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Beyond the coverage of information spreading: Analytical and empirical evidence of re-exposure in large-scale online social networks

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arXiv 2019年

作者： Lu, Xin Qin, Shuo Holme, Petter Meng, Fanhui Hu, Yanqing Liljeros, Fredrik Allon, Gad School of Business Central South University Changsha410083 China College of Systems Engineering National University of Defense Technology Changsha410073 China Department of Public Health Sciences Karolinska Institutet Stockholm17177 Sweden Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8503 Japan School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519082 China Department of Sociology Stockholm University Stockholm17177 Sweden Wharton School University of Pennsylvania 19104 PA United States

Peer influence and social contagion are key denominators in the adoption and participation of information spreading, such as news propagation, word-of-mouth or viral marketing. In this study, we argue that it is biased to only focus on the scale and coverage of information spreading, and propose that the level of influence reinforcement, quantified by the re-exposure rate, i.e., the rate of individuals who are repeatedly exposed to the same information, should be considered together to measure the effectiveness of spreading. We show that local network structural characteristics significantly affects the probability of being exposed or re-exposed to the same information. After analyzing trending news on the super large-scale online network of Sina Weibo (China's Twitter) with 430 million connected users, we find a class of users with extremely low exposure rate, even they are following tens of thousands of others;and the re-exposure rate is substantially higher for news with more transmission waves and stronger secondary forwarding. While exposure and re-exposure rate typically grow together with the scale of spreading, we find exceptional cases where it is possible to achieve a high exposure rate while maintaining low re-exposure rate, or vice versa. Copyright © 2019, The Authors. All rights reserved.

关键词： Social networking (online)

Deep learning towards mobile applications

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arXiv 2018年

作者： Wang, Ji Cao, Bokai Yu, Philip S. Sun, Lichao Bao, Weidong Zhu, Xiaomin College of Systems Engineering National University of Defense Technology Changsha Hunan China Department of Computer Science University of Illinois at Chicago ChicagoIL United States Institute for Data Science Tsinghua University Beijing China

Recent years have witnessed an explosive growth of mobile devices. Mobile devices are permeating every aspect of our daily lives. With the increasing usage of mobile devices and intelligent applications, there is a soaring demand for mobile applications with machine learning services. Inspired by the tremendous success achieved by deep learning in many machine learning tasks, it becomes a natural trend to push deep learning towards mobile applications. However, there exist many challenges to realize deep learning in mobile applications, including the contradiction between the miniature nature of mobile devices and the resource requirement of deep neural networks, the privacy and security concerns about individuals' data, and so on. To resolve these challenges, during the past few years, great leaps have been made in this area. In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: Training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning. The former two aspects cover the primary tasks of deep learning. Then, we go through our two recent applications that apply the data collected by mobile devices to inferring mood disturbance and user identification. Finally, we conclude this paper with the discussion of the future of this area. Copyright © 2018, The Authors. All rights reserved.

关键词： Mobile computing

Thalamo-cortical structural co-variation networks are related to familial risk for schizophrenia in the context of lower nuclei volume estimates in patients: an ENIGMA study

