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

1 Introduction 2 AsySVRG 3 SCOPE 4 Conclusion

I. Program Analysis II. Data Flow Analysis ✓ Available Expressions ✓ Liveness Analysis ✓ Reaching Definitions ✓ Very Busy Expressions III.Theory Behind IV. Sensitivity V. Summary

• Background • Software Security: Control-flow Hijack Attack ➢ Memory Layout , Stack frame, & Procedure ➢ Buffer Overflow: Vulnerability, & Defenses ➢ RILC, Return-Oriented Programming ➢ ASLR & CFI • Software Security: Non-control Data Attack ➢ Data Oriented Programming • Summary

◼ Introduction of Spatial data ◼ Spatial Query Language ◼ Spatial Index Structures ◼ Spatial Query Process and Optimization

Reduced-Error Pruning Split data into training and validation set Do until further pruning is harmful: 1. Evaluate impact on validation set of pruning each possible node (plus those below it) 2. Greedily remove the one that most improves validation set accuracy produces smallest version of most accurate subtree · What if data is limited?

• Study increasingly sophisticated parallel merge kernels • Observe the combined effects of data - dependent execution and a lack of data parallelism on GPU algorithm design