检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Chu, M.C. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Ge, T.T. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hannuksela, O.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Y.Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, H.B. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Khangulyan, D. Kuleshov, D. Kurinov, K. Li, B.B. Li, C.M. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, S.D. 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, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Luo, Q. Luo, Y. 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. Ng, K.C.Y. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. Sáiz, A. Saeed, M. Semikoz, D. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Key Laboratory of Particle Astrophysics & 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 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany 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 Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Hebei Shijiazhuang050024 China University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Zhuhai Guan

We report a dedicated study of the newly discovered extended UHE γ-ray source 1LHAASO J0056+6346u. Analyzing 979 days of LHAASO-WCDA data and 1389 days of LHAASO-KM2A data, we observed a significant excess of γ-ray events with both WCDA and KM2A. Assuming a point power-law source with a fixed spectral index, the significance maps reveal excesses of 12.65 σ, 22.18 σ, and 10.24 σ in the energy ranges of 1–25 TeV, 25–100 TeV, and > 100 TeV, respectively. We use a 3D likelihood algorithm to derive the morphological and spectral parameters, and the source is detected with significances of 13.72 σ by WCDA and 25.27 σ by KM2A. The best-fit positions derived from WCDA and KM2A data are (R.A. = 13.96◦ ± 0.09◦, Decl. = 63.92◦ ± 0.05◦) and (R.A. = 14.00◦ ± 0.05◦, Decl. = 63.79◦ ± 0.02◦), respectively. The angular size (r39) of 1LHAASO J0056+6346u is 0.34◦ ± 0.04◦ at 1-25 TeV and 0.24◦ ± 0.02◦ at > 25 TeV. The differential flux of this UHE γ-ray source can be described by an exponential cutoff power-law function: (2.67±0.25)×10−15 (E / 20 TeV)(−1.97±0.10) e−E / (55.1±7.2) TeV TeV−1cm−2s−1. To explore potential sources of γ-ray emission, we investigated the gas distribution around 1LHAASO J0056+6346u. 1LHAASO J0056+6346u is likely to be a TeV PWN powered by an unknown pulsar, which would naturally explain both its spatial and spectral properties. Another explanation is that this UHE γ-ray source might be associated with gas content illuminated by a nearby CR accelerator, possibly the SNR candidate G124.0+1.4. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmic rays

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Identification of mobile device threats based on PCA-fused neural network

Boletin Tecnico/Technical Bulletin

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Boletin Tecnico/Technical Bulletin 2017年第3期55卷 278-285页

作者： Chu, Sijun Wu, Hao Lu, Yulin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

To monitor and identify whether a mobile device is under threat or at risk effectively, in this paper, we summarize feature data from threat information of common mobile devices and set up an identification model of mobile device threats based on PCA-fused BP neural network. When extracting features from raw sample data, we consider primary vector information of all training samples. While more than 95% of the information in raw data is reserved, interference information in the data is eliminated and the robustness of the BP network model built subsequently is optimized. Finally, the trained model is tested and compared with other model algorithms. Results show that the overall identification accuracy of the proposed model algorithm reaches 91%. The identification accuracy of test samples reaches 90.4%. Compared with previous identification models, it has higher identification accuracy.

关键词： Neural networks

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Excited-state Half-Lives in Cd130 and the Isospin Dependence of Effective Charges

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Physical Review Letters 2024年第22期132卷 222501-222501页