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Biological psychiatry 2025年

作者： Annalisa Lella Linda A Antonucci Roberta Passiatore Loredana Bellantuono Pierluigi Selvaggi Teresa Popolizio Guido Di Sciascio Alessandro Saponaro Patrizia Ricci Mario Altamura Giuseppe Blasi Antonio Rampino Chris Vriend Vince D Calhoun Kelly Rootes-Murdy Aaron L Goldman Inmaculada Baeza Josefina Castro-Fornieles Gisela Sugranyes Elena De la Serna Edith Pomarol-Clotet Mar Fatjó-Vilas Raymond Salvador Andriana Karuk Paola Fuentes-Claramonte David C Glahn Amanda L Rodrigue John Blangero Lei Wang Taeyoung Lee Karolin E Einenkel Saskia Hamers Oliver Gruber Adrian Preda Young-Chul Chung Soyolsaikhan Odkhuu Corentin Vallée Paola Dazzan Machteld Marcelis Stijn Michielse Katharina Brosch Frederike Stein Igor Nenadić Benjamin Straube Florian Thomas-Odenthal Tilo Kircher Sean Carruthers Susan L Rossell Phillip J Sumner Tamsyn E Van Rheenen Caroline Demro Ian S Ramsay Scott R Sponheim Rebekka Lencer Susanne Meinert Tim Hahn Udo Dannlowski Dominik Grotegerd Mariateresa Ciccarelli Felice Iasevoli Giuseppe Pontillo Godfrey D Pearlson Derin Cobia Fabrizio Piras Nerisa Banaj Daniela Vecchio Marjolein E A Barendse Neeltje E M van Haren Hang Joon Jo Kang Sim Yann Quidé Melissa J Green Rachael Slate Giacomo Cecere Wolfgang Omlor Stephanie Homan Philipp Homan Sophia I Thomopoulos Jessica A Turner Theo G M van Erp Paul M Thompson Alessandro Bertolino Giulio Pergola Department of Translational Biomedicine and Neuroscience (DiBraiN)-University of Bari Aldo Moro Bari Italy. Department of Translational Biomedicine and Neuroscience (DiBraiN)-University of Bari Aldo Moro Bari Italy Lieber Institute for Brain Development Johns Hopkins Medical Campus Baltimore MD USA. Istituto Nazionale di Fisica Nucleare Bari Italy. Azienda Ospedaliero-Universitaria Consorziale Policlinico Bari Italy. Neuroradiology Unit Scientific Institute for Research Hospitalization and Health Care Casa Sollievo della Sofferenza 71013 San Giovanni Rotondo Foggia Italy. Department of Mental Health ASL Bari Bari Italy. Department of Mental Health ASL Brindisi Brindisi Italy. Department of Mental Health ASL Barletta Andria Trani BAT Italy. Department of Clinical and Experimental Medicine University of Foggia Italy. Amsterdam UMC Vrije Universiteit Amsterdam Department of Psychiatry and Department of Anatomy and Neuroscience de Boelelaan 1117 Amsterdam the Netherlands Compulsivity Impulsivity and Attention Amsterdam Neuroscience de Boelelaan 1117 Amsterdam the Netherlands. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia Institute of Technology Georgia State University Emory University Atlanta GA USA. Department of Child and Adolescent Psychiatry and Psychology 2021 SGR 01319 Institute of Neuroscience Hospital Clínic Barcelona Barcelona University of Barcelona Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) CIBERSAM Spain. FIDMAG Germanes Hospitalàries Research Foundation CIBERSAM ISCIII Barcelona Spain. Department of Psychiatry and Behavioral Sciences Boston Children's Hospital Boston MA USA Department of Psychiatry Harvard Medical School Boston MA USA Olin Neuropsychiatry Research Center Institute of Living Hartford CT USA. Department of Psychiatry Harvard Medical School Boston MA USA. Department of Human Genetics and South Texas Diabetes and Obesity Institute School of

BACKGROUND:Structural brain differences in the thalamus and the cortex have been widely reported in schizophrenia (SCZ) relative to neurotypical controls (NC). Most prior studies examined the thalamus as a whole, single region-of-interest. Additionally, findings in individuals at familial high-risk for schizophrenia (FHR) remain inconclusive. Here, we investigated whether local and network-wide thalamic-related structural alterations vary as a function of familial risk for schizophrenia. METHODS:Structural MRI scans were obtained from 5,197 participants (3,409 NC, 257 FHR, 1,531 SCZ) across 32 cross-sectional samples within the ENIGMA Consortium. Random-effects meta-analyses, and network analyses were conducted on (i) local thalamic alterations (volume estimates of seven thalamic subdivisions), and (ii) network-wide thalamic alterations (thickness and surface-related thalamo-cortical/cortico-cortical co-variation patterns) across groups (NC, FHR, SCZ). RESULTS:Individuals with SCZ showed significantly lower gray matter volume estimates in the anterior, pulvinar, medial, posterior, and ventral thalamic subdivisions compared to NC (q<0.05). FHR did not differ from NC. At the network-wide level, thalamo-cortical co-variations discriminated FHR from NC (q<0.05), with FHR showing intermediate co-variation between SCZ and NC. Cortico-cortical co-variation patterns revealed that SCZ and FHR shared similarly disconnected clustering configurations, distinct from NC (q<0.05). CONCLUSIONS:Results revealed lower thalamic volume estimates in SCZ but not in FHR, hence yielding no evidence of a familial risk trait, whereas thalamo-cortical and cortico-cortical co-variation estimates were associated with familial risk for SCZ These findings suggest that, once the thalamus is parsed into subdivisions, network-wide thalamo-cortical features may identify trait-dependent, neurobiological correlates of genetic risk for SCZ.

