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Intro to Software Engineering

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ECOM 6330 Java Parallel and Distributed computing

______________________ Hatem M. Hamad Computer Engineering Department Islamic University of Gaza _____________________________________________________________. ECOM 6330 Java Parallel and Distributed computing . Introduction.

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ECE1747 Parallel Programming

ECE1747 Parallel Programming. Shared Memory Multithreading Pthreads. Shared Memory. All threads access the same shared memory data space. Shared Memory Address Space. proc1. proc2. proc3. procN. Shared Memory (continued).

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17. Assertions

17. Assertions. Jelle Slowack , Bart Smets, Glenn Van Loon, Tom Verheyen. Outline. Introduction (BIT, a ssertion, executable assertion, why?) Implementation-based vs responsability-based assertions Implementation Percolation pattern Deployment Limitations & Caveats Some Tools.

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ECE1747 Parallel Programming

ECE1747 Parallel Programming. Shared Memory Multithreading Pthreads. Shared Memory. All threads access the same shared memory data space. Shared Memory Address Space. proc1. proc2. proc3. procN. Shared Memory (continued).

1.15k views • 64 slides

Expectation Maximization Algorithm

Expectation Maximization Algorithm. Rong Jin. A Mixture Model Problem. Apparently, the dataset consists of two modes How can we automatically identify the two modes?. Gaussian Mixture Model (GMM). Assume that the dataset is generated by two mixed Gaussian distributions Gaussian model 1:

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Control Dependences

Control Dependences. Chapter 7. Control Dependences. Roadmap If-conversion Control dependence. Control Dependences. Constraints posed by control flow DO 100 I = 1, N S 1 IF (A(I-1).GT. 0.0) GO TO 100 S 2 A(I) = A(I) + B(I)*C 100 CONTINUE If we vectorize by...

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Cost-effective Outbreak Detection in Networks

Cost-effective Outbreak Detection in Networks. Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen, Natalie Glance. Scenario 1: Water network. Given a real city water distribution network And data on how contaminants spread in the network

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Informed Search and Exploration

Informed Search and Exploration. Search Strategies Heuristic Functions Local Search Algorithms. Vilalta&Eick: Informed Search. Introduction. Informed search strategies: Use problem specific knowledge Can find solutions more efficiently than

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Boosting and Additive Trees (Part 1)

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Ancestry OCR Project: Extractors

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The University of Jordan Mechatronics Engineering Department Dr. Osama M. Al-Habahbeh 2012

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Human Action Recognition Week 9

Taylor Rassmann. Human Action Recognition Week 9. Hierarchical SVMs. Look at a confusion matrix of the UCF50 dataset Dollar Features Find the two most confused classes Train an SVM specifically on these two. Hierarchical SVMs.

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BOOSTED MMI FOR MODEL AND FEATURE-SPACE DISCRIMINATIVE TRAINING

BOOSTED MMI FOR MODEL AND FEATURE-SPACE DISCRIMINATIVE TRAINING. Daniel Povey, Dimitri Kanevsky, Brian Kingsbury, Bhuvana Ramabhadran, George Saon and Karthik Visweswariah IBM T.J. Watson Research Center Yorktown Heights, NY, USA Presented by Yueng-Tien ,Lo. Outline. MMI review BMMI

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Racquetball Launcher Project

Racquetball Launcher Project. [Names]. Initial Launcher Design Drawing. [Insert your first design drawing here.]. Launcher Version 1.0. [Insert launcher photo(s)]. Aiming/firing protocol:. [Insert description of aiming/firing protocol].

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Human Action Recognition Week 10

Taylor Rassmann. Human Action Recognition Week 10. Overview Human Action Recognition. UCF50 dataset Begin with optical flow Lucas- Kanade with pyramids. Overview Human Action Recognition: Features. Kinematic Features Divergence Vorticity Symmetric Flow Fields Asymmetric Flow Fields.

311 views • 10 slides