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arXiv 2024年

作者： Apraez, Daniel Ordoñez Turrisi, Giulio Kostic, Vladimir Martin, Mario Agudo, Antonio Moreno-Noguer, Francesc Pontil, Massimiliano Semini, Claudio Mastalli, Carlos Italy Computational Statistics and Machine Learning IIT Italy Institut de Robòtica i Informàtica Industrial CSIC-UPC Spain Robot Motor Intelligence – Heriot-Watt University United Kingdom IHMC Robotics Florida Institute for Human & Machine Cognition United States

We present a comprehensive framework for studying and leveraging morphological symmetries in robotic systems. These are intrinsic properties of the robot’s morphology, frequently observed in animal biology and robotics, which stem from the replication of kinematic structures and the symmetrical distribution of mass. We illustrate how these symmetries extend to the robot’s state space and both proprioceptive and exteroceptive sensor measurements, resulting in the equivariance of the robot’s equations of motion and optimal control policies. Thus, we recognize morphological symmetries as a relevant and previously unexplored physics-informed geometric prior, with significant implications for both data-driven and analytical methods used in modeling, control, estimation and design in robotics. For data-driven methods, we demonstrate that morphological symmetries can enhance the sample efficiency and generalization of machine learning models through data augmentation, or by applying equivariant/invariant constraints on the model’s architecture. In the context of analytical methods, we employ abstract harmonic analysis to decompose the robot’s dynamics into a superposition of lower-dimensional, independent dynamics. We substantiate our claims with both synthetic and real-world experiments conducted on bipedal and quadrupedal robots. Lastly, we introduce the repository MORPHOSYMM to facilitate the practical use of the theory and applications outlined in this *** Codes 68T40 © 2024, CC BY.

关键词： Robots

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3D Human Pose Estimation Based on Multi-Input Multi-Output Convolutional Neural Network and Event Cameras: A Proof of Concept on the DHP19 Dataset 25th

3D Human Pose Estimation Based on Multi-Input Multi-Output C...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Manilii, Alessandro Lucarelli, Leonardo Rosati, Riccardo Romeo, Luca Mancini, Adriano Frontoni, Emanuele Università Politecnica delle Marche Via Brecce Bianche 12 Ancona60131 Italy Computational Statistics and Machine Learning and Cognition Motion and Neuroscience Istituto Italiano di Tecnologia Genova Italy

ISBN: (纸本)9783030687625

Nowadays Human Pose Estimation (HPE) represents one of the main research themes in the field of computer vision. Despite innovative methods and solutions introduced for frame processing algorithms, the use of standard frame-based cameras still has several drawbacks such as data redundancy and fixed frame-rate. The use of event-based cameras guarantees higher temporal resolution with lower memory and computational cost while preserving the significant information to be processed and thus it represents a new solution for real-time applications. In this paper, the DHP19 dataset was employed, the first and, to date, the only one with HPE data recorded from Dynamic Vision Sensor (DVS) event-based cameras. Starting from the baseline single-input single-output (SISO) Convolutional Neural Network (CNN) model proposed in the literature, a novel multi-input multi-output (MIMO) CNN-based architecture was proposed in order to model simultaneously two different single camera views. Experimental results show that the proposed MIMO approach outperforms the standard SISO model in terms of accuracy and training time. © 2021, Springer Nature Switzerland AG.

关键词： Cameras

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Dynamics Harmonic Analysis of Robotic Systems: Application in Data-Driven Koopman Modelling

arXiv

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arXiv 2023年

作者： Ordoñez-Apraez, Daniel Felipe Kostic, Vladimir Turrisi, Giulio Novelli, Pietro Mastalli, Carlos Semini, Claudio Pontil, Massimiliano Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Italy Dynamic Legged Systems Istituto Italiano di Tecnologia Italy Robot Motor Intelligence Heriot-Watt University United Kingdom Florida Institute for Human and Machine Cognition United States Department of Computer Science University College London United Kingdom

We introduce the use of harmonic analysis to decompose the state space of symmetric robotic systems into orthogonal isotypic subspaces. These are lower-dimensional spaces that capture distinct, symmetric, and synergistic motions. For linear dynamics, we characterize how this decomposition leads to a subdivision of the dynamics into independent linear systems on each subspace, a property we term dynamics harmonic analysis (DHA). To exploit this property, we use Koopman operator theory to propose an equivariant deep-learning architecture that leverages the properties of DHA to learn a global linear model of the system dynamics. Our architecture, validated on synthetic systems and the dynamics of locomotion of a quadrupedal robot, exhibits enhanced generalization, sample efficiency, and interpretability, with fewer trainable parameters and computational *** Codes 43-08 © 2023, CC BY.

