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[null,"1.","Record Nr.",null,"UNISA996210048603316"]
[null,null,"Titolo",null,"Champ pénal = : Penal field"]
[null,null,"Pubbl/distr/stampa",null,"[France?], : Revues.org"]
[null,null,"Soggetti",null,"Criminology"]
[{"$nulls":4},"Public welfare"]
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[{"$nulls":4},"Periodicals."]
[null,null,"Lingua di pubblicazione",null,"Francese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Periodico"]
[null,null,null,"Note generali","Refereed/Peer-reviewed"]
[{"$nulls":4},"Most articles available in both French and English."]
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[null,null,"Titolo",null,"Connectomics in NeuroImaging : Third International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / / edited by Markus D. Schirmer, Archana Venkataraman, Islem Rekik, Minjeong Kim, Ai Wern Chung"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019"]
[null,null,"ISBN",null,"3-030-32391-9"]
[null,null,"Edizione",null,"[1st ed. 2019.]"]
[null,null,"Descrizione fisica",null,"1 online resource (X, 139 p. 53 illus., 51 illus. in color.)"]
[null,null,"Collana",null,"Image Processing, Computer Vision, Pattern Recognition, and Graphics, , 3004-9954 ; ; 11848"]
[null,null,"Disciplina",null,"612.82"]
[{"$nulls":4},"616.80475"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Computer science"]
[{"$nulls":4},"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Models of Computation"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Unsupervised Feature Selection via Adaptive Embedding and Sparse Learning for Parkinson's Disease Diagnosis -- A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity -- Graph Morphology-Based Genetic Algorithm for Classifying Late Dementia States -- Covariance Shrinkage for Dynamic Functional Connectivity -- Rapid Acceleration of the Permutation Test via Transpositions -- Heat kernels with functional connectomes reveal atypical energy transport in peripheral subnetworks in autism -- A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences -- Adversarial Connectome Embedding for Mild Cognitive Impairment Identification using Cortical Morphological Networks -- A Machine Learning Framework for Accurate Functional Connectome "]
[{"$nulls":4},"Fingerprinting and an Application of a Siamese Network -- Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation.-Constraining Disease Progression Models Using Subject Specific Connectivity Priors -- Hemodynamic Matrix Factorization for Functional Magnetic Resonance Imaging -- Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An application to autism."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of the Third International Workshop on Connectomics in NeuroImaging, CNI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 13 full papers presented were carefully reviewed and selected from 14 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications."]