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Consistent with the structural scheme depicted in Figure 141, markers of High and Low Positive Affect do tend to be inversely related, with a weighted mean correlation of 41 Contrary to Figure 141, however, the Low Positive Affect terms also are moderately correlated with markers of High Negative Affect (mean r = 30) Similarly, the Low Negative Affect scales are moderately related to both High Negative Affect (mean r = 39) and High Positive Affect (mean r = 30) These results establish some signi cant inaccuracies in the original formulation of the affect circumplex (see Watson et al, 1999, for a discussion) Moreover, they further suggest that the inclusion of these low-end terms actually would lessen the construct validity of the PANAS scales Watson and Clark (1997) examined this issue by constructing a bipolar form of the PANAS.



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Theorem 25.1 (Stability of E-NLMS) Consider the E-NLMS recursion (25.1) and assume the data { d ( i ) , u i } satisfy model (22.1) and the independence assumption (22.23). The regressors need not be Gaussian. Then the filter is convergent in the mean and is also mean-square stable for any p < 2. Moreover, the transient behavior of the filter is characterized by the statespace recursion (25.10)-(25.12), and the mean-square deviation and the excess mean-square error are given by





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They created a bipolar Positive Affect scale by reverse-keying ratings on the four items comprising the PANAS-X Fatigue scale (sleepy, tired, sluggish, drowsy) and adding them to the 10 regular high-end terms; in parallel fashion, they created a bipolar Negative Affect scale by reverse-scoring the three terms included in the PANAS-X Serenity scale (calm, relaxed, at ease) and adding them to the 10 regular highend descriptors Both the original unipolar PANAS scales and these alternative bipolar versions then were correlated with regression-based factor scores in each of 13 data sets The results established that the original PANAS scales were superior measures of the underlying dimensions, in that they consistently showed both better convergent validity (ie, higher correlations with the target factor score) and superior discriminant validity (ie, lower correlations with the other factor score).

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In other words, unipolar scales consistently provide better measures of these dimensions than do bipolar scales Thus, for most assessment purposes, researchers.

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As we mentioned earlier, we denoted the probabilities that the medium is idle on rst and second CCA with a and b, respectively, and the probability that the transmission is successful with g. Note that the rst CCA may fail because a packet transmission from another node is in progress; this particular backoff period may be at any position with respect to that packet. The second CCA, however, will fail only if some other node has just started its transmission that is, the backoff period in which the second CCA is undertaken must be the rst backoff period of that packet. Note that the rst medium access by any node will happen within the rst 16 backoff periods of the superframe. Let the clusters contain nbot, nmid, and ntop ordinary sensor nodes, respectively, with the packet arrival rate of l per node. (References to speci c clusters will use the subscripts bot, mid, and top, respectively.) The top cluster coordinator acts as the network sink.

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