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Gaia data Release 3: Apsis II - Stellar Parameters

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arXiv 2022年

作者： Fouesneau, Morgan Frémat, Y. Andrae, R. Korn, A.J. Soubiran, C. Kordopatis, G. Vallenari, A. Heiter, U. Creevey, O.L. Sarro, L.M. de Laverny, P. Lanzafame, A.C. Lobel, A. Sordo, R. Rybizki, J. Slezak, I. Álvarez, M.A. Drimmel, R. Garabato, D. Delchambre, L. Bailer-Jones, C.A.L. Hatzidimitriou, D. Lorca, A. Le Fustec, Y. Pailler, F. Mary, N. Robin, C. Utrilla, E. Aramburu, A. Abreu Bakker, J. Bellas-Velidis, I. Bijaoui, A. Blomme, R. Bouret, J.-C. Brouillet, N. Brugaletta, E. Burlacu, A. Carballo, R. Casamiquela, L. Chaoul, L. Chiavassa, A. Contursi, G. Cooper, W.J. Dafonte, C. Demouchy, C. Dharmawardena, T.E. García-Lario, P. García-Torres, M. Gomez, A. González-Santamaría, I. Piccolo, A. Jean-Antoine Kontizas, M. Lebreton, Y. Licata, E.L. Lindstrøm, H.E.P. Livanou, E. Romeo, A. Magdaleno Manteiga, M. Marocco, F. Martayan, C. Marshall, D.J. Nicolas, C. Ordenovic, C. Palicio, P.A. Pallas-Quintela, L. Pichon, B. Poggio, E. Recio-Blanco, A. Riclet, F. Santoveña, R. Schultheis, M.S. Segol, M. Silvelo, A. Smart, R.L. Süveges, M. Thévenin, F. Elipe, G. Torralba Ulla, A. van Dillen, E. Zhao, H. Zorec, J. Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany Royal Observatory of Belgium Ringlaan 3 Brussels1180 Belgium 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 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 Padova Vicolo Osservatorio 5 Padova35122 Italy Dpto. de Inteligencia Artificial UNED c/Juan del Rosal 16 Madrid28040 Spain 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 CIGUS CITIC Department of Computer Science and Information Technologies University of A Coruña Campus de Elviña s/n A Coruña15071 Spain INAF - Osservatorio Astrofisico di Torino via Osservatorio 20 TO Pino Torinese10025 Italy Institut d'Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium Department of Astrophysics Astronomy and Mechanics National and Kapodistrian University of Athens Panepistimiopolis Zografos Athens15783 Greece National Observatory of Athens I. Metaxa and Vas. Pavlou Palaia Penteli Athens15236 Greece Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Telespazio for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Thales Services for 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 Madrid

Context. The third Gaia data release (Gaia DR3) contains, beyond the astrometry and photometry, dispersed light for hundreds of millions of sources from the Gaia prism spectra (BP and RP) and the spectrograph (RVS). This data release opens a new window on the chemo-dynamical properties of stars in our Galaxy, essential knowledge for understanding the structure, formation, and evolution of the Milky Way. Aims. To provide insight into the physical properties of Milky Way stars, we used these data to produce a uniformly-derived, all-sky catalog of stellar astrophysical parameters (APs): atmospheric properties (Teff, log g, [M/H], [α/Fe], activity index, emission lines, rotation), 13 chemical abundance estimates, evolution characteristics (radius, age, mass, bolometric luminosity), distance, and dust extinction. Methods. We developed the astrophysical parameters inference system (Apsis) pipeline to infer APs of Gaia objects by analyzing their astrometry, photometry, BP/RP, and RVS spectra. We validate our results against other works in the literature, including benchmark stars, interferometry, and asteroseismology. Here we assessed the stellar analysis performance from Apsis statistically. Results. We describe the quantities we obtained, including the underlying assumptions and the limitations of our results. We provide guidance and identify regimes in which our parameters should and should not be used. Conclusions. Despite some limitations, this is the most extensive catalog of uniformly-inferred stellar parameters to date. These comprise Teff, log g, and [M/H] (470 million using BP/RP, 6 million using RVS), radius (470 million), mass (140 million), age (120 million), chemical abundances (5 million), diffuse interstellar band analysis (1/2 million), activity indices (2 million), Hα equivalent widths (200 million), and further classification of spectral types (220 million) and emission-line stars (50 thousand). More precise and detailed astrophysical parameters based on

