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Self-Compassion Around the World: Measurement Invariance of the Short Form of the Self-Compassion Scale (SCS-SF) Across 65 Nations, 40 Languages, Gender Identities, and Age Groups

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Mindfulness 2025年 1-28页

作者： Swami, Viren Tran, Ulrich S. Voracek, Martin Aavik, Toivo Ranjbar, Hamed Abdollahpour Adebayo, Sulaiman Olanrewaju Afhami, Reza Ahmed, Oli Aimé, Annie Akel, Marwan Al Halbusi, Hussam Alexias, George Ali, Khawla F. Alp-Dal, Nursel Alsalhani, Anas B. Álvarez-Solas, Sara Amaral, Ana Carolina Soares Andrianto, Sonny Aspden, Trefor Argyrides, Marios Aruta, John Jamir Benzon R. Atkin, Stephen Ayandele, Olusola Baceviciene, Migle Bahbouh, Radvan Ballesio, Andrea Barron, David Bellard, Ashleigh Bender, Sóley Sesselja Beydaǧ, Kerime Derya Birovljević, Gorana Blackburn, Marie-Ève Borja-Alvarez, Teresita Borowiec, Joanna Bozogáňová, Miroslava Bratland-Sanda, Solfrid Browning, Matthew H. E. M. Brytek-Matera, Anna Burakova, Marina Çakır-Koçak, Yeliz Camacho, Pablo Camilleri, Vittorio Emanuele Cazzato, Valentina Cerea, Silvia Chaiwutikornwanich, Apitchaya Chaleeraktrakoon, Trawin Chambers, Tim Chen, Qing-Wei Chen, Xin Chien, Chin-Lung Chobthamkit, Phatthanakit Choompunuch, Bovornpot Compte, Emilio J. Corrigan, Jennifer Cosmas, Getrude Cowden, Richard G. Czepczor-Bernat, Kamila Czub, Marcin da Silva, Wanderson Roberto Dadfar, Mahboubeh Dalley, Simon E. Dany, Lionel Datu, Jesus Alfonso D. de Carvalho, Pedro Henrique Berbert de Holanda Coelho, Gabriel Lins De Jesus, Avila Odia S. Debbabi, Sonia Harzallah Dhakal, Sandesh Di Bernardo, Francesca Dimitrova, Donka D. Dion, Jacinthe Dixson, Barnaby Donofrio, Stacey M. Drysch, Marius Du, Hongfei Dzhambov, Angel M. El-Jor, Claire Enea, Violeta Eskin, Mehmet Farbod, Farinaz Farrugia, Lorleen Fian, Leonie Fisher, Maryanne L. Folwarczny, Michał Frederick, David A. Fuller-Tyszkiewicz, Matthew Furnham, Adrian García, Antonio Alías Geller, Shulamit Ghisi, Marta Ghorbani, Alireza Martinez, Maria Angeles Gomez Gradidge, Sarah Graf, Sylvie Grano, Caterina Gyene, Gyöngyvér Hallit, Souheil Hamdan, Motasem Handelzalts, Jonathan E. Hanel, Paul H. P. Hawks, Steven R. Hekmati, Issa Helmy, Mai Hill, Tetiana Hina, Farah Holenweger, Geraldine Hřebíčková, Martina Ijabadeniyi, Olasupo Augustine Imam, Asma İ School of Psychology and Sport Science Anglia Ruskin University Cambridge United Kingdom Centre for Psychological Medicine Perdana University Kuala Lumpur Malaysia Department of Cognition Emotion and Methods in Psychology Faculty of Psychology University of Vienna Vienna Austria Institute of Psychology University of Tartu Tartu Estonia Department of Psychology Istinye University Istanbul Turkey Department of Psychology and Behavioural Studies Ekiti State University Ado-Ekiti Nigeria Department of Art Studies Tarbiat Modares University Tehran Iran Department of Psychology University of Chittagong Chattogram Bangladesh Department of Psychoeducation and Psychology Université du Québec en Outaouais Saint-Jérôme Canada INSPECT-LB: National Institute of Public Health Clinical Epidemiology and Toxicology Beirut Lebanon School of Pharmacy Lebanese International University Beirut Lebanon Department of Management Ahmed Bin Mohammad Military College Doha Qatar Faculty of Psychology Panteion University of Social and Political Sciences Athens Greece Royal College of Surgeons Ireland-Bahrain Adliya Bahrain Department of Midwifery Faculty of Health Sciences Munzur University Tunceli Turkey Department of Oral Medicine and Diagnostic Sciences Vision College of Dentistry and Nursing Vision Colleges Riyadh Saudi Arabia Facultad de Salud Universidad Internacional de La Rioja Logroño Spain Science and Technology of Southeast Minas Gerais Federal Institute of Education Barbacena Brazil Department of Psychology Universitas Islam Indonesia Yogyakarta Indonesia Department of Psychology Aberystwyth University Ceredigion United Kingdom Department of Psychology Neapolis University Pafos Paphos Cyprus Department of Psychology De La Salle University Manila Philippines Department of General Studies The Polytechnic Ibadan Nigeria Department of Psychology University of Ibadan Ibadan Nigeria Health Research and Innovation Science Centre Faculty of Health Sciences Klai

