Author: "Li, Yichen" / Publication Year Range: Last 10 years - Searchworks@Jio Institute Digital Library Search Results

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1,978 results on '"Li, Yichen"'

1. Editable Fairness: Fine-Grained Bias Mitigation in Language Models

Author: Chen, Ruizhe, Li, Yichen, Yang, Jianfei, Zhou, Joey Tianyi, and Liu, Zuozhu
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Generating fair and accurate predictions plays a pivotal role in deploying large language models (LLMs) in the real world. However, existing debiasing methods inevitably generate unfair or incorrect predictions as they are designed and evaluated to achieve parity across different social groups but leave aside individual commonsense facts, resulting in modified knowledge that elicits unreasonable or undesired predictions. In this paper, we first establish a new bias mitigation benchmark, BiaScope, which systematically assesses performance by leveraging newly constructed datasets and metrics on knowledge retention and generalization. Then, we propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases. FAST identifies the decisive layer responsible for storing social biases and then calibrates its outputs by integrating a small modular network, considering both bias mitigation and knowledge-preserving demands. Comprehensive experiments demonstrate that FAST surpasses state-of-the-art baselines with superior debiasing performance while not compromising the overall model capability for knowledge retention and downstream predictions. This highlights the potential of fine-grained debiasing strategies to achieve fairness in LLMs. Code will be publicly available., Comment: arXiv admin note: substantial text overlap with arXiv:2405.09341
Published: 2024

2. Automated Defects Detection and Fix in Logging Statement

Author: Zhong, Renyi, Li, Yichen, Kuang, Jinxi, Gu, Wenwei, Huo, Yintong, and Lyu, Michael R.
Subjects: Computer Science - Software Engineering
Abstract: Developers use logging statements to monitor software, but misleading logs can complicate maintenance by obscuring actual activities. Existing research on logging quality issues is limited, mainly focusing on single defects and manual fixes. To address this, we conducted a study identifying four defect types in logging statements through real-world log changes analysis. We propose LogFixer, a two-stage framework for automatic detection and updating of logging statements. In the offline stage, LogFixer uses a similarity-based classifier on synthetic defective logs to identify defects. During the online phase, this classifier evaluates logs in code snippets to determine necessary improvements, and an LLM-based recommendation framework suggests updates based on historical log changes. We evaluated LogFixer on real-world and synthetic datasets, and new real-world projects, achieving an F1 score of 0.625. LogFixer significantly improved static text and dynamic variables suggestions by 48.12\% and 24.90\%, respectively, and achieved a 61.49\% success rate in recommending correct updates for new projects. We reported 40 problematic logs to GitHub, resulting in 25 confirmed and merged changes across 11 projects.
Published: 2024

3. Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching

Author: Li, Yichen, Xu, Wenchao, Wang, Haozhao, Li, Ruixuan, Qi, Yining, and Guo, Jingcai
Subjects: Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: This paper focuses on Federated Domain-Incremental Learning (FDIL) where each client continues to learn incremental tasks where their domain shifts from each other. We propose a novel adaptive knowledge matching-based personalized FDIL approach (pFedDIL) which allows each client to alternatively utilize appropriate incremental task learning strategy on the correlation with the knowledge from previous tasks. More specifically, when a new task arrives, each client first calculates its local correlations with previous tasks. Then, the client can choose to adopt a new initial model or a previous model with similar knowledge to train the new task and simultaneously migrate knowledge from previous tasks based on these correlations. Furthermore, to identify the correlations between the new task and previous tasks for each client, we separately employ an auxiliary classifier to each target classification model and propose sharing partial parameters between the target classification model and the auxiliary classifier to condense model parameters. We conduct extensive experiments on several datasets of which results demonstrate that pFedDIL outperforms state-of-the-art methods by up to 14.35\% in terms of average accuracy of all tasks.
Published: 2024

4. Performance of the plastic scintillator modules for the top veto tracker of the Taishan Antineutrino Observatory

Author: Luo, Guang, Yin, Xiaohao, An, Fengpeng, Wang, Zhimin, Hor, Y. K., Lu, Peizhi, Li, Ruhui, Li, Yichen, He, Wei, Wang, Wei, and Xiao, Xiang
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: For tracking and tagging the cosmic-ray muon (CR-muon), the Taishan Antineutrino Observatory (TAO) experiment is equipped with a top veto tracker (TVT) system composed of 160 modules, each consisting of plastic scintillator (PS) strip as target material, embedded wavelength shifting fiber (WLS-fiber) as photon collection and transmission medium, and silicon photomultipliers (SiPMs) at both ends as read-out. This article introduces the unique design of the module and reports the excellent performance of all modules, providing guidance and important reference for the process design of scintillation detectors with WLS-fibers. In general, when the CR-muon hits the center of plastic scintillator and without optical grease, the most probable value of the signal amplitude at one end of the PS strip is greater than 40.8 p.e. and 51.5 p.e. for all the 2 m-length modules and 1.5 m-length modules respectively. The CR-muon tagging efficiency of PS module is measured to be more than 99.3%, which meets the requirement of TAO.
Published: 2024

5. Technique Report of CVPR 2024 PBDL Challenges

Author: Fu, Ying, Li, Yu, You, Shaodi, Shi, Boxin, Chen, Linwei, Zou, Yunhao, Wang, Zichun, Li, Yichen, Han, Yuze, Zhang, Yingkai, Wang, Jianan, Liu, Qinglin, Yu, Wei, Lv, Xiaoqian, Li, Jianing, Zhang, Shengping, Ji, Xiangyang, Chen, Yuanpei, Zhang, Yuhan, Peng, Weihang, Zhang, Liwen, Xu, Zhe, Gou, Dingyong, Li, Cong, Xu, Senyan, Zhang, Yunkang, Jiang, Siyuan, Lu, Xiaoqiang, Jiao, Licheng, Liu, Fang, Liu, Xu, Li, Lingling, Ma, Wenping, Yang, Shuyuan, Xie, Haiyang, Zhao, Jian, Huang, Shihua, Cheng, Peng, Shen, Xi, Wang, Zheng, An, Shuai, Zhu, Caizhi, Li, Xuelong, Zhang, Tao, Li, Liang, Liu, Yu, Yan, Chenggang, Zhang, Gengchen, Jiang, Linyan, Song, Bingyi, An, Zhuoyu, Lei, Haibo, Luo, Qing, Song, Jie, Liu, Yuan, Li, Qihang, Zhang, Haoyuan, Wang, Lingfeng, Chen, Wei, Luo, Aling, Li, Cheng, Cao, Jun, Chen, Shu, Dou, Zifei, Liu, Xinyu, Zhang, Jing, Zhang, Kexin, Yang, Yuting, Gou, Xuejian, Wang, Qinliang, Liu, Yang, Zhao, Shizhan, Zhang, Yanzhao, Yan, Libo, Guo, Yuwei, Li, Guoxin, Gao, Qiong, Che, Chenyue, Sun, Long, Chen, Xiang, Li, Hao, Pan, Jinshan, Xie, Chuanlong, Chen, Hongming, Li, Mingrui, Deng, Tianchen, Huang, Jingwei, Li, Yufeng, Wan, Fei, Xu, Bingxin, Cheng, Jian, Liu, Hongzhe, Xu, Cheng, Zou, Yuxiang, Pan, Weiguo, Dai, Songyin, Jia, Sen, Zhang, Junpei, and Chen, Puhua
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to inform and enhance deep learning models, we can develop more robust and accurate vision systems. Physics-based vision aims to invert the processes to recover scene properties such as shape, reflectance, light distribution, and medium properties from images. In recent years, deep learning has shown promising improvements for various vision tasks, and when combined with physics-based vision, these approaches can enhance the robustness and accuracy of vision systems. This technical report summarizes the outcomes of the Physics-Based Vision Meets Deep Learning (PBDL) 2024 challenge, held in CVPR 2024 workshop. The challenge consisted of eight tracks, focusing on Low-Light Enhancement and Detection as well as High Dynamic Range (HDR) Imaging. This report details the objectives, methodologies, and results of each track, highlighting the top-performing solutions and their innovative approaches., Comment: CVPR 2024 PBDL Challenges: https://pbdl-ws.github.io/pbdl2024/challenge/index.html
Published: 2024

