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201. Shadow thermodynamics of non-linear charged Anti-de Sitter black holes

Author

Du, Yun-Zhi, Li, Huai-Fan, Zhou, Xiang-Nan, Guo, Wei-Qi, and Zhao, Ren

Subjects
High Energy Physics - Theory
Abstract

Non-linear interaction between the electromagnetic fields (EMF) are occurred when vacuum polarization in quantum electrodynamics (QED) happens. The field of non-linear electrodynamics which may be resulting from this interaction could have important effects on black hole physics. In this work, we investigate the relationship between the observable quantity, the shadow radius and the first-order phase transition for the non-linear charged AdS black hole in the frame of the Einstein-power-Yang-Mills (EPYM) gravity. Through the analysis, we find with a certain condition there exist the non-monotonic behaviors between the shadow radius, the horizon radius, and temperature (or pressure). And from the viewpoint of the shadow radius, the phase transition temperature is higher than that from the viewpoint of the horizon radius with the same condition. These indicate that the shadow radius can be regarded a probe to reveal the thermodynamic phase transition information of black holes. When the system is undergoing the phase transition in two cases of the different non-linear YM charge parameter values: $\gamma=1,~1.5$, the thermal profiles of the coexistent big and small black hole phases with the temperature are presented. Furthermore, the effects of non-linear YM charge parameter on the shadow radius and the thermal profile are also investigated., Comment: arXiv admin note: substantial text overlap with arXiv:2204.01007; text overlap with arXiv:1507.04217 by other authors

Published
2022
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202. Marginal Interventional Effects

Author

Zhou, Xiang and Opacic, Aleksei

Subjects
Statistics - Methodology
Abstract

Conventional causal estimands, such as the average treatment effect (ATE), reflect how the mean outcome in a population or subpopulation would change if all units received treatment versus control. Real-world policy changes, however, are often incremental, changing the treatment status for only a small segment of the population who are at or near "the margin of participation." To capture this notion, two parallel lines of inquiry have developed in economics and in statistics and epidemiology that define, identify, and estimate what we call interventional effects. In this article, we bridge these two strands of literature by defining interventional effect (IE) as the per capita effect of a treatment intervention on an outcome of interest, and marginal interventional effect (MIE) as its limit when the size of the intervention approaches zero. The IE and MIE can be viewed as the unconditional counterparts of the policy-relevant treatment effect (PRTE) and marginal PRTE (MPRTE) proposed in the economics literature. However, different from PRTE and MPRTE, IE and MIE are defined without reference to a latent index model, and, as we show, can be identified either under unconfoundedness or through the use of instrumental variables. For both scenarios, we show that MIEs are typically identified without the strong positivity assumption required of the ATE, highlight several "stylized interventions" that may be of particular interest in policy analysis, discuss several parametric and semiparametric estimation strategies, and illustrate the proposed methods with an empirical example.

Published
2022

203. Improving the Adversarial Robustness of NLP Models by Information Bottleneck

Author

Zhang, Cenyuan, Zhou, Xiang, Wan, Yixin, Zheng, Xiaoqing, Chang, Kai-Wei, and Hsieh, Cho-Jui

Subjects
Computer Science - Computation and Language
Abstract

Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool NLP models. In this study, we explore the feasibility of capturing task-specific robust features, while eliminating the non-robust ones by using the information bottleneck theory. Through extensive experiments, we show that the models trained with our information bottleneck-based method are able to achieve a significant improvement in robust accuracy, exceeding performances of all the previously reported defense methods while suffering almost no performance drop in clean accuracy on SST-2, AGNEWS and IMDB datasets.

Published
2022
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206. Prospects for Detecting the Diffuse Supernova Neutrino Background with JUNO

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, 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, 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, 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, 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, Yufeng, Li, Zepeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Liang, Hao, Liao, Jiajun, 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, Luo, Xiaojie, 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, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, 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, 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, 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, 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, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology, Physics - Instrumentation and Detectors
Abstract

We present the detection potential for the diffuse supernova neutrino background (DSNB) at the Jiangmen Underground Neutrino Observatory (JUNO), using the inverse-beta-decay (IBD) detection channel on free protons. We employ the latest information on the DSNB flux predictions, and investigate in detail the background and its reduction for the DSNB search at JUNO. The atmospheric neutrino induced neutral current (NC) background turns out to be the most critical background, whose uncertainty is carefully evaluated from both the spread of model predictions and an envisaged \textit{in situ} measurement. We also make a careful study on the background suppression with the pulse shape discrimination (PSD) and triple coincidence (TC) cuts. With latest DSNB signal predictions, more realistic background evaluation and PSD efficiency optimization, and additional TC cut, JUNO can reach the significance of 3$\sigma$ for 3 years of data taking, and achieve better than 5$\sigma$ after 10 years for a reference DSNB model. In the pessimistic scenario of non-observation, JUNO would strongly improve the limits and exclude a significant region of the model parameter space., Comment: 29 pages, 11 figures, final published version in JCAP

