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Advancing image understanding in poor visibility environments: A collective benchmark study

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arXiv 2019年

作者： Yang, Wenhan Yuan, Ye Ren, Wenqi Liu, Jiaying Scheirer, Walter J. Wang, Zhangyang Zhang, Taiheng Zhong, Qiaoyong Xie, Di Pu, Shiliang Zheng, Yuqiang Qu, Yanyun Xie, Yuhong Chen, Liang Li, Zhonghao Hong, Chen Jiang, Hao Yang, Siyuan Liu, Yan Qu, Xiaochao Wan, Pengfei Zheng, Shuai Zhong, Minhui Su, Taiyi He, Lingzhi Guo, Yandong Zhao, Yao Zhu, Zhenfeng Liang, Jinxiu Wang, Jingwen Chen, Tianyi Quan, Yuhui Xu, Yong Liu, Bo Liu, Xin Sun, Qi Lin, Tingyu Li, Xiaochuan Lu, Feng Gu, Lin Zhou, Shengdi Cao, Cong Zhang, Shifeng Chi, Cheng Zhuang, Chubin Lei, Zhen Li, Stan Z. Wang, Shizheng Liu, Ruizhe Yi, Dong Zuo, Zheming Chi, Jianning Wang, Huan Wang, Kai Liu, Yixiu Gao, Xingyu Chen, Zhenyu Guo, Chang Li, Yongzhou Zhong, Huicai Huang, Jing Guo, Heng Yang, Jianfei Liao, Wenjuan Yang, Jiangang Zhou, Liguo Feng, Mingyue Qin, Likun Wangxuan Institute of Computer Technology Peking University Beijing100080 China Department of Computer Science and Engineering Texas A&M University TX77843 United States State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Department of Mechanical Engineering Zhejiang University Hangzhou310027 China Hikvision Research Institute Hangzhou310051 China Mtlab Meitu Inc. Beijing100080 China Insitute of Information Science Beijing Jiaotong University Beijing100044 China Department of Computer Science and Technology Tongji University Shanghai201804 China XPENGMOTORS Beijing China School of Computer Science and Engineering at South China University of Technology Guangzhou510006 China Tencent AI Lab Shenzhen518000 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Northeastern University Shenyang110819 China Nanyang Technological University Singapore639798 Big Data Center State Grid Corporation of China Beijing China Winsense Inc. Beijing100080 China University of Chinese Academy of Sciences Beijing100049 China Department of Informatics Technical University of Munich Garching85748 Germany Institute of Microelectronics Chinese Academy of Sciences Beijing100190 China China Electric Power Research Institute Beijing100031 China Westlake University Hangzhou310024 China Department of Computer Science Durham University Durham United Kingdom Australian National University ActonACT0200 Australia Sunway-AI Co. Ltd Zhuhai China Chinese Academy of Sciences R&D Center for Internet of Things Wuxi214200 China

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG2+challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

Gaia data Release 3: Analysis of RVS spectra using the General Stellar Parametriser from spectroscopy

