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A Framework for the Integration of Coarse Sensing Information and Environmental Constraints*

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A Framework for the Integration of Coarse Sensing Informatio...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Rui Li Yingbai Hu Yanjun Cao Mengyao Li School of Automation Chongqing University Chongqing China Informatics 6 Technical University of Munich Munich Germany Department of Computer and Software Engineering École Polytechnique de Montréal Boul Édouard-Montpetit Québec Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

A series of previous work has found that the environmental constraint (EC), which is the natural result of the contact of the robot and the interacting objects, is immensely helpful for the realization of high-precision robotic tasks. However, due to the existence of multifarious errors, such as mechanical error, modeling error and sensing error, there would be discrepancy between the actual constraints and the ideal models. In such case, it is hard to realize manipulation with EC-based strategies. Inspired by human, a preliminary framework which aims at the integration of the coarse sensing information and the environmental constraints is proposed for robotic manipulation. By mapping the sensing information into the new space, where the environmental constraint can be formally described, the region that integrates the sensing information and the environmental constraint is constructed and the conditions to achieve high-precision manipulation are derived. Based on the conditions, a motion planning strategy is proposed to achieve the required task. The effectiveness of this strategy is verified by case studies.

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An Implicit Brain computer Interface Supported by Gaze Monitoring for Virtual Therapy

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An Implicit Brain Computer Interface Supported by Gaze Monit...

IEEE International Conference on Systems, Man and Cybernetics

作者： David Achanccaray George Mylonas Javier Andreu-Perez Neuro-Robotics Laboratory Tohoku University Sendai Japan Department of Surgery and Cancer Imperial College London Human-centred Automation Robotics and Monitoring for Surgery London UK School of Computer Science and Electronic Engineering University of Essex Colchester UK

Advanced Brain-computer Interface (BCI) paradigms aim to solve some problems as BCI illiteracy and unfamiliarity of the subjects to be able to control their elicited motor imagery (MI) successfully, hence improving training time and performance of BCI systems. This work evaluates the effect and performance of an Implicit BCI supported by the Gaze Monitoring (IBCI-GM) paradigm for virtual rehabilitation therapy of patients suffering from partial or total paralysis of their upper limbs; this paradigm also was compared with alternative forms of advanced BCI methods such as Virtual Reality-based BCI (VR-BCI) with a head-mounted display (HMD) and a computer screen (CS). Eight subjects participated in the experiments; four subjects tested the VR-BCI with a CS, and the rest of them tested both BCI advanced methods (IBCI-GM and VR-BCI with an HMD). The subjects were asked to control a virtual arm through MI of flexion and extension movements. The VR-BCI HMD was the approached best method; however, IBCI-GM had significant results and was more practical for users, but it depends on the ability to perform eye movements to be applied by patients. Therefore, these methods should be tested with more subjects to have definitive results.

关键词： Resists Task analysis Electroencephalography Support vector machines Training Monitoring Brain

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

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arXiv 2022年

作者： Nwoye, Chinedu Innocent Alapatt, Deepak Yu, Tong Vardazaryan, Armine Xia, Fangfang Zhao, Zixuan Xia, Tong Jia, Fucang Yang, Yuxuan Wang, Hao Yu, Derong Zheng, Guoyan Duan, Xiaotian Getty, Neil Sanchez-Matilla, Ricardo Robu, Maria Zhang, Li Chen, Huabin Wang, Jiacheng Wang, Liansheng Zhang, Bokai Gerats, Beerend Raviteja, Sista Sathish, Rachana Tao, Rong Kondo, Satoshi Pang, Winnie Ren, Hongliang Abbing, Julian Ronald Sarhan, Mohammad Hasan Bodenstedt, Sebastian Bhasker, Nithya Oliveira, Bruno Torres, Helena R. Ling, Li Gaida, Finn Czempiel, Tobias Vilaça, João L. Morais, Pedro Fonseca, Jaime Egging, Ruby Mae Wijma, Inge Nicole Qian, Chen Bian, Guibin Li, Zhen Balasubramanian, Velmurugan Sheet, Debdoot Luengo, Imanol Zhu, Yuanbo Ding, Shuai Aschenbrenner, Jakob-Anton van der Kar, Nicolas Elini Xu, Mengya Islam, Mobarakol Seenivasan, Lalithkumar Jenke, Alexander Stoyanov, Danail Mutter, Didier Mascagni, Pietro Seeliger, Barbara Gonzalez, Cristians Padoy, Nicolas ICube University of Strasbourg CNRS France IHU Strasbourg France Department of Computer Science University of Chicago United States Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Management Hefei University of Technology Hefei China Institue of Medical Robotics Shanghai Jiao Tong University Shanghai China Argonne National Laboratory 9700 S Cass Ave LemontIL60439 United States Digital Surgery A Medtronic Company London United Kingdom Institute of Automation Chinese Academy of Sciences China Department of Computer Science School of Informatics Xiamen University Xiamen China Johnson & Johnson Meander Medical Centre Netherlands Indian Institute of Technology Kharagpur India Muroran Institute of Technology Japan Department of Biomedical Engineering National University of Singapore Singapore Department for Translational Surgical Oncology National Center for Tumor Diseases Partner Site Dresden Germany 2Ai School of Technology IPCA Barcelos Portugal School of Medicine University of Minho Braga Portugal Algoritimi Center School of Engineering University of Minho Guimeraes Portugal Technical University of Munich Germany Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Wellcome EPSRC Center for Interventional and Surgical Science University College London United Kingdom Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy University Hospital of Strasbourg France

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity;yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery. © 2022, CC BY-NC-SA.

