'Initial guess' diaporamas de présentation

Phase Transitions in the Information Distortion

Phase Transitions in the Information Distortion. NIPS 2003 workshop on Information Theory and Learning: The Bottleneck and Distortion Approach December 13, 2003 Albert E. Parker. Department of Mathematical Sciences Center for Computational Biology Montana State University

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Maximizing Broadcast Coverage Using Range Control for Dense Wireless Networks Richard Martin, Xiaoyan Li, Thu Nguyen

Maximizing Broadcast Coverage Using Range Control for Dense Wireless Networks Richard Martin, Xiaoyan Li, Thu Nguyen Department of Computer Science Rutgers University May, 2003. Future Building Blocks . Small complete systems CPU, memory, stable storage, wireless network Low cost

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Introduction to Information Retrieval (Part 2)

Introduction to Information Retrieval (Part 2). By Evren Ermis. Introduction to Information Retrieval. Retrieval models Vector-space-model Probabilistic model Relevance feedback Evaluation Performance evaluation Retrieval Performance evaluation Reference Collections Evaluation measures.

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Bayesian inference

Bayesian inference. Lee Harrison York Neuroimaging Centre 01 / 05 / 2009. Overview. Probabilistic modeling and representation of uncertainty Introduction Curve fitting without Bayes Bayesian paradigm Hierarchical models Variational methods (EM, VB) SPM applications

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Longitudinal FreeSurfer

Longitudinal FreeSurfer. Martin Reuter mreuter@nmr.mgh.harvard.edu http:// reuter.mit.edu. What can we do with FreeSurfer ?. measure volume of cortical or subcortical structures compute thickness (locally) of the cortical sheet study differences of populations (diseased, control).

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Gauss-Siedel Method

Gauss-Siedel Method. Chemical Engineering Majors Authors: Autar Kaw http://numericalmethods.eng.usf.edu Transforming Numerical Methods Education for STEM Undergraduates. Gauss-Seidel Method http://numericalmethods.eng.usf.edu. Gauss-Seidel Method. An iterative method. Basic Procedure :

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Nick Gayeski, PhD Building Technology, MIT

Data-Driven Model Predictive Control OF Low-Lift Chillers Pre-Cooling Thermo-Active Building Systems. Nick Gayeski, PhD Building Technology, MIT IBPSA Model Predictive Control Workshop June 2011 Advisors: Dr. Leslie K. Norford, Dr. Peter R. Armstrong.

519 views • 33 slides

CN700: HST 10.6-10.13

CN700: HST 10.6-10.13. Neil Weisenfeld (notes were recycled and modified from Prof. Cohen and an unnamed student) April 12, 2005. Robust Loss Functions for Classification. Loss functions that lead to simple boosting solutions (squared error and exponential) are not always the most robust.

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Development of an incremental 4D-VAR system for ocean model downscaling

Development of an incremental 4D-VAR system for ocean model downscaling. Yoichi Ishikawa 1 , Toshiyuki Awaji 1,2 , Teiji In 3 , Satoshi Nakada 2 , Tsuyoshi Wakamatsu 1 , Yoshimasa Hiyoshi 1 , Yuji Sasaki 1 1 DrC, JAMSTEC 2 Kyoto University 3 Japan Marine Science Foundation .

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Interactive Motion Editing

Interactive Motion Editing. Presented by Troy McMahon. Interactive Motion Editing. Adapting existing motions to compensate for variations in characters and environments. . Motivation. Reusability: Interactive Motion Editing allows us to generate new motions using existing motion clips to

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Multiple Shooting:

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By Jiaji Pan and Jason Hung

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PDBeFold (SSM ) pdbe /fold

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A Parallel Implicit Harmonic Balance Solver for Forced Motion Transonic Flow

A Parallel Implicit Harmonic Balance Solver for Forced Motion Transonic Flow . Ken Badcock & Mark Woodgate Department of Engineering University of Liverpool Liverpool L69 3GH. WCCM8 ECCOMAS 2008 June 30 - July 4 2008 Venice Italy. Time domain VS Frequency domain solvers.

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Implementing the Probability Matrix Technique for Positron Emission Tomography

By: Chris Markson Student Adviser: Dr. Kaufman. Implementing the Probability Matrix Technique for Positron Emission Tomography. Introduction. PET vs. CAT PET ~ metabolic CAT ~ anatomical The Process Tagged chemical compound Positrons in compound meet electrons in the body~ annihilation

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Balancing a trip matrix

Balancing a trip matrix. Initial guess. Column sum. Row sum. Doubly constrained model. Desired Column sum Dj. Desired Row sum Oi. Balancing factor. 11/6=1.8333. Balancing factor. 11/6=1.8333. We need to multiply first row with 1.8333. Balancing factor.

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Solution of Nonlinear Functions

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Solving Equations

Solving Equations. One equation, one unknown Linear --- simple algebra Non-linear. Multiple equations, multiple unknowns Linear -- matrix methods Non-linear. Solving Equations. Finding parameters to equations Linear --- Least Squares Non-linear

365 views • 13 slides