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Variant Plateau's Law in Atomically Thin Transition Metal Dichalcogenide Dome Networks

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arXiv 2023年

作者： Liu, Boqing Yildirim, Tanju Lü, Tieyu Blundo, Elena Wang, Li Jiang, Lixue Zou, Hongshuai Zhang, Lijun Zhao, Huijun Yin, Zongyou Tian, Fangbao Polimeni, Antonio Lu, Yuerui School of Engineering College of Engineering and Computer Science The Australian National University CanberraACT2601 Australia 1-1 Namiki Ibaraki Tsukuba305-0044 Japan Department of Physics Institute of Theoretical Physics and Astrophysics Xiamen University Xiamen361005 China Dipartimento di Fisica Sapienza Università di Roma Roma00185 Italy School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Centre for Catalysis and Clean Energy Griffith University Gold Coast Campus QLD4222 Australia State Key Laboratory of Integrated Optoelectronics School of Materials Science and Engineering Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials Jilin University Changchun130012 China Research School of Chemistry College of Science The Australian National University CanberraACT2601 Australia Arc Centre of Excellence in Quantum Computation and Communication Technology Anu Node CanberraACT2601 Australia

Since its fundamental inception from soap bubbles, Plateau's law has sparked extensive research in equilibrated states. However, most studies primarily relied on liquids, foams or cellular structures, whereas its applicability has yet to be explored in nano-scale solid films. Here, we observed a variant Plateau's law in networks of atomically thin domes made of solid two-dimensional (2D) transition metal dichalcogenides (TMDs). Discrete layer-dependent van der Waals (vdWs) interaction energies were experimentally and theoretically obtained for domes protruding in different TMD layers. Significant surface tension differences from layer-dependent vdWs interaction energies manifest in a variant of this fundamental law. Meanwhile, the remarkable mechanical properties, gas impermeability and interlayer vdWs interaction energy of TMD films enable domes and the networks to sustain high gas pressure and exist in a fundamentally variant nature for several years. Our findings pave the way towards exploring variant discretised states with applications in opto-electro-mechanical devices. Copyright © 2023, The Authors. All rights reserved.

关键词： Surface tension

Adaptive Fuzzy Sliding Mode Control for Active Front Steering System

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Adaptive Fuzzy Sliding Mode Control for Active Front Steerin...

第三十九届中国控制会议

作者： Tian Tian Liandi Fang Shihong Ding Wei Xing Zheng School of Electrical and Information Engineering Jiangsu University High-tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province College of Mathematics and Computer Science Tongling University School of Computing Engineering and Mathematics Western Sydney University

In order to alleviate the adverse effects of uncertainty in the active front steering（AFS） system, a new type of observer is designed in this paper that can simultaneously estimate the sideslip angle and disturbance, which provides the controller with accurate compensation and simplifies the system model. In addition, an active front steering controller based on adaptive fuzzy sliding mode（AFSM） is proposed, which uses adaptive algorithms to adjust and update fuzzy system parameters in real *** stability of the closed-loop AFS system based on the Lyapunov stability theory is analyzed. Compared with traditional PI control, the simulation results show the effectiveness of the proposed AFSM control method.

关键词： AFS adaptive fuzzy control sliding mode control disturbance observer

Optimizing Partial Area Under the Top-k Curve: Theory and Practice

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arXiv 2022年

作者： Wang, Zitai Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Security Department Alibaba Group Hangzhou311121 China School of Cyber Science and Technology Sun Yat-Sen University Shenzhen Campus Shenzhen518107 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

Top-k error has become a popular metric for large-scale classification benchmarks due to the inevitable semantic ambiguity among classes. Existing literature on top-k optimization generally focuses on the optimization method of the top-k objective, while ignoring the limitations of the metric itself. In this paper, we point out that the top-k objective lacks enough discrimination such that the induced predictions may give a totally irrelevant label a top rank. To fix this issue, we develop a novel metric named partial Area Under the top-k Curve (AUTKC). Theoretical analysis shows that AUTKC has a better discrimination ability, and its Bayes optimal score function could give a correct top-K ranking with respect to the conditional probability. This shows that AUTKC does not allow irrelevant labels to appear in the top list. Furthermore, we present an empirical surrogate risk minimization framework to optimize the proposed metric. Theoretically, we present (1) a sufficient condition for Fisher consistency of the Bayes optimal score function;(2) a generalization upper bound which is insensitive to the number of classes under a simple hyperparameter setting. Finally, the experimental results on four benchmark datasets validate the effectiveness of our proposed framework. © 2022, CC BY.

