### Table 2 Experimental data using the Graphplan-based encoding

### Table 2: Algorithm to Compute Critical Path Length.

"... In PAGE 42: ... The heart of PERT is a network of tasks needed to complete a project, showing the order in which the tasks need to be completed and their dependencies between them. As shown in Table2 , PERT scheduling reduces to a derivate of Dijkstra apos;s single shortest path algorithm within acyclic graphs (Cormen, Leiserson, amp;Rivest, 1990). In the algorithm, e(O i ) is the tentative earliest end time of operator O i , i 2f1;;:::;;kg, while the earliest starting times t i for all operators in the optimal plan are given by t i = e(O i );d(O i ).... In PAGE 93: ... Additionally, if we have to cope with reactive scheduling then feasible solutions and user interaction will become more important. Table2 . Evaluation of the results solution late orders lateness transport costs 1 2 (1 product) 12 24 2 3 (2 products) 8 22 3 2 (2 products) 9 23 4 4 (3 products) 14 21 For a solution approach it is necessary to describe what information is needed to model the problem and how it is used.... ..."

### Table 2 - Average deviations from the critical path based lower bound for J60

"... In PAGE 18: ...olution is just 0.25 and 0.11 percent for 1000 schedules and 5000 schedules, respectively. For j60 and j90 problems set, some of the optimal solutions are not known, so we measure the average percentage deviation from the critical-path based lower bound for comparison purposes. Table2 summarizes the results for j60 test instances. 295 out of 480 instances are solved to critical path based lower bound.... ..."

### Table 2. Different critical paths considered during layout optimization

2006

"... In PAGE 4: ... Baseline processor parameters Instruction Cache 32KB, 32B/block, 2-way Decode Width 8 ROB Size 128 entries Issue Queue 32 entries Issue Width 8 Register File 70 INT and 70 FP Functional Units Units 4 IntALU, 1 FPALU, 2 IntMult, 1 FPMult Load/Store Queue 32 entries L1Data Cache 16KB, 32B/block, 4-way, 2RW ports Unified L2 cache 1MB, 64B/block, 8-way 4.1 Performance Impact of 3D Integration In order to consider the impact of pipelining based on interconnect delays, we use the critical paths in Table2 for this study. The area and delay of the blocks were derived based on [22, 27] for a futuristic 70nm process technology.... ..."

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### Table 3b. Solution Statistics for Model 2 (Minimization)

1999

"... In PAGE 4: ...6 Table 2. Problem Statistics Model 1 Model 2 Pt Rows Cols 0/1 Vars Rows Cols 0/1 Vars 1 4398 4568 4568 4398 4568 170 2 4546 4738 4738 4546 4738 192 3 3030 3128 3128 3030 3128 98 4 2774 2921 2921 2774 2921 147 5 5732 5957 5957 5732 5957 225 6 5728 5978 5978 5728 5978 250 7 2538 2658 2658 2538 2658 120 8 3506 3695 3695 3506 3695 189 9 2616 2777 2777 2616 2777 161 10 1680 1758 1758 1680 1758 78 11 5628 5848 5848 5628 5848 220 12 3484 3644 3644 3484 3644 160 13 3700 3833 3833 3700 3833 133 14 4220 4436 4436 4220 4436 216 15 2234 2330 2330 2234 2330 96 16 3823 3949 3949 3823 3949 126 17 4222 4362 4362 4222 4362 140 18 2612 2747 2747 2612 2747 135 19 2400 2484 2484 2400 2484 84 20 2298 2406 2406 2298 2406 108 Table3 a. Solution Statistics for Model 1 (Maximization) Pt Initial First Heuristic Best Best LP Obj.... In PAGE 5: ...) list the elapsed time when the heuristic procedure is first called and the objective value corresponding to the feasible integer solution returned by the heuristic. For Table3 a, the columns Best LP Obj. and Best IP Obj.... In PAGE 5: ... report, respectively, the LP objective bound corresponding to the best node in the remaining branch-and-bound tree and the incumbent objective value corresponding to the best integer feasible solution upon termination of the solution process (10,000 CPU seconds). In Table3 b, the columns Optimal IP Obj., bb nodes, and Elapsed Time report, respectively, the optimal IP objective value, the total number of branch-and-bound tree nodes solved, and the total elapsed time for the solution process.... ..."

### Table 4 Classification results with different reducts 1: Number of rules; 2: Classification accuracy POSAR CEAR DISMAR GAAR PSORSAR

"... In PAGE 25: ... So, all the particles have a powerful search capability, which can help the swarm avoid dead ends. The comparison of the number of decision rules and the classification accuracy with different reducts are shown in Table4... ..."

### Table 2 shows the number of critical paths with at least one unique gates among 10,000 critical paths extracted for each of the ISCAS benchmark circuits.

1995

"... In PAGE 16: ... The distribution of unique gates for the 10 most critical paths in C432. Table2 . The number of critical path with at least one unique gate in the ISCAS85 benchmark circuits.... ..."

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### Table 7: The optimal investment plan

"... In PAGE 38: ...) Solution time* (CPU sec.) Figures 11 and 12, together with Table7 show the final solution obtained for the largest pr oblem instance of 25 wells for the planning horizon of 24 periods. The final configuration is shown in Figure ... ..."

### Table 3: The coverage of the full-profile critical path for the 98%-critical-path instructions using 20% sampling and 5% sampling. Also given is the adjusted profile time for the training inputs.

1998

"... In PAGE 7: ... Because we are tracking paths of dependen- cies, we need to sample instructions in consecutive sequences rather than one at a time. For these measurements ( Table3 ), we profile 5000 instructions at a time, then turn off profiling for a time (e.g.... In PAGE 8: ... Also given is the adjusted profile time for the training inputs. We also provide in Table3 the execution time of the profiler on a 500 MHz Alpha 21164. In the initial coding of the profiler, we have made no effort to optimize the code for efficiency.... ..."

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### Table 1. Results for test set 1

1993

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