### Table 3 Link density in clusters

"... In PAGE 13: ...6). --------------------------- Insert Figure 2 here --------------------------- Table3 shows the total number of forward and backward link clusters, size of clusters, and density of links in the clusters for all participants under BF and SK conditions. The results in Table 3 indicate that the number of backward clusters is higher than the number of forward clusters in all BF sessions.... In PAGE 13: ...umber of links is 18, the size of the cluster is 5 and the link density is 18/5 = 3.6). --------------------------- Insert Figure 2 here --------------------------- Table 3 shows the total number of forward and backward link clusters, size of clusters, and density of links in the clusters for all participants under BF and SK conditions. The results in Table3 indicate that the number of backward clusters is higher than the number of forward clusters in all BF sessions. In SK sessions the number of backward and forward clusters are closer to each other.... In PAGE 13: ... In SK sessions the number of backward and forward clusters are closer to each other. ----------------------- Insert Table3 here ----------------------- Table 3 shows that the fore-link density is relatively higher than back-link density under the BF conditions for all architects except for A1. Note that for A1 the fore-link density in the BF and SK conditions are very close (2.... In PAGE 13: ... In SK sessions the number of backward and forward clusters are closer to each other. ----------------------- Insert Table 3 here ----------------------- Table3 shows that the fore-link density is relatively higher than back-link density under the BF conditions for all architects except for A1. Note that for A1 the fore-link density in the BF and SK conditions are very close (2.... In PAGE 14: ... A higher density of fore-links in clusters should indicate more idea generation. Table3 showed that the fore-link density was relatively higher under the BF conditions for all architects except A1. Hypothesis 1 can be accepted based on the results of five out of six architects; the use of imagery alone enhanced idea generation more than sketching did.... In PAGE 20: ... Thus, Group 2 under the BF conditions demonstrated higher conectedness of ideas, probably due to the familiarity through sketching effect. The link density in forward clusters was found to be significantly higher under BF conditions for Group 2 compared to all other sessions ( Table3 ). This can be taken as an evidence for the improved idea generation due to the familiarity/learning effect.... ..."

### Table 4: Results of genetic algorithms testing

"... In PAGE 36: ...Table4 summarizes the results of a benchmark testing. In the testing we ran- domly retrieved ve test cases of 1-document, 2-document, 3-document, 4-document, 5-document, and 10-document examples, respectively, from the 3000-document DIALOG-extracted database discussed earlier.... In PAGE 36: ... For each test case, an initial tness based on the Jaccard apos;s score was computed. For 1-document and 2-document test cases, their initial tness tended to be higher due to the smaller sample size (see Column 2 of Table4 ). In Table 4 we also report per- formance measures in terms of the Jaccard apos;s scores for the GA processes, the CPU times, and the average improvements in tness.... ..."

### TABLE 1 Electron density

1977

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### Table 1. Genetic algorithms parameters.

"... In PAGE 9: ... Basically, the implementation of GA-Stacking combines two parts: a part coming from Weka that includes all the base learning algorithms and another part, which was integrated into Weka, that implements a GA. The parameters used for the GA in the experiments are shown in Table1 . The elite rate is the proportion of the population carried forward unchanged from each generation.... ..."

### Table 1 Comparison of core/periphery fitness measures using Beck et al. (2003; ND) data

2004

"... In PAGE 5: ....P Boyd, W.J. Fitzgerald, R.J. Beck/Social Networks columns 4 and 5 of Table1 . Column 6 of Table 1 compares the results from the UCINET (Version 6.... In PAGE 5: ... For all 12 groups, all three of these algorithms matched the exhaustive search by consistently finding the global optimum from several starting configurations. [ Table1 about here] From the results in Table 1, the genetic algorithm in UCINET finds the global optimum in two out of our 12 cases. The UCINET fit statistic is among the five best for seven of the 12 cases, and among the ten best for nine of the 12 cases.... In PAGE 5: ... For all 12 groups, all three of these algorithms matched the exhaustive search by consistently finding the global optimum from several starting configurations. [Table 1 about here] From the results in Table1 , the genetic algorithm in UCINET finds the global optimum in two out of our 12 cases. The UCINET fit statistic is among the five best for seven of the 12 cases, and among the ten best for nine of the 12 cases.... In PAGE 7: ... A low probability along with an intuitively high observed fitness value suggests that the observed data may have a core/periphery structure. To illustrate this permutation test, we used Mathematica to program a random permutation generator based upon the observed within group distribution of messages for each of the 12 groups from Table1 . As with the observed data, diagonal cells were also ignored for these permutations.... In PAGE 7: ... For Group 1, for example, no random permutation in each of the 3 runs produced an optimal fitness value equal to or greater than the observed fitness value of 0.867 (see Table1 ). For Group 3, 43 of the random permutations in the first run produced optimal fitness values equal to or greater than the observed fitness value (0.... ..."