作者： A. Jungclaus M. Górska M. Mikołajczuk J. Acosta J. Taprogge S. Nishimura P. Doornenbal G. Lorusso G. S. Simpson P.-A. Söderström T. Sumikama Z. Xu P. Kumar G. Martínez-Pinedo F. Nowacki P. Van Isacker H. Baba F. Browne N. Fukuda R. Gernhäuser G. Gey N. Inabe T. Isobe H. S. Jung D. Kameda G. D. Kim Y.-K. Kim I. Kojouharov T. Kubo N. Kurz Y. K. Kwon Z. Li H. Sakurai H. Schaffner Y. Shimizu K. Steiger H. Suzuki H. Takeda Zs. Vajta H. Watanabe J. Wu A. Yagi K. Yoshinaga G. Benzoni S. Bönig K. Y. Chae J.-M. Daugas F. Drouet A. Gadea S. Ilieva F. G. Kondev T. Kröll G. J. Lane A. Montaner-Pizá K. Moschner F. Naqvi M. Niikura H. Nishibata A. Odahara R. Orlandi Z. Patel Zs. Podolyák A. Wendt Instituto de Estructura de la Materia CSIC E-28006 Madrid Spain GSI Helmholtzzentrum für Schwerionenforschung D-64291 Darmstadt Germany Faculty of Physics University of Warsaw Warsaw PL-02093 Poland Departamento de Física Teórica Universidad Autónoma de Madrid E-28049 Madrid Spain RIKEN Nishina Center RIKEN 2-1 Hirosawa Wako-shi Saitama 351-0198 Japan LPSC Université Joseph Fourier Grenoble 1 CNRS/IN2P3 Institut National Polytechnique de Grenoble F-38026 Grenoble Cedex France Department of Physics Tohoku University Aoba Sendai Miyagi 980-8578 Japan Department of Physics University of Tokyo Hongo 7-3-1 Bunkyo-ku 113-0033 Tokyo Japan Institut für Kernphysik (Theoriezentrum) Fachbereich Physik Technische Universität Darmstadt D-64289 Darmstadt Germany Université de Strasbourg CNRS IPHC UMR Strasbourg 7178 F-67000 France Grand Accélérateur National d’Ions Lourds CEA/DRF–CNRS/IN2P3 F-14076 Caen France School of Computing Engineering and Mathematics University of Brighton Brighton BN2 4GJ United Kingdom Physik Department E12 Technische Universität München D-85748 Garching Germany Institut Laue-Langevin B.P. 156 F-38042 Grenoble Cedex 9 France Department of Physics Chung-Ang University Seoul 156-756 Republic of Korea Rare Isotope Science Project Institute for Basic Science Daejeon 305-811 Republic of Korea Department of Nuclear Engineering Hanyang University Seoul 133-791 Republic of Korea School of Physics and State key Laboratory of Nuclear Physics and Technology Peking University Beijing 100871 China MTA Atomki P.O. Box 51 Debrecen H-4001 Hungary Department of Physics Osaka University Machikaneyama-machi 1-1 Osaka 560-0043 Toyonaka Japan Department of Physics Faculty of Science and Technology Tokyo University of Science 2641 Yamazaki Noda Chiba Japan INFN Sezione di Milano via Celoria 16 I-20133 Milano Italy Institut für Kernphysik Technische Universität Darmstadt D-64289 Darmstadt Germany Department of Physics Sungkyunkwan Univer

The known Iπ=81+, Ex=2129−keV isomer in the semimagic nucleus Cd13082 was populated in the projectile fission of a U238 beam at the Radioactive Isotope Beam Factory at RIKEN. The high counting statistics of the accumulated data allowed us to determine the excitation energy, Ex=2001.2(7) keV, and half-life, T1/2=57(3) ns, of the Iπ=61+ state based on γγ coincidence information. Furthermore, the half-life of the 81+ state, T1/2=224(4) ns, was remeasured with high precision. The new experimental information, combined with available data for Sn134 and large-scale shell model calculations, allowed us to extract proton and neutron effective charges for Sn132, a doubly magic nucleus far-off stability. A comparison to analogous information for Sn100 provides first reliable information regarding the isospin dependence of the isoscalar and isovector effective charges in heavy nuclei.

关键词： Electromagnetic transitions Nuclear structure & decays Shell model

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Word2Cluster: A New Multi-Label Text Clustering Algorithm with an Adaptive Clusters Number

