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Author Correction: Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms

Nature methods 2022年第1期19卷 129页

作者： Emmanuel Moebel Antonio Martinez-Sanchez Lorenz Lamm Ricardo D Righetto Wojciech Wietrzynski Sahradha Albert Damien Larivière Eric Fourmentin Stefan Pfeffer Julio Ortiz Wolfgang Baumeister Tingying Peng Benjamin D Engel Charles Kervrann Serpico Project-Team Centre Inria Rennes-Bretagne Atlantique and CNRS-UMR 144 Inria CNRS Institut Curie PSL Research University Campus Universitaire de Beaulieu Rennes Cedex France. Department of Computer Science Faculty of Sciences University of Oviedo Oviedo Spain. Health Research Institute of Asturias (ISPA) Avenida Hospital Universitario s/n Oviedo Spain. Institute of Neuropathology Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' University of Göttingen Göttingen Germany. Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Helmholtz AI Helmholtz Zentrum München Neuherberg Germany. Max Planck Institute of Biochemistry Martinsried Germany. Fourmentin-Guilbert Scientific Foundation Noisy-le-Grand France. Zentrum für Molekulare Biologie der Universität Heidelberg Heidelberg Germany. Ernst Ruska-Centre Wilhelm-Johnen-Straße Jülich Germany. Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. ben.engel@helmholtz-muenchen.de. Department of Chemistry Technical University of Munich Garching Germany. ben.engel@helmholtz-muenchen.de. Serpico Project-Team Centre Inria Rennes-Bretagne Atlantique and CNRS-UMR 144 Inria CNRS Institut Curie PSL Research University Campus Universitaire de Beaulieu Rennes Cedex France. charles.kervrann@inria.fr.

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Label distribution learning forests 17

Label distribution learning forests

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Proceedings of the 31st International Conference on Neural Information Processing Systems

作者： Wei Shen Kai Zhao Yilu Guo Alan Yuille Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University and Department of Computer Science Johns Hopkins University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Department of Computer Science Johns Hopkins University

ISBN: (纸本)9781510860964

Label distribution learning (LDL) is a general learning framework, which assigns to an instance a distribution over a set of labels rather than a single label or multiple labels. Current LDL methods have either restricted assumptions on the expression form of the label distribution or limitations in representation learning, e.g., to learn deep features in an end-to-end manner. This paper presents label distribution learning forests (LDLFs) - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1) Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions. 2) The learning of differentiable decision trees can be combined with representation learning. We define a distribution-based loss function for a forest, enabling all the trees to be learned jointly, and show that an update function for leaf node predictions, which guarantees a strict decrease of the loss function, can be derived by variational bounding. The effectiveness of the proposed LDLFs is verified on several LDL tasks and a computer vision application, showing significant improvements to the state-of-the-art LDL methods.

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OTHR-04. DEVELOPMENT OF A FUNCTIONAL PLATFORM FOR REAL-TIME PERSONALIZED DRUG SENSITIVITY PROFILING OF PATIENT-DERIVED 3D FRESH TUMOR TISSUE CULTURES IN THE PEDIATRIC PRECISION ONCOLOGY PROGRAM INFORM

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Neuro-Oncology 2023年第SUPPLEMENT_1期25卷 i74–i75页

作者： Heike Peterziel Nora Jamaladdin Dina ElHarouni Xenia F Gerloff Sonja Herter Petra Fiesel Yannick Berker Mirjam Blattner-Johnson Kathrin Schramm Barbara C Jones David Reuss Laura Turunen Aileen Friedenauer Tim Holland-Letz Martin Sill Lena Weiser Christopher Previti Gnanaprakash Balasubramanian Nicolas U Gerber Johannes Gojo Caroline Hutter Ingrid Øra Olli Lohi Antonis Kattamis Bram de Wilde Frank Westermann Stephan Tippelt Norbert Graf Michaela Nathrath Monika Sparber-Sauer Astrid Sehested Christof M Kramm Uta Dirksen Olli Kallioniemi Stefan M Pfister Cornelis M van Tilburg David T W Jones Jani Saarela Vilja Pietiäinen Natalie Jäger Matthias Schlesner Annette Kopp-Schneider Sina Oppermann Till Milde Olaf Witt Ina Oehme Hopp Children’s Cancer Center Heidelberg (KiTZ) Heidelberg Germany Clinical Cooperation Unit Pediatric Oncology German Cancer Research Center (DKFZ) National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Consortium (DKTK) Heidelberg Germany Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Division of Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Clinical Cooperation Unit Neuropathology German Cancer Research Center (DKFZ) Heidelberg Germany Division of Pediatric Glioma Research German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Division of Pediatric Glioma Research German Cancer Research Center (DKFZ) Department of Pediatric Oncology Hematology Immunology and Pulmonology Heidelberg University Hospital and German Cancer Consortium (DKTK) Heidelberg Germany Department Neuropathology at Institute of Pathology Heidelberg University Hospital Heidelberg Germany Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Heidelberg Germany Division of Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Core Facility Omics IT and Data Management (ODCF) German Cancer Research Center (DKFZ) Heidelberg Germany Department of Oncology University Children’s Hospital Zürich Zürich Switzerland Department of Pediatrics and Adolescent Medicine Comprehensive Cancer Center and Comprehensive Center for Pediatrics Medical University of Vienna Vienna Austria St. Anna Children’s Hospital Department of Pediatrics and Adolescent Medicine Medical University of Vienna and St. Anna Children’s Cancer Research Institute (CCRI) Vienna Austria Children’s Hospital Pediatric Oncology Skåne University Hospital Lund &amp Karolinska Univ

