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Measurement of solar pp neutrino flux using electron recoil data from PandaX-4T commissioning run

Chinese Physics C 2024年第9期48卷 1-7,300页

作者： Xiaoying Lu Abdusalam Abdukerim Zihao Bo Wei Chen Xun Chen Yunhua Chen Chen Cheng Zhaokan Cheng Xiangyi Cui Yingjie Fan Deqing Fang Lisheng Geng Karl Giboni Xuyuan Guo Chencheng Han Ke Han Changda He Jinrong He Di Huang Junting Huang Zhou Huang Ruquan Hou Yu Hou Xiangdong Ji Yonglin Ju Chenxiang Li Jiafu Li Mingchuan Li Shuaijie Li Tao Li Qing Lin Jianglai Liu Congcong Lu Lingyin Luo Yunyang Luo Wenbo Ma Yugang Ma Yajun Mao Yue Meng Xuyang Ning Binyu Pang Ningchun Qi Zhicheng Qian Xiangxiang Ren Nasir Shaheed Xiaofeng Shang Xiyuan Shao Guofang Shen Manbin Shen Lin Si Wenliang Sun Yi Tao Anqing Wang Meng Wang Qiuhong Wang Shaobo Wang Siguang Wang Wei Wang Xiuli Wang Xu Wang Zhou Wang Yuehuan Wei Mengmeng Wu Weihao Wu Yuan Wu Mengjiao Xiao Xiang Xiao Kaizhi Xiong Binbin Yan Xiyu Yan Yong Yang Chunxu Yu Ying Yuan Zhe Yuan Youhui Yun Xinning Zeng Minzhen Zhang Peng Zhang Shibo Zhang Shu Zhang Tao Zhang Wei Zhang Yang Zhang Yingxin Zhang Yuanyuan Zhang Li Zhao Jifang Zhou Ning Zhou Xiaopeng Zhou Yubo Zhou Zhizhen Zhou Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary ScienceShandong UniversityQingdao 266237China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong UniversityQingdao 266237China School of Physics and Astronomy Shanghai Jiao Tong UniversityKey Laboratory for Particle Astrophysics and Cosmology(MoE)Shanghai Key Laboratory for Particle Physics and CosmologyShanghai 200240China New Cornerstone Science Laboratory Tsung-Dao Lee InstituteShanghai Jiao Tong UniversityShanghai 200240China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province Liangshan 615000China Yalong River Hydropower Development Company Ltd.288 Shuanglin RoadChengdu 610051China School of Physics Sun Yat-Sen UniversityGuangzhou 510275China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China Department of Physics Yantai UniversityYantai 264005China Key Laboratory of Nuclear Physics and Ion-beam Application(MOE) Institute of Modern PhysicsFudan UniversityShanghai 200433China School of Physics Beihang UniversityBeijing 102206China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang UniversityBeijing 102206China Peng Huanwu Collaborative Center for Research and Education Beihang UniversityBeijing 100191China Southern Center for Nuclear-Science Theory(SCNT) Institute of Modern PhysicsChinese Academy of SciencesHuizhou 516000China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Department of Physics University of MarylandCollege ParkMaryland 20742USA State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China School of Physics Peking UniversityBeijing 100871Ch

The proton-proton(pp)fusion chain dominates the neutrino production in the *** uncertainty of the predicted pp neutrino flux is at the sub-percent level,whereas that of the best measurement is O(10%).In this study,for the first time,we measure solar pp neutrinos in the electron recoil energy range from 24 to 144 keV using the PandaX-4T commissioning data with 0.63 tonne×year *** pp neutrino flux is determined as(8.0±3.9(stat)±10.0(syst))×1010 s^(-1)cm^(-2),which is consistent with the Standard Solar Model and existing measurements,corresponding to an upper flux limit of 23.3×10^(10)s^(-1)cm^(-2)at 90%C.L..

