### Table 2 The effect on Apriori frequent itemset by the time-windows based data preprocessing algorithm

### TABLE II WINDOWS-BASED (COMPARISON OF DIFFERENT WINDOWS-BASED ISOTOPE PATTERN CALCULATOR)

### Table 1. Overall results. All window-based algorithms are evaluated at their best window size. In addition to general recall and precision rates, more precise estimates of the odds multipliers and their 95% confidence intervals are given.

"... In PAGE 3: ... Fortunately, there are a number of non-parametric al- gorithms that perform much better than the window-based set. Table1 presents a complete list of the performance of all algorithms. It is divided into the two traditional met- rics of recall, or the percentage of symbols on the page that were found during the OMR process, and precision, or the percentage of symbols in the OMR output that were in fact on the page.... In PAGE 4: ...44, the difference is statistically significant. Table1 is ordered by recall performance, which is the most important measure to optimise when trying to reduce human editing costs after the OMR process. The clear winner is Brink and Pendock 1996, which performs sig- nificantly better than all others in both performance and recall; a sample of its output appears in figure 2a.... In PAGE 4: ... None of them has received much attention to date, and indeed, the most commonly used binarisation algorithm is the notably mediocre Otsu 1979 (see figure 2c). A more visual representation of some of the data in Table1 appears in figure 4. This figure is a box-and- whisker plot on recall performance for every image in the test set.... ..."

### Table 3: Window based reordering: performance and quality comparison.

1997

Cited by 7

### Table 7. Comparison of the computing times of window based schemes

2002

Cited by 4

### Table 3. Median crossover lengths for Window-based design.

2004

"... In PAGE 18: ... The lines represents the median of SPECint and SPECfp benchmarks with different tech- nologies and transcoder designs. The resulting crossover lengths are given in Table3 . As technology shrinks, the crossover point becomes shorter, which is what we would expect since wire power grows more dominant in smaller technologies.... ..."

Cited by 2

### Table 3. Median crossover lengths for Window-based design.

2004

"... In PAGE 18: ... The lines represents the median of SPECint and SPECfp benchmarks with different tech- nologies and transcoder designs. The resulting crossover lengths are given in Table3 . As technology shrinks, the crossover point becomes shorter, which is what we would expect since wire power grows more dominant in smaller technologies.... ..."

Cited by 2

### Table 11. Results from dynamic window-based pivoting

"... In PAGE 17: ... In general, a window size of 10%-15% of the time constraints generally seemed to give good results. Looking at Table11 , the rst example shown (DIFFEQ) is small enough that 5% of the time constraints is statistically insigni cant, leading to results that are dominated by the area-axis exploration. However, the EWF results give a strong argument for using dynamic pivoting { here a bad a priori choice of using only latency-axis exploration or area-axis exploration (as shown in Table 10) could lead to a signi cantly larger execution time than 15% dynamic pivoting.... ..."

### Table 3. Median crossover lengths for Window-based design.

"... In PAGE 17: ... The lines represents the median of SPECint and SPECfp benchmarks with different tech- nologies and transcoder designs. The resulting crossover lengths are given in Table3 . As technology shrinks, the crossover point becomes shorter, which is what we would expect since wire power grows more dominant in smaller technologies.... ..."