Krasnov, Alexander, Kuflik, Eric, Kuo, Chia-Ming, Kwok, King Wai, Lagarde, Francois, Lai, Pei-Zhu, Laktineh, Imad, Lan, Boyang, Lan, Xiaofei, Lavezzi, Lia, Lee, Seung J., Lei, Ge, Leng, Yongbin, Leung, Sze Ching, Li, Bingzhi, Li, Bo, Li, Boyang, Li, Changhong, Li, Cheng, Li, Congqiao, Li, Dazhang, Li, Dikai, Li, Fengyun, Li, Gexing, Li, Guangrui, Li, Hai-Bo, Li, Haifeng, Li, Haoqing, Li, Hengne, Li, Honglei, Li, Huijing, Li, Jin, Li, Jing, Li, Jinmian, Li, Jinyan, Li, Jungang, Li, Kang, Li, Ke, Li, Li, Li, Lianming, Li, Long, Li, Mengran, Li, Minxian, Li, Peirong, Li, Peiyu, Li, Peng, Li, Qiaodan, Li, Quansheng, Li, Rui, Li, Shaopeng, Li, Shiyuan, Li, Shu, Li, Tianjun, Li, Tong, Li, Weiguo, Li, Wenjun, Li, Xiaoling, Li, Xiaomei, Li, Xiaoping, Li, Xin, Li, Xingguo, Li, Xin-Qiang, Li, Yanwei, Li, Yiming, Ying Li, Li, Ying-Ying, Li, Yufeng, Li, Zhao, Li, Zhenghua, Li, Zhihui, Li, Zhongquan, Li, Ziyuan, Liang, Chaohui, Liang, Hao, Liang, Jing, Liang, Jinhan, Liang, Zuotang, Liao, Hean, Liao, Hongbo, Liao, Jinfeng, Liao, Libo, Liao, Wei, Liao, Yi, Liao, Yunfeng, Lin, Chuangxin, Lin, Hai, Lin, Haiying, Ling, Jiajie, Ling, Pan, Lisanti, Mariangela, Liu, Ao, Liu, Baiqi, Liu, Beijiang, Liu, Bing, Liu, Bo, Liu, Dianyu, Liu, Dong, Liu, Fu-Hu, Liu, Hongbang, Liu, Hu, Liu, Jia, Liu, Jiaming, Liu, Jian, Liu, Jianfei, Liu, Jiangtao, Liu, Jie, Liu, Jindong, Liu, Kexin, Liu, Ling, Liu, Liqiang, Liu, Ming, Liu, Ning, Liu, Peilian, Liu, Qian, Liu, Qingyuan, Liu, Shubin, Liu, Shulin, Liu, Tao, Liu, Weitao, Liu, Wendi, Liu, Xiang, Liu, Xiaohui, Liu, Xin, Liu, Xuesong, Liu, Xuewen, Liu, Xuyang, Liu, Yandong, Liu, Yang, Liu, Yanlin, Liu, Yi, Liu, Yong, Liu, Yudong, Liu, Zengqiang, Liu, Zeyuan, Liu, Zhanfeng, Liu, Zhaofeng, Liu, Zhen, Liu, Zhenchao, Liu, Zhongxiu, Liu, Zuowei, Llorente Merino, Javier, Lo, Cheuk Yee, Logashenko, Ivan B., Long, Andrew, Lou, Xinchou, Low, Ian, Low, Matthew, Lu, Cai-Dian, Lu, Wei, Lu, Weiguo, Lu, Yuanrong, Lu, Yunpeng, Lu, Zhijun, Lukic, Strahinja, Luo, Mingcheng, Luo, Pengwei, Luo, Qing, Luo, Tao, Luo, Xiaofeng, Luo, Yanting, Luo, Zhenhuan, Lyu, Kunfeng, Lyu, Xiaorui, Ma, Huizhou, Ma, Kai, Ma, Lianliang, Ma, Na, Ma, Qiang, Ma, Rui, Ma, Wen-Gan, Ma, Xiao, Ma, Xiaotian, Ma, Xinpeng, Ma, Yanling, Ma, Yanqing, Ma, Yongsheng, Ma, Yue, Ma, Zhongjian, Maggiora, Marco, Magniette, Frédéric, Malamud, Ernie, Mangano, Michelangelo, Mao, Lijun, Mao, Mingling, Mao, Yajun, Mao, Yanmin, Mao, Yanyan, Martin, Adam, Martinez Outschoorn, Verena