LEADER 02856oam 22006134a 450 001 9910480231903321 005 20201016234716.0 010 $a2-7605-1873-6 010 $a1-4356-8554-7 035 $a(CKB)1000000000576739 035 $a(EBL)599895 035 $a(OCoLC)681484514 035 $a(SSID)ssj0000734485 035 $a(PQKBManifestationID)11974091 035 $a(PQKBTitleCode)TC0000734485 035 $a(PQKBWorkID)10721927 035 $a(PQKB)11497412 035 $a(CaBNvSL)thg00603837 035 $a(MiAaPQ)EBC3257824 035 $a(OCoLC)417071039 035 $a(MdBmJHUP)muse21160 035 $a(MiAaPQ)EBC599895 035 $a(PPN)170254968 035 $a(EXLCZ)991000000000576739 100 $a20060119d2006 uy 0 101 0 $afre 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 12$aL'analyse multivariée avec SPSS$b[electronic resource] /$fJean Stafford, Paul Bodson avec la collaboration de Marie-Christine Stafford 210 1$aQue?bec, Que?bec :$cPresses de l'Universite? du Que?bec,$d2007. 210 4$d©2006 215 $a1 online resource (258 p.) 300 $aDescription based upon print version of record. 311 $a2-7605-1392-0 320 $aComprend des ref. bibliogr.: p. [233]-238. 327 $aTable des matie?res; Introduction; 1. L'analyse des donne?es; 2. Le traitement des donne?es par ordinateur; 3. L'analyse factorielle en composantes principales; 4. L'analyse factorielle des correspondances; 5. Analyse bivarie?e : Tableau de contingence et khi-carre?; 6. Analyse bivarie?e : L'analyse de variance; 7. Analyse bivarie?e : Corre?lation et re?gression simple; 8. La re?gression multiple; 9. La re?gression logistique; E?pilogue; Bibliographie; Tables statistiques 330 $aLes auteurs proposent une approche pratique et empirique qui allie l'analyse statistique a? l'utilisation d'un logiciel facile d'acce?s : SPSS. En de?crivant les diverses me?thodes de l'analyse multivarie?e, ils pre?sentent les interrelations entre plusieurs variables d'une base de donne?es et en ge?ne?ralisent les conclusions par infe?rence statistique du traitement informatique des donne?es jusqu'a? l'interpre?tation des re?sultats. 606 $aFactor analysis$xData processing 606 $aRegression analysis$xData processing 608 $aElectronic books. 615 0$aFactor analysis$xData processing. 615 0$aRegression analysis$xData processing. 676 $a364/.07/27 700 $aStafford$b Jean$0196397 701 $aStafford$b Marie-Christine$0992780 701 $aBodson$b Paul$0992781 801 0$bMdBmJHUP 801 1$bMdBmJHUP 906 $aBOOK 912 $a9910480231903321 996 $aL'analyse multivariée avec SPSS$92273303 997 $aUNINA LEADER 01726nam 2200409Ia 450 001 9910699298103321 005 20230902161632.0 035 $a(CKB)5470000002400920 035 $a(OCoLC)469186804 035 $a(EXLCZ)995470000002400920 100 $a20091124d2009 ua 0 101 0 $aeng 135 $aurmnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGroundwater quality, age, and probability of contamination, Eagle River watershed valley-fill aquifer, north-central Colorado, 2006-2007$b[electronic resource] /$fby Michael G. Rupert and L. Niel Plummer ; prepared in cooperation with Eagle County ... [and others] 210 1$aReston, Va. :$cU.S. Geological Survey,$d2009. 215 $a1 online resource (vii, 59 pages) $cillustrations, maps 225 1 $aScientific investigations report ;$v2009-5082 300 $aTitle from PDF title screen (viewed Aug. 25, 2009). 320 $aIncludes bibliographical references (pages 54-59). 410 0$aScientific investigations report ;$v2009-5082. 606 $aGroundwater$xQuality$zColorado$zEagle River Watershed 606 $aGroundwater$xPollution$zColorado$zEagle River Watershed 615 0$aGroundwater$xQuality 615 0$aGroundwater$xPollution 700 $aRupert$b Michael G$01406403 701 $aPlummer$b L. Niel$01384178 712 02$aEagle County (Colo.) 712 02$aGeological Survey (U.S.) 