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Potential to identify neutrino mass ordering with reactor antineutrinos at JUNO

Chinese Physics C 2025年第3期49卷 37-63页

作者： Angel Abusleme Thomas Adam Shakeel Ahmad Rizwan Ahmed Sebastiano Aiello Muhammad Akram Abid Aleem Fengpeng An Qi An Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina Burin Asavapibhop João Pedro Athayde Marcondes de André Didier Auguste Weidong Bai Nikita Balashov Wander Baldini Andrea Barresi Davide Basilico Eric Baussan Marco Bellato Marco Beretta Antonio Bergnoli Daniel Bick Lukas Bieger Svetlana Biktemerova Thilo Birkenfeld Iwan Morton-Blake David Blum Simon Blyth Anastasia Bolshakova Mathieu Bongrand Clément Bordereau Dominique Breton Augusto Brigatti Riccardo Brugnera Riccardo Bruno Antonio Budano Jose Busto Anatael Cabrera Barbara Caccianiga Hao Cai Xiao Cai Yanke Cai Zhiyan Cai Stéphane Callier Antonio Cammi Agustin Campeny Chuanya Cao Guofu Cao Jun Cao Rossella Caruso Cédric Cerna Vanessa Cerrone Chi Chan Jinfan Chang Yun Chang Auttakit Chatrabhuti Chao Chen Guoming Chen Pingping Chen Shaomin Chen Yixue Chen Yu Chen Zhangming Chen Zhiyuan Chen Zikang Chen Jie Cheng Yaping Cheng Yu Chin Cheng Alexander Chepurnov Alexey Chetverikov Davide Chiesa Pietro Chimenti Yen-Ting Chin Ziliang Chu Artem Chukanov Gérard Claverie Catia Clementi Barbara Clerbaux Marta Colomer Molla Selma Conforti DiLorenzo Alberto Coppi Daniele Corti Simon Csakli Flavio Dal Corso Olivia Dalager Jaydeep Datta Christophe De La Taille Zhi Deng Ziyan Deng Xiaoyu Ding Xuefeng Ding Yayun Ding Bayu Dirgantara Carsten Dittrich Sergey Dmitrievsky Tadeas Dohnal Dmitry Dolzhikov Georgy Donchenko Jianmeng Dong Evgeny Doroshkevich Wei Dou Marcos Dracos Frédéric Druillole Ran Du Shuxian Du Katherine Dugas Stefano Dusini Hongyue Duyang Jessica Eck Timo Enqvist Andrea Fabbri Ulrike Fahrendholz Lei Fan Jian Fang Wenxing Fang Marco Fargetta Dmitry Fedoseev Zhengyong Fei Li-Cheng Feng Qichun Feng Federico Ferraro Amélie Fournier Haonan Gan Feng Gao Alberto Garfagnini Arsenii Gavrikov Marco Giammarchi Nunzio Giudice Maxim Gonchar Guanghua Gong Hui Gong Yuri Gornushkin Alexandre Göttela Marco Grassi Maxim Gromov Vasily Gromov Minghao Gu X Yerevan Physics Institute YerevanArmenia Université Libre de Bruxelles BrusselsBelgium Universidade Estadual de Londrina LondrinaBrazil Pontificia Universidade Catolica do Rio de Janeiro Rio de JaneiroBrazil Millennium Institute for SubAtomic Physics at the High-energy Frontier(SAPHIR) ANIDChile Pontificia Universidad Católica de Chile SantiagoChile Beijing Institute of Spacecraft Environment Engineering BeijingChina Beijing Normal University BeijingChina China Institute of Atomic Energy BeijingChina Institute of High Energy Physics BeijingChina North China Electric Power University BeijingChina School of Physics Peking UniversityBeijingChina Tsinghua University BeijingChina University of Chinese Academy of Sciences BeijingChina Jilin University ChangchunChina College of Electronic Science and Engineering National University of Defense TechnologyChangshaChina Chongqing University ChongqingChina Dongguan University of Technology DongguanChina Jinan University GuangzhouChina Sun Yat-Sen University GuangzhouChina Harbin Institute of Technology HarbinChina University of Science and Technology of China HefeiChina The Radiochemistry and Nuclear Chemistry Group in University of South China HengyangChina Wuyi University JiangmenChina Shandong University JinanChinaand Key Laboratory of Particle Physics and Particle Irradiation of Ministry of EducationShandong UniversityQingdaoChina Nanjing University NanjingChina Guangxi University NanningChina East China University of Science and Technology ShanghaiChina School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghaiChina Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghaiChina Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological SciencesShijiazhuangChina Nankai University TianjinChina Wuhan University WuhanChina Xi’an Jiaotong University Xi’anChina Xiamen University XiamenChina School of Physics and Microelectronics Zhengzhou UniversityZhengzhouChina Institute of Physics National