Word2Cluster: A New Multi-Label Text Clustering Algorithm wi...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Kaili Mao Jianwei Niu Xuefeng Liu Shui Yu Longbo Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University School of Computer Science and Cyber Engineering Guangzhou University Guangdong China China aerospace science and industry corporation China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Text clustering has been widely used in many Natural Language Processing (NLP) applications such as text summarization and news recommendation. However, most of the current algorithms need to predefine a clustering number, which is difficult to obtain. Moreover, the mutli-label clustering is useful in multiple clustering tasks in many applications, but related works are rarely available. Although several studies have attempted to solve above two problems, there is a need for methods that can solve the two issues simultaneously. Therefore, we propose a new text clustering algorithm called Word2Cluster. Word2Cluster can automatically generate an adaptive number of clusters and support multi-label clustering. To test the performance of Wrod2Cluster, we build a Chinese text dataset, Hotline, according to real world applications. To evaluate the clustering results better, we propose an improved evaluation method based on basic accuracy, precision and recall for multi-label text clustering. Experimental results on a Chinese text dataset (Hotline) and a public English text dataset (Reuters) demonstrate that our algorithm can achieve better F1-measure and runs faster than the state-of- the-art baselines.

关键词： Clustering algorithms Biological system modeling Merging Task analysis Computer science Technological innovation Natural language processing

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A WeChat user geolocating algorithm based on the relation between reported and actual distance 1

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9th International Symposium on Cyberspace Safety and Security, CSS 2017

作者： Shi, Wenqi Luo, Xiangyang Zhao, Fan Peng, Ziru Gan, Yong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Science and Technology Institute Zhengzhou China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China

ISBN: (数字)9783319694719

ISBN: (纸本)9783319694702

The accuracy of existing geolocation methods for WeChat users depends on the stable correspondence between reported distance and actual distance. In view of the difficulty to pinpoint users’ location in real-world due to WeChat location protection strategy, a WeChat User geolocating Algorithm based on reported and actual distance relation analysis is proposed. Firstly, statistical characteristics of the relation between reported distance and actual distance are obtained based on collected data. Secondly, optimization parameters are selected based on these characteristics to determine the space where the target user is located. Finally, stepwise strategies are taken to improve the accuracy rate of space partition. Experimental results show that, on the premise that target users can be discovered, the proposed algorithm could achieve higher accuracy compared with the classical space partition based algorithm and the heuristic number theory based algorithm. The highest geolocating accuracy is within 10 m and 56% of geolocation results are within 60 m. © 2017, Springer International Publishing AG.

关键词： Number theory

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ShenTu: Processing Multi-Trillion Edge Graphs on Millions of Cores in Seconds 18

ShenTu: Processing Multi-Trillion Edge Graphs on Millions of...

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ACM/IEEE International Conference for High Performance computing, Networking, Storage, and Analysis

作者： Heng Lin Xiaowei Zhu Bowen Yu Xiongchao Tang Wei Xue Wenguang Chen Lufei Zhang Torsten Hoefler Xiaosong Ma Xin Liu Weimin Zheng Jingfang Xu Tsinghua University and Fma Technology Tsinghua University and Qatar Computing Research Institute Tsinghua University State Key Laboratory of Mathematical Engineering and Advanced Computing ETH Zurich Qatar Computing Research Institute National Research Centre of Parallel Computer Engineering and Technology Beijing Sogou Technology Development Co. Ltd

ISBN: (纸本)9781538683859

Graphs are an important abstraction used in many scientific fields. With the magnitude of graph-structured data constantly increasing, effective data analytics requires efficient and scalable graph processing systems. Although HPC systems have long been used for scientific computing, people have only recently started to assess their potential for graph processing, a workload with inherent load imbalance, lack of locality, and access irregularity. We propose ShenTu ⁸ the first general-purpose graph processing framework that can efficiently utilize an entire Petascale system to process multi-trillion edge graphs in seconds. ShenTu embodies four key innovations: hardware specialization, supernode routing, on-chip sorting, and degree-aware messaging, which together enable its unprecedented performance and scalability. It can traverse a record-size 70-trillion-edge graph in seconds. Furthermore, ShenTu enables the processing of a spam detection problem on a 12-trillion edge Internet graph, making it possible to identify trustworthy and spam webpages directly at the fine-grained page level.