Objectives: The 12-item Self-Compassion Scale–Short Form (SCS–SF) is a widely used instrument for the assessment of self-compassion. To date, there have been few examinations of this instrument’s psychometric properties, particularly across nations and languages. Therefore, we used data from the Body Image in Nature Survey (BINS) to assess measurement invariance of the SCS–SF across nations, languages, gender identities, and age groups. Methods: Participants (N = 56,968) from 65 nations completed the SCS–SF in 40 languages. Using these data, we tested various hypothesised models of the SCS–SF in the total sample and, using multi-group confirmatory factor analysis, tested for invariance of the optimal model across national groups, languages, gender identities, and age groups. Results: In the total dataset, we found that an 11-item, 2-factor model (i.e., SCS-11) provided best fit to the data, with the two factors tapping distinct constructs of compassionate and uncompassionate self-responding. The SCS-11 was found to be partially scalar invariant across national groups and languages, and fully scalar invariant across gender identities and age groups. There was wide variation in latent means for the two factors, particularly across national groups and languages. Further analyses showed negligible associations between the two factors and sociodemographic variables, including marital status, financial security, and urbanicity. Conclusions: Our results suggest that it may be possible to derive a stable 2-factor model of the SCS–SF for use in cross-cultural research, but also highlight the likelihood of cross-national and cross-linguistic variations in the way that self-compassion is understood. © The Author(s) 2025.

关键词： Cross-cultural Measurement invariance Multi-group confirmatory factor analysis (MG-CFA) Self-Compassion Scale Short Form

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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

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Nature 2024年第8003期627卷 347-357页

