1 Introduction Problem Definition Existing Methods 2 Isotropic Hashing 3 Supervised Hashing with Latent Factor Model 4 Supervised Multimodal Hashing with SCM 5 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 6 Conclusion 7 Reference

1 Introduction 2 Learning to Hash Isotropic Hashing Supervised Hashing with Latent Factor Models Supervised Multimodal Hashing with SCM Multiple-Bit Quantization 3 Distributed Learning Coupled Group Lasso for Web-Scale CTR Prediction Distributed Power-Law Graph Computing 4 Stochastic Learning Distributed Stochastic ADMM

1 Introduction Problem Definition Existing Methods 2 Isotropic Hashing Model Learning Experiment 3 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 4 Conclusion 5 Reference

1 Introduction Problem Definition Existing Methods Motivation and Contribution 2 Isotropic Hashing Model Learning Experimental Results 3 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 4 Conclusion 5 Reference

The halting problem is undecidable Decidability

◼ knowledge of a basic formalism for modeling timed systems ◼ basic understanding of verification algorithms for timed systems (useful for practical modeling and verification)

南京大学：《形式语言与自动机 Formal Languages and Automata》课程教学资源（PPT课件讲稿）Petri Net

南京大学：《形式语言与自动机 Formal Languages and Automata》课程教学资源（PPT课件讲稿）Transition System

Turing Machines Recursive and Recursively Enumerable Languages

Statement Applications