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Publisher Correction: Extreme risk induced by communities in interdependent networks

Communications Physics 2019年第1期2卷 1页

作者： Jiachen Sun Rui Zhang Ling Feng Christopher Monterola Xiao Ma Céline Rozenblat H. Eugene Stanley Boris Podobnik Yanqing Hu School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Institute for Molecular Engineering University of Chicago Chicago USA Institute of High Performance Computing A*STAR Singapore Singapore Department of Physics National University of Singapore Singapore Singapore Asian Institute of Management Metro Manila Philippines School of Data and Computer Science Sun Yat-sen University Guangzhou China Institute of Geography University of Lausanne Lausanne Switzerland Department of Physics Boston University Boston USA Faculty of Civil Engineering University of Rijeka Rijeka Croatia Faculty of Information Studies Novo Mesto Slovenia Zagreb School of Economics and Management Zagreb Croatia Luxembourg School of Business Luxembourg Luxembourg

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An interview based study of pioneering experiences in teaching and learning complex systems in higher education

arXiv

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arXiv 2018年

作者： Lizier, Joseph T. Harré, Michael S. Mitchell, Melanie DeDeo, Simon Finn, Conor Lindgren, Kristian Lizier, Amanda L. Sayama, Hiroki Centre for Complex Systems Complex Systems Research Group Faculty of Engineering and IT University of Sydney NSW Australia Department of Computer Science Portland State University PortlandOR United States Santa Fe Institute Santa FeNM United States School of Informatics and Computing Indiana University BloomingtonIN United States Department of Social and Decision Sciences Carnegie Mellon University PittsburghPA United States CSIRO Data 61 MarsfieldNSW Australia Department of Space Earth and Environment Chalmers University of Technology Göteborg Sweden School of Education University of Technology Sydney NSW Australia Center for Collective Dynamics of Complex Systems Department of Systems Science and Industrial Engineering Binghamton University State University of New York NY United States

Due to the interdisciplinary nature of complex systems as a field, students studying complex systems at University level have diverse disciplinary backgrounds. This brings challenges (e.g. wide range of computer programming skills) but also opportunities (e.g. facilitating interdisciplinary interactions and projects) for the classroom. However, there is little published regarding how these challenges and opportunities are handled in teaching and learning Complex Systems as an explicit subject in higher education, and how this differs in comparison to other subject areas. We seek to explore these particular challenges and opportunities via an interview-based study of pioneering teachers and learners (conducted amongst the authors) regarding their experiences. We compare and contrast those experiences, and analyse them with respect to the educational literature. Our discussions explored: approaches to curriculum design, how theories/models/frameworks of teaching and learning informed decisions and experience, how diversity in student backgrounds was addressed, and assessment task design. We found a striking level of commonality in the issues expressed as well as the strategies to handle them, for example a significant focus on problem-based learning, and the use of major student-led creative projects for both achieving and assessing learning outcomes. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

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IDTxl: The information dynamics toolkit xl: A python package for the efficient analysis of multivariate information dynamics in networks

arXiv

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arXiv 2018年

作者： Wollstadt, Patricia Lizier, Joseph T. Vicente, Raul Finn, Conor Martinez-Zarzuela, Mario Mediano, Pedro Novelli, Leonardo Wibral, Michael MEG Unit Brain Imaging Center Goethe-University Frankfurt Fankfurt am Main Germany Centre for Complex Systems Faculty of Engineering and IT University of Sydney SydneyNSW Australia Computational Neuroscience Lab Institute of Computer Science Tartu Estonia Data61 CSIRO EppingNSW Australia Communications and Signal Theory and Telematics Engineering University of Valladolid Valladolid Spain Computational Neurodynamics Group Department of Computing Imperial College London London United Kingdom Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Campus Institute for Dynamics of Biological Networks Georg-August Universität Göttingen Germany

