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Electromagnetic-Thermal Co-simulation of Large Antenna Array on Platform Using Enhanced Finite Element Solver with Massively Parallel Computing Capability

Electromagnetic-Thermal Co-simulation of Large Antenna Array...

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International Applied computational Electromagnetics Society Symposium (ACES)

作者： Hao-Xuan Zhang Li Huang Liang Zhou Z.G Zhao Yu-Teng Zheng G. D. Zhu Wen-Yan Yin Key Lab of Ministry of Education of Design and EMC of High-Speed Electronic Systems Shanghai Jiao Tong University Shanghai CHINA Software Center for High Performance Numerical Simulation of CAPE Institute of Applied Physics and Computational Mathematics Beijing CHINA Innovative Institute of Electromagnetic Information and Electronic Integration Zhejiang University Hangzhou CHINA

ISBN: (数字)9780996007894

ISBN: (纸本)9781728146331

Electromagnetic-thermal co-simulation of large antenna array on platform is performed using in-house enhanced finite element solver with massively parallel computing capability. It appropriately integrates both domain decomposition method (DDM) so as to solve very large complex matrix efficiently during the implementation of finite element procedure. Its massively parallel computing capability is examined on the supercomputer, where the antenna arrays of 16·16 operating at 2.5 GHz, together with their platform are simulated, respectively, and its accuracy is validated in comparison with the numerical result obtained by commercial software but just with small scale. The computational efficiency of such in-house developed solver can reach a speedup of 7.149 and strong scalability efficiency of 64.2% on 512 CPU cores of 32 computing nodes, and its speedup, scalability and efficiency are also examined. In particular, not only the radiation characteristics but also the surface temperature distribution of large antenna arrays are for the first time addressed in the high -power operating state, which directly support their reliability design.

关键词： Finite element analysis Microstrip antenna arrays Microstrip antennas Parallel processing Scalability Antenna feeds

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Electronic nature of chiral charge order in the kagome superconductor CsV3Sb5

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Physical Review B 2021年第7期104卷 075148-075148页

作者： Zhiwei Wang Yu-Xiao Jiang Jia-Xin Yin Yongkai Li Guan-Yong Wang Hai-Li Huang Sen Shao Jinjin Liu Peng Zhu Nana Shumiya Md Shafayat Hossain Hongxiong Liu Youguo Shi Junxi Duan Xiang Li Guoqing Chang Pengcheng Dai Zijin Ye Gang Xu Yanchao Wang Hao Zheng Jinfeng Jia M. Zahid Hasan Yugui Yao Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) School of Physics Beijing Institute of Technology Beijing 100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing 100081 China Laboratory for Topological Quantum Matter and Advanced Spectroscopy (B7) Department of Physics Princeton University Princeton New Jersey 08544 USA School of Physics and Astronomy Key Laboratory of Artificial Structures and Quantum Control (Ministry of Education) Shenyang National Laboratory for Materials Science Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun 130012 China International Center for Computational Method and Software College of Physics Jilin University Changchun 130012 China Beijing National Laboratory for Condensed Matter Physics and Institute of Physics Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Department of Physics and Astronomy Rice University Houston Texas 77005 USA Wuhan National High Magnetic Field Center Huazhong University of Science and Technology Wuhan Hubei 430074 China Princeton Institute for the Science and Technology of Materials Princeton University Princeton New Jersey 08544 USA Materials Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Quantum Science Center Oak Ridge Tennessee 37831 USA

Kagome superconductors with TC up to 7 K have been discovered for over 40 y. Recently, unconventional chiral charge order has been reported in kagome superconductor KV3Sb5, with an ordering temperature of one order of magnitude higher than the TC. However, the chirality of the charge order has not been reported in the cousin kagome superconductor CsV3Sb5, and the electronic nature of the chirality remains elusive. In this paper, we report the observation of electronic chiral charge order in CsV3Sb5 via scanning tunneling microscopy (STM). We observe a 2 × 2 charge modulation and a 1 × 4 superlattice in both topographic data and tunneling spectroscopy. 2 × 2 charge modulation is highly anticipated as a charge order by fundamental kagome lattice models at van Hove filling, and is shown to exhibit intrinsic chirality. We find that the 1 × 4 superlattices form various small domain walls, and can be a surface effect as supported by our first-principles calculations. Crucially, we find that the amplitude of the energy gap opened by the charge order exhibits real-space modulations, and features 2 × 2 wave vectors with chirality, highlighting the electronic nature of the chiral charge order. STM study at 0.4 K reveals a superconducting energy gap with a gap size 2Δ=0.85meV, which estimates a moderate superconductivity coupling strength with 2Δ/kBTC=3.9. When further applying a c-axis magnetic field, vortex core bound states are observed within this gap, indicative of clean-limit superconductivity.

