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These terms may be inserted into (1019) to evaluate the rate of growth of extinction and polarization with distance through any interstellar dust cloud More realistic modelling may be effected by also applying an assembly of dust grains with a distribution of values for a , b and c , or with a size distribution based on the three cross sections maintaining a constant ratio Polarization and extinction data are not uniquely correlated as can be seen in Figure 101, for example Around galactic longitudes of l 85 95 (Perseus), the p /A V ratio is high and the polarization vectors related to individual stars are closely aligned On the other hand, for l 40 50 (Cygnus), the p /A V ratio is low with the alignment of polarization vectors being more random In the rst case, the line of sight is across a spiral arm while, for the latter, it is more along the direction of the spiral arm The differences in p /A V values might be brought about by changes in the associated values of , perhaps also there being variations in the values of F, according to the galactic location Differences in the interstellar reddening curve according to galactic longitude could also be in uenced by the value of F (see, for example, Greenberg & Meltzer, 1960 and Wilson, 1960)

As with communicating with a custom server, we will probably want to communicate asynchronously for performance reasons. This works in just the same way using an OnRead() event.

A di erent approach is proposed by Kim and Bentley [457] where ALCs are not randomly generated and tested with negative selection, but an evolutionary process is used to evolve ALCs towards non-self and to maintain diversity and generality among the ALCs The model by Potter and De Jong [689] applies a coevolutionary genetic

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It is slightly easier to debug a socket server than it is to debug a custom server, because it is a separate process that can be debugged in its own right. The emulator bearer has a deliberately long timeout, so it is possible to run a socket server in the debugger and not lose the connection. To debug a socket server using the Metrowerks IDE, load the socket server and run the debugger. This will start the debugger and the socket server will connect to the Service Broker (starting it in the process) and register itself before you connect the emulated mobile phone to the PC. This is not the usual sequence that you will nd on a real mobile phone, where you will not get the socket server started until after a connection is made. This has one drawback if you are not aware of the consequences. When you run the Connect UI application in the emulator to get the connection, it causes the MServerSocketObserver::ServerSocketStopped

Figure 192 Adapted Negative Selection algorithm to evolve ALCs towards the selected class of non-self patterns in the training set and further away from the selected class of self patterns Once the tness of the ALC set evolves to a point where all the non-self patterns and none of the self patterns are detected, the ALCs represent a description of the concept If the training set of self and non-self patterns is noisy, the ALC set will be evolved until most of the non-self patterns are detected and as few as possible self patterns are detected The evolved ALCs can discriminate between examples and counter-examples of a given concept Each class of patterns in the training set is selected in turn as self and all other classes as non-self to evolve the di erent concept in the training set Gonzalez et al [327] present a negative selection method that is able to train ALCs with continuous-valued self patterns The ALCs are evolved away from the training set of self patterns and well separated from one another to maximize the coverage of nonself, ie the least possible overlap among the evolved set of ALCs is desired A similar approach is presented in the model of Graa and Engelbrecht [332] All patterns are represented as binary strings and the Hamming distance is used as a nity measure A genetic algorithm is used to evolve ALCs away from the training set of self patterns towards a maximum non-self space coverage and a minimum overlap among existing ALCs in the set The di erence to the model of Gonzalez et al [327], is that each ALC in the set has an a nity threshold The ALCs are trained with an adapted negative selection method as illustrated in Figure 192 With the adapted negative selection method the a nity threshold, aneg , of an ALC is determined by the distance to the closest self pattern from the ALC The a nity threshold, aneg , is used to determine a match with a non-self pattern Thus, if the measured a nity between a pattern and an ALC is less than the ALC s a nity threshold, aneg , the pattern is classi ed as a non-self pattern Figure 192 also illustrates the drawback of false positives and false negatives when the ALCs are trained with the adapted negative selection method These drawbacks are due to an incomplete static self set The known self is the incomplete static self set that is used to train the ALCs.

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