00934nam0-2200313---450-99000971475040332120130411143431.0000971475FED01000971475(Aleph)000971475FED0100097147520130411d1977----km-y0itay50------bafreFRy-------001yy<<La >>vie quotidienne en France au temps du front populaire1935-1938Henri NoguèresParisHachette1977312 p.21 cm<<La >>vie quotidienneFRONTE POPOLAREFRANCIASTORIA1934-1938944.081521itaNogueres,Henri242530ITUNINAREICATUNIMARCBK990009714750403321XIV B 1625181FSPBCFSPBCVie quotidienne en France au temps du front populaire844037UNINA05795nam 2200757 a 450 991045239630332120200520144314.01-118-53486-71-118-53485-91-118-53484-0(CKB)2550000001114516(EBL)1367688(OCoLC)862821341(SSID)ssj0000981909(PQKBManifestationID)11590924(PQKBTitleCode)TC0000981909(PQKBWorkID)10982687(PQKB)10404978(MiAaPQ)EBC1367688(DLC) 2013030942(Au-PeEL)EBL1367688(CaPaEBR)ebr10753375(CaONFJC)MIL514368(EXLCZ)99255000000111451620130726d2014 uy 0engur|n|---|||||txtccrPractical multiscaling[electronic resource] /Jacob FishChichester, West Sussex, U.K. John Wiley & Sons Inc.20141 online resource (416 p.)Description based upon print version of record.1-118-41068-8 1-299-83117-6 Includes bibliographical references and index.Chapter 1. Introduction to multiscale methods -- The rationale for multiscale computations -- The hype and the reality -- Examples and qualification of multiscale methods -- Nomenclature and definitions -- Notation -- Chapter 2. Upscaling/Downscaling of Continua -- Homogenizaton of linear heterogeneous media -- Upscaling based on enhanced kinematics -- Homogenization of nonlinear heterogeneous media -- Higher order homogenization -- Multiple-scale homogenization -- Going beyond upscaling : homogenization-based multigrid -- Chapter 3. Upscaling/Downscaling of Atomistic/Continuum Media -- Governing equations -- Generalized mathematical homogenization (GMH) -- Finite element implementation and numerical verification -- Statistical ensemble -- Verification -- Going beyond upscaling -- Chapter 4. Reduced Order Homogenization -- Reduced order homogenization for two-scale problems -- Lower order approximation of eigenstrains -- Extension to nonlocal heterogeneous media -- Extension to dispersive heterogeneous media -- Extension to multiple spatial scales -- Extension to large deformations -- Extension to multiple temporal scales with application to fatigue -- Extension to multiphysics problems -- Chapter 5. Scale-separation-free Upscaling/Downscaling of Continua -- Computational continua (C2) -- Reduced order computational continua (RC2) -- Nonlocal quadrature in multidimensions -- Model Verification -- Chapter 6. Multiscale Design Software -- Microanalysis with MDS-Lite -- Macroanalysis with MDS-Lite."Practical Multiscaling covers fundamental modelling techniques aimed at bridging diverse temporal and spatial scales ranging from the atomic level to a full-scale product level. It focuses on practical multiscale methods that account for fine-scale (material) details but do not require their precise resolution. The text material evolved from over 20 years of teaching experience at Rensselaer and Columbia University, as well as from practical experience gained in the application of multiscale software. This book comprehensively covers theory and implementation, providing a detailed exposition of the state-of-the-art multiscale theories and their insertion into conventional (single-scale) finite element code architecture. The robustness and design aspects of multiscale methods are also emphasised, which is accomplished via four building blocks: upscaling of information, systematic reduction of information, characterization of information utilizing experimental data, and material optimization. To ensure the reader gains hands-on experience, a companion website hosting a lite version of the multiscale design software (MDS-Lite) is available. Key features: Combines fundamental theory and practical methods of multiscale modelling Covers the state-of-the-art multiscale theories and examines their practical usability in design; covers applications of