关键词： Charge density waves Density of states Electronic structure Superconducting gap Topological materials

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How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

arXiv

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arXiv 2023年

作者： Bosoni, Emanuele Beal, Louis Bercx, Marnik Blaha, Peter Blügel, Stefan Bröder, Jens Callsen, Martin Cottenier, Stefaan Degomme, Augustin Dikan, Vladimir Eimre, Kristjan Flage-Larsen, Espen Fornari, Marco Garcia, Alberto Genovese, Luigi Giantomassi, Matteo Huber, Sebastiaan P. Janssen, Henning Kastlunger, Georg Krack, Matthias Kresse, Georg Kühne, Thomas D. Lejaeghere, Kurt Madsen, Georg K.H. Marsman, Martijn Marzari, Nicola Michalicek, Gregor Mirhosseini, Hossein Müller, Tiziano M.A. Petretto, Guido Pickard, Chris J. Poncé, Samuel Rignanese, Gian-Marco Rubel, Oleg Ruh, Thomas Sluydts, Michael Vanpoucke, Danny E.P. Vijay, Sudarshan Wolloch, Michael Wortmann, Daniel Yakutovich, Aliaksandr V. Yu, Jusong Zadoks, Austin Zhu, Bonan Pizzi, Giovanni Institut de Ciència de Materials de Barcelona ICMAB-CSIC Campus UAB Bellaterra08193 Spain Univ. Grenoble-Alpes CEA IRIG-MEM-L Sim Grenoble38000 France LausanneCH-1015 Switzerland Institute for Materials Chemistry Technical University of Vienna Getreidemarkt 9/165-TC ViennaA-1060 Austria Peter Grünberg Institut Institute for Advanced Simulation Forschungszentrum Jülich JARA JülichD-52425 Germany Forschungszentrum Jülich JülichD-52425 Germany Department of Electromechanical Systems and Metal Engineering Ghent University Belgium Ghent University Belgium Institute of Atomic and Molecular Sciences Academia Sinica Taipei10617 Taiwan Norwegian EuroHPC Competence Center Sigma2 AS Norway SINTEF Industry Materials Physics Oslo Norway Department of Physics Science of Advanced Materials Program Central Michigan University Mount PleasantMI48859 United States Université catholique de Louvain Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium LausanneCH-1015 Switzerland Kongens Lyngby2800 Denmark VilligenCH-5232 Switzerland University of Vienna Faculty of Physics Center for Computational Materials Science Kolingasse 14-16 ViennaA-1090 Austria VASP Software GmbH Sensengasse 8 ViennaA-1090 Austria Helmholtz-Zentrum Dresden-Rossendorf GörlitzD-02826 Germany Center for Sustainable Systems Design University of Paderborn PaderbornD-33098 Germany OCAS NV ArcelorMittal Global R&D Gent Pres. J. F. Kennedylaan 3 ZelzateB-9060 Belgium Dynamics of Condensed Matter Theoretical Chemistry University of Paderborn PaderbornD-33098 Germany HPE HPC EMEA Research Lab BaselCH-4051 Switzerland Department of Materials Science & Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom Advanced Institute for Materials Research Tohoku University 2-1-1 Katahira Aoba Sendai980-8577 Japan Department of Materials Science and Engineering McMaster University 1280 Main Street West HamiltonONL8S 4L8 Canada ePot

In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. We discuss here general recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z=1 to 96 and characterizing 10 prototypical cubic compounds for each element: 4 unaries and 6 oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies). © 2023, CC BY.

关键词： Density functional theory

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Electronic nature of chiral charge order in the kagome superconductor CsV3Sb5

arXiv

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arXiv 2021年

作者： Wang, Zhiwei Jiang, Yu-Xiao Yin, Jia-Xin Li, Yongkai Wang, Guan-Yong Huang, Hai-Li Shao, Shen Liu, Jinjin Zhu, Peng Shumiya, Nana Hossain, Md Shafayat Liu, Hongxiong Shi, Youguo Duan, Junxi Li, Xiang Chang, Guoqing Dai, Pengcheng Ye, Zijin Xu, Gang Wang, Yanchao Zheng, Hao Jia, Jinfeng Hasan, M. Zahid Yao, Yugui School of Physics Beijing Institute of Technology Beijing100081 China Department of Physics Princeton University PrincetonNJ United States School of Physics and Astronomy Key Laboratory of Artificial Structures and Quantum Control Ministry of Education Shenyang National Laboratory for Materials Science Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Department of Physics and Astronomy Rice University HoustonTX77005 United States Wuhan National High Magnetic Field Center Huazhong University of Science and Technology Hubei Wuhan430074 China Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Quantum Science Center Oak RidgeTN37831 United States Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing100081 China

