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Speci cally, in a N -node system the rst N media data blocks {v0, j | j = 0, 1, . . . , N 1} will be distributed to all N nodes in pseudo-random order, with each node storing exactly one of the N data blocks as shown in Figure 15.2. This process repeats for the next N data blocks {v1, j | j = 0, 1, . . . , N 1}, and so on until all data blocks are distributed. As long as the client receives a whole parity group before decoding it for playback, this row-permutated placement policy can achieve perfect streaming load balance, same as the original round-robin placement policy.

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Naturally, you need some conditions as well. This kind of usage usually covers it when it comes to categories, since most well planned WordPress theme designs are built around using as few categories as possible. Tags, however, are different so you may want to check for several tags at the same time using an array:

After an initial transient, the sample mean square error eeAvg exhibits relatively small fluctuations about a fixed expected value eeAvg is a useful statistical control-system performance measure One can investigate effects of different servo-parameter combinations and also modify the input-noise amplitude and bandwidth by changing a and w

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To determine which data blocks will need to be moved after adding a new node, we rst re-index all the media data blocks according to the new con guration. Figure 15.3 shows an example of reorganizing from a 4-node system to a 5-node system. For example, media blocks v1,0 and v1,1 will be re-indexed to v0,4 and v1,0 respectively in the 5-node con guration. If we consider the rst group of media blocks in the new con guration, we can see that node n 1 now needs to send two media blocks v0,1 and v0,4 while the new node is not used. This is the reason why load imbalance will occur if the data blocks are not reorganized.

That will return true whenever the tags pirates, ninjas, or mushrooms are used. The final way to put tagging to good use is utilizing the fact that post_class() returns every tag as a class to post div. That means that you can have a lot of potential classes for more in-depth styling. This, for example, is a post with the ID 129, which you can tell from the id="post-129", as well as the fact that it even has the class post-129:

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Original block indices (4 nodes)

-- VECTORIZED MONTE CARLO STUDY OF A NOISE-INPUT TEST -- note noise sampling and initialization ---------------------------------------------------------------------------------------------------a=4 | w=1 k = 40 | r = 2 | g1 = 10000 | -controller parameters B = 300 | maxtrq = 1 | g2 = 2 | R = 06 | -servo parameters ---------------------------------------------------------------------------------------------------TMAX = 10 | DT = 0001 | NN = 5000 | scale = 1 display N1 | display C8 | display R | -display colors -------n = 1000 STATE p[n], unoise[n], x[n], xdot[n], v[n] ARRAY noise[n], voltage[n], torque[n], e[n] -initialize noise for i = 1 to n | noise[i] = a * (ran()+ran()+ran()+ran()) | next -drun write "eAvg = ";eAvg;" eeAvg = ";eeAvg ---------------------------------------------------------------------------------------------------DYNAMIC ---------------------------------------------------------------------------------------------------Vectr d/dt p = - w * p + noise | -two-section Vectr d/dt unoise = - w * unoise + p | -low-pass filter -Vector e = x - unoise | -servo error Vector voltage = - k * e - r * xdot | -motor voltage Vectr d/dt v = - B * v + g1 * voltage | -motor-field buildup -Vector torque = maxtrq * tanh(g2 * v/maxtrq) | -dynamics Vectr d/dt x = xdot | Vectr d/dt xdot = torque-R * xdot ------------------------------------------------------------------sample at sampling points OUT Vector noise = a * (ran()+ran()+ran() +ran()) | -sampled noise -DOT eSum = e * 1 | DOT eeSum = e * e | -compute averages eAvg = eSum/n | eeAvg = eeSum/n ----------------------------------------------------------------------------------------------------offset curves for a rescaled stripchart display -X = x[17] + 05 * scale | Unoise = unoise[17] + 05 * scale error5 = 5 * e[17] eAvg100 = 100 * eAvg - 05 * scale eeAvg500 = 500 * eeAvg - scale dispt X, Unoise, error5, eAvg100, eeAvg500

New block indices (5 nodes)

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