Mathematical modeling and validation in physiology : applications to the cardiovascular and respiratory systems / / Jerry J. Batzel, Mostafa Bachar, Franz Kappel, editors

• Record Nr.: UNINA9910438149703321
• Pubbl/distr/stampa: Berlin ; ; New York, : Springer, c2013
• ISBN: 3-642-32882-2
• Edizione: [1st ed. 2013.]
• Descrizione fisica: 1 online resource (XX, 254 p. 83 illus., 34 illus. in color.)
• Collana: Lecture notes in mathematics ; ; 2064
• Altri autori (Persone): BatzelJerry J; BacharMostafa; KappelF
• Disciplina: 571.015118
• Soggetti: Human physiology - Mathematical models; Cardiovascular system - Mathematical models; Respiratory organs - Mathematical models
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 1 Merging Mathematical and Physiological Knowledge: Dimensions and Challenges -- 2 Mathematical Modeling of Physiological Systems -- 3 Parameter Selection Methods in Inverse Problem Formulation.- 4 Application of the Unscented Kalman Filtering to Parameter Estimation -- 5 Integrative and Reductionist Approaches to Modeling of Control of Breathing -- 6 Parameter Identification in a Respiratory Control System Model with Delay -- 7 Experimental Studies of Respiration and Apnea -- 8 Model Validation and Control Issues in the Respiratory System -- 9 Experimental Studies of the Baroreflex -- 10 Development of Patient Specific Cardiovascular Models Predicting Dynamics in Response to Orthostatic Stress Challenges -- 11 Parameter Estimation of a Model for Baroreflex Control of Unstressed Volume.
• Sommario/riassunto: This volume synthesizes theoretical and practical aspects of both the mathematical and life science viewpoints needed for modeling of the cardiovascular-respiratory system specifically and physiological systems generally. Theoretical points include model design, model complexity and validation in the light of available data, as well as control theory approaches to feedback delay and Kalman filter ; applications to parameter identification. State of the art approaches using parameter sensitivity are discussed for enhancing model identifiability through joint analysis of model structure and data. Practical examples illustrate model development at various levels of complexity based on given physiological information. The sensitivity-based approaches for examining model identifiability are illustrated by means of specific modeling examples. The themes presented address the current problem of patient-specific model adaptation in the clinical setting, where data is typically limited.

Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part V / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin

• Record Nr.: UNINA9910851987103321
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
• ISBN: 9789819722624; 9819722624
• Edizione: [1st ed. 2024.]
• Descrizione fisica: 1 online resource (431 pages)
• Collana: Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 14649
• Disciplina: 006.3
• Soggetti: Artificial intelligence; Algorithms; Education - Data processing; Computer science - Mathematics; Signal processing; Computer networks; Artificial Intelligence; Design and Analysis of Algorithms; Computers and Education; Mathematics of Computing; Signal, Speech and Image Processing; Computer Communication Networks
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part V -- Multimedia and Multimodal Data -- Re-thinking Human Activity Recognition with Hierarchy-Aware Label Relationship Modeling -- 1 Introduction -- 2 Related Work -- 2.1 Human Activity Recognition (HAR) -- 2.2 Hierarchical Label Modeling -- 3 Problem Formulation -- 4 Our Proposals -- 4.1 Hierarchy-Aware Label Encoding -- 4.2 Activity Data Encoding -- 4.3 Label-Data Joint Embedding Learning -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 5.3 Ablation Study -- 6 Discussions and Conclusion -- References -- Geometrically-Aware Dual Transformer Encoding Visual and Textual Features for Image Captioning -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Features Extractor -- 3.2 Caption Generator -- 3.3 Attention Block -- 3.4 Training and Objectives -- 4 Experiments -- 4.1 Experiments Setup -- 4.2 Experiment Result -- 5 Conclusions -- References -- MHDF: Multi-source Heterogeneous Data Progressive Fusion for Fake News Detection -- 1 Introduction -- 2 Related Work -- 3 MHDF Model -- 3.1 Model Overview -- 3.2 Multi-source Heterogeneous Data Amplification -- 3.3 News Textual Feature Fusion -- 3.4 News Visual Feature Fusion -- 3.5 Sentiment Feature Extractor -- 3.6 Feature Integration Classifier -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Performance Comparison -- 4.4 Ablation Experiments and Validity Verification -- 4.5 Conclusions -- References -- Accurate Semi-supervised Automatic Speech Recognition via Multi-hypotheses-Based Curriculum Learning -- 1 Introduction -- 2 Related Works -- 2.1 Automatic Speech Recognition Methods -- 2.2 Connectionist Temporal Classification (CTC) Loss -- 3 Proposed Method -- 3.1 Multiple Hypotheses for Unlabeled Instances.; 3.2 Training ASR Model with Multiple Hypotheses -- 3.3 Curriculum Learning -- 3.4 Theoretical Analysis -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Transcription Performance (Q1) -- 4.3 Speed of Convergence (Q2) -- 4.4 Ablation Study (Q3) -- 5 Conclusions -- References -- MM-PhyQA: Multimodal Physics Question-Answering with Multi-image CoT Prompting -- 1 Introduction -- 2 Related Works -- 2.1 Available Datasets -- 2.2 Large Multimodal Models and Chain-of-Thought -- 3 Novel Dataset -- 3.1 Original Dataset Creation -- 3.2 Data Augmentation Procedure -- 3.3 Chain of Thought Variant -- 3.4 MM-PhyQA Dataset Topics -- 4 Methodology -- 4.1 Multi-image Chain-of-Thought (MI-CoT) -- 5 Experiments -- 5.1 Models -- 6 Results and Discussion -- 6.1 Model Performance -- 6.2 Zero Shot Prompting Vs Supervised Fine-Tuning -- 6.3 Effect of Chain of Thought Prompting -- 6.4 Error Analysis -- 7 Conclusion -- References -- Adversarial Text Purification: A Large Language Model Approach for Defense -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Large Language Models -- 3.2 Adversarial Text Purification -- 4 LLM-Guided Adversarial Text Purification -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Results and Discussion -- 6 Conclusion -- References -- lil'HDoC: An Algorithm for Good Arm Identification Under Small Threshold Gap -- 1 Introduction -- 2 Background -- 2.1 Good Arm Identification -- 3 Problem Setting -- 4 Preliminary -- 5 Algorithm -- 5.1 Correctness of lil'HDoC -- 5.2 First Arms Sampling Complexity -- 5.3 Total Sample Complexity -- 6 Experiment -- 6.1 Dataset -- 6.2 Baseline -- 6.3 Results -- 7 Conclusion -- References -- Recommender Systems -- ScaleViz: Scaling Visualization ; Recommendation Models on Large Data -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 4 Proposed Solution -- 4.1 Cost Profiling -- 4.2 RL Agent.; 5 Evaluations -- 5.1 Experimental Setup -- 5.2 Speed-Up in Visualization Generation -- 5.3 Budget vs. Error Trade-Off -- 5.4 Need for Dataset-Specific Feature Selection -- 5.5 Scalability with Increasing Data Size -- 6 Conclusion -- References -- Collaborative Filtering in Latent Space: A Bayesian Approach for Cold-Start Music Recommendation -- 1 Introduction -- 2 Related Work and Problem Formulation -- 2.1 Problem Formulation -- 3 Methodology -- 3.1 Overview -- 3.2 Statistical Model in CFLS -- 3.3 Optimization -- 3.4 Prediction -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Performance Comparisons -- 4.4 Influence of Different Cold-Start Levels -- 4.5 Diversity, Interpretability and User Controllability -- 5 Conclusions -- References -- On Diverse and Precise Recommendations for Small and Medium-Sized Enterprises -- 1 Introduction -- 2 Related Work -- 3 Definitions and Problem Statement -- 4 Variants of a Session-Based Recommender System -- 4.1 Quality Metrics -- 5 Experiments and Evaluation -- 5.1 Selection of Real-World Datasets -- 5.2 Task Definition and Parameter Configuration -- 5.3 Evaluation of Experimental Results -- 6 Conclusion and Future Work -- References -- HMAR: Hierarchical Masked Attention for Multi-behaviour Recommendation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 HMAR -- 2.3 Multi-task Learning -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Evaluation Protocol -- 3.3 Model Performance (RQ1) -- 3.4 Effect of Auxiliary Behaviors and Individual Model Components (RQ2 &amp -- RQ3) -- 4 Related Work -- 5 Conclusion -- References -- Residual Spatio-Temporal Collaborative Networks for Next POI Recommendation -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Problem Formulation -- 3.2 Long-Term Dependence Module -- 3.3 Short-Term Dependence Module -- 3.4 Sample Balancer.; 4 Experiments -- 4.1 Experimental Settings -- 4.2 Recommendation Performance -- 4.3 Ablation Study -- 5 Conclusions -- References -- Conditional Denoising Diffusion for Sequential Recommendation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Stepwise Diffuser -- 3.2 Sequence Encoder -- 3.3 Cross-Attentive Conditional Denoising Decoder -- 3.4 Optimization -- 4 Experiments -- 4.1 Plateau of Ranking Prediction -- 4.2 Overall Experiments -- 4.3 Ablation Study -- 4.4 Hyperparameter Sensitivity -- 4.5 Case Study for Stepwise Generation -- 5 Conclusion -- References -- UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering -- 1 Introduction -- 2 Related Work -- 2.1 Collaborative Filtering -- 2.2 Explainable and Transparent Recommender Models -- 2.3 The Prototype-Based Collaborative Filtering -- 3 Methodology -- 3.1 User-Item Prototypes Connections Matrix Factorization (UIPC-MF) -- 3.2 Loss Function -- 4 Experiments and Discussion -- 4.1 Evaluation Metrics -- 4.2 Baseline Models -- 4.3 Training Details -- 4.4 Evaluation Results -- 4.5 Explaining UIPC-MF Recommendations -- 4.6 The Impact of L1-Norm in Reduction of Learning Bias -- 5 Conclusion -- References -- Towards Multi-subsession Conversational Recommendation -- 1 Introduction -- 2 Related Works -- 3 MSMCR Scenario -- 3.1 Definition -- 3.2 General Framework -- 4 Methodology -- 4.1 Context-Aware Recommendation -- 4.2 Policy Learning -- 4.3 Model Training -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Overall Performance -- 5.3 Further Experiments -- 6 Conclusion -- References -- False Negative Sample Aware Negative Sampling for Recommendation -- 1 Introduction -- 2 ; Related Work -- 3 Preliminary -- 4 Methodology -- 4.1 False Negatives Identification -- 4.2 False Negatives Elimination -- 5 Experiment -- 5.1 Experiment Settings.; 5.2 Performance Comparison -- 5.3 Study of EDNS -- 6 Conclusion -- References -- Multi-sourced Integrated Ranking with Exposure Fairness -- 1 Introduction -- 2 Problem Formulation -- 3 Proposed Model -- 3.1 Input Layer -- 3.2 Dual RNN Module -- 3.3 Multi-task Module -- 3.4 Model Training -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Baselines -- 4.3 Model Selection -- 4.4 Performance Comparison -- 4.5 Ablation Study -- 4.6 Online A/B Testing -- 5 Conclusion -- References -- Soft Contrastive Learning for Implicit Feedback Recommendations -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Notations -- 3.2 The SCLRec Framework -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Overall Performance (RQ1) -- 4.3 Ablation Study (RQ2) -- 4.4 Robustness to Interaction Noises (RQ3) -- 5 Conclusion -- References -- Dual-Graph Convolutional Network and Dual-View Fusion for Group Recommendation -- 1 Introduction -- 2 Problem Formulation -- 3 Approach -- 3.1 Dual-Graph Construction -- 3.2 Dual-Graph Network for Member Preference -- 3.3 Dual-View Fusion for Group Preference -- 3.4 Group Recommendation and Model Training -- 4 Experiments -- 4.1 Experimental Dataset and Setup -- 4.2 Experimental Results and Analysis -- 4.3 Parameter Sensitivity -- 5 Related Works -- 6 Conclusion and Future Work -- References -- TripleS: A Subsidy-Supported Storage for Electricity with Self-financing Management System -- 1 Introduction -- 2 Literature Review -- 2.1 Electricity Subsidy and Operating Reserve -- 2.2 Electricity Management System -- 2.3 Electricity Storage -- 3 Problem Definition and Simulation Environment -- 4 Proposed TripleS -- 5 Experimental Results -- 5.1 Performance Evaluation -- 5.2 Performance Evaluation Under MS Attack -- 5.3 Influence of Self-discharge -- 6 Conclusion -- References -- Spatio-temporal Data.; Mask Adaptive Spatial-Temporal Recurrent Neural Network for Traffic Forecasting.
• Sommario/riassunto: The 6-volume set LNAI 14645-14650 constitutes the proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, which took place in Taipei, Taiwan, during May 7–10, 2024. The 177 papers presented in these proceedings were carefully reviewed and selected from 720 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.