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arXiv 2022年

作者： Recio-Blanco, Alejandra de Laverny, Patrick Palicio, P.A. Kordopatis, G. Álvarez, M.A. Schultheis, M. Contursi, G. Zhao, H. Torralba Elipe, G. Ordenovic, C. Manteiga, M. Dafonte, C. Oreshina-Slezak, I. Bijaoui, A. Frémat, Y. Seabroke, G. Pailler, F. Spitoni, E. Poggio, E. Creevey, O.L. Abreu Aramburu, A. Accart, S. Andrae, R. Bailer-Jones, C.A.L. Bellas-Velidis, I. Brouillet, N. Brugaletta, E. Burlacu, A. Carballo, R. Casamiquela, L. Chiavassa, A. Cooper, W.J. Dapergolas, A. Delchambre, L. Dharmawardena, T.E. Drimmel, R. Edvardsson, B. Fouesneau, M. Garabato, D. García-Lario, P. García-Torres, M. Gavel, A. Gomez, A. González-Santamaría, I. Hatzidimitriou, D. Heiter, U. Jean-Antoine Piccolo, A. Kontizas, M. Korn, A.J. Lanzafame, A.C. Lebreton, Y. Le Fustec, Y. Licata, E.L. Lindstrøm, H.E.P. Livanou, E. Lobel, A. Lorca, A. Magdaleno Romeo, A. Marocco, F. Marshall, D.J. Mary, N. Nicolas, C. Pallas-Quintela, L. Panem, C. Pichon, B. Riclet, F. Robin, C. Rybizki, J. Santoveña, R. Silvelo, A. Smart, R.L. Sarro, L.M. Sordo, R. Soubiran, C. Süveges, M. Ulla, A. Vallenari, A. Zorec, J. Utrilla, E. Bakker, J. Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Nice France CIGUS CITIC Department of Computer Science and Information Technologies University of A Coruña Campus de Elviña s/n A Coruña15071 Spain CIGUS CITIC Department of Nautical Sciences and Marine Engineering University of A Coruña Paseo de Ronda 51 A Coruña15071 Spain Royal Observatory of Belgium 3 avenue circulaire Brussels1180 Belgium Mullard Space Science Laboratory University College London Holmbury St Mary Dorking SurreyRH5 6NT United Kingdom CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany National Observatory of Athens I. Metaxa and Vas. Pavlou Palaia Penteli Athens15236 Greece Laboratoire d'astrophysique de Bordeaux Univ. Bordeaux CNRS B18N allée Geoffroy Saint-Hilaire Pessac33615 France INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Telespazio for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Dpto. de Matemática Aplicada y Ciencias de la Computación Univ. de Cantabria ETS Ingenieros de Caminos Canales y Puertos Avda. de los Castros s/n Santander39005 Spain GEPI Observatoire de Paris Université PSL CNRS 5 Place Jules Janssen Meudon92190 France Centre for Astrophysics Research University of Hertfordshire College Lane HatfieldAL10 9AB United Kingdom 10025 Italy Institut d'Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium Theoretical Astrophysics Division of Astronomy and Space Physics Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Data Science and

Context. The chemo-physical parametrisation of stellar spectra is essential for understanding the nature and evolution of stars and of Galactic stellar populations. A worldwide observational effort from the ground has provided, in one century, an extremely heterogeneous collection of chemical abundances for about two million stars in total, with fragmentary sky coverage. Aims. This situation is revolutionised by the Gaia third data release (DR3), which contains the parametrisation of Radial Velocity Spectrometer (RVS) data performed by the General Stellar Parametriser-spectroscopy, GSP-Spec, module. Here we describe the parametrisation of the first 34 months of Gaia RVS observations. Methods. GSP-Spec estimates the chemo-physical parameters from combined RVS spectra of single stars, without additional inputs from astrometric, photometric, or spectro-photometric BP/RP data. The main analysis workflow described here, MatisseGauguin, is based on projection and optimisation methods and provides the stellar atmospheric parameters;the individual chemical abundances of N, Mg, Si, S, Ca, Ti, Cr, Fe i, Fe ii, Ni, Zr, Ce and Nd;the differential equivalent width of a cyanogen line;and the parameters of a diffuse interstellar band (DIB) feature. Another workflow, based on an artificial neural network (ANN) and referred to with the same acronym, provides a second set of atmospheric parameters that are useful for classification control. For both workflows, we implement a detailed quality flag chain considering different error sources. Results. With about 5.6 million stars, the Gaia DR3 GSP-Spec all-sky catalogue is the largest compilation of stellar chemo-physical parameters ever published and the first one from space data. Internal and external biases have been studied taking into account the implemented flags. In some cases, simple calibrations with low degree polynomials are suggested. The homogeneity and quality of the estimated parameters enables chemo-dynamical studies of G

关键词： Stars

data integration and visualisation for demanding marine operations

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Data integration and visualisation for demanding marine oper...

