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arXiv 2024年

作者： Xiao, Shuixin Wang, Yuanlong Zhang, Jun Dong, Daoyi Mooney, Gary J. Petersen, Ian R. Yonezawa, Hidehiro School of Engineering and Technology University of New South Wales CanberraACT2600 Australia University of Michigan–Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China School of Engineering The Australian National University CanberraACT2601 Australia Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China CIICADA Lab School of Engineering Australian National University ACT2601 Australia School of Physics The University of Melbourne ParkvilleVIC3010 Australia School of Physics University of Melbourne ParkvilleVIC3010 Australia School of Engineering Australian National University ACT2601 Australia RIKEN Center for Quantum Computing Japan Centre for Quantum Computation and Communication Technology Australian Research Council CanberraACT2600 Australia

Quantum process tomography is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose a two-stage solution for both trace-preserving and non-trace-preserving quantum process tomography. Utilizing a tensor structure, our algorithm exhibits a computational complexity of O(MLd2) where d is the dimension of the quantum system and M, L represent the numbers of different input states and measurement operators, respectively. We establish an analytical error upper bound and then design the optimal input states and the optimal measurement operators, which are both based on minimizing the error upper bound and maximizing the robustness characterized by the condition number. Numerical examples and testing on IBM quantum devices are presented to demonstrate the performance and efficiency of our algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Error analysis

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Quantum Nondemolition Detection of a Propagating Microwave Photon

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Physical Review Letters 2014年第9期112卷 093601-093601页

作者： Sankar R. Sathyamoorthy L. Tornberg Anton F. Kockum Ben Q. Baragiola Joshua Combes C. M. Wilson Thomas M. Stace G. Johansson Department of Microtechnology and Nanoscience MC2 Chalmers University of Technology S-41296 Gothenburg Sweden Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131-0001 USA Institute for Quantum Computing and Electrical and Computer Engineering Department University of Waterloo Waterloo N2 L 3G1 Canada Centre for Engineered Quantum Systems School of Physical Sciences University of Queensland Saint Lucia Queensland 4072 Australia

The ability to nondestructively detect the presence of a single, traveling photon has been a long-standing goal in optics, with applications in quantum information and measurement. Realizing such a detector is complicated by the fact that photon-photon interactions are typically very weak. At microwave frequencies, very strong effective photon-photon interactions in a waveguide have recently been demonstrated. Here we show how this type of interaction can be used to realize a quantum nondemolition measurement of a single propagating microwave photon. The scheme we propose uses a chain of solid-state three-level systems (transmons) cascaded through circulators which suppress photon backscattering. Our theoretical analysis shows that microwave-photon detection with fidelity around 90% can be realized with existing technologies.

关键词： MICROWAVE photonics RESEARCH QUANTUM mechanics QUANTUM information science PHOTON-photon interactions WAVEGUIDES PHOTON scattering BACKSCATTERING

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Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

Size-invariance matters: rethinking metrics and losses for i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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Haptic interfaces for the rescue walking robots motion in the disaster areas

Haptic interfaces for the rescue walking robots motion in th...

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UKACC International Conference on control (control)

作者： Luige Vladareanu Octavian Melinte Adrian Bruja Hongbo Wang Xiaojie Wang Shuang Cang Hongnian Yu Zeng-Guang Hou Xiao-Liang Xie Robotics and Mechatronics Department Institute of Solid Mechanics C-tin Mille 15 Bucharest 1 ROMANIA School of Design Engineering and Computing Boumemouth University Poole Dorset UK Parallel Robot and Mechatronic System Laboratory of Hebei Province University Qinhuangdao CHINA State Key Laboratory of Management and Control for Complex Systems Beijing P.R.CHINA

In the recent years haptic interfaces became a reliable solution in order to solve problems which arise when humans interact with the environment. If in the research area of the haptic interaction between human and environment there are important researches, a innovative approach for the interaction between the robot and the environment using haptic interfaces and virtual projection method is presented in this paper. In order to control this interaction we used the Virtual Projection Method where haptic control interfaces of impedance and admittance will be embedded. The obtained results, validated by simulations assure stability, stiffness, high maneuverability and adaptability for rescue walking robots in order to move in disaster, dangerous and hazardous areas.

