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The length of the myo bril and also the muscle cell is dependent on the number of sarcomeres For example, the semitendinosus, a long muscle, has been estimated to have somewhere in the neighborhood of 58 104 to 66 104 sarcomeres per muscle ber, while the soleus has been estimated to have approximately 14 104 (Wickiewicz et al 1983) Adjacent myo brils are attached to each other at the Z-line.

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If no reply is received by the client within a preset length of time, the client will retransmit the request up to an administered amount of times. The retransmission is regulated in that it will randomly send retransmission requests. This is to ensure that the network will not be flooded with requests should clients somehow sync their transmissions (purely by coincidence). Before retransmission, the Secs field is updated.

operation of addition in the set must be de ned. For 0 i < 2m 1, X i is divided by p(X ), resulting in X i = qi (X ) p(X ) + ai (X ) (18)

e g(x) e3.365884 = = 0.966621 1 + e g(x) 1 + e3.365884 e g(x) e3.365945 = = 0.966623 1 + e g(x) 1 + e3.365945

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Figure 15.13 illustrates the link performance of an FM ber-optic system for video channels. Table 15.1 shows typical link budgets for an HFC AM system.

Subsequent chapters deal with establishing backend and frontend roles for your MythTV project build. Much of this targets Fedora Core users, but if you opted to install KnoppMyth, most of this preparation is already done. In 6, you configure MythTV as a slave backend, interact (once again) with mythfill database, and get some expert advice on tweaking channel performance. In 7, you find guidance on configuring the mythfrontend controls (General, Appearance, Program Guide, Playback, and Recording). Shortly thereafter, MythTV keyboard commands and theme packages are explored, considerations for DirecTV and DishTV are examined, and further tuner options are explained. Part IV introduces topics involved in personalizing the sights and sounds in your MythTV environment, including adding DVD support, customizing MythTV menu features, tweaking parameters for playback on widescreen displays, and advanced performance hacks. You also learn how to manipulate the MythTV environment keybindings, exporting the native .nuv format to one playable in any reader, outputting to high-definition televisions (HDTV), and even how to rip old VHS tapes onto CD.

through the second iterative loop, the resulting position. Then, a veri cation of the initial hypothesis concerning the integer ambiguities is required, possibly leading to a new updated set of integer ambiguities, thus a new second loop process.

Note: All results are simple consequences of the results in the previous subsection and the rules for vector valued functions with vector arguments given in the following sections.

How will we know when our models are performing well Is 80% classi cation accuracy good enough 90% 95% To be able to calibrate the performance of our candidate models, we need to establish benchmarks against which these models can be compared These benchmarks often come in the form of baseline model performance for some simple models Two of these simple models are (1) the don t send a marketing promotion to anyone model, and (2) the send a marketing promotion to everyone model Clearly, the company does not need to employ data miners to use either of these two models Therefore, if after arduous analysis, the performance of the models reported by the data miner is lower than the performance of either of the baseline models above, the data miner better try again.

R R interval (msec.)

In other words, the models reported by the data miner absolutely need to outperform these baseline models, hopefully by a margin large enough to justify the project Recall Table 72, the cost/bene t decision table for this case study Applying those costs and bene ts to these two baseline models, we obtain for the test data set (5908 negative responses and 1151 positive responses) the performance measures shown in Table 710 Which of these two baseline models performs better A comparison of the overall error rates would lead one to prefer the send to everyone model However, as we discussed earlier, data mining models need to take into account real-world considerations such as costs and bene ts, so that traditional model performance measures such as false positive rate, false negative rate, and overall error rate are deemphasized.

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