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arXiv 2024年

作者： Ruan, Xinhui Wang, Jia-Heng He, Dong Song, Pengtao Li, Shengyong Zhao, Qianchuan Kuang, L.M. Tsai, Jaw-Shen Zou, Chang-Ling Zhang, Jing Zheng, Dongning Astafiev, O.V. Liu, Yu-Xi Peng, Zhihui Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center of Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Integrated Circuits Tsinghua University Beijing100084 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100190 China Center for Quantum Computing RIKEN Saitama351-0198 Japan Graduate School of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku Tokyo162-0825 Japan CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui230026 China Hefei National Laboratory Hefei230088 China School of Automation Science and Engineering Xi'an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China Skolkovo Institute of Science and Technology Nobel str. 3 Moscow143026 Russia Moscow Institute of Physics and Technology Institutskiy Pereulok 9 Dolgoprudny141701 Russia Royal Holloway University of London Surrey EghamTW20 0EX United Kingdom

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical one by controlling the photon number inside the cavity. The device consists of a transmon qubit strongly coupled to a one-dimensional transmission line and a coplanar waveguide resonator. We observe a sideband anti-crossing and asymmetry in the emission spectra of the system through a one-dimensional transmission line, which is fundamentally different from the weak coupling case. By changing the photon number inside the cavity, the emission spectrum of our doubly driven system approaches to the case when the atom is driven by two classical bichromatic fields. We also measure the dynamical evolution of the system through the transmission line and study the properties of the first-order correlation function, Rabi oscillations and energy relaxation in the system. The study of resonance fluorescence from an atom driven by two fields promotes understanding decoherence in superconducting quantum circuits and may find applications in superconducting quantum computing and quantum networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Electric lines

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Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)

arXiv

引用

arXiv 2021年

作者： Chen, Yuntian Luo, Yingtao Liu, Qiang Xu, Hao Zhang, Dongxiao Intelligent Energy Laboratory Frontier Research Center Peng Cheng Laboratory Shenzhen China Department of Computer Science University of Washington SeattleWA United States Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China BIC-ESAT ERE and SKLTCS College of Engineering Peking University Beijing China School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China

Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes and predicting future responses. However, the PDEs of many real-world problems are uncertain, which calls for PDE discovery. We propose the symbolic genetic algorithm (SGA-PDE) to discover open-form PDEs directly from data without prior knowledge about the equation structure. SGA-PDE focuses on the representation and optimization of PDE. Firstly, SGA-PDE uses symbolic mathematics to realize the flexible representation of any given PDE, transforms a PDE into a forest, and converts each function term into a binary tree. Secondly, SGA-PDE adopts a specially designed genetic algorithm to efficiently optimize the binary trees by iteratively updating the tree topology and node attributes. The SGA-PDE is gradient-free, which is a desirable characteristic in PDE discovery since it is difficult to obtain the gradient between the PDE loss and the PDE structure. In the experiment, SGA-PDE not only successfully discovered nonlinear Burgers' equation, Korteweg-de Vries (KdV) equation, and Chafee-Infante equation, but also handled PDEs with fractional structure and compound functions that cannot be solved by conventional PDE discovery methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Genetic algorithms

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LMS-Net: A learned Mumford-Shah network for binary few-shot medical image segmentation

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Medical Image Analysis 2025年

作者： Shengdong Zhang Fan Jia Xiang Li Hao Zhang Jun Shi Liyan Ma Shihui Ying Department of Mathematics School of Science Shanghai University Shanghai 200444 China Department of Mathematics and Scientific Computing and Imaging (SCI) Institute University of Utah Salt Lake City 84102 UT USA School of Computer Science and Technology East China Normal University Shanghai 200444 China School of Communication and Information Engineering Shanghai University Shanghai 200444 China School of Computer Engineering and Science Shanghai University Shanghai 200444 China School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai 200444 China Shanghai Institute of Applied Mathematics and Mechanics Shanghai 200072 China School of Mechanics and Engineering Science Shanghai University Shanghai 200072 China

Few-shot semantic segmentation (FSS) methods have shown great promise in handling data-scarce scenarios, particularly in medical image segmentation tasks. However, most existing FSS architectures lack sufficient interpretability and fail to fully incorporate the underlying physical structures of semantic regions. To address these issues, in this paper, we propose a novel deep unfolding network, called the Learned Mumford-Shah Network (LMS-Net), for the FSS task. Specifically, motivated by the effectiveness of pixel-to-prototype comparison in prototypical FSS methods and the capability of deep priors to model complex spatial structures, we leverage our learned Mumford-Shah model (LMS model) as a mathematical foundation to integrate these insights into a unified framework. By reformulating the LMS model into prototype update and mask update tasks, we propose an alternating optimization algorithm to solve it efficiently. Further, the iterative steps of this algorithm are unfolded into corresponding network modules, resulting in LMS-Net with clear interpretability. Comprehensive experiments on three publicly available medical segmentation datasets verify the effectiveness of our method, demonstrating superior accuracy and robustness in handling complex structures and adapting to challenging segmentation scenarios. These results highlight the potential of LMS-Net to advance FSS in medical imaging applications. Our code will be available at: https://***/SDZhang01/LMSNet .

