### Table 6 Computational Complexity of Dynamic Learning Image No.

2004

"... In PAGE 20: ... Finally, the newly computed top 5 matches resulting from the dynamic search are shown in Figure 6h. Table6 provides a comparison of the computation complexity of the dynamic learning mechanism and the normal hierarchical content matching. The latter produces the same object labeling as the proposed dynamic learning by searching all possible region combinations.... ..."

Cited by 2

### Table 4: Complex undo with dynamic pointers

1992

"... In PAGE 18: ... That is, the e ort is linear rather than quadratic in the number of commands e ected. By way of example, dynamic pointers can handle quite complex sequences of operations such as those depicted in Table4 . Even though user2 apos;s actions movethepointatwhich user1 apos;s deletion took place, the dynamic pointers still track this and the two sequences of actions commute.... ..."

Cited by 9

### Table 4: Complex undo with dynamic pointers

1992

"... In PAGE 18: ... That is, the e ort is linear rather than quadratic in the number of commands e ected. By way of example, dynamic pointers can handle quite complex sequences of operations such as those depicted in Table4 . Even though user2 apos;s actions move the point at which user1 apos;s deletion took place, the dynamic pointers still track this and the two sequences of actions commute.... ..."

Cited by 9

### Table 1. Complexity of existing dynamic AC algorithms.

in An improved algorithm for maintaining arc consistency in dynamic constraint satisfaction problems

2005

Cited by 2

### Table 1. Complexity of existing dynamic AC algorithms.

in An improved algorithm for maintaining arc consistency in dynamic constraint satisfaction problems

2005

Cited by 2

### Table 1. Time and space complexity of existing dynamic arc consistency algorithms.

2004

Cited by 4

### Table 1: Complexities, implementation and static/dynamic compliance of the various algorithms.

"... In PAGE 3: ... The Slack stealing scheduling was rst de ned for the static rate monotonic scheduler [2] and then adapt- ed to the dynamic deadline driven scheduler [7]. Table1 summarizes the time and the space com- plexities of the various algorithms presented in this section, where m is the maximal number of simul- taneous aperiodic requests and Ts the period of the server. Table 1 shows also if the scheduling algorithm is compliant (a19 or not a23) with a static or a dynamic priority based rule.... In PAGE 3: ... Table 1 summarizes the time and the space com- plexities of the various algorithms presented in this section, where m is the maximal number of simul- taneous aperiodic requests and Ts the period of the server. Table1 shows also if the scheduling algorithm is compliant (a19 or not a23) with a static or a dynamic priority based rule. When we implemented of the var- ious algorithms from their \formal quot; de nitions in the literature we had to lift some imprecisions and ambi- guities; Table 1 displays also this aspect (see [5] for details).... In PAGE 3: ... Table 1 shows also if the scheduling algorithm is compliant (a19 or not a23) with a static or a dynamic priority based rule. When we implemented of the var- ious algorithms from their \formal quot; de nitions in the literature we had to lift some imprecisions and ambi- guities; Table1 displays also this aspect (see [5] for details). 4.... ..."

### Table 1. Temporal complexity of environmental, behavioural and cognitive dynamics.

2008

"... In PAGE 14: ...The data given in Table1 confirm the Complexity Monotonicity Thesis put forward in this paper, that the more complex the environmental dynamics, the more complex the types of behaviour an agent needs to deal with the environmental complexity, and the more complex the behaviour, the more complex the internal ... ..."

Cited by 1

### Table 3: Amortized time complexity of DynamicList structure T1 T2 T3 Enqueue O(log(n)) O(1) O(1)

"... In PAGE 5: ... The tree-like structure ensures that each subsequent enqueue into T1 is of O(log(n)) complexity. Table3 summarizes the theoretical performance of DynamicList. Table 3: Amortized time complexity of DynamicList structure T1 T2 T3 Enqueue O(log(n)) O(1) O(1) ... ..."

### Table 4: The performance of FairShare Policy in dynamic, complex distributed environment.

2003

"... In PAGE 19: ... Consequently, the max-min rates for flow[1-5] are 50, 0, 0, 50, and 50, respectively. In Table4 , the status of flows, the expected (analytically) max-mix rates of flows, and the measured goodputs of lows are given. Figure 12(b) shows that with our FairShare, all flows quickly determine and stabilize at their max-min share.... ..."

Cited by 2