6. JUNO Sensitivity to Invisible Decay Modes of Neutrons

Author: JUNO Collaboration, Abusleme, Angel, Adam, Thomas, Adamowicz, Kai, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, de André, João Pedro Athayde Marcondes, Auguste, Didier, Bai, Weidong, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Beretta, Marco, Bergnoli, Antonio, Bick, Daniel, Bieger, Lukas, Biktemerova, Svetlana, Birkenfeld, Thilo, Blake, Iwan, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Busto, Jose, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callier, Stéphane, Calvez, Steven, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Cerrone, Vanessa, Chang, Jinfan, Chang, Yun, Chatrabhuti, Auttakit, Chen, Chao, Chen, Guoming, Chen, Pingping, Chen, Shaomin, Chen, Xin, Chen, Yiming, Chen, Yixue, Chen, Yu, Chen, Zelin, Chen, Zhangming, Chen, Zhiyuan, Chen, Zikang, Cheng, Jie, Cheng, Yaping, Cheng, Yu Chin, Chepurnov, Alexander, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chin, Yen-Ting, Chou, Po-Lin, Chu, Ziliang, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Molla, Marta Colomer, Di Lorenzo, Selma Conforti, Coppi, Alberto, Corti, Daniele, Csakli, Simon, Cui, Chenyang, Corso, Flavio Dal, Dalager, Olivia, Datta, Jaydeep, De La Taille, Christophe, Deng, Zhi, Deng, Ziyan, Ding, Xiaoyu, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dittrich, Carsten, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dou, Wei, Dracos, Marcos, Druillole, Frédéric, Du, Ran, Du, Shuxian, Duan, Yujie, Dugas, Katherine, Dusini, Stefano, Duyang, Hongyue, Eck, Jessica, Enqvist, Timo, Fabbri, Andrea, Fahrendholz, Ulrike, Fan, Lei, Fang, Jian, Fang, Wenxing, Fedoseev, Dmitry, Feng, Li-Cheng, Feng, Qichun, Ferraro, Federico, Fournier, Amélie, Fritsch, Fritsch, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guizzetti, Rosa Maria, Guo, Cong, Guo, Wanlei, Hagner, Caren, Han, Hechong, Han, Ran, Han, Yang, He, Jinhong, He, Miao, He, Wei, He, Xinhai, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jun, Hu, Peng, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Jinhao, Huang, Junting, Huang, Kaixuan, Huang, Shengheng, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Imbert, Leonard, Ioannisian, Ara, Isocrate, Roberto, Jafar, Arshak, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Cailian, Jiang, Di, Jiang, Guangzheng, Jiang, Wei, Jiang, Xiaoshan, Jiang, Xiaozhao, Jiang, Yixuan, Jing, Xiaoping, Jollet, Cécile, Kang, Li, Karaparabil, Rebin, Kazarian, Narine, Khan, Ali, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kuleshov, Sergey, Kumaran, Sindhujha, Kutovskiy, Nikolay, Labit, Loïc, Lachenmaier, Tobias, Lai, Haojing, Landini, Cecilia, Leblanc, Sébastien, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Hongjian, Li, Huang, Li, Jiajun, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Shuo, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, An-An, Liang, Hao, Liao, Jiajun, Liao, Yilin, Liao, Yuzhong, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Ling, Xin, Lippi, Ivano, Liu, Caimei, Liu, Fang, Liu, Fengcheng, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hongyang, Liu, Jianglai, Liu, Jiaxi, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shenghui, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Xuewei, Liu, Yankai, Liu, Zhen, Loi, Lorenzo, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Meishu, Lu, Peizhi, Lu, Shuxiang, Lu, Xianguo, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Fengjiao, Luo, Guang, Luo, Jianyi, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Mai, Jingyu, Malabarba, Marco, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meraviglia, Anita, Meregaglia, Anselmo, Meroni, Emanuela, Miramonti, Lino, Mohan, Nikhil, Montuschi, Michele, Reveco, Cristobal Morales, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parker, George, Parmeggiano, Sergio, Patsias, Achilleas, Pei, Yatian, Pelicci, Luca, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Percalli, Elisa, Perrin, Willy, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qi, Xiaohui, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Qu, Manhao, Qu, Zhenning, Ranucci, Gioacchino, Re, Alessandra, Rebii, Abdel, Redchuk, Mariia, Reina, Gioele, Ren, Bin, Ren, Jie, Ren, Yuhan, Ricci, Barbara, Rientong, Komkrit, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sandanayake, Deshan, Sangka, Anut, Sava, Giuseppe, Sawangwit, Utane, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settimo, Mariangela, Shao, Junyu, Sharov, Vladislav, Shi, Hexi, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Singhal, Apeksha, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Smirnov, Mikhail, Smirnov, Oleg, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Strizh, Michail, Studenikin, Alexander, Su, Aoqi, Su, Jun, Sun, Guangbao, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Takenaka, Akira, Tan, Xiaohan, Tang, Jian, Tang, Jingzhe, Tang, Qiang, Tang, Quan, Tang, Xiao, Hariharan, Vidhya Thara, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Triossi, Andrea, Trzaska, Wladyslaw, Tung, Yu-Chen, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Venettacci, Carlo, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, von Sturm, Katharina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Hanwen, Wang, Jian, Wang, Jun, Wang, Li, Wang, Lu, Wang, Meng, Wang, Mingyuan, Wang, Qianchuan, Wang, Ruiguang, Wang, Sibo, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuyi, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Watcharangkool, Apimook, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wei, Yuehuan, Wen, Liangjian, Weng, Jun, Wiebusch, Christopher, Wirth, Rosmarie, Wu, Chengxin, Wu, Diru, Wu, Qun, Wu, Yinhui, Wu, Yiyang, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xian, Shishen, Xiang, Ziqian, Xiao, Fei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yijun, Xie, Yuguang, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jiayang, Xu, Jilei, Xu, Jing, Xu, Jinghuan, Xu, Meihang, Xu, Xunjie, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Qiyu, Yan, Taylor, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Fengfan, Yang, Jie, Yang, Lei, Yang, Pengfei, Yang, Xiaoyu, Yang, Yifan, Yang, Yixiang, Yang, Zekun, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Guojun, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zamogilnyi, Kirill, Zavadskyi, Vitalii, Zeng, Fanrui, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Hangchang, Zhang, Haosen, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jingbo, Zhang, Jinnan, Zhang, Junwei, Zhang, Lei, Zhang, Peng, Zhang, Ping, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Shuihan, Zhang, Siyuan, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Yibing, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Jie, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zhao, Tianhao, Zheng, Hua, Zheng, Yangheng, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhou, Xing, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, and Zou, Jiaheng
Subjects: High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract: We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $ n \rightarrow { inv} $ and $ nn \rightarrow { inv} $. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$., Comment: 28 pages, 7 figures, 4 tables
Published: 2024

7. Large Language Model Bias Mitigation from the Perspective of Knowledge Editing

Author: Chen, Ruizhe, Li, Yichen, Xiao, Zikai, and Liu, Zuozhu
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Existing debiasing methods inevitably make unreasonable or undesired predictions as they are designated and evaluated to achieve parity across different social groups but leave aside individual facts, resulting in modified existing knowledge. In this paper, we first establish a new bias mitigation benchmark BiasKE leveraging existing and additional constructed datasets, which systematically assesses debiasing performance by complementary metrics on fairness, specificity, and generalization. Meanwhile, we propose a novel debiasing method, Fairness Stamp (FAST), which enables editable fairness through fine-grained calibration on individual biased knowledge. Comprehensive experiments demonstrate that FAST surpasses state-of-the-art baselines with remarkable debiasing performance while not hampering overall model capability for knowledge preservation, highlighting the prospect of fine-grained debiasing strategies for editable fairness in LLMs.
Published: 2024

8. Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs

Author: Shan, Shiwen, Huo, Yintong, Su, Yuxin, Li, Yichen, Li, Dan, and Zheng, Zibin
Subjects: Computer Science - Software Engineering, Computer Science - Machine Learning
Abstract: Configurable software systems are prone to configuration errors, resulting in significant losses to companies. However, diagnosing these errors is challenging due to the vast and complex configuration space. These errors pose significant challenges for both experienced maintainers and new end-users, particularly those without access to the source code of the software systems. Given that logs are easily accessible to most end-users, we conduct a preliminary study to outline the challenges and opportunities of utilizing logs in localizing configuration errors. Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause configuration properties based on logs. We further implement a tool, LogConfigLocalizer, aligned with the design of the aforementioned strategy, hoping to assist end-users in coping with configuration errors through log analysis. To the best of our knowledge, this is the first work to localize the root-cause configuration properties for end-users based on Large Language Models~(LLMs) and logs. We evaluate the proposed strategy on Hadoop by LogConfigLocalizer and prove its efficiency with an average accuracy as high as 99.91%. Additionally, we also demonstrate the effectiveness and necessity of different phases of the methodology by comparing it with two other variants and a baseline tool. Moreover, we validate the proposed methodology through a practical case study to demonstrate its effectiveness and feasibility., Comment: 13 pages, accepted by ISSTA 2024 (The 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis)
Published: 2024
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9. Towards Efficient Replay in Federated Incremental Learning

Author: Li, Yichen, Li, Qunwei, Wang, Haozhao, Li, Ruixuan, Zhong, Wenliang, and Zhang, Guannan
Subjects: Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: In Federated Learning (FL), the data in each client is typically assumed fixed or static. However, data often comes in an incremental manner in real-world applications, where the data domain may increase dynamically. In this work, we study catastrophic forgetting with data heterogeneity in Federated Incremental Learning (FIL) scenarios where edge clients may lack enough storage space to retain full data. We propose to employ a simple, generic framework for FIL named Re-Fed, which can coordinate each client to cache important samples for replay. More specifically, when a new task arrives, each client first caches selected previous samples based on their global and local importance. Then, the client trains the local model with both the cached samples and the samples from the new task. Theoretically, we analyze the ability of Re-Fed to discover important samples for replay thus alleviating the catastrophic forgetting problem. Moreover, we empirically show that Re-Fed achieves competitive performance compared to state-of-the-art methods.
Published: 2024

10. FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems

Author: Huang, Junjie, Liu, Jinyang, Chen, Zhuangbin, Jiang, Zhihan, LI, Yichen, Gu, Jiazhen, Feng, Cong, Yang, Zengyin, Yang, Yongqiang, and Lyu, Michael R.
Subjects: Computer Science - Software Engineering, Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: Postmortem analysis is essential in the management of incidents within cloud systems, which provides valuable insights to improve system's reliability and robustness. At CloudA, fault pattern profiling is performed during the postmortem phase, which involves the classification of incidents' faults into unique categories, referred to as fault pattern. By aggregating and analyzing these fault patterns, engineers can discern common faults, vulnerable components and emerging fault trends. However, this process is currently conducted by manual labeling, which has inherent drawbacks. On the one hand, the sheer volume of incidents means only the most severe ones are analyzed, causing a skewed overview of fault patterns. On the other hand, the complexity of the task demands extensive domain knowledge, which leads to errors and inconsistencies. To address these limitations, we propose an automated approach, named FaultProfIT, for Fault pattern Profiling of Incident Tickets. It leverages hierarchy-guided contrastive learning to train a hierarchy-aware incident encoder and predicts fault patterns with enhanced incident representations. We evaluate FaultProfIT using the production incidents from CloudA. The results demonstrate that FaultProfIT outperforms state-of-the-art methods. Our ablation study and analysis also verify the effectiveness of hierarchy-guided contrastive learning. Additionally, we have deployed FaultProfIT at CloudA for six months. To date, FaultProfIT has analyzed 10,000+ incidents from 30+ cloud services, successfully revealing several fault trends that have informed system improvements., Comment: Accepted by Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice (ICSE SEIP 2024)
Published: 2024
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11. Go Static: Contextualized Logging Statement Generation

Author: Li, Yichen, Huo, Yintong, Zhong, Renyi, Jiang, Zhihan, Liu, Jinyang, Huang, Junjie, Gu, Jiazhen, He, Pinjia, and Lyu, Michael R.
Subjects: Computer Science - Software Engineering
Abstract: Logging practices have been extensively investigated to assist developers in writing appropriate logging statements for documenting software behaviors. Although numerous automatic logging approaches have been proposed, their performance remains unsatisfactory due to the constraint of the single-method input, without informative programming context outside the method. Specifically, we identify three inherent limitations with single-method context: limited static scope of logging statements, inconsistent logging styles, and missing type information of logging variables. To tackle these limitations, we propose SCLogger, the first contextualized logging statement generation approach with inter-method static contexts. First, SCLogger extracts inter-method contexts with static analysis to construct the contextualized prompt for language models to generate a tentative logging statement. The contextualized prompt consists of an extended static scope and sampled similar methods, ordered by the chain-of-thought (COT) strategy. Second, SCLogger refines the access of logging variables by formulating a new refinement prompt for language models, which incorporates detailed type information of variables in the tentative logging statement. The evaluation results show that SCLogger surpasses the state-of-the-art approach by 8.7% in logging position accuracy, 32.1% in level accuracy, 19.6% in variable precision, and 138.4% in text BLEU-4 score. Furthermore, SCLogger consistently boosts the performance of logging statement generation across a range of large language models, thereby showcasing the generalizability of this approach., Comment: This paper was accepted by The ACM International Conference on the Foundations of Software Engineering (FSE 2024)
Published: 2024

12. Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context

Author: Li, Yichen, Peng, Yun, Huo, Yintong, and Lyu, Michael R.
Subjects: Computer Science - Software Engineering, Computer Science - Artificial Intelligence
Abstract: Large Language Models (LLMs) have achieved remarkable success in code completion, as evidenced by their essential roles in developing code assistant services such as Copilot. Being trained on in-file contexts, current LLMs are quite effective in completing code for single source files. However, it is challenging for them to conduct repository-level code completion for large software projects that require cross-file information. Existing research on LLM-based repository-level code completion identifies and integrates cross-file contexts, but it suffers from low accuracy and limited context length of LLMs. In this paper, we argue that Integrated Development Environments (IDEs) can provide direct, accurate and real-time cross-file information for repository-level code completion. We propose IDECoder, a practical framework that leverages IDE native static contexts for cross-context construction and diagnosis results for self-refinement. IDECoder utilizes the rich cross-context information available in IDEs to enhance the capabilities of LLMs of repository-level code completion. We conducted preliminary experiments to validate the performance of IDECoder and observed that this synergy represents a promising trend for future exploration.
Published: 2024

