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is the presence of redundancy between facts. Specifically, a fact derived from one bar is likely to be fairly similar to a fact derived from an immediately adjacent bar. Because of the similarity, the effective number of data points, in terms of contributing statistically independent information, will be smaller than the actual number of data points. The two corrected correlation columns represent adjustments assuming two differently reduced numbers of facts. The process of correcting correlations is analogous to that of correcting probabilities for multiple tests in optimization: As a parameter is stepped through a number of values, results are likely to be similar for nearby parameter values, meaning the effective number of tests is sotnewhat less tbau the actual number of tests.



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As mentioned earlier, dispersion is the optical term for the spreading of the transmitted light pulse as it transits the fiber. It is a bandwidthlimiting phenomenon and comes in two forms: multimode dispersion, and chromatic dispersion. Chromatic dispersion is further subdivided into material dispersion and waveguide dispersion.

To understand multimode dispersion, it is first important to understand the concept of a mode. Figure 7-19 shows a fiber with a relatively wide





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Explanation of the data templates and the Binding markup extension was given in 2. In 3, you were introduced to a number of controls, including a brief introduction to data integration and manipulation controls such as ListBox and DataGrid. Controls such as ListBox enable you to connect a user interface element to a data source and automatically display data. The DataGrid control is specifically designed for displaying data in rows and columns. It provides a lot of flexibility for displaying the data and the column headers and footers. We ll take a detailed look at data controls in this section.

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As evident from Table 1 l-l, the raw correlations rose monotonically with the size of the network in terms of numbers of connections. When adjusted for shrinkage, by assuming an effective sample size of 13,000, the picture changed dramatically: The nets that stood out were the small Mayer net with 6 middle layer neurons, and the smaller of the two 4-layer networks. With the more moderate shrinkage correction, the two large 4-layer networks had the highest estimated predictive ability, as indicated by the multiple correlation of their outputs with the target. On the basis of the more conservative statistics (those assuming a smaller effective sample size and, hence, more shrinkage due to curve-fitting) in Table 1 l1, two neural nets were selected for use in the entry model: the 18-6-l network (nn2.nei) and the 18-14-4-l network (nn&rer). These were considered the best bets for nets that might hold up out-of-sample. For the test of the entry model using these nets, the model implementation was run with mode set to 2. As usual, all order types (at open, on limit, on stop) were tested.

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core. Because of the width of the core, it allows light rays arriving from the source at a variety of angles (three in this case) to enter the fiber and be transmitted to the receiver. Because of the different paths that each ray, or mode, will take, they will arrive at the receiver at different times, resulting in a dispersed signal. Now consider the system shown in Figure 7-20. The core is much narrower and only allows a single ray, or mode, to be sent down the fiber. This results in less end-to-end energy loss and avoids the dispersion problem that occurs in multimode installations.

For these models, two additional fact sets are needed. Except for their targets, these fact sets are identical to the one constructed for the time-reversed Slow %K. The target for the first fact set is a 1, indicating a bottom turning point, if tomorrow s open is lower than the 3 preceding bars and 10 succeeding bars. If not, this target is set to 0. The target for the second fact set is a 1, indicating a top, if tomorrow s open has a price higher than the preceding 3 and succeeding 10 opens. Otherwise this target is set to 0. Assuming there are consistent patterns in the matket, the networks should be able to learn them and, therefore, predict whether tomorrow s open is going to be a top, a bottom, or neither. Unlike the fact set for the time-reversed Slow %K model, the facts in the sets for these models are generated only if tomorrow s open could possibly be a turn ing point. If, for example, tomorrow s open is higher than today s open, then

The speed at which an optical signal travels down a fiber is absolutely dependent upon its wavelength. If the signal comprises multiple wavelengths, then the different wavelengths will travel at different speeds, resulting in an overall spreading or smearing of the signal. As discussed earlier, chromatic dispersion comprises two subcategories: material dispersion and waveguide dispersion.

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