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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Author Correction: Aerial additive manufacturing with multiple autonomous robots

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Nature 2024年第7998期626卷 E5页

作者： Ketao Zhang Pisak Chermprayong Feng Xiao Dimos Tzoumanikas Barrie Dams Sebastian Kay Basaran Bahadir Kocer Alec Burns Lachlan Orr Talib Alhinai Christopher Choi Durgesh Dattatray Darekar Wenbin Li Steven Hirschmann Valentina Soana Shamsiah Awang Ngah Clément Grillot Sina Sareh Ashutosh Choubey Laura Margheri Vijay M Pawar Richard J Ball Chris Williams Paul Shepherd Stefan Leutenegger Robert Stuart-Smith Mirko Kovac Department of Aeronautics Imperial College London London UK. School of Engineering and Materials Science Queen Mary University of London London UK. Department of Computing Imperial College London London UK. Department of Architecture and Civil Engineering University of Bath Bath UK. Department of Computer Science University College London London UK. Materials and Technology Centre of Robotics Swiss Federal Laboratories for Materials Science and Technology (Empa) Dübendorf Switzerland. Department of Informatics Technical University of Munich Garching Germany. Stuart Weitzman School of Design University of Pennsylvania Philadelphia PA USA. Department of Aeronautics Imperial College London London UK. m.kovac@imperial.ac.uk. Materials and Technology Centre of Robotics Swiss Federal Laboratories for Materials Science and Technology (Empa) Dübendorf Switzerland. m.kovac@imperial.ac.uk.

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Transfer learning for scalability of neural-network quantum states

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Physical Review E 2020年第5期101卷 053301-053301页

作者： Remmy Zen Long My Ryan Tan Frédéric Hébert Mario Gattobigio Christian Miniatura Dario Poletti Stéphane Bressan School of Computing National University of Singapore 117417 Singapore Singapore Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Singapore Université Côte d'Azur CNRS Institut de Physique de Nice 06560 Valbonne France MajuLab CNRS-Université Côte d'Azur-Sorbonne Université-National University of Singapore-Nanyang Technological University Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore Singapore Department of Physics National University of Singapore 2 Science Drive 3 117542 Singapore Singapore School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Singapore Yale-NUS College 16 College Avenue West 138527 Singapore Singapore Science and Mathematics Cluster Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Singapore

Neural-network quantum states have shown great potential for the study of many-body quantum systems. In statistical machine learning, transfer learning designates protocols reusing features of a machine learning model trained for a problem to solve a possibly related but different problem. We propose to evaluate the potential of transfer learning to improve the scalability of neural-network quantum states. We devise and present physics-inspired transfer learning protocols, reusing the features of neural-network quantum states learned for the computation of the ground state of a small system for systems of larger sizes. We implement different protocols for restricted Boltzmann machines on general-purpose graphics processing units. This implementation alone yields a speedup over existing implementations on multicore and distributed central processing units in comparable settings. We empirically and comparatively evaluate the efficiency (time) and effectiveness (accuracy) of different transfer learning protocols as we scale the system size in different models and different quantum phases. Namely, we consider both the transverse field Ising and Heisenberg XXZ models in one dimension, as well as in two dimensions for the latter, with system sizes up to 128 and 8×8 spins. We empirically demonstrate that some of the transfer learning protocols that we have devised can be far more effective and efficient than starting from neural-network quantum states with randomly initialized parameters.