The international precision oncology program INFORM enrolls relapsed/refractory pediatric cancer patients for comprehensive molecular analysis. We report a two-year pilot study implementing ex vivo drug sensitivity profiling (DSP) using a library of 75-78 clinically relevant drugs. We included 132 viable tumor samples from 35 pediatric oncology centers in seven countries. DSP was conducted on multicellular fresh tumor tissue spheroid cultures in 384-well plates with an overall mean processing time of three weeks. In 89 cases (67%), sufficient viable tissue was received; 69 (78%) passed internal quality controls. The DSP results matched the identified molecular targets, including BRAF, ALK, MET and TP53 status. Drug vulnerabilities were identified in 80% of cases lacking actionable (very) high-evidence molecular events, adding value to the molecular data. Striking parallels between clinical courses and the DSP results were observed in selected patients. Overall, DSP in clinical real-time is feasible in international multicenter precision oncology programs.

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Label distribution learning forests

arXiv

引用

arXiv 2017年

作者： Shen, Wei Zhao, Kai Guo, Yilu Yuille, Alan Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Department of Computer Science Johns Hopkins University

关键词： Decision trees

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Essential guidelines for computational method benchmarking

arXiv

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arXiv 2018年

作者： Weber, Lukas M. Saelens, Wouter Cannoodt, Robrecht Soneson, Charlotte Hapfelmeier, Alexander Gardner, Paul P. Boulesteix, Anne-Laure Saeys, Yvan Robinson, Mark D. Institute of Molecular Life Sciences University of Zurich Zurich Switzerland SIB Swiss Institute of Bioinformatics University of Zurich Zurich Switzerland Data Mining and Modelling for Biomedicine VIB Center for Inflammation Research Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Institute of Medical Informatics Statistics and Epidemiology Technical University of Munich Munich Germany Department of Biochemistry University of Otago Dunedin New Zealand Institute for Medical Information Processing Biometry and Epidemiology Ludwig-Maximilians-University Munich Germany Friedrich Miescher Institute for Biomedical Research SIB Swiss Institute of Bioinformatics Basel Switzerland

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology. Copyright © 2018, The Authors. All rights reserved.

关键词： Benchmarking

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SN 2018agk: A prototypical Type Ia Supernova with a smooth power-law rise in Kepler (K2)