OCEANS 2016 - Shanghai

作者： Wang, Hao Zhuge, Xu Strazdins, Girts Wei, Zheng Li, Guoyuan Zhang, Houxiang Big Data Lab Faculty of Engineering and Natural Sciences Norwegian Unversity of Science and Technology in Aalesund Postboks 1517 Aalesund6025 Norway Mechatronics Lab Faculty of Maritime Technology and Operations Norwegian Unversity of Science and Technology in Aalesund Norway

ISBN: (纸本)9781467397247

Marine operations face the major challenge of increased complexity. Much more data are being collected for diagnostic and monitoring purposes with sensors on board a vessel. However, due to the lack of easy data presentation and interaction, the crew members are overwhelmed by the large volume of information and alert messages, causing serious information overload problems. In this paper, we explore data integration and visualisation techniques for maritime operations and present a proof-of-concept prototype using real offline data from our industrial collaborators. This work is an important part of our initial efforts towards a visual analytics framework for maritime operations. © 2016 IEEE.

关键词： data integration

Gaia data Release 3: Analysis of the Gaia BP/RP spectra using the General Stellar Parameterizer from Photometry

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arXiv 2022年

作者： Andrae, Rene Fouesneau, M. Sordo, R. Bailer-Jones, C.A.L. Dharmawardena, T.E. Rybizki, J. De Angeli, F. Lindstrøm, H.E.P. Marshall, D.J. Drimmel, R. Korn, A.J. Soubiran, C. Brouillet, N. Casamiquela, L. Rix, H.-W. Abreu Aramburu, A. Álvarez, M.A. Bakker, J. Bellas-Velidis, I. Bijaoui, A. Brugaletta, E. Burlacu, A. Carballo, R. Chaoul, L. Chiavassa, A. Contursi, G. Cooper, W.J. Creevey, O.L. Dafonte, C. Dapergolas, A. de Laverny, P. Delchambre, L. Demouchy, C. Edvardsson, B. Frémat, Y. Garabato, D. García-Lario, P. García-Torres, M. Gavel, A. Gomez, A. González-Santamaría, I. Hatzidimitriou, D. Heiter, U. Jean-Antoine Piccolo, A. Kontizas, M. Kordopatis, G. Lanzafame, A.C. Lebreton, Y. Licata, E.L. Livanou, E. Lobel, A. Lorca, A. Magdaleno Romeo, A. Manteiga, M. Marocco, F. Mary, N. Nicolas, C. Ordenovic, C. Pailler, F. Palicio, P.A. Pallas-Quintela, L. Panem, C. Pichon, B. Poggio, E. Recio-Blanco, A. Riclet, F. Robin, C. Santoveña, R. Sarro, L.M. Schultheis, M.S. Segol, M. Silvelo, A. Slezak, I. Smart, R.L. Süveges, M. Thévenin, F. Torralba Elipe, G. Ulla, A. Utrilla, E. Vallenari, A. van Dillen, E. Zhao, H. Zorec, J. Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany INAF - Osservatorio astronomico di Padova Vicolo Osservatorio 5 Padova35122 Italy Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom INAF - Osservatorio Astrofisico di Torino via Osservatorio 20 TO Pino Torinese10025 Italy Niels Bohr Institute University of Copenhagen Juliane Maries Vej 30 Copenhagen Ø2100 Denmark DXC Technology Retortvej 8 Valby2500 Denmark IRAP Université de Toulouse CNRS UPS CNES 9 Av. colonel Roche BP 44346 Toulouse Cedex 431028 France Observational Astrophysics Division of Astronomy and Space Physics Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden Laboratoire d'astrophysique de Bordeaux Univ. Bordeaux CNRS B18N allée Geoffroy Saint-Hilaire Pessac33615 France GEPI Observatoire de Paris Université PSL CNRS 5 Place Jules Janssen Meudon92190 France Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain CIGUS CITIC Department of Computer Science and Information Technologies University of A Coruña Campus de Elviña s/n A Coruña15071 Spain National Observatory of Athens I. Metaxa and Vas. Pavlou Palaia Penteli Athens15236 Greece Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Bd de l'Observatoire CS 34229 Nice Cedex 406304 France INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Telespazio for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Dpto. de Matemática Aplicada y Ciencias de la Computación Univ. de Cantabria ETS Ingenieros de Caminos Canales y Puertos Avda. de los Castros s/n Santander39005 Spain CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Centre for Astrophysics Research University of Hertfordshire College Lane HatfieldAL10 9AB United Kingdom In

