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8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2017, in conjunction with the Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： He, Yuanye Yu, Liang-Chih Lai, K. Robert Liu, Weiyi Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan School of Information Science and Engineering Yunnan University Kunming China

ISBN: (纸本)9781945626951

The EmoInt-2017 task aims to determine a continuous numerical value representing the intensity to which an emotion is expressed in a tweet. Compared to classification tasks that identify 1 among n emotions for a tweet, the present task can provide more fine-grained (real-valued) sentiment analysis. This paper presents a system that uses a bi-directional LSTM-CNN model to complete the competition task. Combining bi-directional LSTM and CNN, the prediction process considers both global information in a tweet and local important information. The proposed method ranked sixth among twenty-one teams in terms of Pearson Correlation Coefficient. © 2017 Association for Computational Linguistics.

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Building a precision medicine infrastructure at a national level: The Swedish experience

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Cambridge prisms. Precision medicine 2023年 1卷 e15页

作者： Anders Edsjö Anna Lindstrand David Gisselsson Paula Mölling Mikaela Friedman Lucia Cavelier Maria Johansson Hans Ehrencrona Therese Fagerqvist Tobias Strid Lovisa Lovmar Bo Jacobsson Åsa Johansson Lars Engstrand Craig E Wheelock Per Sikora Valtteri Wirta Thoas Fioretos Richard Rosenquist Department of Clinical Genetics Pathology and Molecular Diagnostics Office for Medical Services Region Skåne Lund Sweden. Division of Pathology Department of Clinical Sciences Lund University Lund Sweden. Department of Molecular Medicine and Surgery Karolinska Institutet Stockholm Sweden. Clinical Genetics Karolinska University Hospital Solna Sweden. Genomic Medicine Center Karolinska Karolinska University Hospital Stockholm Sweden. Division of Clinical Genetics Department of Laboratory Medicine Lund University Lund Sweden. Department of Laboratory Medicine Faculty of Medicine and Health Örebro University Örebro Sweden. Lund University Collaboration Office Lund University Lund Sweden. Innovation Partnership Office Uppsala University Uppsala Sweden. Department of Clinical Pathology Biological and Clinical Sciences Linköping University Linköping Sweden. Clinical Genomics Linköping Linköping University Linköping Sweden. Department of Clinical Genetics and Genomics Sahlgrenska University Hospital Gothenburg Sweden. Department of Obstetrics and Gynecology Sahlgrenska Academy Gothenburg University Gothenburg Sweden. Department of Obstetrics and Gynecology Sahlgrenska University Hospital Gothenburg Sweden. Department of Immunology Genetics and Pathology Science for Life Laboratory Uppsala University Uppsala Sweden. Department of Microbiology Tumor and Cell Biology Centre for Translational Microbiome Research Karolinska Institutet Solna Sweden. Unit of Integrative Metabolomics Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden. Department of Respiratory Medicine and Allergy Karolinska University Hospital Stockholm Sweden. Clinical Genomics Gothenburg Science for Life Laboratory University of Gothenburg Gothenburg Sweden. Bioinformatics Data Center Core Facilities Sahlgrenska Academy University of Gothenburg Gothenburg Sweden. Department of Microbiology Tumor and Cell Biology Clinical Genomics Stockholm Science Life Labor

Precision medicine has the potential to transform healthcare by moving from one-size-fits-all to personalised treatment and care. This transition has been greatly facilitated through new high-throughput sequencing technologies that can provide the unique molecular profile of each individual patient, along with the rapid development of targeted therapies directed to the Achilles heels of each disease. To implement precision medicine approaches in healthcare, many countries have adopted national strategies and initiated genomic/precision medicine initiatives to provide equal access to all citizens. In other countries, such as Sweden, this has proven more difficult due to regionally organised healthcare. Using a bottom-up approach, key stakeholders from academia, healthcare, industry and patient organisations joined forces and formed Genomic Medicine Sweden (GMS), a national infrastructure for the implementation of precision medicine across the country. To achieve this, Genomic Medicine Centres have been established to provide regionally distributed genomic services, and a national informatics infrastructure has been built to allow secure data handling and sharing. GMS has a broad scope focusing on rare diseases, cancer, pharmacogenomics, infectious diseases and complex diseases, while also providing expertise in informatics, ethical and legal issues, health economy, industry collaboration and education. In this review, we summarise our experience in building a national infrastructure for precision medicine. We also provide key examples how precision medicine already has been successfully implemented within our focus areas. Finally, we bring up challenges and opportunities associated with precision medicine implementation, the importance of international collaboration, as well as the future perspective in the field of precision medicine.

