### Table 1: Results for fixed query workload and con- tent

"... In PAGE 5: ... We also provide the achieved social and workload cost. Table1 summarizes our results. In the first scenario (Table 1 lines 1-4), all strate- gies reach a Nash equilibrium rather fast (20 rounds in the worst case).... In PAGE 5: ... Table 1 summarizes our results. In the first scenario ( Table1 lines 1-4), all strate- gies reach a Nash equilibrium rather fast (20 rounds in the worst case). The peers form the desired number of clusters (10).... In PAGE 5: ... Since all data requests of a peer are contained into its own cluster, the cost for the recall is zero, thus both the so- cial and workload costs are equal to the cluster membership cost. In the second scenario ( Table1 lines 5-8), again, we reach the desired number of clusters, but since this number is larger than the one in the first scenario, it takes more rounds to do so. The recall factor in this case is not zero, since there are data items from the same category a peer re- quires that are located in different clusters.... In PAGE 5: ... Also, since the queries are not uniformly distributed among the peers, the social cost differs from the workload cost. Finally, the third scenario ( Table1 lines 9-12) does not reach convergence. In this case, the data and query distributions are such that do not favor the creation of clusters.... ..."

### Table 1. Statistics that summarize the con- tents of the log file considered in our experi- ments.

2005

Cited by 4

### Table 2. Requirements for performance data con- tent to construct a space-time view

1998

"... In PAGE 7: ... 2.2 Refining Our Requirements Based on these observations, the re- quirements for constructing space-time views can be stated in terms of re- quirements to construct its three major components: the color bars, white spaces and message lines as summa- rized in Table2 . For example, re- quirement-1 in Table 2 implies that the control flow (for example, the branch sequence of a conditional) has to be monitored and repro- duced in the right order.... In PAGE 7: ...2 Refining Our Requirements Based on these observations, the re- quirements for constructing space-time views can be stated in terms of re- quirements to construct its three major components: the color bars, white spaces and message lines as summa- rized in Table 2. For example, re- quirement-1 in Table2 implies that the control flow (for example, the branch sequence of a conditional) has to be monitored and repro- duced in the right order. The requirements for making trace file length fixed and predictable be- fore program execution can be formalized as shown in Table 3.... ..."

Cited by 4

### Table 2: Metrics for eqntott based on superblock scheduling with different two sets of unrolling and peeling parameters. Under mild enlargement, we used unrolling and peeling factors of 8 and 2. Under aggressive enlargement, we used unrolling and peeling factors of 16 and 4.

"... In PAGE 11: ... We can demonstrate the truth of this statement even with SUPER alone. Table2 compares the useful-IPC and IC-expansion metrics for eqntott when scheduled by SUPER under two different unrolling and peeling limits. Of our benchmarks, eqntott most clearly 5.... ..."

### Table 2:N15, No Enlargement

"... In PAGE 9: ... I refer to this case under the provisions of the treaty as N27, and the weighted majority system as W27, respectively. Table2 shows the member countries together with their voting weights, the quota and the decision rule for N15. The big four countries all have 29 votes, 12.... ..."

### Table 2:N15, No Enlargement

"... In PAGE 10: ... I refer to this case under the provisions of the treaty as N27, and the weighted majority system as W27, respectively. Table2 shows the member countries together with their voting weights, the threshold and the decision rule for N15. The big four countries all have 29 votes, 12.... ..."

### Table 3 Target Enlargement Experimental Results

1998

"... In PAGE 2: ... While the size of the Binary Decision Diagrams(BDDs) is not very large, computing the next larger preimage for all the examples except Inbox, MC1, and MC2 (Small), exceeded the memory limit [1]. Design Max Cycles of Target Enlargement BDD Size of Largest Target Enlargement CM (4 Slots) 4 55,301 CM (6 Slots) 4 33,374 CM (8 Slots) 4 25,235 Inbox 528 MC1 595 MC2 (Small) 41,47 MC2 (Big) 6 23,885 LSC 4 13,172 Table 2 Target Enlargement Table3 shows the number of visited states and explored states. The number of visited states includes those that eventually became explored states.... In PAGE 3: ...Table 3 Target Enlargement Experimental Results Table3 shows that Target Enlargement can have significant reductions in the number of visited and explored states to find a violation of the assertions. While Yuan et al.... In PAGE 3: ... While Yuan et al. also reported some reductions in the number of states needed to find bugs on two small examples, Table3 illustrates the use of Target Enlargement on a larger and more realistic set of examples [12]. In addition, the reduction on this set of examples can be up to two orders of magnitude, which is much larger than shown by Yuan et al.... ..."

Cited by 58

### Table 11: The impact of successive enlargements on the size of the Community*

"... In PAGE 25: ... Implications for budgetary expenditures and receipts The extent and speed of acceptance of the acquis communautaire by new members has always constituted a central difficulty for successive EU enlargements. In the case of eastward enlargement the large number of applicants, their relatively low level of income (the increase in EU population would far exceed the addition to GDP as shown in Table11 ), and the fact that most CEECs have large agricultural sectors pose particular difficulties. One of the key questions which arises concerns how far enlargement in stages will ease the problem of extending the structural funds and the Common Agricultural Policy (CAP) to the CEECs.... ..."

### Table 3 Target Enlargement Experimental Results

"... In PAGE 2: ... While the size of the Binary Decision Diagrams(BDDs) is not very large, computing the next larger preimage for all the examples except Inbox, MC1, and MC2 (Small), exceeded the memory limit [1]. Design Max Cycles of Target Enlargement BDD Size of Largest Target Enlargement CM (4 Slots) 4 55,301 CM (6 Slots) 4 33,374 CM (8 Slots) 4 25,235 Inbox 528 MC1 595 MC2 (Small) 41,47 MC2 (Big) 6 23,885 LSC 4 13,172 Table 2 Target Enlargement Table3 shows the number of visited states and explored states. The number of visited states includes those that eventually became explored states.... In PAGE 3: ...Table 3 Target Enlargement Experimental Results Table3 shows that Target Enlargement can have significant reductions in the number of visited and explored states to find a violation of the assertions. While Yuan et al.... In PAGE 3: ... While Yuan et al. also reported some reductions in the number of states needed to find bugs on two small examples, Table3 illustrates the use of Target Enlargement on a larger and more realistic set of examples [12]. In addition, the reduction on this set of examples can be up to two orders of magnitude, which is much larger than shown by Yuan et al.... ..."

### Table 3 Target Enlargement Experimental Results

"... In PAGE 2: ... While the size of the Binary Decision Diagrams(BDDs) is not very large, computing the next larger preimage for all the examples except Inbox, MC1, and MC2 (Small), exceeded the memory limit [1]. Design Max Cycles of Target Enlargement BDD Size of Largest Target Enlargement CM (4 Slots) 4 55,301 CM (6 Slots) 4 33,374 CM (8 Slots) 4 25,235 Inbox 528 MC1 595 MC2 (Small) 41,47 MC2 (Big) 6 23,885 LSC 4 13,172 Table 2 Target Enlargement Table3 shows the number of visited states and explored states. The number of visited states includes those that eventually became explored states.... In PAGE 3: ...Table 3 Target Enlargement Experimental Results Table3 shows that Target Enlargement can have significant reductions in the number of visited and explored states to find a violation of the assertions. While Yuan et al.... In PAGE 3: ... While Yuan et al. also reported some reductions in the number of states needed to find bugs on two small examples, Table3 illustrates the use of Target Enlargement on a larger and more realistic set of examples [12]. In addition, the reduction on this set of examples can be up to two orders of magnitude, which is much larger than shown by Yuan et al.... ..."