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Manifold-adaptive dimension estimation revisited

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arXiv 2020年

作者： Benkő, Zsigmond Stippinger, Marcell Rehus, Roberta Bencze, Attila Fabó, Dániel Hajnal, Boglárka Erőss, Loránd Telcs, András Somogyvári, Zoltán Department of Computational Sciences Wigner Research Centre for Physics H-1121 Hungary János Szentágothai Doctoral School of Neurosciences Semmelweis University H-1085 Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics BudapestH-1111 Hungary Epilepsy Center Department of Neurology National Institute of Clinical Neurosciences BudapestH-1145 Hungary Department of Functional Neurosurgery National Institute of Clinical Neurosciences BudapestH-1145 Hungary Faculty of Information Technology and Bionics Péter Pázmány Catholic University BudapestH-1083 Hungary Department of Quantitative Methods University of Pannonia Faculty of Business and Economics VeszprémH-8200 Hungary Neuromicrosystems ltd. BudapestH-1113 Hungary

Data dimensionality informs us about data complexity and sets limit on the structure of successful signal processing pipelines. In this work we revisit and improve the manifold adaptive Farahmand-Szepesvári-Audibert (FSA) dimension estimator, making it one of the best nearest neighbor-based dimension estimators available. We compute the probability density function of local FSA estimates, if the local manifold density is uniform. Based on the probability density function, we propose to use the median of local estimates as a basic global measure of intrinsic dimensionality, and we demonstrate the advantages of this asymptotically unbiased estimator over the previously proposed statistics: the mode and the mean. Additionally, from the probability density function, we derive the maximum likelihood formula for global intrinsic dimensionality, if i.i.d. holds. We tackle edge and finite-sample effects with an exponential correction formula, calibrated on hypercube datasets. We compare the performance of the corrected-median-FSA estimator with kNN estimators: maximum likelihood (ML, Levina-Bickel) and two implementations of DANCo (R and matlab). We show that corrected-median-FSA estimator beats the ML estimator and it is on equal footing with DANCo for standard synthetic benchmarks according to mean percentage error and error rate metrics. With the median-FSA algorithm, we reveal diverse changes in the neural dynamics while resting state and during epileptic seizures. We identify brain areas with lower-dimensional dynamics that are possible causal sources and candidates for being seizure onset zones. Copyright © 2020, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

Comparison for the effects of different components of temperature variability on mortality: A multi-country time-series study

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Environment International 2024年 187卷 108712页

作者： Wen, Bo Wu, Yao Guo, Yuming Gasparrini, Antonio Tong, Shilu Overcenco, Ala Urban, Aleš Schneider, Alexandra Entezari, Alireza Vicedo-Cabrera, Ana Maria Zanobetti, Antonella Analitis, Antonis Zeka, Ariana Tobias, Aurelio Nunes, Baltazar Alahmad, Barrak Armstrong, Ben Forsberg, Bertil Pan, Shih-Chun Íñiguez, Carmen Ameling, Caroline Valencia, César De la Cruz Åström, Christofer Houthuijs, Danny Van Dung, Do Royé, Dominic Indermitte, Ene Lavigne, Eric Mayvaneh, Fatemeh Acquaotta, Fiorella de'Donato, Francesca Rao, Shilpa Sera, Francesco Carrasco-Escobar, Gabriel Kan, Haidong Orru, Hans Kim, Ho Holobaca, Iulian-Horia Kyselý, Jan Madureira, Joana Schwartz, Joel Jaakkola, Jouni J.K. Katsouyanni, Klea Diaz, Magali Hurtado Ragettli, Martina S. Hashizume, Masahiro Pascal, Mathilde Coélho, Micheline de Sousa Zanotti Stagliorio Ortega, Nicolás Valdés Ryti, Niilo Scovronick, Noah Michelozzi, Paola Matus Correa, Patricia Goodman, Patrick Saldiva, Paulo Hilario Nascimento Raz, Raanan Abrutzky, Rosana Osorio, Samuel Dang, Tran Ngoc Colistro, Valentina Huber, Veronika Lee, Whanhee Seposo, Xerxes Honda, Yasushi Kim, Yoonhee Guo, Yue Leon Bell, Michelle L. Li, Shanshan Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne Australia Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London United Kingdom Centre for Statistical Methodology London School of Hygiene & Tropical Medicine London United Kingdom Centre on Climate Change & Planetary Health London School of Hygiene & Tropical Medicine London United Kingdom Shanghai Children's Medical Centre Shanghai Jiao Tong University Shanghai China School of Public Health Institute of Environment and Population Health Anhui Medical University Hefei China Center for Global Health Nanjing Medical University Nanjing China School of Public Health and Social Work Queensland University of Technology Brisbane Australia National Agency for Public Health of the Ministry of Health Labour and Social Protection of the Republic of Moldova Moldova Institute of Atmospheric Physics Czech Academy of Sciences Prague Czech Republic Faculty of Environmental Sciences Czech University of Life Sciences Prague Czech Republic Institute of Epidemiology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany Faculty of Geography and Environmental Sciences Hakim Sabzevari University Sabzevar Iran Institute of Social and Preventive Medicine University of Bern Bern Switzerland Oeschger Center for Climate Change Research University of Bern Bern Switzerland Department of Environmental Health Harvard T.H. Chan School of Public Health Harvard University BostonMA United States Department of Hygiene Epidemiology and Medical Statistics National and Kapodistrian University of Athens Athens Greece Institute for Environment Health and Societies Brunel University London London United Kingdom Institute of Environmental Assessment and Water Research Spanish Council for Scientific Research Barcelona Spain School of Tropical Medicine and Global Health Nagasaki University Naga

