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Euclid preparation. Sensitivity to neutrino parameters

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arXiv 2024年

作者： Archidiacono, M. Lesgourgues, J. Casas, S. Pamuk, S. Schöneberg, N. Sakr, Z. Parimbelli, G. Schneider, A. Hervas Peters, F. Pace, F. Sabarish, V.M. Costanzi, M. Camera, S. Carbone, C. Clesse, S. Frusciante, N. Fumagalli, A. Monaco, P. Scott, D. Viel, M. Amara, A. Andreon, S. Auricchio, N. Baldi, M. Bardelli, S. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Capobianco, V. Cardone, V.F. Carretero, J. Castellano, M. Cavuoti, S. Cimatti, A. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. Douspis, M. Dubath, F. Duncan, C.A.J. Dupac, X. Dusini, S. Ealet, A. Farina, M. Farrens, S. Ferriol, S. Frailis, M. Franceschi, E. Galeotta, S. Gillis, B. Giocoli, C. Grazian, A. Grupp, F. Guzzo, L. Haugan, S.V.H. Hoekstra, H. Hormuth, F. Hornstrup, A. Jahnke, K. Joachimi, B. Keihänen, E. Kermiche, S. Kiessling, A. Kilbinger, M. Kitching, T. Kubik, B. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maino, D. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinet, N. Marulli, F. Massey, R. Maurogordato, S. McCracken, H.J. Medinaceli, E. Mei, S. Mellier, Y. Meneghetti, M. Merlin, E. Meylan, G. Moresco, M. Moscardini, L. Munari, E. Niemi, S.-M. Nightingale, J.W. Padilla, C. Paltani, S. Pasian, F. Pedersen, K. Percival, W.J. Pettorino, V. Pires, S. Polenta, G. Poncet, M. Popa, L.A. Pozzetti, L. Raison, F. Rebolo, R. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Saglia, R. Sapone, D. Sartoris, B. Scaramella, R. Schirmer, M. Schneider, P. Schrabback, T. Secroun, A. Seidel, G. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Tallada-Crespí, P. Taylor, A.N. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Tutusaus, I. Valenziano, L. Vassallo, T. Veropalumbo, A. Wang, Y. Weller, J. Zamorani, G. Zoubian, J. Zucca, E. Biviano, A. Boucaud, A. Bozzo, E. Burigana, C. Calabrese, M. Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 Milano20133 Italy INFN-Sezione di Milano Via Celoria 16 Milano20133 Italy RWTH Aachen University Aachen52056 Germany Martí i Franquès 1 Barcelona08028 Spain Institut für Theoretische Physik University of Heidelberg Philosophenweg 16 Heidelberg69120 Germany Université de Toulouse CNRS UPS CNES 14 Av. Edouard Belin Toulouse31400 France Université St Joseph Faculty of Sciences Beirut Lebanon Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain Dipartimento di Fisica Università degli studi di Genova INFN-Sezione di Genova via Dodecaneso 33 Genova16146 Italy SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy Department of Astrophysics University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland AIM CEA CNRS Université Paris-Saclay Université de Paris Gif-sur-Yvette91191 France Dipartimento di Fisica Università degli Studi di Torino Via P. Giuria 1 Torino10125 Italy INFN-Sezione di Torino Via P. Giuria 1 Torino10125 Italy INAF-Osservatorio Astrofisico di Torino Via Osservatorio 20 TO Pino Torinese10025 Italy Hamburger Sternwarte University of Hamburg Gojenbergsweg 112 Hamburg21029 Germany Dipartimento di Fisica Sezione di Astronomia Università di Trieste Via Tiepolo 11 Trieste34131 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INAF-IASF Milano Via Alfonso Corti 12 Milano20133 Italy Service de Physique Théorique CP225 Boulevard du Triophe Bruxelles1050 Belgium Department of Physics "E. Pancini" University Federico II Via Cinthia 6 Napoli80126 Italy Ludwig-Maximilians-University Schellingstrasse 4 Munich80799 Germany INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy Department of Physics and Astronomy University of British Colu

