### Table 1: Tableau comparison of existing approaches.

1994

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### Table 2. Partial assignments

"... In PAGE 3: ... For each column n and each partial assignment x we denote the number of specified carries with cn x. Let us suppose we have the partial assignment X1X1 and 1X1X on the inputs as shown in Table2 . At least two addends in column 4 are one, thus we know that at least one carry in column 5 has to be generated, hence we have c5 x = 1.... In PAGE 3: ... At least two addends in column 4 are one, thus we know that at least one carry in column 5 has to be generated, hence we have c5 x = 1. In a tabular representation as in Table2 it is simple to derive this information. However, if the addition... ..."

### (Table 2) Partially

2000

Cited by 11

### Table 1. XP Core Practices Experience Summary XP Core

in 1

"... In PAGE 9: ... Coding standards have also long been incorporated into projects run under traditional methodologies although the imperative for them might seem less since code ownership is usually not collective. 5 ISSUES FOR DISCUSSION AND PROJECT INSIGHTS The project at NextEd was a success story that applied many of the core practices (see Table1 ) of XP. From both customer and developer perspectives, it delivered on the requirements, limited as they were in initial detail, to produce a publishing system for... ..."

### Table 2. Effect of the partial instances

2003

"... In PAGE 4: ... The usefulness of such justified rejections can be measured by providing our learner with partial instances. In the fol- lowing experiment ( Table2 ), the teacher provides the learner with 90 partial negative instances (after 10 complete positive ones) in the training data. We consider partial in- stances involving 2, 5, 10 variables, and report the size of the version space and of the set of clauses (effective space used to represent the general bound) after 100 instances have been given.... ..."

Cited by 3

### Table 2. Improvements by partial evaluation

2002

"... In PAGE 15: ...iously annotated to be partially evaluated (i.e., cost centers are introduced on the same expressions to be specialized). Table2 shows our profiling results for several well-known benchmarks of partial evaluation. Some of them are typical from deforestation (the case of all ones, app last, double app, double flip, length app) and kmp is the well-known KMP test .... In PAGE 15: ...5 For each benchmark, we show the number of steps, pattern matching operations and applications for the original and residual programs, respectively; we do not include information about the amount of nondeterminism since it is not changed by the partial evaluator. The last column of Table2 shows the actual speedup (i.e.... In PAGE 15: ... Runtime input goals were chosen to give a reasonably long overall time. From the figures in Table2 , we observe that the cost information collected by the profiler allows us to quantify the potential improvement which has been achieved by the residual program. For instance, the more significant improve- ments on the symbolic costs are produced for the KMP benchmark which, in- deed, shows an actual speedup of 12.... ..."

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### Table 8: Partial Derivatives of Chile

"... In PAGE 14: ... While the neural network analysis shows that one can explain almost 2 4 percent of the variation in the Chilean stock market with a feed-forward model with ten neurons, the question remains, can one make sense of this model, or of any of the models? To interpret the linear model and the best polynomial and neural network , one needs to examine the partial derivatives implied by the alternative parameter estimates. The partial derivatives appear in Table8 , below. The linear model shows that only the lagged home-market is significant for the Chilean index.... ..."

### Table 3: Negation and Partial Negation

1997

"... In PAGE 14: ... postcondition: Returns conflict if A and B conflict, no-conflict otherwise. BEGIN-- Use Table3 (Negation table) to negate A. neg-A = Negate(A) IF (neg-A syntactically equals B) RETURN conflict ELSEIF (the relation operator in A is one of f gt;; lt;;=g) -- Use Table 3 (Negation table) to negate B.... In PAGE 14: ... BEGIN-- Use Table 3 (Negation table) to negate A. neg-A = Negate(A) IF (neg-A syntactically equals B) RETURN conflict ELSEIF (the relation operator in A is one of f gt;; lt;;=g) -- Use Table3 (Negation table) to negate B. partneg-A = PartialNegate1(A) IF (partneg1-A syntactically equals B) RETURN conflict ELSEpartneg2-A = PartialNegate2(A) IF (partneg2-A syntactically equals B) RETURN conflict ELSERETURN no-conflict END IF END IF END IF END IF END Negation Figure 4: The Negation Algorithm { Decides if Two Constraints Con ict with Each... ..."

Cited by 8

### Table 3. Partial NICETEXT grammar.

2001

"... In PAGE 4: ... Writing grammars with thousands of rules was not something that we could require most users to do. The following demonstrates the results of a very small grammar (partially shown in Table3 ) using a small part of the original dictionary with the ciphertext in Table 2 as input: Jodie, Ernesto Lauriston and Roger met Cristie Mackzum. In 1720, Maurise Leigh met Gordan.... ..."

Cited by 4