作者： Ken Suzuki Konstantinos Hatzikotoulas Lorraine Southam Henry J Taylor Xianyong Yin Kim M Lorenz Ravi Mandla Alicia Huerta-Chagoya Giorgio E M Melloni Stavroula Kanoni Nigel W Rayner Ozvan Bocher Ana Luiza Arruda Kyuto Sonehara Shinichi Namba Simon S K Lee Michael H Preuss Lauren E Petty Philip Schroeder Brett Vanderwerff Mart Kals Fiona Bragg Kuang Lin Xiuqing Guo Weihua Zhang Jie Yao Young Jin Kim Mariaelisa Graff Fumihiko Takeuchi Jana Nano Amel Lamri Masahiro Nakatochi Sanghoon Moon Robert A Scott James P Cook Jung-Jin Lee Ian Pan Daniel Taliun Esteban J Parra Jin-Fang Chai Lawrence F Bielak Yasuharu Tabara Yang Hai Gudmar Thorleifsson Niels Grarup Tamar Sofer Matthias Wuttke Chloé Sarnowski Christian Gieger Darryl Nousome Stella Trompet Soo-Heon Kwak Jirong Long Meng Sun Lin Tong Wei-Min Chen Suraj S Nongmaithem Raymond Noordam Victor J Y Lim Claudia H T Tam Yoonjung Yoonie Joo Chien-Hsiun Chen Laura M Raffield Bram Peter Prins Aude Nicolas Lisa R Yanek Guanjie Chen Jennifer A Brody Edmond Kabagambe Ping An Anny H Xiang Hyeok Sun Choi Brian E Cade Jingyi Tan K Alaine Broadaway Alice Williamson Zoha Kamali Jinrui Cui Manonanthini Thangam Linda S Adair Adebowale Adeyemo Carlos A Aguilar-Salinas Tarunveer S Ahluwalia Sonia S Anand Alain Bertoni Jette Bork-Jensen Ivan Brandslund Thomas A Buchanan Charles F Burant Adam S Butterworth Mickaël Canouil Juliana C N Chan Li-Ching Chang Miao-Li Chee Ji Chen Shyh-Huei Chen Yuan-Tsong Chen Zhengming Chen Lee-Ming Chuang Mary Cushman John Danesh Swapan K Das H Janaka de Silva George Dedoussis Latchezar Dimitrov Ayo P Doumatey Shufa Du Qing Duan Kai-Uwe Eckardt Leslie S Emery Daniel S Evans Michele K Evans Krista Fischer James S Floyd Ian Ford Oscar H Franco Timothy M Frayling Barry I Freedman Pauline Genter Hertzel C Gerstein Vilmantas Giedraitis Clicerio González-Villalpando Maria Elena González-Villalpando Penny Gordon-Larsen Myron Gross Lindsay A Guare Sophie Hackinger Liisa Hakaste Sohee Han Andrew T Hattersley Christian Herder Momoko Horikoshi Annie-Green Howard Willa Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences University of Manchester Manchester UK. Department of Diabetes and Metabolic Diseases Graduate School of Medicine University of Tokyo Tokyo Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. konstantinos.hatzikotoulas@helmholtz-munich.de. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. Center for Precision Health Research National Human Genome Research Institute National Institutes of Health Bethesda MD USA. British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Heart and Lung Research Institute University of Cambridge Cambridge UK. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor MI USA. Corporal Michael J. Crescenz VA Medical Center Philadelphia PA USA. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Genetics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Programs in Metabolism and Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Diabetes Unit and Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. TIMI Study Group Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London L

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

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Secrecy and robustness for active attacks in secure network coding and its application to network quantum key distribution

arXiv

引用

arXiv 2017年

作者： Hayashi, Masahito Owari, Masaki Kato, Go Cai, Ning Graduate School of Mathematics Nagoya University Japan Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Centre for Quantum Technologies National University of Singapore Singapore Singapore Department of Computer Science Faculty of Informatics Shizuoka University Japan Ntt Communication Science Laboratories Ntt Corporation Japan School of Information Science and Technology ShanghaiTech University

In network coding, we discuss the effect of sequential error injection on information leakage. We show that there is no improvement when the operations in the network are linear operations. However, when the operations in the network contains non-linear operations, we find a counterexample to improve Eve's obtained information. Furthermore, we discuss the asymptotic rate in a linear network under the secrecy and robustness conditions as well as under the secrecy condition alone. Finally, we apply our results to network quantum key distribution, which clarifies the type of network that enables us to realize secure long distance communication via short distance quantum key distribution. Copyright © 2017, The Authors. All rights reserved.

关键词： Linear networks

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A novel reversible watermarking scheme for relational databases protection based on histogram shifting

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Journal of information Hiding and Multimedia Signal Processing 2016年第2期7卷 266-276页

作者： Ma, Yu-Jia Zhu, Yue-Sheng Liu, Xi-Yao Communication and Information Security Lab Institute of Big Data Technologies Shenzhen Graduate School Peking University Shenzhen China School of Information Science and Engineering Central South University Changsha China

The watermarking technique can be used to protect the ownership of relational databases by hiding some ownership information into the relational databases. In this paper, a novel robust reversible watermarking scheme for relational database protection is proposed to increase the embedding capacity by developing an improved histogram shifting with binary tree structure and an attribute selection scheme. The iteratively embedded watermarks are utilized by a majority voting mechanism to enhance the robustness of the proposed scheme. In addition, the attribute selection and cell selection are employed to reduce the distortion caused by the watermark embedding. The experimental results show that our proposed scheme can provide higher embedding capacity and stronger robustness with low distortion compared with other watermarking schemes for relational databases. © 2016.

