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11 J H Laning and R H Battin, Random Processes in Automatic Control, McGraw-Hill, New York, 1956 12 G S Fishman, Monte Carlo Simulation, Springer, New York, 1995 13 J M Hammersley and D C Handscomb, Advanced Monte Carlo Methods, Methuen, London, 1964 14 H D Mittelman and P Spelucci, Decision Tree for Optimization Software, http://platoasuedu/guidehtml, 2005 15 J J More and S J Wright, Optimization Software, SIAM Publications, 1993



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And then you d just add the images in the proper way using CSS. The link would get the left-hand side s rounded corners fixed to the left, and then it would fill out with the color of choice, and then you d apply the right-hand side corners to the span but fixed to the right. What you get in effect is a button that can have any width you want, since the actual link background is a filler after the lefthand side s corners are displayed. Or, to make it even clearer, here s some dummy CSS that you d want to alter before usage, but it still explains how it works.





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will need to update the redundant data blocks according to the newly reorganized data placements. In this section we develop a Sequential Redundant Data Update (SRDU) algorithm that exploits structure of the Reed-Solomon erasure correction (RSE) code [4 6] to substantially reduce the number of block movements required in redundant data update.

NEURAL-NETWORK SIMULATION Neural-network simulation began as an attempt to reverse-engineer adaptive biological systems, with neuron pulse rates represented as numerical variables Connection-parameter adjustments imitate biological learning Hardware models using hundreds of small processors can implement artificial sense organs, but most artificial neural networks (and all we will discuss here) are abstract computer models, in effect large multi-input function generators that compute statistics for regression, pattern recognition, and control systems1 This chapter is not a treatise on neural-network theory or a roadmap for neural-network development Our goal here is to demonstrate the use of DESIRE vector operations for programming compact and efficient neural-network models 6-1 Neural-network Models and Pattern Vectors Figure 6-1a represents a model neural-network layer with nx input activations x[1], x[2], , x[nx] and nv output activations v[1], v[2], , v[nv] A simple neuron-layer model implements

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Figure 15.11 depicts the placement of data and redundant blocks in a system of 6 nodes. There are two redundant data blocks per parity group, denoted by ci,0 and ci,1 for parity group i. This enables the system to survive two node failures. Note that while the round-robin data placement order is shown in Figure 15.11, it is not a requirement. The exact ordering of the data blocks within a parity group is irrelevant to redundant data block generation. Thus, the RPDR algorithm is also compatible with redundant data generation. After adding a new node to the system and reorganizing the data blocks, the new data block placements become the one shown in Figure 15.12. Again the order of data block placements is arbitrary and irrelevant to the process of redundant data generation. Under this new con guration the original redundant data {ci, j | i, j 0, 1, . . .} will need to be updated. In general, for a system of N nodes and h redundancies, we will need all (N h) data blocks in a parity group to compute the corresponding h redundant blocks. As data and redundant blocks of the same parity group are all stored individually in separate nodes, the system will need to send all data blocks to the redundant nodes to generate the new redundant blocks. This simple algorithm is clearly very inef cient.

ul#navigation li { oat:left; padding: 5px; list-style:none; } ul#navigation a:link, ul#navigation a:visited { display:block; } ul#navigation a:hover, ul#navigation a:active { background: #888 url(corners-left.gif) no-repeat left; oat:left; }

r0 c 0,0 c 1,0 c 2,0 c 3,0 c 4,0

W[i, k]x[k]

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r0 c 0,0 c 1,0 c 2,0 c 3,0

(i=1, 2, , n)

On the other hand, if a central archive server storing a copy of all media data is available, then it can simply regenerate the new redundant data blocks locally and send them to the redundant nodes to replace the obsolete redundant data blocks. In this case, the number of block movements required will be equal to (B/(N h)). Nevertheless maintaining this central archive server will incur additional costs and complicate management of the system. Depending on the particular application this approach may not be cost-effective.

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