关键词： genomic medicine implementation national infrastructure precision medicine

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Diversity and longitudinal records: Genetic architecture of disease associations and polygenic risk in the Taiwanese Han population

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science advances 2025年第23期11卷 eadt0539页

作者： Ting-Yuan Liu Hsing-Fang Lu Yu-Chia Chen Chi-Chou Liao Ying-Ju Lin Jai-Sing Yang Wen-Ling Liao Wei-De Lin Shih-Yin Chen Yu-Chuen Huang Wei-Yong Lin Yu-Huei Liu Kai-Cheng Hsu Shih-Sheng Chang Hong-Da Chen Yu-Pao Chou Jan-Gowth Chang Chung-Hsing Wang Chwen-Tzuei Chang Chung-Ming Huang Kai-Jieh Yeo Tzu-Yuan Wang Chin-Chung Yeh Jiunn-Horng Chen Chi-Ping Huang Hsueh-Chou Lai Rong-Hsing Chen Hui-Ju Lin Po-Yuan Wu Jiu-Yao Wang Chin-Chi Kuo Der-Yang Cho Chang-Hai Tsai Fuu-Jen Tsai Million-person precision medicine initiative Department of Medical Research China Medical University Hospital Taichung 40402 Taiwan. Master Program for Digital Health Innovation China Medical University Taichung 406040 Taiwan. Department of Medical Research China Medical University Hospital Taichung 40402 Taiwan. Genetic Center Department of Medical Research China Medical University Hospital Taichung 40402 Taiwan. School of Post Baccalaureate Chinese Medicine China Medical University Taichung 404333 Taiwan. Graduate Institute of Integrated Medicine College of Chinese Medicine China Medical University Taichung 406040 Taiwan. Center for Personalized Medicine Department of Medical Research China Medical University Hospital Taichung 406040 Taiwan. School of Chinese Medicine China Medical University Taichung 406040 Taiwan. Drug Development Center China Medical University Taichung 406040 Taiwan. Artificial Intelligence Center China Medical University Hospital Taichung 40402 Taiwan. Department of Neurology China Medical University Hospital Taichung 40402 Taiwan. Department of Medicine China Medical University Taichung 406040 Taiwan. Department of Laboratory Medicine China Medical University Hospital Taichung 40402 Taiwan. Department of Bioinformatics and Medical Engineering Asia University Taichung 41300 Taiwan. Department of Medical Laboratory Science and Biotechnology China Medical University Taichung Taiwan. Division of Genetics and Metabolism Children's Hospital of China Medical University Taichung 404447 Taiwan. Intelligent Diabetes Metabolism and Exercise Center Department of Internal Medicine China Medical University Hospital Taichung 40402 Taiwan. Division of Immunology and Rheumatology Department of Internal Medicine China Medical University Hospital Taichung 40402 Taiwan. Division of Endocrine and Metabolism China Medical University Hospital Taichung 40402 Taiwan. Department of Urology China Medical University Hospital Taichung 40402 Taiwan. Cen

We addressed the underrepresentation of non-European populations in genome-wide association studies (GWASs) by building HiGenome, a large-scale genetic resource for the Taiwanese Han population. Using a custom genotyping array, we integrated deidentified electronic medical records (2003 to 2021) with genomic data to enable GWASs, phenome-wide association studies, and polygenic risk score (PRS) analysis. Among 413,000 participants, 323,397 passed ancestry and quality control filtering. GWASs covered 1085 traits, focusing on diseases prevalent in Taiwan such as type 2 diabetes, chronic kidney disease, gout, and alcoholic liver damage. PRSs were calculated for 238 traits, with the strongest associations observed in musculoskeletal disorders. Incorporating PRS into clinical practice supports early risk prediction and personalized prevention. To further expand translational value, we also conducted pharmacogenomic analysis and human leukocyte antigen typing. HiGenome offers a large-scale genetic and clinical dataset from the Taiwanese Han population, supporting population-specific analyses and precision medicine development in East Asia. The hospital-based design enables continuous follow-up and longitudinal data expansion.

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Random asynchronous iterations in distributed coordination algorithms

arXiv

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arXiv 2018年

作者： Chen, Yao Xia, Weiguo Cao, Ming Lü, Jinhu Department of Computer Science Southwestern University of Finance and Economics Chengdu611130 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China Faculty of Science and Engineering ENTEG University of Groningen Groningen9747 AG Netherlands School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (stochastic, indecomposable, aperiodic) matrix generate consensus asymptotically via synchronous iterations, the dynamics of DCA with asynchronous iterations have not been studied extensively, especially when viewed as stochastic processes. This paper aims to show that for any given irreducible stochastic matrix, even non-SIA, the corresponding DCA lead to consensus successfully via random asynchronous iterations under a wide range of conditions on the transition probability. Particularly, the transition probability is neither required to be independent and identically distributed, nor characterized by a Markov chain. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Thermal pedestrian detection using block LBP with multi-level classifier

Thermal pedestrian detection using block LBP with multi-leve...