13. An Empirical Study on Large Language Models in Accuracy and Robustness under Chinese Industrial Scenarios

Author: Li, Zongjie, Qiu, Wenying, Ma, Pingchuan, Li, Yichen, Li, You, He, Sijia, Jiang, Baozheng, Wang, Shuai, and Gu, Weixi
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Recent years have witnessed the rapid development of large language models (LLMs) in various domains. To better serve the large number of Chinese users, many commercial vendors in China have adopted localization strategies, training and providing local LLMs specifically customized for Chinese users. Furthermore, looking ahead, one of the key future applications of LLMs will be practical deployment in industrial production by enterprises and users in those sectors. However, the accuracy and robustness of LLMs in industrial scenarios have not been well studied. In this paper, we present a comprehensive empirical study on the accuracy and robustness of LLMs in the context of the Chinese industrial production area. We manually collected 1,200 domain-specific problems from 8 different industrial sectors to evaluate LLM accuracy. Furthermore, we designed a metamorphic testing framework containing four industrial-specific stability categories with eight abilities, totaling 13,631 questions with variants to evaluate LLM robustness. In total, we evaluated 9 different LLMs developed by Chinese vendors, as well as four different LLMs developed by global vendors. Our major findings include: (1) Current LLMs exhibit low accuracy in Chinese industrial contexts, with all LLMs scoring less than 0.6. (2) The robustness scores vary across industrial sectors, and local LLMs overall perform worse than global ones. (3) LLM robustness differs significantly across abilities. Global LLMs are more robust under logical-related variants, while advanced local LLMs perform better on problems related to understanding Chinese industrial terminology. Our study results provide valuable guidance for understanding and promoting the industrial domain capabilities of LLMs from both development and industrial enterprise perspectives. The results further motivate possible research directions and tooling support.
Published: 2024

14. MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection

Author: Liu, Jinyang, Gu, Wenwei, Chen, Zhuangbin, Li, Yichen, Su, Yuxin, and Lyu, Michael R.
Subjects: Computer Science - Software Engineering, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Key Performance Indicators (KPIs) are essential time-series metrics for ensuring the reliability and stability of many software systems. They faithfully record runtime states to facilitate the understanding of anomalous system behaviors and provide informative clues for engineers to pinpoint the root causes. The unprecedented scale and complexity of modern software systems, however, make the volume of KPIs explode. Consequently, many traditional methods of KPI anomaly detection become impractical, which serves as a catalyst for the fast development of machine learning-based solutions in both academia and industry. However, there is currently a lack of rigorous comparison among these KPI anomaly detection methods, and re-implementation demands a non-trivial effort. Moreover, we observe that different works adopt independent evaluation processes with different metrics. Some of them may not fully reveal the capability of a model and some are creating an illusion of progress. To better understand the characteristics of different KPI anomaly detectors and address the evaluation issue, in this paper, we provide a comprehensive review and evaluation of twelve state-of-the-art methods, and propose a novel metric called salience. Particularly, the selected methods include five traditional machine learning-based methods and seven deep learning-based methods. These methods are evaluated with five multivariate KPI datasets that are publicly available. A unified toolkit with easy-to-use interfaces is also released. We report the benchmark results in terms of accuracy, salience, efficiency, and delay, which are of practical importance for industrial deployment. We believe our work can contribute as a basis for future academic research and industrial application., Comment: The code and datasets are available at https://github.com/OpsPAI/MTAD
Published: 2024

15. Synthesis of Nano-sheet SAPO-34 Zeolites Via Aminothermal Method and Its Methanol to Olefins (MTO) Performance

Author: Li, Shengpeng, Cheng, Shimin, Yuan, Long, Tan, Guangyu, Cheng, Ying, Li, Li, Li, Yichen, and Wang, Chuang
Published: 2024
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16. Backward reasoning through AND/OR trees to solve problems

Author: Olieslagers, Jeroen, Bnaya, Zahy, Li, Yichen, and Ma, Wei Ji
Subjects: Psychology, Problem Solving, Reasoning, Representation, Computational Modeling
Abstract: Whether travelling, playing games, or debugging code, any situation where an agent desires change can be framed as a problem. Despite this ubiquity, there is no unifying framework describing how people reason backwards when solving problems. We introduce AND/OR trees, which chain together subgoals and actions to attain them, as a way to represent this process. To investigate whether actions from AND/OR trees were predictive of human behavior, we conducted a study in which participants solved deterministic, long-horizon puzzles. AND/OR trees were able to explain most of the actions the participants took. Next, we modeled search through these trees using a psychologically plausible, single-parameter search algorithm. We fit this model to the data of individual participants and found that it captures trends in summary statistics of human play. Our results show the promise of AND/OR trees as a representation for backward reasoning in problem solving.
Published: 2024

17. Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms

Author: Li, Yichen and Zhang, Chicheng
Subjects: Computer Science - Machine Learning
Abstract: We study interactive imitation learning, where a learner interactively queries a demonstrating expert for action annotations, aiming to learn a policy that has performance competitive with the expert, using as few annotations as possible. We focus on the general agnostic setting where the expert demonstration policy may not be contained in the policy class used by the learner. We propose a new oracle-efficient algorithm MFTPL-P (abbreviation for Mixed Follow the Perturbed Leader with Poisson perturbations) with provable finite-sample guarantees, under the assumption that the learner is given access to samples from some ``explorative'' distribution over states. Our guarantees hold for any policy class, which is considerably broader than prior state of the art. We further propose Bootstrap-Dagger, a more practical variant that does not require additional sample access. Empirically, MFTPL-P and Bootstrap-Dagger notably surpass online and offline imitation learning baselines in continuous control tasks., Comment: 38 pages, 59 figures, under review of AISTATS conference
Published: 2023

18. Efficient Human-AI Coordination via Preparatory Language-based Convention

Author: Guan, Cong, Zhang, Lichao, Fan, Chunpeng, Li, Yichen, Chen, Feng, Li, Lihe, Tian, Yunjia, Yuan, Lei, and Yu, Yang
Subjects: Computer Science - Machine Learning, Computer Science - Computation and Language
Abstract: Developing intelligent agents capable of seamless coordination with humans is a critical step towards achieving artificial general intelligence. Existing methods for human-AI coordination typically train an agent to coordinate with a diverse set of policies or with human models fitted from real human data. However, the massively diverse styles of human behavior present obstacles for AI systems with constrained capacity, while high quality human data may not be readily available in real-world scenarios. In this study, we observe that prior to coordination, humans engage in communication to establish conventions that specify individual roles and actions, making their coordination proceed in an orderly manner. Building upon this observation, we propose employing the large language model (LLM) to develop an action plan (or equivalently, a convention) that effectively guides both human and AI. By inputting task requirements, human preferences, the number of agents, and other pertinent information into the LLM, it can generate a comprehensive convention that facilitates a clear understanding of tasks and responsibilities for all parties involved. Furthermore, we demonstrate that decomposing the convention formulation problem into sub-problems with multiple new sessions being sequentially employed and human feedback, will yield a more efficient coordination convention. Experimental evaluations conducted in the Overcooked-AI environment, utilizing a human proxy model, highlight the superior performance of our proposed method compared to existing learning-based approaches. When coordinating with real humans, our method achieves better alignment with human preferences and an average performance improvement of 15% compared to the state-of-the-art.
Published: 2023

19. Siamese-DETR for Generic Multi-Object Tracking

Author: Liu, Qiankun, Li, Yichen, Jiang, Yuqi, and Fu, Ying
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: The ability to detect and track the dynamic objects in different scenes is fundamental to real-world applications, e.g., autonomous driving and robot navigation. However, traditional Multi-Object Tracking (MOT) is limited to tracking objects belonging to the pre-defined closed-set categories. Recently, Open-Vocabulary MOT (OVMOT) and Generic MOT (GMOT) are proposed to track interested objects beyond pre-defined categories with the given text prompt and template image. However, the expensive well pre-trained (vision-)language model and fine-grained category annotations are required to train OVMOT models. In this paper, we focus on GMOT and propose a simple but effective method, Siamese-DETR, for GMOT. Only the commonly used detection datasets (e.g., COCO) are required for training. Different from existing GMOT methods, which train a Single Object Tracking (SOT) based detector to detect interested objects and then apply a data association based MOT tracker to get the trajectories, we leverage the inherent object queries in DETR variants. Specifically: 1) The multi-scale object queries are designed based on the given template image, which are effective for detecting different scales of objects with the same category as the template image; 2) A dynamic matching training strategy is introduced to train Siamese-DETR on commonly used detection datasets, which takes full advantage of provided annotations; 3) The online tracking pipeline is simplified through a tracking-by-query manner by incorporating the tracked boxes in previous frame as additional query boxes. The complex data association is replaced with the much simpler Non-Maximum Suppression (NMS). Extensive experimental results show that Siamese-DETR surpasses existing MOT methods on GMOT-40 dataset by a large margin.
Published: 2023

20. LILAC: Log Parsing using LLMs with Adaptive Parsing Cache

Author: Jiang, Zhihan, Liu, Jinyang, Chen, Zhuangbin, Li, Yichen, Huang, Junjie, Huo, Yintong, He, Pinjia, Gu, Jiazhen, and Lyu, Michael R.
Subjects: Computer Science - Software Engineering
Abstract: Log parsing transforms log messages into structured formats, serving as the prerequisite step for various log analysis tasks. Although a variety of log parsing approaches have been proposed, their performance on complicated log data remains compromised due to the use of human-crafted rules or learning-based models with limited training data. The recent emergence of powerful large language models (LLMs) demonstrates their vast pre-trained knowledge related to code and logging, making it promising to apply LLMs for log parsing. However, their lack of specialized log parsing capabilities currently hinders their accuracy in parsing. Moreover, the inherent inconsistent answers, as well as the substantial overhead, prevent the practical adoption of LLM-based log parsing. To address these challenges, we propose LILAC, the first practical log parsing framework using LLMs with adaptive parsing cache. To facilitate accurate and robust log parsing, LILAC leverages the in-context learning (ICL) capability of the LLM by performing a hierarchical candidate sampling algorithm and selecting high-quality demonstrations. Furthermore, LILAC incorporates a novel component, an adaptive parsing cache, to store and refine the templates generated by the LLM. It helps mitigate LLM's inefficiency issue by enabling rapid retrieval of previously processed log templates. In this process, LILAC adaptively updates the templates within the parsing cache to ensure the consistency of parsed results. The extensive evaluation on public large-scale datasets shows that LILAC outperforms state-of-the-art methods by 69.5% in terms of the average F1 score of template accuracy. In addition, LILAC reduces the query times to LLMs by several orders of magnitude, achieving a comparable efficiency to the fastest baseline., Comment: This paper was accepted by The ACM International Conference on the Foundations of Software Engineering (FSE 2024)
Published: 2023

21. Advancing cardiac regeneration through 3D bioprinting: methods, applications, and future directions

Author: Zheng, Zilong, Tang, Weijie, Li, Yichen, Ai, Yinze, Tu, Zhi, Yang, Jinfu, and Fan, Chengming
Published: 2024
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22. ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility

Author: Tian, Yunsheng, Willis, Karl D. D., Omari, Bassel Al, Luo, Jieliang, Ma, Pingchuan, Li, Yichen, Javid, Farhad, Gu, Edward, Jacob, Joshua, Sueda, Shinjiro, Li, Hui, Chitta, Sachin, and Matusik, Wojciech
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Graphics
Abstract: The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together. In this paper, we present ASAP, a physics-based planning approach for automatically generating such a sequence for general-shaped assemblies. ASAP accounts for gravity to design a sequence where each sub-assembly is physically stable with a limited number of parts being held and a support surface. We apply efficient tree search algorithms to reduce the combinatorial complexity of determining such an assembly sequence. The search can be guided by either geometric heuristics or graph neural networks trained on data with simulation labels. Finally, we show the superior performance of ASAP at generating physically realistic assembly sequence plans on a large dataset of hundreds of complex product assemblies. We further demonstrate the applicability of ASAP on both simulation and real-world robotic setups. Project website: asap.csail.mit.edu, Comment: ICRA 2024
Published: 2023

23. Missing-modality Enabled Multi-modal Fusion Architecture for Medical Data

Author: Wang, Muyu, Fan, Shiyu, Li, Yichen, and Chen, Hui
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Fusing multi-modal data can improve the performance of deep learning models. However, missing modalities are common for medical data due to patients' specificity, which is detrimental to the performance of multi-modal models in applications. Therefore, it is critical to adapt the models to missing modalities. This study aimed to develop an efficient multi-modal fusion architecture for medical data that was robust to missing modalities and further improved the performance on disease diagnosis.X-ray chest radiographs for the image modality, radiology reports for the text modality, and structured value data for the tabular data modality were fused in this study. Each modality pair was fused with a Transformer-based bi-modal fusion module, and the three bi-modal fusion modules were then combined into a tri-modal fusion framework. Additionally, multivariate loss functions were introduced into the training process to improve model's robustness to missing modalities in the inference process. Finally, we designed comparison and ablation experiments for validating the effectiveness of the fusion, the robustness to missing modalities and the enhancements from each key component. Experiments were conducted on MIMIC-IV, MIMIC-CXR with the 14-label disease diagnosis task. Areas under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC) were used to evaluate models' performance. The experimental results demonstrated that our proposed multi-modal fusion architecture effectively fused three modalities and showed strong robustness to missing modalities. This method is hopeful to be scaled to more modalities to enhance the clinical practicality of the model.
Published: 2023

24. Your Code Secret Belongs to Me: Neural Code Completion Tools Can Memorize Hard-Coded Credentials

Author: Huang, Yizhan, Li, Yichen, Wu, Weibin, Zhang, Jianping, and Lyu, Michael R.
Subjects: Computer Science - Cryptography and Security
Abstract: Neural Code Completion Tools (NCCTs) have reshaped the field of software engineering, which are built upon the language modeling technique and can accurately suggest contextually relevant code snippets. However, language models may emit the training data verbatim during inference with appropriate prompts. This memorization property raises privacy concerns of NCCTs about hard-coded credential leakage, leading to unauthorized access to applications, systems, or networks. Therefore, to answer whether NCCTs will emit the hard-coded credential, we propose an evaluation tool called Hard-coded Credential Revealer (HCR). HCR constructs test prompts based on GitHub code files with credentials to reveal the memorization phenomenon of NCCTs. Then, HCR designs four filters to filter out ill-formatted credentials. Finally, HCR directly checks the validity of a set of non-sensitive credentials. We apply HCR to evaluate three representative types of NCCTs: Commercial NCCTs, open-source models, and chatbots with code completion capability. Our experimental results show that NCCTs can not only return the precise piece of their training data but also inadvertently leak additional secret strings. Notably, two valid credentials were identified during our experiments. Therefore, HCR raises a severe privacy concern about the potential leakage of hard-coded credentials in the training data of commercial NCCTs. All artifacts and data are released for future research purposes in https://github.com/HCR-Repo/HCR., Comment: Accepted by FSE '24
Published: 2023
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25. Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Author: Abusleme, Angel, Adam, Thomas, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, Akram, Muhammad, Aleem, Abid, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, Asavapibhop, Burin, de André, João Pedro Athayde Marcondes, Auguste, Didier, Bai, Weidong, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Beretta, Marco, Bergnoli, Antonio, Bick, Daniel, Bieger, Lukas, Biktemerova, Svetlana, Birkenfeld, Thilo, Morton-Blake, Iwan, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Busto, Jose, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callier, Stéphane, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Cerrone, Vanessa, Chan, Chi, Chang, Jinfan, Chang, Yun, Chatrabhuti, Auttakit, Chen, Chao, Chen, Guoming, Chen, Pingping, Chen, Shaomin, Chen, Yixue, Chen, Yu, Chen, Zhangming, Chen, Zhiyuan, Chen, Zikang, Cheng, Jie, Cheng, Yaping, Cheng, Yu Chin, Chepurnov, Alexander, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chin, Yen-Ting, Chu, Ziliang, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Molla, Marta Colomer, Di Lorenzo, Selma Conforti, Coppi, Alberto, Corti, Daniele, Csakli, Simon, Corso, Flavio Dal, Dalager, Olivia, Datta, Jaydeep, De La Taille, Christophe, Deng, Zhi, Deng, Ziyan, Ding, Xiaoyu, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dittrich, Carsten, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dou, Wei, Dracos, Marcos, Druillole, Frédéric, Du, Ran, Du, Shuxian, Dugas, Katherine, Dusini, Stefano, Duyang, Hongyue, Eck, Jessica, Enqvist, Timo, Fabbri, Andrea, Fahrendholz, Ulrike, Fan, Lei, Fang, Jian, Fang, Wenxing, Fargetta, Marco, Fedoseev, Dmitry, Fei, Zhengyong, Feng, Li-Cheng, Feng, Qichun, Ferraro, Federico, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guo, Cong, Guo, Wanlei, Guo, Xinheng, Hagner, Caren, Han, Ran, Han, Yang, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Jinhao, Huang, Junting, Huang, Kaixuan, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Imbert, Leonard, Ioannisian, Ara, Isocrate, Roberto, Jafar, Arshak, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Cailian, Jiang, Di, Jiang, Wei, Jiang, Xiaoshan, Jing, Xiaoping, Jollet, Cécile, Kampmann, Philipp, Kang, Li, Karaparambil, Rebin, Kazarian, Narine, Khan, Ali, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kuleshov, Sergey, Kutovskiy, Nikolay, Labit, Loïc, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Huiling, Li, Jiajun, Li, Mengzhao, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, Hao, Liao, Jiajun, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Ling, Xin, Lippi, Ivano, Liu, Caimei, Liu, Fang, Liu, Fengcheng, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jiaxi, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shenghui, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Xuewei, Liu, Yankai, Liu, Zhen, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Peizhi, Lu, Shuxiang, Lu, Xianguo, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Daibin, Luo, Fengjiao, Luo, Guang, Luo, Jianyi, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Magoni, Marco, Mai, Jingyu, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meraviglia, Anita, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Miramonti, Lino, Mohan, Nikhil, Montuschi, Michele, Müller, Axel, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parmeggiano, Sergio, Pei, Yatian, Pelicci, Luca, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qi, Xiaohui, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Qu, Manhao, Qu, Zhenning, Ranucci, Gioacchino, Rasheed, Reem, Re, Alessandra, Rebii, Abdel, Redchuk, Mariia, Ren, Bin, Ren, Jie, Ricci, Barbara, Rientong, Komkrit, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sandanayake, Deshan, Sangka, Anut, Sava, Giuseppe, Sawangwit, Utane, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settimo, Mariangela, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Singhal, Apeksha, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Su, Aoqi, Su, Jun, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Szelezniak, Michal, Takenaka, Akira, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Hariharan, Vidhya Thara, Theisen, Eric, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Tortorici, Francesco, Treskov, Konstantin, Triossi, Andrea, Triozzi, Riccardo, Trzaska, Wladyslaw, Tung, Yu-Chen, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Venettacci, Carlo, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, von Sturm, Katharina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Li, Wang, Lu, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuyi, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Watcharangkool, Apimook, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wei, Yuehuan, Wen, Kaile, Wen, Liangjian, Weng, Jun, Wiebusch, Christopher, Wirth, Rosmarie, Wonsak, Bjoern, Wu, Diru, Wu, Qun, Wu, Yiyang, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xiao, Fei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yuguang, Xie, Zhangquan, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Xunjie, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Qiyu, Yan, Taylor, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Guojun, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zavadskyi, Vitalii, Zeng, Fanrui, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Haosen, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jingbo, Zhang, Jinnan, ZHANG, Lei, Zhang, Mohan, Zhang, Peng, Zhang, Ping, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Shuihan, Zhang, Siyuan, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Jie, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zheng, Dongqin, Zheng, Hua, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, Zou, Jiaheng, and Züfle, Jan
Subjects: High Energy Physics - Experiment, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: The core-collapse supernova (CCSN) is considered one of the most energetic astrophysical events in the universe. The early and prompt detection of neutrinos before (pre-SN) and during the supernova (SN) burst presents a unique opportunity for multi-messenger observations of CCSN events. In this study, we describe the monitoring concept and present the sensitivity of the system to pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton liquid scintillator detector currently under construction in South China. The real-time monitoring system is designed to ensure both prompt alert speed and comprehensive coverage of progenitor stars. It incorporates prompt monitors on the electronic board as well as online monitors at the data acquisition stage. Assuming a false alert rate of 1 per year, this monitoring system exhibits sensitivity to pre-SN neutrinos up to a distance of approximately 1.6 (0.9) kiloparsecs and SN neutrinos up to about 370 (360) kiloparsecs for a progenitor mass of 30 solar masses, considering both normal and inverted mass ordering scenarios. The pointing ability of the CCSN is evaluated by analyzing the accumulated event anisotropy of inverse beta decay interactions from pre-SN or SN neutrinos. This, along with the early alert, can play a crucial role in facilitating follow-up multi-messenger observations of the next galactic or nearby extragalactic CCSN., Comment: 24 pages, 9 figures, accepted for the publication at JCAP
Published: 2023

26. A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We?

Author: Jiang, Zhihan, Liu, Jinyang, Huang, Junjie, Li, Yichen, Huo, Yintong, Gu, Jiazhen, Chen, Zhuangbin, Zhu, Jieming, and Lyu, Michael R.
Subjects: Computer Science - Software Engineering
Abstract: Log data have facilitated various tasks of software development and maintenance, such as testing, debugging and diagnosing. Due to the unstructured nature of logs, log parsing is typically required to transform log messages into structured data for automated log analysis. Given the abundance of log parsers that employ various techniques, evaluating these tools to comprehend their characteristics and performance becomes imperative. Loghub serves as a commonly used dataset for benchmarking log parsers, but it suffers from limited scale and representativeness, posing significant challenges for studies to comprehensively evaluate existing log parsers or develop new methods. This limitation is particularly pronounced when assessing these log parsers for production use. To address these limitations, we provide a new collection of annotated log datasets, denoted Loghub-2.0, which can better reflect the characteristics of log data in real-world software systems. Loghub-2.0 comprises 14 datasets with an average of 3.6 million log lines in each dataset. Based on Loghub-2.0, we conduct a thorough re-evaluation of 15 state-of-the-art log parsers in a more rigorous and practical setting. Particularly, we introduce a new evaluation metric to mitigate the sensitivity of existing metrics to imbalanced data distributions. We are also the first to investigate the granular performance of log parsers on logs that represent rare system events, offering in-depth details for software diagnosis. Accurately parsing such logs is essential, yet it remains a challenge. We believe this work could shed light on the evaluation and design of log parsers in practical settings, thereby facilitating their deployment in production systems., Comment: This paper was accepted by 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2024)
Published: 2023

27. AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection

Author: Huo, Yintong, Li, Yichen, Su, Yuxin, He, Pinjia, Xie, Zifan, and Lyu, Michael R.
Subjects: Computer Science - Software Engineering
Abstract: The rapid progress of modern computing systems has led to a growing interest in informative run-time logs. Various log-based anomaly detection techniques have been proposed to ensure software reliability. However, their implementation in the industry has been limited due to the lack of high-quality public log resources as training datasets. While some log datasets are available for anomaly detection, they suffer from limitations in (1) comprehensiveness of log events; (2) scalability over diverse systems; and (3) flexibility of log utility. To address these limitations, we propose AutoLog, the first automated log generation methodology for anomaly detection. AutoLog uses program analysis to generate run-time log sequences without actually running the system. AutoLog starts with probing comprehensive logging statements associated with the call graphs of an application. Then, it constructs execution graphs for each method after pruning the call graphs to find log-related execution paths in a scalable manner. Finally, AutoLog propagates the anomaly label to each acquired execution path based on human knowledge. It generates flexible log sequences by walking along the log execution paths with controllable parameters. Experiments on 50 popular Java projects show that AutoLog acquires significantly more (9x-58x) log events than existing log datasets from the same system, and generates log messages much faster (15x) with a single machine than existing passive data collection approaches. We hope AutoLog can facilitate the benchmarking and adoption of automated log analysis techniques., Comment: The paper has been accepted by ASE 2023 (Research Track)
Published: 2023

28. Exploring the Effectiveness of LLMs in Automated Logging Generation: An Empirical Study

Author: Li, Yichen, Huo, Yintong, Jiang, Zhihan, Zhong, Renyi, He, Pinjia, Su, Yuxin, Briand, Lionel, and Lyu, Michael R.
Subjects: Computer Science - Software Engineering
Abstract: Automated logging statement generation supports developers in documenting critical software runtime behavior. Given the great success in natural language generation and programming language comprehension, large language models (LLMs) might help developers generate logging statements, but this has not yet been investigated. To fill the gap, this paper performs the first study on exploring LLMs for logging statement generation.We first build a logging statement generation dataset, LogBench, with two parts: (1) LogBench-O: logging statements collected from GitHub repositories, and (2) LogBench-T: the transformed unseen code from LogBench-O. Then, we leverage LogBench to evaluate the effectiveness and generalization capabilities (using LogBench-T) of eleven top-performing LLMs. In addition, we examine the performance of these LLMs against classical retrieval-based and machine learning-based logging methods from the era preceding LLMs. We further evaluate LLM's logging generalization capabilities using unseen data (LogBench-T) derived from code transformation techniques. While existing LLMs deliver decent predictions on logging levels and logging variables, our study indicates that they only achieve a maximum BLEU score of 0.249, thus calling for improvements. The paper also highlights the importance of prompt constructions and external factors (e.g., programming contexts and code comments) for LLMs' logging performance. Based on these findings, we identify five implications and provide practical advice for future logging research. Our empirical analysis discloses the limitations of current logging approaches while showcasing the potential of LLM-based logging tools, and provides actionable guidance for building more practical models.
Published: 2023

29. Hypernetwork-driven centralized contrastive learning for federated graph classification

Author: Zhu, Jianian, Li, Yichen, Wang, Haozhao, Qi, Yining, and Li, Ruixuan
Published: 2024
Full Text: View/download PDF

30. Collaborative security model in East Asia: Possibilities and challenges

Author: Jiang, Mingyue, primary, Li, Yichen, additional, and Zhang, Ziren, additional
Published: 2024
Full Text: View/download PDF

31. JUNO sensitivity to the annihilation of MeV dark matter in the galactic halo

Author: JUNO Collaboration, Abusleme, Angel, Adam, Thomas, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, Akram, Muhammad, Aleem, Abid, Alexandros, Tsagkarakis, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, Asavapibhop, Burin, de André, João Pedro Athayde Marcondes, Auguste, Didier, Bai, Weidong, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Bergnoli, Antonio, Bick, Daniel, Birkenfeld, Thilo, Blin, Sylvie, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Busto, Jose, Butorov, Ilya, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callegari, Riccardo, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Chan, Chi, Chang, Jinfan, Chang, Yun, Chen, Guoming, Chen, Pingping, Chen, Po-An, Chen, Shaomin, Chen, Yixue, Chen, Yu, Chen, Zhiyuan, Chen, Zikang, Cheng, Jie, Cheng, Yaping, Cheng, Yu Chin, Chepurnov, Alexander, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chu, Ziliang, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Molla, Marta Colomer, Di Lorenzo, Selma Conforti, Corti, Daniele, Corso, Flavio Dal, Dalager, Olivia, De La Taille, Christophe, Deng, Zhi, Deng, Ziyan, Depnering, Wilfried, Diaz, Marco, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dou, Wei, Dracos, Marcos, Druillole, Frédéric, Du, Ran, Du, Shuxian, Dusini, Stefano, Dvorak, Martin, Eck, Jessica, Enqvist, Timo, Fabbri, Andrea, Fahrendholz, Ulrike, Fan, Donghua, Fan, Lei, Fang, Jian, Fang, Wenxing, Fargetta, Marco, Fedoseev, Dmitry, Fei, Zhengyong, Feng, Li-Cheng, Feng, Qichun, Ford, Richard, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guo, Cong, Guo, Wanlei, Guo, Xinheng, Guo, Yuhang, Hagner, Caren, Han, Ran, Han, Yang, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Kaixuan, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Ioannisian, Ara, Isocrate, Roberto, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Di, Jiang, Wei, Jiang, Xiaoshan, Jing, Xiaoping, Jollet, Cécile, Kalousis, Leonidas, Kampmann, Philipp, Kang, Li, Karaparambil, Rebin, Kazarian, Narine, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kutovskiy, Nikolay, Kuusiniemi, Pasi, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Daozheng, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Huiling, Li, Mengzhao, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zepeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, Hao, Liao, Jiajun, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Lippi, Ivano, Liu, Fang, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Yan, Liu, Yunzhe, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Peizhi, Lu, Shuxiang, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Daibin, Luo, Fengjiao, Luo, Guang, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Mai, Jingyu, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martin-Chassard, Gisele, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meinusch, Artur, Meng, Yue, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Mezzetto, Mauro, Miller, Jonathan, Miramonti, Lino, Montini, Paolo, Montuschi, Michele, Müller, Axel, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parmeggiano, Sergio, Pei, Yatian, Pelicci, Luca, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Ranucci, Gioacchino, Rasheed, Reem, Re, Alessandra, Rebber, Henning, Rebii, Abdel, Redchuk, Mariia, Ren, Bin, Ren, Jie, Ricci, Barbara, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sanfilippo, Simone, Sangka, Anut, Sawangwit, Utane, Sawatzki, Julia, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settanta, Giulio, Settimo, Mariangela, Shao, Zhuang, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Slupecki, Maciej, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Štefánik, Dušan, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Su, Jun, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Szelezniak, Michal, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Hariharan, Vidhya Thara, Theisen, Eric, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Treskov, Konstantin, Triossi, Andrea, Troni, Giancarlo, Trzaska, Wladyslaw, Tung, Yu-Chen, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, Volpe, Cristina, von Sturm, Katharina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Lu, Wang, Meifen, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuman, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Wang, Zongyi, Watcharangkool, Apimook, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wen, Kaile, Wen, Liangjian, Weng, Jun, Wiebusch, Christopher, Wonsak, Bjoern, Wu, Diru, Wu, Qun, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yuguang, Xie, Zhangquan, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Qiyu, Yan, Taylor, Yan, Wenqi, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Huan, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zavadskyi, Vitalii, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Guoqing, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jin, Zhang, Jingbo, Zhang, Jinnan, Zhang, Mohan, Zhang, Peng, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Jie, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zheng, Dongqin, Zheng, Hua, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, Zou, Jiaheng, and Zwickel, Sebastian
Subjects: High Energy Physics - Experiment, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: We discuss JUNO sensitivity to the annihilation of MeV dark matter in the galactic halo via detecting inverse beta decay reactions of electron anti-neutrinos resulting from the annihilation. We study possible backgrounds to the signature, including the reactor neutrinos, diffuse supernova neutrino background, charged- and neutral-current interactions of atmospheric neutrinos, backgrounds from muon-induced fast neutrons and cosmogenic isotopes. A fiducial volume cut, as well as the pulse shape discrimination and the muon veto are applied to suppress the above backgrounds. It is shown that JUNO sensitivity to the thermally averaged dark matter annihilation rate in 10 years of exposure would be significantly better than the present-day best limit set by Super-Kamiokande and would be comparable to that expected by Hyper-Kamiokande., Comment: 25 pages, 9 figures, matches the publised version
Published: 2023
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32. Learning Preconditioner for Conjugate Gradient PDE Solvers

Author: Li, Yichen, Chen, Peter Yichen, Du, Tao, and Matusik, Wojciech
Subjects: Mathematics - Numerical Analysis
Abstract: Efficient numerical solvers for partial differential equations empower science and engineering. One of the commonly employed numerical solvers is the preconditioned conjugate gradient (PCG) algorithm which can solve large systems to a given precision level. One challenge in PCG solvers is the selection of preconditioners, as different problem-dependent systems can benefit from different preconditioners. We present a new method to introduce \emph{inductive bias} in preconditioning conjugate gradient algorithm. Given a system matrix and a set of solution vectors arise from an underlying distribution, we train a graph neural network to obtain an approximate decomposition to the system matrix to be used as a preconditioner in the context of PCG solvers. We conduct extensive experiments to demonstrate the efficacy and generalizability of our proposed approach in solving various 2D and 3D linear second-order PDEs.
Published: 2023

33. Photoelectron Storage at the WO3/TiO2 Interface: Modeling in Ambient Conditions from First-Principles Calculations

Author: Li, Yichen, Cheng, Dongfang, Wei, Ziyang, and Sautet, Philippe
Subjects: photocatalysis, titanium dioxide, tungstentrioxide, first-principles calculation, electronstorage, proton-coupled electron transfer, oxygenreduction reaction, Inorganic Chemistry, Organic Chemistry, Chemical Engineering
Published: 2023

34. Exploiting Diffusion Model as Prompt Generator for Object Localization

Author: Jiang, Yuqi, Liu, Qiankun, Li, Yichen, Jia, Hao, Fu, Ying, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Ye, Long, editor, Yang, Hua, editor, An, Ping, editor, and Yang, Xiaokang, editor
Published: 2024
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35. Sand Particle Monitoring for the High-Production Gas Well Based on EMD-CNN Method

Author: Wang, Kai, Chang, Ziang, Lu, Jiaqi, Tian, Jiaqi, Yang, Kui, Li, Yichen, Wang, Gang, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Sun, Baojiang, editor, Sun, Jinsheng, editor, Wang, Zhiyuan, editor, Chen, Litao, editor, and Chen, Meiping, editor
Published: 2024
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36. Sliding Window Detection and Distance-Based Matching for Tracking on Gigapixel Images