关键词： Random access memory Servers Data analysis Supercomputers Task analysis Computer architecture Optimization supercomputer task analysis random access machine Computer Architecture Servers Data analysis core Core samples Scalability Edge of a figure or solid Unsolicited electronic mail

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Stego key searching for LSB steganography on JPEG decompressed image

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Science China(Information Sciences) 2016年第3期59卷 60-74页

作者： Jiufen LIU Yuguo TIAN Tao HAN Junchao WANG Xiangyang LUO Zhengzhou Information Science and Technology Institute State Key Laboratory of Mathematical Engineering and Advanced Computing

At present, steganalysis research focuses on detecting the existence of a hidden message. However,extracting the hidden information, i.e., an extracting attack, is crucial in obtaining effective evidence in computer forensics. Due to the difficulty of an extracting attack, research in this field is limited. In steganography with a stego key, an extracting attack is equivalent to recovering the stego key. In this paper we study a method for recovering the stego key in least significant bit(LSB) steganography with a decompressed JPEG image as the cover image. Firstly, the recovery of the stego key is translated into a cryptanalysis problem for a sequential cipher. The method for recovering the stego key is based on estimating the modification positions. The minimum size of the data used to recover the stego key successfully is discussed. Secondly, when a decompressed JPEG image is used as the cover image, the probability of recovering the cover pixels using recompression is *** is used to compute the error of the estimated sequence. Finally, an algorithm to recover the stego key in LSB steganography with a decompressed JPEG image as the cover image is proposed. The experimental results for the steganographic software, Hide and Seek 4.1 and its variant, which is a typical representative of LSB steganography, show that the proposed method can successfully recover the stego key in LSB replacement and LSB matching, i.e., the extracting attack is successful, and it outperforms three previous methods in terms of computational complexity.

关键词： steganalysis extracting attack LSB steganography decompressed JPEG image recompression stego key

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Classification of Arrhythmia Based on Extreme Learning Machine

Classification of Arrhythmia Based on Extreme Learning Machi...

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International Conference on Intelligent Human-Machine Systems and Cybernetics

作者： Xingjin Zhang Runchuan Li Yongpeng Liu Shan Gao Hongpo Zhang Shengya Shen Zongmin Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou Henan China School of Foreign Languages Zhengzhou University Zhengzhou Henan China

This paper proposes an arrhythmia classification method based on Extreme Learning Machine (ELM). Firstly, we extract a feature vector from the electrocardiogram (ECG) dataset as the input of the ELM model for each heartbeat and use the 10-fold cross validation method to test the model. Secondly, we complete the experiment on the MIT-BIH database according to the AAMI-reported arrhythmia classification criteria. Finally, compared with other methods the average accuracy of classification method proposed in this paper has a 1.7% increase, and the sensitivity of the N, S, V, and Q categories are 99.12%, 75.2%, 93.72%, and 98.64%, respectively. We found that the performance about class F is lower than the best performance in the literature, because the optimal outcome of neural network requires a relatively high number of training samples.

关键词： Arrhythmia Extreme learning machine Heartbeat classification

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Photonic topological valley-locked waveguides

arXiv

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arXiv 2020年

作者： Chen, Qiaolu Zhang, Li Yan, Qinghui Xi, Rui Chen, Hongsheng Yang, Yihao Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China International Joint Innovation Center ZJU-UIUC Institute Zhejiang University Haining314400 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore637371 Singapore Centre for Disruptive Photonic Technologies Photonics Institute Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

Topological valley kink states have become a significant research frontier with considerable intriguing applications such as robust on-chip communications and topological lasers. Unlike guided modes with adjustable widths in most conventional waveguides, the valley kink states are usually highly confined around the domain walls and thus lack the mode width degree of freedom (DOF), posing a serious limitation to potential device applications. Here, by adding a photonic crystal (PhC) featuring a Dirac point between two valley PhCs with opposite valley-Chern numbers, we design and experimentally demonstrate topological valley-locked waveguides (TVLWs) with tunable mode widths. The photoinc TVLWs could find unique applications, such as high-energy-capacity topological channel intersections, valley-locked energy concentrators, and topological cavities with designable confinement, as verified numerically and experimentally. The TVLWs with width DOF could be beneficial to interface with the exsisting photonic waveguides and devices, and serve as a novel platform for practical use of topological lasing, field enhancement, on-chip communicaitons, and high-capacity energy transport. Copyright © 2020, The Authors. All rights reserved.

关键词： Landforms

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