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2017 IEEE International Conference on Applied System innovation, ICASI 2017

作者： Liu, You-Chen Huang, Shih-Shinh Lu, Ching-Hu Chang, Feng-Chia Lin, Pei-Yu Dept. of Computer and Communication Engineering National Kaohsiung First University of Science and Technology Taiwan Dept. of Electrical Engineering National Taiwan University of Science and Technology Taiwan National Chung-Shan Institute Science and Technology Taiwan Dept. of Information Communication Innovation Center for Big Data and Digital Convergence Yuan Ze University Taiwan

ISBN: (纸本)9781509048977

Pedestrian detection is an important topic in many applications, such as intelligent transportation systems (ITSs) or surveillance. For the purpose of applications used around the clock, the work for detecting pedestrian based on thermal sensors has attracted significant attention. To achieve this, this paper proposes a LBP (local binary pattern) encoded multi-level classifier for detecting pedestrians in thermal images. The proposed method consists of training and detection stages. The training stage firstly encodes the texture information of all training samples in all possible blocks by using LBP. For a particular block, the patches of all positive and negative samples with the same LBP code are collected to train a SVM (support vector machine) classifier for discrimination. HOGs (Histogram of Oriented Gradients) are used for the description of the patch appearance. By considering a set of learned SVMs for a particular block as a weak classifier, a set of discriminative weak classifiers is selected to form a final classifier (strong classifier) by using Adaboost. The experimental results on the constructed dataset demonstrate that the proposed method outperforms those in the literature. © 2017 IEEE.

关键词： Adaptive boosting

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Publisher’s Note: “Microstrip-line resonator with broadband, circularly polarized, uniform microwave field for nitrogen vacancy center ensembles in diamond” [AIP Advances 9, 075213 (2019)]

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AIP Advances 2019年第10期9卷

作者： Xiaoying Yang Ning Zhang Heng Yuan Guodong Bian Pengcheng Fan Mingxin Li 1School of Instrument Science and Optoelectronic Engineering Beihang University Beijing 100191 China 2Quantum Sensing Center Zhejiang Lab Hangzhou 310000 China 3Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing 100191 China

This article was originally published online on 19 July 2019 with a mistake in the affiliations for Ning Zhang. The affiliation was added as “Quantum Sensing Ce

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Low-resolution face recognition in the wild via selective knowledge distillation

arXiv

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arXiv 2018年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Li, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security at University of Chinese Academy of Sciences China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation. In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine-tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources: approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification. Experimental results show that the student stream performs impressively in recognizing low-resolution faces and costs only 0.15MB memory and runs at 418 faces per second on CPU and 9, 433 faces per second on GPU. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

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The Past, Present, and Future of the Brain Imaging data Structure (BIDS)