Author: Li, Yichen, Liu, Qiankun, Wang, Xiaoyong, Fu, Ying, 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, Fang, Lu, editor, Pei, Jian, editor, Zhai, Guangtao, editor, and Wang, Ruiping, editor
Published: 2024
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37. Clinical Trial Histology Image Based End-to-End Biomarker Expression Levels Prediction and Visualization Using Constrained GANs

Author: Zhao, Wei, Qi, Bozhao, Li, Yichen, Trullo, Roger, Attieh, Elham, Bauchet, Anne-Laure, Tang, Qi, Pochet, Etienne, 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, Wu, Shandong, editor, Shabestari, Behrouz, editor, and Xing, Lei, editor
Published: 2024
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38. Improving the Transferability of Adversarial Samples by Path-Augmented Method

Author: Zhang, Jianping, Huang, Jen-tse, Wang, Wenxuan, Li, Yichen, Wu, Weibin, Wang, Xiaosen, Su, Yuxin, and Lyu, Michael R.
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon negatively affects their deployment in real-world scenarios, especially security-related ones. To evaluate the robustness of a target model in practice, transfer-based attacks craft adversarial samples with a local model and have attracted increasing attention from researchers due to their high efficiency. The state-of-the-art transfer-based attacks are generally based on data augmentation, which typically augments multiple training images from a linear path when learning adversarial samples. However, such methods selected the image augmentation path heuristically and may augment images that are semantics-inconsistent with the target images, which harms the transferability of the generated adversarial samples. To overcome the pitfall, we propose the Path-Augmented Method (PAM). Specifically, PAM first constructs a candidate augmentation path pool. It then settles the employed augmentation paths during adversarial sample generation with greedy search. Furthermore, to avoid augmenting semantics-inconsistent images, we train a Semantics Predictor (SP) to constrain the length of the augmentation path. Extensive experiments confirm that PAM can achieve an improvement of over 4.8% on average compared with the state-of-the-art baselines in terms of the attack success rates., Comment: 10 pages + appendix, CVPR 2023
Published: 2023

39. Category-Level Multi-Part Multi-Joint 3D Shape Assembly

Author: Li, Yichen, Mo, Kaichun, Duan, Yueqi, Wang, He, Zhang, Jiequan, Shao, Lin, Matusik, Wojciech, and Guibas, Leonidas
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Shape assembly composes complex shapes geometries by arranging simple part geometries and has wide applications in autonomous robotic assembly and CAD modeling. Existing works focus on geometry reasoning and neglect the actual physical assembly process of matching and fitting joints, which are the contact surfaces connecting different parts. In this paper, we consider contacting joints for the task of multi-part assembly. A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment. We propose a hierarchical graph learning approach composed of two levels of graph representation learning. The part graph takes part geometries as input to build the desired shape structure. The joint-level graph uses part joints information and focuses on matching and aligning joints. The two kinds of information are combined to achieve the bilateral objectives. Extensive experiments demonstrate that our method outperforms previous methods, achieving better shape structure and higher joint alignment accuracy.
Published: 2023

40. The JUNO experiment Top Tracker

Author: JUNO Collaboration, Abusleme, Angel, Adam, Thomas, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, Akram, Muhammad, Aleem, Abid, Alexandros, Tsagkarakis, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, Asavapibhop, Burin, de André, João Pedro Athayde Marcondes, Auguste, Didier, Bai, Weidong, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Beretta, Marco, Bergnoli, Antonio, Bick, Daniel, Birkenfeld, Thilo, Blin, Sylvie, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Busto, Jose, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callier, Stéphane, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Cerrone, Vanessa, Chan, Chi, Chang, Jinfan, Chang, Yun, Chen, Chao, Chen, Guoming, Chen, Pingping, Chen, Shaomin, Chen, Yixue, Chen, Yu, Chen, Zhiyuan, Chen, Zikang, Cheng, Jie, Cheng, Yaping, Cheng, Yu Chin, Chepurnov, Alexander, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chu, Ziliang, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Molla, Marta Colomer, Di Lorenzo, Selma Conforti, Coppi, Alberto, Corti, Daniele, Corso, Flavio Dal, Dalager, Olivia, De La Taille, Christophe, Deng, Zhi, Deng, Ziyan, Depnering, Wilfried, Diaz, Marco, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dou, Wei, Dracos, Marcos, Drapier, Olivier, Druillole, Frédéric, Du, Ran, Du, Shuxian, Dugas, Katherine, Dusini, Stefano, Duyang, Hongyue, Eck, Jessica, Enqvist, Timo, Fabbri, Andrea, Fahrendholz, Ulrike, Fan, Lei, Fang, Jian, Fang, Wenxing, Fargetta, Marco, Fedoseev, Dmitry, Fei, Zhengyong, Felici, Giulietto, Feng, Li-Cheng, Feng, Qichun, Ferraro, Federico, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Gerasimov, Vladimir, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guo, Cong, Guo, Wanlei, Guo, Xinheng, Guo, Yuhang, Gursky, Semen, Hagner, Caren, Han, Ran, Han, Yang, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Jinhao, Huang, Kaixuan, Huang, Wenhao, Huang, Qinhua, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Ioannisian, Ara, Isocrate, Roberto, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Di, Jiang, Wei, Jiang, Xiaoshan, Jing, Xiaoping, Jollet, Cécile, Kalousis, Leonidas, Kampmann, Philipp, Kang, Li, Karaparambil, Rebin, Kazarian, Narine, Khan, Ali, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kuleshov, Sergey, Kutovskiy, Nikolay, Kuusiniemi, Pasi, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Huiling, Li, Mengzhao, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zepeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, Hao, Liao, Jiajun, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Lippi, Ivano, Liu, Caimei, Liu, Fang, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jiaxi, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shenghui, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Yankai, Liu, Yunzhe, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Peizhi, Lu, Shuxiang, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Daibin, Luo, Fengjiao, Luo, Guang, Luo, Jianyi, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Magoni, Marco, Mai, Jingyu, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meraviglia, Anita, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Miller, Jonathan, Miramonti, Lino, Montini, Paolo, Montuschi, Michele, Müller, Axel, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parmeggiano, Sergio, Pei, Yatian, Pelicci, Luca, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Ranucci, Gioacchino, Rasheed, Reem, Re, Alessandra, Rebii, Abdel, Redchuk, Mariia, Ren, Bin, Ren, Jie, Ricci, Barbara, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Romanov, Victor, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sandanayake, Deshan, Sanfilippo, Simone, Sangka, Anut, Sawangwit, Utane, Sawatzki, Julia, Schever, Michaela, Schuler, Jacky, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settanta, Giulio, Settimo, Mariangela, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Slupecki, Maciej, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Štefánik, Dušan, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Su, Jun, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Szelezniak, Michal, Takenaka, Akira, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Hariharan, Vidhya Thara, Theisen, Eric, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Tortorici, Francesco, Treskov, Konstantin, Triossi, Andrea, Triozzi, Riccardo, Troni, Giancarlo, Trzaska, Wladyslaw, Tung, Yu-Chen, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, von Sturm, Katharina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Lu, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Watcharangkool, Apimook, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wen, Kaile, Wen, Liangjian, Weng, Jun, Wiebusch, Christopher, Wirth, Rosmarie, Wonsak, Bjoern, Wu, Diru, Wu, Qun, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yuguang, Xie, Zhangquan, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Qiyu, Yan, Taylor, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Guojun, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zavadskyi, Vitalii, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Haosen, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jin, Zhang, Jingbo, Zhang, Jinnan, Zhang, Mohan, Zhang, Peng, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Shuihan, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Jie, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zheng, Dongqin, Zheng, Hua, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, Zou, Jiaheng, and Zwickel, Sebastian
Subjects: High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: The main task of the Top Tracker detector of the neutrino reactor experiment Jiangmen Underground Neutrino Observatory (JUNO) is to reconstruct and extrapolate atmospheric muon tracks down to the central detector. This muon tracker will help to evaluate the contribution of the cosmogenic background to the signal. The Top Tracker is located above JUNO's water Cherenkov Detector and Central Detector, covering about 60% of the surface above them. The JUNO Top Tracker is constituted by the decommissioned OPERA experiment Target Tracker modules. The technology used consists in walls of two planes of plastic scintillator strips, one per transverse direction. Wavelength shifting fibres collect the light signal emitted by the scintillator strips and guide it to both ends where it is read by multianode photomultiplier tubes. Compared to the OPERA Target Tracker, the JUNO Top Tracker uses new electronics able to cope with the high rate produced by the high rock radioactivity compared to the one in Gran Sasso underground laboratory. This paper will present the new electronics and mechanical structure developed for the Top Tracker of JUNO along with its expected performance based on the current detector simulation., Comment: 20 pages
Published: 2023
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41. JUNO sensitivity to $^7$Be, $pep$, and CNO solar neutrinos