We present IDTxl (the Information Dynamics Toolkit xl), a new open source Python toolbox for effective network inference from multivariate time series using information theory, available from GitHub (https://***/pwollstadt/IDTxl). Information theory (Cover & Thomas, 2006;MacKay, 2003;Shannon, 1948) is the mathematical theory of information and its transmission over communication channels. Information theory provides quantitative measures of the information content of a single random variable (entropy) and of the information shared between two variables (mutual information). The defined measures build on probability theory and solely depend on the probability distributions of the variables involved. As a consequence, the dependence between two variables can be quantified as the information shared between them, without the need to explicitly model a specific type of dependence. Hence, mutual information is a model-free measure of dependence, which makes it a popular choice for the analysis of systems other than communication channels. Transfer entropy (TE) (Schreiber, 2000) is an extension of mutual information that measures the directed information transfer between time series of a source and a target variable. TE has become popular in many scientific disciplines to infer dependencies and whole networks from data. Notable application domains include neuroscience (Wibral, Vicente, & Lindner, 2014) and dynamical systems analysis (Lizier, Prokopenko, & Zomaya, 2014) (see Bossomaier, Barnett, Harré, & Lizier (2016) for an introduction to TE and a comprehensive discussion of its application). In the majority of the applications, TE is used in a bivariate fashion, where information transfer is quantified between all sourcetarget pairs. In a multivariate setting, however, such a bivariate analysis may infer spurious or redundant interactions, where multiple sources provide the same information about the target. Conversely, bivariate analysis may also miss synergistic

关键词： High level languages

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An improved computational framework using one stage filtration by incorporating knowledge in gene expression clustering 16

An improved computational framework using one stage filtrati...

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2016 International Conference on Artificial Intelligence and Robotics, ICAIR 2016 and 2016 International Conference on Automation, Control and Robotics Engineering, CACRE 2016

作者： Kasim, Shahreen Fudzee, Mohd Farhan Md. Salamat, Mohamad Aizi Ramli, Azizul Azhar Mahdin, Hairulnizam Abdullah, Mohd Hafizul Afifi Soft Computing and Data Mining Centre Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat86400 Malaysia Web Technology Department Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat86400 Malaysia

ISBN: (纸本)9781450342353

The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. Given a gene expression dataset, the challenge is to predict gene function given the intensity and redundancy of the data and the lack of similarity of expression profiles without degrading the analysis performance and also without assigning genes at random. At the same time, the computational method must be capable of producing highly compacted clusters with furthest separation, high consistency, and accuracy. In this paper, we present a new computational framework for clustering gene expression data. From this experiment, we can conclude that our new framework capable to improve the accuracy thus determined the dominant gene. © 2016 ACM.

关键词： Gene expression

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SPT clusters with DES and HST weak lensing. I. Cluster lensing and Bayesian population modeling of multiwavelength cluster datasets

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Physical Review D 2024年第8期110卷 083509页

