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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Juan David Hernandez Mark Moll Eduard Vidal Marc Carreras Lydia E. Kavraki Underwater Vision and Robotics Research Center (CIRS) University of Girona Spain Department of Computer Science at Rice University Houston TX USA

ISBN: (纸本)9781509037636

We present a framework for planning collision-free and safe paths online for autonomous underwater vehicles (AUVs) in unknown environments. We build up on our previous work and propose an improved approach. While preserving its main modules (mapping, planning and mission handler), the framework now considers motion constraints to plan feasible paths, i.e., those that meet vehicle's motion capabilities. The new framework also incorporates a risk function to avoid navigating close to nearby obstacles, and reuses the last best known solution to eliminate time-consuming pruning routines. To evaluate this approach, we use the Sparus II AUV, a torpedo-shaped vehicle performing autonomous missions in a 2-dimensional workspace. We validate the framework's new features by solving tasks in both simulation and real-world in-water trials and comparing results with our previous approach.

关键词： Planning Navigation Path planning Underwater autonomous vehicles Differential equations Vehicle dynamics

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Author Correction: Observation of an ultra-high-energy cosmic neutrino with KM3NeT

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Nature 2025年第8057期640卷 E3页

作者： S. Aiello R. Bruno G. Ferrara E. Leonora F. Longhitano N. Randazzo A. Sinopoulou I. Tosta e Melo A. Albert D. Drouhin T. Pradier A. R. Alhebsi S. Thoudam A. van Vliet M. Alshamsi V. Bertin M. Breuhaus J. Brunner J. Busto L. Cerisy P. Coyle B. De Martino D. Dornic J.-G. Ducoin A. Enzenhöfer S. Henry D. Joly S. Lancelin S. Le Stum N. Lumb F. Magnani C. Paries M. Perrin-Terrin G. Vannoye S. Alves Garre A. Bariego-Quintana D. Calvo V. Cecchini A. Garcia Soto S. R. Gozzini J. J. Hernández-Rey A. Lazo N. Lessing J. Palacios González E. J. Pastor Gomez J. Prado D. Real A. Šaina F. Salesa Greus A. Sánchez Losa J. D. Zornoza J. Zúñiga A. Ambrosone M. Bendahman M. Benhassi C. Bozza R. M. Bozza R. Buompane A. Condorelli A. De Benedittis L. A. Fusco L. Gialanella W. Idrissi Ibnsalih A. Manfreda A. Marinelli F. Marzaioli S. Mastroianni G. Miele P. Migliozzi M. L. Mitsou C. M. Mollo L. Morales-Gallegos M. R. Musone V. Oliviero C. Poirè I. C. Rea A. Simonelli B. Spisso D. Vivolo F. Ameli A. Capone S. Celli I. Di Palma S. Gagliardini M. Mastrodicasa C. A. Nicolau A. Tudorache A. Veutro A. Zegarelli M. Andre M. Anghinolfi F. Badaracco B. Caiffi V. Ellajosyula C. Guidi V. Kulikovskiy L. Malerba M. Marconi B. Ó Fearraigh V. Parisi A. Romanov M. Sanguineti M. Taiuti S. Zavatarelli L. Aphecetche F. Bretaudeau R. Dallier V. Decoene L. Martin M. Ardid S. Ardid J. García Méndez C. A. Quiroz-Rangel C. Argüelles N. Kamp A. Y. Wen H. Atmani J. Boumaaza A. Eddymaoui Y. Tayalati J. Aublin B. Baret Y. Becherini T. Cartraud J. A. B. Coelho A. Coleiro S. Colonges A. Creusot C. Donzaud M. Eff S. El Hedri I. Goos P. Goswami A. Ilioni A. Kouchner M. Lindsey Clark E. Oukacha S. Peña Martínez R. Pestes A. Sharma B. Trocmé V. Van Elewyck C. Verilhac I. Lhenry-Yvon L. Bailly-Salins M. Bennani B. Guillon M. Labalme C. Lastoria G. Lehaut I. Mozun Mateo V. Pestel A. Vacheret Z. Bardačová E. Eckerová F. Mamedov Y. Shitov I. Štekl R. Dvornický F. Šimkovic F. Benfenati Gualandi F. Carenini P. Castaldi T. Chiarusi I. Del Rosso G. Levi A. Margiotta R. Muller INFN Sezione di Catania (INFN-CT) Catania Italy INFN Laboratori Nazionali del Sud (LNS) Catania Italy Dipartimento di Fisica e Astronomia “Ettore Majorana” Università di Catania (INFN-CT) Catania Italy Université de Strasbourg CNRS IPHC UMR 7178 Strasbourg France Université de Haute Alsace Mulhouse France Department of Physics Khalifa University Abu Dhabi United Arab Emirates Aix Marseille Université CNRS/IN2P3 CPPM Marseille France IFIC - Instituto de Física Corpuscular (CSIC - Universitat de València) Paterna Spain INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Napoli Italy Università di Napoli “Federico II” Dip. Scienze Fisiche “E. Pancini” Complesso Universitario di Monte S. Angelo Napoli Italy Dipartimento di Matematica e Fisica Università degli Studi della Campania “Luigi Vanvitelli” Caserta Italy Dipartimento di Fisica Università di Salerno e INFN Gruppo Collegato di Salerno Fisciano Italy INFN Sezione di Roma Roma Italy Dipartimento di Fisica Università La Sapienza Roma Italy Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Universitat Politècnica de Catalunya Vilanova i la Geltrú Spain INFN Sezione di Genova Genova Italy Università di Genova Genova Italy LPC CAEN Normandie Université ENSICAEN UNICAEN CNRS/IN2P3 Caen France Subatech IMT Atlantique IN2P3-CNRS Nantes Université Nantes France Instituto de Investigación para la Gestión Integrada de las Zonas Costeras Universitat Politècnica de València Gandia Spain Department of Physics Harvard University Cambridge MA USA Laboratory for Particle Physics and Cosmology Lyman Laboratory Harvard University Cambridge MA USA Faculty of Sciences University Mohammed V in Rabat Rabat Morocco Institute of Applied Physics Mohammed VI Polytechnic University Ben Guerir Morocco Astroparticule et Cosmologie Université Paris Cité CNRS Paris France Institut Universitaire de France Paris France Institute of Experimental and Applied Physics