关键词： Dynamical systems

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Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

arXiv

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arXiv 2019年

作者： Kuijf, Hugo J. Biesbroek, J. Matthijs Bresser, Jeroen de Heinen, Rutger Andermatt, Simon Bento, Mariana Berseth, Matt Belyaev, Mikhail Cardoso, M. Jorge Casamitjana, Adrià Collins, D. Louis Dadar, Mahsa Georgiou, Achilleas Ghafoorian, Mohsen Jin, Dakai Khademi, April Knight, Jesse Li, Hongwei Lladó, Xavier Luna, Miguel Mahmood, Qaiser McKinley, Richard Mehrtash, Alireza Ourselin, Sébastien Park, Bo-yong Park, Hyunjin Park, Sang Hyun Pezold, Simon Puybareau, Elodie Rittner, Leticia Sudre, Carole H. Valverde, Sergi Vilaplana, Verónica Wiest, Roland Xu, Yongchao Xu, Ziyue Zeng, Guodong Zhang, Jianguo Zheng, Guoyan Chen, Christopher Flier, Wiesje van der Barkhof, Frederik Viergever, Max A. Biessels, Geert Jan Image Sciences Institute UMC Utrecht Utrecht University Netherlands Brain Center Rudolf Magnus UMC Utrecht Utrecht University Netherlands Department of Radiology UMC Utrecht Department of Radiology LUMC Leiden Netherlands Department of Biomedical Engineering University of Basel Allschwil Switzerland Radiology and Clinical Neuroscience Hotchkiss Brain Institute University of Calgary AB Canada NLP Logix Skolkovo Institute of Science and Technology School of Biomedical Engineering and Imaging Sciences King’s College London Centre for Medical Image Computing University College London Signal Theory and Communications Department Universitat Politècnica de Catalunya BarcelonaTech Barcelona Spain McGill University Canada Computational Statistics and Machine Learning MSc University College London TomTom Amsterdam Netherlands Department of Radiology and Imaging Science National Institutes of Health United States Ryerson University Canada University of Guelph Canada Sun Yat-sen University University of Dundee Technical University of Munich Research institute of Computer Vision and Robotics University of Girona Spain Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu Korea Republic of Pakistan Institute of Nuclear Science and Technology Support Center for Advanced Neuroimaging Institute for Diagnostic and Interventional Neuroradiology Inselspital University of Bern Switzerland Electrical and Computer Engineering Department University of British Columbia Vancouver Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston School of Biomedical Engineering and Imaging Sciences King’s College London Suwon Korea Republic of School of Electronic and Electrical Engineering Sungkyunkwan University Suwon Korea Republic of France School of Electrical and Computer Engineering University of Campinas SP Brazil School of Biomedical Engineering and Imaging Sciences King’s College London Centre for Medical I

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://***/).Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. Segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute log-transformed volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner *** participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all methods generalize to unseen *** challenge remains open for future submissions and provides a public platform for method evaluation. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Rejoinder

引用

American Statistician 2009年第2期63卷 122-123页

作者： Brown, Emery N. Kass, Robert E. Department of Anesthesia and Critical Care Massachusetts General Hospital Boston MA 02114 Computational Neuroscience and Health Sciences and Technology Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA 02139 Department of Statistics Center for the Neural Basis of Cognition and Machine Learning Department Carnegie Mellon University Pittsburgh PA 15217

The article presents the authors' response to the comments about their article in which they argue that the statistic discipline has a problem, and it needs to recognize changes that include encouraging statistical thinking and statistics students' participation in cross-disciplinary work. They assert that with their suggestions, they did not intend to be directive concerning its implementations, but on what faculty can do for changes to take effect. They agree to the argument that resource are important, but indicate that a creative approach to resource-neutral curriculum reform may be implemented.

关键词： Statisticians statistics education Curriculum change Curricula (Courses of study) Curriculum planning Curriculum evaluation Instructional systems Educational change Educational innovations

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