'Source model' diaporamas de présentation

CSE584: Software Engineering Lecture 7: Evolution (B)

CSE584: Software Engineering Lecture 7: Evolution (B). David Notkin Computer Science & Engineering University of Washington http://www.cs.washington.edu/education/courses/584/. Outline. Reverse engineering Visualization Software summarization Miscellaneous visualization, etc.

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Recipe of strong motion prediction

Seminar at Charles University, Prague, Czech September 5, 2003. Recipe of strong motion prediction. for future earthquakes. Kojiro Irikura Disaster Prevention Research Institute Kyoto University, Japan.

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Prof. David R. Jackson ECE Dept.

ECE 6341. Spring 2014. Prof. David R. Jackson ECE Dept. Notes 38. Spectral Domain Immittance (SDI) Method. SDI Method (cont.). k s. The finite current sheet is thus represented as a set of infinite phased current sheets :. where.

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The Porosity of Additive Noise Sequences

Vinith Misra, Tsachy Weissman { vinith , tsachy }@ stanford.edu. The Porosity of Additive Noise Sequences. ISIT, 7/5/ 2012. Outline. Universality and porosity Related work Main results Converse ideas Predictability and practicality. (Lossless) Source Coding. Known source model

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Model Transformation By Demonstration

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A Pseudo-Dynamic Rupture Model Generator for Earthquakes on Geometrically Complex Faults

A Pseudo-Dynamic Rupture Model Generator for Earthquakes on Geometrically Complex Faults. Daniel Trugman , July 2013. 2D Rough-Fault Dynamic Simulations. Homogenous background stress + complex fault geometry ? heterogeneity in tractions

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CellML in practice: modularity & reuse; versioning & provenance; embedded workspaces

CellML in practice: modularity & reuse; versioning & provenance; embedded workspaces. David Nickerson Auckland Bioengineering Institute University of Auckland New Zealand. A useful reference. http://abibook.readthedocs.org/en/EMBC-2013-tutorial/tutorials/embc13/ index.html.

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SAFETY AND LOSS PREVENTION ERT 322

SAFETY AND LOSS PREVENTION ERT 322. SOURCE MODEL. Prepared by; Miss Syazwani Mahmad Puzi School of Bioprocess Engineering. INTRODUCTION. Most accidents in chemical plants result in spills of toxic, flammable and explosive materials.

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What is CTSenTest ?

What is CTSenTest ?. "Crowd-source model" based virtual testing platform embracing the Future of work.

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Contents

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Likelihood analysis general tutorial (including source specifications)

Gamma-ray Large Area Space Telescope. Likelihood analysis general tutorial (including source specifications). G. Tosti INFN - Perugia. REFERENCES. DC2 code releases : https://confluence.slac.stanford.edu/display/DC2/DC2+code+releases GLAST Data Access:

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Multi-scale Simulation of tsunami from Tohoku Earthquake and Diffracted Tsunami Waves in Hokkaido

Multi-scale Simulation of tsunami from Tohoku Earthquake and Diffracted Tsunami Waves in Hokkaido. ZHU AIYU, Zhang Dongning Institute of Geophysics, China Earthquake Administration Yuen Dave A. University of Minnisota Song Shenyi Computer Network Information Center, China Academy of Science.

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Professor Martin Williams

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A Synthesis-based Approach to Transformations in an MDA Software Development Process

A Synthesis-based Approach to Transformations in an MDA Software Development Process Ivan Kurtev, Klaas van den Berg University of Twente. Outline. Alternative Transformations in MDA; Synthesis-based Software Development Applied to MDA Transformations;

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Lecture 13

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Gamma-ray Large Area Space Telescope

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Generative and Discriminative Models in Text Classification

Generative and Discriminative Models in Text Classification. David D. Lewis Independent Consultant Chicago, IL, USA Dave@DavidDLewis.com www.DavidDLewis.com Workshop on Challenges in Information Retrieval and Language Modeling, U Mass, CIIR, Amherst, MA, 11 Sept 2002 . Text Classification.

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Embedded workspaces

Embedded workspaces. David Nickerson 7 th International CellML Workshop 25 March 2013 Waiheke Island, Auckland, New Zealand. Background. PMR Workspaces Independent mercurial repositories. Detailed provenance record. Contains many types of data. CellML 1.1 model hierarchies

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Combining Decision Procedures

Combining Decision Procedures. Individual DPs Software Engineering Aspects. Sergey Berezin Stanford University. 2*x+3*y<17. Integer. Real. x-y<17. Special Fragments. Linear Arithmetic. x@y[7:5] & 10010 = ~z. car(cons(x,y))=x. Recursive Datatypes. Bitvectors. x*y+z>=17.

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