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Efficient visual recognition with deep neural networks: A survey on recent advances and new directions

arXiv

引用

arXiv 2021年

作者： Wu, Yang Wang, Dingheng Lu, Xiaotong Yang, Fan Li, Guoqi Dong, Weisheng Shi, Jianbo Institute for Research Initiatives Nara Institute of Science and Technology Takayama-cho Ikoma Nara630-0192 Japan School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'An Jiaotong University Shaanxi Xi'an710049 China School of Artificial Intelligence Xidian University China Division of Information Science Nara Institute of Science and Technology Japan Department of Precision Instrumentation Center for Brain Inspired Computing Research Beijing Innovation Center for Future Chip Tsinghua University Beijing100084 China Department of Computer and Information Science University of Pennsylvania United States

Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence. It has great fundamental importance and strong industrial needs. Deep neural networks (DNNs) have largely boosted their performances on many concrete tasks, with the help of large amounts of training data and new powerful computation resources. Though recognition accuracy is usually the first concern for new progresses, efficiency is actually rather important and sometimes critical for both academic research and industrial applications. Moreover, insightful views on the opportunities and challenges of efficiency are also highly required for the entire community. While general surveys on the efficiency issue of DNNs have been done from various perspectives, as far as we are aware, scarcely any of them focused on visual recognition systematically, and thus it is unclear which progresses are applicable to it and what else should be concerned. In this paper, we present the review of the recent advances with our suggestions on the new possible directions towards improving the efficiency of DNNrelated visual recognition approaches. We investigate not only from the model but also the data point of view (which is not the case in existing surveys), and focus on three most studied data types (images, videos and points). This paper attempts to provide a systematic summary via a comprehensive survey which can serve as a valuable reference and inspire both researchers and practitioners who work on visual recognition problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data

arXiv

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arXiv 2023年

作者： Zhong, Tianyang Zhao, Wei Zhang, Yutong Pan, Yi Dong, Peixin Jiang, Zuowei Kui, Xiaoyan Shang, Youlan Yang, Li Wei, Yaonai Yang, Longtao Chen, Hao Zhao, Huan Liu, Yuxiao Zhu, Ning Li, Yiwei Wang, Yisong Yao, Jiaqi Wang, Jiaqi Zeng, Ying He, Lei Zheng, Chao Zhang, Zhixue Li, Ming Liu, Zhengliang Dai, Haixing Wu, Zihao Zhang, Lu Zhang, Shu Cai, Xiaoyan Hu, Xintao Zhao, Shijie Jiang, Xi Zhang, Xin Li, Xiang Zhu, Dajiang Guo, Lei Shen, Dinggang Han, Junwei Liu, Tianming Liu, Jun Zhang, Tuo School of Automation Northwestern Polytechnical University Xi’an710072 China The Second Xiangya Hospital Central South University Changsha410011 China Clinical Research Center for Medical Imaging in Hunan Province Changsha China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute of Medical Research Northwestern Polytechnical University Xi’an710072 China Glasgow College University of Electronic Science and Technology of China Chengdu611731 China School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering Central South University Hunan Province Changsha410083 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Lingang Laboratory Shanghai200031 China Imaging Center The Second Affiliated Hospital of Xinjiang Medical University Urumuqi830000 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China Department of Radiology Xiangtan Central Hospital Xiangtan411199 China Department of Radiology Yueyang Central Hospital City Yueyang414000 China Department of Radiology The First People's Hospital of Changde City Changde415003 China Department of Radiology The First Hospital of Hunan University of Chinese Medicine Changsha410021 China Department of Radiology Huadong Hospital Affiliated to Fudan University Shanghai200040 China School of Computing University of Georgia GA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States Department of Radiology Massachusetts General Hospital Harvard Medical School MA United States Shanghai United Imaging Intelligence Co. Ltd. China Shanghai Clinical Research and Trial Center China

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial & neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of 332,673 observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports. © 2023, CC BY.

关键词： Decision making

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SiNED-ancillary services for reliable power grids in times of progressive German energiewende and digital transformation

SiNED-ancillary services for reliable power grids in times o...

