LEADER 02586nam 2200649Ia 450 001 9910459465003321 005 20200520144314.0 010 $a1-136-92155-9 010 $a1-282-89861-2 010 $a9786612898617 010 $a0-203-84464-5 035 $a(CKB)2670000000051655 035 $a(EBL)515396 035 $a(OCoLC)681484148 035 $a(SSID)ssj0000457084 035 $a(PQKBManifestationID)12210193 035 $a(PQKBTitleCode)TC0000457084 035 $a(PQKBWorkID)10414920 035 $a(PQKB)11610272 035 $a(MiAaPQ)EBC515396 035 $a(Au-PeEL)EBL515396 035 $a(CaPaEBR)ebr10428063 035 $a(CaONFJC)MIL289861 035 $a(EXLCZ)992670000000051655 100 $a20100310d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBolshevism, an international danger$b[electronic resource] $eits doctrine and its practice through war and revolution /$fby Paul Miliukov 210 $a[London] $cRoutledge$d2010 215 $a1 online resource (149 p.) 225 1 $aRoutledge revivals 300 $aReprint. Originally published: London : George Allen & Unwin, 1920. 311 $a0-415-59006-X 311 $a0-415-58939-8 320 $aIncludes bibliographical references. 327 $aBOOK COVER; TITLE; COPYRIGHT; PREFACE; CONTENTS; PART I: THE INTERNATIONAL DOCTRINE OF BOLSHEVISM; PART II: THE PROGRESS OF BOLSHEVISM THROUGH WAR AND REVOLUTION; PART III: BOLSHEVISM OUT FOR A WORLD REVOLUTION; CONCLUSION; EPILOGUE 330 $aFirst published in 1920, Paul Miliukov's book concerns the international nature of Bolshevism, both in terms of its ideologically internationalist doctrine of World Revolution and in terms of the attempts to spread Bolshevism in the period immediately preceding and following the First World War and the Russian revolution of October 1917. This reissue is a must for anyone interested in the rise of Bolshevism as an international force. 410 0$aRoutledge revivals. 606 $aCommunism 606 $aCommunism$zSoviet Union 606 $aCommunist strategy 608 $aElectronic books. 615 0$aCommunism. 615 0$aCommunism 615 0$aCommunist strategy. 676 $a335.430947 700 $aMili?ukov$b P. N$g(Pavel Nikolaevich),$f1859-1943.$0836309 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910459465003321 996 $aBolshevism, an international danger$92146490 997 $aUNINA LEADER 02092nam 2200409 n 450 001 996390746503316 005 20200824121622.0 035 $a(CKB)4940000000102192 035 $a(EEBO)2248578691 035 $a(UnM)99841401e 035 $a(UnM)99841401 035 $a(EXLCZ)994940000000102192 100 $a19910401d1601 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 04$aThe discoueries of the world from their first originall vnto the yeere of our Lord 1555. Briefly written in the Portugall tongue by Antonie Galuano, gouernour of Ternate, the chiefe island of the Malucos: corrected, quoted, and now published in English by Richard Hakluyt, sometimes student of Christ church in Oxford$b[electronic resource] 210 $aLondini $c[Printed at Eliot's Court Press] impensis G. Bishop$d1601 215 $a[12], 97, [3] p 300 $aA translation of: Tratato. Que compo?s o nobre & notavel capita?o Antonio Galva?o, dos diversos & desvayrados caminhos, por onde nos tempos passados a pimenta & especearia veyo da India a?s nossas partes. 300 $aPrinter identified by STC. 300 $aThe last leaf is blank. 300 $aReproduction of the original in the British Library. 330 $aeebo-0018 606 $aDiscoveries in geography$vEarly works to 1800 607 $aAmerica$xEarly accounts to 1600 607 $aAmerica$xDiscovery and exploration, Spanish$vEarly works to 1800 615 0$aDiscoveries in geography 700 $aGalva?o$b Anto?nio$fd. 1557.$01012319 701 $aHakluyt$b Richard$f1552?-1616.$0203293 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996390746503316 996 $aThe discoueries of the world from their first originall vnto the yeere of our Lord 1555. Briefly written in the Portugall tongue by Antonie Galuano, gouernour of Ternate, the chiefe island of the Malucos: corrected, quoted, and now published in English by Richard Hakluyt, sometimes student of Christ church in Oxford$92349778 997 $aUNISA LEADER 07122nam 22007335 450 001 9910484379503321 005 20251225212349.0 010 $a3-319-67389-0 024 7 $a10.1007/978-3-319-67389-9 035 $a(CKB)4100000000587150 035 $a(DE-He213)978-3-319-67389-9 035 $a(MiAaPQ)EBC6296727 035 $a(MiAaPQ)EBC5578647 035 $a(Au-PeEL)EBL5578647 035 $a(OCoLC)1003646157 035 $a(PPN)204533678 035 $a(EXLCZ)994100000000587150 100 $a20170906d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning in Medical Imaging $e8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings /$fedited by Qian Wang, Yinghuan Shi, Heung-Il Suk, Kenji Suzuki 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XV, 391 p. 134 illus.