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Cognitive-Behavioral Treatments for Insomnia Description Go to bed only when sleepy; get out of bed when unable to sleep; use the bed/bedroom for sleep only (no reading, watching TV, etc.); arise at the same time every morning; no napping. Curtail time in bed to the actual sleep time, thereby creating mild sleep deprivation, which results in more consolidated and more ef cient sleep. Methods aimed at reducing somatic tension (e.g., progressive muscle relaxation, autogenic training, biofeedback) or intrusive thoughts (e.g., imagery training, hypnosis, thought stopping) interfering with sleep. Psychotherapeutic method aimed at changing dysfunctional beliefs and attitudes about sleep and insomnia (e.g., unrealistic sleep expectations; fear of the consequences of insomnia). Avoid stimulants (e.g., caffeine and nicotine) and alcohol around bedtime; do not eat heavy or spicy meals too close to bedtime; exercise regularly but not too late in the evening; maintain a dark, quiet, and comfortable sleep environment.

8.4 ALGORITHMS FOR WIRELESS FAIR QUEUEING In the last section, we described the key components of a generic wireless fair queueing algorithm and discussed possible design choices for each of the components. In this section, we consider six contemporary wireless fair queueing algorithms and compare their characteristics. A detailed performance evaluation of many of these algorithms can be found in [15]. Among the algorithms proposed in contemporary literature, we choose six representative algorithms for discussion. The algorithms that we choose not to describe behave very similarly to one of the algorithms described here. The six algorithms chosen are the idealized wireless fair queueing algorithm (IWFQ) [10], the wireless packet scheduling algorithm [10], the channel-condition-independent fair queueing algorithm (CIF-Q) [16], the server-based fairness approach (SBFA) [19], the wireless fair service algorithm (WFS) [11], and the effort-limited fair scheduling algorithm (ELF) [6].

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=== Predictions on test set === inst#, 1 2 3 4 actual, predicted, error, 1:neg 1:neg 1:neg 1:neg 2:pos 2:pos 2:pos 2:pos probability distribution *0.521 0.479 *0.521 0.479 0.423 *0.577 0.389 *0.611

the rst and the nal parts of a code vector In this case the error pattern is of the form e(X ) = X j B(X ), where 0 j n 1, and B(X ) is a polynomial of degree equal to or less than n k 1, which will not contain g(X ) as a factor A modi cation of the Meggitt decoder is the error-trapping decoder This algorithm is particularly ef cient in the correction of single error patterns, double error patterns in some codes and burst errors Its ef ciency falls for other error patterns The error-trapping decoder can be applied to error patterns with certain properties, such as a sequence of n k bits or less placed the most signi cant positions of the received vector.

In this case it is possible to show that the error polynomial e(X ) is equal to X k S (n k) (X ), where S (n k) (X ) is the syndrome polynomial of the received polynomial r (X ) shifted by n k positions This property makes easier the correction of such an error pattern, because it is necessary to calculate only S (n k) (X ) and then add X k S (n k) (X ) to r (X ) In the same way, it is possible to perform the decoding of error patterns of size n k or less even when they do not occur in the most signi cant positions of the code vector, hence the name of the error-trapping algorithm.

RFC Format (continued)

It can be shown that, for a given cyclic code, capable of correcting error patterns of t random errors or less, error-trapping happens if the weight of the syndrome vector is less than or equal to t Details of this algorithm can be found in [1]..

moved from the early phases of involvement to the middle and later years The athletes were provided with stimulating environments at home and with coaches who guided their development In a qualitative study of socialization into elite sport, Carlson (1988) analyzed the development of 10 elite tennis players in Sweden and compared their training activities with a control group of 10 subelite players He found that the players in the control group specialized in tennis at a younger age (age 11) than the elite players (age 14) Furthermore, between the ages of 13 and 15 the players from the control group played more tennis than the elite players did The elite players practiced more tennis only after the age of 15 Carlson concluded that early specialization and professional-like training in tennis did not favor the development of elite players; instead, an all-around sport engagement was more important before adolescence.

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