'Acyclic graphs' diaporamas de présentation

Part II: How to make a Bayesian model

1.12k views • 80 slides

Typestate Verification: Abstraction Techniques and Complexity Results

Typestate Verification: Abstraction Techniques and Complexity Results. J. Field, D. Goyal, G. Ramalingam IBM T.J. Watson Research Center. E. Yahav School of Computer Science Tel-Aviv University. Motivation. Component a library with cleanly encapsulated state. Client

606 views • 52 slides

ParaTimer : A progress Indicator for MapReduce DAGs

ParaTimer : A progress Indicator for MapReduce DAGs. Kristi Morton, Magdalena Balazinska, and Dan Grossman Computer Science and Engineering Department, University of Washington. Advisor Martin Theobald. Isha Khosla Masters in Informatics. Overview. Parallel Database Management Systems.

753 views • 46 slides

Break-points detection with atheoretical regression trees

Marco Reale University of Canterbury Universidade Federal do Parana, 27 th November 2006. Break-points detection with atheoretical regression trees. Acknowledgements. The results presented are the outcome of joint work with: Carmela Cappelli and William Rea. Structural Breaks.

459 views • 39 slides

Group Communications in Mobile Ad hoc Networks

Group Communications in Mobile Ad hoc Networks. Jian Li http://networks.cs.ucdavis.edu/~lijian/slides/ecs257/. References. P. Mohapatra, C. Gui, and J. Li. Group Communications in Mobile Ad hoc Networks. IEEE Computer Magazine, Feb. 2004, pp. 52-59. Agenda. Introduction Group Comm. Models

569 views • 36 slides

Bayesian Networks and Causal Modelling

Ann Nicholson. Bayesian Networks and Causal Modelling. School of Computer Science and Software Engineering Monash University. Overview. Introduction to Bayesian Networks (BNs) Summary of BN research projects Varieties of Causal intervention

658 views • 34 slides

The Synergy of Computer Science and Mathematics

The Synergy of Computer Science and Mathematics. Dr. Raymond Greenlaw Armstrong Atlantic State University School of Computing. Outline. Introduction Influence of Math on Computer Science Influence of Computer Science on Math Computational Mathematics Conclusions References. Introduction.

494 views • 31 slides

Can PRAM Graph Algorithms Provide Practical Speedups on Many-Core Machines?

Can PRAM Graph Algorithms Provide Practical Speedups on Many-Core Machines?. George Caragea, and Uzi Vishkin University of Maryland. Speaker James Edwards. Introduction.

417 views • 27 slides

Gene Clustering

691 views • 26 slides

Ontology-Guided Search and Text Mining for Intelligence Gathering Kurt Godden, Ph.D. MSR Lab, R&D kurt.godden@gm.c

283 views • 25 slides

479 views • 24 slides

Generalized Tree Alignment: The Deferred Path Heuristic

Generalized Tree Alignment: The Deferred Path Heuristic. Stinus Lindgreen stinus@diku.dk. Overview: What is a phylogeny? The Generalized Tree Alignment problem Sequence Graphs and their algorithms The Deferred Path Heuristic. Phylogeny: Describes evolutionary model Common ancestor

339 views • 24 slides

477 views • 23 slides

Grouping Heuristics for Word-Level Decision Diagrams

Grouping Heuristics for Word-Level Decision Diagrams. Rolf Drechsler Marc Herbstritt Bernd Becker Institute of Computer Science University of Freiburg 79110 Freiburg, Germany. Outline. Introduction Word-Level Decision Diagrams (WLDDs) Variable Grouping Topology-based Heuristic

370 views • 23 slides

307 views • 17 slides

TESLA-Based Defense Against Pollution Attacks in P2P Systems with Network Coding

TESLA-Based Defense Against Pollution Attacks in P2P Systems with Network Coding. Anh Le, Athina Markopoulou University of California, Irvine. Pollution Attacks in P2P Systems with NC. b 1 + 2b 2. b 2. b 2. b 1. b 1. b 3. b 3. b 3. S. A. B. b' 1. 2b' 1 + b 3. C.

345 views • 16 slides

Intro to Computer Algorithms Lecture 2

Intro to Computer Algorithms Lecture 2. Phillip G. Bradford Computer Science University of Alabama. Announcements. ACM initial meeting of the year . Thursday, August 28th, 5:00pm in East Eng. 119 Industrial Colloquium Series Tuesday, September 2nd, 5:00pm in EE 119

138 views • 13 slides

Interval Partitioning of a Flow Graph

Interval Partitioning of a Flow Graph. Input: a flow graph G = (N, E, n 0 ). Output: a partition of G into a set of disjoint intervals. Method: for each n, compute I(n). I(n) := {n}; while there exists a node m ? n 0 all of whose predecessors are in I(n) do I(n) := I(n) ? {m}

372 views • 11 slides