644 research outputs found
Erratum: Meaningful Big Data Integration for a Global COVID-19 Strategy (IEEE Computational Intelligence Magazine DOI: 10.1109/MCI.2020.3019898)
No full textThe byline of “Meaningful Big Data Integration for a Global COVID-19 Strategy” in November 2020 issue of IEEE Computational Intelligence Magazine is corrected as follows:Joao Pita Costa, Marko Grobelnik, Flavio Fuart, Luka Stopar, Gorka Epelde, Scott Fischaber, Piotr Poliwoda, Debbie Rankin, Jonathan Wallace, Michaela Black, Raymond Bond, Maurice Mulvenna, Dale Weston, Paul Carlin, Roberto Bilbao, Gorana Nikolic, Xi Shi, Bart De Moor, Minna Pikkarainen, Jarmo Pääkkönen, Anthony Staines, Regina Connolly, Paul Davis, Juha Pajula, and Adil UmerJuha Pajula and Adil Umer are with VTT, Finland
Mflme Juha
No full textThis collaborative project began when the Ford Foundation approached Gerry Mulgrew (director of Communicado Co. theatre group), in search of experienced arts practitioners to form links and opportunities with the Tanzanian theatre group, Parapanda Arts Lab. Mulgrew chose McIntyre to help him develop workshop techniques that involved ‘performed’ drawing and storytelling. The aim was to use these techniques to enable creative conversations between actors, that cut across cultural and linguistic boundaries. McIntyre was invited to undertake a residency with Communicado Co. in order to explore both conventional theatrical approaches to improvisatory workshop and those related to the placement and community-based activities he organises for fine art undergraduates at Northumbria. The residency generated the workshop process that equipped McIntyre to be Visual Art Director of the multi-media performance Mflme Juha. McIntyre also used the drawing practices on which the workshop technique was constructed to explore the visualisation of short stories for children. Seven Stories (the National Centre for Children’s Books) commissioned McIntyre to contribute to a documentary TV programme (McIntyre worked with author David Almond [winner of the Whitbread award], actor Kevin Wheatley [Inspector Morse and Auf Weidesein Pet] and the director/producer Lesley Duncanson), to help describe the development of a narrative idea through the contrasting acts of writing, drawing and reading. The result was ‘The Savage’ (screened on ITV, 3rd September 2006). As a follow-up, McIntyre was invited to be artist-in-residence for ‘the Big Draw’, a weekend event in which visual narratives were developed in partnership with writers and actors
How Many Is Enough? Effect of Sample Size in Inter-Subject Correlation Analysis of fMRI
No full textInter-subject correlation (ISC) is a widely used method for analyzing functional magnetic resonance imaging (fMRI) data acquired during naturalistic stimuli. A challenge in ISC analysis is to define the required sample size in the way that the results are reliable. We studied the effect of the sample size on the reliability of ISC analysis and additionally addressed the following question: How many subjects are needed for the ISC statistics to converge to the ISC statistics obtained using a large sample? The study was realized using a large block design data set of 130 subjects. We performed a split-half resampling based analysis repeatedly sampling two nonoverlapping subsets of 10-65 subjects and comparing the ISC maps between the independent subject sets. Our findings suggested that with 20 subjects, on average, the ISC statistics had converged close to a large sample ISC statistic with 130 subjects. However, the split-half reliability of unthresholded and thresholded ISC maps improved notably when the number of subjects was increased from 20 to 30 or more.</p
Inter-Subject Correlation Analysis for Functional Magnetic Resonance Imaging: Properties and Validation
Full text linkInter-subject correlation (ISC) analysis for functional magnetic resonance imaging (fMRI)is a data driven approach to detect the brain activity during complex stimuli. TheISC measures are computed as a correlation between the fMRI time courses of thestudied group of subjects. The ISC analysis is developed especially for fMRI studies withnaturalistic stimuli, like movies, music, video games or annotated stories. The naturalisticstimuli are typically used to study the higher cognitive functions of a human brain suchas emotions or humor.This thesis investigates the properties of ISC analysis for fMRI. Three major aspects ofthe ISC analysis were studied: the accuracy of the analysis when compared with thegeneral linear model (GLM) based analysis, the effects of spatial smoothing and the effectof sample size on the results. In addition, the openly available implementation of ISCanalysis that was used in this study, ISCtoolbox for Matlab, was improved by developinga built-in support for cluster computing environments. The improvements were publishedwith the new version of the ISCtoolbox. These improvements were mandatory due to thehigh computational costs of ISC analysis and the high number of ISC analyses requiredfor the studies of this thesis.Four international journal publications are included in the thesis. The first one describesthe properties of the ISC analysis and ISCtoolbox for Matlab implementation. The secondone investigates the accuracy of the ISC analysis with a block design fMRI data when compared with the GLM analysis. The third study investigates the effects of spatialsmoothing on the ISC analysis and uses the GLM analysis as a reference for the testing. The fourth study tests how the sample size affects the ISC analysis results.The ISC analysis was verified to be an effcient non-parametric data analysis method forfMRI data especially