作者： S. Bocquet S. Grandis L. E. Bleem M. Klein J. J. Mohr M. Aguena A. Alarcon S. Allam S. W. Allen O. Alves A. Amon B. Ansarinejad D. Bacon M. Bayliss K. Bechtol M. R. Becker B. A. Benson G. M. Bernstein M. Brodwin D. Brooks A. Campos R. E. A. Canning J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero R. Cawthon C. Chang R. Chen A. Choi J. Cordero M. Costanzi L. N. da Costa M. E. S. Pereira C. Davis J. DeRose S. Desai T. de Haan J. De Vicente H. T. Diehl S. Dodelson P. Doel C. Doux A. Drlica-Wagner K. Eckert J. Elvin-Poole S. Everett I. Ferrero A. Ferté A. M. Flores J. Frieman J. García-Bellido M. Gatti G. Giannini M. D. Gladders D. Gruen R. A. Gruendl I. Harrison W. G. Hartley K. Herner S. R. Hinton D. L. Hollowood W. L. Holzapfel K. Honscheid N. Huang E. M. Huff D. J. James M. Jarvis G. Khullar K. Kim R. Kraft K. Kuehn N. Kuropatkin F. Kéruzoré S. Lee P.-F. Leget N. MacCrann G. Mahler A. Mantz J. L. Marshall J. McCullough M. McDonald J. Mena-Fernández R. Miquel J. Myles A. Navarro-Alsina R. L. C. Ogando A. Palmese S. Pandey A. Pieres A. A. Plazas Malagón J. Prat M. Raveri C. L. Reichardt J. Roberson R. P. Rollins A. K. Romer C. Romero A. Roodman A. J. Ross E. S. Rykoff L. Salvati C. Sánchez E. Sanchez D. Sanchez Cid A. Saro T. Schrabback M. Schubnell L. F. Secco I. Sevilla-Noarbe K. Sharon E. Sheldon T. Shin M. Smith T. Somboonpanyakul B. Stalder A. A. Stark V. Strazzullo E. Suchyta M. E. C. Swanson G. Tarle C. To M. A. Troxel I. Tutusaus T. N. Varga A. von der Linden N. Weaverdyck J. Weller P. Wiseman B. Yanny B. Yin M. Young Y. Zhang J. Zuntz University Observatory Faculty of Physics Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 6020 Innsbruck Austria Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Max Planck Institute for Extraterrestrial Physics Gießenbachstr. 1 85748 Garching Germany Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 Rio de Janeiro RJ-20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 USA Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom School of Physics University of Melbourne Parkville Victoria 3010 Australia Institute of Cosmology and Gravitation University of Portsmouth Portsmouth PO1 3FX United Kingdom Department of Physics University of Cincinnati Cincinnati Ohio 45221 USA Physics Department 2320 Chamberlin Hall University of Wisconsin-Madison 1150 University Avenue Madison Wisconsin 53706-1390 USA Department of Astronomy and Astrophysics University of Chicago Chicago Illinois 60637 USA Fermi National Accelerator Laboratory Batavia Illinois 60510-0500 USA Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA Department of Physics and Astronomy University of Missouri 5110 Rockhill Road Kansas City Missouri 64110 USA Department of Physics and Astronomy University Coll

We present a Bayesian population modeling method to analyze the abundance of galaxy clusters identified by the South Pole Telescope (SPT) with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). We discuss and validate the modeling choices with a particular focus on a robust, weak-lensing-based mass calibration using DES data. For the DES Year 3 data, we report a systematic uncertainty in weak-lensing mass calibration that increases from 1% at z=0.25 to 10% at z=0.95, to which we add 2% in quadrature to account for uncertainties in the impact of baryonic effects. We implement an analysis pipeline that joins the cluster abundance likelihood with a multiobservable likelihood for the Sunyaev-Zel’dovich effect, optical richness, and weak-lensing measurements for each individual cluster. We validate that our analysis pipeline can recover unbiased cosmological constraints by analyzing mocks that closely resemble the cluster sample extracted from the SPT-SZ, SPTpol ECS, and SPTpol 500d surveys and the DES Year 3 and HST-39 weak-lensing datasets. This work represents a crucial prerequisite for the subsequent cosmological analysis of the real dataset.

关键词： Cosmological parameters Gravitational lenses Large scale structure of the Universe

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The associations of childhood adversities and mental disorders with suicidal thoughts and behaviors - Results from the World Mental Health International College Student Initiative

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Psychiatry research 2025年 350卷 116555页