Background: Temperature variability (TV) is associated with increased mortality risk. However, it is still unknown whether intra-day or inter-day TV has different effects. Objectives: We aimed to assess the association of intra-day TV and inter-day TV with all-cause, cardiovascular, and respiratory mortality. methods: We collected data on total, cardiovascular, and respiratory mortality and meteorology from 758 locations in 47 countries or regions from 1972 to 2020. We defined inter-day TV as the standard deviation (SD) of daily mean temperatures across the lag interval, and intra-day TV as the average SD of minimum and maximum temperatures on each day. In the first stage, inter-day and intra-day TVs were modelled simultaneously in the quasi-Poisson time-series model for each location. In the second stage, a multi-level analysis was used to pool the location-specific estimates. Results: Overall, the mortality risk due to each interquartile range [IQR] increase was higher for intra-day TV than for inter-day TV. The risk increased by 0.59% (95% confidence interval [CI]: 0.53, 0.65) for all-cause mortality, 0.64% (95% CI: 0.56, 0.73) for cardiovascular mortality, and 0.65% (95% CI: 0.49, 0.80) for respiratory mortality per IQR increase in intra-day TV0–7 (0.9 °C). An IQR increase in inter-day TV0–7 (1.6 °C) was associated with 0.22% (95% CI: 0.18, 0.26) increase in all-cause mortality, 0.44% (95% CI: 0.37, 0.50) increase in cardiovascular mortality, and 0.31% (95% CI: 0.21, 0.41) increase in respiratory mortality. The proportion of all-cause deaths attributable to intra-day TV0–7 and inter-day TV0–7 was 1.45% and 0.35%, respectively. The mortality risks varied by lag interval, climate area, season, and climate type. Conclusions: Our results indicated that intra-day TV may explain the main part of the mortality risk related to TV and suggested that comprehensive evaluations should be proposed in more countries to help protect human health. © 2024

关键词： Location

An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes

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arXiv 2018年

作者： Speicher, Nora K. Pfeifer, Nico Department of Computational Biology and Applied Algorithmics Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Methods in Medical Informatics Department of Computer Science University of Tübingen Germany

Due to the complexity of cancer, clustering algorithms have been used to disentangle the observed heterogeneity and identify cancer subtypes that can be treated specifically. While kernel based clustering approaches allow the use of more than one input matrix, which is an important factor when considering a multidimensional/manifold disease like cancer, the clustering results remain hard to evaluate and, in many cases, it is unclear which piece of information had which impact on the final result. In this paper, we propose an extension of multiple kernel learning clustering that enables the characterization of each identified patient cluster based on the features that had the highest impact on the result. To this end, we combine feature clustering with multiple kernel dimensionality reduction and introduce FIPPA, a score which measures the feature cluster impact on a patient cluster. Results: We applied the approach to different cancer types described by four different data types with the aim of identifying integrative patient subtypes and understanding which features were most important for their identification. Our results show that our method does not only have state-of-the-art performance according to standard measures (e.g., survival analysis), but, based on the high impact features, it also produces meaningful explanations for the molecular bases of the subtypes. This could provide an important step in the validation of potential cancer subtypes and enable the formulation of new hypotheses concerning individual patient groups. Similar analysis are possible for other disease phenotypes. Availability: Source code for rMKL-LPP with feature clustering and the calculation of fFIPPA scores is available at https://***/nora/fFIPPA. Copyright © 2018, The Authors. All rights reserved.