关键词： Quantum statistical mechanics Quantum spin models Artificial neural networks Many-body techniques Quantum spin chains Variational wave functional methods

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PointNLM: Point N onlocal M eans for vegetation segmentation based on middle echo point clouds

arXiv

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arXiv 2019年

作者： Li, Jonathan Wu, Rongren Chen, Yiping Zhu, Qing Luo, Zhipeng Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University School of Information Science and Engineering Xiamen Fujian361005 China Departments of Geography Environmental Management and Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Middle echo which cover s one or a few corresponding points is a specific type of 3D point cloud acquired by a multi echo laser scanner. In this paper, we propose a novel approach for automatic segmentation of trees that leverages middle echo information from LiDAR point clouds . First, using a convolution classification method, the proposed type of point clouds reflected by the middle echoes are identified from all point clouds T he middle echo point clouds are distinguished from the first and last echoes. Hence , the crown positions of the tree s are quickly detect ed from the huge number of point clouds. Second, to accurately extract trees from all point clouds, we propose a 3D deep learning network , PointNLM to semantically segment tree crowns. PointNLM captures the long range relationship betw een the point clouds via a non local branch and extracts high level features via max pooling applied to unordered points. T he whole framework is evaluated using the Semantic 3D reduced test set . T he IoU of tree point cloud segmentation reached 0.864. In a ddition, the semantic segmentation network was tested using the Paris Lille 3D dataset T he average IoU outperformed several other popular methods . The experiment al results indicate that the proposed algorithm provide s an excellen t solution for vegetation segmentation from LiDAR point clouds. Copyright © 2019, The Authors. All rights reserved.

关键词： Vegetation

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Human-centered manipulation and navigation with Robot DE NIRO

arXiv

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arXiv 2018年

作者： Falck, Fabian Doshi, Sagar Smuts, Nico Lingi, John Rants, Kim Kormushev, Petar Imperial College London Robot Intelligence Lab Department of Computing Dyson School of Design Engineering

Social assistance robots in health and elderly care have the potential to support and ease human lives. Given the macrosocial trends of aging and long-lived populations, robotics-based care research mainly focused on helping the elderly live independently. In this paper, we introduce Robot DE NIRO, a research platform that aims to support the supporter (the caregiver) and also offers direct human-robot interaction for the care recipient. Augmented by several sensors, DE NIRO is capable of complex manipulation tasks. It reliably interacts with humans and can autonomously and swiftly navigate through dynamically changing environments. We describe preliminary experiments in a demonstrative scenario and discuss DE NIRO's design and *** put particular emphases on safe, human-centered interaction procedures implemented in both hardware and software, including collision avoidance in manipulation and navigation as well as an intuitive perception stack through speech and face recognition. Copyright © 2018, The Authors. All rights reserved.

关键词： Human robot interaction

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Data-driven pedestrian re-identification based on hierarchical semantic representation

Data-driven pedestrian re-identification based on hierarchic...

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作者： Cheng, Keyang Xu, Fangjie Tao, Fei Qi, Man Li, Maozhen School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China School of Law Criminal Justice and Computing Canterbury Christ Church University CanterburyCT1 1QU United Kingdom School of Engineering and Design Brunei University UxbridgeLondonUB8 3PH United Kingdom

Limited number of labeled data of surveillance video causes the training of supervised model for pedestrian re-identification to be a difficult task. Besides, applications of pedestrian re-identification in pedestrian retrieving and criminal tracking are limited because of the lack of semantic representation. In this paper, a data-driven pedestrian re-identification model based on hierarchical semantic representation is proposed, extracting essential features with unsupervised deep learning model and enhancing the semantic representation of features with hierarchical mid-level ‘attributes’. Firstly, CNNs, well-trained with the training process of CAEs, is used to extract features of horizontal blocks segmented from unlabeled pedestrian images. Then, these features are input into corresponding attribute classifiers to judge whether the pedestrian has the attributes. Lastly, with a table of ‘attributes-classes mapping relations’, final result can be calculated. Under the premise of improving the accuracy of attribute classifier, our qualitative results show its clear advantages over the CHUK02, VIPeR, and i-LIDS data set. Our proposed method is proved to effectively solve the problem of dependency on labeled data and lack of semantic expression, and it also significantly outperforms the state-of-the-art in terms of accuracy and semanteme. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Deep learning