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910699298103321 996 $aGroundwater quality, age, and probability of contamination, Eagle River watershed valley-fill aquifer, north-central Colorado, 2006-2007$93485313 997 $aUNINA LEADER 13094nam 22008295 450 001 9910886089803321 005 20251225202208.0 010 $a3-031-70365-0 024 7 $a10.1007/978-3-031-70365-2 035 $a(MiAaPQ)EBC31629522 035 $a(Au-PeEL)EBL31629522 035 $a(CKB)34674273000041 035 $a(DE-He213)978-3-031-70365-2 035 $a(EXLCZ)9934674273000041 100 $a20240901d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning and Knowledge Discovery in Databases. Research Track $eEuropean Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9?13, 2024, Proceedings, Part VI /$fedited by Albert Bifet, Jesse Davis, Tomas Krilavi?ius, Meelis Kull, Eirini Ntoutsi, Indr? ?liobait? 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (509 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v14946 311 08$a3-031-70364-2 327 $aIntro -- Preface -- Organization -- Invited Talks Abstracts -- The Dynamics of Memorization and Unlearning -- The Emerging Science of Benchmarks -- Enhancing User Experience with AI-Powered Search and Recommendations at Spotify -- How to Utilize (and Generate) Player Tracking Data in Sport -- Resource-Aware Machine Learning-A User-Oriented Approach -- Contents - Part VI -- Research Track -- Rejection Ensembles with Online Calibration -- 1 Introduction -- 2 Notation and Related Work -- 2.1 Related Work -- 3 A Theoretical Investigation of Rejection -- 3.1 Three Distinct Situations Can Occur When Training the Rejector -- 3.2 Even a Perfect Rejector Will Overuse Its Budget -- 3.3 A Rejector Should Not Trust fs and fb -- 4 Training a Rejector for a Rejection Ensemble -- 5 Experiments -- 5.1 Experiments with Deep Learning Models -- 5.2 Experiments with Decision Trees -- 5.3 Conclusion from the Experiments -- 6 Conclusion -- References -- Lighter, Better, Faster Multi-source Domain Adaptation with Gaussian Mixture Models and Optimal Transport -- 1 Introduction -- 2 Preliminaries -- 2.1 Gaussian Mixtures -- 2.2 Domain Adaptation -- 2.3 Optimal Transport -- 3 Methodological Contributions -- 3.1 First Order Analysis of MW2 -- 3.2 Supervised Mixture-Wasserstein Distances -- 3.3 Mixture Wasserstein Barycenters -- 3.4 Multi-source Domain Adaptation Through GMM-OT -- 4 Experiments -- 4.1 Toy Example -- 4.2 Multi-source Domain Adaptation -- 4.3 Lighter, Better, Faster Domain Adaptation -- 5 Conclusion -- References -- Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering -- 1 Introduction -- 2 Related Work -- 2.1 Commonsense Question Answering -- 2.2 Graph-Text Alignment -- 3 Task Formulation -- 4 Methods -- 4.1 Graph-Text Alignment -- 4.2 Subgraph Retrieval Module -- 4.3 Prediction -- 5 Experiments -- 5.1 Datasets. 327 $a5.2 Baselines -- 5.3 Implementation Details -- 5.4 Main Results -- 5.5 Ablation Study -- 5.6 Low-Resource Setting -- 5.7 Evaluation with other GNNs -- 5.8 Hyper-parameter Analysis -- 6 Ethical Considerations and Limitations -- 6.1 Ethical Considerations -- 6.2 Limitations -- 7 Conclusion -- References -- HetCAN: A Heterogeneous Graph Cascade Attention Network with Dual-Level Awareness -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Heterogeneous Information Network -- 3.2 Graph Neural Networks -- 3.3 Transformer-Style Architecture -- 4 The Proposed Model -- 4.1 Overall Architecture -- 4.2 Type-Aware Encoder -- 4.3 Dimension-Aware Encoder -- 4.4 Time Complexity Analysis -- 5 Experiments -- 5.1 Experimental Setups -- 5.2 Node Classification -- 5.3 Link Prediction -- 5.4 Model Analysis -- 6 Conclusion -- References -- Interpetable Target-Feature Aggregation for Multi-task Learning Based on Bias-Variance Analysis -- 1 Introduction -- 2 Preliminaries -- 2.1 Related Works: Dimensionality Reduction, Multi-task Learning -- 3 Bias-Variance Analysis: Theoretical Results -- 4 Multi-task Learning via Aggregations: Algorithms -- 5 Experimental Validation -- 5.1 Synthetic Experiments and Ablation Study -- 5.2 Real World Datasets -- 6 Conclusions and Future Developments -- References -- The Simpler The Better: An Entropy-Based Importance Metric to Reduce Neural Networks' Depth -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 How Layers Can Degenerate -- 3.2 Entropy for Rectifier Activations -- 3.3 EASIER -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 4.3 Ablation Study -- 4.4 Limitations and Future Work -- 5 Conclusion -- References -- Towards Few-Shot Self-explaining Graph Neural Networks -- 1 Introduction -- 2 Problem Definition -- 3 The Proposed MSE-GNN -- 3.1 Architecture of MSE-GNN -- 3.2 Optimization Objective. 327 $a3.3 Meta Training -- 4 Experiments -- 4.1 Datasets and Experimental Setup -- 5 Related Works -- 6 Conclusion -- References -- Uplift Modeling Under Limited Supervision -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Uplift Modeling with Graph Neural Networks (UMGNet) -- 3.2 Active Learning for Uplift GNNs (UMGNet-AL) -- 4 Experimental Evaluation -- 4.1 Datasets -- 4.2 Benchmark Models -- 4.3 Experiments -- 5 Conclusion -- References -- Self-supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 STEN: Spatial-Temporal Normality Learning -- 3.1 Problem Statement -- 3.2 Overview of The Proposed Approach -- 3.3 OTN: Order Prediction-Based Temporal Normality Learning -- 3.4 DSN: Distance Prediction-Based Spatial Normality Learning -- 3.5 Training and Inference -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Main Results -- 4.3 Ablation Study -- 4.4 Qualitative Analysis -- 4.5 Sensitivity Analysis -- 4.6 Time Efficiency -- 5 Conclusion -- References -- Modeling Text-Label Alignment for Hierarchical Text Classification -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Text Encoder -- 3.2 Graph Encoder -- 3.3 Generation of Composite Representation -- 3.4 Loss Functions -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Experimental Results -- 4.4 Analysis -- 5 Conclusion -- A Details of Statistical Test -- B Performance Analysis on Additional Datasets -- References -- Secure Aggregation Is Not Private Against Membership Inference Attacks -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Privacy Analysis of Secure Aggregation -- 4.1 Threat Model -- 4.2 SecAgg as a Noiseless LDP Mechanism -- 4.3 Asymptotic Privacy Guarantee -- 4.4 Upper Bounding M() via Dominating Pairs of Distributions. 327 $a4.5 Lower Bounding M() and Upper Bounding fM() via Privacy Auditing -- 5 Experiments and Discussion -- 6 Conclusions -- A Correlated Gaussian Mechanism -- A.1 Optimal LDP Curve: Proof of Theorem 2 -- A.2 The Case Sd={xRd:||x||2 rd} -- A.3 Trade-Off Function: Proof of Proposition 1 -- B LDP Analysis of the Mechanism (1) in a Special Case: Proof of Theorem 3 -- References -- Evaluating Negation with Multi-way Joins Accelerates Class Expression Learning -- 1 Introduction -- 2 Preliminaries -- 2.1 The Description Logic ALC -- 2.2 Class Expression Learning -- 2.3 Semantics and Properties of SPARQL -- 2.4 Worst-Case Optimal Multi-way Join Algorithms -- 3 Mapping ALC Class Expressions to SPARQL Queries -- 4 Negation in Multi-way Joins -- 4.1 Rewriting Rule for Negation and UNION Normal