检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Photocatalytic Hydrogen Production on a sp2-Carbon-Linked Covalent Organic Framework

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Angewandte Chemie 2022年第42期134卷

作者： Si Ma Prof. Dr. Tianqi Deng Dr. Ziping Li Zhenwei Zhang Ji Jia Qizhen Li Prof. Dr. Gang Wu Prof. Dr. Hong Xia Prof. Dr. Shuo-Wang Yang Prof. Dr. Xiaoming Liu College of Chemistry Jilin University Changchun 130012 P.R. China Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 311200 P.R. China State Key Laboratory of Silicon Materials & School of Materials Science and Engineering Zhejiang University Hangzhou 310027 P.R. China Department of Biomedical Engineering Southern University of Science and Technology Shenzhen 518055 P.R. China Institute of High Performance Computing Agency for Science Technology and Research 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Singapore State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Technology Jilin University Changchun 130012 P.R. China

Two-dimensional covalent organic frameworks (2D-COFs) have emerged as attractive platforms for solar-to-chemical energy conversion. In this study, we have implemented a gradient heating strategy to synthesize a sp 2 -carbon-linked triazine-based COF, COF-JLU100, exhibiting high crystallinity, large surface area, good durability and carrier mobility for solar-driven photocatalytic hydrogen evolution. The Pt-doped COF-JLU100 demonstrated a high hydrogen evolution rate of over 100 000 μmol g −1 h −1 for water splitting under visible-light illumination (λ>420 nm). Experimental and theoretical studies corroborate that the cyano-vinylene segments in COF-JLU100 extend the π-delocalization and enable fast charge transfer and separation rates as well as good dispersion in water. Moreover, COF-JLU100 can be prepared by low-cost and easily available monomers and has excellent stability, which is desirable for practical solar-driven hydrogen production.

关键词： Covalent Organic Frameworks Gradient Heating Hydrogen Evolution Photocatalysis sp2 Carbon Linkage

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PS-HOMO: A Protection Scheme for iOS SMS

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Journal of Physics: Conference Series 2020年第2期1486卷

作者： Guomiao Zhou Qianqiong Wu Ming Duan Qi Xi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450000 China Institute of Electrical Engineering and Automation Luoyang Institute of Science and Technology Luoyang Henan 471000 China

iOS Short Message Service (SMS) transmits messages in plaintext, which would lead to potential security threats of data. It is meaningful to design a safe and effective SMS. In this paper, all iOS Security Guides are referred to and all the cryptographic algorithms in iOS data protection are analyzed. Because there is no homomorphic encryption, a library of homomorphic encryption algorithms for iOS based on RSA and Paillier is developed. Using this library, a protection scheme PS-HOMO for protecting iOS SMS data is proposed and implemented. Under the premise of ensuring the data confidentiality, PS-HOMO also has the function of ciphertext operation due to the multiplicative homomorphism of RSA and additive homomorphism of Paillier. A comprehensive performance test was performed on PS-HOMO. The results show that the performance is in line with our expectations and will not have a major impact on the original system. The security of PS-HOMO is theoretically analyzed from man-in-the-middle attack, replay attack, and traffic analysis attack. Two application scenarios of the PS-HOMO, remote anonymous voting and privacy telemedicine service are envisaged. We hope PS-HOMO would play a significant role in privacy protection.

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Improved Impossible Polytopic Attacks on Round-reduced DES

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Journal of Physics: Conference Series 2020年第3期1486卷

作者： Jing Wang Qianqiong Wu Chaohui Fu Guomiao Zhou Ming Duan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450000 China College of electrical engineering and automation Luoyang Institute of Science and Technology Luoyang Henan 471000 China

In Eurocrypt 2016, Tyge Tiessen introduced the d-difference which considering the differences between multiple plaintexts, and proposed the impossible polytopic attack on block cipher which effectively reducing the data complexity. In this paper, we improve the impossible polytopic attacks of round-reduced DES by some ideas like truncated differentials. Given the input 3-difference of each S-box in the third round, the number of the output 3-difference is actually smaller than the theoretical upper bound, which helps us reduce the memory complexity of the attack on 5-round DES from 212 bytes to 26.9 bytes and increase the success rate of the attack. Using the idea of truncated differentials, the time complexity of the attack on 6-round DES is reduced from 232.2 encryptions to 225.8 encryptions by selecting the output 3-differences of 6 S-boxes for key recovery. We also improve the attack on 7-round DES by using more plaintexts based on our improved attack on 6-round DES.

