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arXiv 2018年

作者： Binder, Alexander Bockmayr, Michael Hägele, Miriam Wienert, Stephan Heim, Daniel Hellweg, Katharina Stenzinger, Albrecht Parlow, Laura Budczies, Jan Goeppert, Benjamin Treue, Denise Kotani, Manato Ishii, Masaru Dietel, Manfred Hocke, Andreas Denkert, Carsten Müller, Klaus-Robert Klauschen, Frederick Machine-Learning Group Department of Software Engineering and Theoretical Computer Science Technical University of Berlin Marchstr. 23 Berlin10587 Germany Systems Pathology Lab Institute of Pathology Charité University Hospital Charitéplatz 1 Berlin10117 Germany Department of Internal Medicine Infectious Diseases and Pulmology Charitéplatz 1 Berlin10117 Germany Institute of Pathology University of Heidelberg Im Neuenheimer Feld 224 Heidelberg Germany Laboratory of Cellular Dynamics Immunology Frontier Research Center Osaka University 2-2 Yamada-oka Suita Osaka565-0871 Japan ISTD Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Charité Berlin Charitéplatz 1 Berlin10117 Germany Department of Brain and Cognitive Engineering Korea University 145 Anam-ro Seongbukgu Seoul136-713 Korea Republic of Max-Planck-Institute for Informatics Saarbrücken Germany Department of Pediatric Hematology and Oncology University Medical Center Hamburg-Eppendorf Hamburg Germany

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment. Copyright © 2018, The Authors. All rights reserved.

关键词： Fluorescence microscopy

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Automatic differentiation in machine learning: a survey

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The Journal of Machine Learning Research 2017年第1期18卷

作者： Atılım Günes Baydin Barak A. Pearlmutter Alexey Andreyevich Radul Jeffrey Mark Siskind Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science National University of Ireland Maynooth Maynooth Co. Kildare Ireland Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA School of Electrical and Computer Engineering Purdue University West Lafayette IN

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply "auto-diff", is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs. AD is a small but established field with applications in areas including computational uid dynamics, atmospheric sciences, and engineering design optimization. Until very recently, the fields of machine learning and AD have largely been unaware of each other and, in some cases, have independently discovered each other's results. Despite its relevance, general-purpose AD has been missing from the machine learning toolbox, a situation slowly changing with its ongoing adoption under the names "dynamic computational graphs" and "differentiable programming". We survey the intersection of AD and machine learning, cover applications where AD has direct relevance, and address the main implementation techniques. By precisely defining the main differentiation techniques and their interrelationships, we aim to bring clarity to the usage of the terms "autodiff", "automatic differentiation", and "symbolic differentiation" as these are encountered more and more in machine learning settings.

关键词： backpropagation differentiable programming

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Author Correction: Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets

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Nature communications 2021年第1期12卷 7260页

作者： Merel C Postema Daan van Rooij Evdokia Anagnostou Celso Arango Guillaume Auzias Marlene Behrmann Geraldo Busatto Filho Sara Calderoni Rosa Calvo Eileen Daly Christine Deruelle Adriana Di Martino Ilan Dinstein Fabio Luis S Duran Sarah Durston Christine Ecker Stefan Ehrlich Damien Fair Jennifer Fedor Xin Feng Jackie Fitzgerald Dorothea L Floris Christine M Freitag Louise Gallagher David C Glahn Ilaria Gori Shlomi Haar Liesbeth Hoekstra Neda Jahanshad Maria Jalbrzikowski Joost Janssen Joseph A King Xiang Zhen Kong Luisa Lazaro Jason P Lerch Beatriz Luna Mauricio M Martinho Jane McGrath Sarah E Medland Filippo Muratori Clodagh M Murphy Declan G M Murphy Kirsten O'Hearn Bob Oranje Mara Parellada Olga Puig Alessandra Retico Pedro Rosa Katya Rubia Devon Shook Margot J Taylor Michela Tosetti Gregory L Wallace Fengfeng Zhou Paul M Thompson Simon E Fisher Jan K Buitelaar Clyde Francks Department of Language & Genetics Max Planck Institute for Psycholinguistics Nijmegen The Netherlands. Department of Cognitive Neuroscience Donders Institute for Brain Cognition and Behaviour Donders Centre for Cognitive Neuroimaging Radboud University Medical Centre Nijmegen The Netherlands. Bloorview Research Institute Holland Bloorview Kids Rehabilitation Hospital and Department of Pediatrics University of Toronto Toronto ON USA. Child and Adolescent Psychiatry Department Gregorio Marañón General University Hospital School of Medicine Universidad Complutense IiSGM CIBERSAM Madrid Spain. Institut de Neurosciences de la Timone UMR 7289 Aix Marseille Université CNRS Marseille France. Department of Psychology Carnegie Mellon University Pittsburgh PA USA. Laboratory of Psychiatric Neuroimaging (LIM-21) Departamento e Instituto de Psiquiatria Hospital das Clinicas HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. IRCCS Stella Maris Foundation viale del Tirreno 331 56128 Pisa Italy. Department of Clinical and Experimental Medicine University of Pisa Pisa Italy. Department of Child and Adolescent Psychiatry and Psychology Hospital Clinic Psychiatry Unit Department of Medicine 2017SGR881 University of Barcelona IDIBAPS CIBERSAM Barcelona Spain. Department of Forensic and Neurodevelopmental Sciences Institute of Psychiatry Psychology & Neuroscience King's College London London UK. Institute for Pediatric Neuroscience NYU Child Study Center New York NY USA. Department of Psychology Ben-Gurion University of the Negev Beer Sheva Israel. Brain Center Rudolf Magnus Department of Psychiatry University Medical Center Utrecht Utrecht The Netherlands. Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University Hospital Goethe University Frankfurt am Main Frankfurt Germany. Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University o