作者： Philippe Mortier Xue Yang Yasmin A Altwaijri Jacob A Holdcraft Sue Lee Nancy A Sampson Yesica Albor Ahmad N Alhadi Jordi Alonso Nouf K Al-Saud Claes Andersson Lukoye Atwoli Randy P Auerbach Caroline Ayuya Muaka Patricia M Báez-Mansur Laura Ballester Jason Bantjes Harald Baumeister Marcus Bendtsen Corina Benjet Anne H Berman Ronny Bruffaerts Paula Carrasco Silver C N Chan Irina Cohut María Anabell Covarrubias Díaz Couder Marcelo A Crockett Pim Cuijpers Oana A David Dong Dong David D Ebert Jorge Gaete Mireia Felez-Nobrega Carlos García Forero Margalida Gili Raúl A Gutiérrez-García Josep Maria Haro Penelope Hasking Xanthe Hunt Mathilde M Husky Florence Jaguga Leontien Jansen Álvaro I Langer Yan Liu Scarlett Mac-Ginty Vania Martínez Andre Mason Muthoni Mathai Margaret McLafferty Andrea Miranda-Mendizabal Elaine K Murray Catherine M Musyoka Siobhan M O'Neill Claudiu C Papasteri José A Piqueras Codruta A Popescu Charlene Rapsey Kealagh Robinson Tiscar Rodriguez-Jimenez Damian Scarf Oi-Ling Siu Dan J Stein Sascha Y Struijs Cristina T Tomoiaga Karla Patricia Valdés-García Shelby Vereecke Daniel V Vigo Angel Y Wang Samuel Y S Wong Ronald C Kessler Health Services Research Group Hospital del Mar Research Institute Carrer del Doctor Aiguader 88 08003 Barcelona Spain Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública Instituto de Salud Carlos III (CIBERESP ISCIII) Av. Monforte de Lemos 3-5. Pabellón 11. Planta 0 28029 Madrid Spain. The Jockey Club School of Public Health and Primary Care Faculty of Medicine The Chinese University of Hong Kong Hong Kong SAR PR China. Biostatistics Epidemiology and Scientific Computing Department King Faisal Specialist Hospital and Research Centre Riyadh Saudi Arabia. Department of Health Care Policy Harvard Medical School Boston MA USA. Center for Global Mental Health Research Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz Mexico City Mexico. Department of Psychiatry College of Medicine King Saud University Riyadh Saudi Arabia SABIC Psychological Health Research & Applications Chair (SPHRAC) Department of Psychiatry College of Medicine King Saud University Riyadh Saudi Arabia. Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública Instituto de Salud Carlos III (CIBERESP ISCIII) Av. Monforte de Lemos 3-5. Pabellón 11. Planta 0 28029 Madrid Spain Department of Medicine and Life Sciences Pompeu Fabra University (UPF) Barcelona Spain. Electronic address: jalonso@***. Department of Criminology Malmö University Malmö Sweden. Department of Medicine Medical College East Africa the Aga Khan University Nairobi Kenya Brain and Mind Institute the Aga Khan University Nairobi Kenya Department of Mental Health and Behavioural Sciences Moi University School of Medicine Eldoret Kenya. Department of Psychiatry Columbia University New York NY USA. Department of Psychology & Counselling Daystar University Nairobi Kenya. Coordinación de Desarrollo Académico y Servicios Educativos Universidad la Salle Ciudad Victoria Ciudad Victoria Mexico. Mental Health Alcohol Substance Use and Tobacco Research Unit South African Medical Resear

OBJECTIVE:To investigate the associations of demographic variables, childhood adversities (CAs), and mental disorders (MDx) with onset, transition, and persistence of suicidal thoughts and behaviors (STB) among first-year university students. METHOD:Poisson regression models within a discrete-time survival framework were constructed using web-based self-report survey data from 72,288 incoming university students across 18 countries (response rate=20.9%; median age=19 years, 57.9% female, 1.4% transgender, 21.0% non-heterosexual). These models examined the associations of four demographic variables, five CAs, and eight MDx with STB outcomes. RESULTS:Lifetime prevalence of suicidal ideation, plans, and attempts was 47.0%, 26.0%, and 9.6%, respectively; 12-month estimates were 30.6%, 14.0%, and 2.3%. In unadjusted analyses, associations were strongest between lifetime onset of suicidal ideation and CAs (RR range 4.4-7.0), particularly parental psychopathology (relative risk [RR]=7.0 [95% CI 6.5-7.7]), followed by MDx (RR range 1.3-3.0). Of the demographic subgroups, transgender students had highest risk of STB (lifetime ideation onset RR=2.4 [2.3-2.6]; ideation-to-attempt transition RR=1.5 [1.3-1.8]). In fully adjusted models, strongest predictors of lifetime ideation onset were emotional abuse (RR=2.1 [1.9-2.2]), major depressive disorder (RR=2.0 [1.9-2.1]), and bipolar disorder (RR=1.8 [1.6-2.0]). Ideation-to-attempt transition remained most strongly associated with panic disorder (RR=1.5 [1.3-1.7]), bipolar disorder (RR=1.4 [1.2-1.7]), and sexual abuse (RR=1.4 [1.2-1.7]). Most predictors were significantly but weakly associated with persistence of ideation and plan, while only physical abuse remained associated with repeated suicide attempts (RR=1.3 [1.0-1.8]). CONCLUSION:CAs and MDx are strong predictors of both onset of and transition within the STB spectrum, underscoring the importance of implementing early-life prevention interventions.