Context. The Euclid mission of the European Space Agency will deliver weak gravitational lensing and galaxy clustering surveys that can be used to constrain the standard cosmological model and extensions thereof. Aims. We present forecasts from the combination of the Euclid photometric galaxy surveys (weak lensing, galaxy clustering and their cross-correlation) and its spectroscopic redshift survey on the sensitivity to cosmological parameters including the summed neutrino mass ∑ mν and the effective number of relativistic species Neff in the standard ΛCDM scenario and in a scenario with dynamical dark energy (w0waCDM). Methods. We compare the accuracy of different algorithms predicting the nonlinear matter power spectrum for such models. We then validate several pipelines for Fisher matrix and Markov Chain Monte Carlo (MCMC) forecasts, using different theory codes, algorithms for numerical derivatives, and assumptions concerning the non-linear cut-off scale. Results. The Euclid primary probes alone will reach a sensitivity of σ(∑ mν = 60 meV) = 56 meV in the ΛCDM+∑ mν model, whereas the combination with cosmic microwave background (CMB) data from Planck is expected to achieve σ(∑ mν) = 23 meV and raise the evidence for a non-zero neutrino mass to at least the 2.6 σ level. This can be pushed to a 4 σ detection if future CMB data from LiteBIRD and CMB Stage-IV are included. In combination with Planck, Euclid will also deliver tight constraints on ∆Neff ν+Neff model, or ∆Neff eff remains stable, while that to ∑ mν degrades at most by a factor two. Conclusions. This work illustrates the complementarity between the Euclid spectroscopic and imaging/photometric surveys and between Euclid and CMB constraints. Euclid will have a great potential for measuring the neutrino mass and excluding well-motivated scenarios with additional relativistic particles. Copyright © 2024, The Authors. All rights reserved.

关键词： Galaxies

Ab initio search of polymer crystals with high thermal conductivity

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arXiv 2018年

作者： Utimula, Keishu Ichibha, Tom Maezono, Ryo Hongo, Kenta School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan

Lattice thermal conductivities (LTC) for a subset of polymer crystals from the Polymer Genome Library were investigated to explore high LTC polymer systems. We employed a first-principles approach to evaluating phonon lifetimes within the third-order perturbation theory combined with density functional theory, and then solved the linearized Boltzmann transport equation with single-mode relaxation time approximated by the computed lifetime. Typical high LTC polymer systems, polyethylene (PE) crystal and fiber, were benchmarked, which is reasonably consistent with previous references, validating our approach. We then applied it to not only typical polymer crystals, but also some selected ones having structural similarities to PE. Among the latter crystals, we discovered that beta phase of Poly(vinylidenesurely fluoride) (PVF-β) crystal has higher LTC than PE at low temperature. Our detailed mode analysis revealed that the phonon lifetime of PVF-β is more locally distributed around lower frequency modes and four-times larger than that of PE. It was also found from a simple data analysis that the LTC relatively correlates with curvature of energy-volume plot. The curvature would be used as a descriptor for further exploration of high LTC polymer crystals by means of a data-driven approach beyond human-based one. Copyright © 2018, The Authors. All rights reserved.

关键词： Perturbation techniques

Joint power and admission control based on channel distribution information: A novel two-timescale approach

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arXiv 2017年

作者： Chen, Qitian Kang, Dong He, Yichu Chang, Tsung-Hui Liu, Ya-Feng School of Mathematical Sciences Peking University Beijing100871 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Science and Engineering Chinese University of Hong Kong Shenzhen Research Institute of Big Data Shenzhen518172 China

In this letter, we consider the joint power and admission control (JPAC) problem by assuming that only the channel distribution information (CDI) is available. Under this assumption, we formulate a new chance (probabilistic) constrained JPAC problem, where the signal to interference plus noise ratio (SINR) outage probability of the supported links is enforced to be not greater than a prespecified tolerance. To efficiently deal with the chance SINR constraint, we employ the sample approximation method to convert them into finitely many linear constraints. Then, we propose a convex approximation based deflation algorithm for solving the sample approximation JPAC problem. Compared to the existing works, this letter proposes a novel two-timescale JPAC approach, where admission control is performed by the proposed deflation algorithm based on the CDI in a large timescale and transmission power is adapted instantly with fast fadings in a small timescale. The effectiveness of the proposed algorithm is illustrated by simulations. Copyright © 2017, The Authors. All rights reserved.