Kagome superconductors with TC up to 7K have been discovered over 40 years. Recently, unconventional chiral charge order has been reported in kagome superconductor KV3Sb5, with an ordering temperature of one order of magnitude higher than the TC. However, the chirality of the charge order has not been reported in the cousin kagome superconductor CsV3Sb5, and the electronic nature of the chirality remains elusive. In this letter, we report the observation of electronic chiral charge order in CsV3Sb5 via scanning tunneling microscopy (STM). We observe a 2×2 charge modulation and a 1×4 superlattice in both topographic data and tunneling spectroscopy. 2×2 charge modulation is highly anticipated as a charge order by fundamental kagome lattice models at van Hove filling, and is shown to exhibit intrinsic chirality. We find that the 1×4 superlattices forms various small domain walls, and can be a surface effect as supported by our first-principles calculations. Crucially, we find that the amplitude of the energy gap opened by the charge order exhibits real space modulations, and features 2×2 wave vectors with chirality, highlighting the electronic nature of the chiral charge order. STM study at 0.4K reveals a superconducting energy gap with a gap size 2Δ=0.85meV, which estimates a moderate superconductivity coupling strength with 2Δ/kBTC=3.9. When further applying a c-axis magnetic field, vortex core bound states are observed within this gap, indicative of clean-limit superconductivity. © 2021, CC BY.

关键词： Chirality

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On Autonomous systems: From Reflexive, Imperative and Adaptive Intelligence to Autonomous and Cognitive Intelligence

On Autonomous Systems: From Reflexive, Imperative and Adapti...

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IEEE International Conference on Cognitive Informatics

作者： Yingxu Wang Konstantinos N. Plataniotis Sam Kwong Henry Leung Svetlana Yanushkevich Fakhri Karray Ming Hou Newton Howard Rodolfo A. Fiorini Paolo Soda Edward Tunstel Jianmin Wang Shushma Patel International Institute of Cognitive Informatics and Cognitive Computing (ICIC) Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary Canada University of Toronto Canada City University of Hong Kong Hong Kong University of Waterloo Canada Toronto Research Centre DRDC Canada Computational Neurosciences Lab University of Oxford UK Information & Bioengg. Politecnico di Milano University Italy Univ. Campus Bio-Medico of Rome Italy Autonomous and Intelligent Systems President IEEE SMC School of Software Tsinghua University China School of Engineering London South Bank University UK

ISBN: (数字)9781728114194

ISBN: (纸本)9781728104966

Autonomous systems underpinned by cognitive intelligence represent advanced forms of artificial intelligence studied in intelligence science, systems science, and computational intelligence. Traditional theories and technologies of autonomous systems put emphases on human-system interactions and humans in-the-loop. This paper explores the intelligence and system foundations of autonomous systems. It focuses on what structural and behavioral properties constitute the intelligence power of autonomous systems. It explains how system intelligence aggregates from reflexive, imperative, adaptive intelligence to autonomous and cognitive intelligence. A Hierarchical Intelligence Model (HIM) is introduced to elaborate the evolution of human and system intelligence as an inductive process. A set of properties of system autonomy is formally analyzed towards a wide range of autonomous system applications in computational intelligence and systems engineering.

关键词： Frequency modulation Electronic mail Handheld computers Cognitive systems Robots Animals

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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GB-RC4: Effective brute force attacks on RC4 algorithm using GPU 7

GB-RC4: Effective brute force attacks on RC4 algorithm using...

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7th International Green and Sustainable Computing Conference, IGSC 2016

作者： Xue, Pei Li, Tao Dong, Han Liu, Chunbo Ma, Wenjing Pei, Songwen College of Computer and Control Engineering Nankai University Tianjin300071 China Information Security Evaluation Center Civil Aviation University of China Tianjin300300 China Laboratory of Parallel Software and Computational Science State Key Laboratory of Computing Science Institute of Software CAS Beijing China Shanghai Key Lab of Modern Optical Systems Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781509051175

Encryption algorithms are applied to a variety of fields and the security of encryption algorithms depends heavily on the computational infeasibility of exhaustive key-space search. RC4 algorithm has an extensive application for stream encryption, however, the disadvantages of traditional RC4 serial algorithm are large computational quantity and slow computation speed, which means a great challenge for throughput of computing system. Graphics processors recently have been widely used as accelerators for high throughput computing. In this paper, GPU is used to improve the efficiency and performance of attacking RC4 algorithm. First, we revised RC4 algorithm to make it easier to parallelize on a GPU. Then we take advantage of constant memory and shared memory in GPU to search the key space of RC4 algorithm efficiently. Besides, we solved the bank conflict problem in shared memory to further improve the performance. Our approach was evaluated in the whole key space of 40-bit, and the results show it can complete searching of the whole key space within 12.8 hours. © 2016 IEEE.