Form -- 4.2 Multi-way Join Algorithm -- 4.3 Implementation -- 5 Experimental Results -- 5.1 Systems, Setup and Execution -- 5.2 Datasets and Queries -- 5.3 Results and Discussion -- 6 Related Work -- 7 Conclusion And Future Work -- References -- LayeredLiNGAM: A Practical and Fast Method for Learning a Linear Non-gaussian Structural Equation Model -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 LiNGAM -- 3.2 DirectLiNGAM -- 4 LayeredLiNGAM -- 4.1 Generalization of Lemma 2 -- 4.2 Algorithm -- 4.3 Adaptive Thresholding -- 5 Experiments -- 5.1 Datasets and Evaluation Metrics -- 5.2 Determining Threshold Parameters -- 5.3 Results on Synthetic Datasets -- 5.4 Results on Real-World Datasets -- 6 Conclusion -- References -- Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties -- 1 Introduction -- 2 Background -- 2.1 Bayesian Optimization -- 2.2 Rank GP Distributions -- 3 Framework -- 3.1 Expert Preferential Inputs on Abstract Properties -- 3.2 Augmented GP with Abstract Property Preferences -- 3.3 Overcoming Inaccurate Expert Inputs. 327 $a4 Convergence Remarks -- 5 Experiments -- 5.1 Synthetic Experiments -- 5.2 Real-World Experiments -- 6 Conclusion -- References -- Enhancing LLM's Reliability by Iterative Verification Attributions with Keyword Fronting -- 1 Introduction -- 2 Related Work -- 2.1 Retrieval-Augmented Generation -- 2.2 Text Generation Attribution -- 3 Methodology -- 3.1 Task Formalization -- 3.2 Overall Framework -- 3.3 Keyword Fronting -- 3.4 Attribution Verification -- 3.5 Iterative Optimization -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Main Results -- 4.3 Ablation Studies -- 4.4 Impact of Hyperparameters -- 4.5 The Performance of the Iteration -- 5 Conclusion -- References -- Reconstructing the Unseen: GRIOT for Attributed Graph Imputation with Optimal Transport -- 1 Introduction -- 2 Related Works -- 3 Multi-view Optimal Transport Loss for Attribute Imputation -- 3.1 Notations -- 3.2 Optimal Transport and Wasserstein Distance -- 3.3 Definition of the `3?9`42`"?613A``45`47`"603AMultiW Loss Function -- 3.4 Instantiation of `3?9`42`"?613A``45`47`"603AMultiW Loss with Attributes and Structure -- 4 Imputing Missing Attributes with `3?9`42`"?613A``45`47`"603AMultiW Loss -- 4.1 Architecture of GRIOT -- 4.2 Accelerating the Imputation -- 5 Experimental Analysis -- 5.1 Experimental Protocol -- 5.2 Imputation Quality v.s. Node Classification Accuracy -- 5.3 Imputing Missing Values for Unseen Nodes -- 5.4 Time Complexity -- 6 Conclusion and Perspectives -- References -- Introducing Total Harmonic Resistance for Graph Robustness Under Edge Deletions -- 1 Introduction -- 2 Problem Statement and a New Robustness Measure -- 2.1 Problem Statement and Notation -- 2.2 Robustness Measures -- 3 Related Work -- 4 Comparison of Exact Solutions -- 5 Greedy Heuristic for k-GRoDel -- 5.1 Total Harmonic Resistance Loss After Deleting an Edge. 327 $a5.2 Forest Index Loss After Deleting an Edge. 330 $aThis multi-volume set, LNAI 14941 to LNAI 14950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024. The papers presented in these proceedings are from the following three conference tracks: - Research Track: The 202 full papers presented here, from this track, were carefully reviewed and selected from 826 submissions. These papers are present in the following volumes: Part I, II, III, IV, V, VI, VII, VIII. Demo Track: The 14 papers presented here, from this track, were selected from 30 submissions. These papers are present in the following volume: Part VIII. Applied Data Science Track: The 56 full papers presented here, from this track, were carefully reviewed and selected from 224 submissions. These papers are present in the following volumes: Part IX and Part X. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v14946 606 $aArtificial intelligence 606 $aComputer engineering 606 $aComputer networks 606 $aComputers 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aSoftware engineering 606 $aArtificial Intelligence 606 $aComputer Engineering and Networks 606 $aComputing Milieux 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aSoftware Engineering 615 0$aArtificial intelligence. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aComputers. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aSoftware engineering. 615 14$aArtificial Intelligence. 615 24$aComputer Engineering and Networks. 615 24$aComputing Milieux. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aSoftware Engineering. 676 $a006.3 700 $aBifet$b Albert$01167471 701 $aDavis$b Jesse$01730652 701 $aKrilavi?ius$b Tomas$01769151 701 $aKull$b Meelis$01769152 701 $aNtoutsi$b Eirini$01769153 701 $a?liobait?$b Indr?$01769154 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910886089803321 996 $aMachine Learning and Knowledge Discovery in Databases. Research Track$94236908 997 $aUNINA LEADER 00935nam 2200253la 450 001 9910482642203321 005 20221102161743.0 035 $a(UK-CbPIL)2090361431 035 $a(CKB)5500000000092752 035 $a(EXLCZ)995500000000092752 100 $a20221102d1628 uy | 101 0 $alat 135 $aurcn||||a|bb| 200 10$aDe studio theologico compendiaria et genuina tamen ratione incoando et continuando breve consilium / [Caspar Bartholin] 210 $aCopenhagen $cHeinrich Waldkirch$d1628 215 $aOnline resource ([24] leaves (last blank) , (8vo)) 300 $aReproduction of original in The Wellcome Library, London. 700 $aBartholin$b Caspar$f1585-1629.$0796334 801 0$bUk-CbPIL 801 1$bUk-CbPIL 906 $aBOOK 912 $a9910482642203321 996 $aDe studio theologico compendiaria et genuina tamen ratione incoando et continuando breve consilium$92025416 997 $aUNINA LEADER 05891nam 22008055 450 001 996647969603316 005 20250301115242.0 010 $a9783031820212$b(electronic bk.) 010 $z9783031820205 024 7 $a10.1007/978-3-031-82021-2 035 $a(MiAaPQ)EBC31928864 035 $a(Au-PeEL)EBL31928864 035 $a(CKB)37744242000041 035 $a(DE-He213)978-3-031-82021-2 035 $a(OCoLC)1505731817 035 $a(EXLCZ)9937744242000041 100 $a20250301d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Computer Graphics $e41st Computer Graphics International Conference, CGI 2024, Geneva, Switzerland, July 1?5, 2024, Proceedings, Part II /$fedited by Nadia Magnenat-Thalmann, Jinman Kim, Bin Sheng, Zhigang Deng, Daniel Thalmann, Ping Li 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (596 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15339 311 08$aPrint version: Magnenat-Thalmann, Nadia Advances in Computer Graphics Cham : Springer,c2025 9783031820205 327 $aGraphics and VR/AR -- VR Isle Academy: A VR Digital Twin Approach for Robotic Surgical Skill Development -- BoneStory: Visual Storytelling in 3D Virtual Surgical Planning for Bone Fracture Reduction -- The Significance of Interaction in Determining Learning Outcomes in Serious Games -- A Computational Medical XR Discipline, by George Papagiannakis, Walter Greenleaf -- Physiological Factors based Depression Assessment in Virtual Reality -- Assessing Cognitive Load in Distraction and Task Switching: Implications for Developing Realistic Clinical XR Training -- SPARC: Shared Perspective with Avatar Distortion for Remote Collaboration in VR -- Numerical