关键词： Familial high risk for schizophrenia meta-analyses morphometric measures structural neuroimaging thalamic subdivisions thalamo-cortical networks

X-ray Polarization of BL Lacertae in Outburst

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arXiv 2023年

作者： Peirson, Abel L. Negro, Michela Liodakis, Ioannis Middei, Riccardo Kim, Dawoon E. Marscher, Alan P. Marshall, Herman L. Pacciani, Luigi Romani, Roger W. Wu, Kinwah Marco, Alessandro Di Lalla, Niccoló Di Omodei, Nicola Jorstad, Svetlana G. Agudo, Iván Kouch, Pouya M. Lindfors, Elina Aceituno, Francisco José Bernardos, Maria I. Bonnoli, Giacomo Casanova, Víctor García-Comas, Maya Agís-González, Beatriz Husillos, César Marchini, Alessandro Sota, Alfredo Casadio, Carolina Escudero, Juan Myserlis, Ioannis Sievers, Albrecht Gurwell, Mark Rao, Ramprasad Imazawa, Ryo Sasada, Mahito Fukazawa, Yasushi Kawabata, Koji S. Uemura, Makoto Mizuno, Tsunefumi Nakaoka, Tatsuya Akitaya, Hiroshi Cheong, Whee Yeon Jeong, Hyeon-Woo Kang, Sincheol Kim, Sang-Hyun Lee, Sang-Sung Angelakis, Emmanouil Kraus, Alexander Cibrario, Nicoló Donnarumma, Immacolata Poutanen, Juri Tavecchio, Fabrizio Antonelli, Lucio A. Bachetti, Matteo Baldini, Luca Baumgartner, Wayne H. Bellazzini, Ronaldo Bianchi, Stefano Bongiorno, Stephen D. Bonino, Raffaella Brez, Alessandro Bucciantini, Niccoló Capitanio, Fiamma Castellano, Simone Cavazzuti, Elisabetta Chen, Chien-Ting Ciprini, Stefano Costa, Enrico De Rosa, Alessandra Monte, Ettore Del Gesu, Laura Di Doroshenko, Victor Dovčiak, Michal Ehlert, Steven R. Enoto, Teruaki Evangelista, Yuri Fabiani, Sergio Ferrazzoli, Riccardo Garcia, Javier A. Gunji, Shuichi Hayashida, Kiyoshi Heyl, Jeremy Iwakiri, Wataru Kaaret, Philip Karas, Vladimir Kitaguchi, Takao Kolodziejczak, Jeffery J. Krawczynski, Henric Monaca, Fabio La Latronico, Luca Madejski, Grzegorz Maldera, Simone Manfreda, Alberto Marin, Frédéric Marinucci, Andrea Massaro, Francesco Matt, Giorgio Mitsuishi, Ikuyuki Muleri, Fabio Ng, C.-Y. O’Dell, Stephen L. Oppedisano, Chiara Papitto, Alessandro Pavlov, George G. Perri, Matteo Pesce-Rollins, Melissa Petrucci, Pierre-Olivier Pilia, Maura Possenti, Andrea Puccetti, Simonetta Ramsey, Brian D. Rankin, John Ratheesh, Ajay Roberts, Oliver J. Sgró, Carmelo Slane, Patrick Soffitta, Paolo Spandre, Gloria Swartz, Douglas Department of Physics Kavli Institute for Particle Astrophysics and Cosmology Stanford University StanfordCA94305 United States University of Maryland Baltimore County BaltimoreMD21250 United States NASA Goddard Space Flight Center GreenbeltMD20771 United States Center for Research and Exploration in Space Science and Technology NASA/GSFC GreenbeltMD20771 United States Finnish Centre for Astronomy with ESO University of Turku 20014 Finland Space Science Data Center Agenzia Spaziale Italiana Via del Politecnico snc Roma00133 Italy 00078 Italy INAF Istituto di Astrofisica e Planetologia Spaziali Via del Fosso del Cavaliere 100 RomaI-00133 Italy Dipartimento di Fisica Università degli Studi di Roma "La Sapienza" Piazzale Aldo Moro 5 RomaI-00185 Italy Dipartimento di Fisica Università degli Studi di Roma "Tor Vergata" Via della Ricerca Scientifica 1 RomaI-00133 Italy Institute for Astrophysical Research Boston University 725 Commonwealth Avenue BostonMA02215 United States MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States INAF Istituto di Astrofisica e Planetologia Spaziali Via del Fosso del Cavaliere 100 Roma00133 Italy Mullard Space Science Laboratory University College London Holmbury St Mary Dorking SurreyRH5 6NT United Kingdom Astronomical Institute St. Petersburg State University 28 Universitetsky prospekt Peterhof St. Petersburg198504 Russia Instituto de Astrofísica de Andalucía IAA-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Department of Physics and Astronomy University of Turku 20014 Finland INAF Osservatorio Astronomico di Brera Via E. Bianchi 46 LC Merate23807 Italy University of Siena Astronomical Observatory Via Roma 56 Siena53100 Italy Institute of Astrophysics Foundation for Research and Technology - Hellas Voutes Heraklion7110 Greece Department of Physics University of Crete Heraklion70013 Greece