关键词： Signal interference

Ab initio thermodynamic properties of certain compounds in Nd-Fe-B system

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arXiv 2018年

作者： Hanindriyo, Adie Tri Sridar, Soumya Hari Kumar, K.C. Hongo, Kenta Maezono, Ryo School of Material Science Jaist Asahidai 1-1 Nomi Ishikawa923-1292 Japan Department of Metallurgical and Materials Engineering Indian Institute of Technology Madras Chennai600 036 India Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Research Center for Advanced Computing Infrastructure Jaist Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan Presto Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama332-0012 Japan School of Information Science Jaist Asahidai 1-1 Nomi Ishikawa923-1292 Japan

In this work, we report the results of ab initio calculations of thermochemical properties of several compounds in the Fe-Nd, B-Nd and B-Fe-Nd systems. We have performed DFT+U calculations to compute the enthalpy of formation of the compounds NdB6, NdB4, Nd2B5, Nd2Fe17and Nd5Fe2B6. It was found that the values obtained with an effective Hubbard U correction have better agreement with the experimental data. We have also computed the vibrational contribution to the heat capacity (Cp) of the compounds as a function of temperature was computed using the quasharmonic approximation. For most of the compounds these properties have not been experimentally determined until now. Hence, the computed ab initio thermodynamic properties will serve as useful input for the Gibbs energy model parameter assessment using the CALPHAD method. Copyright © 2018, The Authors. All rights reserved.

关键词： Iron alloys

The first fermi-LAT solar flare catalog

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arXiv 2021年

作者： Ajello, M. Baldini, L. Bastieri, D. Bellazzini, R. Berretta, A. Bissaldi, E. Blandford, R.D. Bonino, R. Bruel, P. Buson, S. Cameron, R.A. Caputo, R. Cavazzuti, E. Cheung, C.C. Chiaro, G. Costantin, D. Cutini, S. D’Ammando, F. de Palma, F. Desiante, R. Di Lalla, N. Di Venere, L. Fana Dirirsa, F. Fegan, S.J. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giordano, F. Giroletti, M. Green, D. Guiriec, S. Hays, E. Hewitt, J.W. Horan, D. Jóhannesson, G. Kovacevic, M. Kuss, M. Larsson, S. Latronico, L. Li, J. Longo, F. Lovellette, M.N. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. Mereu, I. Michelson, P.F. Mizuno, T. Monzani, M.E. Morselli, A. Moskalenko, I.V. Negro, M. Omodei, N. Orienti, M. Orlando, E. Paneque, D. Pei, Z. Persic, M. Pesce-Rollins, Melissa Petrosian, V. Piron, F. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Rani, B. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Tak, D. Troja, E. Valverde, J. Wood, K. Zaharijas, G. Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Dipartimento di Fisica Università degli Studi di Perugia PerugiaI-06123 Italy Dipartimento di Fisica"M. Merlin" dell’Università e del Politecnico di Bari via Amendola 173 BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France NASA Goddard Space Flight Center GreenbeltMD20771 United States Italian Space Agency Via del Politecnico snc Roma00133 Italy Space Science Division Naval Research Laboratory WashingtonDC20375-5352 United States INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 MilanoI-20133 Italy University of Padua Department of Statistical Science Via 8 Febbraio 2 Padova35122 Italy Istituto Nazionale di Fisica Nucleare Sezione di Perugia PerugiaI-06123 Italy INAF Istituto di Radioastronomia BolognaI-40129 Italy Department of Physics University of Johannesburg PO Box 524 Auckland Park2006 South Africa Department of Physical Sciences Hiroshima University Higashi-HiroshimaHiroshima739-8526 Japan Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Centre for Astroparticle Ph

We present the first Fermi-Large Area Telescope (LAT) solar flare catalog covering the 24th solar cycle. This catalog contains 45 Fermi-LAT solar flares (FLSFs) with emission in the γ-ray energy band (30 MeV - 10 GeV) detected with a significance ≥5σ over the years 2010-2018. A subsample containing 37 of these flares exhibit delayed emission beyond the prompt-impulsive hard X-ray phase with 21 flares showing delayed emission lasting more than two hours. No prompt-impulsive emission is detected in four of these flares. We also present in this catalog the observations of GeV emission from 3 flares originating from Active Regions located behind the limb (BTL) of the visible solar disk. We report the light curves, spectra, best proton index and localization (when possible) for all the FLSFs. The γ-ray spectra is consistent with the decay of pions produced by >300 MeV protons. This work contains the largest sample of high-energy γ-ray flares ever reported and provides the unique opportunity to perform population studies on the different phases of the flare and thus allowing to open a new window in solar physics. Copyright © 2021, The Authors. All rights reserved.

