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Figs 6.9 and 6.10 summarize the excitonic wave function corresponding to the minimized variational energies of Fig. 6.8. The binding energy is essentially calculated over a range of discrete values of both A and (in this case) B, hence the accuracy of these parameters is known only to the resolution of the mesh. The most important result of the variational calculation is the energy, which around the minima is a weak function of the parameters, and thus even a coarse mesh would have little consequences for the binding energy; Fig. 6.6 illustrates this nicely. The minima can be determined to 0.001 meV with a A resolution of 1 A and a /3 resolution of 0.01. The symbols in Fig. 6.9 are drawn open and of a size in order to illustrate the accuracy in the determination of the Bohr radius A. In many ways the functional dependencies of A on well width and barrier height mirror that of the binding energy EX0 it is a non-monotonic function of well width, decreases with increasing barrier height and tends towards its bulk value of 115 A at large well widths.

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Reinforcement learning is slow learning process, but works well in situations where all the training data is not available at once. In such environments, the NN learns to adapt to new situations easily as they arise.

A (\/i:r^i<w:white.i} A red.w } swap(r,w) { red.r } . The first requirement is met because the quantification (\/i:r^i<w:white.i) weakens to white.r (using the splitting and one-point rules) when r <w. The second holds because red.w and red.r are both equivalent to red.r A red.w whenr -w. This completes the verification of the guarded command red.w - swap(r,w) ; r,w := r+l,w + l . Exercise 14.1. Check formally the correctness of the guarded commands white.w - w := w + I

The symmetry parameter is displayed in Fig. 6.10 as both J3 (as calculated) and as its transformation into . Recalling that the trial wave function of the relative motion is given by:

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blue.w < b := b-l ; swap(b,w) .

then C ~0.5 implies that:

1. Discuss how reinforcement learning can be used to guide a robot out of a room filled with obstacles. 2. Why is the SSE not a good measure to compare the performance of NNs on different data set sizes 3. One approach to incremental learning is to select from the candidate training set the most informative pattern as the one with the largest error. Justify and criticize this approach. Assume that a new pattern is selected at each epoch. 4. Explain the role of the steepness coefficient in supervised NNs. -\net

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Exercise 2.1 was about identifying the invariant properties in Hoare's algorithm for finding the 20 best values among 100 values in a deck of cards. In this section, we develop an implementation of the algorithm. If the exercise is no longer fresh in your mind, it may be worthwhile reading through it once more. The implementation developed below differs from Hoare's algorithm, but the essence remains the same. The algorithm is one of the first truly non-trivial algorithms to be developed with the intention of demonstrating formal program construction techniques; it is called the Find algorithm. The algorithm can be used to sort a sequence of values into percentiles without doing a complete sort (for example, finding the best 10% of students in an examination).

and the three-dimensional case:

Before we can begin the development of the algorithm, we must agree on exactly what is to be computed. Hoare's description of the problem and its solution is informal, a drawback of which is a certain amount of ambiguity. Using a formal, mathematical language to specify the algorithm forces us to be completely precise in our specification of the problem. This is, just by itself, a useful exercise; Section 14.2.1 provides the details. Having agreed on a formal specification, the development of the algorithm is discussed in Section 14.2.2.

While the implications for the binding energy, by using either one of the simpler wave functions above, might be relatively small, i.e. probably less than 1 meV, the fact remains that excitons in quantum wells do have mixed symmetry. In this example the C value implies that 'equi-surfaces' of Vv are prolate spheroids, i.e. they look like rugby footballs (American footballs), with the major axis along the growth (z-) direction of the heterostructure.

5. Explain how architecture can be used to avoid overfitting. 6. Explain how active learning can be used to avoid overfitting. 7. Consider the sigmoid activation function. Discuss how scaling of the training data affects the performance of NNs.

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