We report the first > 99% confidence detection of X-ray polarization in BL Lacertae. During a recent X-ray/γ-ray outburst, a 287 ksec observation (2022 November 27-30) was taken using the Imaging X-ray Polarimetry Explorer (IXPE), together with contemporaneous multiwavelength observations from the Neil Gehrels Swift observatory and XMM-Newton in soft X-rays (0.3–10 keV), NuSTAR in hard X-rays (3–70 keV), and optical polarization from the Calar Alto, and Perkins Telescope observatories. Our contemporaneous X-ray data suggest that the IXPE energy band is at the crossover between the low- and high-frequency blazar emission humps. The source displays significant variability during the observation, and we measure polarization in three separate time bins. Contemporaneous X-ray spectra allow us to determine the relative contribution from each emission hump. We find > 99% confidence X-ray polarization Π2−4keV = 21.7+5−7.69% and electric vector polarization angle ψ2−4keV = −28.7 ±8.7◦ in the time bin with highest estimated synchrotron flux contribution. We discuss possible implications of our observations, including previous IXPE BL Lacertae pointings, tentatively concluding that synchrotron self-Compton emission dominates over hadronic emission processes during the observed epochs. © 2023, CC BY.

关键词： Polarization

Diagnosis of celiac disease and environmental enteropathy on biopsy images using color balancing on convolutional neural networks

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arXiv 2019年

作者： Kowsari, Kamran Sali, Rasoul Khan, Marium N. Adorno, William Asad Ali, S. Moore, Sean R. Amadi, Beatrice C. Kelly, Paul Syed, Sana Brown, Donald E. Department of Systems & Information Engineering University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia CharlottesvilleVA United States Department of Pediatrics School of Medicine University of Virginia CharlottesvilleVA United States Aga Khan University Karachi Pakistan Tropical Gastroenterology and Nutrition group University of Zambia School of Medicine Lusaka Zambia Blizard Institute Barts and The London School of Medicine Queen Mary University of London London United Kingdom

Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. CD is an autoimmune disorder that is prevalent worldwide and is caused by an increased sensitivity to gluten. Gluten exposure destructs the small intestinal epithelial barrier, resulting in nutrient mal-absorption and childhood under-nutrition. EE also results in barrier dysfunction but is thought to be caused by an increased vulnerability to infections. EE has been implicated as the predominant cause of under-nutrition, oral vaccine failure, and impaired cognitive development in low-and-middle-income countries. Both conditions require a tissue biopsy for diagnosis, and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose a convolutional neural network (CNN) to classify duodenal biopsy images from subjects with CD, EE, and healthy controls. We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images. Our evaluations show that the proposed model achieves an area under ROC of 0.99, 1.00, and 0.97 for CD, EE, and healthy controls, respectively. These results demonstrate the discriminative power of the proposed model in duodenal biopsies classification. Copyright © 2019, The Authors. All rights reserved.

