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5.14b plot the overlap between weekdays August 15 (Tuesday) and August 21 (Monday) versus other days (both weekdays and weekend days). Note that, the curves with points are for pairs of weekdays, and those without points are for a weekday and weekend. Figure 5.15 plots the overlap between weekend days. Note that the x-axis data values for the top N case are not always equal to N in the graphs. The reason is that when we consider the top N documents, the next few documents after the top Nth document may have the same access frequency as the Nth document; so these documents are included as the top N documents. Looking at Figure 5.14, we make the following observations: 1. The overlap of documents between different days is signi cant. For example, the document overlap for the top 100 documents is more than 80%, and the overlap for the top 1000 documents is more than 70%. This indicates that the set of popular queries remains relatively stable, and suggests that we can cache a stable set of popular query results or optimize the data layout to improve the performance of these queries. For example, workload-based techniques can be used to generate indices and materialized views automatically for a database [1]; these techniques are largely applicable if the database query workload is relatively stable, which is the case for our browser queries. 2. As shown in Figure 5.14, the document overlap initially uctuates with an increasing number of documents and then decreases when the number of top documents picked is more than 100. The initial uctuation is due to the

this test is conducted here for pedagogical purposes. We rst specify the null hypothesis that the population regression coef cient 1 is zero that is to say, H0 : 1 = 0. For this test we require an estimate of the standard error of the regression coef cient, sb1. The standard error is a measure of instability of the regression coef cient (i.e., how much variability we would expect in the regression coef cient if we estimated the same regression equation in repeated random samples from a single population). The standard error depends upon MSresidual and on the variation of the predictor SSX = (Xi MX ) 2 and is given as sb1 = MS residual = SSx .772 = .076 134.557 (19.13)

You can modify shapes by converting lines to fills, expanding the shape of a filled object, or softening the edges of a filled shape by modifying the curves of the shape. The Convert Lines to Fills feature changes lines to fills, which allows you to fill lines with gradients or to erase a portion of a line. The Expand Shape and Soften Edges features allow you to expand filled shapes and blur the edges of shapes. The Expand Fill and Soften Fill Edges features work best on small shapes that do not contain many small details. Applying Soften Edges to shapes with extensive detail can increase the file size of a Flash document and the resulting SWF file.

In this section, we obtain an approximation of the BER P(H) for a particular channel realization H. For simplicity, we refer to this method as Method I. As noted above and discussed in more detail in Section 11.5.3.3, the main strength of this method is the ability to obtain the outage BER. 11.5.3.1 Pairwise Error Probability. The PEP for an error vector e (1r rL) with the error event starting in a position i (1rirLc) is given by Eq: (11.17). For a given H, we obtain the expression 0s 1 H x zi; 2 A PEPi; H Q@ N 0

Set yourself up with a /usr/local/bin/change_chan.sh script that contains something like the following lines (here, we assume you re working with a Motorola DCT-2000 series cable box):