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swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor

arXiv

引用

arXiv 2019年

作者： Li, Mingzhen Liu, Changxi Liao, Jianjin Zheng, Xuegui Yang, Hailong Sun, Rujun Xu, Jun Gan, Lin Yang, Guangwen Luan, Zhongzhi Qian, Depei School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Beijing China National University of Singapore Singapore Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM achieves 1.79× on average compared to the state-of-the-art deep learning framework on Sunway, across six representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor. Copyright © 2019, The Authors. All rights reserved.

关键词： Program compilers

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Application of the nitrogen laser calibration system in LHAASO-WFCTA

Application of the nitrogen laser calibration system in LHAA...

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Chen, Long Li, Xin Ge, Lisi Liu, Hu Sun, Qinning Wang, Yang Xia, Junji Zhu, Fengrong Zhang, Yong Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D’Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. School of Physical Science and Technology Southwest Jiaotong University No.111 North Section 1 Second Ring Road Chengdu China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences 19B Yuquan RoadShijingshan District Beijing China Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Dipartimento di Fisica dell’Università di Napoli `‘Federico II" Complesso Universitario di Monte Sant’Angelo via Cinthia Napoli80126 Italy Sc

The nitrogen laser calibration system for the Wide Field of View Cherenkov Telescope Array (WFCTA) is one of the most important components of the Large High Altitude Air Shower Observatory (LHAASO). This calibration system is composed of three parts, including a high-precision 3D lifting and rotating platform (HiRoP), a nitrogen laser and the wide field of view Cherenkov telescope prototypes. The accuracy of the HiRoP is of great importance for the precise calibration of WFCTA since it controls the laser beam’s pointing direction. A home-made HiRoP was introduced and the measurements implies that the angular and lifting accuracies of HiRoP are better than 0.003° and 0.075 mm, respectively. Furthermore, the pulse energy stability of the nitrogen laser beam is another remarkably important factor. The nitrogen laser system with a wavelength of 337.1 nm located in a high-precision temperature and humidity’s controlling container, resulting in a pulse energy fluctuation less than 3 percent. Besides, the method of a standard zero point verification is employed to improve the long-term accuracy of the beam pointing direction. © Copyright owned by the author(s).

关键词： Laser beams

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Stealthy Malware Detection Based on Deep Neural Network

引用

Journal of Physics: Conference Series 2020年第1期1437卷

作者： Shoupu Lu Qingbao Li Xinbing Zhu State Key laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan Province 450001 China Henan University of Economics and Law Zhengzhou Henan Province 450000 China

Network attacks using advanced local hiding technology have not only increased, but also become a serious threat. However, attacks using these technologies can not be detected through traffic detection, and some attacks imitate benign traffic to avoid detection. To solve these problems, a malware process detection method based on process behavior in possibly infected terminals is proposed. In this method, a deep neural network is introduced to classify malware processes. Firstly, the recurrent neural network is trained to extract the characteristics of process behavior. Secondly, training convolutional neural network is used to classify feature images generated by trained RNN features. The experiments results show that this method can effectively extract the features of malicious processes, and the AUC of ROC curve is 0.97 in the best case.