The Jiangmen Underground Neutrino Observatory(JUNO)is a multi-purpose neutrino experiment under construction in South *** paper presents an updated estimate of JUNO’s sensitivity to neutrino mass ordering using the reactor antineutrinos emitted from eight nuclear reactor cores in the Taishan and Yangjiang nuclear power *** measurement is planned by studying the fine interference pattern caused by quasi-vacuum oscillations in the oscillated antineutrino spectrum at a baseline of 52.5 km and is completely independent of the CP violating phase and neutrino mixing angleθ_(23).The sensitivity is obtained through a joint analysis of JUNO and Taishan Antineutrino Observatory(TAO)detectors utilizing the best available knowledge to date about the location and overburden of the JUNO experimental site,local and global nuclear reactors,JUNO and TAO detector responses,expected event rates and spectra of signals and backgrounds,and systematic uncertainties of analysis *** find that a 3σmedian sensitivity to reject the wrong mass ordering hypothesis can be reached with an exposure of about 6.5 years×26.6 GW thermal power.

关键词： JUNO neutrino neutrino mass ordering

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Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices

arXiv

引用

arXiv 2020年

作者： Li, Guangli Ma, Xiu Wang, Xueying Liu, Lei Xue, Jingling Feng, Xiaobing State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia

The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural network pruning methods have been proposed to compress the models, prevailing approaches focus only on parametric operators (e.g., convolution), which may miss optimization opportunities. In this paper, we present a novel fusion-catalyzed pruning approach, called FUPRUNER, which simultaneously optimizes the parametric and non-parametric operators for accelerating neural networks. We introduce an aggressive fusion method to equivalently transform a model, which extends the optimization space of pruning and enables non-parametric operators to be pruned in a similar manner as parametric operators, and a dynamic filter pruning method is applied to decrease the computational cost of models while retaining the accuracy requirement. Moreover, FUPRUNER provides configurable optimization options for controlling fusion and pruning, allowing much more flexible performance-accuracy trade-offs to be made. Evaluation with state-of-the-art residual neural networks on five representative intelligent edge platforms, Jetson TX2, Jetson Nano, Edge TPU, NCS, and NCS2, demonstrates the effectiveness of our approach, which can accelerate the inference of models on CIFAR-10 and ImageNet datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Economic and social effects

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Warburg Effects in Cancer and Normal Proliferating Cells:Two Tales of the Same Name

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Genomics, Proteomics & Bioinformatics 2019年第3期17卷 273-286页

作者： Huiyan Sun Liang Chen Sha Cao Yanchun Liang Ying Xu The China-Japan Union Hospital Jilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA Faculty of Health Sciences University of MacaoTaipaMacao SAR 999078China Department of Biostatistics School of MedicineIndiana UniversityIndianapolisIN 46202USA Zhuhai Laboratory of MOE Key Laboratory of Symbolic Computation and Knowledge Engineering Zhuhai College of Jilin UniversityZhuhai 519041China

It has been observed that both cancer tissue cells and normal proliferating cells(NPCs)have the Warburg *** goal here is to demonstrate that they do this for different *** accomplish this,we have analyzed the transcriptomic data of over 7000 cancer and control tissues of 14 cancer types in TCGA and data of five NPC types in *** analyses reveal that NPCs accumulate large quantities of ATPs produced by the respiration process before starting the Warburg effect,to raise the intracellular pH from 6.8 to 7.2 and to prepare for cell division *** cell cycle starts,the cells start to rely on glycolysis for ATP generation followed by ATP hydrolysis and lactic acid release,to maintain the elevated intracellular pH as needed by cell division since together the three processes are pH *** cells go back to the normal respirationbased ATP production once the cell division phase *** comparison,cancer cells have reached their intracellular pH at 7.4 from top down as multiple acid-loading transporters are up-regulated and most acid-extruding ones except for lactic acid exporters are *** cells use continuous glycolysis for ATP production as way to acidify the intracellular space since the lactic acid secretion is decoupled from glycolysis-based ATP generation and is pH balanced by increased expressions of acid-loading ***-expression analyses suggest that lactic acid secretion is regulated by external,non-pH related ***,our data strongly suggest that the two cell types have the Warburg effect for very different reasons.

关键词： Cancer Warburg effect Fenton reaction Cell proliferation pH homeostasis

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The Research of the Feature Extraction and Classification Algorithm Based on EEG Signal of Motor Imagery

The Research of the Feature Extraction and Classification Al...

