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arXiv 2023年

作者： Lou, Rui Zhou, Liqin Song, Wenhua Fedorov, Alexander Tu, Zhijun Jiang, Bei Wang, Qi Li, Man Liu, Zhonghao Chen, Xuezhi Rader, Oliver Büchner, Bernd Sun, Yujie Weng, Hongming Lei, Hechang Wang, Shancai Leibniz Institute for Solid State and Materials Research IFW Dresden Dresden01069 Germany Helmholtz-Zentrum Berlin für Materialien und Energie Albert-Einstein-Straße 15 Berlin12489 Germany Joint Laboratory "Functional Quantum Materials" BESSY II Berlin12489 Germany Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-nano Devices Renmin University of China Beijing100872 China School of Physical Science and Technology ShanghaiTech University Shanghai201210 China ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China School of Information Network Security People’s Public Security University of China Beijing100038 China Institute of High-Pressure Physics School of Physical Science and Technology Ningbo University Ningbo315211 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201800 China Institute for Solid State and Materials Physics TU Dresden Dresden01062 Germany Department of Physics Southern University of Science and Technology Shenzhen518055 China Songshan Lake Materials Laboratory Guangdong Dongguan523808 China

Kagome magnets provide a fascinating platform for the realization of correlated topological quantum phases under various magnetic ground states. However, the effect of the magnetic spin configurations on the characteristic electronic structure of the kagome lattice layer remains elusive. Here, utilizing angle-resolved photoemission spectroscopy and density functional theory calculations, we report the spectroscopic evidence for the spin-reorientation effect of a kagome ferromagnet Fe3Ge, which is composed solely of kagome planes. As the Fe moments cant from the c axis into the ab plane upon cooling, the two kinds of kagome-derived Dirac fermions respond quite differently. The one with less-dispersive bands (kz ∼ 0) containing the 3dz2 orbitals evolves from gapped into nearly gapless, while the other with linear dispersions (kz ∼ π) embracing the 3dxz/3dyz components remains intact, suggesting that the effect of spin reorientation on the Dirac fermions has an orbital selectivity. Moreover, we demonstrate that there is no signature of charge order formation in Fe3Ge, contrasting with its sibling compound FeGe, a newly established charge-density-wave kagome magnet. Copyright © 2023, The Authors. All rights reserved.

关键词： Fermions

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衰亡链揭示互利共生生态系统中安全与崩溃的中间阶段（英文）

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Engineering 2024年第12期 89-98页

作者：王光炜刘雪明肖颖袁烨潘林强管晓宏高建喜张海涛 The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and Technology Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi'an Jiaotong University Tsinghua National Laboratory of Information Science and Technology Department of Automation Tsinghua University Department of Computer Science Rensselaer Polytechnic Institute

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities, such as overfishing and deforestation, and the effects of such damage on ecological stability are uncertain. Despite recent advances in experimental and theoretical studies on regime shifts and tipping points, theoretical tools for understanding the extinction chain, which is the sequence of species extinctions resulting from overexploitation, are still lacking, especially for large-scale nonlinear networked systems. In this study, we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and densities. We discovered five phases during the exploitation process: safe,partial extinction, bistable, tristable, and collapse, which enabled the optimal design of restoration strategies for degraded or collapsed systems. We validated our approach using a 20-year dataset from an eelgrass restoration project. Counterintuitively, we also found a specific region in the diagram spanning exploitation rates and competition intensities, where exploiting more species helps increase *** computational tool provides insights into harvesting, fishing, exploitation, or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

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Optimizing network resource allocation with graph pointer neural network in large-scale AI systems

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Digital Communications and networks 2024年

作者： Peiying Zhang Yilin Li Athanasios V. Vasilakos Konstantin Igorevich Kostromitin Jianyong Zhang Mohsen Guizani Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an 710071 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250013 China Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Physics of Nanoscale Systems South Ural State University Chelyabinsk 454080 the Russian Federation Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 China Key Lab of All Optical Network and Advanced Telecommunication of EMC Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi 999041 United Arab Emirates