关键词： Graphics processing unit

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Online Machine Learning for Big Data Analytics by Cognitive Robots

Online Machine Learning for Big Data Analytics by Cognitive ...

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2016 IEEE International Conference of Online Analysis and Computing Science(ICOACS 2016)

作者： Yingxu Wang FICIC FWIF Information Systems Lab Stanford University Laboratory for Computational Intelligence Denotational Mathematicsand Software Science Dept.of Electrical and Computer EngineeringSchulich School of Engineering and Hotchkiss Brain InstituteUniversity of Calgary

It is recognized that both sources of and solutions to the big data challenges are human collective *** are an abstract representation of the quantity of realistic entities and perceived *** data play an indispensable role not only in a wide range of engineering applications,but also in the cognitive mechanisms of both humans and cognitive robots such as sensation,quantification,qualification,estimation,memory,and *** keynote lecture presents online big data analytic theories by machine learning as well as knowledge extraction by cognitive ***,information,knowledge,and intelligence are the four hierarchical layers of cognitive objects in the brain and cognitive systems from the bottom *** is discovered by the author that,although the cognitive unit of data is bit,that of knowledge is bir,i.e.,a binary relation.A resent finding towards big data science is that big data systems in nature are a recursive ndimensional typed hyperstructure(RNDTHS).This topological property of big data reveals that the mathematical foundation of big data science is underpinned by big data algebra(BDA),which is a denotational mathematical structure for efficiently dealing with the inherited complexities and unprecedented challenges in big data *** leads to a coherent theory for big data modeling,analyses,mining,information elicitation,knowledge extraction,and intelligent generation for cognitive *** experiments demonstrate that cognitive robots may autonomously transform big data in vast linguistic databases(corpuses) into sophistic knowledge bases by cognitive machine learning.

关键词： Big data big data science denotational mathematics mathematical models recursively hierarchical hyperstructures big data algebra cogniitve machine learning formal concepts knowledge extraction knowledge generation cognitive informatics cognitive computing computational intelligence applications

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Experiments on the supervised learning algorithm for formal concept elicitation by cognitive robots

Experiments on the supervised learning algorithm for formal ...

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IEEE International Conference on Cognitive Informatics

作者： Omar A. Zatarain Yingxu Wang Laboratory for Computational Intelligence Denotational Mathematics and Software Science International Institute of Cognitive Informatics and Cognitive Computing (ICIC) Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary Calgary Alberta Canada Information Systems Lab Stanford University Stanford CA USA

ISBN: (纸本)9781509038473

Concept elicitation is a fundamental methodology for knowledge extraction and representation in cognitive robot learning. Traditional machine learning technologies deal with object identification, cluster classification, functional regression, and behavior acquisition. This paper presents a supervised machine knowledge learning methodology for concept elicitation from sample dictionaries in natural languages. Formal concepts are autonomously generated based on collective intention of attributes and collective extension of objects elicited from informal definitions in dictionaries. A system of formal concept generation for a cognitive robot is implemented by the Algorithm of Machine Concept Elicitation (AMCE) in MATlab. Experiments on machine learning for creating a set of twenty formal concepts reveal that the cognitive robot is able to learn synergized concepts in human knowledge in order to build its own cognitive knowledge base. The results of machine-generated concepts demonstrate that the AMCE algorithm can over perform human knowledge expressions in dictionaries in terms of relevance, accuracy, quantitativeness, and cohesiveness.

关键词： Algebra Handheld computers Writing Instruments Animals Semantics

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Semantic classes and relevant domains on WSD

Semantic classes and relevant domains on WSD

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17th International Conference on Text, Speech, and Dialogue, TSD 2014

作者： Izquierdo, Rubén Vázquez, Sonia Montoyo, Andrés Computational Lexicology and Terminology Lab. Vrije University of Amsterdam Netherlands Department of Software and Computing Systems University of Alicante Spain

ISBN: (纸本)9783319108155

Language ambiguities are a problem in various fields. For example, in Machine Translation the major cause of errors is ambiguity. Moreover, ambiguous words can be confusing for Information Extraction algorithms. Our purpose in this work is to provide a new approach to solve semantic ambiguities by dealing with the problem of the fine granularity of sense inventories. Our goal is to replace word senses with Semantic Classes that share properties, features and meanings. Also another semantic resources, Relevant Domains, is used to extract extract semantic information and enrich the process. The results obtained are evaluated in the Evaluation Exercises for the Semantic Analysis of Text (SensEval) framework. © 2014 Springer International Publishing.

关键词： Semantics

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