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The horizontal axis measures company X s asset value (actually the logarithm of asset value) and the vertical axis measures company Y s asset value. Note that the default point for company X is indicated on the horizontal axis and the default point for company Y is indicated on the vertical axis. The concentric ovals are equal probability lines. Every point on a given oval represents the same probability, and the inner ovals indicate higher probability. If the asset value for company X were uncorrelated with the asset value for company Y, the equal probability line would be a circle. If the asset values were perfectly correlated, the equal probability line would be a straight line. The ovals indicate that the asset values for companies X and Y are positively correlated, but less than perfectly correlated. The probability that company X s asset value is less than DPTX is EDFX; and the probability that company Y s asset value is less than DPTY is EDFY. The joint probability that company X s asset value is less than DPTX and company Y s asset value is less than DPTX is J. Finally, the probability that company X s asset value exceeds DPTX and company Y s asset value exceeds DPTX is 1 EDFX EDFY + J. Assuming that the asset values for company X and company Y are jointly normally distributed, the correlation of default for companies X and Y can be calculated as

As you can see in the IDE s Output panel, the IDE did not find any errors in HelloBlackBerry.java, so it proceeded to successfully create HelloBlackBerry.cod in the HelloBlackBerry project folder. If the IDE finds errors in the .java file, it will describe each error in the Output panel and will not build the .cod file for your program. Because I supplied you with the correct code to HelloBlackBerry, there were no errors this time. But making mistakes or forgetting the proper Java syntax is extremely common when writing new code. Therefore, building a newly written BlackBerry program is usually an iterative process of writing code, building code, correcting errors in code, building code, correcting errors in code, and repeating until the build is successful.

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The method described here selects a few samples from each class as representative cases. (For the sake of convenience, the samples that are not selected as cases are referred to as patterns in subsequent discussion.) Let ^ x1 ; x2 ; . . . ; xi ; . . . ; xn be a e pattern vector of known classi cation in an n-dimensional feature space containing M classes. The variable elk xlk 1 ; xlk 2 ; . . . ; xlk i ; . . . ; xlk n denotes the lk th case from the kth class Ck . mlk ^ represents the degree of similarity of ^ to a case elk . dlk ^ e e e stands for the distance between ^ and elk . The degree of similarity between a pattern e ^ and a case elk is de ned as e 8 ! > e 2 > 1 2 dlk ^ > 0 dlk ^ < l e > 2 > > l < ! mlk ^ e 3:57 e 2 > 2 1 dlk ^ l > dlk ^ < l e > 2 > > l > : 0 otherwise e Here l is the bandwidth of mlk ^ [i.e., the separation between its two (crossover) points where mlk 0:5]. Note that mlk ^ can be viewed as a p-type membership e function characterizing a fuzzy set of points representing a region Rglk with elk as its center [72]. The distance dlk ^ may be expressed in many ways. Considering e a Euclidean norm, we have " e dlk ^

assume to be used in the network. Should this change, then the network can easily compensate for this as the terminal s assumption is known to the base station. CQI reporting is illustrated in Figure 4.15, showing that when the terminal is close to the base station and assumes high HS-DSCH power allocation (based on the value given by the network), a high CQI value is reported. Respectively, when the terminal is closer to the cell edge (the lowest curve) then the reported CQI is much lower, especially if the expected HSDPA Node B power allocation is low as well. When at or close to the cell edge, most of the interference comes from other cells and, thus, the representative geometry value is low around 0 dB or in some cases the value is negative.

Conner et al., 1981, Conner and Day, 1992 Conner and Day, 1976 Megonigal et al., 1997 Megonigal et al., 1997 Brown, 1981 Deghi et al., 1980 Richardson et al., 1983 Natural Natural Natural Natural Natural Natural T. distichum Dominant 905 136 769 528 35 556 All Sites 477 63 414 254 44 278 T. distichumt T. distichum var. nutans T. distichumt T. distichumt T. distichumt 501 591 315 345 177 Duever, 1984 Duever, 1984 Burns, 1984 Burns, 1984 Brown et al., 1984 Impounded 350 63 287 208 39 212

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