关键词： Stars

Gaia data Release 3: Astrophysical parameters inference system (Apsis) I - methods and content overview

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arXiv 2022年

作者： Creevey, Orlagh L. Sordo, R. Pailler, F. Frémat, Y. Heiter, U. Thévenin, F. Andrae, R. Fouesneau, M. Lobel, A. Bailer-Jones, C.A.L. Garabato, D. Bellas-Velidis, I. Brugaletta, E. Lorca, A. Ordenovic, C. Palicio, P.A. Sarro, L.M. Delchambre, L. Drimmel, R. Rybizki, J. Torralba Elipe, G. Korn, A.J. Recio-Blanco, A. Schultheis, M.S. De Angeli, F. Montegriffo, P. Abreu Aramburu, A. Accart, S. Álvarez, M.A. Bakker, J. Brouillet, N. Burlacu, A. Carballo, R. Casamiquela, L. Chiavassa, A. Contursi, G. Cooper, W.J. Dafonte, C. Dapergolas, A. de Laverny, P. Dharmawardena, T.E. Edvardsson, B. Le Fustec, Y. García-Lario, P. García-Torres, M. Gomez, A. González-Santamaría, I. Hatzidimitriou, D. Jean-Antoine Piccolo, A. Kontizas, M. Kordopatis, G. Lanzafame, A.C. Lebreton, Y. Licata, E.L. Lindstrøm, H.E.P. Livanou, E. Magdaleno Romeo, A. Manteiga, M. Marocco, F. Marshall, D.J. Mary, N. Nicolas, C. Pallas-Quintela, L. Panem, C. Pichon, B. Poggio, E. Riclet, F. Robin, C. Santoveña, R. Silvelo, A. Slezak, I. Smart, R.L. Soubiran, C. Süveges, M. Ulla, A. Utrilla, E. Vallenari, A. Zhao, H. Zorec, J. Barrado, D. Bijaoui, A. Bouret, J.-C. Blomme, R. Brott, I. Cassisi, S. Kochukhov, O. Martayan, C. Shulyak, D. Silvester, J. Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Bd de l'Observatoire CS 34229 Cedex 4 Nice06304 France INAF - Osservatorio astronomico di Padova Vicolo Osservatorio 5 Padova35122 Italy CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Cedex 9 Toulouse31401 France Royal Observatory of Belgium Ringlaan 3 Brussels1180 Belgium Observational Astrophysics Division of Astronomy and Space Physics Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany 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 INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Dpto. de Inteligencia Artificial UNED c/Juan del Rosal 16 Madrid28040 Spain Institut d'Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium INAF - Osservatorio Astrofisico di Torino via Osservatorio 20 TO Pino Torinese10025 Italy Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna via Piero Gobetti 93/3 Bologna40129 Italy Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain 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 Cedex 9 Toulouse31401 France Dpto. de Matemática Aplicada y C

