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International Automatic Control Conference

作者： Kai-Hsiang Su Chiao-Hsin Chen Chia-Hsin Chen Yong-Liang Zhang Li-Wei Ko Institute of Biological Science and Technology National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) College of Biological Science and Technology National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Institute of Molecular Medicine and Bioengineering National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Rehabilitation Department of Kaohsiung Medical University Chung-Ho Memorial Hospital Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Department of Electrical and Computer Engineering Brain Research Center National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan

With the advent of the global aging era, the impact of stroke on humans is continuously increasing. Therefore, new therapeutic approaches to enhance patient recovery are receiving growing attention. Stroke is a severe neurological disorder that causes brain damage, requiring extended periods of rehabilitation for patients to return to normal life. Currently, common clinical methods largely rely on traditional passive limb rehabilitation, primarily targeting peripheral nerve training. Nowadays, an increasing number of people are recognizing the importance of post-stroke neuroplasticity. This study aims to activate patients' central nervous system and proposes a brain-machine interface (BCI) exoskeleton system based on human cognition. By designing three cognitive brainwave training models, including an attention-based standing-up model detecting θ-band power decrease, a relaxation-based sitting-down model detecting α-band power increase, and a walking model detecting MRCP, patients can actively control lower limb exoskeleton movement using *** study provides cross-validation accuracy through both normal subjects and stroke patients, with accuracies of 87.13±9.38 and 80.83±9.68, respectively. Envisioning a rehabilitation system guided by human cognition, our team has developed an intuitive BCI exoskeleton system.

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Single-shot fringe projection profilometry based on deep learning and computer graphics

arXiv

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arXiv 2021年

作者： Wang, Fanzhou Wang, Chenxing Guan, Qingze School of Automation Southeast University 2# Sipailou Xuanwu Nanjing210096 China Key laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Computer Science University College London Gower St Bloomsbury LondonWC1E 6BT United Kingdom

Multiple works have applied deep learning to fringe projection profilometry (FPP) in recent years. However, to obtain a large amount of data from actual systems for training is still a tricky problem, and moreover, the network design and optimization still worth exploring. In this paper, we introduce computer graphics to build virtual FPP systems in order to generate the desired datasets conveniently and simply. The way of constructing a virtual FPP system is described in detail firstly, and then some key factors to set the virtual FPP system much close to the reality are analyzed. With the aim of accurately estimating the depth image from only one fringe image, we also design a new loss function to enhance the quality of the overall and detailed information restored. And two representative networks, U-Net and pix2pix, are compared in multiple aspects. The real experiments prove the good accuracy and generalization of the network trained by the data from our virtual systems and the designed loss, implying the potential of our method for applications. Copyright © 2021, The Authors. All rights reserved.

关键词： computer graphics

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Neuromorphic processing at 11 Tera-OPs with soliton crystal Kerr microcombs

Neuromorphic processing at 11 Tera-OPs with soliton crystal ...

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2021 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2021

作者： Tan, M. Xu, X. Wu, J. Boes, A. Corcoran, B. Nguyen, T. Chu, S.T. Little, B.E. Morandotti, R. Mitchell, A. Moss, D.J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Xi'an Institute of Optics and Precision Mechanics Precision Mechanics of CAS Xi'an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada

ISBN: (纸本)9781665416009

We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds. We demonstrate a single neuron perceptron at 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We then demonstrate a convolutional accelerator operating beyond 11 TeraOPs/s (TOPs). We test the perceptron on handwritten-digit recognition and cancer-cell detection - achieving over 90% and 85% accuracy, respectively. © 2021 IEEE.

关键词： Character recognition

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Ecology in the digital world of Wikipedia

arXiv

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arXiv 2021年

作者： Ogushi, Fumiko Kertész, János Kaski, Kimmo Shimada, Takashi Center for Mathematical Modeling and Data Science Osaka University Toyonaka Osaka560-8531 Japan Department of Network and Data Science Central European University Quellenstrasse 51 Vienna1100 Austria Department of Computer Science Aalto University School of Science P. O. Box 15500 Espoo Finland Alan Turing Institute British Library 96 Euston Road LondonNW1 2DB United States Mathematics and Infomatics Center University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Systems Innovation Graduate School of Engineering University of Tokyo

Wikipedia, a paradigmatic example of online knowledge space is organized in a collaborative, bottom-up way with voluntary contributions, yet it maintains a level of reliability comparable to that of traditional encyclopedias. The lack of selected professional writers and editors makes the judgement about quality and trustworthiness of the articles a real challenge. Here we show that a self-consistent metrics for the network defined by the edit records captures well the character of editors’ activity and the articles’ level of complexity. Using our metrics, one can better identify the human-labeled high-quality articles, e.g., "featured" ones, and differentiate them from the popular and controversial articles. Furthermore, the dynamics of the editor-article system is also well captured by the metrics, revealing the evolutionary pathways of articles and diverse roles of editors. We demonstrate that the collective effort of the editors indeed drives to the direction of article improvement. © 2021, CC BY.