关键词： solar pp neutrino flux PandaX-4T electron recoil spectrum

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Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation Via Decoupling Optimization

SSRN

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SSRN 2023年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Visual Media Intelligent Processing Research Group Department of Film and Television Engineering Shanghai University Shanghai200072 China School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Youtu Lab Tencent China

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-label generation to select unlabeled points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Ex-✩Fully documented templates are available in the elsarticle package on CTAN. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler

arXiv

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arXiv 2023年

作者： Lin, Shaohui Huang, Wenxuan Xie, Jiao Zhang, Baochang Shen, Yunhang Yu, Zhou Han, Jungong Doermann, David The School of Computer Science and Technology East China Normal University Shanghai China The Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China The Department of Automation School of Aerospace Engineering Xiamen University Xiamen China The Institute of Artificial Intelligence Beihang University Beijing China Zhongguancun Laboratory Beijing China The Youtu Lab Tencent Shanghai China The School of Statistics East China Normal University Shanghai China The Department of Computer Science University of Sheffield United Kingdom The University at Buffalo BuffaloNY United States

Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs, which can be effectively applied to edge devices and cloud services. In this paper, we propose a novel Knowledge-driven Differential Filter Sampler (KDFS) with Masked Filter Modeling (MFM) framework for filter pruning, which globally prunes the redundant filters based on the prior knowledge of a pre-trained model in a differential and non-alternative optimization. Specifically, we design a differential sampler with learnable sampling parameters to build a binary mask vector for each layer, determining whether the corresponding filters are redundant. To learn the mask, we introduce masked filter modeling to construct PCA-like knowledge by aligning the intermediate features from the pre-trained teacher model and the outputs of the student decoder taking sampling features as the input. The mask and sampler are directly optimized by the Gumbel-Softmax Straight-Through Gradient Estimator in an end-to-end manner in combination with global pruning constraint, MFM reconstruction error, and dark knowledge. Extensive experiments demonstrate the proposed KDFS’s effectiveness in compressing the base models on various datasets. For instance, the pruned ResNet-50 on ImageNet achieves 55.36% computation reduction, and 42.86% parameter reduction, while only dropping 0.35% Top-1 accuracy, significantly outperforming the state-of-the-art methods. The code is available at https://***/Osilly/KDFS. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Sparse generalized canonical correlation analysis: Distributed alternating iteration based approach

arXiv

引用

arXiv 2020年

作者： Cai, Jia Lv, Kexin Huo, Junyi Huang, Xiaolin Yang, Jie School of Statistics and Mathematics Guangdong University of Finance & Economics Big Data and Educational Statistics Application Laboratory 21 Chisha Road Guangzhou Guangdong510320 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China School of Electronics and Computer Science University of Southampton University Road SouthamptonSO17 1BJ United Kingdom

Sparse canonical correlation analysis (CCA) is a useful statistical tool to detect latent information with sparse structures. However, sparse CCA works only for two datasets, i.e., there are only two views or two distinct objects. To overcome this limitation, in this paper, we propose a sparse generalized canonical correlation analysis (GCCA), which could detect the latent relations of multiview data with sparse structures. Moreover, the introduced sparsity could be considered as Laplace prior on the canonical variates. Specifically, we convert the GCCA into a linear system of equations and impose 1 minimization penalty for sparsity pursuit. This results in a nonconvex problem on Stiefel manifold, which is difficult to solve. Motivated by Boyd’s consensus problem, an algorithm based on distributed alternating iteration approach is developed and theoretical consistency analysis is investigated elaborately under mild conditions. Experiments on several synthetic and real world datasets demonstrate the effectiveness of the proposed algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistical mechanics

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Generalized Estimating Equations for Hearing Loss data with Specified Correlation Structures

arXiv

引用

arXiv 2023年

作者： Wei, Zhuoran Zhu, Hanbing Curhan, Sharon Curhan, Gary Wang, Molin Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai China Harvard Medical School BostonMA United States Channing Division of Network Medicine Department of Medicine Brigham and Women’s Hospital BostonMA United States Division of Renal Medicine Department of Medicine Brigham and Women’s Hospital BostonMA United States Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States