Gaia data Release 3 contains a wealth of new data products for the community. Astrophysical parameters are a major component of this release. They were produced by the Astrophysical parameters inference system (Apsis) within the Gaia data Processing and Analysis Consortium. The aim of this paper is to describe the overall content of the astrophysical parameters in Gaia DR3 and how they were produced. In Apsis we use the mean BP/RP and mean RVS spectra along with astrometry and photometry, and we derive the following parameters: source classification and probabilities for 1.6 billion objects, interstellar medium characterisation and distances for up to 470 million sources, including a 2D total Galactic extinction map, 6 million redshifts of quasar candidates and 1.4 million redshifts of galaxy candidates, along with an analysis of 50 million outlier sources through an unsupervised classification. The astrophysical parameters also include many stellar spectroscopic and evolutionary parameters for up to 470 million sources. These comprise Teff, log g, and [M/H] (470 million using BP/RP, 6 million using RVS), radius (470 million), mass (140 million), age (120 million), chemical abundances (up to 5 million), diffuse interstellar band analysis (0.5 million), activity indices (2 million), Hα equivalent widths (200 million), and further classification of spectral types (220 million) and emission-line stars (50 thousand). This paper is part of a series of three papers: this Paper I focusses on describing the global content of the parameters in Gaia DR3. The accompanying papers II and III focus on the validation and use of the stellar and non-stellar products, respectively. This catalogue is the most extensive homogeneous database of astrophysical parameters to date, and it is based uniquely on Gaia data. It will only be superseded in Gaia data Release 4. This catalogue will therefore remain a key reference over the next four years, providing astrophysical parameters independ

关键词： Stars

Digital image splicing detection based on Markov features in QDCT and QWT domain

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arXiv 2017年

作者： Wang, Ruxin Lu, Wei Xiang, Shijun Zhao, Xianfeng Wang, Jinwei School of Data and Computer Science Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China College of Information Science and Technology Jinan University Guangzhou510632 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. Firstly, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real part and three imaginary parts of QDCT coefficients respectively. Then, additional Markov features are extracted from luminance (Y) channel in quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet subband coefficients. Finally, ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperforms some state-of-the-art methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Discrete cosine transforms

Quantum homomorphic signature using coherent states 2

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Quantum homomorphic signature using coherent states

2nd IEEE International Conference on computer and Communications, ICCC 2016

作者： Luo, Qingbin Yang, Guowu She, Kun Li, Xiaoyu Yuqi, Wang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Edward S. Rogers Sr. Department of Electrical Computer Engineering Department University of Toronto TorontoONM5S 3G4 Canada Big Data Research Center and School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China College of Computer Minnan Normal University Zhangzhou China

ISBN: (纸本)9781467390262

In this paper, a novel scheme for quantum homomorphic signature based on coherent states is proposed, which is an effective approach to identity authentication for multiple streams to enhance the security of quantum networks. The state used for signature are arbitrarily close in trace distance to states of O(log2n), if the length of the nodes' messages is n-bit. The scheme analysis shows that the scheme is a quantum group homomorphic signature scheme and the signature is unforgeable. © 2016 IEEE.

关键词： Quantum theory

Image segmentation using membrane computing: A literature survey 11th

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Image segmentation using membrane computing: A literature su...

11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Yahya, Rafaa I. Shamsuddin, Siti Mariyam Yahya, Salah I. Hasan, Shafatnnur Al-Salibi, Bisan Al-Khafaji, Ghada Faculty of Computing Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Computer College of Science University of Al-Mustansiriyah Baghdad Iraq UTM Big Data Center Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Software Engineering Koya University Iraq School of Computer Sciences Universiti Sains Malaysia USM GelugorPenang11800 Malaysia Department of Computer Science College of Science Baghdad University Baghdad Iraq

ISBN: (纸本)9789811036101

Membrane computing, a recent branch of natural computing, has been gaining momentum attention in the last few decades due to its massive parallelism and efficient computation. Many researchers in the field of membrane computing have proposed sophisticated techniques inspired by cell biology for computer science applications, especially when they considered cell organization in tissues, organs, and most recently, from the organization of neurons. The interdisciplinary applications of membrane computing include, but not limited to computer science, biology, biomedicine, bioinformatics and several other fields such as mathematics, artificial intelligence, automation, economics, to name but a few. Their applications are pertaining to computer graphics, approximate optimization, cryptography, parallel computing and image processing. Hence, in this paper we present an up to date comprehensive literature review pertaining to the application of membrane computing in the area of digital image analysis, especially image segmentation, comprehensively and systematically. We thoroughly investigate the recent advancement in the field of image segmentation using membrane system. Furthermore, we highlight the merits and demerits of various software tools and methods. Finally, we suggest some intuitive future directions in light of the current limitations. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Image segmentation