arXiv

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arXiv 2023年

作者： Poldrack, Russell A. Markiewicz, Christopher J. Appelhoff, Stefan Ashar, Yoni K. Auer, Tibor Baillet, Sylvain Bansal, Shashank Beltrachini, Leandro Bénar, Christian G. Bertazzoli, Giacomo Bhogawar, Suyash Blair, Ross W. Bortoletto, Marta Boudreau, Mathieu Brooks, Teon L. Calhoun, Vince D. Castelli, Filippo Maria Clement, Patricia Cohen, Alexander L. Cohen-Adad, Julien D'Ambrosio, Sasha de Hollander, Gilles de la Iglesia-Vayá, María de la Vega, Alejandro Delorme, Arnaud Devinsky, Orrin Draschkow, Dejan Duff, Eugene Paul DuPre, Elizabeth Earl, Eric Esteban, Oscar Feingold, Franklin W. Flandin, Guillaume Galassi, Anthony Gallitto, Giuseppe Ganz, Melanie Gau, Rémi Gholam, James Ghosh, Satrajit S. Giacomel, Alessio Gillman, Ashley G. Gleeson, Padraig Gramfort, Alexandre Guay, Samuel Guidali, Giacomo Halchenko, Yaroslav O. Handwerker, Daniel A. Hardcastle, Nell Herholz, Peer Hermes, Dora Honey, Christopher J. Innis, Robert B. Ioanas, Horea-Ioan Jahn, Andrew Karakuzu, Agah Keator, David B. Kiar, Gregory Kincses, Balint Laird, Angela R. Lau, Jonathan C. Lazari, Alberto Legarreta, Jon Haitz Li, Adam Li, Xiangrui Love, Bradley C. Lu, Hanzhang Maumet, Camille Mazzamuto, Giacomo Meisler, Steven L. Mikkelsen, Mark Mutsaerts, Henk Nichols, Thomas E. Nikolaidis, Aki Nilsonne, Gustav Niso, Guiomar Norgaard, Martin Okell, Thomas W. Oostenveld, Robert Ort, Eduard Park, Patrick J. Pawlik, Mateusz Pernet, Cyril R. Pestilli, Franco Petr, Jan Phillips, Christophe Poline, Jean-Baptiste Pollonini, Luca Raamana, Pradeep Reddy Ritter, Petra Rizzo, Gaia Robbins, Kay A. Rockhill, Alexander P. Rogers, Christine Rokem, Ariel Rorden, Chris Routier, Alexandre Saborit-Torres, Jose Manuel Salo, Taylor Schirner, Michael Smith, Robert E. Spisak, Tamas Sprenger, Julia Swann, Nicole C. Szinte, Martin Takerkart, Sylvain Thirion, Bertrand Thomas, Adam G. Torabian, Sajjad Varoquaux, Gael Voytek, Bradley Welzel, Julius Wilson, Martin Yarkoni, Tal Gorgolewski, Krzysztof J. Department of Psychology Stanford University StanfordCA United States Max Planck Institute for Human Development Berlin Germany University of Colorado Anschutz Medical Campus AuroraCO United States School of Psychology University of Surrey Guildford United Kingdom McConnell Brain Imaging Centre Montréal Neurological Institute McGill University Montréal Canada Department of Bioengineering University of California San Diego La Jolla CA United States School of Physics Astronomy Cardiff University Wales United Kingdom Institut de Neurosciences des Systèmes Marseille France Neurophysiology Lab IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy Center for Mind/Brain Sciences - CIMeC University of Trento TN Rovereto Italy Brigham and Women’s Hospital BostonMA United States Massachusetts General Hospital BostonMA United States Harvard Medical School BostonMA United States Rackspace Technology San AntonioTX United States NeuroPoly Lab Polytechnique Montréal MontréalQC Canada Georgia State Georgia Tech Emory AtlantaGA United States University of Florence Sesto Fiorentino Italy Bioretics srl Cesena Italy Department of Medical Imaging Ghent University Hospital Ghent Belgium Department of Diagnostic Sciences Ghent University Ghent Belgium Department of Neurology Boston Children's Hospital BostonMA United States Dipartimento di Scienze della Salute dell'Università degli Studi di Milano Milan Italy Department of Clinical and Experimental Epilepsy University College London United Kingdom Zurich Center for Neuroeconomics Department of Economics University of Zurich Zurich Switzerland UMIB-FISABIO Valencia Spain The University of Texas at Austin AustinTX United States SCCN University of California San Diego La Jolla CA United States Department of Neurology NYU Langone Medical Center New YorkNY United States Department of Experimental Psychology University of Oxford Oxford United Kingdom UK Dementia Research Ins

The Brain Imaging data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS. © 2023, CC BY.

关键词： data structures

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Measurement of ω meson production in pp collisions at = 13 TeV

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Journal of High Energy Physics 2025年第4期2025卷 1-30页

