### Table 1: Constant time accessor functions

2003

"... In PAGE 5: ... In this paper, we will denote the document ordering by a5 . Throughout this paper, we impose a specific physical data model on our XML database, which gives a set of accessor functions (as summarized in Table1 ) which take constant time to run. We have carefully chosen this set of accessors so that it is likely that any reasonable XML database would need to be able to implement these accessors in constant time.... ..."

Cited by 1

### Table 1: Constant time accessor functions

2003

"... In PAGE 6: ... We have carefully chosen this set of accessors so that it is likely that any reasonable native XML database would need to be able to implement these accessors in constant time. The accessors needed are summarized in Table1 . Of these accessors, PREORDER-PREVIOUS and PREORDER-NEXT can easily be implemented in terms of the others, although in worst cast time linear in the depth of the database.... ..."

Cited by 3

### Table 3.1: The complexity of flows over time with constant transit times in comparison to the corresponding flow problems with inflow-dependent and load-dependent transit times, respectively. The entry poly ( pseudo-poly ) means that the problem can be solved exactly in polynomial (pseudo-polynomial) time.

2003

### Table 7: The 2D shallow water experiment; a comparison of and F computed by the new algorithm and Mehlum apos;s spline method at t = 5. For moderate sizes of constant time steps (0:05 t 0:25), the number of PCG iterations required for solving the corresponding Laplace equation is at most 5 regardless of the grid size. The number decreases for smaller time 16

"... In PAGE 18: ...Table7 we observe rapid convergence of the solution with our new algorithm towards the reference solution computed by Mehlum apos;s spline method. In fact, errors less than the line thickness in the plot are easily achieved even on a coarse grid, see Figures 2 and 3.... ..."

### Table 2: Damage constants and time constants extracted from the irradiation period

### Table 2: Damage constants and time constants extracted from the irradiation period

1995

### Table 2. Comparison of the Time Constant of the Initial Folding Phase with Protein Size and Secondary Structure Type

"... In PAGE 22: ... These early structural events can be attributed to accumulation of partially folded intermediates, which may act as stepping stones in finding the unique native conforma- tion. Table2 lists the time constants of the earliest folding phase measured by continuous-flow fluorescence for some of the proteins discussed in this review, along with their size and structural type. While the number of proteins studied is still too small to warrant general conclusions, it is interesting to note that the initial folding times vary by an order of magnitude but show no apparent correlation with protein size.... ..."

### Table 6: The 2D shallow water experiment; a comparison of and F computed by the new algorithm and Mehlum apos;s spline method at t = 5. For moderate sizes of constant time steps (0:05 t 0:25), the number of PCG iterations required for solving the corresponding Laplace equation is at most 5 regardless of the grid size. The number decreases for smaller time steps. From Table 6 we observe rapid convergence of the solution with our new algorithm towards the reference solution computed by Mehlum apos;s spline method. In fact, errors less than the line thickness in the plot are easily achieved even on a coarse grid, see Figures 2 and 3.

### Table 4: Performance results (constant time value) for the Rage512 data set.

2000

"... In PAGE 4: ... Even though the larger data sets show better improve- ments, the T-BON algorithm is over twice as fast on average for the smaller data sets. Table4 demonstrates the performance of the T-BON algorithm, using the Rage512 data set, when the user changes isovalues in the same time step. This query behavior favors the pure BONO ap- proach, since the T-BON algorithm must read more of the tree and data at each new isovalue.... In PAGE 4: ... Eventually, the entire tree and all of the data will be copied to memory and the performance of the T-BON algorithm will approach that of the pure BONO technique. The per- formance deficit in Table4 can be explained by the layering effect in the Rage data set, as is evident in Figure 3. Each cell contains only a small range of values.... ..."

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### Table 5: Performance results (constant time value) for the Rage256 data set.

2000

"... In PAGE 4: ... When the isovalue changes, a new layer of cells must be read from disk, making little use of the pre- vious layer. Table5 shows similar results for the same experiment using the Rage256 data set. Table 6 summarizes the results for the Jet256 data set at time step 50.... ..."

Cited by 21