Context. The astrophysical characterisation of sources is among the major new data products in the third Gaia data release (DR3). In particular, there are stellar parameters for 471 million sources estimated from low-resolution BP/RP spectra. Aims. We present the General Stellar Parameterizer from Photometry (GSP-Phot), which is part of the astrophysical parameters inference system (Apsis). GSP-Phot is designed to produce a homogeneous catalogue of parameters for hundreds of millions of single non-variable stars based on their astrometry, photometry, and low-resolution BP/RP spectra. These parameters are effective temperature, surface gravity, metallicity, absolute MG magnitude, radius, distance, and extinction for each star. Methods. GSP-Phot uses a Bayesian forward-modelling approach to simultaneously fit the BP/RP spectrum, parallax, and apparent G magnitude. A major design feature of GSP-Phot is the use of the apparent flux levels of BP/RP spectra to derive, in combination with isochrone models, tight observational constraints on radii and distances. We carefully validate the uncertainty estimates by exploiting repeat Gaia observations of the same source. Results. The data release includes GSP-Phot results for 471 million sources with G eff and 0.2-0.25 for log g, but these depend strongly on data quality. In particular, GSP-Phot results are significantly better for stars with good parallax measurements ($/σ$ > 20), mostly within 2kpc. Metallicity estimates exhibit substantial biases compared to literature values and are only useful at a qualitative level. However, we provide an empirical calibration of our metallicity estimates that largely removes these biases. Extinctions A0 and ABP show typical differences from reference values of 0.07-0.09 mag. MCMC samples of the parameters are also available for 95% of the sources. Conclusions. GSP-Phot provides a homogeneous catalogue of stellar parameters, distances, and extinctions that can be used for various purposes

关键词： Photometry

Gaia DR3: Apsis III - Non-stellar content and source classification

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arXiv 2022年

作者： Delchambre, L. Bailer-Jones, C.A.L. Bellas-Velidis, I. Drimmel, R. Garabato, D. Carballo, R. Hatzidimitriou, D. Marshall, D.J. Andrae, R. Dafonte, C. Livanou, E. Fouesneau, M. Licata, E.L. Lindstrøm, H.E.P. Manteiga, M. Robin, C. Silvelo, A. Abreu Aramburu, A. Álvarez, M.A. Bakker, J. Bijaoui, A. Brouillet, N. Brugaletta, E. Burlacu, A. Casamiquela, L. Chaoul, L. Chiavassa, A. Contursi, G. Cooper, W.J. Creevey, O.L. Dapergolas, A. de Laverny, P. Demouchy, C. Dharmawardena, T.E. Edvardsson, B. Frémat, Y. García-Lario, P. García-Torres, M. Gavel, A. Gomez, A. González-Santamaría, I. Heiter, U. Jean-Antoine Piccolo, A. Kontizas, M. Kordopatis, G. Korn, A.J. Lanzafame, A.C. Lebreton, Y. Lobel, A. Lorca, A. Magdaleno Romeo, A. Marocco, F. Mary, N. Nicolas, C. Ordenovic, C. Pailler, F. Palicio, P.A. Pallas-Quintela, L. Panem, C. Pichon, B. Poggio, E. Recio-Blanco, A. Riclet, F. Rybizki, J. Santoveña, R. Sarro, L.M. Schultheis, M.S. Segol, M. Slezak, I. Smart, R.L. Sordo, R. Soubiran, C. Süveges, M. Thévenin, F. Torralba Elipe, G. Ulla, A. Utrilla, E. Vallenari, A. van Dillen, E. Zhao, H. Zorec, J. Institut d'Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium Max Planck Institute for Astronomy Königstuhl 17 Heidelberg69117 Germany National Observatory of Athens I. Metaxa and Vas. Pavlou Palaia Penteli Athens15236 Greece INAF - Osservatorio Astrofisico di Torino via Osservatorio 20 TO Pino Torinese10025 Italy CIGUS CITIC Department of Computer Science and Information Technologies University of A Coruña Campus de Elviña s/n A Coruña15071 Spain Dpto. de Matemática Aplicada y Ciencias de la Computación Univ. de Cantabria ETS Ingenieros de Caminos Canales y Puertos Avda. de los Castros s/n Santander39005 Spain Department of Astrophysics Astronomy and Mechanics National and Kapodistrian University of Athens Panepistimiopolis Zografos Athens15783 Greece IRAP Université de Toulouse CNRS UPS CNES 9 Av. colonel Roche BP 44346 ToulouseCedex 4 31028 France Niels Bohr Institute University of Copenhagen Juliane Maries Vej 30 Copenhagen Ø2100 Denmark DXC Technology Retortvej 8 Valby2500 Denmark CIGUS CITIC Department of Nautical Sciences and Marine Engineering University of A Coruña Paseo de Ronda 51 A Coruña15071 Spain Thales Services for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin ToulouseCedex 9 31401 France Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Bd de l'Observatoire CS 34229 NiceCedex 4 06304 France Laboratoire d'astrophysique de Bordeaux Univ. Bordeaux CNRS B18N allée Geoffroy Saint-Hilaire Pessac33615 France INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Telespazio for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin ToulouseCedex 9 31401 France GEPI Observatoire de Paris Université PSL CNRS 5 Place Jules Janssen Meudon92190 France CNES Centre Spatial de Toulouse 18 avenue