关键词： Medical imaging

Uplift modeling with multiple treatments and general response types 17

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Uplift modeling with multiple treatments and general respons...

17th SIAM International Conference on data Mining, SDM 2017

作者： Zhao, Yan Fang, Xiao Simchi-Levi, David Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Institute for Data Systems and Society Department of Civil and Environmental Engineering Operations Research Center Massachusetts Institute of Technology United States

ISBN: (纸本)9781611974874

Randomized experiments have been used to assist decision-making in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods. Copyright © by SIAM.

关键词： Decision making

Discovery of X-ray polarization angle rotation in active galaxy Mrk 421

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arXiv 2023年

作者： Gesu, Laura Di Marshall, Herman L. Ehlert, Steven R. Kim, Dawoon E. Donnarumma, Immacolata Tavecchio, Fabrizio Liodakis, Ioannis Kiehlmann, Sebastian Agudo, Iván Jorstad, Svetlana G. Muleri, Fabio Marscher, Alan P. Puccetti, Simonetta Middei, Riccardo Perri, Matteo Pacciani, Luigi Negro, Michela Romani, Roger W. Marco, Alessandro Di Blinov, Dmitry Bourbah, Ioakeim G. Kontopodis, Evangelos Mandarakas, Nikos Romanopoulos, Stylianos Skalidis, Raphael Vervelaki, Anna Casadio, Carolina Escudero, Juan Myserlis, Ioannis Gurwell, Mark A. Rao, Ramprasad Keating, Garrett K. Kouch, Pouya M. Lindfors, Elina Aceituno, Francisco José Bernardos, Maria I. Bonnoli, Giacomo Casanova, Víctor García-Comas, Maya Agís-González, Beatriz Husillos, César Marchini, Alessandro Sota, Alfredo Imazawa, Ryo Sasada, Mahito Fukazawa, Yasushi Kawabata, Koji S. Uemura, Makoto Mizuno, Tsunefumi Nakaoka, Tatsuya Akitaya, Hiroshi Savchenko, Sergey S. Vasilyev, Andrey A. Gómez, José L. Antonelli, Lucio A. Barnouin, Thibault Bonino, Raffaella Cavazzuti, Elisabetta Costamante, Luigi Chen, Chien-Ting Cibrario, Nicolò De Rosa, Alessandra Pierro, Federico Di Errando, Manel Kaaret, Philip Karas, Vladimir Krawczynski, Henric Lisalda, Lindsey Madejski, Grzegorz Malacaria, Christian Marin, Frédéric Marinucci, Andrea Massaro, Francesco Matt, Giorgio Mitsuishi, Ikuyuki O’Dell, Stephen L. Paggi, Alessandro Peirson, Abel L. Petrucci, Pierre-Olivier Ramsey, Brian D. Tennant, Allyn F. Wu, Kinwah Bachetti, Matteo Baldini, Luca Baumgartner, Wayne H. Bellazzini, Ronaldo Bianchi, Stefano Bongiorno, Stephen D. Brez, Alessandro Bucciantini, C. Capitanio, Fiamma Castellano, Simone Ciprini, Stefano Costa, Enrico Monte, Ettore Del Lalla, Niccolò Bucciantini Di Doroshenko, Victor Dovčiak, Michal Enoto, Teruaki Evangelista, Yuri Fabiani, Sergio Ferrazzoli, Riccardo Garcia, Javier A. Gunji, Shuichi Hayashida, Kiyoshi Heyl, Jeremy Iwakiri, Wataru Kislat, Fabian Kitaguchi, Takao Kolodziejczak, Jeffery J. Monaca, Fabio La Latronico, Luca Maldera, Simone Manfreda, Alberto Ng, C.-Y. ASI Agenzia Spaziale Italiana Via del Politecnico snc Roma00133 Italy MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States NASA Marshall Space Flight Center HuntsvilleAL35812 United States INAF Istituto di Astrofisica e Planetologia Spaziali Via del Fosso del Cavaliere 100 Roma00133 Italy Dipartimento di Fisica Università degli Studi di Roma "La Sapienza" Piazzale Aldo Moro 5 RomaI00185 Italy Dipartimento di Fisica Università degli Studi di Roma "Tor Vergata" Via della Ricerca Scientifica 1 Roma00133 Italy 23807 Italy Finnish Centre for Astronomy with ESO University of Turku 20014 Finland Institute of Astrophysics Foundation for Research and Technology-Hellas HeraklionGR-71110 Greece Department of Physics University of Crete Heraklion70013 Greece Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Institute for Astrophysical Research Boston University 725 Commonwealth Avenue BostonMA02215 United States Department of Astrophysics St. Petersburg State University Universitetsky pr. 28 Petrodvoretz St. Petersburg198504 Russia Space Science Data Center Agenzia Spaziale Italiana Via del Politecnico snc Roma00133 Italy INAF Osservatorio Astronomico di Roma Via Frascati 33 RM Monte Porzio Catone00078 Italy University of Maryland Baltimore County BaltimoreMD21250 United States NASA Goddard Space Flight Center GreenbeltMD20771 United States Center for Research and Exploration in Space Science and Technology NASA/GSFC GreenbeltMD20771 United States Department of Physics Kavli Institute for Particle Astrophysics and Cosmology Stanford University StanfordCA94305 United States Owens Valley Radio Observatory California Institute of Technology MC 249-17 PasadenaCA91125 United States Institut de Radioastronomie Millimétrique Avenida Divina Pastora 7 Local 20 GranadaE18012 Spain Center for Astrophys