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Siamese network features for image matching

Siamese network features for image matching

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International Conference on Pattern Recognition

作者： Iaroslav Melekhov Juho Kannala Esa Rahtu Department of Computer Science Aalto University Finland Center for Machine Vision Research University of Oulu

ISBN: (纸本)9781509048489

Finding matching images across large datasets plays a key role in many computer vision applications such as structure-from-motion (SfM), multi-view 3D reconstruction, image retrieval, and image-based localisation. In this paper, we propose finding matching and non-matching pairs of images by representing them with neural network based feature vectors, whose similarity is measured by Euclidean distance. The feature vectors are obtained with convolutional neural networks which are learnt from labeled examples of matching and non-matching image pairs by using a contrastive loss function in a Siamese network architecture. Previously Siamese architecture has been utilised in facial image verification and in matching local image patches, but not yet in generic image retrieval or whole-image matching. Our experimental results show that the proposed features improve matching performance compared to baseline features obtained with networks which are trained for image classification task. The features generalize well and improve matching of images of new landmarks which are not seen at training time. This is despite the fact that the labeling of matching and non-matching pairs is imperfect in our training data. The results are promising considering image retrieval applications, and there is potential for further improvement by utilising more training image pairs with more accurate ground truth labels.

关键词： Image retrieval Training Neural networks Euclidean distance Network architecture Image matching

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Approximate hybrid model predictive control for multi-contact push recovery in complex environments

Approximate hybrid model predictive control for multi-contac...