Context. As part of the third Gaia data release, we present the contributions of the non-stellar and classification modules from the eighth coordination unit (CU8) of the data Processing and Analysis Consortium, which is responsible for the determination of source astrophysical parameters using Gaia data. This is the third in a series of three papers describing the work done within CU8 for this release. Aims. For each of the five relevant modules from CU8, we summarise their objectives, the methods they employ, their performance, and the results they produce for Gaia DR3. We further advise how to use these data products and highlight some limitations. Methods. The Discrete Source Classifier (DSC) module provides classification probabilities associated with five types of sources: quasars, galaxies, stars, white dwarfs, and physical binary stars. A subset of these sources are processed by the Outlier Analysis (OA) module, which performs an unsupervised clustering analysis, and then associates labels with the clusters to complement the DSC classification. The Quasi Stellar Object Classifier (QSOC) and the Unresolved Galaxy Classifier (UGC) determine the redshifts of the sources classified as quasar and galaxy by the DSC module. Finally, the Total Galactic Extinction (TGE) module uses the extinctions of individual stars determined by another CU8 module to determine the asymptotic extinction along all lines of sight for Galactic latitudes |b| > 5◦. Results. Gaia DR3 includes 1591 million sources with DSC classifications;56 million sources to which the OA clustering is applied;1.4 million sources with redshift estimates from UGC;6.4 million sources with QSOC redshift;and 3.1 million level 9 HEALPixes of size 0.013 deg2 where the extinction is evaluated by TGE. Conclusions. Validation shows that results are in good agreement with values from external catalogues;for example 90% of the QSOC redshifts have absolute error lower than 0.1 for sources with empty warning flags, wh

关键词： Galaxies

Gaia data Release 3. Stellar chromospheric activity and mass accretion from Ca II IRT observed by the Radial Velocity Spectrometer