关键词： Iterative methods

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LUC Algorithm in Visual Cryptography

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Ratnadewi Christian Dio Alpha G Darmawan Napitupulu Heri Nurdiyanto Nuning Kurniasih Abdurrozaq Hasibuan GS Achmad Daengs Rosida Tiurma Manurung Indrya Mulyaningsih Robbi Rahim Castaka Agus Sugianto Dahlan Abdullah Department of Electrical Engineering Universitas Kristen Maranatha Bandung Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Banten Indonesia STMIK Dharma Wacana Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Universitas Islam Sumatera Utara Indonesia University of 45 Surabaya Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Bandung Indonesia Department of Education Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia Politeknik TEDC Bandung Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia

Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients. All of this system can be realization by using cryptography. LUC algorithms are introduced by Smith and Lennon in 1993. LUC algorithm is an algorithm that based on the use of Lucas sequence (specific arithmetic operations derived from Lucas row) that rarely used to enhance the security of the messages. In this research, LUC algorithm is combined with visual cryptography to process the encryption and the description of a colored image. Four different images were used in the trial here. The performance of the system is assessed using Structural similarity (SSIM) which has an assessment similar to human eye. If the image quality is the same as the original image, then the SSIM value is one. Whereas if the quality of decryption image is very different from the original image then the value of SSIM is zero.

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Retraction: LUC Algorithm in Visual Cryptography (J. Phys.: Conf. Ser. 1114 012090)

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Journal of Physics: Conference Series 2018年第1期1114卷

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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Minimizing thin film thickness in TiN coatings using genetic algorithms

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AIP Conference Proceedings 2018年第1期2016卷

作者： M. I. Jarrah A. S. M. Jaya M. A. Azam M. R. Muhamad H. Akbar 1Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka (UTeM) Hang Tuah Jaya 76100 Durian Tunggal Melaka Malaysia 2Faculty of Manufacturing Engineering Universiti Teknikal Malaysia Melaka (UTeM) Hang Tuah Jaya 76100 Durian Tunggal Melaka Malaysia 3Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Optimization of coating parameters is important in the machining process, as an optimized coating layer enhances efficient machining result by providing high wear resistance, saving time and effort, reducing cost, increasing productivity and leading to extend tool life for better surface finish. In terms of tool wear resistance, tools with suitable film coating are forty times better than uncoated tools. Cutting tools are usually coated by Titanium Nitride (TiN), since it has properties such as resistance to wear and hardness resulting in increased cutting performance. Genetic algorithms (GAs) belong to the powerful artificial intelligence optimization family of evolutionary algorithm-based metaheuristic techniques. Since their inception, they have been successfully applied in numerous areas of industrial sectors as material design, tools coating and cutting, alloy design, and so on. In this research work, predicting thin film coating thickness of titanium nitride (TiN) on tungsten carbide (WC) inserts is presented. First, (WC) inserts were coated by TiN in different conditions using Physical Vapor Deposition (PVD), and the surface profilometer was used to measure thickness of the WC coated specimens. Second, Response Surface Methodology (RSM) was used to generate the objective function which represents the process parameters and coating thickness. Third, using a fitness function model, GAs were established to optimize the thickness value of TiN layer with respect to changes in three process parameters, namely Nitrogen gas pressure (N2), Argon gas pressure (Ar), and Turntable Speed (TT). Finally, for performance measurement, three real experimental tests were carried out in terms of Prediction Interval (PI) and Residual Error (e) to validate the RSM model. The actual coating thickness of validation data fell within the 95% (PI) and the (e) values percentage were very low. In terms of optimization, GAs is capable optimizing thickness better than the average experimen

