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Ten of the 16 methods in the SAXParser class are parse() methods with different signatures. You also have the getProperty() and setProperty() methods, which are similar to the getFeature() and setFeature() methods you saw in the SAXParserFactory. You also have the getter methods getParser(), getXMLReader(), isNamespaceAware(), and isValidating(), which you can use to see the properties that have been set in the XMLReader and in the parser. But, for the most part, the job of a parser is to parse, and so that's what the bulk of the methods enable you to do. Let's put all of this together to create a SAX 2.0 based parser and instruct it to parse Richard III. Create the following code and save it as CueMyLine.java in the cue directory:

In situations where the relationship between two variables is nonlinear, a simple way of generating a regression equation is to transform the nonlinear relationship to a

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be a piece of a yellow Post-it note placed over the car s speedometer, so that every time the patient looks at the speedometer, it reminds him or her to stop any habit and release any tension in the masticatory muscles. Therapy also entails teaching patients what relaxed feels like and how to relax their masticatory muscles. With practice patients are able to learn to quickly drain any tension found in these muscles. As patients are able to make themselves pain free by relaxing their muscles, psychologists often teach patients to use internal cues. An internal cue that is often used for TMD patients is to constantly be aware of the level of their masticatory muscle tension. Whenever any tension develops within a masticatory muscle, this immediately alerts the patients. By this time these patients are usually aware that if they allow the tension to build, it will develop into pain. Over time this becomes an unconscious habit for them and hence they are able to remain pain free throughout the day. More examples of external and internal cues are provided in Breaking Daytime Habits in chapter 14. RELAXATION Progressive muscle relaxation, imagery, hypnosis, yoga, prayer, and meditation appear to provide a similar physiological relaxation response. This response counters the hyperarousal state individuals may have from overstimulation of their ght-or- ight mechanism.10 In a survey of TMD patients, relaxation was one of the self-management therapies that provided patients the most relief from their TMD pain.19

Since the channel is memoryless, log Pr{r/I} = log Pr{rj/Ij}, the criterion has thus the following equivalent form: To find I* such that log Pr{r/I*} = Min Pr{r/I}, or To find I* such that log Pr{r/I*} = Min log Pr{rj/Ij} This is equivalent to: To find c* such that log Pr{r/c*} = Min log Pr{rj/cj} From the received sequence r and each code sequence c, a path metric can be further defined for an AWGN channel Note that log Pr{rj/cj} = log (2 ) ||rj cj||2, the path length is now ||rj cj||2, that is just the Euclidean distance between r and c This is why the maximum likelihood decoding is to find the code sequence, or the corresponding trellis path, that is the closest to the received sequence in Euclidean distance.

Relational databases are excellent tools to store and manipulate data. In addition to storing data in a normal form, which reduces redundancy, they provide many advantages. To enable the performance of multiple and frequent data updates without any failures, databases usually support transactions. Database transactions enable concurrency without the corruption of data. In addition to supporting transactions, relational databases have several other features, such as a rich query language, optimized indexes, and proven scalability. In spite of these features, there are enough reasons to use XML for storing data. Some of these are listed here:

since all the latent attitude variables have been standardized, whilst for the categorical variables this refers to the difference between the category of interest and the reference category. For example, for whether or not the respondent began family care, it is the difference between yes and no . For comparison, standardized parameter estimates for the (na ve) linear and logistic regression models used to construct the GCM are also presented in Tables 22.3 and 22.4. Here the attitude variables constructed by summing

The criterion is still expressed as: To find c* such that log Pr{r/c*} = Min ||rj cj||2 The Viterbi algorithm solves this problem by applying the dynamic programming principle that assures the global optimality with local optimum decisions The local decisions reduce the computation efforts on the trellis In practice, much simpler integer metrics quantized to 3-bit are used, see [Ref 3-10] and [Ref 3-17] Roughly speaking, to find the shortest path among the 2rL ones, r being the code rate, L the sequence length, 2rL path metrics are to be evaluated and compared, if a brute-force method is used In the Viterbi algorithm (VA), much less calculation effort is necessary From the first trellis section to the L-th one, the VA progresses section by section.

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