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hence using the end technique Thus, Eq (55) is an implementation of lines 5 and 6 of the simple list scheduling (Algorithm 9) or the dynamic list scheduling (Algorithm 11) As the selection of the processor is not performed anymore in these algorithms, the complexity of the second part of list scheduling reduces by the factor |P| The second part of simple list scheduling is then O(V + E) From Theorem 51 it is known that the end technique is optimal for a given node order and processor allocation As a result, scheduling with a given processor allocation reduces to nding the best node order Both static and dynamic priorities can be employed to order the nodes for their scheduling However, since the processor allocations are already determined, task graph characteristics, like node levels and the critical path, can be computed using the allocated path length (Section 44), that is, the path length based on the known processor allocations These characteristics do not change during the entire scheduling; hence, dynamic priorities are only sensible when considering the state of the partial schedules, for example, choosing the node among the free nodes that can start earliest, that is, the ready node (Section 513) One might wonder whether this scheduling problem with a given preallocation is still NP-hard After all, it is only about nding the best node order Unfortunately it is still NP-hard, even for task graphs without communication costs, unit execution time, and very simple graph structures such as forest (Goyal [79]) or chains (Rayward-Smith et al [159]); see also Hoogeveen et al [91] 53 CLUSTERING As mentioned before in Section 422, task scheduling under the classic model is a trade-off between minimizing interprocessor communication costs and maximizing the concurrency of the task execution A natural idea is therefore to determine rst before the actual scheduling which nodes should always be executed on the same processor Obvious candidates for grouping are nodes that depend on each other, especially nodes of the critical path Clustering is a technique that follows this idea It is therefore only suitable for scheduling with communication costs In its core it is a scheduling technique.

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QR Code printer for net generate, create qr barcode none in On this basis the simple parity-check code can be de ned as a matrix code by the following de ition: De nition 223 A k 1; k simple parity-check code is de ned and expressed by 1 k 1 parity-check matrix having k 1 1 s, that is, one row vector composed of k 1 1 s & Let us explore the error control capability of a simple parity-check code C It can be easily understood that the minimum Hamming distance of C is two, meaning dmin 2 From Subsection 224 we know that a simple parity-check code can detect single-bit errors The following theorem, however, clari es the error detection capability of the simple parity-check code Theorem 2.

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3 Simple parity-check codes detect any odd number of bit errors but never detect even number of bit errors Theorem 23 can be easily proved by Eq (27) Recall that an odd number of 1 s in e, meaning w e odd number, always leads to S 1, whereas an even number of 1 s in e always leads to S 0 Theorem 23 tells us that only one additional check bit to the original input word will lead to an error detection of any odd number of bit errors This is why the simple parity-check codes are so ef cient and hence so much in use in digital systems The simple parity-check code is usually expressed over GF 2 The corresponding nonbinary check code is called a checksum code over integer set modulo q, meaninig Zq , where q is an integer larger than 2.

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Encode QR In Visual C#.NET Using Barcode generator for Visual Studio .NET Control to generate, create QR image . If we have such a picture, we will be able to use it to de ne test sets that do more than just test one simple function. he extreme X-machine (XXM) is a suitable candidate. These were introduced by Thomson (2003, 2005), and we have been using these for a number of years with great success. They are popular with programmers, and customers can also make sense of them. The simple machine of the type in Fig. 6.6 is called an X-machine by Eilenberg (1974) and is discussed at length in Holcombe (1998). It can be used to describe many things, but it is not always convenient. One problem is that each state transition (indicated by an arrow) needs to be triggered by an external input or event. Sometimes, especially in object-oriented programs, we would like to allow the system to call some other function or method without an explicit external input. Thus we might have the diagram shown in Fig. 6.7. Here the input from a potential user is supplied in one go, by completing a simple form, and the Validate user state then generates a result that triggers one of the three transitions Fail, User access, or Admin access internally. The operations that are labeling the transitions (arrows) represent functions of various levels of complexity. The real power of these machine diagrams is that they can be hierarchical. In other words, the functions labeling the transitions can themselves be broken down into a series of operations at a lower level described by a subsidiary machine.Related: Intelligent Mail Generation .NET

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Fig 67 The phase delay of a compound half-wave plate made of quartz and MgF2 , designed to be achromatic at 425 and 575 nm, is compared with a simple plate of uartz cut to be half-wave at 500 nm. P larimetry. Making Code 128A In Java Using Barcode printer for Java Control to generate, create Code 128 .Related: 

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The acid content data are "well behaved" in the sense that they are quite compatible with the assumptions of the simple linear regression odel However, among regression data sets with more than one explanatory variable, such good behavior is much rarer See Daniel and Wood (1980, p 60) 93b In Table 91, the p-values for the LAD procedure were obtained using the F distribution rather than the chi-squared distribution The p-values using the chi-squared distribution were smaller 93c Estimation methods have been developed for dealing with regression data that contain both collinearity and outliers See Pfaffenberger and Dielman (1990) 93d Three of the four outliers detected by LAD and M-regression in the turnip green data can also be detected using least-squares diagnostic procedures.

This requires a more sophisticated procedure than simply looking at the LS residuals divided by aLS We have mentioned studentized residuals in Note 24a Also, in order to judge whether a data point is an outlier, it is a good precaution to eliminate its influence on the estimates ~, and a that are used in forming its studentized residual This is the idea behind the externally studentized residual (see Weisberg, 1985, Section 52) The externally studentized residual of a data point can be used to test the null hypothesis that the point is not an outlier The p-values for data points 10, 15, 19, and 20 are 00343,00329,00111, and 01157 93e The collinearity problem in the turnip green data can be reduced by standardizing X 2 before forming its square.

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