multiscale methods; accompanied by a continuously updated website hosting the multiscale design software; illustrated with colour images. Practical Multiscaling is an ideal textbook for graduate students studying multiscale science and engineering. It is also a must-have reference for government laboratories, researchers and practitioners in civil, aerospace, pharmaceutical, electronics, and automotive industries, and commercial software vendors"--Provided by publisher.Mechanical engineeringMathematical modelsContinuum mechanicsMathematical modelsMaterialsMathematical modelsMultiscale modelingScaling laws (Statistical physics)Mechanical engineeringComputer simulationContinuum mechanicsComputer simulationMaterialsComputer simulationElectronic books.Mechanical engineeringMathematical models.Continuum mechanicsMathematical models.MaterialsMathematical models.Multiscale modeling.Scaling laws (Statistical physics)Mechanical engineeringComputer simulation.Continuum mechanicsComputer simulation.MaterialsComputer simulation.620.001/51SCI041000bisacshFish J(Jacob)882821MiAaPQMiAaPQMiAaPQBOOK9910452396303321Practical multiscaling2270030UNINA13868nam 22008775 450 99658786800331620231228101139.0981-9985-43-910.1007/978-981-99-8543-2(CKB)29476193100041(DE-He213)978-981-99-8543-2(MiAaPQ)EBC31046393(Au-PeEL)EBL31046393(EXLCZ)992947619310004120231228d2024 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierPattern Recognition and Computer Vision[electronic resource] 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part VIII /edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji1st ed. 2024.Singapore :Springer Nature Singapore :Imprint: Springer,2024.1 online resource (XIV, 513 p. 157 illus., 152 illus. in color.) Lecture Notes in Computer Science,1611-3349 ;144329789819985425 Intro -- Preface -- Organization -- Contents - Part VIII -- Neural Network and Deep Learning I -- A Quantum-Based Attention Mechanism in Scene Text Detection -- 1 Introduction -- 2 Related Work -- 2.1 Attention Mechanism -- 2.2 Revisit Quantum-State-based Mapping -- 3 Approach -- 3.1 QSM-Based Channel Attention (QCA) Module and QSM-Based Spatial Attention (QSA) Module -- 3.2 Quantum-Based Convolutional Attention Module (QCAM) -- 3.3 Adaptive Channel Information Transfer Module (ACTM) -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Performance Comparison -- 4.3 Ablation Study -- 5 Discussion and Conclusion -- References -- NCMatch: Semi-supervised Learning with Noisy Labels via Noisy Sample Filter and Contrastive Learning -- 1 Introduction -- 2 Related Work -- 2.1 Semi-supervised Learning -- 2.2 Self-supervised Contrastive Learning -- 2.3 Learning with Noisy Labels -- 3 Method -- 3.1 Preliminaries -- 3.2 Overall Framework -- 3.3 Noisy Sample Filter (NSF) -- 3.4 Semi-supervised Contrastive Learning (SSCL) -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental for SSL -- 4.3 Experimental for SSLNL -- 4.4 Ablation Study -- 5 Conclusion -- References -- Data-Free Low-Bit Quantization via Dynamic Multi-teacher Knowledge Distillation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Preliminaries -- 3.2 More Insight on 8-Bit Quantized Models -- 3.3 Dynamic Multi-teacher Knowledge Distillation -- 4 Experiments -- 4.1 Experimental Setups -- 4.2 Comparison with Previous Data-Free Quantization Methods -- 4.3 Ablation Studies -- 5 Conclusion -- References -- LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Architecture of LeViT-UNet -- 3.2 LeViT as Encoder -- 3.3 CNNs as Decoder -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Implementation Details.4.3 Experiment Results on Synapse Dataset -- 4.4 Experiment Results on ACDC Dataset -- 5 Conclusion -- References -- DUFormer: Solving Power Line Detection Task in Aerial Images Using Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Vision Transformer -- 2.2 Semantic Segmentation -- 3 Proposed Architecture -- 3.1 Overview -- 3.2 Double U Block (DUB) -- 3.3 Power Line Aware Block (PLAB) -- 3.4 BiscSE Block -- 3.5 Loss Function -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Comparative Experiments -- 4.3 Ablation Experiments -- 5 Conclusion -- References -- Space-Transform Margin Loss with Mixup for Long-Tailed Visual Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Mixup and Its Space Transformation -- 2.2 Long-Tailed Learning with Mixup -- 2.3 Re-balanced Loss Function Modification Methods -- 3 Method -- 3.1 Space Transformation in Mixup -- 3.2 Space-Transform Margin Loss Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementations Details -- 4.3 Main Results -- 4.4 Feature Visualization and Analysis of STM Loss -- 4.5 Ablation Study -- 5 Conclusion -- References -- A Multi-perspective Squeeze Excitation Classifier Based on Vision Transformer for Few Shot Image Classification -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Definition -- 3.2 Meta-Training Phase -- 3.3 Meta-test Phase -- 4 Experimental Results -- 4.1 Datasets and Training Details -- 4.2 Evaluation Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- ITCNN: Incremental Learning Network Based on ITDA and Tree Hierarchical CNN -- 1 Introduction -- 2 Proposed Network -- 2.1 Network Structure -- 2.2 ITDA -- 2.3 Branch Route -- 2.4 Training Strategies -- 2.5 Optimization Strategies -- 3 Experiments and Results -- 3.1 Experiment on Classification -- 3.2 Experiment on CIL -- 4 Conclusion -- References.Periodic-Aware Network for Fine-Grained Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Skeleton-Based Action Recognition -- 2.2 Periodicity Estimation of Videos -- 2.3 Squeeze and Excitation Module -- 3 Method -- 3.1 3D-CNN Backbone -- 3.2 Periodicity Feature Extraction Module -- 3.3 Periodicity Fusion Module -- 4 Experiment -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Learning Domain-Invariant Representations from Text for Domain Generalization -- 1 Introduction -- 2 Related Work -- 2.1 Domain Generalization -- 2.2 CLIP in Domain Generalization -- 3 Method -- 3.1 Problem Formulation -- 3.2 Text Regularization -- 3.3 CLIP Representations -- 4 Experiments and Results -- 4.1 Datasets and Experimental Settings -- 4.2 Comparison with Existing DG Methods -- 4.3 Ablation Study -- 5 Conclusions -- References -- TSTD:A Cross-modal Two Stages Network with New Trans-decoder for Point Cloud Semantic Segmentation -- 1 Introduction -- 2 Related Works -- 2.1 Image Transformers -- 2.2 Point Cloud Transformer -- 2.3 Joint 2D-3D Network -- 3 Method -- 3.1 Overall Architecture -- 3.2 2D-3D Backprojection -- 3.3 Trans-Decoder -- 4 Experiments -- 4.1 Dataset and Metric -- 4.2 Performance Comparison -- 4.3 Ablation Experiment -- 5 Conclusion -- References -- NeuralMAE: Data-Efficient Neural Architecture Predictor with Masked Autoencoder -- 1 Introduction -- 2 Related Work -- 2.1 Neural Architecture Performance Predictors -- 2.2 Generative Self-supervised Learning -- 3 Method -- 3.1 Overall Framework -- 3.2 Pre-training -- 3.3 Fine-Tuning -- 3.4 Multi-head Attention-Masked Transformer -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Experiments on NAS-Bench-101 -- 4.3 Experiments on NAS-Bench-201 -- 4.4 Experiments on NAS-Bench-301.4.5 Ablation Study -- 5 Conclusion -- References -- Co-regularized Facial Age Estimation with Graph-Causal Learning -- 1 Introduction -- 2 Method -- 2.1 Problem Formulation -- 2.2 Ordinal Decision Mapping -- 2.3 Bilateral Counterfactual Pooling -- 3 Experiments -- 3.1 Datasets and Evaluation Settings -- 3.2 Comparison with State-of-the-Art Methods -- 3.3 Ablation Study -- 3.4 Performance Under Out-of-Distribution Settings -- 3.5 Qualitative Results -- 4 Conclusion -- References -- Online Distillation and Preferences Fusion for Graph Convolutional Network-Based Sequential Recommendation -- 1 Introduction -- 2 Method -- 2.1 Graph Construction -- 2.2 Collaborative Learning -- 2.3 Feature Fusion -- 3 Experiment -- 3.1 Experimental Setup -- 3.2 Experimental Results -- 3.3 Ablation Studies -- 4 Conclusion -- References -- Grassmann Graph Embedding for Few-Shot Class Incremental Learning -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Problem