### Table 1: Two proven upper bounds and the conjectured exact bound

"... In PAGE 9: ... So if this conjecture is correct, the family of complete graphs K4k+4 is an example of a family of bar k-visibility graphs with the maximum number of edges. Table1 shows the two proven upper bounds on the number of edges in a bar k-visibility graph, together with the conjectured exact bound. 4 Thickness of Bar k-Visibility Graphs By Theorem 4, K8 is a bar 1-visibility graph, and thus there are non-planar bar 1-visibility graphs.... ..."

### Table 7: Effects of conjectured nonmember feedbacks.

1996

"... In PAGE 24: ... Thus, member industries approximate the (n ? m) non-member industries as a single external industry . Table7 illustrates the effect of incorporating this restrictive aggregated view of the outside world on the performance of three of the algorithms described in section 4. Each shows only a modest improvement.... ..."

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### Table 7: Effects of conjectured nonmember feedbacks.

1996

"... In PAGE 24: ... Thus, member industries approximate the n28n n00 mn29 non-member industries as a single external industry . Table7 illustrates the effect of incorporating this restrictive aggregated view of the outside world on the performance of three of the algorithms described in section 4. Each shows only a modest improvement.... ..."

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### Table 4. Improvement in classification accuracy using majority voting ensembles. Optimal unweighted majority-voting ensemble classifiers were formed by selecting classifiers from all 8 classifiers for each feature set listed and the average classification accuracy for 10-fold cross-validation was calculated. A paired-t test was performed for each ensemble classifier against the previous neural network classifier for each feature subset (SLF15 and SLF16 were compared against the previous classifier for SLF8 and SLF13, respectively). Each ensemble classifier was also compared against the optimal classifier for each feature set listed in Table 2 (SLF15 and SLF16 were compared with the individual optimal classifiers for SLF8 and SLF13, respectively).

"... In PAGE 10: ... Therefore, we constructed an unweighted majority-voting ensemble of all possible combinations of the 8 classifiers for each feature set. Table4 shows the optimal majority-voting classifiers found for each feature set. The accuracies on both SLF8 and SLF13 feature sets were improved by 1% by combining three classifiers for each: exponential-rbf-kernel SVM, AdaBoost, and Bagging for SLF8; rbf-kernel SVM, AdaBoost, and Mixtures-of-Experts for SLF13.... In PAGE 12: ...11 SVM, exponential-rbf-kernel SVM, polynomial-kernel SVM, and AdaBoost for SLF16, and rbf-kernel SVM, exponential-rbf-kernel SVM, and polynomial-kernel SVM for SLF15 ( Table4 ). We achieved a 92.... In PAGE 12: ... The benefits of including the new texture features can be represented by a 2% improvement on classifying 2D protein fluorescence microscope images both with and without DNA features. Table4 also showed that the accuracy upper bounds for SLF16 and SLF15 are higher than those of SLF13 and SLF8 feature sets respectively. To gain insight into the basis for the improvement, we compared the distributions in the two feature spaces of those images that were misclassified by the neural network classifier using SLF13 but were correctly classified by the ensemble classifier using SLF16.... In PAGE 13: ... Furthermore, the relatively independent errors (Table 3) among the classifiers of a majority-voting ensemble contribute to a more robust prediction. For instance, linear-kernel SVM, one of the five classifiers in the best performing ensemble classifier of SLF16 ( Table4 ), predicted the image of the transferrin receptor pattern in Figure 6 as tubulin, while all other classifiers in the ensemble made the accurate prediction. This error would not be avoided if the linear-kernel SVM was selected as the only classifier.... In PAGE 14: ... Firstly, different image set sizes were tested for the six feature sets. Figure 8A shows the average performance of the majority-voting classifier for each feature set ( Table4 ) over 1000 random trials of image sets drawn from each class in the test set. The dominant predicted class in an image set was taken as the output, while random choice was made if several classes tied.... ..."

### Table 4.1: The sizes of the largest known critical sets of small order, with conjectured bounds

### Table 3 illustrates how the number of feasible weekend-off distributions decreases for a 12-week-rota when successively adding these constraints.

"... In PAGE 17: ... Table3 : Reducing the Number of Feasible Weekends-off Distributions In order to analyse the effect of pre-generating all feasible weekends-off distributions on the runtime we chose an example with six weeks. The following figure illustrates the Branch-and-Bound process.... ..."

### Table 2: Improved Upper Bounds

"... In PAGE 6: ... Algorithm IFlatIRelax was implemented in COMET, which is constraint-based language for local search (Michel amp; Van Hentenryck 2002; 2003). New Upper Bounds Table2 shows the lower and upper bound reported in (Nuijten amp; Aarts 1996) and used in the evaluation in (Cesta, Oddi, amp; Smith 2000). It also reports the new best upper bounds found by IFlatIRelax during the course of this research.... ..."

### Table 2. Conjectured sizes of X(J; Q)

2001

"... In PAGE 7: ... In [Sto4], some improvements of the results and algorithms in [FS] and [Sto3] are discussed. The regulators in Table2 have been double-checked using these improved algorithms. 3.... In PAGE 17: ...In Table2 , we list the curve CN simply by N,thelevelfromwhichitarose.Let r denote the rank.... ..."

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### Table 2. Upper Bounds for 3-D Orthogonal Box-Drawings

1999

"... In PAGE 6: ... A uni ed approach to drawing line-, cube- and box-drawings in general position is presented in [28]. Table2 summarizes the known bounds for orthogonal graph drawings. Table 2.... In PAGE 6: ...Problem 7. Can the bounds in Table2 be improved? Problem 8. Biedl [2] shows that any orthogonal drawing in general position re- quires (max n3; m2 ) bounding box volume, and conjectured that this lower bound can be improved to (n2m).... ..."

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