'Minimum cut' diaporamas de présentation

Discrete Optimization Lecture 1

Discrete Optimization Lecture 1. M. Pawan Kumar pawan.kumar@ecp.fr. Background. Ph.D., Computing Oxford Brookes University, UK, 2003-2007 Postdoc, Department of Engineering University of Oxford, UK, 2008 Postdoc, Computer Science Department Stanford University, USA, 2009-2011

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Online Social Networks

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Fitting : Voting and the Hough Transform

Fitting : Voting and the Hough Transform. Thursday, September 19 th 2013 Devi Parikh Virginia Tech.

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UNDERWATER ACUSTIC SENSOR NETWORKS (UW-ASNs)

UNDERWATER ACUSTIC SENSOR NETWORKS (UW-ASNs). Daladier Jabba Molinares Department of Computer Science and Engineering University of South Florida Tampa, FL 33620 daladier@cse.usf.edu. UNDERWATER ACUSTIC SENSOR NETWORKS (UW-ASNs). Introduction Communication architecture

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HCS Clustering Algorithm

HCS Clustering Algorithm. A Clustering Algorithm Based on Graph Connectivity. Presentation Outline. The Problem HCS Algorithm Overview Main Players General Algorithm Properties Improvements Conclusion. The Problem. Clustering:

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Maximum Flows

Maximum Flows. CONTENTS Introduction to Maximum Flows (Section 6.1) Introduction to Minimum Cuts (Section 6.1) Applications of Maximum Flows (Section 6.2) Flows and Cuts (Section 6.3) Generic Augmenting Path Algorithm (Section 6.4) Max-Flow Min-Cut Theorem (Section 6.5)

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Introduction to Markov Random Fields and Graph Cuts

Introduction to Markov Random Fields and Graph Cuts. Simon Prince s.prince@cs.ucl.ac.uk. Plan of Talk. Denoising problem Markov random fields (MRFs) Max-flow / min-cut Binary MRFs (exact solution). Binary Denoising. Before. After.

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Review of: Detecting Network Intrusions via Sampling: A Game Theoretic Approach

CS7701: Research Seminar on Networking http://arl.wustl.edu/~jst/cse/770/. Review of: Detecting Network Intrusions via Sampling: A Game Theoretic Approach. Paper by: Murali Kodialam (Bell Labs) T.V. Lakshman (Bell Labs) Published in: IEEE Infocom 2003 Reviewed by: James Moscola

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Lecture 5: Project Planning 2

Lecture 5: Project Planning 2. Outline. Time/Cost Tradeoffs Linear and non-linear Adding Workforce Constraints Slides borrowed from Twente & Iowa See Pinedo CD. Time/Cost Trade-Offs. What if you could spend money to reduce the job duration More money ? shorter processing time

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CS216: Program and Data Representation University of Virginia Computer Science Spring 2006 David Evans

CS216: Program and Data Representation University of Virginia Computer Science Spring 2006 David Evans. Lecture 26. http://www.cs.virginia.edu/cs216. Bad News/Good News. Bad News 54 people listed review as their most-preferred class, but only 1 person sent in any review questions!

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Health and Safety Training Standards in Design and Technology

Health and Safety Training Standards in Design and Technology . S8HS PLANER/THICKNESSER MACHINE. Specialist Extension Level Standards:. Specialist Extension Level S8HS: PLANER/THICKNESSER MACHINES.

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Approximate computation and implicit regularization in large-scale data analysis

Approximate computation and implicit regularization in large-scale data analysis. Michael W. Mahoney Stanford University Sept 2011 (For more info, see: http://cs.stanford.edu/people/mmahoney). Motivating observation . Theory of NP-completeness is a very useful theory

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Approximate computation and implicit regularization in large-scale data analysis

Approximate computation and implicit regularization in large-scale data analysis. Michael W. Mahoney Stanford University Sept 2011 (For more info, see: http://cs.stanford.edu/people/mmahoney). Motivating observation . Theory of NP-completeness is a very useful theory

349 views • 19 slides

Can Network Coding Help in P2P Networks?

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Static Process Scheduling

Static Process Scheduling. Martha Garcia. Static Process scheduling. Goals of Static Process Scheduling Types of Static Process Scheduling Future Research References. Static Process scheduling. GOALS :

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Network Flow Multiple Sources and Sinks

Network Flow Multiple Sources and Sinks. Sometimes a network will have multiple sources and multiple sinks. We deal with this situation by introducing a supersource and a supersink. 8. 12. A. D. T 1. 9. 10. S 1. 3. 18. 30. 16. T 2. E. B. 15. S 2. 8. 10. 3. 17. T 3. 12.

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CS 585 Computational Photography

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Consumer Decision Processes

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Maximum Flow and Minimum Cut Problems

Maximum Flow and Minimum Cut Problems. In this handout: Duality theory Upper bounds for maximum flow value Minimum Cut Problem Relationship between Maximum Flow and Minimum Cut . Primal and Dual Problems in Optimization. For many optimization problems, there is a dual problem

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Applications of Maximum Flow and Minimum Cut Problems

Applications of Maximum Flow and Minimum Cut Problems. In this handout Transshipment problem Assignment Problem. 4. 5. D 1. B. S 1. 5. 2. 2. 5. A. 2. 15. 5. 4. 19. 9. C. S 2. D 2. Transshipment Problem. Given : Directed network G=(V, E)

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