关键词： Diseases

Comparison of weather station and climate reanalysis data for modelling temperature-related mortality (vol 12, 5178, 2022)

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SCIENTIFIC REPORTS 2022年第1期12卷 1-14页

作者： Mistry, Malcolm N. Schneider, Rochelle Masselot, Pierre Roye, Dominic Armstrong, Ben Kysely, Jan Orru, Hans Sera, Francesco Tong, Shilu Lavigne, Eric Urban, Ales Madureira, Joana Garcia-Leon, David Ibarreta, Dolores Ciscar, Juan-Carlos Feyen, Luc de Schrijver, Evan de Sousa Zanotti Stagliorio Coelho, Micheline Pascal, Mathilde Tobias, Aurelio Guo, Yuming Vicedo-Cabrera, Ana M. Gasparrini, Antonio Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK Department of Economics Ca’ Foscari University of Venice Venice Italy The Centre on Climate Change & Planetary Health London School of Hygiene & Tropical Medicine London UK Forecast Department European Centre for Medium-Range Weather Forecast (ECMWF) Reading UK Ф-Lab European Space Agency (ESA-ESRIN) Frascati Italy Department of Statistics Computer Science and Applications ‘G. Parenti’ University of Florence Florence Italy Centre for Statistical Methodology London School of Hygiene & Tropical Medicine London UK Department of Geography University of Santiago de Compostela Santiago de Compostela Spain CIBER de Epidemiología y Salud Pública (CIBERESP) Madrid Spain Institute of Atmospheric Physics of the Czech Academy of Sciences Prague Czech Republic Faculty of Environmental Sciences Czech University of Life Sciences Prague Czech Republic Department of Family Medicine and Public Health University of Tartu Tartu Estonia Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine Shanghai China School of Public Health Institute of Environment and Population Health Anhui Medical University Hefei China School of Public Health and Social Work Queensland University of Technology Brisbane QLD Australia Center for Global Health School of Public Health Nanjing Medical University Nanjing China Air Health Science Division Health Canada Ottawa ON Canada School of Epidemiology and Public Health University of Ottawa Ottawa ON Canada Department of Environmental Health Instituto Nacional de Saúde Dr Ricardo Jorge Porto Portugal EPIUnit—Instituto de Saúde Pública Universidade do Porto Porto Portugal Centro de Investigação em Saúde Pública Escola Nacional de Saúde Pública Universidade NOVA de Lisboa Lisbon Portugal The Joint Research Center (JRC) European Commission Seville Spain The Joint Research Center (JRC) European Commission

Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk.

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Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality in low-income and middle-income countries, 2000-17: analysis for the Global Burden of Disease Study 2017 (vol 395, pg 1779, 2020)

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LANCET 2020年第10239期395卷 1762-1762页

作者： Reiner, R. C., Jr. Hay, S., I Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Global Health School of Medicine University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States College of Health and Medical Sciences Haramaya University Harar Ethiopia Department of Epidemiology and Biostatistics Haramaya University Harar Ethiopia Department of Medical Laboratory Sciences Haramaya University Harar Ethiopia School of Nursing and Midwifery Haramaya University Harar Ethiopia School of Pharmacy Haramaya University Harar Ethiopia School of Public Health Haramaya University Harar Ethiopia Haramaya University Harar Ethiopia Advanced Diagnostic and Interventional Radiology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Biology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Research Institute Tehran University of Medical Sciences Tehran Iran Department of Economics and Management Sciences for Health Tehran University of Medical Sciences Tehran Iran Department of Environmental Health Engineering Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran Department of Microbiology Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran Hematology-Oncology and Stem Cell Transplantation Research Center Tehran University of Medical Sciences Tehran Iran Iran National Institute of Health Research Tehran University of Medical Sciences Tehran Iran Metabolomics and