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Orthogonally polarized RF optical single sideband generation with integrated ring resonators

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Journal of Semiconductors 2021年第4期42卷 53-66页

作者： Mengxi Tan Xingyuan Xu Jiayang Wu Thach G.Nguyen Sai T.Chu Brent E.Little Arnan Mitchell Roberto Morandotti David J.Moss Optical Sciences Centre Swinburne University of TechnologyHawthornVIC 3122Australia Electro-Photonics Laboratory Department of Electrical and Computer Systems EngineeringMonash UniversityVIC3800Australia School of Engineering RMIT UniversityMelbourneVIC 3001Australia Department of Physics City University of Hong KongHong KongChina State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision MechanicsChinese Academy of ScienceXi'an 710119China INRS-Énergie Matériaux et Télécommunications1650 Boulevard Lionel-BouletVarennesQuébecJ3X 1S2Canada Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China

We review recent work on narrowband orthogonally polarized optical RF single sideband generators as well as dualchannel equalization,both based on high-Q integrated ring *** devices operate in the optical telecommunications C-band and enable RF operation over a range of either fixed or thermally tuneable *** operate via TE/TM mode birefringence in the *** achieve a very large dynamic tuning range of over 55 dB for both the optical carrier-to-sideband ratio and the dual-channel RF equalization for both the fixed and tunable devices.

关键词： microwave photonics micro-ring resonators radio frequency

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Photonic radio frequency channelizers based on Kerr optical micro-combs

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Journal of Semiconductors 2021年第4期42卷 17-32页

作者： Mengxi Tan Xingyuan Xu Jiayang Wu Thach G.Nguyen Sai T.Chu Brent E.Little Roberto Morandotti Arnan Mitchell David J.Moss Optical Sciences Centre Swinburne University of TechnologyHawthornVIC 3122Australia Electro-Photonics Laboratory Department of Electrical and Computer Systems EngineeringMonash UniversityVIC3800Australia School of Engineering RMIT UniversityMelbourneVIC 3001Australia Department of Physics City University of Hong KongHong KongChina State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision MechanicsChinese Academy of ScienceXi'an 710119China INRS-Énergie Matériaux et Télécommunications1650 Boulevard Lionel-BouletVarennesQuébecJ3X 1S2Canada Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China

We review recent work on broadband RF channelizers based on integrated optical frequency Kerr micro-combs combined with passive micro-ring resonator filters,with microcombs having channel spacings of 200 and 49 *** approach to realizing RF channelizers offers reduced complexity,size,and potential cost for a wide range of applications to microwave signal detection.

关键词： microwave photonic signal channelization integrated optical frequency comb

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

arXiv

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arXiv 2020年

作者： Jooken, Jorik Leyman, Pieter Wauters, Tony De Causmaecker, Patrick Department of Computer Science KU Leuven Kulak Etienne Sabbelaan 53 Kortrijk8500 Belgium Department of Industrial Systems Engineering and Production Design Ghent University Technologiepark Zwijnaarde 46 Zwijnaarde9052 Belgium Industrial Systems Engineering Flanders Make@UGent Sint-Martens-Latemlaan 2B Kortrijk8500 Belgium Department of Computer Science CODeS KU Leuven Gebroeders De Smetstraat 1 Ghent9000 Belgium

In this article we propose a heuristic algorithm to explore search space trees associated with instances of combinatorial optimization problems. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees and represents the state-of-the-art algorithm for a number of games. Several enhancements to Monte Carlo tree search are proposed that make the algorithm more suitable in a combinatorial optimization context. These enhancements exploit the combinatorial structure of the problem and aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domains of variables by eliminating dominated value assignments and using a beam width. The algorithm was implemented with its components specifically tailored to two combinatorial optimization problems: the quay crane scheduling problem with non-crossing constraints and the 0-1 knapsack problem. For the first problem our algorithm surpasses the state-of-the-art results and several new best solutions are found for a benchmark set of instances. For the second problem our algorithm typically produces near-optimal solutions that are slightly worse than the state-of-the-art results, but it needs only a small fraction of the time to do so. These results indicate that the algorithm is competitive with the state-of-the-art for two entirely different combinatorial optimization problems. © 2020, CC BY-NC-ND.

关键词： Combinatorial optimization

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CHARMS: Cognitive Hierarchical Agent with Reasoning and Motion Styles

arXiv

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arXiv 2025年

作者： Wang, Jingyi Chu, Duanfeng Deng, Zejian Lu, Liping School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan430063 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L3G1 Canada School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China

To address the current challenges of low intelligence and simplistic vehicle behavior modeling in autonomous driving simulation scenarios, this paper proposes the Cognitive Hierarchical Agent with Reasoning and Motion Styles (CHARMS). The model can reason about the behavior of other vehicles like a human driver and respond with different decision-making styles, thereby improving the intelligence and diversity of the surrounding vehicles in the driving scenario. By introducing the Level-k behavioral game theory, the paper models the decision-making process of human drivers and employs deep reinforcement learning to train the models with diverse decision styles, simulating different reasoning approaches and behavioral characteristics. Building on the Poisson cognitive hierarchy theory, this paper also presents a novel driving scenario generation method. The method controls the proportion of vehicles with different driving styles in the scenario using Poisson and binomial distributions, thus generating controllable and diverse driving environments. Experimental results demonstrate that CHARMS not only exhibits superior decision-making capabilities as ego vehicles, but also generates more complex and diverse driving scenarios as surrounding vehicles. We will release code for CHARMS at https://***/WUTADWjy/CHARMS. Copyright © 2025, The Authors. All rights reserved.

关键词： Poisson distribution

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Roadmap on Deep Learning for Microscopy

arXiv

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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