Coarsening for Tetrahedral Meshes -- Reconstruction -- Towards Finer Human Reconstruction for Single RGB-D Images -- UrgRF: Radiance Field Reconstruction Guided by Low-Resolution Grids -- DeGraRec: 3D Deformable Object Reconstruction using Graph Neural Networks and Depth Estimation -- DUE-MVSNet: Learning Multi-View Stereo Based on Dual Uncertainty Estimation -- GRD: Garment Reconstruction and Draping with Preserved Design Based on 2D Image -- Survey on Multi-Person 3D Reconstruction from Monocular View -- SewPCT: Sewing Pattern Reconstruction from Point Cloud with Transformer -- Rendering and Animation -- Neural Metameric Enhancement for Foveated Rendering -- Expression Fusion to Enhance Video- and Speech-driven 3D Facial Animation -- Real-ESRGAN based EXR Upscale for VFX Pipeline -- Unsupervised Real-time Garment Deformation Prediction Driven by Human Body Pose and Shape -- GPN: Generative Point-based NeRF -- Interactive Ray Tracing of 3D Indoor Scanned Point Clouds -- Theoretical Analysis -- A Manifold Representation of the Key in Vision Transformers -- Mamba-Spike: Enhancing the Mamba Architecture with a Spiking Front-End for Efficient Temporal Data Processing -- PDGC: Properly Disentangle by Gating and Contrasting for Cross-Domain Few-Shot Classification -- Multi-scale Similarity Information Fusion Hashing for Unsupervised Cross-modal Retrieval -- YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment -- Automated Data Exploration and Analysis. 330 $aThe three-volume set LNCS 15338, 15339 and 15340 constitutes the refereed proceedings from the 41st Computer Graphics International Conference, CGI 2024, held during July 1?5, 2024, in Geneva, Switzerland. The 84 full papers presented in these proceedings were carefully reviewed and selected from 211 submissions. The papers are organized in the following topical sections: Part I: Colors, painting and layout; detection and recognition; image analysis and processing; image restoration and enhancement; and visual analytics and modeling. Part II: Graphics and VR/AR; reconstruction; rendering and animation; and theoretical analysis. Part III: Image analysis and visualization; image attention and perception; medical imaging and robotics; synthesis and generation; and empowering novel geometric algebra for graphics & engineering workshop. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15339 606 $aApplication software 606 $aComputer systems 606 $aComputer networks 606 $aData structures (Computer science) 606 $aInformation theory 606 $aCoding theory 606 $aComputer science 606 $aComputer and Information Systems Applications 606 $aComputer System Implementation 606 $aComputer Communication Networks 606 $aData Structures and Information Theory 606 $aCoding and Information Theory 606 $aTheory of Computation 615 0$aApplication software. 615 0$aComputer systems. 615 0$aComputer networks. 615 0$aData structures (Computer science) 615 0$aInformation theory. 615 0$aCoding theory. 615 0$aComputer science. 615 14$aComputer and Information Systems Applications. 615 24$aComputer System Implementation. 615 24$aComputer Communication Networks. 615 24$aData Structures and Information Theory. 615 24$aCoding and Information Theory. 615 24$aTheory of Computation. 676 $a005.3 700 $aMagnenat-Thalmann$b Nadia$0534913 701 $aKim$b Jinman$01784485 701 $aSheng$b Bin$01358931 701 $aDeng$b Zhigang$01784486 701 $aThalmann$b Daniel$060546 701 $aLi$b Ping$01784487 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a996647969603316 996 $aAdvances in Computer Graphics$94316084 997 $aUNISA