### Table 3: Comparison of the system costs for Differential Equation Solver, RLS-laguerre Lattice Filter, and Elliptic filter, with different time constraints.

2005

"... In PAGE 23: ...3. All experiments shown in Table 2 and Table3 are finished in less than one second and the longest running time is 0.11 seconds which occurs when Algorithm DFG Assign Repeat is experimented on Elliptic Filter with timing constraint 100 time units.... In PAGE 26: ...s 27.9% and that for Tree Assign is 30.1%. The experimental results for differential equation solver, RLS-laguerre lattice filter and elliptic filter, are shown in Table3 . In the table, each column cost under different fields presents the system costs ob- tained from different algorithms: the greedy algorithm (Field Greedy ), the ILP model (Field ILP ), DFG Assign Once (Field DFG Assign Once ), DFG Assign Repeat (Field DFG Assign Repeat ), and DFG Assign CP (Field DFG Assign CP ).... ..."

Cited by 2

### Table 3: Comparison of the system costs for Differential Equation Solver, RLS-laguerre Lattice Filter, and Elliptic filter, with different time constraints.

2005

"... In PAGE 23: ...3. All experiments shown in Table 2 and Table3 are finished in less than one second and the longest running time is 0.11 seconds which occurs when Algorithm DFG Assign Repeat is experimented on Elliptic Filter with timing constraint 100 time units.... In PAGE 26: ...s 27.9% and that for Tree Assign is 30.1%. The experimental results for differential equation solver, RLS-laguerre lattice filter and elliptic filter, are shown in Table3 . In the table, each column cost under different fields presents the system costs ob- tained from different algorithms: the greedy algorithm (Field Greedy ), the ILP model (Field ILP ), DFG Assign Once (Field DFG Assign Once ), DFG Assign Repeat (Field DFG Assign Repeat ), and DFG Assign CP (Field DFG Assign CP ).... ..."

Cited by 2

### Table 2. Comparison of the system costs for differential equation solver, RLS-laguerre lat- tice filter and elliptic filter when the timing constraint varies.

2004

Cited by 1

### Table 6: Simulated Wholesale Price Differential Due to Fuel Compatibility (cents per gallon, standard errors in parentheses)

2006

"... In PAGE 19: ... Conditioning on outage allows separation of the persistent effects of regulatory differentia- tion, such as increased production costs or changes to local competition, from the dynamic effect of the constraints placed on refiners responding to supply shocks by s regulatory differentiation. The average price differential (column 3, Table6 ) between the base case and counterfactual in months without local outages identifies the persistent effects of regulatory differentiation. The incremental difference in months with local outages identifies the additional dynamic effects of incompatible gasoline regulations.... ..."

### Table 1. Examples for single-source problems at schema level (violated integrity constraints) For both schema- and instance-level problems we can differentiate different problem scopes: attribute (field), record, record type and source; examples for the various cases are shown in Tables 1 and 2. Note that uniqueness constraints specified at the schema level do not prevent duplicated instances, e.g., if information on the same real world entity is entered twice with different attribute values (see example in Table 2). Scope/Problem Dirty Data Reasons/Remarks

2000

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### Table 1. A qualitative comparison of the algebraic and constructive methods by some differentiating positive qualities

1999

"... In PAGE 6: ... An example is the determination of the planar configuration of n points using 2n-3 distance constraints by genetic evolution. A qualitative comparison of some prevailing factors of the two main approaches is provided in Table1 . The table lists some differentiating positive qualities that are of interest to CAD users and developers and attributes them to the appropriate approach.... ..."

Cited by 2

### Table 2. Performance Results on the Magic Series Program. Interestingly, local search performs reasonably well on this problem as indicated in Table 2. The table gives the best, average, and worst times in seconds for 50 runs on a 2.4Ghz Pentium, as well as the standard deviation. The contributions here are twofold. On the one hand, Comet naturally ac- commodates logical and cardinality operators as differentiable objects, allowing very similar modelings for constraint programming and local search. On the other hand, implementations of logical/cardinality operators directly exploit incremen- tal algorithms for the constraints they combine, providing compositionality both at the language and implementation level. The implementations can in fact be shown optimal in terms of the input/output incremental model [14], assuming optimality of the incremental algorithms for the composed constraints.

### Table 2. Comparison of the feasible region of LP-QDMC and single QDMC strategy.

1980

Cited by 1

### Table 2. Delay differentiation

2001

"... In PAGE 4: ... 3.3 Delay Differentiation Table2 presents the ability of PP in providing delay dif- ferentiation service. This ability is once again compared with WFQ and WRR disciplines.... In PAGE 4: ... For Table 2, D6 BD BPBCBMBG, D6 BE BPBCBMBF, D6 BE BPBCBMBE and D6 BG BPBCBMBD; and AQ BD BPBCBMBFBI, AQ BE BPBCBMBEBJ, AQ BF BPBCBMBDBK and AQ BG BPBCBMBCBL. Table2 shows that in this case, the achieved average waiting time by PP disci- pline accurately approximates that by WFQ or WRR with... ..."

Cited by 10

### Table 3: Semantic differentials

2000

Cited by 7