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Resource-efficient verification of quantum computing using Serfling's bound

arXiv

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arXiv 2018年

作者： Takeuchi, Yuki Mantri, Atul Morimae, Tomoyuki Mizutani, Akihiro Fitzsimons, Joseph F. Graduate School of Engineering Science Osaka University Toyonaka Osaka560-8531 Japan NTT Communication Science Laboratories NTT Corporation 3-1 Morinosato Wakamiya Atsugi-shi Kanagawa243-0198 Japan Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Centre for Quantum Technologies National University of Singapore Block S15 3 Science Drive 2 Singapore117543 Singapore School of Informatics University of Edinburgh 10 Crichton Street EdinburghEH8 9AB United Kingdom Yukawa Institute for Theoretical Physics Kyoto University Kitashirakawa Oiwakecho Sakyo-ku Kyoto606-8502 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan Mitsubishi Electric Corporation Information Technology R & D Center 5-1-1 Ofuna Kamakura-shi Kanagawa247-8501 Japan Horizon Quantum Computing 79 Ayer Rajah Crescent #03-01 BASH Singapore139955

Verifying quantum states is central to certifying the correct operation of various quantum information processing tasks. In particular, in measurement-based quantum computing, checking whether correct graph states are generated is essential for reliable quantum computing. Several verification protocols for graph states have been proposed, but none of these are particularly resource efficient: multiple copies are required to extract a single state that is guaranteed to be close to the ideal one. The best protocol currently known requires O(n15) copies of the state, where n is the size of the graph state. In this paper, we construct a significantly more resource-efficient verification protocol for graph states that only requires O(n5 log n) copies. The key idea is to employ Serfling's bound, which is a probability inequality in classical statistics. Utilizing Serfling's bound also enables us to generalize our protocol for qudit and continuous-variable graph states. Constructing a resource-efficient verification protocol for them is non-trivial. For example, the previous verification protocols for qubit graph states that use the quantum de Finetti theorem cannot be generalized to qudit and continuous-variable graph states without tremendously increasing the resource overhead. This is because the overhead caused by the quantum de Finetti theorem depends on the local dimension. On the other hand, in our protocol, the resource overhead is independent of the local dimension, and therefore generalizing to qudit or continuous-variable graph states does not increase the overhead. The flexibility of Serfling's bound also makes our protocol robust: our protocol accepts slightly noisy but still useful graph states. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum optics

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SciPy 1.0—Fundamental algorithms for scientific computing in python

arXiv

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arXiv 2019年

作者： Virtanen, Pauli Gommers, Ralf Oliphant, Travis E. Haberland, Matt Reddy, Tyler Cournapeau, David Burovski, Evgeni Peterson, Pearu Weckesser, Warren Bright, Jonathan van der Walt, Stéfan J. Brett, Matthew Wilson, Joshua Millman, K. Jarrod Mayorov, Nikolay Nelson, Andrew R.J. Jones, Eric Kern, Robert Larson, Eric Carey, C.J. Polat, Ilhan Feng, Yu Moore, Eric W. VanderPlas, Jake Laxalde, Denis Perktold, Josef Cimrman, Robert Henriksen, Ian Quintero, E.A. Harris, Charles R. Archibald, Anne M. Ribeiro, Antônio H. Pedregosa, Fabian van Mulbregt, Paul University of Jyväskylä University of Jyväskylä FI-40014 Finland Quansight LLC AustinTX United States Ultrasound Imaging Mayo Clinic RochesterMN55902 United States Electrical Engineering Brigham Young University ProvoUT84604 United States Enthought Inc. AustinTX78701 United States Anaconda Inc AustinTX78701 United States BioResource and Agricultural Engineering Department California Polytechnic State University San Luis ObispoCA93407 United States Department of Mathematics University of California Los AngelesCA90025 United States Los Alamos National Laboratory Los AlamosNM87544 United States Independent Researcher Higher School of Economics Myasnitskaya 20 Moscow101000 Russia Independent Researcher Saue Estonia Department of Mechanics and Applied Mathematics Institute of Cybernetics Tallinn Technical University Akadeemia Rd 21 Tallinn Estonia Independent Researcher NY United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States School of Psychology University of Birmingham Edgbaston BirmighamB15 2TT United Kingdom Division of Biostatistics University of California BerkeleyCA94720 United States WayRay LLC Skolkovo Innovation Center Moscow143026 Russia Australian Nuclear Science and Technology Organisation Locked Bag 2001 Kirrawee DCNSW2232 Australia Institute for Learning and Brain Sciences University of Washington SeattleWA98195 United States College of Information and Computing Sciences University of Massachusetts Amherst AmherstMA01002 United States Berkeley Center for Cosmological Physics University of California BerkeleyCA94720 United States Bruker Biospin Corp. 15 Fortune Drive BillericaMA01821 United States University of Washington SeattleWA98195 United States New Technologies - Research Centre University of West Bohemia Univerzitní 8 Plzeň306 14 Czech Republic Oden Institute for Computational Engineering and Sciences University of Texas at Austin AustinTX7

