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Change, stages of (transtheoretical model), 320 321, 524 525, 548 550 Character: education, through physical education, 675 morality and (see Morality) terminology, 662 theoretical ref lections, development of, 676 678 Cheating, moral functioning and, 10 Check-backs, 279 Cheese Roll, 340 Chemotherapy, 580 Childhood: development of self, 649 model of parental inf luences on children s motivation and achievement, 687, 688 parental belief in children s competence and success expectancy, 693 695 Choice: intrinsic motivation and physical activity, 65 morality and, 667, 670 673 Choking under pressure, 425 442 alleviating pressure-induced failure, 437 439 defined, 425 426 dispositional self-consciousness, 436 distraction under pressure, 438 forms of, 439 441 high profile examples, 441 home choke, 428 implicit learning, 438 439 individual differences and susceptibility, 435 437 mechanisms of, 428 435 attentional theories, 430 434 behavioral theories, 434 435 cusp catastrophe model (CCM), 429 distraction theories, 430 431 drive theories, 428 430 explicit monitoring theories, 431 434 freezing degrees of freedom (df), 435 pressure s double whammy, 434 social facilitation, 429 430 Yerkes-Dodson effect (inverted-U theory), 39, 429 overview/introduction, 425, 441 442 reinvestment, 436 437 skill monitoring, 437 438 stereotype threat, 439 440 study methods, 426 428 trait anxiety, 437 yips, 439 Chronological challenges (RESPECTFUL model), 839 Citizenship and civic responsibility, 678 Claimed handicaps, 145 Classical test theory (CTT), 737, 739, 741, 750 751, 806 Classification system, theoretical framework in exercise psychology, 537 538 CLR. See Conditional likelihood ratio (CLR) test CML. See Conditional maximum likelihood (CML) method Coach-athlete model of motivation, 67 68 Coaching Behavior Assessment System (CBAS), 113 117, 120, 124 Coaching Behavior Questionnaire (CBQ), 116 117 Coaching Feedback Questionnaire (CFQ), 116 Perceived Behavior Scale (CBAS-PBS), 115, 120 response categories, 115 Coaching effectiveness model, 123 124

It can be veri ed that this code is a slice code, codes known as alphabetic codes because the codewords are ordered alphabetically 4 Huffman codes and Shannon codes Using codeword lengths of 1 log pi (which is called Shannon coding) may be much worse than the optimal code for some particular symbol For example, consider two symbols, one of which occurs with probability 09999 and the other with probability 00001 Then using codeword lengths of 1 log pi gives codeword lengths of 1 bit and 14 bits, respectively The optimal codeword length is obviously 1 bit for both symbols Hence, the codeword for the infrequent symbol is much longer in the Shannon code than in the optimal code Is it true that the codeword lengths for an optimal code are always 1 less than log pi The following example illustrates that this is not always true.

When you request an authorization, by rule from the card associations you are supposed to be requesting an authorization of the amount you expect to settle on If you get an authorization for $100, you cannot process a settlement of $500 against it The amounts don t have to be exact the association has built-in variances to account for.

Example 573 Consider a random variable X with a distribution 1 1 1 1 3 , 3 , 4 , 12 The Huffman coding procedure results in codeword lengths of (2, 2, 2, 2) or (1, 2, 3, 3) [depending on where one puts the merged probabilities, as the reader can verify (Problem 5512)] Both these codes achieve the same expected codeword length In the second code, the third symbol has length 3, which is greater than 1 log p3 Thus, the codeword length for a Shannon code could be less than the codeword length of the corresponding symbol of an optimal (Huffman) code This example also illustrates the fact that the set of codeword lengths for an optimal code is not unique (there may be more than one set of lengths with the same expected value).

Although either the Shannon code or the Huffman code can be shorter for individual symbols, the Huffman code is shorter on average Also, the Shannon code and the Huffman code differ by less than 1 bit in expected codelength (since both lie between H and H + 1).

3.3.5.1 Function Description Users may use different devices to access applications, even concurrently, each device supporting different input and output modality services, different connectivity, and with different capabilities. To face this scenario without creating a speci c user interface representation for each device, the Mobile Services Architecture must support user interface adaptation. The UIAF provides functionalities to allow service developers to make services available through multiple devices using multiple modalities. The determination of the devices and their supported modalities to be used as user interfaces is also a necessary feature of the UIAF. It relies on the detection of surrounding devices and their capabilities, and also on user context and preferences, in order to decide which combination of devices and modalities will provide the best user experience.