For increasingly complex communication demands of large-scale AI communication systems, the Space-Air-Ground Integrated network (SAGIN) better caters to demands but also raises concerns about resource scarcity and diversity. This paper innovatively combines Graph Pointer Neural networks (GPNN) and Reinforcement Learning (RL) to enhance resource allocation efficiency. The method leverages the advantages of GPNN in handling graph data and RL in optimizing decisions in dynamic environments. It also targets the optimization goal of maximizing resource allocation while minimizing deployment latency. This paper begins by modeling SAGIN and elucidating the SAGIN logical architecture based on Software-defined networking (SDN). Subsequently, it introduces an SFC deployment algorithm aimed at joint optimization of resource allocation and latency. The algorithm leverages GPNN and RL to deploy virtual nodes and links, with the goal of optimizing resource allocation and deployment latency. Experiment findings conclusively demonstrate that the efficacy of proposed algorithm in effectively weighing limited heterogeneous resources and minimum mapping delay. Notably, when compared to three other SFC mapping algorithms MLRL, NFVdeep, and RL, the proposed algorithm consistently outperforms them, with an average improvement of 10.17% in long-term average reward/cost, 11.21% in link resource utilization ratio, 15.34% in node resource utilization ratio, and 16.38% in acceptance ratio.

关键词： Space-air-ground integrated network Topological structure Reinforcement learning Service function chain Graph pointer neural network

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Advancing image understanding in poor visibility environments: A collective benchmark study

arXiv

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arXiv 2019年

作者： Yang, Wenhan Yuan, Ye Ren, Wenqi Liu, Jiaying Scheirer, Walter J. Wang, Zhangyang Zhang, Taiheng Zhong, Qiaoyong Xie, Di Pu, Shiliang Zheng, Yuqiang Qu, Yanyun Xie, Yuhong Chen, Liang Li, Zhonghao Hong, Chen Jiang, Hao Yang, Siyuan Liu, Yan Qu, Xiaochao Wan, Pengfei Zheng, Shuai Zhong, Minhui Su, Taiyi He, Lingzhi Guo, Yandong Zhao, Yao Zhu, Zhenfeng Liang, Jinxiu Wang, Jingwen Chen, Tianyi Quan, Yuhui Xu, Yong Liu, Bo Liu, Xin Sun, Qi Lin, Tingyu Li, Xiaochuan Lu, Feng Gu, Lin Zhou, Shengdi Cao, Cong Zhang, Shifeng Chi, Cheng Zhuang, Chubin Lei, Zhen Li, Stan Z. Wang, Shizheng Liu, Ruizhe Yi, Dong Zuo, Zheming Chi, Jianning Wang, Huan Wang, Kai Liu, Yixiu Gao, Xingyu Chen, Zhenyu Guo, Chang Li, Yongzhou Zhong, Huicai Huang, Jing Guo, Heng Yang, Jianfei Liao, Wenjuan Yang, Jiangang Zhou, Liguo Feng, Mingyue Qin, Likun Wangxuan Institute of Computer Technology Peking University Beijing100080 China Department of Computer Science and Engineering Texas A&M University TX77843 United States State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Department of Mechanical Engineering Zhejiang University Hangzhou310027 China Hikvision Research Institute Hangzhou310051 China Mtlab Meitu Inc. Beijing100080 China Insitute of Information Science Beijing Jiaotong University Beijing100044 China Department of Computer Science and Technology Tongji University Shanghai201804 China XPENGMOTORS Beijing China School of Computer Science and Engineering at South China University of Technology Guangzhou510006 China Tencent AI Lab Shenzhen518000 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Northeastern University Shenyang110819 China Nanyang Technological University Singapore639798 Big Data Center State Grid Corporation of China Beijing China Winsense Inc. Beijing100080 China University of Chinese Academy of Sciences Beijing100049 China Department of Informatics Technical University of Munich Garching85748 Germany Institute of Microelectronics Chinese Academy of Sciences Beijing100190 China China Electric Power Research Institute Beijing100031 China Westlake University Hangzhou310024 China Department of Computer Science Durham University Durham United Kingdom Australian National University ActonACT0200 Australia Sunway-AI Co. Ltd Zhuhai China Chinese Academy of Sciences R&D Center for Internet of Things Wuxi214200 China