Summary Background Across low-income and middle-income countries (LMICs), one in ten deaths in children younger than 5 years is attributable to diarrhoea. The substantial between-country variation in both diarrhoea incidence and mortality is attributable to interventions that protect children, prevent infection, and treat disease. Identifying subnational regions with the highest burden and mapping associated risk factors can aid in reducing preventable childhood *** We used Bayesian model-based geostatistics and a geolocated dataset comprising 15 072 746 children younger than 5 years from 466 surveys in 94 LMICs, in combination with findings of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, to estimate posterior distributions of diarrhoea prevalence, incidence, and mortality from 2000 to 2017. From these data, we estimated the burden of diarrhoea at varying subnational levels (termed units) by spatially aggregating draws, and we investigated the drivers of subnational patterns by creating aggregated risk factor *** The greatest declines in diarrhoeal mortality were seen in south and southeast Asia and South America, where 54·0% (95% uncertainty interval [UI] 38·1-65·8), 17·4% (7·7-28·4), and 59·5% (34·2-86·9) of units, respectively, recorded decreases in deaths from diarrhoea greater than 10%. Although children in much of Africa remain at high risk of death due to diarrhoea, regions with the most deaths were outside Africa, with the highest mortality units located in Pakistan. Indonesia showed the greatest within-country geographical inequality; some regions had mortality rates nearly four times the average country rate. Reductions in mortality were correlated to improvements in water, sanitation, and hygiene (WASH) or reductions in child growth failure (CGF). Similarly, most high-risk areas had poor WASH, high CGF, or low oral rehydration therapy *** By co-analysing geospatial trends in d

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ON THE EXISTENCE AND UNIQUENESS OF THE SOLUTION OF A PARABOLIC OPTIMAL CONTROL PROBLEM WITH UNCERTAIN INPUTS

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arXiv 2018年

作者： Benner, Peter Onwunta, Akwum Stoll, Martin Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstrasse 1 Magdeburg39106 Germany Department of Computer Science University of Maryland College ParkMD20742 United States Technische Universität Chemnitz Faculty of Mathematics Professorship Scientific Computing Chemnitz09107 Germany

In this note, we consider the existence and uniqueness of the solution of a time-dependent optimal control problem constrained by a partial differential equation with uncertain inputs. Relying on the Lions’ Lemma for deterministic problems, we furthermore characterize the optimal control of the stochastic problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Optimal control systems

Global, regional, and national burden of mortality associated with short-term temperature variability from 2000–19: a three-stage modelling study

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The Lancet Planetary Health 2022年第5期6卷 e410-e421页

作者： Wu, Yao Li, Shanshan Zhao, Qi Wen, Bo Gasparrini, Antonio Tong, Shilu Overcenco, Ala Urban, Aleš Schneider, Alexandra Entezari, Alireza Vicedo-Cabrera, Ana Maria Zanobetti, Antonella Analitis, Antonis Zeka, Ariana Tobias, Aurelio Nunes, Baltazar Alahmad, Barrak Armstrong, Ben Forsberg, Bertil Pan, Shih-Chun Íñiguez, Carmen Ameling, Caroline De la Cruz Valencia, César Åström, Christofer Houthuijs, Danny Van Dung, Do Royé, Dominic Indermitte, Ene Lavigne, Eric Mayvaneh, Fatemeh Acquaotta, Fiorella Rao, Shilpa Sera, Francesco Carrasco-Escobar, Gabriel Kan, Haidong Orru, Hans Kim, Ho Holobaca, Iulian-Horia Kyselý, Jan Madureira, Joana Schwartz, Joel Jaakkola, Jouni J K Katsouyanni, Klea Hurtado Diaz, Magali Ragettli, Martina S Hashizume, Masahiro Pascal, Mathilde de Sousa Zanotti Stagliorio Coélho, Micheline Ortega, Nicolás Valdés Ryti, Niilo Scovronick, Noah Michelozzi, Paola Correa, Patricia Matus Goodman, Patrick Nascimento Saldiva, Paulo Hilario Abrutzky, Rosana Osorio, Samuel Dang, Tran Ngoc Colistro, Valentina Huber, Veronika Lee, Whanhee Seposo, Xerxes Honda, Yasushi Guo, Yue Leon Bell, Michelle L Guo, Yuming Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia Department of Epidemiology School of Public Health Cheeloo College of Medicine Shandong University Jinan China Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine UK London United Kingdom Centre for Statistical Methodology London School of Hygiene & Tropical Medicine UK London United Kingdom Centre on Climate Change & Planetary Health London School of Hygiene & Tropical Medicine UK London United Kingdom Shanghai Children's Medical Centre Shanghai Jiao Tong University Shanghai China School of Public Health Institute of Environment and Population Health Anhui Medical University Hefei China Center for Global Health Nanjing Medical University Nanjing China School of Public Health and Social Work Queensland University of Technology Brisbane QLD Australia National Agency for Public Health of the Ministry of Health Labour and Social Protection of the Republic of Moldova Chișinău Moldova Institute of Atmospheric Physics Czech Academy of Sciences Prague Czech Republic Faculty of Environmental Sciences Czech University of Life Sciences Prague Czech Republic Institute of Epidemiology Helmholtz Zentrum München—German Research Center for Environmental Health Neuherberg Germany Faculty of Geography and Environmental Sciences Hakim Sabzevari University Sabzevar Iran Institute of Social and Preventive Medicine University of Bern Bern Switzerland Oeschger Center for Climate Change Research University of Bern Bern Switzerland Department of Environmental Health Harvard TH Chan School of Public Health Harvard University Boston MA United States Department of Hygiene Epidemiology and Medical Statistics National and Kapodistrian University of Athens Athens Gr