in studies with naturalistic stimuli. In addition to this the studiesindicated that ISC can successfully be applied also to the traditional block design data,where it is able to detect activations with similar accuracy as the GLM analysis. Thespatial smoothing was found to be a mandatory pre-processing step for the ISC analysis.When the thresholds for ISC results were corrected with false discovery rate multiplecomparisons correction, the ISC analysis was able to tolerate slightly larger Gaussiansmoothing kernels than the GLM analysis. The sample size investigation verified that theISC analysis can produce fairly stable results when the number of subjects included in thestudy was more than 20. At least 30 subjects were found to guarantee the high similarityof the results. The implementation of the ISC analysis as ISCtoolbox was found to behighly efficient in cluster computing environments
Inter-Subject Correlation Analysis for Functional Magnetic Resonance Imaging: Properties and Validation
No full textInter-subject correlation (ISC) analysis for functional magnetic resonance imaging (fMRI)is a data driven approach to detect the brain activity during complex stimuli. TheISC measures are computed as a correlation between the fMRI time courses of thestudied group of subjects. The ISC analysis is developed especially for fMRI studies withnaturalistic stimuli, like movies, music, video games or annotated stories. The naturalisticstimuli are typically used to study the higher cognitive functions of a human brain suchas emotions or humor.<br/><br/>This thesis investigates the properties of ISC analysis for fMRI. Three major aspects ofthe ISC analysis were studied: the accuracy of the analysis when compared with thegeneral linear model (GLM) based analysis, the effects of spatial smoothing and the effectof sample size on the results. In addition, the openly available implementation of ISCanalysis that was used in this study, ISCtoolbox for Matlab, was improved by developinga built-in support for cluster computing environments. The improvements were publishedwith the new version of the ISCtoolbox. These improvements were mandatory due to thehigh computational costs of ISC analysis and the high number of ISC analyses requiredfor the studies of this thesis.<br/><br/>Four international journal publications are included in the thesis. The first one describesthe properties of the ISC analysis and ISCtoolbox for Matlab implementation. The secondone investigates the accuracy of the ISC analysis with a block design fMRI data when compared with the GLM analysis. The third study investigates the effects of spatialsmoothing on the ISC analysis and uses the GLM analysis as a reference for the testing. The fourth study tests how the sample size affects the ISC analysis results.<br/><br/>The ISC analysis was verified to be an effcient non-parametric data analysis method forfMRI data especially in studies with naturalistic stimuli. In addition to this the studiesindicated that ISC can successfully be applied also to the traditional block design data,where it is able to detect activations with similar accuracy as the GLM analysis. Thespatial smoothing was found to be a mandatory pre-processing step for the ISC analysis.When the thresholds for ISC results were corrected with false discovery rate multiplecomparisons correction, the ISC analysis was able to tolerate slightly larger Gaussiansmoothing kernels than the GLM analysis. The sample size investigation verified that theISC analysis can produce fairly stable results when the number of subjects included in thestudy was more than 20. At least 30 subjects were found to guarantee the high similarityof the results. The implementation of the ISC analysis as ISCtoolbox was found to behighly efficient in cluster computing environments.Peer reviewe
Endpoint Sobolev bounds for fractional Hardy–Littlewood maximal operators
No full textFunding Information: I would like to thank my supervisor, Juha Kinnunen, for all of his support. I would like to thank Olli Saari for introducing me to this problem. I am also thankful for the discussions with Juha Kinnunen, Panu Lahti and Olli Saari who made me aware of a version of the coarea formula [, Theorem 3.11], which was used in the first draft of the proof, and for discussions with David Beltran, Cristian González-Riquelme and Jose Madrid, in particular about the centered fractional maximal operator. The author has been supported by the Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters. Publisher Copyright: © 2022, The Author(s).Let 0 0 the fractional maximal function does not use certain small balls. For α= 0 the proof collapses.Peer reviewe
Median-Type John–Nirenberg Space in Metric Measure Spaces
No full textFunding Information: The author would like to thank Juha Kinnunen and Riikka Korte for valuable discussions. The author would also like to thank the anonymous referee for carefully reading the paper and for constructive comments. The research was supported by the Academy of Finland. Publisher Copyright: © 2022, The Author(s).We study the so-called John–Nirenberg space that is a generalization of functions of bounded mean oscillation in the setting of metric measure spaces with a doubling measure. Our main results are local and global John–Nirenberg inequalities, which give weak-type estimates for the oscillation of a function. We consider medians instead of integral averages throughout, and thus functions are not a priori assumed to be locally integrable. Our arguments are based on a Calderón–Zygmund decomposition and a good-λ inequality for medians. A John–Nirenberg inequality up to the boundary is proven by using chaining arguments. As a consequence, the integral-type and the median-type John–Nirenberg spaces coincide under a Boman-type chaining assumption.Peer reviewe