Due to the nature of pure-tone audiometry test, hearing loss data often has a complicated correlation structure. Generalized estimating equation (GEE) is commonly used to investigate the association between exposures and hearing loss, because it is robust to misspecification of the correlation matrix. However, this robustness typically entails a moderate loss of estimation efficiency in finite samples. This paper proposes to model the correlation coefficients and use second-order generalized estimating equations to estimate the correlation parameters. In simulation studies, we assessed the finite sample performance of our proposed method and compared it with other methods, such as GEE with independent, exchangeable and unstructured correlation structures. Our method achieves an efficiency gain which is larger for the coefficients of the covariates corresponding to the within-cluster variation (e.g., ear-level covariates) than the coefficients of cluster-level covariates. The efficiency gain is also more pronounced when the within-cluster correlations are moderate to strong, or when comparing to GEE with an unstructured correlation structure. As a real-world example, we applied the proposed method to data from the Audiology Assessment Arm of the Conservation of Hearing Study, and studied the association between a dietary adherence score and hearing loss. Copyright © 2023, The Authors. All rights reserved.

关键词： Audition

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Dependency, Reciprocity, and Mentorship in Long-Term Research Collaboration: A Co-Authorship Matrix-Based Multivariate Time Series Analysis

SSRN

引用

SSRN 2023年

作者： Zhu, Yongjun Kim, Donghun Jiang, Ting Zhao, Yi He, Jiangen Chen, Xinyi Lou, Wen Department of Library and Information Science Yonsei University Seoul Korea Republic of Department of Library and Information Science Sungkyunkwan University Seoul Korea Republic of School of Economics and Management Nanjing University of Science and Technology Nanjing China School of Information Sciences The University of Tennessee Knoxville United States Department of Cultural Media Yonsei University Seoul Korea Republic of Faculty of Economics and Management East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Ministry of Education of China Shanghai China

In this study, we examine the roles of dependency, reciprocity, and mentorship in the prediction of long-term research collaboration in five disciplines. We use co-authorship matrix-based multivariate time series features and interpretable machine learning to train long-term collaboration prediction models and interpret the feature importance of trained models. Overall, long-term research collaboration that is defined using various standards was rare across the examined disciplines, and the prediction results were moderate to good. We found dependency, reciprocity, and mentorship to have different roles in different disciplines. Among the three, mentorship was important in predicting long-term research collaboration in Agriculture, Geology, and Library and Information science. Reciprocity, which measures the interdependence between two researchers was important to prediction in the fields of Agriculture and Geology. Finally, dependency was important in all the disciplines with varying degrees of importance. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

引用

Nucleic acids research 2023年第13期51卷 6578-6592页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Bo-Jia Chen Yu-Te Lin Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Program in Integrative & Complex Diseases Department of Nutrition Texas A&M University College Station TX 77843 USA. Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

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Coupled chemical reactions: Effects of electric field, diffusion, and boundary control

引用

Physical Review E 2023年第6期108卷 064413-064413页

作者： Shixin Xu Robert Eisenberg Zilong Song Huaxiong Huang Zu Chongzhi Center for Mathematics and Computational Sciences Duke Kunshan University 8 Duke Ave Kunshan Jiangsu 215316 China Department of Applied Mathematics Illinois Institute of Technology Chicago Illinois 60616 USA and Department of Physiology and Biophysics Rush University Chicago Ilinois 60612 USA Math and Statistics Department Utah State University Old Main Hill Logan Utah 84322 USA Research Center for Mathematics Advanced Institute of Natural Sciences Beijing Normal University Zhuhai Guangdong 519088 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai Guangdong 519088 China Laboratory of Mathematics and Complex Systems MOE Beijing Normal University Beijing 100875 China and Department of Mathematics and Statistics York University Toronto Ontario Canada M3J 1P3

Chemical reactions involve the movement of charges, and this paper presents a mathematical model for describing chemical reactions in electrolytes. The model is developed using an energy variational method that aligns with classical thermodynamics principles. It encompasses both electrostatics and chemical reactions within consistently defined energetic and dissipative functionals. Furthermore, the energy variation method is extended to account for open systems that involve the input and output of charge and mass. Such open systems have the capability to convert one form of input energy into another form of output energy. In particular, a two-domain model is developed to study a reaction system with self-regulation and internal switching, which plays a vital role in the electron transport chain of mitochondria responsible for ATP generation—a crucial process for sustaining life. Simulations are conducted to explore the influence of electric potential on reaction rates and switching dynamics within the two-domain system. It shows that the electric potential inhibits the oxidation reaction while accelerating the reduction reaction.