Cited by 1

### Table 4 Genetic-guided clustering algorithms and their characteristics.

### Table 3. Average improvement in different edge densities for various genetic parameters (crossover rate, mutation rate).

### Table 2: Biological RNA structures.

2007

"... In PAGE 5: ...) We study the behaviour of the algorithm on biological structures since it will have an impact in biological appli- cations such as ribozyme design. Because of the limited availability of true biological structures, we generated structures with biological characteristics based on the set of real structures listed in Table2 . The statistics reported in Table 3 summarise salient structural properties of these naturally occurring RNAs.... In PAGE 5: ... Figure 2a shows the median expected run time for differ- ent structure lengths (where the median is over the struc- tures in a set and the expectation is over multiple runs of the algorithm on a given structure), as well as the expected run time for the structure at the 10% and the 90% quan- tile for the biologically motivated structures. We also show the expected run times for the set of real biological structures summarised in Table2 . Notice that the empiri- cal complexity for designing these real structures fits well within the range of complexity observed for our biologi- cally motivated sets of structures, which provides some evidence that the probabilistic model underlying these sets is reasonably plausible for the purposes of this study.... In PAGE 6: ... Hairpins Stems 2-Branch loops Multiloops Bulges Size [4,8] [3,12] [4, 11] [6,17] [1,3] Number - - [1,8] [0,5] [0,0.17]* Branches - - - [3,4] - Properties of the structures from Table2 ; the intervals specify the minimal and maximal values observed for the respective features. These parameters were used to generate structures with biological properties.... In PAGE 9: ...5 Performance of RNA-SSD with different number and locations of primary base constraints In a second series of experiments, we studied the correla- tion between the number of bases constrained and the performance of the RNA-SSD algorithm. The experiments were conducted using some biological structures from Table2 as well as biologically motivated structures. Table 4 shows some features of these structures.... In PAGE 14: ... Structures with biological char- acteristics were generated with the help of an RNA struc- ture generator [9] that allows us to directly control salient properties of the structures being generated, including the overall size as well as the number and size of bulge, inter- nal, and multiloops, and the length of stems. In order to determine these properties, we selected from the biologi- cal literature ten structures that are consistent with exper- imental evidence and empirical data, ranging from 60 to 600 bases in length (see Table2 ). Average values of each of the features captured in the parameters of the RNA structure generator over our set of structures were used to roughly summarise the structural properties of naturally occurring RNAs (see Table 3).... ..."

### TABLE 1.1 Examples of Commonly Used Structure-Based Drug Design Packages

### Table 3: Comparison to Iterative Improvement Partitioners LSR based algorithms are always faster than LR based algorithms since SNT promotes higher rate of convergence due to its minor perturbation of the current partition compared to an entirely new random initial partition.

"... In PAGE 5: ... 4.2 Comparison to Other Partitioning Algorithms Table3 shows the comparison of our heap based FM-LSRb to state-of-the-art iterative improvement partitioning algorithms LIFO-LA4 (LIFO bucket and modi ed lookahead formulation based), K-DualFM (dual net representation based), GMetis (genetic multi-level graph partitioning based), LA3-CDIP (LIFO bucket, lookahead and cluster detection based), and CLIP-PROPf (CLIP, PROP, followed by PROP post- re nement). The experiment shows that FM-LSRb outperforms LIFO-LA4, K-DualFM, GMetis, LA3- CDIP, and CLIP-PROPf by 70.... ..."