Background: Increased mortality risk is associated with short-term temperature variability. However, to our knowledge, there has been no comprehensive assessment of the temperature variability-related mortality burden worldwide. In this study, using data from the MCC Collaborative Research Network, we first explored the association between temperature variability and mortality across 43 countries or regions. Then, to provide a more comprehensive picture of the global burden of mortality associated with temperature variability, global gridded temperature data with a resolution of 0·5° × 0·5° were used to assess the temperature variability-related mortality burden at the global, regional, and national levels. Furthermore, temporal trends in temperature variability-related mortality burden were also explored from 2000–19. methods: In this modelling study, we applied a three-stage meta-analytical approach to assess the global temperature variability-related mortality burden at a spatial resolution of 0·5° × 0·5° from 2000–19. Temperature variability was calculated as the SD of the average of the same and previous days’ minimum and maximum temperatures. We first obtained location-specific temperature variability related-mortality associations based on a daily time series of 750 locations from the Multi-country Multi-city Collaborative Research Network. We subsequently constructed a multivariable meta-regression model with five predictors to estimate grid-specific temperature variability related-mortality associations across the globe. Finally, percentage excess in mortality and excess mortality rate were calculated to quantify the temperature variability-related mortality burden and to further explore its temporal trend over two decades. Findings: An increasing trend in temperature variability was identified at the global level from 2000 to 2019. Globally, 1 753 392 deaths (95% CI 1 159 901–2 357 718) were associated with temperature variability per year, accounting for

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Social and moral psychology of COVID-19 across 69 countries

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Scientific data 2023年第1期10卷 272页

