'Error bound' presentation slideshows

Scalable many-light methods

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Statistical Inference

Statistical Inference. Making decisions regarding the population base on a sample. Decision Types. Estimation. Deciding on the value of an unknown parameter. Hypothesis Testing. Deciding a statement regarding an unknown parameter is true of false.

1.26k views • 112 slides

Unifying Topology and Numerical Accuracy in Geometric Modeling Surface to Surface Intersections

Unifying Topology and Numerical Accuracy in Geometric Modeling Surface to Surface Intersections. K. H. Ko School of Mechatronics Gwangju Institute of Science and Technology. Motivations. Digital Laboratory, digital manufacturing, computer simulation, etc.

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SECTION 6.5

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PEG Breakout

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PEG Breakout

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On Computing Multiple Eigenvalues , the Jordan Structure and their Sensitivity

On Computing Multiple Eigenvalues , the Jordan Structure and their Sensitivity. Zhonggang Zeng Northeastern Illinois University. Sixth International Workshop on Accurate Solution of Eigenvalue Problems (IWASEP VI). May 23, 2006, Penn State Univ. l l l l. + + +

398 views • 28 slides

On Computing Multiple Eigenvalues , the Jordan Structure and their Sensitivity

On Computing Multiple Eigenvalues , the Jordan Structure and their Sensitivity. Zhonggang Zeng Northeastern Illinois University. Sixth International Workshop on Accurate Solution of Eigenvalue Problems (IWASEP VI). May 23, 2006, Penn State Univ. l l l l. + + +

397 views • 28 slides

RACE: Time Series Compression with Rate Adaptivity and Error Bound for Sensor Networks

RACE: Time Series Compression with Rate Adaptivity and Error Bound for Sensor Networks. Huamin Chen, Jian Li, and Prasant Mohapatra Presenter: Jian Li. Agenda. Motivation Background RACE Algorithm Numerical Evaluation Conclusion. Motivation. Sensor Networks Limited energy source

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Mining Discrete Patterns via Binary Matrix Factorization

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Contour Map Matching for Event Detection in Sensor Networks

Contour Map Matching for Event Detection in Sensor Networks. Wenwei Xue Joint Work with Qiong Luo, Lei Chen and Yunhao Liu Department of Computer Science and Engineering Hong Kong University of Science and Technology. Surveillance Applications of WSNs.

359 views • 20 slides

Rajagopal Iyengar Rensselaer Polytechnic Institute, ECSE Dept., Networks Lab iyengr@rpi

On the Packet Header Size and Network State Tradeoff for Trajectory-Based Routing in Wireless Networks. Rajagopal Iyengar Rensselaer Polytechnic Institute, ECSE Dept., Networks Lab iyengr@rpi.edu http://networks.ecse.rpi.edu/~iyeng. Murat Yuksel University of Nevada-Reno, CSE Department

311 views • 19 slides

Performance of Convolutional Coded Dual Header Pulse Interval Modulation in Infrared Links

Performance of Convolutional Coded Dual Header Pulse Interval Modulation in Infrared Links. S. Rajbhandari, Z. Ghassemlooy, and N. M. Aldibbiat Optical Communications Research Group, School of Computing, Engineering and Information Sciences, The University of Northumbria, Newcastle, U.K.

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Exercise 7.1 a. Find and report the four seasonal factors for quarter 1, 2, 3 and 4

Exercise 7.1 a. Find and report the four seasonal factors for quarter 1, 2, 3 and 4 sn 1 = 1.191, sn 2 = 1.521, sn 3 = 0.804, sn 4 = 0.484 b. What is the equation of the estimated trend that has been calculated using the deaseasonalized data? tr t = 220.53893 + 19.949897 ? t

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????????? - IMPROVED PRECISION CONSTRAINED DATA AGGREGATION METHOD IN WIRELESS SENSOR NETWORK

????????? - IMPROVED PRECISION CONSTRAINED DATA AGGREGATION METHOD IN WIRELESS SENSOR NETWORK. Advisor : Prof. Chin-Liang Wang Presented by Cheng-Hsun Hsieh 2007/06/04. Precision Constrained Data Aggregation. Data Aggregation Model We consider one commonly used type of aggregation

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? - Information Inducing Action Action(a) is ? - information inducing if:

Identifying and Exploiting Weak Information Inducing Actions In Solving POMDPs Ekhlas Sonu Grad Student Computer Science Department The University of Georgia sonu@cs.uga.edu. Results. Introduction

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ABSTRACT:

Sparse Q-learning with Mirror Descent Sridhar Mahadevan and Bo Liu , University of Massachusetts Amherst Autonomous Learning Laboratory , {mahadeva, boliu}@cs.umass.edu. Convergence Comparison with LARS-TD:. ABSTRACT:

125 views • 1 slides