### Table 4. Size of matched subgraph for strongly regular graphs. Increasing the degree makes matching more challenging for the LeRP algorithm. But, as with the denser graphs in Table 3, the drop in performance can be recovered with only a slight increase in the dynamic range of node and edge coloring. Test conditions were similar to Table 2, but with the strongly regular graphs.

2003

"... In PAGE 13: ... Results indicate that a slight increase in the dynamic range of coloring can accommodate the denser graphs. Tests with Strongly Regular Graphs Table4 summarizes tests involving strongly regular graphs. Here, the size of the matched subgraph was examined as the degree of the graphs was increased.... ..."

Cited by 3

### Table 6. Semantics of Triples

2005

"... In PAGE 9: ... The semantics of Hoare triples for commands, for closure vari- ables, and for commands in context, resp., is given in Table6 . Note that triples express partial correctness.... ..."

Cited by 4

### Table 5: Fuzzy and neuro-fuzzy software systems.

2003

"... In PAGE 22: ...upports independent rules (i.e., changes in one rule do not effect the result of other rules). FSs and NNs differ mainly on the way they map inputs to outputs, the way they store information or make inference steps. Table5 lists the most popular software and hardware tools based on FSs as well as on merged FSs and NNs methodologies. Neuro-Fuzzy Systems (NFS) form a special category of systems that emerged from the integration of Fuzzy Systems and Neural Networks [65].... ..."

Cited by 2

### Table 5: Average run times over 3 graphs for each of three di erent sets of hand-designed strongly regular graphs (to identify as highly ambiguous). Size=number of nodes in each graph, Degree=degree of any node, and # Edges=no. of edges in each graph

"... In PAGE 23: ... We have also tested the algorithm with hand-designed strongly regular graphs [24] on which most existing algorithms arrive at incorrect decisions or take an exponential time. Our algorithm stopped and classi ed these graphs as highly ambiguous in a reasonable amount of time as Table5 shows. 4.... ..."

### Table 4 Deduction rules for MPA with relative discrete time (a 2 A).

2000

"... In PAGE 61: ... Intuitively, x 1 7! x0 means that x evolves into x0 by passing to the next time slice. We add the rules in Table4 to the rules of Table 2. Note that x 1 67! means that x cannot execute a 1 7! transition, i.... In PAGE 86: ... We detected four signi cant language features which give rise to di erent languages and make three performance-sensitive equiv- alences, performance congruence, lazy equivalence and eager equivalence, to behave di erently over these languages. Table4 summarizes our results. We would like to note that if process synchronization is allowed and eager equivalence and/or lazy equivalence do not coincide with performance con- gruence, then they are not even compositional.... ..."