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IEEE-RAS International Conference on Humanoid Robots

作者： Tobia Marcucci Robin Deits Marco Gabiccini Antonio Bicchi Russ Tedrake Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technolozy Cambridge MA USA Research Center “E. Piaggio” University of Pisa Pisa Italy Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy Dipartimento di Ingegneria Civile e Industriale University of Pisa Pisa Italy

ISBN: (纸本)9781538646793

Feedback control of robotic systems interacting with the environment through contacts is a central topic in legged robotics. One of the main challenges posed by this problem is the choice of a model sufficiently complex to capture the discontinuous nature of the dynamics but simple enough to allow online computations. Linear models have proved to be the most effective and reliable choice for smooth systems; we believe that piecewise affine (PWA) models represent their natural extension when contact phenomena occur. Discrete-time PWA systems have been deeply analyzed in the field of hybrid Model Predictive Control (MPC), but the straightforward application of MPC techniques to complex systems, such as a humanoid robot, leads to mixed-integer optimization problems which are not solvable at real-time rates. Explicit MPC methods can construct the entire control policy offline, but the resulting policy becomes too complex to compute for systems at the scale of a humanoid robot. In this paper we propose a novel algorithm which splits the computational burden between an offline sampling phase and a limited number of online convex optimizations, enabling the application of hybrid predictive controllers to higher-dimensional systems. In doing so we are willing to partially sacrifice feedback optimality, but we set stability of the system as an inviolable requirement. Simulation results of a simple planar humanoid that balances by making contact with its environment are presented to validate the proposed controller.

关键词： Robot kinematics Dynamics Feedback control Optimal control Legged locomotion Computational modeling

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Evaluating race and sex diversity in the world's largest companies using deep neural networks

arXiv

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arXiv 2017年

作者： Chekanov, Konstantin Mamoshina, Polina Yampolskiy, Roman V. Timofte, Radu Scheibye-Knudsen, Morten Zhavoronkov, Alex Youth Laboratories Ltd Diversity Ai Group Skolkovo Innovation Center Nobel Street 5 Moscow143026 Insilico Medicine Emerging Technology Centers Jhu 1101 33rd Street BaltimoreMD21218 United States Computer Science Department University of Oxford Oxford United Kingdom Computer Engineering and Computer Science Duthie Center for Engineering University of Louisville LouisvilleKY40292 United States Computer Vision Lab Department of Information Technology and Electrical Engineering Eth Zürich Switzerland Center for Healthy Aging Department of Cellular and Molecular Medicine University of Copenhagen Biogerontology Research Foundation 2354 Chynoweth House Trevissome Park TruroTR4 8UN United Kingdom Moscow Institute of Physics and Technology Institutskiy per. 9 Dolgoprudny141701 Russia

Diversity is one of the fundamental properties for survival of species, populations and organizations. Recent advances in deep learning allow for the rapid and automatic assessment of organizational diversity and possible discrimination by race, sex, age and other parameters. Automating the process of assessing the organizational diversity using the deep neural networks and eliminating the human factor may provide a set of real-time unbiased reports to all stakeholders. In this pilot study we applied the deep learned predictors of race and sex to the executive management and board member profiles of the 500 largest companies from the 2016 Forbes Global 2000 list and compared the predicted ratios to the ratios within each company's country of origin and ranked them by the sex-, age- and race- diversity index (DI). While the study has many limitations and no claims are being made concerning the individual companies, it demonstrates a method for the rapid and impartial assessment of organizational diversity using deep neural networks. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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Publisher Correction: Multicolor multiscale brain imaging with chromatic multiphoton serial microscopy

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Nature communications 2019年第1期10卷 2160页

作者： Lamiae Abdeladim Katherine S Matho Solène Clavreul Pierre Mahou Jean-Marc Sintes Xavier Solinas Ignacio Arganda-Carreras Stephen G Turney Jeff W Lichtman Anatole Chessel Alexis-Pierre Bemelmans Karine Loulier Willy Supatto Jean Livet Emmanuel Beaurepaire Laboratory for Optics and Biosciences Ecole polytechnique CNRS INSERM IP Paris Palaiseau 91128 France. Sorbonne Université INSERM CNRS Institut de la Vision 17 rue Moreau Paris 75012 France. Cold Spring Harbor Laboratory Cold Spring Harbor 11724 NY USA. Department of Computer Science and Artificial Intelligence University of the Basque Country San Sebastian 20018 Spain. IKERBASQUE Basque Foundation for Science Bilbao 48013 Spain. Donostia International Physics Center (DIPC) San Sebastian 20018 Spain. Center for Brain Science and Department of Molecular and Cellular Biology Harvard University Cambridge 02138 MA USA. Neurodegenerative Diseases Laboratory Molecular Imaging Research Center Institut de Biologie François Jacob CEA CNRS Université Paris-Sud Fontenay-aux-Roses 92265 France. Sorbonne Université INSERM CNRS Institut de la Vision 17 rue Moreau Paris 75012 France. jean.livet@inserm.fr. Laboratory for Optics and Biosciences Ecole polytechnique CNRS INSERM IP Paris Palaiseau 91128 France. emmanuel.beaurepaire@polytechnique.edu.