Published
2022
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207. Volumetric-mapping-based inverse design of 3D architected materials and mobility control by topology reconstruction

Author

Xiao, Kai, Zhou, Xiang, and Ju, Jaehyung

Subjects
Condensed Matter - Materials Science, Computer Science - Computational Geometry, Physics - Applied Physics
Abstract

The recent development of modular origami structures has ushered in a new era for active metamaterials with multiple degrees of freedom (multi-DOF). Notably, no systematic inverse design approach for volumetric modular origami structures has been reported. Moreover, very few topologies of modular origami have been studied for the design of active metamaterials with multi-DOF. Herein, we develop an inverse design method and reconfigurable algorithm for constructing 3D active architected structures - we synthesize modular origami structures that can be volumetrically mapped to a target 3D shape. We can control the reconfigurability by reconstructing the topology of the architected structures. Our inverse design based on volumetric mapping with mobility control by topology reconstruction can be used to construct architected metamaterials with any 3D complex shape that are also transformable with multi-DOF. Our work opens a new path toward 3D reconfigurable structures based on volumetric inverse design. This work is significant for the design of 3D active metamaterials and 3D morphing devices for automotive, aerospace, and biomedical engineering applications., Comment: 36 pages

Published
2022
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208. Multilevel Hierarchical Network with Multiscale Sampling for Video Question Answering

Author

Peng, Min, Wang, Chongyang, Gao, Yuan, Shi, Yu, and Zhou, Xiang-Dong

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

Video question answering (VideoQA) is challenging given its multimodal combination of visual understanding and natural language processing. While most existing approaches ignore the visual appearance-motion information at different temporal scales, it is unknown how to incorporate the multilevel processing capacity of a deep learning model with such multiscale information. Targeting these issues, this paper proposes a novel Multilevel Hierarchical Network (MHN) with multiscale sampling for VideoQA. MHN comprises two modules, namely Recurrent Multimodal Interaction (RMI) and Parallel Visual Reasoning (PVR). With a multiscale sampling, RMI iterates the interaction of appearance-motion information at each scale and the question embeddings to build the multilevel question-guided visual representations. Thereon, with a shared transformer encoder, PVR infers the visual cues at each level in parallel to fit with answering different question types that may rely on the visual information at relevant levels. Through extensive experiments on three VideoQA datasets, we demonstrate improved performances than previous state-of-the-arts and justify the effectiveness of each part of our method., Comment: Accepted by IJCAI 2022. arXiv admin note: text overlap with arXiv:2109.04735

Published
2022

209. Sub-percent Precision Measurement of Neutrino Oscillation Parameters with JUNO

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, 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, Cheng, Yu Chin, 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, 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, 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, Joutsenvaara, Jari, 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, 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, Meinusch, Artur, Meng, Yue, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Mezzetto, Mauro, Miller, Jonathan, Miramonti, Lino, Molla, Marta Colomer, 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, Pelicci, Luca, 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, 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, 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, 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, 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, Xueyao, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Fengyi, 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, Jiang, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, and Zou, Jiaheng

Subjects
High Energy Physics - Experiment
Abstract

JUNO is a multi-purpose neutrino observatory under construction in the south of China. This publication presents new sensitivity estimates for the measurement of the $\Delta m^2_{31}$, $\Delta m^2_{21}$, $\sin^2 \theta_{12}$, and $\sin^2 \theta_{13}$ oscillation parameters using reactor antineutrinos, which is one of the primary physics goals of the experiment. The sensitivities are obtained using the best knowledge available to date on the location and overburden of the experimental site, the nuclear reactors in the surrounding area and beyond, the detector response uncertainties, and the reactor antineutrino spectral shape constraints expected from the TAO satellite detector. It is found that the $\Delta m^2_{31}$, $\Delta m^2_{21}$, and $\sin^2 \theta_{12}$ oscillation parameters will be determined to better than 0.5% precision in six years of data collection, which represents approximately an order of magnitude improvement over existing constraints., Comment: 29 pages, 10 figures, submitted to Chinese Physics C

Published
2022
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210. Methods for Large-scale Single Mediator Hypothesis Testing: Possible Choices and Comparisons

Author

Du, Jiacong, Zhou, Xiang, Hao, Wei, Liu, Yongmei, Smith, Jennifer A., and Mukherjee, Bhramar

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Mediation hypothesis testing for a large number of mediators is challenging due to the composite structure of the null hypothesis, H0:alpha*beta=0 (alpha: effect of the exposure on the mediator after adjusting for confounders; beta: effect of the mediator on the outcome after adjusting for exposure and confounders). In this paper, we reviewed three classes of methods for multiple mediation hypothesis testing. In addition to these existing methods, we developed the Sobel-comp method, which uses a corrected mixture reference distribution for Sobel's test statistic. We performed extensive simulation studies to compare all six methods in terms of the false positive rates under the null hypothesis and the true positive rates under the alternative hypothesis. We found that the class of methods which uses a mixture reference distribution could best maintain the false positive rates at the nominal level under the null hypothesis and had the greatest true positive rates under the alternative hypothesis. We applied all methods to study the mediation mechanism of DNA methylation sites in the pathway from adult socioeconomic status to glycated hemoglobin level using data from the Multi-Ethnic Study of Atherosclerosis (MESA). We also provide guidelines for choosing the optimal mediation hypothesis testing method in practice., Comment: 24 pages, 6 figures, 4 tables

