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arXiv 2018年

作者： Yuan, Weihao Stork, Johannes A. Kragic, Danica Wang, Michael Y. Hang, Kaiyu Department of Electronic and Computer Engineering Hong Kong University of Science and Technology and Hkust Robotics Institute Dept. of Mech. and Aerospace Engineering and the Department of Electronic and Computer Engineering Department of ComputerScience and Engineering and Hkust Institute for Advanced Study Perception and Learning Lab Centre for Autonomous Systems Kth Royal Institute of Technology Sweden

Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on only visual feedback. For this, we model the task with rewards and train a deep Q-network. Our potential field-based heuristic exploration strategy reduces the amount of collisions which lead to suboptimal outcomes and we actively balance the training set to avoid bias towards poor examples. Our training process leads to quicker learning and better performance on the task as compared to uniform exploration and standard experience replay. We demonstrate empirical evidence from simulation that our method leads to a success rate of 85%, show that our system can cope with sudden changes of the environment, and compare our performance with human level performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Microrobots: A Needle‐Type Microrobot for Targeted Drug Delivery by Affixing to a Microtissue (Adv. Healthcare Mater. 7/2020)

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Advanced Healthcare Materials 2020年第7期9卷

作者： Seungmin Lee Jin‐young Kim Junyoung Kim Ali Kafash Hoshiar Jongeon Park Sunkey Lee Jonghyun Kim Salvador Pané Bradley J. Nelson Hongsoo Choi Department of Robotics Engineering DGIST‐ETH Microrobot Research Center Daegu‐Gyeongbuk Institute of Science & Technology (DGIST) 333 Techno jungang‐daero Hyeonpung‐eup Dalseong‐Gun Daegu 42988 Republic of Korea School of Computer Science and Electronic Engineering University of Essex Colchester CO4 3SQ UK School of Mechanical Engineering Sungkyunkwan University 2066 Seobu‐ro Jangan‐gu Suwon‐si Gyeonggi‐do 16419 South Korea Institute of Robotics and Intelligent Systems ETH Zurich Zurich CH‐8092 Switzerland

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A statistical analysis of gamakas in Carnatic music 18

A statistical analysis of gamakas in Carnatic music

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

作者： Viraraghavan, Venkata Subramanian Aravind, R. Murthy, Hema A. TCS Research and Innovation Embedded Systems and Robotics Bangalore India Department of Electrical Engineering Indian Institute of Technology Madras India Department of Computer Science and Engineering Indian Institute of Technology Madras India

ISBN: (纸本)9789811151798

Carnatic Music, a form of classical music prevalent in South India, has a central concept called rāgas, defined as melodic scales and/or a set of characteristic melodic phrases. These definitions also account for the continuous pitch movement in gamakas and micro-tonal adjustments to pitch values. In this paper, we present several statistics of gamakas to arrive at a model of Carnatic music. We draw upon the two-component model of Carnatic Music, which splits it into a slowly varying 'stage' and a detail, called 'dance'. Based on the statistics, we propose slightly altered definitions of two similar components called constant-pitch notes and transients. An automated implementation of these definitions is used in collecting statistics from 84 concert renditions. We then suggest that the constant-pitch notes and transients can be considered as context and detail respectively of the rāga, but add that both are necessary for defining the rāga. This is verified by performing listening tests on only the constant-pitch notes and transients independently. © 2019 Venkata Subramanian Viraraghavan, R Aravind, Hema A Murthy.

关键词： Statistics

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A comparison of nonsmooth, nonconvex, constrained optimization solvers for the design of time-delay compensators

arXiv

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arXiv 2018年

作者： Kungurtsev, Vyacheslav Mitchell, Tim Vyhlídal, Tomáš Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague

We present a detailed set of performance comparisons of two state-of-the-art solvers for the application of designing time-delay compensators, an important problem in the field of robust control. Formulating such robust control mechanics as constrained optimization problems often involves objective and constraint functions that are both nonconvex and nonsmooth, both of which present significant challenges to many solvers and their end-users hoping to obtain good solutions to these problems. In our particular engineering task, the main difficulty in the optimization arises in a nonsmooth and nonconvex stability constraint, which states that the infinite spectrum of zeros of the so-called shaper should remain in the open left half-plane. To perform our evaluation, we make use β-relative minimization profiles, recently introduced visualization tools that are particularly suited for benchmarking solvers on nonsmooth, nonconvex, constrained optimization problems. Furthermore, we also introduce new visualization tools, called Global-Local Profiles, which for a given problem and a fixed computational budget, assess the tradeoffs of distributing the budget over few or many starting points, with the former getting more budget per point and latter less. Copyright © 2018, The Authors. All rights reserved.