The Diary, the Typewriter and Representative Reality in the Genesis of Juha Mannerkorpi's Päivänsinet
Full text linkTämä artikkeli käsittelee siirtymää päiväkirjan pitämisestä tulevan julkaistavan teoksen kirjoittamiseen tarkastelemalla Juha Mannerkorven Päivänsinet: muuan loppukesän merkintöjä (1979) -teoksen syntyprosessia. Päiväkirjaromaani kertoo vakavasti sairaasta kertojasta, joka tunnollisesti seuraa päivänsinen kasvua laskien, mittaillen ja kirjoituskoneella ylöskirjaten sen päivittäin puhkeavia kukkia. Yhdistämällä geneettistä kritiikkiä Philippe Lejeunen ajatuksiin päiväkirjasta, antifiktiosta ja päiväkirjaefektistä artikkelissa analysoidaan Päivänsinissä toistuvia metapoeettisia huomioita kirjoituskoneen käytöstä ja faktan ja fiktion suhteesta päiväkirjan kirjoitusprosessin ja sen romaaniksi uudelleenkirjoittamisen kontekstissa. Yksityiskohtainen käsikirjoitusten tarkastelu tuo esille, ettei kirjoituskone ole läpinäkyvä väline, jonka avulla heikkonäköinen tekijä saattoi kirjoittaa, vaan että koneella kirjoittaminen fyysisenä toimintana vaikutti Mannerkorven tekstin sisältöön monin tavoin. Sen lisäksi, että artikkeli valottaa Mannerkorven teosten monia kokeellisia puolia Päivänsinien syntyprosessien tarkastelu lisää ymmärrystämme kirjoitusvälineiden vaikutuksesta päiväkirjan pitämiseen ja kirjalliseen työskentelyyn.This article investigates the transition from keeping a diary to writing a future published work with reference to the genetic process of Päivänsinet: muuan loppukesän merkintöjä (1979) by Finnish author and translator Juha Mannerkorpi. The diary novel is about a seriously ill narrator who watches the growth of a morning glory, meticulously counting, measuring and registering the daily unravelling flowers with the help of a typewriter. In combining genetic criticism with Philippe Lejeune’s ideas on the diary, antifiction and the diary effect, the article analyses the frequent metapoetic remarks upon the use of the typewriter and the relationship between fact and fiction in the context of the diary-writing process and its subsequent rewriting as a novel. Upon close inspection of the manuscripts it becomes clear that the typewriter was not a transparent medium that helped the weak-sighted author to write, but that the physical act of typing influenced the content of Mannerkorpi’s text in many respects. In addition to shedding light on many experimental features of Mannerkorpi’s works, this study on the genesis of Päivänsinet widens current understanding concerning the impact of writing tools on diary-keeping and literary writing.Peer reviewe
Inter-subject correlation analysis for functional magnetic resonance imaging:Properties and validation: Dissertation
No full textInter-subject correlation (ISC) analysis for functional magnetic resonance imaging (fMRI)is a data driven approach to detect the brain activity during complex stimuli. TheISC measures are computed as a correlation between the fMRI time courses of thestudied group of subjects. The ISC analysis is developed especially for fMRI studies withnaturalistic stimuli, like movies, music, video games or annotated stories. The naturalisticstimuli are typically used to study the higher cognitive functions of a human brain suchas emotions or humor. This thesis investigates the properties of ISC analysis for fMRI. Three major aspects ofthe ISC analysis were studied: the accuracy of the analysis when compared with thegeneral linear model (GLM) based analysis, the effects of spatial smoothing and the effectof sample size on the results. In addition, the openly available implementation of ISCanalysis that was used in this study, ISCtoolbox for Matlab, was improved by developinga built-in support for cluster computing environments. The improvements were publishedwith the new version of the ISCtoolbox. These improvements were mandatory due to thehigh computational costs of ISC analysis and the high number of ISC analyses requiredfor the studies of this thesis. Four international journal publications are included in the thesis. The ?rst one describesthe properties of the ISC analysis and ISCtoolbox for Matlab implementation. The secondone investigates the accuracy of the ISC analysis with a block design fMRI data when compared with the GLM analysis. The third study investigates the effects of spatialsmoothing on the ISC analysis and uses the GLM analysis as a reference for the testing. The fourth study tests how the sample size affects the ISC analysis results. The ISC analysis was veri?ed to be an effcient non-parametric data analysis method forfMRI data especially in studies with naturalistic stimuli. In addition to this the studiesindicated that ISC can successfully be applied also to the traditional block design data,where it is able to detect activations with similar accuracy as the GLM analysis. Thespatial smoothing was found to be a mandatory pre-processing step for the ISC analysis.When the thresholds for ISC results were corrected with false discovery rate multiplecomparisons correction, the ISC analysis was able to tolerate slightly larger Gaussiansmoothing kernels than the GLM analysis. The sample size investigation veri?ed that theISC analysis can produce fairly stable results when the number of subjects included in thestudy was more than 20. At least 30 subjects were found to guarantee the high similarityof the results. The implementation of the ISC analysis as ISCtoolbox was found to behighly efficient in cluster computing environments
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