LEADER 04117nam 2201201z- 450 001 9910639996503321 005 20231214132821.0 010 $a3-0365-6006-8 035 $a(CKB)5470000001633388 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/95797 035 $a(EXLCZ)995470000001633388 100 $a20202301d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances on Scoliogeny, Diagnosis and Management of Scoliosis and Spinal Disorders 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (264 p.) 311 $a3-0365-6005-X 330 $aThis book contains research articles on the advances in the aetiology of idiopathic scoliosis (IS), the spinal growth related to the implementation of growth modulation for the surgical treatment of early-onset IS, the non-surgical treatment of IS using Physiotheraputic Scoliosis Specific Exercises, and braces. Additionally, it focuses on issues related to surgical treatment, issues related to body posture and the quality of life of this sensitive group of people. The high quality of published papers in this Special Issue of the JCM serve these objectives. 606 $aMedicine$2bicssc 610 $aidiopathic scoliosis 610 $ahealth-related quality of life 610 $acultural adaptation 610 $aItalian Spine Youth Quality of Life Questionnaire 610 $asystematic review 610 $ameta-analysis 610 $aadolescent idiopathic scoliosis 610 $abrace therapy 610 $abrace concepts 610 $arigid brace 610 $anight time brace 610 $aring apophysis 610 $amaturation 610 $aossification 610 $afusion 610 $ascoliosis 610 $anighttime orthotic treatment 610 $asurgery 610 $aquality of life 610 $aparaspinal muscles 610 $across-sectional area 610 $aposterior approach 610 $acomputed tomography 610 $aItalian spine youth quality of life questionnaire 610 $aSRS-22 610 $a22q11.2 deletion syndrome 610 $ahuman model 610 $aneuromuscular scoliosis 610 $aradiography 610 $aMRI 610 $acurve morphology 610 $aintraspinal anomaly 610 $abody height 610 $apulmonary function test 610 $aCobb angle 610 $aiTRAQ 610 $a?-actin 610 $aprogressive 610 $adifferentially expressed proteins 610 $abracing 610 $aphysiotherapeutic scoliosis-specific exercise 610 $aphysical activity 610 $aadherence 610 $aspinal appearance 610 $ashared decision-making 610 $apersonalised approach 610 $asclerostin 610 $aosteocytes 610 $a?-catenin 610 $aWnt signaling pathway 610 $ascoliometer 610 $atruncal asymmetry 610 $alateral spinal profile 610 $asurface topography 610 $aaetiology 610 $aspinal deformities 610 $apathobiomechanics 610 $afollow-up study 610 $aRett syndrome 610 $amotor skills 610 $atelerehabilitation 610 $aphysical therapy modalities 610 $ahome exercise program 610 $aneurodynamic functions 610 $aassessment 610 $apain 610 $atreatment 610 $aearly onset scoliosis 610 $anon-operative treatment 610 $abody posture 610 $asports activity 610 $acorrective exercises 610 $adigital photography 615 7$aMedicine 700 $aGrivas$b Theodoros B$4edt$01095356 702 $aGrivas$b Theodoros B$4oth 906 $aBOOK 912 $a9910639996503321 996 $aAdvances on Scoliogeny, Diagnosis and Management of Scoliosis and Spinal Disorders$93032744 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