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For the consumer it must be quite baf ing to understand how each year designers, manufacturers and retailers all seem to know what styles and colours will be in fashion. The reality is that since the 1970s there have been companies who specialize in fashion prediction and act as consultants to interested parties in the fashion world. Companies such as the Paris-based organizations Peclers and Promostyl, France, and London-based Worth Global Style Network (WGSN) sell their predictions on styles, colour and the market for the coming season or even further in advance for up to 18 months. There are at least 10 main organizations of this type in the world, although some specialize in speci c markets such as childrenswear. Their predictions are not all identical, although there are usually many similarities between them. These predictions help manufacturers and retail buyers alike to make and stock the fashions; styles and colours that will be the fashion for a coming season. However, at the end of the day the nal decision rests with the customer in deciding whether to buy or not.

The producers of context information include all the Context Information Sources (CISs) that are attached to speci c active nodes and provide raw context information, such as: information from DINA Brokers (e.g., local MIB variables), information from GPRS and WLAN Wrappers (e.g., on the corresponding coverage), information from other wrappers (e.g., weather sensors), and various other information provided by Context Providing Applications (e.g., user agenda). In addition to these context sources that provide raw context information, producers also include applications that produce complex context information after collecting and aggregating elementary context. The consumers are the SLOs, the instances of the service logic. During the operation phase, each SLO issues requests for acquisition of context information in order to adapt to the imminent context changes and proceed with the enforcement of the appropriate actions. The CIDS Brokers, namely the Context Brokers, act as third-party players between producers and consumers. They accept requests from SLOs, collect context information from producers, and ef ciently disseminate the information from producers to consumers or other Context Brokers, as dictated by a dissemination scheme. In this sense, Context Brokers realize the linkage between producers and consumers. Each Context Broker provides two different types of API: the Producer APIs and the Consumer APIs. The Producer APIs include the interfaces that enable context producers to publish the information they provide, either raw or complex, so that the Context Brokers will then be able to access it. They also include the interfaces that enable the context producers to deliver the information that they have produced. In this sense, the heterogeneity of the different context sources is hidden and each time a modi cation of the context sources takes place, only the local

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crossing line by the coupling capacitance, which in turn depends on the line widths, interline separations, interlevel separation, and dielectric constant of the substrate. As mentioned earlier, this stage cannot be expressed in terms of the transmission line equations because the coupling between the driven line and the crossing lines is not uniform along the length of the driven line. The load section is shown in Fig. 3.4.10. As before, this is the last section of the driven line and includes the portion of the driven line which is after the crossing section. This section can again be represented as a transmission line but now using the coupling and other parameters used in the source section. The output from this transmission line drives the load represented by the capacitance CL resulting in the load impedance ZL 1 sCL

Introduction, 127 United States, 127

Time Coupon payment is made A par bond s clean price and dirty price are the same immediately after payment at $1,000. 6 months later another coupon payment is made At the halfway point between coupon payments, a semiannual coupon-bearing par bond s clean price is still $1,000, but its dirty price is equal to $1,000 (1 + CT), where C is coupon rate divided by 2, and T is time (equal to one-half of 6 months).

It needs to be emphasised that in the above pricing framework there is theoretically no restriction on the dimensionality of the model. Everything will fl w through just as well for a multidimensional model, albeit more slowly, as it would do for a one-dimensional model. The only assumption being made on the underlying numerical container is that it has the arithmetic operators overloaded and a mean operator. To conclude this section, we note that sometimes it is useful for diagnostics purposes to be able to listen to the symbols at various stages of the calculation. The optional symbol table listener argument in the constructor provides this functionality. A concrete example, provided in the test module ppf.test.test lattice pricer, is given below:

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