关键词： Germanium alloys

Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

Sparse Word Embeddings Using 1 Regularized Online Learning

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Sparse Word Embeddings Using 1 Regularized Online Learning

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Sun, Fei Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned in Word2Vec are lacking of interpretability. It is natural to ask if one could improve their interpretability while keeping their performances. Inspired by the success of sparse models in enhancing interpretability, we propose to introduce sparse constraint into Word2Vec. Specifically, we take the Continuous Bag of Words (CBOW) model as an example in our study and add the l regularizer into its learning objective. One challenge of optimization lies in that stochastic gradient descent (SGD) cannot directly produce sparse solutions with 1 regularizer in online training. To solve this problem, we employ the Regularized Dual Averaging (RDA) method, an online optimization algorithm for regularized stochastic learning. In this way, the learning process is very efficient and our model can scale up to very large corpus to derive sparse word representations. The proposed model is evaluated on both expressive power and interpretability. The results show that, compared with the original CBOW model, the proposed model can obtain state-of-the-art results with better interpretability using less than 10% non-zero elements. © 2016 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Optimization

Neural matching models for question retrieval and next question prediction in conversation

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arXiv 2017年

作者： Yang, Liu Zamani, Hamed Zhang, Yongfeng Guo, Jiafeng Bruce Croft, W. Center for Intelligent Information Retrieval University of Massachusetts Amherst AmherstMA United States CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

The recent boom of AI has seen the emergence of many human-computer conversation systems such as Google Assistant, Microsoft Cortana, Amazon Echo and Apple Siri. We introduce and formalize the task of predicting questions in conversations, where the goal is to predict the new question that the user will ask, given the past conversational context. This task can be modeled as a "sequence matching" problem, where two sequences are given and the aim is to learn a model that maps any pair of sequences to a matching probability. Neural matching models, which adopt deep neural networks to learn sequence representations and matching scores, have attracted immense research interests of information retrieval and natural language processing communities. In this paper, we first study neural matching models for the question retrieval task that has been widely explored in the literature, whereas the effectiveness of neural models for this task is relatively unstudied. We further evaluate the neural matching models in the next question prediction task in conversations. We have used the publicly available Quora data and Ubuntu chat logs in our experiments. Our evaluations investigate the potential of neural matching models with representation learning for question retrieval and next question prediction in conversations. Experimental results show that neural matching models perform well for both tasks. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

Fake news propagate differently from real news even at early stages of spreading

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arXiv 2018年

作者： Zhao, Zilong Zhao, Jichang Sano, Yukie Levy, Orr Takayasu, Hideki Takayasu, Misako Li, Daqing Wu, Junjie Havlin, Shlomo School of Reliability and Systems Engineering Beihang University Beijing100191 China Science and Technology on Reliability and Environmental Engineering Laboratory Beijing100191 China School of Economics and Management Beihang University Beijing100191 China Faculty of Engineering Information and Systems University of Tsukuba Tennodai Tsukuba Ibaraki305-8573 Japan Department of Physics Bar-Ilan University Ramat Gan5290002 Israel Sony Computer Science Laboratories 3-14-13 Higashi-Gotanda Tokyo141-0022 Japan Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8502 Japan Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Social media can be a double-edged sword for society, either as a convenient channel exchanging ideas or as an unexpected conduit circulating fake news through a large population. While existing studies of fake news focus on theoretical modeling of propagation or identification methods based on machine learning, it is important to understand the realistic mechanisms between theoretical models and black-box methods. Here we track large databases of fake news and real news in both, Weibo in China and Twitter in Japan from different culture, which include their complete traces of re-postings. We find in both online social networks that fake news spreads distinctively from real news even at early stages of propagation, e.g. five hours after the first re-postings. Our finding demonstrates collective structural signals that help to understand the different propagation evolution of fake news and real news. Different from earlier studies, identifying the topological properties of the information propagation at early stages may offer novel features for early detection of fake news in social media. Copyright © 2018, The Authors. All rights reserved.

关键词： Social networking (online)