关键词： Machine learning

SoK: A modularized approach to study the security of automatic speech recognition systems

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arXiv 2021年

作者： Chen, Yuxuan Zhang, Jiangshan Yuan, Xuejing Zhang, Shengzhi Chen, Kai Wang, XiaoFeng Guo, Shanqing Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University China School of Cyber Science and Technology Shandong University China SKLOIS Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Department of Computer Science Metropolitan College Boston University United States School of Informatics Computing and Engineering Indiana University Bloomington United States

With the wide use of Automatic Speech Recognition (ASR) in applications such as human machine interaction, simultaneous interpretation, audio transcription, etc., its security protection becomes increasingly important. Although recent studies have brought to light the weaknesses of popular ASR systems that enable out-of-band signal attack, adversarial attack, etc., and further proposed various remedies (signal smoothing, adversarial training, etc.), a systematic understanding of ASR security (both attacks and defenses) is still missing, especially on how realistic such threats are and how general existing protection could be. In this paper, we present our systematization of knowledge for ASR security and provide a comprehensive taxonomy for existing work based on a modularized workflow. More importantly, we align the research in this domain with that on security in Image Recognition System (IRS), which has been extensively studied, using the domain knowledge in the latter to help understand where we stand in the former. Generally, both IRS and ASR are perceptual systems. Their similarities allow us to systematically study existing literature in ASR security based on the spectrum of attacks and defense solutions proposed for IRS, and pinpoint the directions of more advanced attacks and the directions potentially leading to more effective protection in ASR. In contrast, their differences, especially the complexity of ASR compared with IRS, help us learn unique challenges and opportunities in ASR security. Particularly, our experimental study shows that transfer attacks across ASR models is feasible, even in the absence of knowledge about models (even their types) and training data. © 2021, CC BY.

关键词： Speech recognition

Measurement of the cosmic ray proton spectrum around the knee region with LHAASO 38

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Measurement of the cosmic ray proton spectrum around the kne...

38th International Cosmic Ray Conference, ICRC 2023

作者： Zhiyong, You Shoushan, Zhang Liqiao, Yin Lingling, Ma Zhen, Cao Yudong, Wang Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. 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. The Institute of High Energy Physics The Chinese Academy of Sciences YuQuan Road.19B shijingshan district Beijing China University of Chinese Academy of Sciences Department YuQuan Road.19A shijingshan district Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 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 School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Departement de Physique Nucleair

One of the main scientific aims of LHAASO is to measure the energy spectra of cosmic rays for individual mass compositions. LHAASO is made up of three detector arrays, which are WFCTA, KM2A, and WCDA. The three detector arrays can achieve hybrid observation, so several extensive air shower observables that are sensitive to mass compositions can be measured simultaneously. ROOT-TMVA package is used to combine these component-sensitive parameters for selecting proton events. The selected proton events have a purity of over 90%. In this paper, we have selected simulated events with shower cores located in the KM2A array to investigate the feasibility of the analysis method for studying the proton energy spectrum. We have also studied the associated systematic uncertainties, including those introduced by the composition models, proton selection, energy reconstruction method, and hadronic interaction models. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

DSC: Deep scan context descriptor for large-scale place recognition

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arXiv 2021年

作者： Cui, Jiafeng Huang, Tengfei Cai, Yingfeng Zhao, Junqiao Xiong, Lu Yu, Zhuoping School of Automobile Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization. We propose Deep Scan Context (DSC), a general and discriminative global descriptor that captures the relationship among segments of a point cloud. Unlike previous methods that utilize either semantics or a sequence of adjacent point clouds for better place recognition, we only use raw point clouds to get competitive results. Concretely, we first segment the point cloud egocentrically to acquire centroids and eigenvalues of the segments. Then, we introduce a graph neural network to aggregate these features into an embedding representation. Extensive experiments conducted on the KITTI dataset show that DSC is robust to scene variants and outperforms existing methods. © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