关键词： Continuum mechanics Dissipative dynamics Electric field effects

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Coupled Chemical Reactions: Effects of Electric Field, Diffusion and Boundary Control

arXiv

引用

arXiv 2023年

作者： Xu, Shixin Eisenberg, Robert Song, Zilong Huang, Huaxiong Zu Chongzhi Center for Mathematics and Computational Sciences Duke Kunshan University 8 Duke Ave Jiangsu Kunshan China Department of Applied Mathematics Illinois Institute of Technology ChicagoIL60616 United States Department of Physiology and Biophysics Rush University Chicago IL60612 United States Math and Statistics Department Utah State University Old Main Hill Logan UT84322 United States Research Center for Mathematics Advanced Institute of Natural Sciences Beijing Normal University Guangdong Zhuhai519088 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Guangdong Zhuhai519088 China Laboratory of Mathematics and Complex Systems MOE Beijing Normal University Beijing100875 China Department of Mathematics and Statistics York University TorontoONM3J 1P3 Canada

MSC Codes 92E20, 92B05, 35Q92Chemical reactions involve the movement of charges, and this work presents a mathematical model for describing chemical reactions in electrolytes. The model is developed using an energy variational method that aligns with classical thermodynamics principles. It encompasses both electrostatics and chemical reactions within consistently defined energetic and dissipative functionals. Furthermore, the energy variation method is extended to account for open systems that involve the input and output of charge and mass. Such open systems have the capability to convert one form of input energy into another form of output energy. In particular, a two-domain model is developed to study a reaction system with self-regulation and internal switching, which plays a vital role in the electron transport chain of mitochondria responsible for ATP generation-a crucial process for sustaining life. Simulations are conducted to explore the influence of electric potential on reaction rates and switching dynamics within the two-domain system. It shows that the electric potential inhibits the oxidation reaction while accelerating the reduction reaction. © 2023, CC BY.

关键词： Open systems

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Multidomain Model for Optic Nerve Potassium Clearance: Roles of Glial Cells and Perivascular Spaces

arXiv

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arXiv 2024年

作者： Xiao, Shanfeng Huang, Huaxiong Eisenberg, Robert Song, Zilong Xu, Shixin School of Mathematical Sciences Soochow University 1 Shi-Zi Street Jiangsu Province Suzhou215006 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai519088 China Laboratory of Mathematics and Complex Systems MOE Beijing China Normal University Beijing100875 China Department of Mathematics and Statistics York University TorontoONM3J 1P3 Canada Department of Applied Mathematics Illinois Institute of Technology ChicagoIL60616 United States Math and Statistics Department Utah State University Old Main Hill LoganUT84322 United States Zu Chongzhi Center for Mathematics and Computational Sciences Duke Kunshan University 8 Duke Ave Kunshan China

The accumulation of potassium in the extracellular space surrounding nerve cells is a fundamental aspect of biophysics that has garnered significant attention in recent research. This phenomenon holds implications for various neurological conditions, including spreading depression, migraine, certain types of epilepsy, and potentially, learning processes. A quantitative analysis is essential for understanding the dynamics of potassium clearance following a series of action potentials. This clearance process involves multiple structures along the nerve, including glia, the extracellular space, axons, and the perivascular space, necessitating a spatially distributed systems approach akin to the cable equations of nerve physiology. In this study, we propose a multi-domain model for the optic nerve to investigate potassium accumulation and clearance dynamics. The model accounts for the convection, diffusion, and electrical migration of fluid and ions, revealing the significant roles of glia and the perivascular space in potassium buffering. Specifically, our findings suggest that potassium clearance primarily occurs through convective flow within the syncytia of glia, driven by osmotic pressure differences. Additionally, the perivascular space serves as a crucial pathway for potassium buffering and fluid circulation, further contributing to the overall clearance process. Importantly, our model’s adaptability allows for its application to diverse structures with distinct channel and transporter distributions across the six compartments, extending its utility beyond the optic nerve. © 2024, CC BY-SA.

关键词： Neurons

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