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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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Contemporary Symbolic Regression Methods and their Relative Performance 35

Contemporary Symbolic Regression Methods and their Relative ...

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35th Conference on Neural Information Processing Systems - Track on Datasets and Benchmarks, NeurIPS Datasets and Benchmarks 2021

作者： La Cava, William Orzechowski, Patryk Burlacu, Bogdan de França, Fabrício Olivetti Virgolin, Marco Jin, Ying Kommenda, Michael Moore, Jason H. Boston Children’s Hospital Harvard Medical School United States Institute for Biomedical Informatics University of Pennsylvania United States Josef Ressel Center for Symbolic Regression University of Applied Sciences Upper Austria Austria Federal University of ABC Santo Andre Brazil Life Sciences and Health Group Centrum Wiskunde & Informatica Netherlands Department of Statistics Stanford University United States Department of Automatics and Robotics AGH University of Science and Technology Krakow Poland Center for Mathematics Computation and Cognition | Heuristics Analysis and Learning Laboratory Brazil Chalmers University of Technology Sweden

Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent benchmarking standards. We address this shortcoming by introducing an open-source, reproducible benchmarking platform for symbolic regression. We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems. Our assessment includes both real-world datasets with no known model form as well as ground-truth benchmark problems. For the real-world datasets, we benchmark the ability of each method to learn models with low error and low complexity relative to state-of-the-art machine learning methods. For the synthetic problems, we assess each method’s ability to find exact solutions in the presence of varying levels of noise. Under these controlled experiments, we conclude that the best performing methods for real-world regression combine genetic algorithms with parameter estimation and/or semantic search drivers. When tasked with recovering exact equations in the presence of noise, we find that several approaches perform similarly. We provide a detailed guide to reproducing this experiment and contributing new methods, and encourage other researchers to collaborate with us on a common and living symbolic regression benchmark. © 2021 Neural information processing systems foundation. All rights reserved.

关键词： Adversarial machine learning

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Contemporary symbolic regression methods and their relative performance

arXiv

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arXiv 2021年

作者： la Cava, William Orzechowski, Patryk Burlacu, Bogdan de França, Fabrício Olivetti Virgolin, Marco Jin, Ying Kommenda, Michael Moore, Jason H. Institute for Biomedical Informatics University of Pennsylvania Josef Ressel Center for Symbolic Regression University of Applied Sciences Upper Austria Federal University of ABC‡ Santo Andre Brazil Mechanics and Maritime Sciences Chalmers University of Technology Department of Statistics Stanford University Department of Automatics and Robotics AGH University of Science and Technology Krakow Poland Center for Mathematics Computation and Cognition | Heuristics Analysis and Learning Laboratory

Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent benchmarking standards. In this paper, we address this shortcoming by introducing an open-source, reproducible benchmarking platform for symbolic regression. We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems. Our assessment includes both real-world datasets with no known model form as well as ground-truth benchmark problems, including physics equations and systems of ordinary differential equations. For the real-world datasets, we benchmark the ability of each method to learn models with low error and low complexity relative to state-of-the-art machine learning methods. For the synthetic problems, we assess each method's ability to find exact solutions in the presence of varying levels of noise. Under these controlled experiments, we conclude that the best performing methods for real-world regression combine genetic algorithms with parameter estimation and/or semantic search drivers. When tasked with recovering exact equations in the presence of noise, we find that deep learning and genetic algorithm-based approaches perform similarly. We provide a detailed guide to reproducing this experiment and contributing new methods, and encourage other researchers to collaborate with us on a common and living symbolic regression benchmark. © 2021, CC BY-NC-SA.

关键词： Regression analysis

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Motor Skill Improvement Using Compressive Garments in a Child with Multidimensional Impairments and Ehlers-Danlos Syndrome

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Case Reports in Psychiatry 2022年第0期2022卷 5819406页

作者： Xavier, Jean Gauthier, Soizic Zammouri, Ingrid Anzalone, Salvatore Maria Cohen, David Department of Child and Adolescent Psychiatry Henri Laborit Hospital Centre Poitiers 86000 France CNRS UMR 7295 Équipe CoCliCo Cognition and Learning Research Center Poitiers France Department of Child and Adolescent Psychiatry Reference Centre for Rare Psychiatric Diseases AP-HP Groupe Hospitalier Pitié-Salpêtrière Sorbonne Université Paris 75006 France Laboratoire CHArt-THIM EA4004 Université Paris 8 Saint-Denis France CNRS UMR 7222 Institute for Intelligent Systems and Robotics Sorbonne Université Paris 75006 France

We report the case of an 8-year-old child with a complex neurodevelopmental disorder, including severe developmental coordination disorder with dysgraphia, anxiety and depression, mild social functioning impairments, headache and chronic musculoskeletal pain, secondary to Ehlers-Danlos syndrome (EDS) hypermobility type. We explored whether wearing whole-body compressive garments (CGs) could improve his motor skills assessed through standardized and experimental procedures. In addition to the effectiveness of CGs on pain, we found partial improvements in his motor skills, specifically postural control, hand movements, and body schema representation, after wearing CGs for 15 days. During an experimental motor imitation task with a virtual tightrope walker, we found improvements in interpersonal synchronization with performances closer to those of typical developing (TD) controls. We conclude that CGs appear to be an innovative and interesting adjuvant treatment for motor skill impairments in children with multidimensional impairments involving EDS. These promising results require confirmation by further evidence-based research. © 2022 Jean Xavier et al.