Affiliation 4 incorrectly read 'University of the Basque Country (Ikerbasque), University of the Basque Country and Donostia International Physics center, San Sebastian 20018, Spain.'Also, the affiliations of Ignacio Arganda-Carreras with 'IKERBASQUE, Basque Foundation for science, Bilbao, 48013, Spain' and 'Donostia International Physics center (DIPC), San Sebastian, 20018, Spain' were inadvertently ***, the third sentence of the first paragraph of the Results section entitled 'Multicontrast organ-scale imaging with ChroMS microscopy' incorrectly read 'For example, one can choose lambda1 = 850 and lambda2 = 110 nm for optimal two-photon excitation of blue and red chromophores.'. The correct version reads 'lambda2 = 1100 nm' instead of 'lambda2 = 110 nm'. These errors have now been corrected in the PDF and HTML versions of the Article.

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A novel multi-articular leg mechanism for biped robots inspired by bi-articular muscle

A novel multi-articular leg mechanism for biped robots inspi...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Jongwoo Lee Giuk Lee Seokmin Hong SangWook Lee Jung Hoon Kim Yonghwan Oh Center for Robotics Research Korea Institute of Science and Technology (KIST) Seoul Korea Department of Human-Computer Interaction and Robotics University of Science and Technology (UST) Korea

ISBN: (纸本)9781509032884

This paper proposes a novel multi-articular leg system for biped robot. The structure and actuation scheme of the mechanism are inspired by redundantly-actuated manipulators, of which actuators span over multiple joints like bi-articular muscles. The proposed mechanism for robotic leg contributes to improved capacity in end-effector force exertion, which allows less performance requirements in individual actuators. For the purpose of evaluation, we provide the static torque-force transmission analysis, as well as walking simulation results conducted in the dynamic simulator. The results indicate that the proposed mechanism can benefit from the improved output force capacity.

关键词： Legged locomotion Force Muscles Actuators Dynamics Manipulators

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Optoelectronic Devices: Thin Metallic Heat Sink for Interfacial Thermal Management in Biointegrated Optoelectronic Devices (Adv. Mater. Technol. 11/2018)

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Advanced Materials Technologies 2018年第11期3卷

作者： Han Hee Jung Juwon Song Shuang Nie Han Na Jung Min Seok Kim Jae-Woong Jeong Young Min Song Jizhou Song Kyung-In Jang Department of Robotics Engineering Department of Information and Communication Engineering Brain Engineering Convergence Center Daegu Gyeongbuk Institute of Science and Technology Daegu 49288 Republic of Korea Department of Engineering Mechanics Soft Matter Research Center and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou 310027 China School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology 123 Cheomdan-gwagiro Buk-gu Gwangju 61005 Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea

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Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases

Joint Challenge on Dominant and Complementary Emotion Recogn...

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International Conference on Automatic Face and Gesture Recognition

作者： Iiris Lüsi Julio C. S. Jacques Junior Jelena Gorbova Xavier Baró Sergio Escalera Hasan Demirel Juri Allik Cagri Ozcinar Gholamreza Anbarjafari ICV Research Group Institute of Technology University of Tartu Tartu Estonia Universitat de Barcelona Barcelona Catalunya ES Computer Vision Center and Universitat Oberta de Catalunya Barcelona Spain Human Pose Recovery and Behavior Analysis University of Barcelona Barcelona Spain Department of Electrical and Electronic Engineering Eastern Mediterranean University Famagusta TRNC Turkey The Estonian Center of Behavioral and Health Sciences Tartu Estonia Institute of Psychology University of Tartu Tartu Estonia Trinity College Dublin School of Computer Science and Statistics Ireland

In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists.

关键词： Databases Emotion recognition Face Psychology Three-dimensional displays Nose

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