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to inquire whether there are any innate and essential sex differences in tastes and abilities, which would afford a scienti c basis for the apparently arbitrary and traditional assumption that the vocational future of all girls must naturally fall in the domestic sphere, and consequently presents no problem, while the future of boys is entirely problematical and may lie in any of a score of different callings, according to personal tness. (p. 223)

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Propagation times can be improved by increasing the size of the inverters by a factor of k, where k is given by [69] k r Rr Ci Ri Cr 3:10:4

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partitioned (as mentioned in Section 5.4.1.2) into 55 clusters (see Table 5.5) using a similarity matrix based clustering algorithm. For the convenience of readers, we describe below the algorithm that rst transforms the similarity matrix, computed based on the similarity between cases, to an equivalent matrix (i.e., the nal similarity matrix SM in step 5 of the following algorithm), and the cases that are equivalent to each other are considered to be in the same cluster. Clustering Algorithm Step 1. De ne a signi cant level (threshold) b 2 0; 1 . Step 2. Determine the similarity matrix SMN N SM w according to equations pq (3.1) and (3.2). w Step 3. Compute SM1 SM SM spq , where spq maxk min SMpk ; w SMkq . Step 4. If SM1 SM, go to step 5; else, replace SM with SM1 and go to step 3. Step 5. Determine the clusters based on the rule case p and case q belong to the same cluster if and only if SMpq ! b in the nal similarity matrix SM, and compute the number M of clusters. The following example illustrates the foregoing clustering algorithm. Let us consider three cases, e1 1;2 ; e2 1;4 , and e3 1;12 , and assume that their

training algorithms, readers can refer to Appendix B.) Following are the steps in using a back-propagation (BP) neural network for case adaptation. Step 1. Learn the feature weights of the problem attributes. Step 2. Cluster the cases using the weighted distances. Step 3. For every case in each cluster, compare the differences between this case and other cases in both problem attributes and solution attributes. Obtain a set of discrepancy vectors. Step 4. Preprocess (e.g., normalize) the discrepancy vectors for training the neural network. Step 5. For each case, use its discrepancy vectors to train the neural network. Step 6. Given a new problem, retrieve the most similar case from the case base. Compute the difference between their problem attributes. Preprocess this difference and use it as the input to the neural network. Step 7. Obtain the adjustment amount from the neural network by inputting the preprocessed differences of the attribute values. Step 8. Postprocess (e.g., denormalize) the adjustment amount and use it to adapt the old solution for the query problem. The steps above are summarized in Figure 4.9.

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Kids INTERNATIONAL This restaurant gives you a splashy look at the mega-resort and water wonderland of Atlantis, even if you re not a guest of the hotel. Paradise Island has better restaurants, notably Five Twins, but none is this dramatic. You feel like a scuba diver as you peer through gigantic picture windows displaying the illuminated ruins of Atlantis. Everywhere you look, rainbow-hued fish swim past stone archaeological remains, and rows of lobsters parade through the sand. With a ceiling that seems miles away, the Caf s multilevel dining areas are located on the lower floor of the Royal Towers. There s a kids menu, and little ones love taking walks along the aquarium walls between courses. In such a setting, the food becomes almost secondary, although it s quite good. The chef imports topquality ingredients for such dishes as rack of lamb with an arugula pesto. Lobster is a specialty, and you can also order well-prepared versions of smoked salmon with lemon grass and jumbo lump Andros crab cakes. Desserts are uniformly delicious.

600 Wavelength (nm)

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