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Building a grid-semantic map for the navigation of service robots through human–robot interaction

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Digital Communications and Networks 2015年第4期1卷 253-266页

作者： Cheng Zhao Weixing Mei Wei Pan School of Computer Science and Electronic Engineering University of Essex Cognitive Science Department Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University

This paper presents an interactive approach to the construction of a grid-semantic map for the navigation of service robots in an indoor environment. It is based on the Robot Operating System(ROS) framework and contains four modules, namely Interactive Module, Control Module, Navigation Module and Mapping Module. Three challenging issues have been focused during its development:(i) how human voice and robot visual information could be effectively deployed in the mapping and navigation process;(ii) how semantic names could combine with coordinate data in an online Grid-Semantic map; and(iii) how a localization–evaluate–relocalization method could be used in global localization based on modified maximum particle weight of the particle swarm. A number of experiments are carried out in both simulated and real environments such as corridors and offices to verify its feasibility and performance.

关键词： Visual–voice interface Grid-semantic map Global localization ROS

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Design and preliminary characterization of a soft wearable exoskeleton for upper limb

Design and preliminary characterization of a soft wearable e...

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

作者： Leonardo Cappello Dinh Khanh Binh Shih-Cheng Yen Lorenzo Masia Department of Robotics Brain and Cognitive Science Istituto Italiano di Tecnologia Genova Italy School of Mechanical & Aerospace Engineering Nanyang Technological University Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)9781509032884

Exoskeletons targeting the upper limb have been broadly developed both for rehabilitation and to augment user's physical performance. Generally, they are rigid robotic interfaces characterized by a non negligible mechanical impedance at the end effector and consequently perceived by the upper limbs as an external body. The rigid frame, moreover, adds kinematic constrains to the natural joint kinematics which may result in discomfort and ultimately in pain. The concept of soft wearable exoskeleton (or exosuit) has been developed and tested for the lower limb and the hand to address such issues thanks to their minimal inertial contribution and influence on the natural kinematics. In the current paper the design of an soft robotic interface for the elbow joint is presented, whose aim is to provide assistance torque to the targeted joint to facilitate the execution of the activities of daily living. Differently from the state-of-the-art design solutions, our system is able to drive both flexion and extension of the same joint with a single motor in an agonist-antagonist fashion, making the actuation stage compact and energy efficient. A clutching mechanism is also included in the design in order to save power during static configuration, preventing the motor to hold the joint position for a large amount of time. An exosuit has been designed to transfer the torque of the actuator to the biomechanical joint by means of Bowden cables. Two series elastic elements are employed to overcome the drawbacks of the agonist-antagonist mechanism and to provide additional compliance at the end effector. A preliminary test has been finally performed in order to characterize the actuation.

关键词： Cable shielding Actuators Springs Gears Brushless DC motors

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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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DAC-h3: A proactive robot cognitive architecture to acquire and express knowledge about the world and the self

arXiv

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arXiv 2017年

作者： Moulin-Frier, Clément Fischer, Tobias Petit, Maxime Pointeau, Grégoire Puigbo, Jordi-Ysard Pattacini, Ugo Low, Sock Ching Camilleri, Daniel Nguyen, Phuong Hoffmann, Matej Chang, Hyung Jin Zambelli, Martina Mealier, Anne-Laure Damianou, Andreas Metta, Giorgio Prescott, Tony J. Demiris, Yiannis Dominey, Peter Ford Verschure, Paul F.M.J. Laboratory for Synthetic Perceptive Emotive and Cognitive Systems Universitat Pompeu Fabra Barcelona08002 Spain Barcelona Spain Personal Robotics Laboratory Department of Electrical and Electronic Engineering Imperial College London SW7 2AZ United Kingdom Robot Cognition Laboratory INSERM U846 Stem Cell and Brain Research Institute Bron69675 France Italian Institute of Technology iCub Facility Via Morego 30 Genova Italy Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Science University of Sheffield United Kingdom

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users. Copyright © 2017, The Authors. All rights reserved.