Published
2022

211. GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models

Author

Prasad, Archiki, Hase, Peter, Zhou, Xiang, and Bansal, Mohit

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Providing natural language instructions in prompts is a useful new paradigm for improving task performance of large language models in a zero-shot setting. Recent work has aimed to improve such prompts via manual rewriting or gradient-based tuning. However, manual rewriting is time-consuming and requires subjective interpretation, while gradient-based tuning can be extremely computationally demanding for large models and may not be feasible for API-based models. In this work, we introduce Gradient-free Instructional Prompt Search (GrIPS), a gradient-free, edit-based search approach for improving task instructions for large language models. GrIPS takes in instructions designed for humans and automatically returns an improved, edited prompt, while allowing for API-based tuning. With InstructGPT models, GrIPS improves the average task performance by up to 4.30 percentage points on eight classification tasks from the Natural Instructions dataset (with similar improvements for OPT, BLOOM, and FLAN-T5). We see improvements for both instruction-only prompts and instruction + k-shot examples prompts. Notably, GrIPS outperforms manual rewriting and purely example-based prompts while controlling for the available compute and data budget. Further, performance of GrIPS is comparable to select gradient-based tuning approaches. Qualitatively, we show our edits can simplify instructions and at times make them incoherent but nonetheless improve accuracy. Our code is available at: https://github.com/archiki/GrIPS, Comment: EACL 2023 (20 pages)

Published
2022

212. Measurements of Rayleigh Ratios in Linear Alkylbenzene

Author

Yu, Miao, Wu, Wenjie, Peng, Na, Yu, Taozhe, Ding, Yayun, Liu, Qian, Ren, Feng, Zhang, Zhenyu, and Zhou, Xiang

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

In present work an experiment has been designed to measure the Rayleigh ratio directly at $405$ nm and $432$ nm for linear alkylbenzene which is a common solvent used in liquid scintillator detectors of neutrino experiments. The perpendicularly polarized Rayleigh ratio is determined to be $(4.52\pm 0.28)\times 10^{-6}$ m$^{-1}\cdot$ sr$^{-1}$ at $405$ nm and $(3.82\pm 0.24)\times 10^{-6}$ m$^{-1}\cdot$ sr$^{-1}$ at $432$ nm, and the corresponding Rayleigh scattering length is $L_{Ray} = 22.9\pm 0.3(\mathrm{stat.})\pm 1.7(\mathrm{sys.})$ m at $405$ nm and $L_{Ray}= 27.0\pm 0.9(\mathrm{stat.})\pm 1.8(\mathrm{sys.})$ m at $432$ nm., Comment: 16 pages, 7 figures

Published
2022
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213. The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights

Author

Gasse, Maxime, Cappart, Quentin, Charfreitag, Jonas, Charlin, Laurent, Chételat, Didier, Chmiela, Antonia, Dumouchelle, Justin, Gleixner, Ambros, Kazachkov, Aleksandr M., Khalil, Elias, Lichocki, Pawel, Lodi, Andrea, Lubin, Miles, Maddison, Chris J., Morris, Christopher, Papageorgiou, Dimitri J., Parjadis, Augustin, Pokutta, Sebastian, Prouvost, Antoine, Scavuzzo, Lara, Zarpellon, Giulia, Yang, Linxin, Lai, Sha, Wang, Akang, Luo, Xiaodong, Zhou, Xiang, Huang, Haohan, Shao, Shengcheng, Zhu, Yuanming, Zhang, Dong, Quan, Tao, Cao, Zixuan, Xu, Yang, Huang, Zhewei, Zhou, Shuchang, Binbin, Chen, Minggui, He, Hao, Hao, Zhiyu, Zhang, Zhiwu, An, and Kun, Mao

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants., Comment: Neurips 2021 competition. arXiv admin note: text overlap with arXiv:2112.12251 by other authors

Published
2022

215. Spatial transcriptomics reveals profound subclonal heterogeneity and T-cell dysfunction in extramedullary myeloma

Author

John, Mara, Helal, Moutaz, Duell, Johannes, Mattavelli, Greta, Stanojkovska, Emilia, Afrin, Nazia, Leipold, Alexander M., Steinhardt, Maximilian J., Zhou, Xiang, Žihala, David, Anilkumar Sithara, Anjana, Mersi, Julia, Waldschmidt, Johannes M., Riedhammer, Christine, Kadel, Sofie-Katrin, Truger, Marietta, Werner, Rudolf A., Haferlach, Claudia, Einsele, Hermann, Kretzschmar, Kai, Jelínek, Tomáš, Rosenwald, Andreas, Kortüm, K. Martin, Riedel, Angela, and Rasche, Leo

Published
2024
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