### Table 4.2 depicts the effect of increasing the number of aggregated points where 45 is a local maximum. The functional values near the local maximum are much lower (around 12). As more number of points are aggregated the effect of local maximum becomes indiscernible. Hence increasing the aggregation affects the revelation of patterns in the overview.

2003

### Table 6.3 Local Maximum Nodal Errors quot; N=8 16 32 64 128 256

1997

Cited by 8

### TABLE 2 EFFECT OF LOCAL MAXIMUM OF AGENTS Agent type Difference with default simulation

### Table 3. Maximum live local variables and unallocatable local variables.

1999

"... In PAGE 32: ....12.1 Register Pressure The first question is to determine how many variables are actually allocated to reg- isters and what is the register pressure in each function. The data in the second column of Table3 shows the maximum number of live local variables in the SPEC95 integer bench- Data Address CRegs aliased: Address Data as optimization SRF aliased: SRF unaliased: Data Reg File Auxiliary Info CRegs unaliased: Reg File Auxiliary Info Figure 11. Contrasting the SRF and CRegs.... In PAGE 33: ... With optimizations such as loop unrolling, function inlining, and software pipe- lining these numbers would be higher. The third column of Table3 cites the number of local variables that were not allo- catable because of aliasing (in the functions which had the highest register pressure). They are marked unallocatable for their entire lifetime because of the potential for aliasing.... ..."

Cited by 4

### Table II. Effect of decomposition on maximum local priorities for M 9

1997

Cited by 28

### Table 19. Maximum Robot Localization Error (E5): Physical System

2003

"... In PAGE 13: ...able 18. Final Map Landmark Error (E1): Physical System ...................................................................... 84 Table19 .... In PAGE 98: ...Mapping Mission Stroupe 84 Quantitative results for small-scale mapping are in Table 17 - Table19 . Confidence intervals are not computed due to the low number of samples in each experiment.... ..."

Cited by 8

### Table 2: total returns (local) total returns (local) excess returns (local) excess returns (local) minimum number maximum number average market

### Table 2: Statistical results of applying the algorithms to the O3 data set. The hit rate of the GMM method with M = 6 certainly refers to a local maximum.

2002

"... In PAGE 11: ... When M = 3, the K-means and the GMM algorithm merged two true clusters and put the outliers in one clusters. Table2 shows the statistics of this experiment. Again, the K-means and GMM algorithm were clearly less robust in finding their respective (local) criterion optimum than the MVC and the K-means was the fastest.... ..."

Cited by 6

### Table 3: Slip Plane Localization error as a function of maximum node depth. (r = 9:64)

2006

"... In PAGE 7: ...Table 3: Slip Plane Localization error as a function of maximum node depth. (r = 9:64) Table3 presents the slip plane localization error as the maximum depth d increases. It is evident from this table that the maximum depth plays a profound role in accuracy.... ..."

Cited by 6