关键词： Robust control

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New design techniques for globally convergent simultaneous localization and mapping: Analysis and implementation

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Workshop on Sensing and Control for Autonomous Vehicles: Applications to Land, Water and Air Vehicles, 2017

作者： Lourenço, Pedro Guerreiro, Bruno Batista, Pedro Oliveira, Paulo Silvestre, Carlos Laboratory for Robotics and Engineering Systems Institute for Systems and Robotics Lisbon Portugal Department of Electrical and Computer Engineering Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Department of Mechanical Engineering Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (纸本)9783319553719

This chapter presents an overview of algorithms deeply rooted in a sensor-based approach to the SLAM problem that provide global convergence guarantees and allow for the use of partially observable landmarks. The presented algorithms address the more usual range-and-bearing SLAM problem, either in 2-D using a LiDAR or in 3-D using an RGB-D camera, as well as the range-only and bearing-only SLAM problems. For each of these formulations a nonlinear system is designed, for which state and output transformations are considered together with augmented dynamics, in such a way that the underlying system structure can be regarded as linear time-varying for observability analysis and filter design purposes. This naturally allows for the design of Kalman filters with, at least, globally asymptotically stable error dynamics, for which several experimental and simulated trials are presented to highlight the performance and consistency of the obtained filters. © Springer International Publishing AG 2017.

关键词： Kalman filters

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Correction to: Editorial

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International Journal of Social robotics 2019年第6期15卷 1077-1077页

作者： Ge, Shuzhi Sam Wykowska, Agnieszka Khatib, Oussama Social Robotics Laboratory Smart Systems Institute Department of Electrical and Computer Engineering The National University of Singapore Singapore Singapore Institute for Future (IFF) Qingdao University Qingdao China Centre for Robotics UESTC Chengdu China Social Cognition in Human-Robot Interaction Istituto Italiano di Tecnologia Genoa Italy Artificial Intelligence Laboratory Department of Computer Science Stanford University Stanford USA

This is to inform you that the following sentence was inadvertently omitted from the final version

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Relaxed conditions for uniform complete observability and controllability of LTV systems with bounded realizations

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IFAC-PapersOnLine 2017年第1期50卷 3598-3605页

作者： Batista P. Petit N. Silvestre C. Oliveira P. Institute for Systems and Robotics Laboratory for Robotics and Engineering Systems Instituto Superior Técnico Universidade de Lisboa Portugal MINES ParisTech PSL Research University CAS – Centre automatique et systémes 60 bd St Michel Paris 75272 France Department of Electrical and Computer Engineering Faculty of Science and Technology of the University of Macau Macau Department of Mechanical Engineering Instituto Superior Técnico Universidade de Lisboa Portugal

Uniform complete observability and controllability are key concepts for the design of state observers and controllers for linear time-varying systems. While the definitions of these properties are simple, it is more often that not very difficult to show they hold for a particular system. This paper presents alternative simpler sufficient conditions. Examples are shown that illustrate the improvement over previous results, the fact that the new conditions are not necessary, and a practical application. © 2017

关键词： linear time-varying systems uniform complete controllability uniform complete observability

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series

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Global Ecology and Biogeography 2025年第5期34卷