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The First LHAASO Catalog of Gamma-Ray Sources

arXiv

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arXiv 2023年

作者： Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Q.W. Tang, Z.B. Key Laboratory of Particle Astrophyics Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China Tianfu Cosmic Ray Research Center Sichuan Chengdu610000 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Physics and Astronomy Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Guangdong Zhuhai519000 China School of Physics Sun Yat-sen University Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet Geneva121

We present the first catalog of very-high energy and ultra-high energy gamma-ray sources detected by the Large High Altitude Air Shower Observatory (LHAASO). The catalog was compiled using 508 days of data collected by the Water Cherenkov Detector Array (WCDA) from March 2021 to September 2022 and 933 days of data recorded by the Kilometer Squared Array (KM2A) from January 2020 to September 2022. This catalog represents the main result from the most sensitive large coverage gamma-ray survey of the sky above 1 TeV, covering declination from −20◦ to 80◦. In total, the catalog contains 90 sources with an extended size smaller than 2◦ and a significance of detection at > 5σ. Based on our source association criteria, 32 new TeV sources are proposed in this study. Among the 90 sources, 43 sources are detected with ultra-high energy (E > 100 TeV) emission at > 4σ significance level. We provide the position, extension, and spectral characteristics of all the sources in this catalog. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

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A loop unrolling method based on machine learning 32

A loop unrolling method based on machine learning

引用

32nd International Conference on Vibroengineering

作者： Liu, Hui Guo, Zhanjie State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China College of Computer and Information Engineering Henan Normal University Xinxiang453007 China Department of Electrical and Electronic Engineering Zhengzhou Technical College Zhengzhou450121 China

In order to improve the accuracy of loop unrolling factor in the compiler, we propose a loop unrolling method based on improved random decision forest. First, we improve the traditional random decision forest through adding weight value. Second, BSC algorithm based on SMOTE algorithm is proposed to solve the problem of unbalanced data sets. Nearly 1000 loops are selected from several benchmarks, and features extracted from these loops constitute the training set of the loop unrolling factor prediction model. The model has a prediction accuracy of 81 % for the unrolling factor, and the existing Open64 compiler gives 36 % only. © 2018 Hui Liu, et al.

关键词： Machine learning

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Nonmonotonic crossover in electronic phase separated manganite superlattices driven by the superlattice period

引用

Physical Review B 2020年第23期102卷 235107-235107页

作者： Yinyan Zhu Biying Ye Qiang Li Hao Liu Tian Miao Lijun Wu Lei Li Lingfang Lin Yi Zhu Zhe Zhang Qian Shi Yulong Yang Kai Du Yu Bai Yang Yu Hangwen Guo Wenbin Wang Xiaoshan Xu Xiaoshan Wu Zhicheng Zhong Shuai Dong Yimei Zhu Elbio Dagotto Lifeng Yin Jian Shen State Key Laboratory of Surface Physics and Department of Physics Fudan University Shanghai 200433 China Institute for Nanoelectronics Devices and Quantum Computing Fudan University Shanghai 200433 China Condensed Matter Physics and Materials Science Department Brookhaven National Laboratory Upton New York 11973 USA Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo 315201 China School of Physics Southeast University Nanjing 211189 China Department of Physics Nanjing University Nanjing 211189 China Department of Physics and Astronomy University of Nebraska–Lincoln Lincoln Nebraska 68588 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA and Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China and Shanghai Qi Zhi Institute Shanghai 200232 China

Studying manganite superlattices [(LCMO)2n/(PCMO)n]t made of La0.625Ca0.375MnO3 (LCMO) and Pr0.625Ca0.375MnO3 (PCMO), we found an unexpected behavior varying the period n. At small n, the ensemble is a three-dimensional ferromagnetic metal due to interfacial charge transfer. At large n, the LCMO layers dominate transport. However, rather than a smooth interpolation between these limits a sharp transport and magnetic anomaly is found at an intermediate critical PCMO thickness n*. Magnetic force microscopy reveals that the phase-separation length scale also maximizes at n* where, unexpectedly, it becomes comparable to that of the (La1–yPry)0.625Ca0.375MnO3 (LPCMO) alloy. We conjecture the phenomenon originates in a disorder-related length scale: Large charge-ordered clusters as in LPCMO can only nucleate when Pr-rich regions reach a critical size related to n*.

关键词： Electrical conductivity Magnetic phase transitions Magnetic multilayers Strongly correlated systems Magnetic force microscopy

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