Author: Abusleme, Angel, Adam, Thomas, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, Akram, Muhammad, Aleem, Abid, Alexandros, Tsagkarakis, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, Asavapibhop, Burin, de André, João Pedro Athayde Marcondes, Auguste, Didier, Bai, Weidong, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Beretta, Marco, Bergnoli, Antonio, Bick, Daniel, Bieger, Lukas, Biktemerova, Svetlana, Birkenfeld, Thilo, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Busto, Jose, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callier, Stéphane, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Cerrone, Vanessa, Chan, Chi, Chang, Jinfan, Chang, Yun, Chen, Chao, Chen, Guoming, Chen, Pingping, Chen, Shaomin, Chen, Yixue, Chen, Yu, Chen, Zhiyuan, Chen, Zikang, Cheng, Jie, Cheng, Yaping, Cheng, Yu Chin, Chepurnov, Alexander, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chu, Ziliang, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Molla, Marta Colomer, Di Lorenzo, Selma Conforti, Coppi, Alberto, Corti, Daniele, Csakli, Simon, Corso, Flavio Dal, Dalager, Olivia, Datta, Jaydeep, De La Taille, Christophe, Deng, Zhi, Deng, Ziyan, Depnering, Wilfried, Ding, Xiaoyu, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dittrich, Carsten, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dou, Wei, Dracos, Marcos, Druillole, Frédéric, Du, Ran, Du, Shuxian, Dugas, Katherine, Dusini, Stefano, Duyang, Hongyue, Eck, Jessica, Enqvist, Timo, Fabbri, Andrea, Fahrendholz, Ulrike, Fan, Lei, Fang, Jian, Fang, Wenxing, Fargetta, Marco, Fedoseev, Dmitry, Fei, Zhengyong, Feng, Li-Cheng, Feng, Qichun, Ferraro, Federico, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guo, Cong, Guo, Wanlei, Guo, Xinheng, Hagner, Caren, Han, Ran, Han, Yang, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Jinhao, Huang, Junting, Huang, Kaixuan, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Imbert, Leonard, Ioannisian, Ara, Isocrate, Roberto, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Cailian, Jiang, Di, Jiang, Wei, Jiang, Xiaoshan, Jing, Xiaoping, Jollet, Cécile, Kampmann, Philipp, Kang, Li, Karaparambil, Rebin, Kazarian, Narine, Khan, Ali, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kuleshov, Sergey, Kutovskiy, Nikolay, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Huiling, Li, Jiajun, Li, Mengzhao, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zepeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, Hao, Liao, Jiajun, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Ling, Xin, Lippi, Ivano, Liu, Caimei, Liu, Fang, Liu, Fengcheng, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jiaxi, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shenghui, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Xuewei, Liu, Yankai, Liu, Zhen, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Peizhi, Lu, Shuxiang, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Daibin, Luo, Fengjiao, Luo, Guang, Luo, Jianyi, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Magoni, Marco, Mai, Jingyu, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meraviglia, Anita, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Miramonti, Lino, Mohan, Nikhil, Montini, Paolo, Montuschi, Michele, Müller, Axel, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parmeggiano, Sergio, Pei, Yatian, Pelicci, Luca, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qi, Xiaohui, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Ranucci, Gioacchino, Rasheed, Reem, Re, Alessandra, Rebii, Abdel, Redchuk, Mariia, Ren, Bin, Ren, Jie, Ricci, Barbara, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sandanayake, Deshan, Sanfilippo, Simone, Sangka, Anut, Sawangwit, Utane, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settimo, Mariangela, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Singhal, Apeksha, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Su, Jun, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Szelezniak, Michal, Takenaka, Akira, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Hariharan, Vidhya Thara, Theisen, Eric, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Tortorici, Francesco, Treskov, Konstantin, Triossi, Andrea, Triozzi, Riccardo, Trzaska, Wladyslaw, Tung, Yu-Chen, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, von Sturm, Katharina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Lu, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuyi, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Watcharangkool, Apimook, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wen, Kaile, Wen, Liangjian, Weng, Jun, Wiebusch, Christopher, Wirth, Rosmarie, Wonsak, Bjoern, Wu, Diru, Wu, Qun, Wu, Yiyang, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yuguang, Xie, Zhangquan, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Qiyu, Yan, Taylor, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Guojun, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zavadskyi, Vitalii, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Haosen, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jingbo, Zhang, Jinnan, Zhang, Mohan, Zhang, Peng, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Shuihan, Zhang, Siyuan, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Jie, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zheng, Dongqin, Zheng, Hua, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, Zou, Jiaheng, Züfle, Jan, and Zwickel, Sebastian
Subjects: High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: The Jiangmen Underground Neutrino Observatory (JUNO), the first multi-kton liquid scintillator detector, which is under construction in China, will have a unique potential to perform a real-time measurement of solar neutrinos well below the few MeV threshold typical for Water Cherenkov detectors. JUNO's large target mass and excellent energy resolution are prerequisites for reaching unprecedented levels of precision. In this paper, we provide estimation of the JUNO sensitivity to 7Be, pep, and CNO solar neutrinos that can be obtained via a spectral analysis above the 0.45 MeV threshold. This study is performed assuming different scenarios of the liquid scintillator radiopurity, ranging from the most opti mistic one corresponding to the radiopurity levels obtained by the Borexino experiment, up to the minimum requirements needed to perform the neutrino mass ordering determination with reactor antineutrinos - the main goal of JUNO. Our study shows that in most scenarios, JUNO will be able to improve the current best measurements on 7Be, pep, and CNO solar neutrino fluxes. We also perform a study on the JUNO capability to detect periodical time variations in the solar neutrino flux, such as the day-night modulation induced by neutrino flavor regeneration in Earth, and the modulations induced by temperature changes driven by helioseismic waves.
Published: 2023

42. Design optimization of JUNO-TAO plastic scintillator with WLS-fiber and SiPM readout

Author: Luo, Guang, Hor, Y. K., Lu, Peizhi, Wang, Zhimin, Li, Ruhui, Li, Min, Li, Yichen, Zhan, Liang, Wang, Wei, Wei, Yuehuan, Chen, Yu, Xiao, Xiang, and An, Fengpeng
Subjects: Physics - Instrumentation and Detectors
Abstract: Plastic scintillators (PSs)embedded with wavelength-shifting fibers are widely used in high-energy particle physics, such as in muon taggers,as well as in medical physics and other applications. In this study,a simulation package was built to evaluate the effects of the diameter and layout of optical fibers on the light yield with different configurations. The optimal optical configuration was designed based on simulations and validated using two PS prototypes under certain experimental conditions. Atop veto tracker (TVT) for the JUNO-TAO experiment, comprising four layers of 160 strips of PS, was designed and evaluated. The threshold was evaluated when the muon tagging efficiency of a PS strip was >99%. The efficiency of three layer out of four layer of TVT is >99%,even with a tagging efficiency of a single strip as low as 97%, using a threshold of 10 photoelectrons and assuming a 40%silicon PM photon detection efficiency.
Published: 2023
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43. Vortex excites unidirectional water waves near ancient Luoyang Bridge

Author: Li, Yichen and Hu, Xinhua
Published: 2024
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44. Sliding Window Detection and Distance-Based Matching for Tracking on Gigapixel Images

Author: Li, Yichen, primary, Liu, Qiankun, additional, Wang, Xiaoyong, additional, and Fu, Ying, additional
Published: 2024
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45. Meta-Path Based Attentional Graph Learning Model for Vulnerability Detection

Author: Wen, Xin-Cheng, Gao, Cuiyun, Ye, Jiaxin, Li, Yichen, Tian, Zhihong, Jia, Yan, and Wang, Xuan
Subjects: Computer Science - Software Engineering
Abstract: In recent years, deep learning (DL)-based methods have been widely used in code vulnerability detection. The DL-based methods typically extract structural information from source code, e.g., code structure graph, and adopt neural networks such as Graph Neural Networks (GNNs) to learn the graph representations. However, these methods fail to consider the heterogeneous relations in the code structure graph, i.e., the heterogeneous relations mean that the different types of edges connect different types of nodes in the graph, which may obstruct the graph representation learning. Besides, these methods are limited in capturing long-range dependencies due to the deep levels in the code structure graph. In this paper, we propose a Meta-path based Attentional Graph learning model for code vulNErability deTection, called MAGNET. MAGNET constructs a multi-granularity meta-path graph for each code snippet, in which the heterogeneous relations are denoted as meta-paths to represent the structural information. A meta-path based hierarchical attentional graph neural network is also proposed to capture the relations between distant nodes in the graph. We evaluate MAGNET on three public datasets and the results show that MAGNET outperforms the best baseline method in terms of F1 score by 6.32%, 21.50%, and 25.40%, respectively. MAGNET also achieves the best performance among all the baseline methods in detecting Top-25 most dangerous Common Weakness Enumerations (CWEs), further demonstrating its effectiveness in vulnerability detection., Comment: Accepted by TSE
Published: 2022

46. JUNO Sensitivity on Proton Decay $p\to \bar\nu K^+$ Searches

Author: JUNO Collaboration, Abusleme, Angel, Adam, Thomas, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, Akram, Muhammad, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, Asavapibhop, Burin, de André, João Pedro Athayde Marcondes, Auguste, Didier, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Bergnoli, Antonio, Birkenfeld, Thilo, Blin, Sylvie, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Buscemi, Mario, Busto, Jose, Butorov, Ilya, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callegari, Riccardo, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Chang, Jinfan, Chang, Yun, Chen, Pingping, Chen, Po-An, Chen, Shaomin, Chen, Xurong, Chen, Yi-Wen, Chen, Yixue, Chen, Yu, Chen, Zhang, Cheng, Jie, Cheng, Yaping, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Di Lorenzo, Selma Conforti, Corti, Daniele, Corso, Flavio Dal, Dalager, Olivia, De La Taille, Christophe, Deng, Zhi, Deng, Ziyan, Depnering, Wilfried, Diaz, Marco, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dracos, Marcos, Druillole, Frédéric, Du, Ran, Du, Shuxian, Dusini, Stefano, Dvorak, Martin, Enqvist, Timo, Enzmann, Heike, Fabbri, Andrea, Fahrendholz, Ulrike, Fan, Donghua, Fan, Lei, Fang, Jian, Fang, Wenxing, Fargetta, Marco, Fedoseev, Dmitry, Feng, Li-Cheng, Feng, Qichun, Ford, Richard, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giaz, Agnese, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Grewing, Christian, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guardone, Nunzio, Gul, Maria, Guo, Cong, Guo, Jingyuan, Guo, Wanlei, Guo, Xinheng, Guo, Yuhang, Hackspacher, Paul, Hagner, Caren, Han, Ran, Han, Yang, Hassan, Muhammad Sohaib, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Chunhao, Huang, Guihong, Huang, Hanxiong, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Ioannisian, Ara, Isocrate, Roberto, Jelmini, Beatrice, Jen, Kuo-Lun, Jeria, Ignacio, Ji, Xiaolu, Ji, Xingzhao, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Di, Jiang, Wei, Jiang, Xiaoshan, Jin, Ruyi, Jing, Xiaoping, Jollet, Cécile, Joutsenvaara, Jari, Jungthawan, Sirichok, Kalousis, Leonidas, Kampmann, Philipp, Kang, Li, Karaparambil, Rebin, Kazarian, Narine, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kruth, Andre, Kutovskiy, Nikolay, Kuusiniemi, Pasi, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Haitao, Li, Huiling, Li, Jiaqi, Li, Mengzhao, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Shanfeng, Li, Tao, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Liang, Hao, Liao, Jiajun, Liebau, Daniel, Limphirat, Ayut, Limpijumnong, Sukit, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Lippi, Ivano, Liu, Fang, Liu, Haidong, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shuangyu, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Yan, Liu, Yunzhe, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Shuxiang, Lu, Xiaoxu, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Fengjiao, Luo, Guang, Luo, Pengwei, Luo, Shu, Luo, Wuming, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Manzali, Francesco, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Marium, Sadia, Martellini, Cristina, Martin-Chassard, Gisele, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Mezzetto, Mauro, Miller, Jonathan, Miramonti, Lino, Montini, Paolo, Montuschi, Michele, Müller, Axel, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parmeggiano, Sergio, Pei, Yatian, Pelliccia, Nicomede, Peng, Anguo, Peng, Haiping, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Pratumwan, Wathan, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Rajput, Muhammad Usman, Ranucci, Gioacchino, Raper, Neill, Re, Alessandra, Rebber, Henning, Rebii, Abdel, Ren, Bin, Ren, Jie, Ricci, Barbara, Rifai, Mariam, Robens, Markus, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Roth, Christian, Ruan, Xiangdong, Ruan, Xichao, Rujirawat, Saroj, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sanfilippo, Simone, Sangka, Anut, Sanguansak, Nuanwan, Sawangwit, Utane, Sawatzki, Julia, Sawy, Fatma, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settanta, Giulio, Settimo, Mariangela, Shao, Zhuang, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Slupecki, Maciej, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Štefánik, Dušan, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Suwonjandee, Narumon, Szelezniak, Michal, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Treskov, Konstantin, Triossi, Andrea, Troni, Giancarlo, Trzaska, Wladyslaw, Tuve, Cristina, Ushakov, Nikita, Boom, Johannes van den, van Waasen, Stefan, Vanroyen, Guillaume, Vedin, Vadim, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, Volpe, Cristina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Kunyu, Wang, Lu, Wang, Meifen, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuman, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Wang, Zongyi, Waqas, Muhammad, Watcharangkool, Apimook, Wei, Lianghong, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wen, Kaile, Wen, Liangjian, Wiebusch, Christopher, Wong, Steven Chan-Fai, Wonsak, Bjoern, Wu, Diru, Wu, Qun, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yuguang, Xie, Zhangquan, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Taylor, Yan, Wenqi, Yan, Xiongbo, Yan, Yupeng, Yang, Anbo, Yang, Changgen, Yang, Chengfeng, Yang, Huan, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yao, Haifeng, Yasin, Zafar, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yegin, Ugur, Yermia, Frédéric, Yi, Peihuai, Yin, Na, Yin, Xiangwei, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zambanini, Andre, Zavadskyi, Vitalii, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Feiyang, Zhang, Guoqing, Zhang, Haiqiong, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jin, Zhang, Jingbo, Zhang, Jinnan, Zhang, Peng, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Xueyao, Zhang, Yan, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Fengyi, Zhao, Jie, Zhao, Rong, Zhao, Shujun, Zhao, Tianchi, Zheng, Dongqin, Zheng, Hua, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jiang, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, and Zou, Jiaheng
Subjects: High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract: The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel., Comment: 14 pages, 12 figures, an author added
Published: 2022
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47. Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly

