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EXAMPLE: Betty Broker has adjusted gross income of $120,000. Betty also has $32,000 in passive tax losses (in excess of her other passive income) from partnership-type equity-sharing arrangements. These losses should qualif y for the special allowance. How much can she use First, the loss in excess of $25,000 is clearly in excess of the allowance. Thus, $7,000 [$32,000 $25,000] can immediately be treated as a loss carryover (suspended loss) to future years. Next, a portion of Betty s allowance will have to be reduced because her adjusted gross income exceeds the $100,000 hurdle. The $20,000 excess will reduce her allowance at a rate of 50 cents for each dollar of excess, or $10,000. Thus, Betty can use only $15,000 of her equity-sharing real estate losses to offset income from her wages as a real estate broker.

Choosing a CODEC can be dif cult, not least because each vendor presents the performance and capabilities of their CODEC in a different way. Issues to consider include availability and

Enabling eBusiness: Integrating Technologies, Architectures and Applications W.S. Whyte Copyright q 2001 John Wiley & Sons, Ltd ISBNs: 0-471-89941-0 (Hardback); 0-470-84602-X (Electronic)

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such that all entries that have a frequency of less than Tr are eliminated from the table, resulting in a reduced attribute value table. Note that the main motive for introducing this threshold function lies in reducing the size of the case base. Attempts can be made to eliminate noisy pattern representatives (having lower values of nki) from the reduced attribute value table. Rough dependency rules are generated from this reduced attribute value table using the methodology described in Section 2.3.3.2. 2.3.3.4 Mapping Dependency Rules to Cases We now describe a technique for mapping rough dependency rules to cases. This algorithm is based on the observation that each dependency rule (having a frequency above some threshold) represents a cluster in the feature space. It may be noted that only a subset of features appear in each of the rules, which indicates that the entire feature set is not always necessary to characterize a cluster. A case is constructed from a dependency rule in the following manner: 1. Consider the antecedent part of the rule: Split it into atomic formulas containing only a conjunction of literals. 2. For each atomic formula, generate a case that contains the centers and radius of the fuzzy linguistic variables (low, medium, and high) that are present in the formula. (Thus, multiple cases may be generated from a rule.) 3. Associate with each case, generated the precedent part of the rule and the case strength equal to the dependency factor of the rule [equation (2.16)]. The strength factor re ects the size of the corresponding cluster and the signi cance of the case. Thus a case has the following structure:

In some space shared by all parties involved, de ne the terminology of the transactions. Set limits to the values that each of the terms can have. Probably, check the compliance of these, on-line, in real-time and raise an exception handling routine to resolve any problems. De ne and execute procedures to complete other aspects of the transaction.

Proteins are molecules that consist of amino acids. There are 20 different amino acids; hence, one can think of a chain or sequence from an alphabet of size 20 as the primary structure of a protein. Each amino acid consists of a central carbon atom, an amino group, a carboxyl group, and a unique side chain attached to the central carbon. Differences in the side chains distinguish different amino acids. As this string is being produced (synthesized) in the cell, it folds back onto itself creating a 3D object. For several decades or more, biologists have tried to discover how a completely unfolded protein with millions of potential folding outcomes almost instantaneously nds the correct 3D structure. This process is very complex and often occurs with the aid of other proteins known as chaperones that guide the folding protein. The majority of protein structure prediction algorithms are primarily based on dynamic simulations and minimal energy requirements. More recently, it has been suggested that the high mechanical strength of a protein ber, for example, is due to the folded structural linking rather than thermodynamic stability. This suggest the feasibility and validity of a graph theoretic approach as a model for the molecule.

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