Definition -- 3.2 Overview -- 3.3 Grassmann Manifold Embedding -- 3.4 Graph Structure Preserving on Grassmann Manifold -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Global Variational Convolution Network for Semi-supervised Node Classification on Large-Scale Graphs -- 1 Introduction -- 2 Related Work -- 3 Proposed Methods -- 3.1 Positive Pointwise Mutual Information on Large-Scale Graphs -- 3.2 Global Variational Aggregation -- 3.3 Variational Convolution Kernels -- 4 Experiments -- 4.1 Comparison Experiments -- 4.2 Ablation Study -- 4.3 Runtime Study -- 5 Conclusion -- References -- Frequency Domain Distillation for Data-Free Quantization of Vision Transformer -- 1 Introduction -- 2 Related Work -- 2.1 Vision Transformer (ViT) -- 2.2 Network Quantization -- 3 Preliminaries -- 3.1 Quantizer.3.2 Fast Fourier Transform (FFT) and Frequency Domain -- 4 Method -- 4.1 Our Insights -- 4.2 Frequency Domain Distillation -- 4.3 The Overall Pipeline -- 5 Experimentation -- 5.1 Comparison Experiments -- 5.2 Ablation Study -- 6 Conclusions -- References -- An ANN-Guided Approach to Task-Free Continual Learning with Spiking Neural Networks -- 1 Introduction -- 2 Related Works -- 2.1 Image Generation in SNNs -- 2.2 Continual Learning -- 3 Preliminary -- 3.1 The Referee Module: WGAN -- 3.2 The Player Module: FSVAE -- 4 Methodology -- 4.1 Problem Setting -- 4.2 Overview of Our Model -- 4.3 Adversarial Similarity Expansion -- 4.4 Precise Pruning -- 5 Experimental Results -- 5.1 Dataset Setup -- 5.2 Classification Tasks Under TFCL -- 5.3 The Impact of Different Thresholds and Buffer Sizes -- 5.4 ANN and SNN Under TFCL -- 6 Conclusion -- References -- Multi-adversarial Adaptive Transformers for Joint Multi-agent Trajectory Prediction -- 1 Introduction -- 2 Related Works -- 2.1 Multi-agent Trajectory Prediction -- 2.2 Domain Adaptation -- 3 Proposed Method -- 3.1 Encoder: Processing Multi-aspect Data -- 3.2 Decoder: Generating Multi-modal Trajectories -- 3.3 Adaptation: Learning Doamin Invaint Feature -- 3.4 Loss Function -- 4 Experiments -- 4.1 Dataset -- 4.2 Problem Setting -- 4.3 Evaluation Metrics -- 4.4 Implementation Details -- 4.5 Quantitative Analysis -- 4.6 Ablation Study -- 5 Conclusion -- References -- Enhancing Open-Set Object Detection via Uncertainty-Boxes Identification -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Preliminary -- 3.2 Baseline Setup -- 3.3 Pseudo Proposal Advisor -- 3.4 Uncertainty-Box Detection -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Comparison with Other Methods -- 4.3 Ablation Studies -- 4.4 Visualization and Qualitative Analysis -- 5 Conclusions -- References.Interventional Supervised Learning for Person Re-identification.The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. .Lecture Notes in Computer Science,1611-3349 ;14432Image processingDigital techniquesComputer visionArtificial intelligenceApplication softwareComputer networksComputer systemsMachine learningComputer Imaging, Vision, Pattern Recognition and GraphicsArtificial IntelligenceComputer and Information Systems ApplicationsComputer Communication NetworksComputer System ImplementationMachine LearningImage processingDigital techniques.Computer vision.Artificial intelligence.Application software.Computer networks.Computer systems.Machine learning.Computer Imaging, Vision, Pattern Recognition and Graphics.Artificial Intelligence.Computer and Information Systems Applications.Computer Communication Networks.Computer System Implementation.Machine Learning.006Liu Qingshanedthttp://id.loc.gov/vocabulary/relators/edtWang Hanziedthttp://id.loc.gov/vocabulary/relators/edtMa Zhanyuedthttp://id.loc.gov/vocabulary/relators/edtZheng Weishiedthttp://id.loc.gov/vocabulary/relators/edtZha Hongbinedthttp://id.loc.gov/vocabulary/relators/edtChen Xilinedthttp://id.loc.gov/vocabulary/relators/edtWang Liangedthttp://id.loc.gov/vocabulary/relators/edtJi Rongrongedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996587868003316Pattern recognition and computer vision1972598UNISA