关键词： Haptic interfaces Legged locomotion Robot sensing systems Impedance Force

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A Survey of Fault Tolerance in Cloud computing

A Survey of Fault Tolerance in Cloud Computing

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Systems, Advanced Technologies and Knowledge (ISSATK), International Symposium of

作者： Om Kolthoum Elhajjej Nouha Alyaoui Samia Dardouri Karim Chabir Research Laboratory Modeling Aanalysis Control of System (MACS) LR16ES22 National engineering school of Gabes (ENIG) University of Gabes Tunisia Computer Science and Telecommunications Department Laboratory of Modeling Analysis and Control of Systems (MACS) Higher Institute of Applied Sciences and Technology Zarroug Gafsa Gafsa University Tunisia Department of Computer Science College of Computing and Information Technology Shaqra University Saudi Arabia InnoV’COM Laboratory-Sup’Com University of Carthage Tunisia

ISBN: (数字)9798331540173

ISBN: (纸本)9798331540180

This paper is designed to provide a comprehensive overview of the latest developments in fault tolerance methods for cloud computing. Maintaining high availability and reliability of cloud environments requires fault tolerance. This paper explores fault tolerance in the context of cloud computing and discusses recent challenges and innovations in the field. Moreover, it examines the ongoing research efforts to improve fault-tolerance architectures. At the end of the paper, the paper presents system-level metrics that are relevant to fault tolerance.

关键词： Measurement Surveys Cloud computing Fault tolerance Technological innovation Scalability Fault tolerant systems Computer architecture Software Software reliability

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TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation

arXiv

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arXiv 2022年

作者： Liu, Ruiping Yang, Kailun Roitberg, Alina Zhang, Jiaming Peng, Kunyu Liu, Huayao Wang, Yaonan Stiefelhagen, Rainer The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The School of Robotics The National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Artificial Intelligence The University of Stuttgart Stuttgart70569 Germany The Institute for Visual Computing ETH Zurich Zurich8092 Switzerland NIO Shanghai201804 China

Semantic segmentation benchmarks in the realm of autonomous driving are dominated by large pre-trained transformers, yet their widespread adoption is impeded by substantial computational costs and prolonged training durations. To lift this constraint, we look at efficient semantic segmentation from a perspective of comprehensive knowledge distillation and aim to bridge the gap between multi-source knowledge extractions and transformer-specific patch embeddings. We put forward the Transformer-based Knowledge Distillation (TransKD) framework which learns compact student transformers by distilling both feature maps and patch embeddings of large teacher transformers, bypassing the long pre-training process and reducing the FLOPs by >85.0%. Specifically, we propose two fundamental modules to realize feature map distillation and patch embedding distillation, respectively: (1) Cross Selective Fusion (CSF) enables knowledge transfer between cross-stage features via channel attention and feature map distillation within hierarchical transformers;(2) Patch Embedding Alignment (PEA) performs dimensional transformation within the patchifying process to facilitate the patch embedding distillation. Furthermore, we introduce two optimization modules to enhance the patch embedding distillation from different perspectives: (1) Global-Local Context Mixer (GL-Mixer) extracts both global and local information of a representative embedding;(2) Embedding Assistant (EA) acts as an embedding method to seamlessly bridge teacher and student models with the teacher’s number of channels. Experiments on Cityscapes, ACDC, NYUv2, and Pascal VOC2012 datasets show that TransKD outperforms state-of-the-art distillation frameworks and rivals the time-consuming pre-training method. The source code is publicly available at https://***/RuipingL/TransKD. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Riemannian Optimistic Algorithms

arXiv

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arXiv 2023年

作者： Wang, Xi Yuan, Deming Hong, Yiguang Hu, Zihao Wang, Lei Shi, Guodong Australian Center for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW2006 Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Automation Nanjing University of Science and Technology Jiangsu210023 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China College of Computing Georgia Institute of Technology AtlantaGA30339 United States College of Control Science and Engineering Zhejiang University Zhejiang310058 China

In this paper, we consider Riemannian online convex optimization with dynamic regret. First, we propose two novel algorithms, namely the Riemannian Online Optimistic Gradient Descent (R-OOGD) and the Riemannian Adaptive Online Optimistic Gradient Descent (R-AOOGD), which combine the advantages of classical optimistic algorithms with the rich geometric properties of Riemannian manifolds. We analyze the dynamic regrets of the R-OOGD and R-AOOGD in terms of regularity of the sequence of cost functions and comparators. Next, we apply the R-OOGD to Riemannian zero-sum games, leading to the Riemannian Optimistic Gradient Descent Ascent algorithm (R-OGDA). We analyze the average iterate and best-iterate of the R-OGDA in seeking Nash equilibrium for a two-player, zero-sum, g-convex-concave games. We also prove the last-iterate convergence of the R-OGDA for g-strongly convex-strongly concave problems. Our theoretical analysis shows that all proposed algorithms achieve results in regret and convergence that match their counterparts in Euclidean spaces. Finally, we conduct several experiments to verify our theoretical findings. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Face recognition algorithm based on improved facial model