关键词： Deep learning

Understanding metric-related pitfalls in image analysis validation

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

Representative task self-selection for flexible clustered lifelong learning

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arXiv 2019年

作者： Sun, Gan Cong, Yang Wang, Qianqian Zhong, Bineng Fu, Yun State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Xidian University. Xian Shanxi710071 China Huaqiao University Xiamen Fujian361021 China Department of Electrical and Computer Engineering Khoury College of Computer and Information Sciences Northeastern University BostonMA02115 United States

Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are with prescribed size, and can degenerate the performance for both learned tasks and coming ones when facing with a new task environment (cluster). To address this challenge, we propose a novel incremental clustered lifelong learning framework with two knowledge libraries: feature learning library and model knowledge library, called Flexible Clustered Lifelong Learning (FCL3). Specifically, the feature learning library modeled by an autoencoder architecture maintains a set of representation common across all the observed tasks, and the model knowledge library can be self-selected by identifying and adding new representative models (clusters). When a new task arrives, our proposed FCL3 model firstly transfers knowledge from these libraries to encode the new task, i.e., effectively and selectively soft-assigning this new task to multiple representative models over feature learning library. Then, 1) the new task with a higher outlier probability will be judged as a new representative, and used to redefine both feature learning library and representative models over time;or 2) the new task with lower outlier probability will only refine the feature learning library. For model optimization, we cast this lifelong learning problem as an alternating direction minimization problem as a new task comes. Finally, we evaluate the proposed framework by analyzing several multi-task datasets, and the experimental results demonstrate that our FCL3 model can achieve better performance than most lifelong learning frameworks, even batch clustered multi-task learning models. Copyright © 2019, The Authors. All rights reserved.

关键词： Libraries

Underwater acoustic intensity field reconstruction by kriged compressive sensing 13

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Underwater acoustic intensity field reconstruction by kriged...

13th ACM International Conference on Underwater Networks and Systems, WUWNet 2018

作者： Sun, Jie Yu, Jiancheng Zhang, Aiqun Song, Aijun Zhang, Fumin State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Electrical Engineering University of Alabama TuscaloosaAL35487 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9781450361934

This paper presents a novel Kriged Compressive Sensing (KCS) approach for the reconstruction of underwater acoustic intensity fields sampled by multiple gliders following sawtooth sampling patterns. Blank areas in between the sampling trajectories may cause unsatisfying reconstruction results. The KCS method leverages spatial statistical correlation properties of the acoustic intensity field being sampled to improve the compressive reconstruction process. Virtual data samples generated from a kriging method are inserted into the blank areas. We show that by using the virtual samples along with real samples, the acoustic intensity field can be reconstructed with higher accuracy when coherent spatial patterns exist. Corresponding algorithms are developed for both unweighted and weighted KCS methods. By distinguishing the virtual samples from real samples through weighting, the reconstruction results can be further improved. Simulation results show that both algorithms can improve the reconstruction results according to the PSNR and SSIM metrics. The methods are applied to process the ocean ambient noise data collected by the Sea-Wing acoustic gliders in the South China Sea. © 2018 Association for Computing Machinery.

关键词： Kriging

Metrics Reloaded: Recommendations for image analysis validation

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

Evolutionary Game Dynamics Driven by Setting a Ceiling in Payoffs of Defectors 36

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Evolutionary Game Dynamics Driven by Setting a Ceiling in Pa...

第36届中国控制会议

作者： ZHANG Jianlei ZHU Yuying CHEN Zengqiang CAO Ming Department of Automation College of Computer and Control EngineeringNankai University Tianjin Key Laboratory of Intelligent Robotics College of Computer and Control EngineeringNankai University

ISBN: (纸本)9781538629185

The collective cooperation dilemma,where players are involved in a public goods game and must decide whether to make an individual contribution or not,has gained increasing attention from game theorists and control *** from the traditional definition,defectors here will face the payoff restriction after adopting the strategy of free riding others' ***,we introduce payoff ceilings applied to the gained benefits of the defectors,which leads to an extensive public goods game *** the number of cooperators is lower than the threshold value,the current model can be transferred to the setups of the general public goods *** further increasing the number of cooperators will bring the restricted payoffs for *** results suggest that the payoff ceilings provide us with more insight into the resolution of cooperative ***,together with other parameters(e.g.,the amplification factor and the size of gaming population) of the introduced model,lower values of the payoff ceilings can better facilitate the domination of cooperation strategy at the steady state.

关键词： Evolutionary game theory Replicator dynamics Public goods games

Evolutionary Games with Different Time Scales of Strategy Updating 29

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Evolutionary Games with Different Time Scales of Strategy Up...

第29届中国控制与决策会议

作者： Jianlei Zhang Yuying Zhu Chunyan Zhang Department of Automation College of Computer and Control EngineeringNankai University Tianjin Key Laboratory of Intelligent Robotics College of Computer and Control EngineeringNankai University

ISBN: (纸本)9781509046584

Variation in learning rates within populations suggests that evolutionary game theory may not necessarily be restricted to uniform time scales associated with the game interaction and strategy adaption evolution. Using agent-based evolutionary simulation in the framework of prisoner's dilemma game, we demonstrate the sustainability of cooperation and the emergence of different macro-effects, when equipping agents with non-uniform time scale preferences. We employ a spatial random regular grid to describe the social interactions among agents. We conclude that the cooperation level has a strong dependence on the population composition, and the suitable fraction of the fast-updating players in the system which is associated with the maximal cooperation frequency has been found out. Besides, the extent of the promotive effect of diversifying time scales is also closely related with the payoff adoption rules in strategy updating,especially when we invent a past history for each agent. Summing up the gained results, a general conclusion can be drawn, saying that the combination of these factors(e.g. time scales and memory) gives rise to rich dynamic behavior of the system.

关键词： Evolutionary Game Theory Complex Networks Evolution Dynamics