作者： Maria Dornelas Laura H. Antão Amanda E. Bates Viviana Brambilla Jonathan M. Chase Cher F. Y. Chow Ada Fontrodona-Eslava Anne E. Magurran Inês S. Martins Faye Moyes Alban Sagouis Samuel Adu-Acheampong Daniel Acquah-Lamptey Dušan Adam Penelope A. Ajani Aitor Albaina Pablo Almaraz Jeongseop An Roger Sigismund Anderson Madelaine Jean Robertson Anderson Alexsander Z. Antunes Ivan Arismendi Linda Armbrecht Pedro Aros-Mardones Sreejith Kalpuzha Ashtamoorthy Narayanan Ayyappan Gal Badihi Joseph J. Bailey Andrew H. Baird Mark Edward Baird Sreekumar Vadakkethil Balakrishnan José António L. Barão-Nóbrega Adi Barash Miguel Barbosa Jos Barlow Claus Bässler Matthieu Beaumont Natalie Beenaerts Tiago Octavio Begot Wallace Beiroz Ricardo Beldade David M. Bell Alecia Bellgrove Jonathan Belmaker Lisandro Benedetti-Cecchi Cassandra E. Benkwitt Pamela Medina-van Berkum Brandon T. Bestelmeyer Matthew G. Betts Maxwell Kelvin Billah Anne D. Bjorkman Magdalena Błażewicz Christopher P. Bloch Shane A. Blowes Antonio Bode Juliano A. Bogoni Thomas Bolger Timothy C. Bonebrake Erik Bonsdorff Roberta Bottarin Luke N. Brokensha Rob W. Brooker Andrew J. Brooks Helge Bruelheide Thiago Almeida Bueno Claire Laguionie Mariana Lopes Campagnoli James Cant Erica Pellegrini Caramaschi Alexandre Caron Tadhg Carroll Tancredi Caruso Juan Carvajal-Quintero Giuseppe Castaldelli Edward Castañeda-Moya Pedro V. Castilho Sonia Zanini Cechin Shahar Chaikin Uchangi Manjunatha Chandrashekara Tory J. Chase Chaolun Allen Chen Jorge José Cherem Sei-Woong Choi Erica M. Christensen Alexander V. Christianini Jackson Wing Four Chu Peter Coad Carl Van Colen Lise Comte Elisabeth J. Cooper J. Hans C. Cornelissen Eddy Cosson Unai Cotano Luc Crevecoeur Shannan Kyle Crow Graeme S. Cumming Vanessa S. Daga Gabriella Damasceno Gergana N. Daskalova Claire H. Davies Robert A. Davis Frank P. Day Sussy De-La-Zerda Amy Elizabeth Deacon Indradatta de Castro-Arrazola Steven Degraer Kharran Deonarinesingh Juan C. Diaz-Ricaurte Christopher R. Dickman Tara Dirilgen Ciaran John Dolan J. Emmet Centre for Biological Diversity School of Biology University of St Andrews Fife UK MARE—Centro de Ciências do Mar e do Ambiente Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Research Centre for Ecological Change Organismal and Evolutionary Biology Research Programme Faculty of Biological and Environmental Sciences University of Helsinki Helsinki Finland Department of Biology University of Victoria Victoria British Columbia Canada German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Leipzig Germany Department of Computer Science Martin Luther University Halle-Wittenberg Halle (Saale) Germany Leverhulme Centre for Anthropocene Biodiversity Department of Biology University of York York UK Department of Animal Biology and Conservation Science University of Ghana Accra Ghana Philipps Universität Marburg Marburg Germany Department of Forest Ecology Silva Tarouca Research Institute Brno Czech Republic School of Life Sciences University of Technology Sydney Sydney New South Wales Australia Zoology and Animal Cell Biology Department University of the Basque Country (UPV/EHU) Leioa Spain Grupo de Oceanografía de Ecosistemas Instituto de Ciencias Marinas de Andalucía CSIC Campus Universitario de Puerto Real Puerto Real Spain National Institute of Ecology Seocheon Republic of Korea African Regional Postgraduate Programme in Insect Science University of Ghana Accra Ghana Département de Biologie Université de Sherbrooke Sherbrooke Quebec Canada Núcleo de Conservação da Biodiversidade Instituto de Pesquisas Ambientais Secretaria de Meio Ambiente Infraestrutura e Logística do Estado de São Paulo São Paulo Brazil Department of Fisheries Wildlife and Conservation Sciences Oregon State University Corvallis Oregon USA Institute for Marine and Antarctic Studies Ecology & Biodiversity Centre University of Tasmania Hobart Tasmania Australia Graduate Program in Oceanography Department of Oceanography Faculty of Natural Scienc

Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL.

关键词： assemblage biodiversity community database ecoinformatics species taxa time

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Reports on the 2017 AAAI spring symposium series

Reports on the 2017 AAAI spring symposium series

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作者： Bohg, Jeannette Boix, Xavier Chang, Nancy Chu, Vivian Churchill, Elizabeth F. Fang, Fei Feldman, Jerome González, Avelino J. Kido, Takashi Lawless, William F. Montaña, José L. Ontañón, Santiago Sinapov, Jivko Sofge, Don Steels, Luc Steenson, Molly Wright Takadama, Keiki Yadav, Amulya Max Planck Institute for Intelligent Systems United States Massachusetts Institute of Technology United States Institute of Robotics and Intelligent Machines Georgia Institute of Technology United States Google United States Harvard University United States University of California Berkeley United States Department of Computer Science University of Central Florida United States Preferred Networks in Japan Japan Paine College in Augusta GA United States Department of Mathematics Statistics and Computer Science University of Cantabria Spain Department of Computer Science Drexel University United States Department of Computer Science University of Texas Austin United States Naval Research Laboratory United States Barcelona Spain Carnegie Mellon University School of Design United States University of Electro-Communications Japan Computer Science Department University of Southern California United States

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's department of computer science, presented the 2017 Spring Symposium Series, held Monday through Wednesday, March 27-29, 2017, on the campus of Stanford University. The eight symposia held were Artificial Intelligence for the Social Good (SS-17-01);Computational Construction Grammar and Natural Language Understanding (SS-17-02);Computational Context: Why It's Important, What It Means, and Can It Be Computed? (SS-17-03);Designing the User Experience of Ma chine-Learning systems (SS-17-04);Interactive Multisensory Object Perception for Embodied Agents (SS-17-05);Learning from Ob servation of Humans (SS-17-06);science of Intelligence: Com putational Principles of Natural and Artificial Intelligence (SS-17-07);and Well-Being AI: From Machine Learning to Subjectivity-Oriented Computing (SS-17-08). This report, compiled from organizers of the symposia, summarizes the research that took place. © Copyright 2017, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Learning systems

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