arXiv

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arXiv 2021年

作者： Wang, Qinan Rest, Armin Zenati, Yossef Ridden-Harper, Ryan Dimitriadis, Georgios Narayan, Gautham Villar, V. Ashley Magee, Mark R. Foley, Ryan J. Shaya, Edward J. Garnavich, Peter Wang, Lifan Hu, Lei Bódi, Attila Armstrong, Patrick Auchettl, Katie Barclay, Thomas Barentsen, Geert Bognár, Zsófia Brimacombe, Joseph Bulger, Joanna Burke, Jamison Challis, Peter Chambers, Kenneth Coulter, David A. Csörnyei, Géza Cseh, Borbála Deckers, Maxime Dotson, Jessie L. Galbany, Lluís González-Gaitán, Santiago Gromadzki, Mariusz Gully-Santiago, Michael Hanyecz, Ottó Hedges, Christina Hiramatsu, Daichi Hosseinzadeh, Griffin Howell, D. Andrew Howell, Steve B. Huber, Mark E. Jha, Saurabh W. Jones, David O. Könyves-Tóth, Réka Kalup, Csilla Kilpatrick, Charles D. Kriskovics, Levente Li, Wenxiong Lowe, Thomas B. Margheim, Steven McCully, Curtis Mitra, Ayan Muñoz, Jose A. Nicholl, Matt Nordin, Jakob Pál, András Pan, Yen-Chen Piro, Anthony L. Rest, Sofia Rino-Silvestre, João Rojas-Bravo, César Sárneczky, Krisztián Siebert, Matthew R. Smartt, Stephen J. Smith, Ken Sódor, Ádám Stritzinger, Maximilian D. Szabó, Róbert Szakáts, Róbert Tucker, Brad E. Vinkó, József Wang, Xiaofeng Wheeler, J. Craig Young, David R. Zenteno, Alfredo Zhang, Kaicheng Zsidi, Gabriella Department of Physics and Astronomy The Johns Hopkins University BaltimoreMD21218 United States Space Telescope Science Institute BaltimoreMD21218 United States Israel Excellence Fellowship Department of Astronomy and Astrophysics University of California Santa CruzCA95064 United States School of Physics Trinity College Dublin The University of Dublin Dublin 2 Ireland Department of Astronomy University of Illinois 1002 W. Green St. Urbana-ChampaignIL61801 United States Center for Astrophysical Surveys National Center for Supercomputing Applications UrbanaIL61801 United States Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Astronomy Department University of Maryland College ParkMD20742 United States University of Notre Dame Notre DameIN46556 United States Mitchell Institute for Fundamental Physics Department of Physics and Astronomy Texas A&M University College StationTX77843 United States Purple Mountain Observatory Chinese Academy of Sciences 10 Yuanhua Road Nanjing210033 China Konkoly Observatory CSFK Konkoly-Thege M. ut 15-17 Budapest1121 Hungary MTA CSFK Lendület Near-Field Cosmology Research Group Konkoly Thege Miklós út 15-17 Budapest1121 Hungary Mt Stromlo Observatory The Research School of Astronomy and Astrophysics Australian National University ACT2601 Australia School of Physics The University of Melbourne VIC3010 Australia University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Bay Area Environmental Research Institute P.O. Box 25 Moffett Field CA94035 United States Coral Towers Observatory Cairns Australia Institute for Astronomy University of Hawai'i 2680 Woodlawn Drive HonoluluHI96

We present the 30-min cadence Kepler/K2 light curve of the Type Ia supernova (SN Ia) SN 2018agk, covering approximately one week before explosion, the full rise phase and the decline until 40 days after peak. We additionally present ground-based observations in multiple bands within the same time range, including the 1-day cadence DECam observations within the first ~5 days after the first light. The Kepler early light curve is fully consistent with a single power-law rise, without evidence of any bump feature. We compare SN 2018agk with a sample of other SNe Ia without early excess flux from the literature. We find that SNe Ia without excess flux have slowly-evolving early colors in a narrow range (g - i ≈ -0.20±0.20 mag) within the first ~ 10 days. On the other hand, among SNe Ia detected with excess, SN 2017cbv and SN 2018oh tend to be bluer, while iPTF16abc's evolution is similar to normal SNe Ia without excess in g - i. We further compare the Kepler light curve of SN 2018agk with companion-interaction models, and rule out the existence of a typical non-degenerate companion undergoing Roche-lobe overflow at viewing angles smaller than 45°. © 2021, CC BY.

关键词： Supernovae

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Deep regression forests for age estimation

arXiv

引用

arXiv 2017年

作者： Shen, Wei Guo, Yilu Wang, Yan Zhao, Kai Wang, Bo Yuille, Alan Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Department of Computer Science Johns Hopkins University College of Computer and Control Engineering Nankai University Hikvision Research

Age estimation from facial images is typically cast as a nonlinear regression problem. The main challenge of this problem is the facial feature space w.r.t. ages is heterogeneous, due to the large variation in facial appearance across different persons of the same age and the nonstationary property of aging patterns. In this paper, we propose Deep Regression Forests (DRFs), an end-to-end model, for age estimation. DRFs connect the split nodes to a fully connected layer of a convolutional neural network (CNN) and deal with heterogeneous data by jointly learning input-dependant data partitions at the split nodes and data abstractions at the leaf nodes. This joint learning follows an alternating strategy: First, by fixing the leaf nodes, the split nodes as well as the CNN parameters are optimized by Back-propagation;Then, by fixing the split nodes, the leaf nodes are optimized by iterating a step-size free and fastconverging update rule derived from Variational Bounding. We verify the proposed DRFs on three standard age estimation benchmarks and achieve state-of-the-art results on all of them. Copyright © 2017, The Authors. All rights reserved.

关键词： Backpropagation

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

arXiv

引用

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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Academic performance and behavioral patterns

arXiv

引用

arXiv 2017年

作者： Kassarnig, Valentin Mones, Enys Bjerre-Nielsen, Andreas Sapiezynski, Piotr Lassen, David Dreyer Lehmann, Sune Institute of Software Technology Graz University of Technology Graz Austria Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark Department of Economics University of Copenhagen Copenhagen Denmark Center for Social Data Science University of Copenhagen Copenhagen Denmark College of Information and Computer Science Northeastern University Boston United States Niels Bohr Institute University of Copenhagen Copenhagen Denmark

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students. Copyright © 2017, The Authors. All rights reserved.

关键词： Population statistics

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