### Table 8: The results for the input{output matrices

"... In PAGE 11: ...75 0.25 In Table8 we present the results obtained for 38 examples of real problems (input- output matrices), in which the number of objects to be ordered did not exceed m = 60. For each example there are three numbers in the table: OP T - the optimal value of the objective function, opt1 - the value of the objective function obtained in only one trial with a randomly generated initial solution,... ..."

### Table 3. Examples of inputs and output for search components with multiple inputs. The nested bracket syntax associates constraints to the type they apply to Component Input Output

"... In PAGE 13: ... This might be provided minimally by a simple form which supports the user in selecting appropriate types and relations from the ontology. A straightfor- ward case is a search of a single class constrained by a number of properties that apply directly to it, such as the constraints on fuel consumption measurements in the first row of Table3 . More complex constraints arise when the property is not directly related to the item being sought.... ..."

### Table 2: Complexity of a fixed multiplier with digit-size four and coefficient 1839 (excluding CPA stage). No pre-accumulation is used.

"... In PAGE 3: ... Again, note that the registers includes the pipeline registers.A fixed coefficient multiplier with coefficient 1839 and digit-size four was also designed and the metrics of the different implementa- tions are shown in Table2 . No pre-accumulation could be used as the number of partial products for any significance level was small- er than three.... ..."

### Table 1: Complexity of a general 16-bit multiplier with digit-size four (excluding partial product generation and CPA stage).

"... In PAGE 3: ... It can be seen that if h is large, no CSA stages must be used as the reduction tree has reduced the par- tial products in the output digit to at most two bits. Table1 shows metrics of a general 16-bit coefficient multiplier with digit-size four using different architectures and maximal criti- cal path lengths. For h = 1 the multiplier without pre-accumulation yields the implementation with the lowest number of registers.... ..."

### Table 2. Input-output relations of Fig. 2; s closed.

2003

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### Table 5: Input-output mapping for the fairy-tale example.

2005

"... In PAGE 25: ... We trained a neural network with data about Nessie from Loch Ness. The learned input-output mapping is given in Table5 . We used the following abbrevi- ations: f nessie is a fairy tale creature, i nessie is immortal, a nessie is an animal, d nessie is a dragon and a nessie is a tourist attraction.... In PAGE 26: ... Those attributes are represented using one or two propositional variables each, as shown in Table 6. For each of the Monks Problems, a set of attribute-class pairs is given, similar to Table5 , but with only one output called class. In problem 1, we find class = 1 whenever the value of attribute a and b coincide, or if e = 1.... ..."

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