01603nam 2200421 n 450 99638867920331620221108051901.0(CKB)1000000000644612(EEBO)2240891925(UnM)99857386(EXLCZ)99100000000064461219921210d1572 uy |laturbn||||a|bb|Sulpitii Verulani carmen iuuenile, de moribus puerorum in mensa seruandis: olim ab Ascensio explanatum[electronic resource][London] Excudebat Ioannes KyngstonusAnno d[omi]ni. 1572[30] pAn edition of: Stans puer ad mensam.Verse with prose commentary.Signatures: A-B (-B8).Running title reads: Carmen iuuenile de moribus mensæ."Dialogi pueriles studiosæ iuuenti valde necessarii", B4v-end.Reproduction of the original in the Henry E. Huntington Library and Art Gallery.eebo-0113Etiquette, MedievalEtiquette for children and teenagersEarly works to 1800Etiquette, Medieval.Etiquette for children and teenagersSulpitius Verulanus Joannes15th cent.1006828Badius Josse1462-1535.311222Cu-RivESCu-RivESCStRLINWaOLNBOOK996388679203316Sulpitii Verulani carmen iuuenile, de moribus puerorum in mensa seruandis: olim ab Ascensio explanatum2401676UNISA05774nam 22006615 450 99646642990331620230302195054.03-030-31901-610.1007/978-3-030-31901-4(CKB)4100000009522815(DE-He213)978-3-030-31901-4(MiAaPQ)EBC5967218(PPN)254989942(EXLCZ)99410000000952281520191008d2019 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierAdolescent Brain Cognitive Development Neurocognitive Prediction[electronic resource] First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /edited by Kilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (XI, 188 p. 57 illus., 49 illus. in color.)Image Processing, Computer Vision, Pattern Recognition, and Graphics ;11791Includes index.3-030-31900-8 A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction -- Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet -- Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction -- Surface-based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019 -- Prediction of Fluid Intelligence From T1-Weighted Magnetic Resonance Images -- Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI -- Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry -- Predict Fluid Intelligence of Adolescent Using Ensemble Learning -- Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach -- Predicting Fluid intelligence from structural MRI using Random Forest regression -- Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data -- An AutoML Approach for the Prediction of Fluid Intelligence From MRI-Derived Features -- Predicting Fluid Intelligence from MRI images with Encoder-decoder Regularization -- ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology -- Ensemble Modeling of Neurocognitive Performance Using MRI-derived Brain Structure Volumes -- ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression -- Predicting fluid intelligence using anatomical measures within functionally defined brain networks -- Sex differences in predicting fluid intelligence of adolescent brain from T1-weighted MRIs -- Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction -- Adolescent fluid intelligence prediction from regional brain volumes and cortical curvatures using BlockPC-XGBoost -- Cortical and Subcortical Contributions to Predicting Intelligence using 3D ConvNets.This book constitutes the refereed proceedings of the First Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. 29 submissions were carefully reviewed and 24 of them were accepted. Some of the 24 submissions were merged and resulted in the 21 papers that are presented in this book. The papers explore methods for predicting fluid intelligence from T1-weighed MRI of 8669 children (age 9-10 years) recruited by the Adolescent Brain Cognitive Development Study (ABCD) study; the largest long-term study of brain development and child health in the United States to date.Image Processing, Computer Vision, Pattern Recognition, and Graphics ;11791Optical data processingMachine learningMathematical statisticsData miningImage Processing and Computer Visionhttps://scigraph.springernature.com/ontologies/product-market-codes/I22021Machine Learninghttps://scigraph.springernature.com/ontologies/product-market-codes/I21010Probability and Statistics in Computer Sciencehttps://scigraph.springernature.com/ontologies/product-market-codes/I17036Data Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Optical data processing.Machine learning.Mathematical statistics.Data mining.Image Processing and Computer Vision.Machine Learning.Probability and Statistics in Computer Science.Data Mining and Knowledge Discovery.616.8047548Pohl Kilian Medthttp://id.loc.gov/vocabulary/relators/edtThompson Wesley(Of University of California, San Diego)edthttp://id.loc.gov/vocabulary/relators/edtAdeli Ehsanedthttp://id.loc.gov/vocabulary/relators/edtLinguraru Marius Georgeedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996466429903316Adolescent Brain Cognitive Development Neurocognitive Prediction2532657UNISA02736nam 2200649Ia 450 991077772180332120230120084604.01-315-59908-21-317-08509-41-317-08508-61-282-24349-797866122434930-7546-9441-0(CKB)1000000000754896(EBL)438944(OCoLC)432428971(SSID)ssj0000305264(PQKBManifestationID)11226313(PQKBTitleCode)TC0000305264(PQKBWorkID)10285738(PQKB)10384132(Au-PeEL)EBL438944(CaPaEBR)ebr10303012(CaONFJC)MIL924870(Au-PeEL)EBL5293370(CaONFJC)MIL224349(MiAaPQ)EBC438944(MiAaPQ)EBC5293370(EXLCZ)99100000000075489620090130d2009 uy 0engur|n|---|||||txtccrOrganizational cooperation in crises[electronic resource] /Lina M. SvedinFarnham, England ;Burlington, VT Ashgate Pub. Co.c20091 online resource (175 p.)Description based upon print version of record.0-7546-7725-7 Includes bibliographical references (p. [145]-158) and index.Cover; Contents; List of Figures and Tables; Acknowledgements; 1 Introduction; 2 Conceptualizing Organizational Crisis Cooperation: The Legacy of Three Traditions; 3 Crisis Cooperation in Light of the Three Traditions: Case Illustrations; 4 Organizational Behavior in Decision-Situations; 5 Cooperative Strategies across Crises; 6 Linking Behavior and Strategies; 7 Cooperating in a Crisis Context; 8 Organizing for Crisis Cooperation: Conclusions and Implications; 9 An Agenda for Continued Research; Bibliography; IndexLina Svedin takes an interdisciplinary approach to present a systematic examination of organizational cooperation in crises. Bringing together three distinct research traditions on cooperation, the author draws on these traditions to examine how their variables fare empirically when applied to a wide set of cases and decision situations.Crisis managementInternational cooperationCrisis management.International cooperation.658.4658.4/056Svedin Lina M1522981MiAaPQMiAaPQMiAaPQBOOK9910777721803321Organizational cooperation in crises3762999UNINA