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments. Copyright © 2019, The Authors. All rights reserved.

关键词： Open source software

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Education for the future

引用

Science 2018年第6396期360卷 1409-1412页

作者： Cheung, Man Kit Schwendimann, Beat A. Ward, Adrian Pietrzak, Barbara Mollaoglu, Gurkan Duong, Michael Tran Allareddy, Veerasathpurush Ulltveit-Moe, Nils Dutton-Regester, Ken Zhang, Jian Scult, Matthew A. Singh, Poonam C. Yan, Hong Young Naz, Saima Isaacson, Kyle Dennis, Allison F. Beardsley, Felicia Rounds Al-Humaidan, Eyad Ibrahim Lo, Cody Jones, Tyler Sood, Prashant Nieuwenhuis, Rense Ali, Basant A. Yu, Kun-Hsing Arthur, Patrick Kobina Kumar, Brijesh Buschke, Falko Chen, Alexander Cingl, Lubomír O’Mullane, Anthony Peter Coetzee, Vinet Zaidi, Syed Shan-e-Ali Konstantinides, Nikos School of Life Sciences Chinese University of Hong Kong Shatin New Territories Hong Kong Graduate School of Education University of California Berkeley Berkeley 94720 CA United States University of Queensland St. Lucia 4072 QLD Australia Department of Hydrobiology Faculty of Biology Biological and Chemical Research Centre University of Warsaw Warsaw 02-089 Poland Department of Oncological Sciences University of Utah School of Medicine Salt Lake City 84112 UT United States Perelman School of Medicine University of Pennsylvania Philadelphia 19104 PA United States University of Illinois College of Dentistry Chicago 60612 IL United States Department of Information and Communication Technology University of Agder 4879 Grimstad Norway Department of Genetics and Computational Biology QIMR Berghofer Medical Research Institute Brisbane 4006 QLD Australia School of Safety and Environmental Engineering Hunan Institute of Technology Hengyang Hunan 421002 China Lahore 54000 Punjab Pakistan Department of Psychology and Neuroscience Duke University Durham 27708 NC United States National Museum of Marine Biology and Aquarium Chercheng Pingtung County 94450 Taiwan Division of Plant Microbe Interactions CSIR–National Botanical Research Institute Lucknow 226001 Uttar Pradesh India Program in Cell Molecular Developmental Biology and Biophysics (CMDB) Johns Hopkins University Baltimore 21218 MD United States Stem Cell and Tissue Re-Engineering Program King Faisal Specialist Hospital and Research Centre Riyadh Saudi Arabia Department of Sociology and Anthropology University of La Verne La Verne 91750 CA United States Department of Bioengineering University of Utah Salt Lake City 84112 UT United States MRC Centre for Medical Mycology University of Aberdeen Aberdeen AB25 2ZD United Kingdom Faculty of Medicine University of British Columbia Vancouver V6T 1Z2 BC Canada School of Engineering Brown University Providence 02912 RI United States

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