### Table 1: Computational complexity of constraint planning problems. Cycles in the constraint

"... In PAGE 5: ... Indeed, a general computational result states that a restriction imposed on a solution does not necessarily make the corresponding problem easier [Papadimitriou 94]. Table1 shows the computational complexity of some existing constraint planning problems. 4 An NP-complete planning problem with method restriction De nition 3 The method restriction imposes that every constraint method of a given problem must use all of the variables in the constraint either as an input or an output variable.... ..."

### Table 1: Transformational Procedures (TPs). Note that the Input column lists only image-based arguments. Model-based arguments are not listed for Subgraph Isomorphism, Geometric Matching and Planar Distance TPs.

"... In PAGE 24: ... Of course, since any two points on the object model can serve as compile-time parameters to a scaling TP, many other parameterizations of the scaling TP could be included in the library. Although the visual procedure library shown in Table1 is su cient for the pur- poses of this experiment, it includes just a few of the computer vision algorithms described in the literature. Unfortunately, the current Lisp implementation of SLS has proved an impediment to building a larger library, since source code for most visual procedures is available only in C.... ..."

### Table 1. p53 cancer rescue mutants predicted using Maximum

"... In PAGE 5: ...RESULTS This section shows how well each of the Active Learning techniques predicted a set of 57 unclassified putative p53 cancer rescue mutants using a training set of 204 mutant. The results for the Active Learning method that performed best, Maximum Curiosity, are presented in Table1 ; the in vitro assay results are presented in Figure 1; and the summary prediction statistics for all Active Learning methods are presented in Table 2. The bold and italicized Accuracy and Correlation Coefficient highlight the best and second best scores on TC,i, respectively Table 1.... ..."

### Table 1. Subgraphs of the MWT.

in Geometry © 1997 Springer-Verlag New York Inc. A Large Subgraph of the Minimum Weight Triangulation ∗

"... In PAGE 11: ... We now present some representative results in tabular form. Table1 contains results from one set of 343 trials in which we computed the LMT-skeleton and the 1:17682- skeleton on the same sets of uniform random points ranging from 100 to 350 points. For the 50-point intervals, each line of data (each value of n) represents the averages of approximately 50 trials.... ..."

### Table 1: Parameter Estimates for the Coordinating and Non-Coordinating Models

"... In PAGE 28: ... For 1994 and 1998 there is a signi#0Ccant tendency for electors who have higher values of #12 i to be more likely to vote than electors who havelower values of #12 i : conservative electors were especially mobilized in those two elections. *** Table1 about here *** In every year, the coordinating model passes the parameter-based tests of the conditions neces- sary for it to describe coordinating behavior. Table 2 reports the LR test statistics for the constraint #0B = 1, imposed separately for eachyear.... In PAGE 29: ... The House position was expected to be closer to the Democratic position in 1978, 1982, 1986 and 1990, closer to the Republican position in 1994 and 1998. The MLEs for #0B in the coordinating model are less than :5inevery year except one #28see Table1 #29, suggesting that electors expected the Presidenttobeweaker than the House in determining post-midterm policy. *** Table 4 about here *** The distribution of the ordering of electors apos; ideal points with respect to the post-election policies electors expect according to the coordinating model shows that the moderating mechanism of the coordinating model is capable of generating a midterm cycle of the kind emphasized by Alesina and Rosenthal #281989; 1995#29, though it need not do so.... In PAGE 37: ... NES survey respondents mayoverreport the frequency with which they vote. Among the 9,639 cases from years 1978#7B98 that we use to compute the parameter estimates reported in Table1 , the ! i -weighted percentage reporting having voted is, by year: 47.... In PAGE 38: ... 19. Table1 shows #0B 90 , #0B 94 , #1A 78 , #1A 86 , #1A 90 and #1A 98 to have MLEs equal to either 0:0or1:0, on the conceptual boundary of the parameter space. Consequently, the asymptotic distributions of the MLEs and the LR test statistics are complicated #28Moran 1971; Self and Liang 1987#29.... In PAGE 39: ...Table1 to tabulate that mixture distribution and estimate the con#0Cdence intervals of Table 3. 20.... In PAGE 48: ...524 .455 Note: Computed using the parameter MLEs in Table1 and 1978#7B98 ANES data. Table 5: Orderings of Ideal Points and Expected PartyPolicy Positions, byYear Ordering year #12 i #3C ~ #12 Mi ; ~ #12 i ~ #12 Mi #3C#12 i #3C ~ #12 i ~ #12 i #3C#12 i #3C ~ #12 Mi ~ #12 Mi ; ~ #12 i #3C#12 i #12 Di = #12 Ri amp; i =0 1978 19.... In PAGE 48: ... Entries show the percentage of electors in eachyear who have #12 Di #3C#12 Ri and the indicated ordering of ideal point and expected policy positions, or who have #12 Di = #12 Ri , or who lack policy position values #28 amp; i = 0#29. Computed using the parameter MLEs in Table1 and 1978#7B98 ANES data. Percentages for those with #12 Di #3E#12 Ri are, byyear: #12 i #3C ~ #12 i #285.... ..."

### Table 2: Matrix utilization of subgraphs

2004

"... In PAGE 4: ... Therefore, only CCAs with maximum depth of 4 to 7 are considered. Width of Subgraphs: Table2 shows the average width statistics of the subgraphs for the 29 applications. A value in the table indicates the percentage of dynamic subgraphs that had an operation in that cell of the matrix layout (higher utilized cells have a darker background).... ..."

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### Table 2: Matrix utilization of subgraphs

2004

"... In PAGE 4: ... Therefore, only CCAs with maximum depth of 4 to 7 are considered. Width of Subgraphs: Table2 shows the average width statistics of the subgraphs for the 29 applications. A value in the table indicates the percentage of dynamic subgraphs that had an operation in that cell of the matrix layout (higher utilized cells have a darker background).... ..."

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### Table 1. The planar Ramsey numbers for cycles

"... In PAGE 8: ... Remarks Remark 1. Some theorems presented in Table1 can be formulated... ..."

### Table 4. Experiments showing the reduction in subgraphs by applying the maxmissing constraint

"... In PAGE 8: ...Table 4. Experiments showing the reduction in subgraphs by applying the maxmissing constraint Finally, in Table4 the results are reported of an experiment in which we com- pared the number of frequent subgraphs in the output of gSpan with and without a maxmissing = 3.0 constraint.... ..."

### Table 3: Average proportion of vertices in induced planar subgraph found in randomly generated graphs of 10,000 vertices. (Standard deviations in paren- theses.)

"... In PAGE 21: ...1.75, 1.85), [1.85, 1.95), . . . , [3.65, 3.75). The behaviour of the other algorithms as d increases in terms of the reduc- tion in proportion of vertices in the induced subgraph is remarkably similar as can be seen in Figures 1 and 2. In Table3 the proportion of vertices in the subgraphs found for graphs of 10,000 vertices are displayed. As n increases, the proportions of vertices in the subgraphs found by the various MIPS algorithms quickly converge to these proportions.... In PAGE 22: ...186 only by a fraction of a percent (see Table3 ). The fact that the average sizes of subgraph produced by these algorithms are so similar even though the classes of induced subgraphs produced are not all the same may suggest that there may be some fundamental limit on the performance of algorithms for finding induced... ..."

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