Face recognition algorithm based on improved facial model

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International Conference on Natural Computation (ICNC)

作者： Chengyun Liu Zhenxue Chen Faliang Chang Kaifang Wang Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei P. R. China School of Control Science & Engineering Shandong University Jinan P. R. China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing P. R. China

ISBN: (纸本)9781479951529

Face recognition is one of typical biometric identification method, which has a great prospect in secure authentication system, file management, human-computer interaction and social security. This paper proposes gray-scale characteristics and creates facial templates to recognize faces method based on a given number of samples. Firstly, it selects the method of building template according to the number of samples to create the facial template image; then, it will compare the difference of first-order edge entropy between recognition image and the template image and find the best match result; finally, the recognition result is output. Experimental results show that the proposed algorithm has good recognition effect on face recognition under non-constraint conditions.

关键词： Face recognition Face Entropy Lighting Image edge detection

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Robot Hand Interaction Using Plastic Deformation control with Inner Position Loop

Robot Hand Interaction Using Plastic Deformation Control wit...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Kenichi Murakami Koki Ishimoto Taku Senoo Masatoshi Ishikawa Data Science Research Division Information Technology Center The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Department of Information Physics and Computing The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Electrical Systems and Control Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima City Hiroshima Japan

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

In this study, the inner position loop addition for plastic deformation control of the error robustness of the model and actual parameters is proposed. The inner position loop is a type of position feedback control based on the estimation of model parameters and dynamics of robots. This position control system with proportional and derivative gains modifies the trajectory of the end effector close to the desired trajectory. The robustness of the proposed control scheme is evaluated by comparing it with plastic deformation control without an inner position loop in both simulation and actual environments. Finally, the stable reaction with the proposed control is demonstrated for the case where a human pushes the robot finger.

关键词： Strain Plastics Force Deformable models Robots Friction Trajectory

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Robust guided image filtering

arXiv

引用

arXiv 2017年

作者： Liu, Wei Chen, Xiaogang Shen, Chunhua Yu, Jingyi Wu, Qiang Yang, Jie Key Laboratory of Ministry of Education for System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China College of Communication and Art Design University of Shanghai for Science and Technology Shanghai200093 China School of Computer Science University of Adelaide AdelaideSA5005 Australia College of Engineering University of Delaware NewarkDE19176 United States School of Computing and Communications University of Technology SydneyNSW2007 Australia

The process of using one image to guide the filtering process of another one is called Guided Image Filtering (GIF). The main challenge of GIF is the structure inconsistency between the guidance image and the target image. Besides, noise in the target image is also a challenging issue especially when it is heavy. In this paper, we propose a general framework for Robust Guided Image Filtering (RGIF), which contains a data term and a smoothness term, to solve the two issues mentioned above. The data term makes our model simultaneously denoise the target image and perform GIF which is robust against the heavy noise. The smoothness term is able to make use of the property of both the guidance image and the target image which is robust against the structure inconsistency. While the resulting model is highly non-convex, it can be solved through the proposed Iteratively Re-weighted Least Squares (IRLS) in an efficient manner. For challenging applications such as guided depth map upsampling, we further develop a data-driven parameter optimization scheme to properly determine the parameter in our model. This optimization scheme can help to preserve small structures and sharp depth edges even for a large upsampling factor (8× for example). Moreover, the specially designed structure of the data term and the smoothness term makes our model perform well in edge-preserving smoothing for single-image tasks (i.e., the guidance image is the target image itself). It performs well in several challenging applications in avoiding halos, gradient reversals and properly preserving edges with noise/texture being well smoothed. Through extensive experimental results, we show that the proposed RGIF can have promising performance in many applications such as guided depth map upsampling, flash/no flash filtering, detail enhancement, HDR tone mapping, structure smoothing and clip-art JPEG compression artifact removal. This paper is an extension of our previous work [1], [2]. Copyright © 2017, T

关键词： Image enhancement

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