Author: Tian, Yunsheng, Xu, Jie, Li, Yichen, Luo, Jieliang, Sueda, Shinjiro, Li, Hui, Willis, Karl D. D., and Matusik, Wojciech
Subjects: Computer Science - Robotics, Computer Science - Graphics
Abstract: Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of dealing with arbitrary 3D shapes and the highly constrained motion required for real-world assemblies. In this work, we propose a novel method to efficiently plan physically plausible assembly motion and sequences for real-world assemblies. Our method leverages the assembly-by-disassembly principle and physics-based simulation to efficiently explore a reduced search space. To evaluate the generality of our method, we define a large-scale dataset consisting of thousands of physically valid industrial assemblies with a variety of assembly motions required. Our experiments on this new benchmark demonstrate we achieve a state-of-the-art success rate and the highest computational efficiency compared to other baseline algorithms. Our method also generalizes to rotational assemblies (e.g., screws and puzzles) and solves 80-part assemblies within several minutes., Comment: Accepted by SIGGRAPH Asia 2022. Project website: http://assembly.csail.mit.edu/
Published: 2022
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48. Model Independent Approach of the JUNO $^8$B Solar Neutrino Program

Author: JUNO Collaboration, Zhao, Jie, Yue, Baobiao, Lu, Haoqi, Li, Yufeng, Ling, Jiajie, Yu, Zeyuan, Abusleme, Angel, Adam, Thomas, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, Akram, Muhammad, Aleem, Abid, Alexandros, Tsagkarakis, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, Asavapibhop, Burin, de André, João Pedro Athayde Marcondes, Auguste, Didier, Bai, Weidong, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Bergnoli, Antonio, Birkenfeld, Thilo, Blin, Sylvie, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Busto, Jose, Butorov, Ilya, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callegari, Riccardo, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Chan, Chi, Chang, Jinfan, Chang, Yun, Chen, Guoming, Chen, Pingping, Chen, Po-An, Chen, Shaomin, Chen, Xurong, Chen, Yixue, Chen, Yu, Chen, Zhiyuan, Chen, Zikang, Cheng, Jie, Cheng, Yaping, Chepurnov, Alexander, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Molla, Marta Colomer, Di Lorenzo, Selma Conforti, Corti, Daniele, Corso, Flavio Dal, Dalager, Olivia, De La Taille, Christophe, Deng, Zhi, Deng, Ziyan, Depnering, Wilfried, Diaz, Marco, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dracos, Marcos, Druillole, Frédéric, Du, Ran, Du, Shuxian, Dusini, Stefano, Dvorak, Martin, Enqvist, Timo, Enzmann, Heike, Fabbri, Andrea, Fan, Donghua, Fan, Lei, Fang, Jian, Fang, Wenxing, Fargetta, Marco, Fedoseev, Dmitry, Fei, Zhengyong, Feng, Li-Cheng, Feng, Qichun, Ford, Richard, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guo, Cong, Guo, Jingyuan, Guo, Wanlei, Guo, Xinheng, Guo, Yuhang, Hackspacher, Paul, Hagner, Caren, Han, Ran, Han, Yang, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Kaixuan, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Ioannisian, Ara, Isocrate, Roberto, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Di, Jiang, Wei, Jiang, Xiaoshan, Jing, Xiaoping, Jollet, Cécile, Kalousis, Leonidas, Kampmann, Philipp, Kang, Li, Karaparambil, Rebin, Kazarian, Narine, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kutovskiy, Nikolay, Kuusiniemi, Pasi, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Daozheng, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Huiling, Li, Mengzhao, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Zepeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, Hao, Liao, Jiajun, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Lippi, Ivano, Liu, Fang, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Yan, Liu, Yunzhe, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Jingbin, Lu, Junguang, Lu, Shuxiang, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Daibin, Luo, Fengjiao, Luo, Guang, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Mai, Jingyu, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Manzali, Francesco, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martellini, Cristina, Martin-Chassard, Gisele, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Mezzetto, Mauro, Miller, Jonathan, Miramonti, Lino, Montini, Paolo, Montuschi, Michele, Müller, Axel, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parmeggiano, Sergio, Pei, Yatian, Pelliccia, Nicomede, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Ranucci, Gioacchino, Raper, Neill, Re, Alessandra, Rebber, Henning, Rebii, Abdel, Redchuk, Mariia, Ren, Bin, Ren, Jie, Ricci, Barbara, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sanfilippo, Simone, Sangka, Anut, Sawangwit, Utane, Sawatzki, Julia, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settanta, Giulio, Settimo, Mariangela, Shao, Zhuang, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Slupecki, Maciej, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Štefánik, Dušan, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Su, Jun, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Szelezniak, Michal, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Treskov, Konstantin, Triossi, Andrea, Troni, Giancarlo, Trzaska, Wladyslaw, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, Volpe, Cristina, von Sturm, Katharina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Lu, Wang, Meifen, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuman, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Wang, Zongyi, Watcharangkool, Apimook, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wen, Kaile, Wen, Liangjian, Wiebusch, Christopher, Wong, Steven Chan-Fai, Wonsak, Bjoern, Wu, Diru, Wu, Qun, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yuguang, Xie, Zhangquan, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Taylor, Yan, Wenqi, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Huan, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, Yin, Na, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Zafar, Noman, Zavadskyi, Vitalii, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Guoqing, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jin, Zhang, Jingbo, Zhang, Jinnan, Zhang, Mohan, Zhang, Peng, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Xueyao, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Fengyi, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zheng, Dongqin, Zheng, Hua, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jiang, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, and Zou, Jiaheng
Subjects: High Energy Physics - Experiment, Astrophysics - Solar and Stellar Astrophysics, High Energy Physics - Phenomenology, Nuclear Experiment
Abstract: The physics potential of detecting $^8$B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model independent manner by using three distinct channels of the charged-current (CC), neutral-current (NC) and elastic scattering (ES) interactions. Due to the largest-ever mass of $^{13}$C nuclei in the liquid-scintillator detectors and the {expected} low background level, $^8$B solar neutrinos would be observable in the CC and NC interactions on $^{13}$C for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC and ES channels to guarantee the $^8$B solar neutrino observation. From the sensitivity studies performed in this work, we show that JUNO, with ten years of data, can reach the {1$\sigma$} precision levels of 5%, 8% and 20% for the $^8$B neutrino flux, $\sin^2\theta_{12}$, and $\Delta m^2_{21}$, respectively. It would be unique and helpful to probe the details of both solar physics and neutrino physics. In addition, when combined with SNO, the world-best precision of 3% is expected for the $^8$B neutrino flux measurement., Comment: 19 pages, 7 figures, accepted version to appear in The Astrophysical Journal. Yufeng Li and Jiajie Ling are corresponding authors
Published: 2022
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49. SYF2 suppression mitigates neurodegeneration in models of diverse forms of ALS.

Author: Linares, Gabriel, Li, Yichen, Chang, Wen-Hsuan, Rubin-Sigler, Jasper, Mendonca, Stacee, Hong, Sarah, Eoh, Yunsun, Guo, Wenxuan, Huang, Yi-Hsuan, Chang, Jonathan, Tu, Sharon, Dorjsuren, Nomongo, Santana, Manuel, Hung, Shu-Ting, Yu, Johnny, Perez, Joscany, Chickering, Michael, Cheng, Tze-Yuan, Huang, Chi-Chou, Lee, Shih-Jong, Deng, Hao-Jen, Bach, Kieu-Tram, Gray, Kamden, Subramanyam, Vishvak, Rosenfeld, Jeffrey, Alworth, Samuel, Goodarzi, Hani, and Ichida, Justin
Subjects: SYF2, TDP-43, amyotrophic lateral sclerosis, androgens, connectivity map, drug screen, induced pluripotent stem cells, nineteen complex, norgestrel, spliceosome, Mice, Animals, Amyotrophic Lateral Sclerosis, Motor Neurons, Neurodegenerative Diseases, Mutation, DNA-Binding Proteins
Abstract: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease caused by many diverse genetic etiologies. Although therapeutics that specifically target causal mutations may rescue individual types of ALS, such approaches cannot treat most patients since they have unknown genetic etiology. Thus, there is a critical need for therapeutic strategies that rescue multiple forms of ALS. Here, we combine phenotypic chemical screening on a diverse cohort of ALS patient-derived neurons with bioinformatic analysis of large chemical and genetic perturbational datasets to identify broadly effective genetic targets for ALS. We show that suppressing the gene-encoding, spliceosome-associated factor SYF2 alleviates TDP-43 aggregation and mislocalization, improves TDP-43 activity, and rescues C9ORF72 and causes sporadic ALS neuron survival. Moreover, Syf2 suppression ameliorates neurodegeneration, neuromuscular junction loss, and motor dysfunction in TDP-43 mice. Thus, suppression of spliceosome-associated factors such as SYF2 may be a broadly effective therapeutic approach for ALS.
Published: 2023

50. On Efficient Online Imitation Learning via Classification

Author: Li, Yichen and Zhang, Chicheng
Subjects: Computer Science - Machine Learning
Abstract: Imitation learning (IL) is a general learning paradigm for tackling sequential decision-making problems. Interactive imitation learning, where learners can interactively query for expert demonstrations, has been shown to achieve provably superior sample efficiency guarantees compared with its offline counterpart or reinforcement learning. In this work, we study classification-based online imitation learning (abbrev. $\textbf{COIL}$) and the fundamental feasibility to design oracle-efficient regret-minimization algorithms in this setting, with a focus on the general nonrealizable case. We make the following contributions: (1) we show that in the $\textbf{COIL}$ problem, any proper online learning algorithm cannot guarantee a sublinear regret in general; (2) we propose $\textbf{Logger}$, an improper online learning algorithmic framework, that reduces $\textbf{COIL}$ to online linear optimization, by utilizing a new definition of mixed policy class; (3) we design two oracle-efficient algorithms within the $\textbf{Logger}$ framework that enjoy different sample and interaction round complexity tradeoffs, and conduct finite-sample analyses to show their improvements over naive behavior cloning; (4) we show that under the standard complexity-theoretic assumptions, efficient dynamic regret minimization is infeasible in the $\textbf{Logger}$ framework. Our work puts classification-based online imitation learning, an important IL setup, into a firmer foundation., Comment: 51 pages, 2 figures, 36th Conference on Neural Information Processing Systems (NeurIPS 2022)
Published: 2022

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