作者： Acharya, S. Adamová, D. Agarwal, A. Aglieri Rinella, G. Aglietta, L. Agnello, M. Agrawal, N. Ahammed, Z. Ahmad, S. Ahn, S. U. Ahuja, I. Akindinov, A. Akishina, V. Al-Turany, M. Aleksandrov, D. Alessandro, B. Alfanda, H. M. Alfaro Molina, R. Ali, B. Alici, A. Alizadehvandchali, N. Alkin, A. Alme, J. Alocco, G. Alt, T. Altamura, A. R. Altsybeev, I. Alvarado, J. R. Anaam, M. N. Andrei, C. Andreou, N. Andronic, A. Andronov, E. Anguelov, V. Antinori, F. Antonioli, P. Apadula, N. Aphecetche, L. Appelshäuser, H. Arata, C. Arcelli, S. Aresti, M. Arnaldi, R. Arneiro, J. G. M. C. A. Arsene, I. C. Arslandok, M. Augustinus, A. Averbeck, R. Azmi, M. D. Baba, H. Badalà, A. Bae, J. Baek, Y. W. Bai, X. Bailhache, R. Bailung, Y. Bala, R. Balbino, A. Baldisseri, A. Balis, B. Banerjee, D. Banoo, Z. Barbasova, V. Barile, F. Barioglio, L. Barlou, M. Barman, B. Barnaföldi, G. G. Barnby, L. S. Barreau, E. Barret, V. Barreto, L. Bartels, C. Barth, K. Bartsch, E. Bastid, N. Basu, S. Batigne, G. Battistini, D. Batyunya, B. Bauri, D. Bazo Alba, J. L. Bearden, I. G. Beattie, C. Becht, P. Behera, D. Belikov, I. Bell Hechavarria, A. D. C. Bellini, F. Bellwied, R. Belokurova, S. Beltran, L. G. E. Beltran, Y. A. V. Bencedi, G. Bensaoula, A. Beole, S. Berdnikov, Y. Berdnikova, A. Bergmann, L. Besoiu, M. G. Betev, L. Bhaduri, P. P. Bhasin, A. Bhat, M. A. Bhattacharjee, B. Bianchi, L. Bianchi, N. Bielčík, J. Bielčíková, J. Bigot, A. P. Bilandzic, A. Biro, G. Biswas, S. Bize, N. Blair, J. T. Blau, D. Blidaru, M. B. Bluhme, N. Blume, C. Boca, G. Bock, F. Bodova, T. Bok, J. Boldizsár, L. Bombara, M. Bond, P. M. Bonomi, G. Borel, H. Borissov, A. Borquez Carcamo, A. G. Bossi, H. Botta, E. Bouziani, Y. E. M. Bratrud, L. Braun-Munzinger, P. Bregant, M. Broz, M. Bruno, G. E. Buckland, M. D. Budnikov, D. Buesching, H. Bufalino, S. Buhler, P. Burmasov, N. Buthelezi, Z. Bylinkin, A. Bysiak, S. A. Cabanillas Noris, J. C. Cabrera, M. F. T. Cai, M. Caines, H. Caliva, A. Calvo Villar, E. Camacho, J. M. M. Camerini, P. Canedo, F. D. M. Cantway, S. L. Carabas, M. Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Nuclear Physics Institute of the Czech Academy of Sciences Husinec-Řež Czech Republic Variable Energy Cyclotron Centre Homi Bhabha National Institute Kolkata India European Organization for Nuclear Research (CERN) Geneva Switzerland Dipartimento di Fisica dell’Università and Sezione INFN Turin Italy Dipartimento DISAT del Politecnico and Sezione INFN Turin Italy Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Bologna Italy Department of Physics Aligarh Muslim University Aligarh India Korea Institute of Science and Technology Information Daejeon Republic of Korea Faculty of Science P.J. Šafárik University Košice Slovak Republic Frankfurt Institute for Advanced Studies Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany Research Division and ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Darmstadt Germany INFN Sezione di Torino Turin Italy Central China Normal University Wuhan China Instituto de Física Universidad Nacional Autónoma de México Mexico City Mexico University of Houston Houston United States Sungkyunkwan University Suwon City Republic of Korea Department of Physics and Technology University of Bergen Bergen Norway INFN Sezione di Cagliari Cagliari Italy Institut für Kernphysik Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany INFN Sezione di Bari Bari Italy Physik Department Technische Universität München Munich Germany High Energy Physics Group Universidad Autónoma de Puebla Puebla Mexico Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest Romania University of Derby Derby United Kingdom Universität Münster Institut für Kernphysik Münster Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany INFN Sezione di Padova Padova Italy INFN Sezione di Bologna Bologna Italy Lawrence Berkeley National Laboratory Berkeley United Sta

The pT-differential cross section of ω meson production in pp collisions at $$ \sqrt{s} $$ = 13 TeV at midrapidity (|y| < 0.5) was measured with the ALICE detector at the LHC, covering an unprecedented transverse-momentum range of 1.6 < pT < 50 GeV/c. The meson is reconstructed via the ω → π+π−π0 decay channel. The results are compared with various theoretical calculations: PYTHIA8.2 with the Monash 2013 tune overestimates the data by up to 50%, whereas good agreement is observed with Next-to-Leading Order (NLO) calculations incorporating ω fragmentation using a broken SU(3) model. The ω/π0 ratio is presented and compared with theoretical calculations and the available measurements at lower collision energies. The presented data triples the pT ranges of previously available measurements. A constant ratio of Cω/π0 = 0.578 ± 0.006 (stat.) ± 0.013 (syst.) is found above a transverse momentum of 4 GeV/c, which is in agreement with previous findings at lower collision energies within the systematic and statistical uncertainties.

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