Key questions for modelling covid-19 exit strategies

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arXiv 2020年

作者： Thompson, Robin N. Hollingsworth, T. Déirdre Isham, Valerie Arribas-Bel, Daniel Ashby, Ben Britton, Tom Challenor, Peter Chappell, Lauren H.K. Clapham, Hannah Cunniffe, Nik J. Dawid, A. Philip Donnelly, Christl A. Eggo, Rosalind Funk, Sebastian Gilbert, Nigel Gog, Julia R. Glendinning, Paul Hart, William S. Heesterbeek, Hans House, Thomas Keeling, Matt Kiss, Istvan Z. Kretzschmar, Mirjam Lloyd, Alun L. McBryde, Emma S. McCaw, James M. Miller, Joel C. McKinley, Trevelyan J. Morris, Martina O’Neill, Philip D. Pearson, Carl A.B. Parag, Kris V. Pellis, Lorenzo Pulliam, Juliet R.C. Ross, Joshua V. Tildesley, Michael J. Tomba, Gianpaolo Scalia Silverman, Bernard W. Struchiner, Claudio J. Trapman, Pieter Webb, Cerian R. Mollison, Denis Restif, Olivier Mathematical Institute University of Oxford Woodstock Road OxfordOX2 6GG United Kingdom University of Oxford St Aldates OxfordOX1 1DP United Kingdom Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine Keppel Street LondonWC1E 7HT United Kingdom Big Data Institute University of Oxford Old Road Campus OxfordOX3 7LF United Kingdom Department of Statistical Science University College London Gower Street LondonWC1E 6BT United Kingdom School of Environmental Sciences University of Liverpool Brownlow Street LiverpoolL3 5DA United Kingdom Alan Turing Institute British Library 96 Euston Road LondonNW1 2DB United Kingdom Department of Mathematical Sciences University of Bath North Road BathBA2 7AY United Kingdom Department of Mathematics Stockholm University Kräftriket Stockholm106 91 Sweden College of Engineering Mathematical and Physical Sciences University of Exeter ExeterEX4 4QE United Kingdom Department of Plant Sciences University of Oxford South Parks Road OxfordOX1 3RB United Kingdom Saw Swee Hock School of Public Health National University of Singapore 12 Science Drive Singapore117549 Singapore Department of Plant Sciences University of Cambridge Downing Street CambridgeCB2 3EA United Kingdom Statistical Laboratory University of Cambridge Wilberforce Road CambridgeCB3 0WB United Kingdom Department of Statistics University of Oxford St Giles’ OxfordOX1 3LB United Kingdom Department of Infectious Disease Epidemiology Imperial College Norfolk Place LondonW2 1PG United Kingdom Department of Sociology University of Surrey Stag Hill GuildfordGU2 7XH United Kingdom Centre for Mathematical Sciences University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Department of Mathematics University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Department of Population Health Sciences Utrecht University Yalelaan Utrecht3584 CL Netherlands IBM Research Hartree Centre Daresbury

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement lockdown exit strategies that allow restrictions to be relaxed while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton institute "Models for an exit strategy" workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, will allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. The roadmap requires a global collaborative effort from the scientific community and policy-makers, and is made up of three parts: i) improve estimation of key epidemiological parameters;ii) understand sources of heterogeneity in populations;iii) focus on requirements for data collection, particularly in Low-to-Middle-Income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health. Copyright © 2020, The Authors. All rights reserved.

关键词： SARS

CScrypt: A Compressive-Sensing-Based Encryption Engine for the Internet of Things: Demo Abstract 16

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CScrypt: A Compressive-Sensing-Based Encryption Engine for t...

Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM

作者： Wanli Xue Chengwen Luo Rajib Rana Wen Hu Aruna Seneviratne School of Computer Science and Engineering University of New South Wales Australia and Data61 College of Computer Science and Software Engineering Shenzhen University China Institute of Resilient Regions University of Southern Queensland Australia Data61

ISBN: (纸本)9781450342636

Internet of Things (IoT) have been connecting the physical world seamlessly and provides tremendous opportunities to a wide range of applications. However, potential risks exist when IoT system collects local sensor data and uploads to the Cloud. The private data leakage can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this demo, we solve this problem of guaranteeing the user data privacy and security using compressive sensing based cryptographic method. We present CScrypt, a compressive-sensing-based encryption engine for the Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Our system exploits the fact that each individual's biometric data can be trained to a unique dictionary which can be used as an encryption key meanwhile to compress the original data. We will demonstrate a functioning prototype of our system using live data stream when attending the conference.

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Provable multiple replication data possession with full dynamics for secure cloud storage

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Provable multiple replication data possession with full dyna...

作者： Zhang, Yafang Ni, Jianbing Tao, Xiaoling Wang, Yong Yu, Yong Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada College of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Cloud Security and Cloud Service Engineering Research Center Guilin University of Electronic Technology Guilin541004 China

Cloud storage has been gaining tremendous popularity among individuals and corporations because of its low maintenance cost and on-demand services for the clients. To improve the availability and the reliability of critical data, storing multiple replicas on multiple servers is a commonly used strategy. Currently, several provable data possession (PDP) protocols for multiple replicas of dynamic data have been proposed to ensure the integrity of outsourced multi-copy data, but the efficiency of these protocols on verifying multiple replicas one by one is not satisfactory. In this paper, we propose a provable multiple replication data possession protocol with full dynamics, named MR-DPDP. In MR-DPDP, we utilize a novel authenticated data structure called Merkle hash tree with rank to support both full dynamic data updates and efficient integrity verification. In addition, our construction with RSA signature can support both variable-sized file blocks and public verification. Through security proof and performance evaluation, we demonstrate that MR-DPDP not only is sound but also incurs less communication overhead when updating data blocks as well as verifying a proof of the integrity of multiple replicas. Copyright © 2015 John Wiley & Sons, Ltd.

关键词： Cloud storage

Utilizing Cat Swarm Optimization in Allocating the Sink Node in the Wireless Sensor Network Environment 3

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Utilizing Cat Swarm Optimization in Allocating the Sink Node...

3rd International Conference on Computing Measurement Control and Sensor Network, CMCSN 2016

作者： Tsai, Pei-Wei Kong, Lingping Vaclav, Snasel Pan, Jeng-Shyang Istanda, Vaci Hu, Zhi-Yong College of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Department of Computer Science VSB Technical University of Ostrava Ostrava Czech Republic Council of Indigenous Peoples Executive Yuan New Taipei City Taiwan

ISBN: (纸本)9781509010936

A general wireless sensor network (WSN) is a system composed of a large number of sensor nodes. In the WSN system, all sensor nodes are responsible for sensing the interested events and transmitting the related information back to the sink node. The sink node acts like the brain of the whole WSN system for processing the collected data and giving control commands. The position of the sink node directly affects the data transmission efficiency and the distance from the terminal sensor node to the sink node. Thus, choosing the correct place to place the sink node in the whole network is a delicate work. In this paper, we propose a sink node placement method by proposing a newly designed object function and utilizing Cat Swarm Optimization (CSO) to find out the near best location for placing the sink node. The simulation results indicate that our proposed method presents good performance compared with the conventional Particle Swarm Optimization (PSO) algorithm in reducing the overall energy consumption and prolongs the lifetime of network in an efficient way. © 2016 IEEE.

关键词： Sensor nodes