### Table 4. Round 3: Constraint: implicitInvocation of targetSystem= yes, sharedData of targetSystem=yes (rigorous quality assurance)

2003

Cited by 2

### TABLE 4: TF model value at t a86 0a87 S a86 100. Data given in Table 1. Comparison of partially implicit constraints (use equations (A.12)-(A.14)) and explicit application of constraints (omit equations (A.12)-(A.14)).

2003

Cited by 3

### Table 5. Round 4: Constraints: abstractDataType of targetSystem= yes, c3=yes, implicitInvocation of targetSystem=yes, sharedData of targetSystem=yes (rigorous quality assurance)

2003

"... In PAGE 8: ... The mean benefit drifted from lt;5.5 before treatment (Table 2) to lt;11 at treatment round 4 ( Table5 ). Moreover, the number of samples fell into the high benefit ranges ( lt;27.... ..."

Cited by 2

### Table 4: Value of an American butter y, S = 105, t = 0, jump di usion, data as in Table 1. Im- plicit constraint, American constraint solved implicitly. Algorithm (5.4-5.5): American constraint imposed explicitly. Algorithm (5.6): fully implicit PDE, explicit correlation integral, explicit Amer- ican constraint. Itns is the total number of iterations required in algorithm (4.5), for all timesteps. Change is the change from one level of re nement and the next. Ratio is ratio of changes. Data as in Table 1. Crank-Nicolson timestepping is used with the timestep selector de ned by (5.1), where dnorm = :05 and the initial timestep dtinit = :005, on the coarsest grid.

1998

"... In PAGE 18: ...3), and we assume that the option can only be early exercised as a unit. Table4 shows a convergence study for the American butter y. On each re nement, new nodes are inserted between each two coarse grid nodes and the timestep size is approximately halved.... In PAGE 20: ...5) In this case, we would expect that the time truncation error is O( ). In fact, this is clearly demonstrated in Table4 , since the ratio of changes appears to be asymptotically 4 for the implicit American approach (which indicates quadratic convergence) compared to the asymptotic ratio of 2 (linear convergence) for the explicit American method. It is interesting to see from Table 4 that the number of iterations for the implicit American method is only slightly greater than for the explicit American technique.... In PAGE 20: ... In fact, this is clearly demonstrated in Table 4, since the ratio of changes appears to be asymptotically 4 for the implicit American approach (which indicates quadratic convergence) compared to the asymptotic ratio of 2 (linear convergence) for the explicit American method. It is interesting to see from Table4 that the number of iterations for the implicit American method is only slightly greater than for the explicit American technique. This indicates that we can impose the American constraint implicitly at very little computational expense compared to an explicit constraint method.... In PAGE 20: ... This indicates that we can impose the American constraint implicitly at very little computational expense compared to an explicit constraint method. For comparison, we also show in Table4 the results for a fully implicit discretization of the PDE term, an explicit evaluation of the correlation integral, and an explicit application of the American constraint. More precisely, [I A]Vn+1 = V n + BV n V n+1 = max(V ; Vn+1) : (5.... ..."

Cited by 37

### Table 3. Implicit Semantic Constraint Enforcement

"... In PAGE 18: ... 18 combinations fully preserve constraint checking on deletions we find there are a total of 8 cases (see Table3 below). Table 3.... In PAGE 18: ... Table3 below (which extends Table 2) provides an exhaustive analysis of all ternary ... In PAGE 21: ... 21 theoretical underpinnings for this position and provides a decompositional framework ( Table3 ) to assist practitioners who may have to deal with these issues. 4 REFERENCES Armstrong, W.... ..."

### Table 3: The percentages of the stable solutions implicit semi-implicit composite

"... In PAGE 11: ...5 convergence property of the iterative method for the nonlinear equation are all good in this step. In Table3 , we give percentages of stable solutions obtained by the implicit Euler method, the semi-implicit Euler method and the composite Euler method, respectively. All of the data about percentages of stable solutions are based on 5000 simulated trajectories.... ..."

### Table 1: The explicit and implicit methods.

"... In PAGE 8: ... The computations were carried out on a PC with an Intel PIII-1GHz CPU, RAM of 256 MB, and Windows 2000 OS. Table1 contains computation times for the two different methods, for a number of small instances. The instances are characterized by that there is a demand between every node-pair and that link capacities are uni- formly distributed over {10, 20, 30, 40, 50}.... In PAGE 8: ... For the first 7 instances there are two paths per demand, and for the 5 last instances there are three paths per demand. The results of Table1 suggest that the implicit method is slightly faster than that of the explicit method. If we sum up the usage of variables and constraints for the MIP corresponding to iteration k in the two different approaches, this is reasonable to expect.... In PAGE 9: ...0770 Table 2: The distribution approach. computation times given in Table1 and Table 2, strongly suggests that the distribution approach should be used whenever its deviation from the true optimum is acceptable. Certainly, such an error tolerance will depend on the details of the application.... ..."

### Table 1: Implicit and explicit knowledge

2003

"... In PAGE 2: ... The engines behind the two knowledge spectrum forces are the knowledge processors, natural or artificial entities able to create abstractions from data and to instantiate abstractions in order to fit reality. Knowledge is traditionally categorized into implicit and explicit ( Table1 ) and ranges from rich representations grounded in a reality, to highly abstracted, symbolic rep- resentations of that reality. The classical distinction between data, meta-data, information, knowledge and meta-knowledge is simplified by our subscription to the unified view of Algorithmic Information Theory (AIT) [4] which recasts all knowledge modalities and their process- ing into a general framework requiring a Universal Turing Machine, its programs and data represented as finite binary sequences.... ..."

### Table 1. Implicit Encoding Contrast

"... In PAGE 79: ...ll data were thresholded at p = 0.001. We also included activity peaks of p lt; 0.01 if they were located in the brain area of interest (medial temporal lobe) and indicate this lower threshold where applicable. Implicit Encoding Contrast Brain activity underlying implicit word reading and the implicit formation of semantic face-profession associations was revealed by comparing the masked presentation of face-profession pairs (incongruent, congruent or identical condition) to the masked presentation of face-nonword pairs (neutral condition) for each experiment (second level contrasts) ( Table1 ). Experiment 1 (incongruent).... In PAGE 124: ... 116 Table1 . A proposal for different memory systems.... ..."

### Table 3.5: Indonesia production function, average productivity of irrigated land, 1971-98 Model C Model D

"... In PAGE 30: ... The public goods grew constantly. The strong trend in the variables is reflected in the correlation between the variables as can be seen in the correlation matrix in Table3 -A. Because of this correlation, the number of linear combination of the variables (principal components) needed to exhaust the information embedded in the regressors is rather sma ll.... In PAGE 31: ... There are two possible explanations for this relatively weak effect of the price variables: First, the price does not matter at all. This is not supported by the data because, as shown in Table3 A, the correlation between output and the wholesale price measure is 0.73.... In PAGE 31: ...73. Second, Table3 A shows that the inputs are also correlated with the price. It is therefore likely that much of the contribution of prices is channeled through the inputs, and it is the net direct effect of the price that is weak.... In PAGE 31: ... The reduction in the land elasticities is compensated by the increase in the labor elasticity. The correlation coefficient of labor with irrigated land and with capital is high ( Table3 A), and this may cause the variability in the estimates. Another striking difference from the results for the other two countries is the low capital elasticity.... In PAGE 66: ...84 1.00 Table3 -A: Correlation matrix for Indonesia variables, 1971-98 Output Irrigated land Rain-fed land Fertilizer Capital Labor Wholesale price ratio Price spread No education Roads Infant mortality Output 1.000 Irrigated land 0.... ..."