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Roadmap on soft robotics: multifunctionality, adaptability and growth without borders

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Multifunctional Materials 2022年第3期5卷 032001-032001页

作者： Mazzolai, Barbara Mondini, Alessio Del Dottore, Emanuela Margheri, Laura Carpi, Federico Suzumori, Koichi Cianchetti, Matteo Speck, Thomas Smoukov, Stoyan K. Burgert, Ingo Keplinger, Tobias Siqueira, Gilberto De Freitas Vanneste, Felix Goury, Olivier Duriez, Christian Nanayakkara, Thrishantha Vanderborght, Bram Brancart, Joost Terryn, Seppe Rich, Steven I. Liu, Ruiyuan Fukuda, Kenjiro Someya, Takao Calisti, Marcello Laschi, Cecilia Sun, Wenguang Wang, Gang Wen, Li Baines, Robert Patiballa, Sree Kalyan Kramer-Bottiglio, Rebecca Rus, Daniela Fischer, Peer Simmel, Friedrich C Lendlein, Andreas Bioinspired Soft Robotics Laboratory Istituto Italiano di Tecnologia Italy Department of Industrial Engineering University of Florence Via di S. Marta 3 Florence50139 Italy Department of Mechanical Engineering Tokyo Institute of Technology Ookayama Meguro Tokyo152-8550 Japan The BioRobotics Institute Scuola Superiore Sant’Anna Pisa56025 Italy Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa56025 Italy Plant Biomechanics Group & Botanic Garden and Cluster of Excellence livMatS @ FIT University of Freiburg Germany Active and Intelligent Materials Laboratory Queen Mary University of London LondonE1 4NS United Kingdom Wood Materials Science Institute for Building Materials ETH Zurich Zurich8093 Switzerland Cellulose and Wood Materials Laboratory Empa Swiss Federal Laboratories for Materials Science and Technology Dübendorf8600 Switzerland University of Lille Inria CNRS Centrale Lille UMR 9189 CRIStAL LilleF-59000 France Dyson School of Design Engineering Imperial College London United Kingdom Vrije Universiteit Brussel and imec Brussels Belgium Thin-Film Device Laboratory RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan Center for Emergent Matter Science RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan Electrical and Electronic Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan The BioRobotics Institute Scuola Superiore Sant’Anna Italy Lincoln Institute for Agri-food Technology University of Lincoln Lincoln United Kingdom Department of Mechanical Engineering National University of Singapore Singapore School of Mechanical Engineering and Automation Beihang University Beijing100191 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06511 United States CSAIL MIT Schwarzman College of Computing United States Max Planck Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart70569 Germany Institute of Physical Chemistry

Soft robotics aims at creating systems with improved performance of movement and adaptability in unknown, challenging, environments and with higher level of safety during interactions with humans. This Roadmap on Soft Robotics covers selected aspects for the design of soft robots significantly linked to the area of multifunctional materials, as these are considered a fundamental component in the design of soft robots for an improvement of their peculiar abilities, such as morphing, adaptivity and growth. The roadmap includes different approaches for components and systems design, bioinspired materials, methodologies for building soft robots, strategies for the implementation and control of their functionalities and behavior, and examples of soft-bodied systems showing abilities across different environments. For each covered topic, the author(s) describe the current status and research directions, current and future challenges, and perspective advances in science and technology to meet the challenges. © 2022 The Author(s). Published by IOP Publishing Ltd.