关键词： Adverse childhood experiences Mental health Prevalence Risk factors Self-injurious behavior Students Suicide

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Machine learning clustering technique applied to powder X-ray diffraction patterns to distinguish alloy substitutions

arXiv

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arXiv 2018年

作者： Utimula, Keishu Hunkao, Rutchapon Yano, Masao Kimoto, Hiroyuki Hongo, Kenta Kawaguchi, Shogo Suwanna, Sujin Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Optical and Quantum Physics Laboratory Department of Physics faculty of Science Mahidol University Bangkok10400 Thailand Advanced Material Engineering Div. Toyota Motor Corporation Toyota-cho 1 Toyota Aichi471-8572 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 JST Kawaguchi Saitama332-0012 Japan Sayo-gun Hyogo679-5148 Japan Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

We applied the clustering technique using DTW (dynamic time wrapping) analysis to XRD (X-ray diffraction) spectrum patterns in order to identify the microscopic structures of substituents introduced in the main phase of magnetic alloys. The clustering is found to perform well to identify the concentrations of the substituents with successful rates (∼90%). The sufficient performance is attributed to the nature of DTW processing to filter out irrelevant informations such as the peak intensities (due to the incontrollability of diffraction conditions in polycrystalline samples) and the uniform shift of peak positions (due to the thermal expansions of lattices). The established framework is applicable not limited to the system treated in this work but widely to the systems to be tuned their properties by atomic substitutions within a phase. The framework has larger potential to predict wider properties from observed XRD patterns in a way that it can provide such properties evaluated from predicted microscopic local structure, such as magnetic moments, optical spectrum etc.). Copyright © 2018, The Authors. All rights reserved.

关键词： Cluster analysis

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SPT clusters with DES and HST weak lensing. II. Cosmological constraints from the abundance of massive halos

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Physical Review D 2024年第8期110卷 083510页

作者： S. Bocquet S. Grandis L. E. Bleem M. Klein J. J. Mohr T. Schrabback T. M. C. Abbott P. A. R. Ade M. Aguena A. Alarcon S. Allam S. W. Allen O. Alves A. Amon A. J. Anderson J. Annis B. Ansarinejad J. E. Austermann S. Avila D. Bacon M. Bayliss J. A. Beall K. Bechtol M. R. Becker A. N. Bender B. A. Benson G. M. Bernstein S. Bhargava F. Bianchini M. Brodwin D. Brooks L. Bryant A. Campos R. E. A. Canning J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero F. J. Castander R. Cawthon C. L. Chang C. Chang P. Chaubal R. Chen H. C. Chiang A. Choi T-L. Chou R. Citron C. Corbett Moran J. Cordero M. Costanzi T. M. Crawford A. T. Crites L. N. da Costa M. E. S. Pereira C. Davis T. M. Davis J. DeRose S. Desai T. de Haan H. T. Diehl M. A. Dobbs S. Dodelson C. Doux A. Drlica-Wagner K. Eckert J. Elvin-Poole S. Everett W. Everett I. Ferrero A. Ferté A. M. Flores J. Frieman J. Gallicchio J. García-Bellido M. Gatti E. M. George G. Giannini M. D. Gladders D. Gruen R. A. Gruendl N. Gupta G. Gutierrez N. W. Halverson I. Harrison W. G. Hartley K. Herner S. R. Hinton G. P. Holder D. L. Hollowood W. L. Holzapfel K. Honscheid J. D. Hrubes N. Huang J. Hubmayr E. M. Huff D. Huterer K. D. Irwin D. J. James M. Jarvis G. Khullar K. Kim L. Knox R. Kraft E. Krause K. Kuehn N. Kuropatkin F. Kéruzoré O. Lahav A. T. Lee P.-F. Leget D. Li H. Lin A. Lowitz N. MacCrann G. Mahler A. Mantz J. L. Marshall J. McCullough M. McDonald J. J. McMahon J. Mena-Fernández F. Menanteau S. S. Meyer R. Miquel J. Montgomery J. Myles T. Natoli A. Navarro-Alsina J. P. Nibarger G. I. Noble V. Novosad R. L. C. Ogando Y. Omori S. Padin S. Pandey P. Paschos S. Patil A. Pieres A. A. Plazas Malagón A. Porredon J. Prat C. Pryke M. Raveri C. L. Reichardt J. Roberson R. P. Rollins C. Romero A. Roodman J. E. Ruhl E. S. Rykoff B. R. Saliwanchik L. Salvati C. Sánchez E. Sanchez D. Sanchez Cid A. Saro K. K. Schaffer L. F. Secco I. Sevilla-Noarbe K. Sharon E. Sheldon T. Shin C. Sievers G. Smecher M. Smith T. Somboonpanyakul M. Sommer B. Stalder A. A. Stark J. Stephen V. Straz University Observatory Faculty of Physics Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstraße 25/8 6020 Innsbruck Austria High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Max Planck Institute for Extraterrestrial Physics Gießenbachstraße 1 85748 Garching Germany Argelander-Institut für Astronomie Auf dem Hügel 71 53121 Bonn Germany Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile School of Physics and Astronomy Cardiff University Cardiff CF24 3YB United Kingdom Laboratório Interinstitucional de e-Astronomia—LIneA Rua Gal. José Cristino 77 Rio de Janeiro Rio de Janeiro—20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 USA Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom School of Physics University of Melbourne Parkville Victoria 3010 Australia NIST Quantum Devices Group 325 Broadway Mailcode 817.03 Boulder Colorado 80305 USA Department of Physics University of Colorado Boulder Colorado 80309 USA Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology Campus UAB 08193 Bellaterra (Barcelona) Spain Institute of Cosmology and Gravitation University of Portsmouth Portsmouth