引用

International Conference on Virtual Reality and Intelligent Systems (ICVRIS)

作者： Yingjie Zhang Qi Li Hui Yan Xiaozhong Geng Department of Computer Technology and Engineering Changchun institute of Technology Changchun Jilin Province China Department of Computer Science and Technology Changchun University of Science and Technology Changchun Jilin Province China

BCI based on machine learning could makes use of the egg signals to communicate to output under the condition of without the participation of peripheral nerves and muscles. Extracting the essential features of the EEG signals in the presence of artifacts, training the classification algorithms and optimalizing the performance of classifier is critical procedure for BCI system. To some extent the BCI system can be treated as a pattern recognition system whose performance depends on both the features extraction and the features classification algorithm employed. Independent component analysis (ICA) can remove the artifact in the electroencephalogram (EEG) signal spontaneous evoked by left and right hand motor imagery and the classifier based on the Support Vector Machine (SVM) algorithm and on the common spatial pattern (CSP) algorithm apply the feature extracted from purified EEG signal to recognize and discriminate distinctive motor imagery pattern.

关键词： Electroencephalography Classification algorithms Feature extraction Covariance matrices Support vector machines Filtering Electrodes

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A new and more informative subtyping scheme for breast cancer based on co-expression of metabolic genes

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Genes & Diseases 2025年

作者： Chongren Pei Yuzhe Zhang Jun Xiao Qing He Wenyong Zhang Ying Xu Systems Biology Lab for Metabolic Reprogramming Department of Human Cell Biology and Genetics School of Medicine Southern University of Science and Technology Shenzhen Guangdong 518055 China The First Laboratory of Cancer Institute The First Hospital of China Medical University Shenyang Liaoning 110001 China Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun Jilin 130012 China The Third People's Hospital of Shenzhen Shenzhen Guangdong 518112 China School of Medicine Southern University of Science and Technology Shenzhen Guangdong 518055 China

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Virtual Machine Failure Prediction Method Based on AdaBoost-Hidden Markov Model

Virtual Machine Failure Prediction Method Based on AdaBoost-...

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作者： Zhixin Li Lei Liu Degang Kong College of Computer Science and Technology Jilin University School of Computer Technology and Engineering Changchun Institute Of Technology

The failure prediction method of virtual machines(VM) guarantees reliability to cloud ***,the uncertainty of VM security state will affect the reliability and task processing capabilities of the entire cloud *** this Study,a failure prediction method of VM based on AdaBoostHidden Markov Model was proposed to improve the reliability of VMs and overall performance of cloud *** method analyzed the deep relationship between the observation state and the hidden state of the VM through the hidden Markov model,proved the influence of the AdaBoost algorithm on the hidden Markov model(HMM),and realized the prediction of the VM failure *** show that the proposed method adapts to the complex dynamic cloud platform environment,can effectively predict the failure state of VMs,and improve the predictive ability of VM security state.

关键词： Virtual Machine Failure Prediction Hidden Markov Model

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Large-scale generation of functional mRNA-encapsulating exosomes via cellular nanoporation (vol 4, pg 69, 020)

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NATURE BIOMEDICAL engineering 2021年第8期5卷 944-945页

作者： Yang, Zhaogang Shi, Junfeng Xie, Jing Wang, Yifan Sun, Jingyao Liu, Tongzheng Zhao, Yarong Zhao, Xiuting Wang, Xinmei Ma, Yifan Malkoc, Veysi Chiang, Chiling Deng, Weiye Chen, Yuanxin Fu, Yuan Kwak, Kwang J. Fan, Yamin Kang, Chen Yin, Changcheng Rhee, June Bertani, Paul Otero, Jose Lu, Wu Yun, Kyuson Lee, Andrew S. Jiang, Wen Teng, Lesheng Kim, Betty Y. S. Lee, L. James Department of Chemical and Biomolecular Engineering The Ohio State University Columbus OH USA Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas TX USA School of Life Sciences Jilin University Changchun China College of Pharmacy Jinan University Guangzhou China College of Medicine The Ohio State University Columbus OH USA Department of Neurological Surgery Mayo Clinic Jacksonville FL USA Department of Neurosurgery The University of Texas MD Anderson Cancer Center Houston TX USA Carver College of Medicine University of Iowa Iowa City IA USA Department of Biophysics Health Science Center Peking University Beijing China Institute for Stem Cell Biology Stanford University Palo Alto CA USA Institute of Cancer Research Shenzhen Bay Laboratory Shenzhen China Department of Electrical and Computer Engineering The Ohio State University Columbus OH USA Department of Neuroscience The Ohio State University Columbus OH USA Peak Center for Pituitary and Brain Tumor Department of Neurosurgery Houston Methodist Research institute Weill-Cornell Medical College Houston TX USA

A Correction to this paper has been published: https://***/10.1038/s41551-021-00725-w.