The magnetic field conditions in astrophysical relativistic jets can be probed by multiwavelength polarimetry, which has been recently extended to X-rays. For example, one can track how the magnetic field changes in the flow of the radiating particles by observing rotations of the electric vector position angle Ψ. Here we report the discovery of a Ψx rotation in the X-ray band in the blazar Mrk 421 at an average flux state. Across the 5 days of Imaging X-ray Polarimetry Explorer (IXPE) observations of 4-6 and 7-9 June 2022, Ψx rotated in total by ≥ 360◦. Over the two respective date ranges, we find constant, within uncertainties, rotation rates (80 ± 9 and 91 ± 8◦/day) and polarization degrees (Πx = 10% ± 1%). Simulations of a random walk of the polarization vector indicate that it is unlikely that such rotation(s) are produced by a stochastic process. The X-ray emitting site does not completely overlap the radio/infrared/optical emission sites, as no similar rotation of Ψ was observed in quasi-simultaneous data at longer wavelengths. We propose that the observed rotation was caused by a helical magnetic structure in the jet, illuminated in the X-rays by a localized shock propagating along this helix. The optically emitting region likely lies in a sheath surrounding an inner spine where the X-ray radiation is released. © 2023, CC BY.

关键词： Rotation

Segregated interactions in urban and online spaces

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arXiv 2019年

作者： Dong, Xiaowen Morales, Alfredo J. Jahani, Eaman Moro, Esteban Lepri, Bruno Bozkaya, Burcin Sarraute, Carlos Bar-Yam, Yaneer Pentland, Alex Department of Engineering Science University of Oxford Oxford United Kingdom MIT Media Lab CambridgeMA United States New England Complex Systems Institute CambridgeMA United States MIT Institute for Data Systems and Society CambridgeMA United States Department of Mathematics and GISC Universidad Carlos III de Madrid Leganés Spain Mobile and Social Computing Lab Fondazione Bruno Kessler Trento Italy Behavioral Analytics & Visualization Lab Sabancı University Istanbul Turkey New College of Florida SarasotaFL United States Grandata Labs Buenos Aires Argentina

Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both shopping activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric, i.e., the amount of interaction from poorer to wealthier neighborhoods is considerably larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications. Copyright © 2019, The Authors. All rights reserved.

关键词： Geographical distribution

A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics

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arXiv 2019年

作者： Wang, Le-Zhi Zhao, Zhi-Dan Jiang, Jun-Jie Guo, Bing-Hui Wang, Xiao Huang, Zi-Gang Lai, Ying-Cheng School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Web Sciences Center Big Data Research Center University of Electronic Science and Technology of China Chengdu610054 China School of Mathematics Beihang University Beijing100191 China School of Biological and Health Systems Engineering Arizona State University TempeAZ85287 United States School of Life Science and Technology Xi'An Jiaotong University Xi'an710049 China Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou Gansu730000 China Department of Physics Arizona State University TempeAZ85287 United States

We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an \S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Social networking (online)