Definitions of Modeling and simulation physics-based modeling empirical modelIng Model/simulation Development Process module identification module ordering: DSM,'s and N2 diagrams module coding fidelity and benchmarking model execution simulation Computational Issues
Chapter 2: Entity-Relationship Model Basic Concepts Constraints Keys Design Issues E-R Diagram Weak Entity Sets Extended E-R Features Design of an E-R Database Schema Reduction of an E-R Schema to Tables
Lecture 1 神经元的整合发放(IF)模型 Lecture 2 The Hodgkin-Huxley Model of Action Potential Lecture 3 Two-dimensional neuron models Lecture 4 The HH model revisited and synaptic dynamics Lecture 5 Brief introduction of synaptic plasticity Lecture 6 Neural noise and neural codes Lecture 7 Functional roles of noise in taming neurodynamics Lecture 8 Network Models in Neuroscience
1.1 A Decision Tree Model and its Analysis 1.2 Another Decision Tree Model and it Analysis 1.3 The Need for a Systematic Theory of Probability 1.4 Exercises
• Models – HMM: Hidden Markov Model – maximum entropy Markov model – CRFs: Conditional Random Fields • Tasks – Chinese word segmentation – part-of-speech tagging – named entity recognition
Big Five Model Extroversion- sociable, assertive Agreeableness-good-natured, cooperative, trusting Conscientiousness- responsible, dependable, persistent Openness to experience-
1. 了解酸碱概念的变迁; 2. 理解布朗斯特酸碱理论的意义和要点; 3. 理解路易斯酸碱理论的意义和要点; 4. 了解软硬酸碱的内容和应用; 5. 了解几种有代表性的路易斯酸。 5.1 布朗斯特酸碱 The Brfnsted-Lowry acid-base model 5.2 路易斯酸碱 The Lewis acid-base model