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG2+challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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Is AI Robust Enough for Scientific Research?

arXiv

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arXiv 2024年

作者： Zhang, Jun-Jie Song, Jiahao Wang, Xiu-Cheng Li, Fu-Peng Liu, Zehan Chen, Jian-Nan Dang, Haoning Wang, Shiyao Zhang, Yiyan Xu, Jianhui Shi, Chunxiang Wang, Fei Pang, Long-Gang Cheng, Nan Zhang, Weiwei Zhang, Duo Meng, Deyu Northwest Institute of Nuclear Technology No. 28 Pingyu Road Shaanxi Xi’an710024 China School of Telecommunications Engineering Xidian University No. 2 South Taibai Road Shaanxi Xi’an710071 China State Key Laboratory of ISN No. 2 South Taibai Road Shaanxi Xi’an710071 China Institute of Particle Physics Central China Normal University No. 152 Luoyu Road Hubei Wuhan30079 China School of Computer Science and Technology Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China Guangzhou Institute of Geography Academy of Sciences No. 100 Xianlie Road Guangdong Guangzhou510070 China National Meteorological Information Center Beijing100044 China MDX Research Center for Element Strategy Institute of Integrated Research Institute of Science Tokyo Midori-ku Yokohama226-8503 Japan School of Physics and Information Technology Shaanxi Normal University No. 620 West Chang’an Avenue Shaanxi Xi’an710119 China School of Aeronautics Northwestern Polytechnical University No. 127 West Youyi Road Shaanxi Xi’an710072 China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China School of Mathematics and Statistics Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China

We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant deviations in their outputs. Through an analysis of five diverse application areas—weather forecasting, chemical energy and force calculations, fluid dynamics, quantum chromodynamics, and wireless communication—we demonstrate that this vulnerability is a broad and general characteristic of AI systems. This revelation exposes a hidden risk in relying on neural networks for essential scientific computations, calling further studies on their reliability and security. © 2024, CC0.

关键词： Weather forecasting

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Author Correction: Orbital-selective effect of spin reorientation on the Dirac fermions in a non-charge-ordered kagome ferromagnet Fe3Ge

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Nature communications 2024年第1期15卷 10655页

作者： Rui Lou Liqin Zhou Wenhua Song Alexander Fedorov Zhijun Tu Bei Jiang Qi Wang Man Li Zhonghao Liu Xuezhi Chen Oliver Rader Bernd Büchner Yujie Sun Hongming Weng Hechang Lei Shancai Wang Leibniz Institute for Solid State and Materials Research IFW Dresden 01069 Dresden Sachsen Germany. lourui09@***. Helmholtz-Zentrum Berlin für Materialien und Energie Albert-Einstein-Straße 15 12489 Berlin Germany. lourui09@***. Joint Laboratory "Functional Quantum Materials" at BESSY II 12489 Berlin Germany. lourui09@***. Beijing National Laboratory for Condensed Matter Physics and Institute of Physics Chinese Academy of Sciences Beijing 100190 China. University of Chinese Academy of Sciences Beijing 100049 China. Department of Physics Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education) and Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-nano Devices Renmin University of China Beijing 100872 China. Leibniz Institute for Solid State and Materials Research IFW Dresden 01069 Dresden Sachsen Germany. Helmholtz-Zentrum Berlin für Materialien und Energie Albert-Einstein-Straße 15 12489 Berlin Germany. Joint Laboratory "Functional Quantum Materials" at BESSY II 12489 Berlin Germany. School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China. ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai 201210 China. School of Information Network Security People's Public Security University of China Beijing 100038 China. Institute of High-Pressure Physics and School of Physical Science and Technology Ningbo University Ningbo 315211 China. Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201800 China. Institute of Solid State and Materials Physics TU Dresden 01062 Dresden Sachsen Germany. Department of Physics and Guangdong Basic Research Center of Excellence for Quantum Science Southern University of Science and Technology (SUSTech) Shenzhen 518055 China. Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong) Shenzhen 518045 China. Institute of Advanced Science Facilities Shenzhen Guangdong 5181

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

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Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

A Correction to this paper has been published: https://***/10.1038/s41597-021-00851-9.

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