Ingrid, Matsumoto, Shigeki, Mccullough, Matthew, Mcmahon, Steve, Meade, Patrick, Mele, Barbara, Mellado, Bruce, Men, Lingling, Meng, Cai, Meng, Fanbo, Mezzadri, Giulio, Mi, Zhenghui, Michelato, Paolo, Min, Tianjue, Ming, Lei, Mittal, Monika, Molson, James, Monaco, Laura, Montagna, Guido, Morello, Gianfranco, Moretti, Mauro, Mu, Zhihui, Nanni, Jérome, Neubert, Matthias, Nicrosini, Oreste, Nie, Changshan, Nikitin, Sergei, Ning, Feipeng, Ning, Guo-Zhu, Nishu, Nishu, Niu, Yazhou, Ojalvo, Isobel, Okunev, Ivan, Ospanov, Rustem, Pagani, Carlo, Paganis, Stathes, Panareo, Marco, Pandurovic, Mila, Pang, Tong, Panizzo, Giancarlo, Paparella, Rocco, Parida, Bibhuti, Passemar, Emilie, Pei, Guoxi, Pei, Shilun, Peng, Quanling, Peng, Xiaohua, Peng, Yuemei, Petrov, Alexey, Pezzotti, Lorenzo, Pierre-Émile, Thomas, Ping, Rong-Gang, Plackett, Richard, Polesello, Giacomo, Poli Lener, Marco, Polini, Alessandro, Popov, Alexandr S., Prell, Soeren, Qi, Ming, Qian, Sen, Qian, Zhuoni, Qiao, Cong-Feng, Qiao, Yusi, Qin, Guang-You, Qin, Qin, Qin, Qing, Qin, Zhonghua, Qu, Huamin, Ramani, Harikrishnan, Ramsey-Musolf, Michael, Redaelli, Stefano, Reece, Matthew, Ren, Jing, Rischke, Dirk, Rivetti, Angelo, Roda, Chiara, Rolandi, Luigi, Rompotis, Nikolaos, Ruan, Xifeng, Ruiz, Richard, Sabbi, Gianluca, Sang, Wen-Long, Santoro, Romualdo, Sanz-Cillero, Juan J., Schlaffer, Matthias, Schmid, Frank, Schuster, Philip, Schuy, Alex, Schwaller, Pedro, Sciuto, Antonella, Sen, Tanaji, Sertore, Daniele, Sha, Peng, Shan, Lianyou, Shang, Feng, Shao, Dingyu, Shao, Jianxiong, Shashkov, Yaroslav, Shelton, Jessie, Shen, Cheng-Ping, Shen, Peixun, Shen, Qiuping, Shen, Yang, Shen, Yuqiao, Shen, Zhongtao, Shi, Can, Shi, Haoyu, Shi, Jingyuan, Shi, Liaoshan, Shi, Renjie, Shi, Yu-Ji, Shi, Yukun, Shi, Yulong, Shin, Seodong, Shipsey, Ian, Shiu, Gary, Shu, Guan, Shu, Jing, Si, Zonguo, Silvestrini, Luca, Sinyatkin, Sergey, Song, Hong, Song, Mao, Song, Weimin, Stamou, Emannuel, Stratakis, Diktys, Su, Dong, Su, Feng, Su, Shufang, Su, Wanyun, Su, Wei, Su, Yangjie, Sui, Yanfeng, Sullivan, Michael, Sun, Baogeng, Sun, Daming, Sun, Guoqiang, Sun, Hao, Sun, Junfeng, Sun, Liang, Sun, Peng, Sun, Qingfeng, Sun, Shengsen, Sun, Sichun, Sun, Tingting, Sun, Xiangming, Sun, Xianjing, Sun, Xilei, Sun, Yanjun, Sun, Yongzhao, Sundrum, Raman, Tang, Chuanxiang, Tang, Guangyi, Tang, Jian, Tang, Jiannan, Tang, Jingyu, Tang, Songzhi, Tang, Yi-Lei, Tao, Jia, Tao, Junquan, Tassielli, Giovanni, Tenchini, Roberto, Teytelman, Dmitry, Thaler, Jesse, Tian, Saike, Tian, Xingcheng, Tonelli, Guido, Tong, Xinyu, Trentadue, Luca, Tsai, Yuhsin, Tsybychev, Dmitri, Tu, Yanjun, Tweedie, Brock, Unwin, James, Verducci, Monica, Vicini, Alessandro, Videau, Henri, Viehhauser, Georg, Vivarelli, Iacopo, Vossebeld, Joost, Vukasinovic, Natasa, Wan, Xia, Wang, Biao, Wang, Bin, Wang, Binlong, Wang, Bo, Wang, Chengtao, Wang, Chengwei, Wang, Chenliang, Wang, Dayong, Wang, Dou, Wang, Fei, Wang, Feng, Wang, Gang, Wang, Haijing, Wang, Haiyun, Wang, Hui, Wang, Jian, Wang, Jianchun, Wang, Jianli, Wang, Jianxiong, Wang, Jiawei, Wang, Jie, Wang, Jin, Wang, Jing, Wang, Jinwei, Wang, Ke, Wang, Kechen, Wang, Kunfu, Wang, Liangliang, Wang, Lijiao, Wang, Linlin, Wang, Longge, Wang, Lu, Wang, Meifen, Wang, Na, Wang, Pengcheng, Wang, Qun, Wang, Qunyao, Wang, Ran, Wang, Ren-Jie, Wang, Rongkun, Wang, Shaobo, Wang, Shaozhe, Wang, Shengchang, Wang, Shuzheng, Wang, Siguang, Wang, Tianhong, Wang, Tong, Wang, Wei, Wang, Weiping, Wang, Wenyu, Wang, Xi, Wang, Xiangjian, Wang, Xiangqi, Wang, Xiaolong, Wang, Xiaoning, Wang, Xiaoping, Wang, Xin, Wang, Xingze, Wang, Xiongfei, Wang, Yan, Wang, Yaqian, Wang, Yi, Wang, Yifang, Wang, Ying, Wang, Yiwei, Wang, Yong, Wang, Youkai, Wang, Yu, Wang, Yufeng, Wang, Yuhao, Wang, Zhaofeng, Wang, Zhigang, Wang, Zhipeng, Wang, Zhiyong, Wang, Zirui, Wang, Zixun, Wang, Zongyuan, Wei, Runing, Wei, Shujun, Wei, Wei, Wei, Xiaomin, Wei, Yuanyuan, Wei, Yuqian, Wen, Shuopin, Wen, Zhiwen, Willocq, Stephane, Witte, Holger, Wu, Jinfei, Wu, Juan, Wu, Kewei, Wu, Lei, Wu, Linghui, Wu, Mengqing, Wu, Peiwen, Wu, Tianya, Wu, Wenhuan, Wu, Xi, Wu, Xiao-Hong, Wu, Xing-Gang, Wu, Xu, Wu, Xueting, Wu, Ye, Wu, Yuwen, Wu, Zhi, Xia, Wenhao, Xiang, Dao, Xiang, Qian-Fei, Xianyu, Zhong-Zhi, Xiao, Bo-Wen, Xiao, Dengjie, Xiao, Ning, Xiao, Ouzheng, Xiao, Rui-Qing, Xiao, Yu, Xiao, Zhen-Jun, Xie, Ke-Pan, Xie, Xinhai, Xie, Yuehong, Xie, Yuguang, Xie, Zongtai, Xing, Qingzhi, Xing, Zhizhong, Xiu, Qinglei, Xu, Chang, Xu, Da, Xu, Fanrong, Xu, Guanglei, Xu, Guangzhi, Xu, Haocheng, Xu, Hongge, Xu, Hongliang, Xu, Hui, Xu, Ji, Xu, Nu, Xu, Qing, Xu, Qingjin, Xu, Qingjun, Xu, Wei, Xu, Yin, Xu, Yongheng, Xu, Zijun, Xue, Wei, Yan, Bin, Yan, Jun, Yan, Liang, Yan, Mingyang, Yan, Qi-Shu, Yan, Tian, Yan, Wenbiao, Yan, Yingbing, Yang, Bingfang, Yang, Huan, Yang, Jiancheng, Yang, Jianquan, Yang, Jin Min, Yang, Jing, Yang, Junfeng, Yang, Li, Yang, Liu, Yang, Mei, Yang, Ping, Yang, Qianwen, Yang, Xingwang, Yang, Xuan, Yang, Ye, Yang, Ying, Yang, Yong, Yang, Yongliang, Yang, Yueling, Yang, Zhenwei, Yao, Weichao, Yatsenko, Elena, Ye, Hanfei, Ye, Jingbo, Ye, Mei, Ye, Qiang, Ye, Ziping, Yi, Fang, Yi, Kai, Yilun, Xue, Yin, Hang, Yin, Pengfei, Yin, Xiangwei, Yin, Ze, Yin, Zhongbao, You, Zhengyun, Yu, Boxiang, Yu, Chenghui, Yu, Chunxu, Yu, Dan, Yu, Felix, Yu, Fusheng, Yu, Lingda, Yu, Lu, Yu, Yue, Yu, Zhao-Huan, Yuan, Li, Yuan, Siyu, Yuan, Ye, Yuan, Youjin, Yuan, Zhiyang, Yue, Chongxing, Yue, Junhui, Yue, Qian, Zaib, Un-Nisa, Zhai, Jian, Zhai, Jiyuan, Zhang, Baotang, Zhang, Ben-Wei, Zhang, Bo, Zhang, Bowen, Zhang, Cen, Zhang, Chad, Zhang, Chunlei, Zhang, Di, Zhang, Gang, Zhang, Guangyi, Zhang, Guoqing, Zhang, Hai-Bin, Zhang, Hao, Zhang, Honghao, Zhang, Hongyu, Zhang, Huaqiao, Zhang, Hui, Zhang, Jian, Zhang, Jianhui, Zhang, Jianqin, Zhang, Jielei, Zhang, Jingru, Zhang, Jinlong, Zhang, Junrui, Zhang, Kaili, Zhang, Lei, Zhang, Liang, Zhang, Liming, Zhang, Linhao, Zhang, Ren-You, Zhang, Rui, Zhang, Sifan, Zhang, Tianjiao, Zhang, Wenchao, Zhang, Xiangke, Zhang, Xiaohui, Zhang, Xinmin, Zhang, Xinying, Zhang, Xueyao, Zhang, Yang, Zhang, Yao, Zhang, Yi, Zhang, Ying, Zhang, Yongchao, Zhang, Yu, Zhang, Yuan, Zhang, Yuhong, Zhang, Yu-Jie, Zhang, Yulei, Zhang, Yulian, Zhang, Yumei, Zhang, Yunlong, Zhang, Yuxuan, Zhang, Zhaoru, Zhang, Zhen, Zhang, Zhenyu, Zhang, Zhiqing, Zhang, Zhiyong, Zhao, Chen, Zhao, Hang, Zhao, Jingxia, Zhao, Jingyi, Zhao, Ling, Zhao, Mei, Zhao, Minggang, Zhao, Mingrui, Zhao, Peng, Zhao, Qiang, Zhao, Shensen, Zhao, Shuai, Zhao, Shu-Min, Zhao, Tongxian, Zhao, Xianghu, Zhao, Xiaoran, Zhao, Xiaoyan, Zhao, Ying, Zhao, Yu, Zhao, Yue, Zhao, Zhengguo, Zhao, Zhen-Xing, Zhao, Zhuo, Zheng, Bo, Zheng, Hongjuan, Zheng, Liang, Zheng, Ran, Zheng, Shuxin, Zheng, Taifan, Zheng, Xuxing, Zheng, Ya-Juan, Zheng, Yangheng, Zhi, Yu, Zhong, Bin, Zhong, Yiming, Zhou, Bing, Zhou, Hai-Qing, Zhou, Hang, Zhou, Jia, Zhou, Jianxin, Zhou, Jing, Zhou, Maosen, Zhou, Nan, Zhou, Ning, Zhou, Ningchuang, Zhou, Shiyu, Zhou, Shun, Zhou, Sihong, Zhou, Siyi, Zhou, Xiang, Zhou, Yang, Zhou, Yi, Zhou, Yu-Feng, Zhou, Zusheng, Zhu, Changhe, Zhu, Chengguang, Zhu, Chenzheng, Zhu, Dechong, Zhu, Hongyan, Zhu, Hua-Xing, Zhu, Jiamin, Zhu, Jiang, Zhu, Jingya, Zhu, Jingyu, Zhu, Junjie, Zhu, Kai, Zhu, Kun, Zhu, Li, Zhu, Ruilin, Zhu, Xianglei, Zhu, Xuezheng, Zhu, Yifan, Zhu, Yingshun, Zhu, Yongfeng, Zhuang, Xuai, Zong, Hongshi, Zou, Cong, Zou, Jiaheng, Zou, Ye, Zupan, Jure, Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire Leprince-Ringuet (LLR), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de l'Accélérateur Linéaire (LAL), Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Galaxies, Etoiles, Physique, Instrumentation (GEPI), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la 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

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