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v10541 311 08$a3-319-67388-2 327 $aFrom Large to Small Organ Segmentation in CT Using Regional Context -- Motion Corruption Detection in Breast DCE-MRI -- Detection and Localization of Drosophila Egg Chambers in Microscopy Images -- Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-specific Coronary Calcium Scoring -- Atlas of Classifiers for Brain MRI Segmentation -- Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis -- Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer?s Disease -- Multi-Factorial Age Estimation from Skeletal and Dental MRI Volumes -- Automatic Classification of Proximal Femur Fractures Based on Attention Models -- Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation -- Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble -- STAR: Spatio-Temporal Architecture for Super-Resolution inLow-Dose CT Perfusion -- Classification of Alzheimer?s Disease by Cascaded Convolutional Neural Networks Using PET Images -- Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images -- Multi-Scale Volumetric ConvNet with Nested Residual Connections for Segmentation of Anterior Cranial Base -- Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-Status Dementia Diagnosis -- 3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Data Labels -- Efficient Groupwise Registration for Brain MRI by Fast Initialization -- Sparse Multi-View Task-centralized Learning for ASD Diagnosis -- Inter-Subject Similarity Guided Brain Network Modelling for MCI Diagnosis -- Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data -- Triple-Crossing 2.5D Convolutional Neural Network for Detecting Neuronal Arbours in 3D Microscopic Images -- Longitudinally-Consistent Parcellation of Infant Population Cortical Surfaces Based on Functional Connectivity -- Gradient Boosted Trees for Corrective Learning -- Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis -- A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling -- Collage CNN for Renal Cell Carcinoma Detection from CT -- Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images -- Localizing Cardiac Structures in Fetal Heart Ultrasound Video -- Deformable Registration Through Learning of Context-Specific Metric Aggregation -- Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-learning Based Cascade Framework -- 3D U-net with Multi-Level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images -- Indecisive Trees for Classification and Prediction of Knee Osteoarthritis -- Whole Brain Segmentation and Labeling from CT using synthetic MR Images -- Structural Connectivity Guided SparseEffective Connectivity for MCI Identification -- Fusion of High-order and Low-order Effective Connectivity Networks for MCI Classification -- Novel Effective Connectivity Network Inference for MCI Identification -- Reconstruction of Thin-Slice Medical Images Using Generative Adversarial Network -- Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to ?Virtual? High-Dose CT Images -- Deep-Fext: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction -- Product Space Decompositions for Continuous Representations of Brain Connectivity -- Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks -- Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging -- Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks. 330 $aThis book constitutes the refereed proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v10541 606 $aComputer vision 606 $aSoftware engineering 606 $aMedical informatics 606 $aData mining 606 $aArtificial intelligence 606 $aComputer Vision 606 $aSoftware Engineering 606 $aHealth Informatics 606 $aData Mining and Knowledge Discovery 606 $aArtificial Intelligence 615 0$aComputer vision. 615 0$aSoftware engineering. 615 0$aMedical informatics. 615 0$aData mining. 615 0$aArtificial intelligence. 615 14$aComputer Vision. 615 24$aSoftware Engineering. 615 24$aHealth Informatics. 615 24$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 676 $a006.31 702 $aWang$b Qian$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aShi$b Yinghuan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSuk$b Heung-Il$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSuzuki$b Kenji$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484379503321 996 $aMachine Learning in Medical Imaging$92998079 997 $aUNINA