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arXiv 2022年

作者： Lanzafame, Alessandro C. Brugaletta, E. Frémat, Y. Sordo, R. Creevey, O.L. Andretta, V. Scandariato, G. Busà, I. Distefano, E. Korn, A.J. de Laverny, P. Recio-Blanco, A. Abreu Aramburu, A. Álvarez, M.A. Andrae, R. Bailer-Jones, C.A.L. Bakker, J. Bellas-Velidis, I. Bijaoui, A. Brouillet, N. Burlacu, A. Carballo, R. Casamiquela, L. Chaoul, L. Chiavassa, A. Contursi, G. Cooper, W.J. Dafonte, C. Dapergolas, A. Delchambre, L. Demouchy, C. Dharmawardena, T.E. Drimmel, R. Edvardsson, B. Fouesneau, M. Garabato, D. García-Lario, P. García-Torres, M. Gavel, A. Gomez, A. González-Santamaría, I. Hatzidimitriou, D. Heiter, U. Jean-Antoine Piccolo, A. Kontizas, M. Kordopatis, G. Lebreton, Y. Licata, E.L. Lindstrøm, H.E.P. Livanou, E. Lobel, A. Lorca, A. Magdaleno Romeo, A. Manteiga, M. Marocco, F. Marshall, D.J. Mary, N. Nicolas, C. Ordenovic, C. Pailler, F. Palicio, P.A. Pallas-Quintela, L. Panem, C. Pichon, B. Poggio, E. Riclet, F. Robin, C. Rybizki, J. Santoveña, R. Sarro, L.M. Schultheis, M.S. Segol, M. Silvelo, A. Slezak, I. Smart, R.L. Soubiran, C. Süveges, M. Thévenin, F. Torralba Elipe, G. Ulla, A. Utrilla, E. Vallenari, A. van Dillen, E. Zhao, H. Zorec, J. INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Dipartimento di Fisica e Astronomia ""Ettore Majorana"" Università di Catania Via S. Sofia 64 Catania95123 Italy Royal Observatory of Belgium Ringlaan 3 Brussels1180 Belgium INAF - Osservatorio astronomico di Padova Vicolo Osservatorio 5 Padova35122 Italy Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Bd de l'Observatoire CS 34229 Nice Cedex 406304 France INAF- Osservatorio Astronomico di Capodimonte Salita Moiariello 16 Naples80131 Italy Observational Astrophysics Division of Astronomy and Space Physics Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain CIGUS CITIC Department of Computer Science and Information Technologies University of A Coruña Campus de Elviña s/n A Coruña15071 Spain Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany National Observatory of Athens I. Metaxa and Vas. Pavlou Palaia Penteli Athens15236 Greece Laboratoire d'astrophysique de Bordeaux Univ. Bordeaux CNRS B18N allée Geoffroy Saint-Hilaire Pessac33615 France Telespazio for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Dpto. de Matemática Aplicada y Ciencias de la Computación Univ. de Cantabria ETS Ingenieros de Caminos Canales y Puertos Avda. de los Castros s/n Santander39005 Spain GEPI Observatoire de Paris Université PSL CNRS 5 Place Jules Janssen Meudon92190 France CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Centre for Astrophysics Research University of Hertfordshire College Lane Hatfield AL10 9AB United Kingdom 10025 Italy Institut d'Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium APAVE SUDEUROPE SAS for CNES Centre Spatial de Toulouse

Context. The Gaia Radial Velocity Spectrometer (RVS) provides the unique opportunity of a spectroscopic analysis of millions of stars at medium-resolution (λ/∆λ ∼11500) in the near-infrared (845 - 872 nm). This wavelength range includes the Ca ii infrared triplet (IRT) at 850.03, 854.44, and 866.45 nm, which is a good diagnostics of magnetic activity in the chromosphere of late-type stars. Aims. Here we present the method devised for inferring the Gaia stellar activity index from the analysis of the Ca ii IRT in the RVS spectrum, together with its scientific validation. Methods. The Gaia stellar activity index is derived from the Ca ii IRT excess equivalent width with respect to a reference spectrum, taking the projected rotational velocity (v sin i) into account. Scientific validation of the Gaia stellar activity index is performed by deriving a R'IRT index, largely independent of the photospheric parameters, and considering the correlation with the R'HK index for a sample of stars. A sample of well studied PMS stars is considered to identify the regime in which the Gaia stellar activity index may be affected by mass accretion. The position of these stars in the colour-magnitude diagram and the correlation with the amplitude of the photometric rotational modulation is also scrutinised. Results. Gaia DR3 contains a stellar activity index derived from the Ca ii IRT for some 2 × 106 stars in the Galaxy. This represents a gold mine for studies on stellar magnetic activity and mass accretion in the solar vicinity. Three regimes of the chromospheric stellar activity are identified, confirming suggestions made by previous authors on much smaller R'HK datasets. The highest stellar activity regime is associated with PMS stars and RS CVn systems, in which activity is enhanced by tidal interaction. Some evidence of a bimodal distribution in MS stars with Teff 5000 K is also found, which defines the two other regimes, without a clear gap in between. Stars with 3500 K>Teff > 50

关键词： Spectrometers

Extortion provides alternative routes to the evolution of cooperation in structured populations

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Physical Review E 2017年第5期95卷 052302-052302页

作者： Xiongrui Xu Zhihai Rong Zhi-Xi Wu Tao Zhou Chi Kong Tse CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Chengdu 611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou Gansu 730000 People's Republic of China

In this paper, we study the evolution of cooperation in structured populations (individuals are located on either a regular lattice or a scale-free network) in the context of repeated games by involving three types of strategies, namely, unconditional cooperation, unconditional defection, and extortion. The strategy updating of the players is ruled by the replicator-like dynamics. We find that extortion strategies can act as catalysts to promote the emergence of cooperation in structured populations via different mechanisms. Specifically, on regular lattice, extortioners behave as both a shield, which can enwrap cooperators inside and keep them away from defectors, and a spear, which can defeat those surrounding defectors with the help of the neighboring cooperators. Particularly, the enhancement of cooperation displays a resonance-like behavior, suggesting the existence of optimal extortion strength mostly favoring the evolution of cooperation, which is in good agreement with the predictions from the generalized mean-field approximation theory. On scale-free network, the hubs, who are likely occupied by extortioners or defectors at the very beginning, are then prone to be conquered by cooperators on small-degree nodes as time elapses, thus establishing a bottom-up mechanism for the emergence and maintenance of cooperation.

关键词： Population dynamics Self-organized systems Social systems

A multi-objective optimal mobile robot path planning based on whale optimization algorithm 13

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A multi-objective optimal mobile robot path planning based o...

13th IEEE International Conference on Signal Processing, ICSP 2016

作者： Dao, Thi-Kien Pan, Tien-Szu Pan, Jeng-Shyang Department of Electronics Engineering National Kaohsiung University of Applied Sciences Taiwan Harbin Institute of Technology Shenzhen Graduate School Fujian University of Technology China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology China

ISBN: (纸本)9781509013449

Due to the complex physical constraints in working space of robot, optimization for mobile robot operations satisfies not only one criterion but also several criteria. In this paper, a novel multi-objective method for optimal mobile robot path planning based on Whale optimization algorithm (WOA) is proposed. In the proposed method, two criteria of distance and smooth path of the robot path planning issue are transformed into a minimization one. The positions of the target and the obstacles in the environment are considered the fitness for the solution in WOA. The position of the globally best whale in each iteration is selected, and reached by the robot in sequence. In addition, the robot processor updates its information during the motion, and the environment is partially unknown for the robot due to the limit of the detection range of its sensors. Series of simulations are implemented in different static environments for the optimal path when the robot reaches its target. The results show that the proposed method provides the robot reaches its target with colliding free obstacles, and the proposed method may be the alternative method of optimization for robot planning. © 2016 IEEE.

关键词： Mobile robots

A self-adaptive dynamic evaluation model for diabetes mellitus, based on evolutionary strategies

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International Journal Bioautomation 2016年第1期20卷 79-88页

作者： Lu, An-Jiang Jin, Li Kan, Hong-Xing College of Big Data and Information Engineering Guizhou University Guiyang Guizhou550025 China School of Medical Information Technology Anhui University of Chinese Medicine Hefei Anhui230012 China

In order to evaluate diabetes mellitus objectively and accurately, this paper builds a self-adaptive dynamic evaluation model for diabetes mellitus, based on evolutionary strategies. First of all, on the basis of a formalized description of the evolutionary process of diabetes syndromes, using a state transition function, it judges whether a disease is evolutionary, through an excitation parameter. It then, provides evidence for the rebuilding of the evaluation index system. After that, by abstracting and rebuilding the composition of evaluation indexes, it makes use of a heuristic algorithm to determine the composition of the evolved evaluation index set of diabetes mellitus, It then, calculates the weight of each index in the evolved evaluation index set of diabetes mellitus by building a dependency matrix and realizes the self-adaptive dynamic evaluation of diabetes mellitus under an evolutionary environment. Using this evaluation model, it is possible to, quantify all kinds of diagnoses and treatment experiences of diabetes and finally to adopt ideal diagnoses and treatment measures for different patients with diabetics.

关键词： Diagnosis