引用

ETG-Kongress 2021: Von Komponenten bis zum Gesamtsystem fur die Energiewende - ETG Congress 2021: From Components to the Overall System for the Energy Turnaround

作者： Wussow, Jonas Babazadeh, Davood Beutel, Vanessa Buchholz, Sebastian Geissendörfer, Stefan Gerlach, Jana Majumdar, Neelopal von Maydell, Karsten Narayan, Anand Hoffmann, Melanie Kahl, Lily Leveringhaus, Thomas Lotz, Marc René Scheunert, Alexandra Baboli, Payam Teimourzadeh Tiemann, Paul Hendrik Huxoll, Nils Werth, Oliver Agert, Carsten Breitner, Michael H. Engel, Bernd Hofmann, Lutz Könemund, Martin Kurrat, Michael Lehnhoff, Sebastian Nieße, Astrid Weyer, Hartmut Technische Universität Braunschweig Elenia Institute for High Voltage Technology and Power Systems Braunschweig Germany Carl von Ossietzky Universität Oldenburg Department of Computing Science Digitalized Energy Systems Group Oldenburg Germany German Aerospace Center Institute of Networked Energy Systems Oldenburg Germany Leibniz Universität Hannover Institute of Computer Science for Business Administration Hannover Germany Leibniz Universität Hannover Institute of Electric Power Systems Electric Power Engineering Section Hannover Germany OFFIS Institute for Information Technology Oldenburg Germany Ostfalia - University of Applied Science Institute of Electrical Systems and Automation Technology Wolfenbüttel Germany Technische Universität Clausthal Institute of German and International Mining and Energy Law Clausthal-Zellerfeld Germany

ISBN: (纸本)9783800755493

Within SiNED research project, several members of the Energy Research Centre of Lower Saxony (Energieforschungszentrum Niedersachsen, EFZN) are working on various issues relating to the future provision of ancillary services and to future congestion management. The questions include energy technology, economic and energy law aspects as well as information and communications technology (ICT) and data. The investigations are based on Lower Saxony and the framework conditions there. The temporal focus of the investigations is the year 2030. © VDE VERLAG GMBH. Berlin. Offenbach

关键词： Electric power transmission networks

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On Design of H∞ Structured Controller for Decentralized Control systems

On Design of H∞ Structured Controller for Decentralized Con...

引用

Control Conference (ANZCC), Australian and New Zealand

作者： Yanpeng Guan Wei Xing Zheng Department of Automation Shanxi University Taiyuan China School of Computing Engineering and Mathematics Western Sydney University Sydney Australia

In this paper the problem of H ∞ structured controller design is addressed. The considered problem arises when selection of actuators in decentralized control systems leads to that some rows of the controller gain matrix should be zero. However, the design of controllers with sparse structure is essentially a combinatorial problem that is hard to handle. Towards this end, this paper adopts a mixed-integer programming method to tackle the resulting row-cardinality constraint. The direct mixed-integer programming method is utilized to assist in designing row-sparse H ∞ state feedback controllers for large-scale distributed systems.

关键词： Sparse matrices State feedback Programming Decentralized control Actuators Australia Optimization

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From video game to real robot: The transfer between action spaces

arXiv

引用

arXiv 2019年

作者： Karttunen, Janne Kanervisto, Anssi Hautamäki, Ville Kyrki, Ville School of Computing University of Eastern Finland Joensuu Finland Department of Electrical Engineering and Automation Aalto University Espoo Finland

Training agents with reinforcement learning based techniques requires thousands of steps, which translates to long training periods when applied to robots. By training the policy in a simulated environment we avoid such limitation. Typically, the action spaces in a simulation and real robot are kept as similar as possible, but if we want to use a generic simulation environment, this strategy will not work. Video games, such as Doom (1993), offer a crude but multi-purpose environments that can used for learning various tasks. However, original Doom has four discrete actions for movement and the robot in our case has two continuous actions. In this work, we study the transfer between these two different action spaces. We begin with experiments in a simulated environment, after which we validate the results with experiments on a real robot. Results show that fine-tuning initially learned network parameters leads to unreliable results, but by keeping most of the neural network frozen we obtain above 90% success rate in simulation and real robot experiments. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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Channel pruning via automatic structure search

arXiv

引用

arXiv 2020年

作者： Lin, Mingbao Ji, Rongrong Zhang, Yuxin Zhang, Baochang Wu, Yongjian Tian, Yonghong Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University China School of Automation Science and Electrical Engineering Beihang University China Co. Ltd China School of Electronics Engineering and Computer Science Peking University Beijing China Peng Cheng Laboratory Shenzhen China

Channel pruning is among the predominant approaches to compress deep neural networks. To this end, most existing pruning methods focus on selecting channels (filters) by importance/ optimization or regularization based on ruleof- thumb designs, which defects in sub-optimal pruning. In this paper, we propose a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i.e., channel number in each layer, rather than selecting "important" channels as previous works did. To solve the intractably huge combinations of pruned structure for deep networks, we first propose to shrink the combinations where the preserved channels are limited to a specific space, thus the combinations of pruned structure can be significantly reduced. And then, we formulate the search of optimal pruned structure as an optimization problem and integrate the ABC algorithm to solve it in an automatic manner to lessen human interference. ABCPruner has been demonstrated to be more effective, which also enables the fine-tuning to be conducted efficiently in an end-to-end manner. Experiments on CIFAR-10 show that ABCPruner reduces 73.68% of FLOPs and 88.68% of parameters with even 0.06% accuracy improvement for VGGNet-16. On ILSVRC-2012, it achieves a reduction of 62.87% FLOPs and removes 60.01% of parameters with negligible accuracy cost for ResNet-152. The source codes can be available at https://***/lmbxmu/ABCPruner. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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