We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel’dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample is constructed from the combined SPT-SZ, SPTpol ECS, and SPTpol 500d surveys, and comprises 1,005 confirmed clusters in the redshift range 0.25–1.78 over a total sky area of 5200 deg2. We use DES Year 3 weak-lensing data for 688 clusters with redshifts z<0.95 and HST weak-lensing data for 39 clusters with 0.6dataset, we place a 95% upper limit on the sum of neutrino masses ∑mν<0.18 eV. When additionally allowing the dark energy equation of state parameter w to vary, we obtain w=−1.45±0.31 from our cluster-based analysis. In combination with Planck data, we measure w=−1.34−0.15+0.22, or a 2.2σ difference with a cosmological constant. We use the cluster abundance to measure σ8 in five redshift bins between 0.25 and 1.8, and we find the results to be consistent with structure growth as predicted by the ΛCDM model fit to Planck primary CMB data.

关键词： Cosmological parameters Dark energy Large scale structure of the Universe Galaxy clusters

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Euclid preparation. Sensitivity to neutrino parameters

arXiv

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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

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Novel speech features of disturbances in laryngeal muscle coordination

Novel speech features of disturbances in laryngeal muscle co...

引用

International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Branimir Dropuljić Davor Petrinović Krešimir Ćosić IN2data Data Science Company Ltd. Zagreb Croatia Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

The present paper proposes speech features derived from the fundamental frequency (Fo) contour decomposition. The decomposition method is designed in order to differentiate, as much as possible, simultaneous neurobiological effects on vocal fold vibration. The focus of this paper is placed on involuntary disturbances of such vibrations, which are analyzed in the context of emotional stress. The proposed features are compared with conventional perturbation measures, i.e. jitter and shimmer, using two datasets: Synthetic perturbations and SUSAS (Speech Under Simulated and Actual Stress) subset - Roller-coaster. Features are additionally analyzed in the context of elimination potential of voluntary effects like Fo contour changes during natural pronunciation. Results of the initial synthetic perturbation analysis indicate that the proposed features could be less affected by the voluntary control and, on the other hand, more related to disturbances in laryngeal muscle coordination. The proposed features generally outperform conventional perturbation features in speech under stress analysis.

关键词： Speech Muscles Stress Maximum likelihood detection Nonlinear filters DH-HEMTs Feature extraction

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