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arXiv 2025年

作者： Imran, Muhammad Krebs, Jonathan R. Sivaraman, Vishal Balaji Zhang, Teng Kumar, Amarjeet Ueland, Walker R. Fassler, Michael J. Huang, Jinlong Sun, Xiao Wang, Lisheng Shi, Pengcheng Rokuss, Maximilian Baumgartner, Michael Kirchhof, Yannick Maier-Hein, Klaus H. Isensee, Fabian Liu, Shuolin Han, Bing Nguyen, Bong Thanh Shin, Dong-Jin Ji-Woo, Park Choi, Mathew Uhm, Kwang-Hyun Ko, Sung-Jea Lee, Chanwoong Chun, Jaehee Kim, Jin Sung Zhang, Minghui Zhang, Hanxiao You, Xin Gu, Yun Pan, Zhaohong Liu, Xuan Liang, Xiaokun Tiefenthaler, Markus Almar-Munoz, Enrique Schwab, Matthias Kotyushev, Mikhail Epifanov, Rostislav Wodzinski, Marek Müller, Henning Qayyum, Abdul Mazher, Moona Niederer, Steven A. Wang, Zhiwei Yang, Kaixiang Ren, Jintao Korreman, Stine Sofia Gao, Yuchong Zeng, Hongye Zheng, Haoyu Zheng, Rui Yue, Jinghua Zhou, Fugen Liu, Bo Cosman, Alexander Liang, Muxuan Zhao, Chang Upchurch, Gilbert R. Ma, Jun Zhou, Yuyin Cooper, Michol A. Shao, Wei Department of Medicine University of Florida GainesvilleFL32611 United States Department of Surgery University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Information Science and Engineering University of Florida GainesvilleFL32611 United States Shanghai Jiao Tong University Shanghai China Electronic & Information Engineering School Harbin Institute of Technology [Shenzhen Shenzhen China Division of Medical Image Computing German Cancer Research Center [DKFZ Heidelberg Germany CO. LTD Beijing China MedAI Seoul Korea Republic of Korea University Seoul Korea Republic of Department of Radiation Oncology Yonsei Cancer Center Heavy Ion Therapy Research Institute Yonsei University College of Medicine Seoul Korea Republic of Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Medical University of Innsbruck Innsbruck Austria Novosibirsk State University Novosibirsk Russia AGH University of Krakow Krakow Poland Sierre Switzerland University of Geneva Geneva Switzerland The Sense Innovation and Research Center Sion Switzerland National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China Aarhus University Department of Clinical Medicine Nordre Palle Juul-Jensens Blvd. 11 Aarhus8200 Denmark Shanghaitech University Pudong Shanghai China Image Processing Center Beihang University Beijing China Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States University Health Network ON Canada Agronomy Department University of F

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://***. © 2025, CC BY-NC-SA.

关键词： computerized tomography

DTCWT-based zinc fast roughing working condition identification

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Chinese Journal of Chemical engineering 2018年第8期26卷 1721-1726页

作者： Zhuo He Zhaohui Tang Zhihao Yan Jinping Liu College of Information Science and Enineering Central South University Changsha 410083China Key Laboratory of High Performance Computing and Stochastic Information Processing of Ministry of Education of China College of Mathematics and Computer ScienceHunan Normal UniversityChangsha 410081China

The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation *** surface texture feature closely relates to the flotation working conditions and hence can be used as a visual indicator for the zinc fast roughing working condition. A novel working condition identification method based on the dual-tree complex wavelet transform(DTCWT) is proposed for process monitoring of zinc fast ***-level DTCWT is implemented to decompose the froth image into different directions and resolutions in advance, and then the energy parameter of each sub-image is extracted as the froth texture feature. Then, an improved random forest integrated classification(i RFIC) with 10-fold cross-validation model is introduced as the classifier to identify the roughing working condition, which effectively improves the shortcomings of the single model and overcomes the characteristic redundancy but achieves higher generalization performance. Extensive experiments have verified the effectiveness of the proposed method.

关键词： DTCWT Working condition Integrated classification model Zinc fast roughing

A Cost-Driven Fuzzy Scheduling Strategy for Intelligent Workflow Decision Making Systems in Uncertain Edge-Cloud Environments

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arXiv 2021年

作者： Lin, Bing Lin, Chaowei Chen, Xing College of Physics and Energy Fujian Normal University Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China School of Computer Science Peking University 100871 China College of Mathematics and Computer Science Fuzhou University Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China

Workflow decision making is critical to performing many practical workflow applications. Scheduling in edge-cloud environments can address the high complexity problem of workflow applications, while decreasing the data transmission delay between the cloud and end devices. However, because of the heterogeneous resources in edge-cloud environments and the complicated data dependencies among the tasks in a workflow, significant challenges for workflow scheduling remain, including the selection of an optimal tasks-servers solution from the possible numerous combinations. The existing studies have been mainly done subject to rigorous conditions without fluctuations, ignoring the fact that workflow scheduling is typically present in uncertain environments. In this study, we focus on reducing the execution cost of workflow applications mainly caused by task computation and data transmission, while satisfying the workflow deadline in uncertain edge-cloud environments. The Triangular Fuzzy Numbers (TFNs) are adopted to represent the task processing time and data transferring time. A cost-driven fuzzy scheduling strategy based on an Adaptive Discrete Particle Swarm Optimization (ADPSO) algorithm is proposed, which employs the operators of Genetic Algorithm (GA). This strategy introduces the randomly two-point crossover operator, neighborhood mutation operator, and adaptive multipoint mutation operator of GA to effectively avoid converging on local optima. The experimental results show that our strategy can effectively reduce the workflow execution cost in uncertain edge-cloud environments, compared with other benchmark solutions. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making