关键词： Human robot interaction

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Model-Free Temporal-Difference Learning and Dopamine in Alcohol Dependence: Examining Concepts From Theory and Animals in Human Imaging

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Biological Psychiatry: cognitive Neuroscience and Neuroimaging 2016年第5期1卷 401-410页

作者： Huys, Quentin J.M. Deserno, Lorenz Obermayer, Klaus Schlagenhauf, Florian Heinz, Andreas Centre for Addictive Disorders Department of Psychiatry Psychotherapy and Psychosomatics Hospital of Psychiatry University of Zurich Zurich Switzerland Translational Neuromodeling Unit Institute for Biomedical Engineering University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich Switzerland Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Charité Universitätsmedizin Campus Charité Mitte Berlin Germany Department of Neurology Otto-von-Guericke University Magdeburg Germany Neural Information Processing Group Institute of Software Engineering and Theoretical Computer Science Technical University Berlin Germany

Dopamine potentially unites two important roles: one in addiction, being involved in most substances of abuse including alcohol, and a second one in a specific type of learning, namely model-free temporal-difference reinforcement learning. Theories of addiction have long suggested that drugs of abuse may usurp dopamine's role in learning. Here, we briefly review the preclinical literature to motivate specific hypotheses about model-free temporal-difference learning and then review the imaging evidence in the drug of abuse with the most substantial societal consequences: alcohol. Despite the breadth of the literature, only a few studies have examined the predictions directly, and these provide at best inconclusive evidence for the involvement of temporal-difference learning alterations in alcohol dependence. We discuss the difficulties of testing the theory in humans, make specific suggestions, and close with a focus on the interaction with other learning mechanisms. © 2016 Society of Biological Psychiatry

关键词： Alcohol Computational psychiatry Cue reactivity Dopamine Habits Model-free Reinforcement learning Ventral striatum

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An easy-to-hard learning paradigm for multiple classes and multiple labels

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The Journal of Machine Learning Research 2017年第1期18卷

作者： Weiwei Liu Ivor W. Tsang Klaus-Robert Müller School of Computer Science and Engineering The University of New South Wales Sydney NSW Australia and Centre for Artificial Intelligence FEIT University of Technology Sydney NSW Australia Centre for Artificial Intelligence FEIT University of Technology Sydney NSW Australia Machine Learning Group Computer Science Berlin Institute of Technology Berlin Germany and Max Planck Institute for Informatics Saarbrcken and Department of Brain and Cognitive Engineering Korea University Seoul Korea

Many applications, such as human action recognition and object detection, can be formulated as a multiclass classification problem. One-vs-rest (OVR) is one of the most widely used approaches for multiclass classification due to its simplicity and excellent performance. However, many confusing classes in such applications will degrade its results. For example, hand clap and boxing are two confusing actions. Hand clap is easily misclassified as boxing, and vice versa. Therefore, precisely classifying confusing classes remains a challenging task. To obtain better performance for multiclass classifications that have confusing classes, we first develop a classifier chain model for multiclass classification (CCMC) to transfer class information between classifiers. Then, based on an analysis of our proposed model, we propose an easy-to-hard learning paradigm for multiclass classification to automatically identify easy and hard classes and then use the predictions from simpler classes to help solve harder classes. Similar to CCMC, the classifier chain (CC) model is also proposed by Read et al. (2009) to capture the label dependency for multi-label classification. However, CC does not consider the order of di_culty of the labels and achieves degenerated performance when there are many confusing labels. Therefore, it is non-trivial to learn the appropriate label order for CC. Motivated by our analysis for CCMC, we also propose the easy-to-hard learning paradigm for multi-label classification to automatically identify easy and hard labels, and then use the predictions from simpler labels to help solve harder labels. We also demonstrate that our proposed strategy can be successfully applied to a wide range of applications, such as ordinal classification and relationship prediction. Extensive empirical studies validate our analysis and the efiectiveness of our proposed easy-to-hard learning strategies.

关键词： classifier Chain easy-to-hard learning paradigm multi-label classification multiclass classification

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