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coupling inductances in nanohenrys per centimeter on the interconnection widths in the range 0.5 5 mm while Fig. 2.7.18 shows those on the interconnection separations in the range 0.5 10 mm. For the bilevel con guration, the dependences of the various coupling inductances on the interconnection widths are shown in Fig. 2.7.19 while those on the interconnection separations are shown in Fig. 2.7.20. For the bilevel con guration, the dependences of the various coupling inductances on the thickness of the GaAs substrate in the range 3 200 mm are shown in Fig. 2.7.21 while those on the interlevel distance T12 in the range 1 50 mm are shown in Fig. 2.7.22. For the trilevel interconnections, the various coupling inductances as functions of the interconnection widths in the range 0.5 4 mm are shown in Fig. 2.7.23, those as functions of the interconnection separations in the range 0.5 5 mm are shown in Fig. 2.7.24, and those as functions of the interlevel distance T23 are shown in Fig. 2.7.25.



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criterion (i.e. more guessing) with other-race faces (see also, MacLin, MacLin and Malpass, 2001). Smith and colleagues (2001) have shown that eyewitness con dence, decision time to make an identi cation from a lineup, and the use of a sequential lineup versus a simultaneously presented lineup are moderated by same-race versus other-race factors. In the sequential lineup procedure witnesses are forced to make an absolute judgement decision about each individual as they are serially presented. In contrast, in the simultaneous procedure witnesses follow a relative decision-making strategy whereby all suspects are compared with each other. Whereas con dence, decision time, and the relative versus absolute judgement strategy can be used to postdict accuracy of identi cation in own-race faces, this was not found to be true with otherrace faces. The fact that some factors have a differential effect on own- and other-race face recognition is probably not appreciated by most lay persons and potential jurors. Smith et al. (2001) reinforce the point that unlike own-race identi cations once otherrace suspects have been selected by an eyewitness from a lineup it is impossible to determine by any postdictor factors whether the decision is likely to be correct or incorrect.





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These agents could be con gured to le automatically the incoming mails into folders that the user had set-up The lling could be done, for example, by sender s address, subject line, and so on The agents operated on the eMails after they had been delivered to the user s inbox That is, storage space in the in-box was used up, even if the user wanted to refer the mail on to someone else This can be a problem if large attachments are provided It also meant that users were alerted every time a message came into the in-box This can be a nuisance in a true processbased operation, where it is desirable to keep a user working at handling mail within the les at a pre-set order ( Work on the urgent box until it is empty .

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Let s = 1, thus the test is f (x) < . One way to determine mn and mx (used in our implementation) is to nd the minimal feature value of the positive examples (example 1 seen here), and maximal negative value (example H seen here) and assign them to mn and mx , respectively. If s = 1, then the de nitions are modi ed accordingly. Suppose that an image is evaluated to be around the letter I in the example (it could be exactly the letter I in the training process or a tested image at runtime). Since f (x) < , the image is estimated as positive. The degree of con dence in the estimation is |(f (x) )/( mn )|, which is about 0.5 in the example. If the ratio is > 1, then it is replaced by 1. The result of the evaluation is then h(x, f, s, , mn , mx ) = 0.5, which is returned as the recommendation. 11.5.11 Strong Classi ers

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