关键词： Machine design

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Numerical modelling of nonlinear extreme waves in presence of wind

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Acta Oceanologica Sinica 2018年第9期37卷 90-98页

作者： NING Dezhi DU Jun BAI Wei ZHANG Chongwei TENG Bin State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Nanjing 210098 China State Key Laboratory of Coastal and Offshore Engineering Dalian University of Technology Dalian 116023 China School of Computing Mathematics and Digital Technology Manchester Metropolitan University Manchester M1 5GD IJK China Communications Planning and Design Institute for Water Transportation Ltd. Beijing 100007 China

A numerical wave flume with fully nonlinear free surface boundary conditions is adopted to investigate the temporal characteristics of extreme waves in the presence of wind at various speeds. Incident wave trains are numerically generated by a piston-type wave maker, and the wind-excited pressure is introduced into dynamic boundary conditions using a pressure distribution over steep crests, as defined by Jeffreys＇ sheltering mechanism.A boundary value problem is solved by a higher-order boundary element method（HOBEM） and a mixed Eulerian-Lagrangian time marching scheme. The proposed model is validated through comparison with published experimental data from a focused wave group. The influence of wind on extreme wave properties,including maximum extreme wave crest, focal position shift, and spectrum evolution, is also studied. To consider the effects of the wind-driven currents on a wave evolution, the simulations assume a uniform current over varying water depth. The results show that wind causes weak increases in the extreme wave crest, and makes the nonlinear energy transfer non-reversible in the focusing and defocusing processes. The numerical results also provide a comparison to demonstrate the shifts at focal points, considering the combined effects of the winds and the wind-driven currents.

关键词： extreme waves fully nonlinear numerical wave flume higher-order boundary element wave focusing Jeffreys＇ sheltering mechanism

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Transient typechecks are (Almost) free [preliminary draft 2019-02-18]

arXiv

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arXiv 2018年

作者： Roberts, Richard Marr, Stefan Homer, Michael Noble, James School of Design Victoria University of Wellington School of Computing University of Kent School of Engineering and Computer Science Victoria University of Wellington

Transient gradual typing imposes run-time type tests that typically cause a linear slowdown in programs' performance. This performance impact discourages the use of type annotations because adding types to a program makes the program slower. A virtual machine can employ standard just-in-time optimizations to reduce the overhead of transient checks to near zero. These optimizations can give gradually-typed languages performance comparable to state-of-the-art dynamic languages, so programmers can add types to their code without affecting their programs' performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Object oriented programming

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Exploring the thermodynamics of disordered materials with quantum computing

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Science advances 2025年第23期11卷 eadt7156页

作者： Bruno Camino John Buckeridge Nicholas Chancellor C Richard A Catlow Anna Maria Ferrari Paul A Warburton Alexey A Sokol Scott M Woodley Chemistry Department University College London 20 Gordon St London WC1H 0AJ UK. School of Engineering and Design London South Bank University 103 Borough Rd London SE10 AA UK. School of Computing Newcastle University 1 Science Square Newcastle upon Tyne NE4 5TG UK. Cardiff Catalysis Institute School of Chemistry Cardiff University Park Place Cardiff CF10 3AT UK. UK Catalysis Hub Research Complex at Harwell Rutherford Appleton Laboratory Harwell OX11 0QX UK. Dipartimento di Chimica Università degli Studi di Torino Via Pietro Giuria 5 Torino 10125 Italy. London Centre for Nanotechnology University College London London WC1H 0AH UK. Department of Electronic & Electrical Engineering University College London Torrington Pl London WC1E 7JE UK.

Alloys, solid solutions, and doped systems are essential in technologies such as energy generation and catalysis, but predicting their properties remains challenging because of compositional disorder. As the concentration of components changes in a binary solid solution [Formula: see text] , the number of possible configurations becomes computationally intractable. Algorithms used in classical optimization methods cannot avoid assessing high-energy states where, for example, simulated annealing is designed to initially spend computational effort. We introduce a scalable, practical, and accurate approach using quantum annealing to efficiently sample low-energy configurations of disordered materials, avoiding the need for excessive high-energy calculations. Our method includes temperature and simulates large unit cells, producing a Boltzmann-like distribution to identify thermodynamically relevant structures. We demonstrate this by predicting bandgap bowing in [Formula: see text] and bulk modulus variations in [Formula: see text] , with results in excellent agreement with experiments.

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