作者： Flavio Azevedo Tomislav Pavlović Gabriel G Rêgo F Ceren Ay Biljana Gjoneska Tom W Etienne Robert M Ross Philipp Schönegger Julián C Riaño-Moreno Aleksandra Cichocka Valerio Capraro Luca Cian Chiara Longoni Ho Fai Chan Jay J Van Bavel Hallgeir Sjåstad John B Nezlek Mark Alfano Michele J Gelfand Michèle D Birtel Aleksandra Cislak Patricia L Lockwood Koen Abts Elena Agadullina John Jamir Benzon Aruta Sahba Nomvula Besharati Alexander Bor Becky L Choma Charles David Crabtree William A Cunningham Koustav De Waqas Ejaz Christian T Elbaek Andrej Findor Daniel Flichtentrei Renata Franc June Gruber Estrella Gualda Yusaku Horiuchi Toan Luu Duc Huynh Agustin Ibanez Mostak Ahamed Imran Jacob Israelashvili Katarzyna Jasko Jaroslaw Kantorowicz Elena Kantorowicz-Reznichenko André Krouwel Michael Laakasuo Claus Lamm Caroline Leygue Ming-Jen Lin Mohammad Sabbir Mansoor Antoine Marie Lewend Mayiwar Honorata Mazepus Cillian McHugh John Paul Minda Panagiotis Mitkidis Andreas Olsson Tobias Otterbring Dominic J Packer Anat Perry Michael Bang Petersen Arathy Puthillam Tobias Rothmund Hernando Santamaría-García Petra C Schmid Drozdstoy Stoyanov Shruti Tewari Bojan Todosijević Manos Tsakiris Hans H Tung Radu G Umbres Edmunds Vanags Madalina Vlasceanu Andrew Vonasch Meltem Yucel Yucheng Zhang Mohcine Abad Eli Adler Narin Akrawi Hamza Alaoui Mdarhri Hanane Amara David M Amodio Benedict G Antazo Matthew Apps Mouhamadou Hady Ba Sergio Barbosa Brock Bastian Anton Berg Maria P Bernal-Zárate Michael Bernstein Michał Białek Ennio Bilancini Natalia Bogatyreva Leonardo Boncinelli Jonathan E Booth Sylvie Borau Ondrej Buchel C Daryl Cameron Chrissie F Carvalho Tatiana Celadin Chiara Cerami Hom Nath Chalise Xiaojun Cheng Kate Cockcroft Jane Conway Mateo Andres Córdoba-Delgado Chiara Crespi Marie Crouzevialle Jo Cutler Marzena Cypryańska Justyna Dabrowska Michael A Daniels Victoria H Davis Pamala N Dayley Sylvain Delouvée Ognjan Denkovski Guillaume Dezecache Nathan A Dhaliwal Alelie B Diato Roberto Di Paolo Marianna Drosinou Uwe Dulleck Jānis Ekmanis Department of Psychology University of Cambridge Cambridge England. fa441@cam.ac.uk. Institute of Communication Science Friedrich-Schiller University Jena Jena Germany. fa441@cam.ac.uk. Institute of Social Sciences Ivo Pilar Zagreb Croatia. Social and Cognitive Neuroscience Laboratory Mackenzie Presbyterian University São Paulo Brazil. Department of Economics Norwegian School of Economics Bergen Norway. Telenor Research Oslo Norway. Macedonian Academy of Sciences and Arts Skopje Republic of North Macedonia. Kieskompas - Election Compass Amsterdam Netherlands. Department of Political Science & Annenberg School for Communication University of Pennsylvania Philadelphia PA USA. Department of Psychology Macquarie University Sydney NSW Australia. Department of Philosophy University of St Andrews St Andrews Scotland. School of Economics and Finance University of St Andrews St Andrews Scotland. Medicine Faculty Cooperative University of Colombia Villavicencio Colombia. Department of Bioethics El Bosque University Bogotá Colombia. School of Psychology University of Kent Canterbury England. Department of Economics Middlesex University London London England. Darden School of Business University of Virginia Charlottesville VA USA. Questrom School of Business Boston University Boston MA USA. School of Economics and Finance Queensland University of Technology Brisbane QLD Australia. Center for Behavioural Economics Society and Technology Queensland University of Technology Brisbane QLD Australia. Department of Psychology and Neural Science New York University New York NY USA. Department of Strategy and Management Norwegian School of Economics Bergen Norway. SWPS University of Social Sciences and Humanities Warsaw Poland. Department of Psychological Sciences College of William and Mary Williamsburg VA USA. Department of Philosophy Macquarie University Sydney NSW Australia. Stanford Graduate School of Business Stanford University Stanford CA USA.

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

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Quantum computing at the frontiers of biological sciences

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arXiv 2019年

作者： Emani, Prashant S. Warrell, Jonathan Anticevic, Alan Bekiranov, Stefan Gandal, Michael McConnell, Michael J. Sapiro, Guillermo Aspuru-Guzik, Alán Baker, Justin Bastiani, Matteo McClure, Patrick Murray, John Sotiropoulos, Stamatios N. Taylor, Jacob Senthil, Geetha Lehner, Thomas Gerstein, Mark B. Harrow, Aram W. Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Yale School of Medicine Department of Psychiatry New HavenCT06511 United States University of Virginia School of Medicine Department of Biochemistry and Molecular Genetics CharlottesvilleVA22903 United States Department of Psychiatry Semel Institute David Geffen School of Medicine University of California–Los Angeles 695 Charles E. Young Drive South Los AngelesCA90095 United States Univ. of Virginia School of Medicine Department of Neuroscience CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Chemistry University of Toronto 80 St. George Street TorontoONM5S 3H6 Canada TorontoONM5S 1M1 Canada CIFAR Artificial Intelligence Research Chair Vector Institute TorontoONM5S 1M1 Canada Department of Computer Science University of Toronto 40 St. George Street TorontoONM5S 2E4 Canada Schizophrenia and Bipolar Disorder Program McLean Hospital BelmontMA02478 United States Department of Psychiatry Harvard Medical School BostonMA02114 United States Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham NottinghamNG7 2RD United Kingdom Machine Learning Team National Institute of Mental Health BethesdaMD20892 United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD20892 United States Department of Psychiatry Yale University School of Medicine New HavenCT06511 United States Department of Physics Yale University New HavenCT06511 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States National Institute of Standards and Technology GaithersburgMD20899 United States Office of Genomics Research Coordination N

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning