Elements of nonequilibrium statistical mechanics / / V. Balakrishnan

• Record Nr.: UNINA9910484098003321
• Autore: Balakrishnan V.
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-62233-9
• Edizione: [1st ed. 2021.]
• Descrizione fisica: 1 online resource (XIX, 314 p. 30 illus.)
• Disciplina: 530.13
• Soggetti: Nonequilibrium statistical mechanics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Chapter 1. Introduction -- Chapter 2. The Langevin equation -- Chapter 3. The fluctuation-dissippation relation -- Chapter 4. Autocorrelation of velocity -- Chapter 5. Markov Process.
• Sommario/riassunto: This book deals with the basic principles and techniques of nonequilibrium statistical mechanics. The importance of this subject is growing rapidly in view of the advances being made, both experimentally and theoretically, in statistical physics, chemical physics, biological physics, complex systems and several other areas. The presentation of topics is quite self-contained, and the choice of topics enables the student to form a coherent picture of the subject. The approach is unique in that classical mechanical formulation takes center stage. The book is of particular interest to advanced undergraduate and graduate students in engineering departments.

KI 2021: advances in artificial intelligence : 44th German Conference on AI, virtual event, September 27 - October 1, 2021 : proceedings / / edited by Stefan Edelkamp, Ralf Möller, Elmar Rueckert

• Record Nr.: UNISA996464524103316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-87626-8
• Descrizione fisica: 1 online resource (388 pages)
• Collana: Lecture Notes in Computer Science Ser. ; ; v.12873
• Disciplina: 006.3
• Soggetti: Optical data processing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Special Events -- Tutorial -- Workshops -- Organization -- Abstracts of Invited Talks -- Monte Carlo Search -- Autonomy in AI: Reactive Synthesis, Planning and Reinforcement Learning in Linear Temporal Logic on Finite Traces -- Ontologies for Providing Map Knowledge to Autonomous Vehicles -- The Third Wave of AI -- Motion Intelligence for Human-Centred Robots -- Human-Compatible Artificial Intelligence -- Contents -- Technical Programme -- RP-DQN: An Application of Q-Learning to Vehicle Routing Problems -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Method -- 4.1 Original Attention-Model -- 4.2 RP-DQN -- 5 Experiments -- 5.1 Baselines -- 5.2 Data -- 5.3 CVRP Results -- 5.4 MDVRP Results -- 5.5 Learning Curves -- 5.6 Runtime Comparison -- 5.7 Generalization Study -- 6 Conclusion -- References -- -Circulant Maximum Variance Bases -- 1 Introduction -- 2 Preliminaries -- 2.1 Principal Component Analysis -- 2.2 Dynamic Principal Component Analysis -- 2.3 -Circulant Matrices -- 3 Maximum Variance Bases -- 3.1 Simple Matched Circulants -- 3.2 Matched -Circulant Matrices -- 3.3 Relation to PCA, DPCA and DFT -- 4 Numerical Results -- 4.1 MA Process -- 4.2 Circular Process -- 5 Conclusion -- References -- Quantified Boolean Solving for Achievement Games -- 1 Introduction -- 2 Quantified Boolean Formulas -- 3 Harary's Tic-Tac-Toe -- 4 Related Work -- 5 The Pairing Encoding -- 6 Experimental Results -- 7 ; Conclusion -- References -- Knowledge Graph Based Question Answering System for Financial Securities -- 1 Introduction -- 2 Framework -- 2.1 Knowledge Graph Construction -- 2.2 Semantic Question Answering System -- 3 Experiments -- 4 Conclusion -- References -- Semi-unsupervised Learning: An In-depth Parameter Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Learning Paradigms.; 3.2 Semi-unsupervised Learning with Deep Generative Models -- 4 Datasets -- 5 Experiments -- 5.1 Semi-unsupervised Classification -- 5.2 Parameter Analysis -- 6 Summary and Outlook -- References -- Combining Transformer Generators with Convolutional Discriminators -- 1 Introduction -- 2 Related Work -- 2.1 Generative Models Using CNNs -- 2.2 Generative Models Using Attention -- 2.3 Hybrid Models -- 3 Model Architecture -- 3.1 Transformer Generator -- 3.2 Convolutional Discriminator -- 4 Experiments -- 4.1 Setup -- 4.2 Results -- 4.3 Frequency Analysis -- 5 Discussion -- 6 Conclusion -- References -- Explanation as a Process: User-Centric Construction of Multi-level and Multi-modal Explanations -- 1 Introduction -- 2 A Relational Knowledge Domain -- 3 Learning an Interpretable Model with ILP -- 4 Multi-level and Multi-modal Explanations -- 4.1 Explanation Generation -- 4.2 Explanatory Dialogue -- 4.3 Proof-of-Concept Implementation -- 5 Conclusion and Outlook -- References -- Multi-Type-TD-TSR - Extracting Tables from Document Images Using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: From OCR to Structured Table Representations -- 1 Introduction -- 2 Related Work -- 3 End-to-End Multistage Pipeline -- 4 Methods -- 4.1 Table Alignment Pre-processing -- 4.2 Table Detection -- 4.3 Bordered TSR -- 4.4 Unbordered TSR -- 4.5 Partially Bordered TSR -- 4.6 Color Invariance Pre-Processing -- 5 Evaluation -- 6 Conclusion -- References -- A High-Speed Neural Architecture Search Considering the Number of Weights -- 1 Introduction and Related Works -- 2 Proposed Method -- 2.1 DARTS Algorithm -- 2.2 Loss Function with the Number of Weights -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Results -- 4 Conclusions -- References -- Semantic Segmentation of Aerial Images Using Binary Space Partitioning -- 1 Introduction.; 2 Related Work -- 3 Semantic Segmentation -- 3.1 BSP-based Segmentation Model -- 3.2 Differentiable BSP Tree Rendering -- 3.3 Comparison to State of the Art -- 4 Evaluation -- 4.1 Datasets -- 4.2 Training and Test Setup -- 4.3 Ground Truth as BSP Trees -- 4.4 Semantic Segmentation -- 4.5 Prediction Confidence -- 5 Conclusion -- A Dataset Class Distributions -- B Hyperparameters and Model Details -- C Metrics -- D Additional Sample Images -- E Confidence -- References -- EVARS-GPR: EVent-Triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 EVARS-GPR -- 5 Experimental Setup -- 5.1 Simulated Data -- 5.2 Real-World Datasets -- 5.3 Evaluation -- 6 Experimental Results -- 6.1 Behavior on Simulated Data -- 6.2 Results on Real-World Datasets -- 6.3 Discussion -- 7 Conclusion -- Appendix A: Gaussian Process Regression -- Appendix B: List of Symbols -- Appendix C: Online Change Point Detection -- Appendix D: Data Augmentation -- Appendix E: EVARS-GPR Parameters -- Appendix F: Real-World Datasets -- Appendix G: Further Simulated Scenarios -- References -- Selective Pseudo-Label Clustering -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Formal Description -- 3.2 Implementation Details -- 4 Proof of Correctness -- 4.1 Agreed Pseudo-Labels Are More Accurate -- 4.2 Increased Pseudo-Label Accuracy Improves Clustering ; -- 5 Experimental Results -- 5.1 Ablation Studies -- 5.2 Ensemble Size -- 6 Conclusion -- A Appendix A: Full Proofs -- A.1 More Accurate Pseudo-Labels Supplement -- A.2 Lemma 1 Supplement -- A.3 Lemma 2 Supplement -- A.4 Lemma 3 Supplement -- A.5 Theorem 5 Supplement -- B Appendix C: Extended Results -- References -- Crop It, but Not Too Much: The Effects of Masking on the Classification of Melanoma Images -- 1 Introduction -- 2 Related Work.; 3 Method Overview -- 4 Experiments -- 5 Visual Inspection -- 6 Discussion -- 7 Conclusions -- A MedNode Results -- B ISIC2016 Results -- References -- A Demonstrator for Interactive Image Clustering and Fine-Tuning Neural Networks in Virtual Reality -- 1 Introduction -- 2 Architecture -- 3 Visualization in VR -- 3.1 PCA/t-SNE Approach -- 3.2 VAE Approach -- 4 Interactive Fine-Tuning -- 5 Conclusion and Future Work -- References -- HUI-Audio-Corpus-German: A High Quality TTS Dataset -- 1 Introduction -- 2 Related Work -- 3 Data Processing Pipeline -- 3.1 Acquisition of Suitable Audio Data -- 3.2 Splitting of Audio Data -- 3.3 Audio Normalization -- 3.4 Transcription of Audio Data for Subsequent Alignment -- 3.5 Acquisition of Text for Audio Data -- 3.6 Text Normalization -- 3.7 Transcript Alignment -- 4 Dataset Summary -- 4.1 Full Dataset -- 4.2 Clean Dataset -- 4.3 Discussion -- 4.4 Evaluation with Tacotron 2 -- 5 Conclusion and Outlook -- References -- Negation in Cognitive Reasoning -- 1 Introduction -- 2 Background and Related Works -- 2.1 Negation in Logic and Natural Language -- 2.2 Commonsense Reasoning and Negation -- 3 Methods -- 3.1 A System for Cognitive Reasoning -- 3.2 Negation Scope and the Negatus - Why Size Matters -- 3.3 Approach to Negation Treatment for Cognitive Reasoning -- 4 Experiments -- 4.1 Data Preparation and Evaluation -- 5 Summary, Conclusions, and Future Work -- References -- Learning to Detect Adversarial Examples Based on Class Scores -- 1 Introduction -- 1.1 Related Work -- 1.2 Contributions -- 2 Detecting Adversarial Examples from Class Scores -- 3 Experimental Setup -- 4 Results -- 5 Conclusion -- References -- An Agent Architecture for Knowledge Discovery and Evolution -- 1 Introduction -- 2 Background and Related Work -- 2.1 The BDI Architecture -- 2.2 Integrating AI into BDI Agents.; 2.3 KDE Systems and Approaches -- 3 Design of the KDE Agent Architecture -- 4 The KDE Agent Architecture -- 4.1 The Exogenous Modules -- 4.2 The Endogenous Modules -- 5 Use Case - Domestic Electricity Consumption -- 5.1 Cluster Analysis Service -- 5.2 Perception -- 5.3 Deliberation to Generate Explanations -- 6 Discussion and Conclusion -- References -- Demystifying Artificial Intelligence for End-Users: Findings from a Participatory Machine Learning Show -- 1 Introduction -- 2 Related Work -- 2.1 Virtual Agents in Education and Edutainment -- 2.2 Explainable AI -- 2.3 Trust in Technical Systems -- 3 Field Study -- 3.1 Demonstrator Setup -- 3.2 Study Procedure -- 3.3 Evaluation Method -- 4 Results -- 4.1 Information About Participants -- 4.2 Results of the ML-show -- 4.3 Comparison Between Participating and Non-participating Museum Visitors -- 5 Discussion -- 5.1 Take Users' Attitudes and Experiences into Account -- 5.2 Think About Who You Want to Reach with XAI Edutainment -- 5.3 Trust and Distrust Are Important Components in XAI Interaction Design -- 6 Conclusion -- References -- Recent Advances in Counting and Sampling Markov Equivalent DAGs -- 1 Introduction -- 2 Main Results -- References -- An Approach to Reduce the Number of Conditional Independence Tests in the PC Algorithm -- 1 Introduction -- 2 Preliminaries -- 3 Detection of V-Structures in Advance -- 4 The ED-PC Algorithm -- 5 Proof of Correctness -- 6 Experimental Analysis -- 7 Conclusions and Outlook ; -- References -- Poster Papers -- Unsupervised Anomaly Detection for Financial Auditing with Model-Agnostic Explanations -- 1 Introduction -- 2 Related Work -- 3 Explainable Anomaly Detection in the Context of Auditing -- 3.1 Data -- 3.2 Feature Engineering -- 3.3 Ensemble-Based Architecture -- 3.4 Model-Agnostic and Receiver-Dependent Explanations -- 4 Conclusion and Future Work.; A Anomaly Detection Ensemble.