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Author Correction: Biomimetic olfactory chips based on large-scale monolithically integrated nanotube sensor arrays

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Nature Electronics 2024年 842页

作者： Chen Wang Zhesi Chen Chak Lam Jonathan Chan Zhu’an Wan Wenhao Ye Wenying Tang Zichao Ma Beitao Ren Daquan Zhang Zhilong Song Yucheng Ding Zhenghao Long Swapnadeep Poddar Weiqi Zhang Zixi Wan Feng Xue Suman Ma Zhiyong Fan Qingfeng Zhou Kai Liu Geyu Lu Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong China The Hong Kong University of Science and Technology–Shenzhen Research Institute Shenzhen China State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and Technology Clear Water Bay Hong Kong China Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong China Ai-Sensing Technology Co. Ltd. Foshan National Torch Innovation Pioneering Park Foshan China State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing China State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun China

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Stimuli-responsive fluorescent supramolecular polymer network based on a monofunctionalized leaning towerarene

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Chinese Chemical Letters 2019年第12期30卷 2299-2303页

作者： Zengjie Liu Jiarui Wu Chunyu Wang Jie Yang Yan Wang Ying-Wei Yang State Key Laboratory of Inorganic Synthesis and Preparative Chemistry International Joint Research Laboratory of Nano-Micro Architecture Chemistry(NMAC)College of ChemistryJilin UniversityChangchun 130012China The State Key Laboratory of Refractories and Metallurgy School of Chemistry and Chemical EngineeringWuhan University of Science and TechnologyWuhan 430081 China State Key Laboratory of Supramolecular Structure and Materials Institute of Theoretical ChemistryJilin UniversityChangchun 130012China California NanoSystems Institute and Department of Chemistry&Biochemistry University of CaliforniaLos AngelesCA 90095United States

A fluorescent supramolecular polymer network with an excellent triple-stimuli responsive property based on metal-ligand coordination and host-guest interactions has been constructed from a terpyridine-monofunctionalized leaning tower[6]arene,a tetraphenylethylene AIEgen,and a bridging coordination ion(Zn^2+).Addition of competitive binding agents,trifluoroacetic acid,and/or pillar[5]arene can break the metal coordination and/or host-guest inclusion complexation,and thermal heating can weaken the non-covalent interactions in the supramolecular polymer gel,all leading to the gel-to-sol transition.

关键词： Aggregation-induced emission Coordination Leaning tower[n]arene Stimuli-responsive materials Supramolecular polymer

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Bacteria Flagella-Mimicking Polymer Multilayer Magnetic Microrobots

SSRN

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SSRN 2024年

作者： Wu, Zhiguang Lu, Liang Bai, Shuang Shi, Jiaqi Zhang, Zhuotao Pan, Yunlu Wang, Wei Wu, Wenfeng Sun, Shoubin Li, Xu Hou, Gang Huang, Tianyun Jia, Yuxin Zuo, Kaiwen Granovsky, Alexander Wu, He Nikolai, Perov School of Medicine and Health Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China The First Affiliated Hospital of Harbin Medical University Harbin China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and Multimorbidity National Clinical Research Center for Respiratory Diseases Institute of Respiratory Medicine Chinese Academy of Medical Sciences Department of Pulmonary and Critical Care Medicine Center of Respiratory Medicine China-Japan Friendship Hospital Beijing China College of Science Sichuan Agricultural University Chengdu China The Fourth Affiliated Hospital of Harbin Medical University Harbin China Department of Ophthalmology The Second Hospital Jilin University Changchun China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Mathematics Imperial College London United Kingdom Department of Computer Science University of Warwick Coventry United Kingdom Magnetism Department Faculty of Physics Lomonosov Moscow State University Moscow Russia Harbin Medical University China

Learning from natural bacteria flagellum, we demonstrate a magnetic polymer multilayer conical microrobot that bestow the controllable propulsion upon external rotating magnetic field with uniform intensity. The magnetic microrobots were constructed by template-assisted Layer-by-Layer technique technique and subsequent functionalization of magnetic particle onto the big opening of the microrobots. Geometric variables of the polymer microrobots, such as the diameter and wall thickness, can be controlled by selection of porous template and layers of assembly. The microrobots perform controllable propulsion through the manipulation of magnetic field. The comparative analysis of the movement behavior reveals that the deformation of microrobots may attributed to the propulsion upon rotating magnetic field, which is similar as that of natural bacteria. The influence of actuation frequencies and frequencies on the velocity of the microrobots is studied. Such use of polymer polymer multilayer tubular micro/nanoarchitectures results in significant savings in the processing costs and provides a simple and cost-effective fabrication route for the mass production of magnetic polymer microrobots. © 2024, The Authors. All rights reserved.

关键词： Bacteria

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