02886nam 2200637 a 450 991045029110332120200520144314.00-7619-6625-01-280-37014-997866103701461-4129-3201-7(CKB)1000000000031144(EBL)254737(OCoLC)271454984(SSID)ssj0000146052(PQKBManifestationID)11912028(PQKBTitleCode)TC0000146052(PQKBWorkID)10182957(PQKB)11759872(MiAaPQ)EBC254737(OCoLC)70773904(StDuBDS)EDZ0000064131(Au-PeEL)EBL254737(CaPaEBR)ebr10076708(CaONFJC)MIL37014(EXLCZ)99100000000003114420120327d2000 fy 0engur|n|---|||||txtccrEmotion in organizations[electronic resource] /edited by Stephen Fineman2nd ed.London SAGEc20001 online resource (305 p.)Description based upon print version of record.1-4462-1985-2 0-7619-6624-2 Includes bibliographical references and indexes.Cover; Contetns; Contributors; Acknowledgements; Chapter 1 - Emotional Arenas Revisited; Chapter 2 - Narratives of Compassion in Organizations; Chapter 3 - Feeling at Work; Chapter 4 - Relational Experiences and Emotion at Work; Chapter 5 - Emotion Metaphors in Management: The Chinese Experience; Chapter 6 - Commodifying the Emotionally Intelligent; Chapter 7 - Bounded Emotionality at The Body Shop; Chapter 8 - Asthetic Symbols as Emotional Cues; Chapter 9 - If Emotions were Honoured: A cultural Analysis; Chapter 10 - Emotional Labour and Authenticity: Views From Service AgentsChapter 11 - Ambivalent Feelings in Organizational RelationshipsChapter 12 - A Detective's Lot: Contours o Morality and Emotion in Police Work; Chapter 13 - How Children Manage Emotion in Schools; Chapter 14 - Emotion and Injustice in the Workplace; Chapter 15 - Concluding Reflections; Author Index; Subject IndexThis study examines how emotion cannot simply be separated from thinking, judgement, decision making and other so-called rational organizational processes. It shows how feeling and emotion lie at the heart of organizational functioning.EmotionsOrganizational behaviorElectronic books.Emotions.Organizational behavior.158.7Fineman Stephen116723StDuBDSStDuBDSBOOK9910450291103321Emotion in organizations2463957UNINA13399nam 22007095 450 99646617870331620200705045314.03-030-32248-310.1007/978-3-030-32248-9(CKB)4100000009522940(DE-He213)978-3-030-32248-9(MiAaPQ)EBC5968212(PPN)256282943(EXLCZ)99410000000952294020191009d2019 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierMedical Image Computing and Computer Assisted Intervention – MICCAI 2019[electronic resource] 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III /edited by Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (XXXVIII, 888 p. 359 illus., 314 illus. in color.) Image Processing, Computer Vision, Pattern Recognition, and Graphics ;11766Includes index.3-030-32247-5 Neuroimage Reconstruction and Synthesis -- Isotropic MRI Super-Resolution Reconstruction with Multi-Scale Gradient Field Prior -- A Two-Stage Multi-Loss Super-Resolution Network For Arterial Spin Labeling Magnetic Resonance Imaging -- Model Learning: Primal Dual Networks for Fast MR imaging -- Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging -- Joint Reconstruction of PET + Parallel-MRI in a Bayesian Coupled-Dictionary MRF Framework -- Deep Learning Based Framework for Direct Reconstruction of PET Images -- Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction -- Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans using Sparse Fidelity Loss and Adversarial Regularization -- Single Image Based Reconstruction of High Field-like MR Images -- Deep Neural Network for QSM Background Field Removal -- RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting -- RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting -- GANReDL: Medical Image enhancement using a generative adversarial network with real-order derivative induced loss functions -- Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks -- Semi-Supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control -- Disease-Image Specific Generative Adversarial Network for Brain Disease Diagnosis with Incomplete Multi-Modal Neuroimages -- Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map -- CoCa-GAN: Common-feature-learning-based Context-aware Generative Adversarial Network for Glioma Grading -- Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression -- Neuroimage Segmentation -- Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation -- 3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI -- Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants -- VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation -- Weakly Supervised Brain Lesion Segmentation via Attentional Representation Learning -- Scalable Neural Architecture Search for 3D Medical Image Segmentation -- Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images -- High Resolution Medical Image Segmentation using Data-swapping Method -- X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies -- Multi-View Semi-supervised 3D Whole Brain Segmentation with a Self-Ensemble Network -- CLCI-Net: Cross-Level Fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke -- Brain Segmentation from k-space with End-to-end Recurrent Attention Network -- Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images -- CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion -- A Joint 3D+2D Fully Convolutional Framework for Subcortical Segmentation -- U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets -- Generative adversarial network for segmentation of motion affected neonatal brain MRI -- Interactive deep editing framework for medical image segmentation -- Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices -- Improving Multi-Atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation -- Unsupervised deep learning for Bayesian brain MRI segmentation -- Online atlasing using an iterative centroid -- ARS-Net: Adaptively Rectified Supervision Network for Automated 3D Ultrasound Image Segmentation -- Complete Fetal Head Compounding from Multi-View 3D Ultrasound -- SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation -- Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation -- RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation -- Deep Cascaded Attention Networks for Multi-task Brain Tumor Segmentation -- Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation -- 3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation -- Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion -- Multi-task Attention-based Semi-supervised Learning for Medical Image Segmentation -- AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation -- Automated Parcellation of the Cortex using Structural Connectome Harmonics -- Hierarchical parcellation of the cerebellum -- Intrinsic Patch-based Cortical Anatomical Parcellation using Graph Convolutional Neural Network on Surface Manifold -- Cortical Surface Parcellation using Spherical Convolutional Neural Networks -- A Soft STAPLE Algorithm Combined with Anatomical Knowledge -- Diffusion Weighted Magnetic Resonance Imaging -- Multi-Stage Image Quality Assessment of Diffusion MRI via Semi-Supervised Nonlocal Residual Networks -- Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks -- Surface-based Tracking of U-fibers in the Superficial White Matter -- Probing Brain Micro-Architecture by Orientation Distribution Invariant Identification of Diffusion Compartments -- Characterizing Non-Gaussian Diffusion in Heterogeneously Oriented Tissue Microenvironments -- Topographic Filtering of Tractograms as Vector Field Flows -- Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE -- Super-Resolved q-Space Deep Learning -- Joint Identification of Network Hub Nodes by Multivariate Graph Inference -- Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions -- Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling -- Optimal experimental design for biophysical modelling in multidimensional diffusion MRI -- DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography‏ -- Fast and Scalable Optimal Transport for Brain Tractograms -- A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes -- Constructing Consistent Longitudinal Brain Networks by Group-wise Graph Learning -- Functional Neuroimaging (fMRI) -- Multi-layer temporal network analysis reveals increasing temporal reachability and spreadability in the first two years of life -- A matched filter decomposition of fMRI into resting and task components -- Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-state fMRI -- Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network -- Invertible Network for Classification and Biomarker Selection for ASD -- Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data -- Revealing Functional Connectivity by Learning Graph Laplacian -- Constructing Multi-Scale Connectome Atlas by Learning Common Topology of Brain Networks -- Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale -- Identify Hierarchical Structures from Task-based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net -- A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI -- A Novel Graph Wavelet Model for Brain Multi-Scale Functional-structural Feature Fusion -- Combining Multiple Behavioral Measures and Multiple Connectomes via Multiway Canonical Correlation Analysis -- Decoding brain functional connectivity implicated in AD and MCI -- Interpretable Feature Learning Using Multi-Output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis -- Interpretable Multimodality Embedding Of Cerebral Cortex Using Attention Graph Network For Identifying Bipolar Disorder -- Miscellaneous Neuroimaging -- Doubly Weak Supervision of Deep Learning Models for Head CT -- Detecting Acute Strokes from Non-Contrast CT Scan Data Using Deep Convolutional Neural Networks -- FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images -- Regression-based Line Detection Network for Delineation of Largely Deformed Brain Midline -- Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage -- Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network -- Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage -- Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting.The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging.Image Processing, Computer Vision, Pattern Recognition, and Graphics ;11766Optical data processingPattern recognitionArtificial intelligenceHealth informaticsImage Processing and Computer Visionhttps://scigraph.springernature.com/ontologies/product-market-codes/I22021Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Health Informaticshttps://scigraph.springernature.com/ontologies/product-market-codes/I23060Optical data processing.Pattern recognition.Artificial intelligence.Health informatics.Image Processing and Computer Vision.Pattern Recognition.Artificial Intelligence.Health Informatics.616.07540285Shen Dinggangedthttp://id.loc.gov/vocabulary/relators/edtLiu Tianmingedthttp://id.loc.gov/vocabulary/relators/edtPeters Terry Medthttp://id.loc.gov/vocabulary/relators/edtStaib Lawrence Hedthttp://id.loc.gov/vocabulary/relators/edtEssert Carolineedthttp://id.loc.gov/vocabulary/relators/edtZhou Seanedthttp://id.loc.gov/vocabulary/relators/edtYap Pew-Thianedthttp://id.loc.gov/vocabulary/relators/edtKhan Aliedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996466178703316Medical Image Computing and Computer Assisted Intervention – MICCAI 20192513530UNISA01737nam 2200433 a 450 991069267060332120040826083637.0(CKB)5470000002354757(OCoLC)56355761ocm56355761(OCoLC)995470000002354757(EXLCZ)99547000000235475720040826d2004 ua 0engurmn||||||txtrdacontentcrdamediacrrdacarrierCoast Guard[electronic resource] Deepwater program acquisition schedule update needed : report to the Chairmen, Subcommittees on Homeland Security, Committees on Appropriations, House of Representatives and U.S. Senate[Washington, D.C.] :U.S. General Accounting Office,[2004]Title from title screen (viewed on Aug. 19, 2004)."June 2004."Paper version available from: U.S. General Accounting Office, 441 G St., NW, Rm. LM, Washington, D.C. 20548."GAO-04-695."Includes bibliographical references.Coast Guard WarshipsUnited StatesCostsAirplanes, MilitaryUnited StatesCostsWarshipsCosts.Airplanes, MilitaryCosts.United States.Coast Guard.United States.Congress.House.Committee on Appropriations.Subcommittee on Homeland Security.United States.Congress.Senate.Committee on Appropriations.Subcommittee on the Department of Homeland Security.GPOGPOBOOK9910692670603321Coast Guard3283728UNINA