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Feature discovery for sequential prediction of monophonic music 18

Feature discovery for sequential prediction of monophonic mu...

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18th International Society for Music Information Retrieval Conference, ISMIR 2017

作者： Langhabel, Jonas Lieck, Robert Toussaint, Marc Rohrmeier, Martin Department of Computer Science TU Berlin Germany Machine Learning and Robotics Lab University of Stuttgart Germany Systematic Musicology and Music Cognition TU Dresden Germany Digital and Cognitive Musicology Lab EPFL Switzerland

ISBN: (纸本)9789811151798

learning a model for sequential prediction of symbolic music remains an open challenge. An important special case is the prediction of pitch sequences based on a corpus of monophonic music. We contribute to this line of research in two respects: (1) Our models improve the stateof- the-art performance. (2) Our method affords learning interpretable models by discovering an explicit set of relevant features. We discover features using the PULSE learning framework, which repetitively suggests new candidate features using a generative operation and selects features while optimizing the underlying model. Defining a domain-specific generative operation allows to combine multiple music-theoretically motivated features in a unified model and to control their interaction on a fine-grained level. We evaluate our models on a set of benchmark corpora of monophonic chorales and folk songs, outperforming previous work. Finally, we discuss the characteristics of the discovered features from a musicological perspective, giving concrete examples. © 2019 Jonas Langhabel, Robert Lieck, Marc Toussaint, Martin Rohrmeier.

关键词： Forecasting

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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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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Reports of the AAAI 2010 spring symposia

Reports of the AAAI 2010 spring symposia

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作者： Barkowsky, Thomas Bertel, Sven Broz, Frank Chaudhri, Vinay K. Eagle, Nathan Genesereth, Michael Halpin, Harry Hamner, Emily Hoffmann, Gabe Hölscher, Christoph Horvitz, Eric Lauwers, Tom McGuinness, Deborah L. Michalowski, Marek Mower, Emily Shipley, Thomas F. Stubbs, Kristen Vogl, Roland Williams, Mary-Anne Cognitive Systems Group University of Bremen Research Center SFB/TR 8 Spatial Cognition Germany Human Factors Division Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign IL United States Adaptive Systems Research Group Computer Science Department University of Hertfordshire United Kingdom Artificial Intelligence Center at SRI International United States Txteagle Inc. MIT Media Laboratory United States Computer Science Department Stanford University United States University of Edinburgh United Kingdom Robotics Institute Carnegie Mellon University United States Palo Alto Research Center United States Center for Cognitive Science University of Freiburg Germany CREATE lab Carnegie Mellon Robotics Institute United States Rensselaer Polytechnic Institute United States University of Southern California United States Department of Psychology Temple University Spatial Intelligence and Learning Center United States IRobot Corporation United States Stanford Program in Law Science and Technology Stanford University Law School United States Innovation and Enterprise Research Laboratory University of Technology Sydney Australia

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2010 Spring Symposium Series Monday through Wednesday, March 22-24, 2010, at Stanford University. The titles of the seven symposia were Artificial Intelligence for Development;Cognitive Shape Processing;Educational robotics and Beyond: Design and Evaluation;Embedded Reasoning: Intelligence in Embedded Systems;Intelligent Information Privacy Management;It's All in the Timing: Representing and Reasoning about Time in Interactive Behavior;and Linked Data Meets Artificial Intelligence. Copyright © 2010.

关键词： Embedded systems

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The marchitecture: A cognitive architecture for a robot baby

The marchitecture: A cognitive architecture for a robot baby

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AAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference

作者： Marc Pickett, I. Oates, Tim Cognition Robotics and Learning Department of Computer Science and Electrical Engineering University of Maryland Baltimore County 1000 Hilltop Circle Baltimore MD 21250

ISBN: (纸本)1577353234

The Marchitecture is a cognitive architecture for autonomous development of representations. The goals of The Marchitecture are domain independence, operating in the absence of knowledge engineering, learning an ontology of parameterized relational concepts, and elegance of design. To this end, The Marchitecture integrates classification, parsing, reasoning, and explanation. The Marchitecture assumes an ample amount of raw data to develop its representations, and it is therefore appropriate for long lived agents. Copyright © 2007, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Cognitive systems

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