### Table 5: Results for exif with complex predicates

"... In PAGE 6: ... Because the bug is non- deterministic, p1 is also true in 335 runs that succeeded, making p1 a partial predictor. Including complex predicates in the analysis produces one complex predicate shown in Table5 . (The second row is the second component of a complex predicate, which is a conjunction as indicated by the keyword and at the start.... ..."

### Table 5: Results for exif with complex predicates

"... In PAGE 6: ... Because the bug is non- deterministic, p1 is also true in 335 runs that succeeded, making p1 a partial predictor. Including complex predicates in the analysis produces one complex predicate shown in Table5 . (The second row is the second component of a complex predicate, which is a conjunction as indicated by the keyword and at the start.... ..."

### Table 3: The complexity of Schur functions on 1-semi-unitary matrices This analysis seems to suggest a hierarchy of di culty: The determinant is always easy. For a given class of matrices M, whenever the permanent is di cult, so are ham and cycle . Finally, whenever cycle? is hard, so is cycle+. Note that the unitary matrices and C( x; y) are closed under some kind of multiplication and the C( x; y) matrices are themselves unitary. Problem 5.1 Is this hierachy of complexity of Schur functions true for any multi- plicatively closed class of matrices ?

in 9

### Table 2. (Adapted from [313]) Complexity of Horn Logic Programs Notation: The complexity results in the above table refer to worst case analysis for skeptical reasoning, i.e. to determining if a given literal is true in every canonical model (with respect to a particular semantics) of the program. For logic programs with no function symbols, the data complexity over an EDB E is presented. The notation used is the following: jPj denotes the length of the program P; jAj denotes the number of propositional letters in P; jEj denotes the total number of symbols that occur in the EDB E.

"... In PAGE 12: ... Schlipf [313] has written a comprehensive survey article that summarizes the results. Some of these results, taken from [313], are listed in Table2 . A user may wish to determine which semantics to be used based upon the complexity expected to nd answers to queries.... In PAGE 17: ... Schlipf [313] and Eiter and Gottlob [106] have written comprehen- sive survey articles that summarize the complexity results that are known for alternative semantics. Some of these results, taken from [104, 106], are listed in Table2 . A user may wish to determine the semantics to be used based upon the complexity expected to nd answers to queries.... ..."

### Table 3: Test run results for dfs with edge classification and disjoint tree searching. The right two columns give the values for the variables in the I/O complexity equation O(E/BD + V )

"... In PAGE 12: ... This term dealt with how often the algorithm needs to scan through the adjacency list in the process of the search structure. Since there is no longer a search structure, this term can be removed, resulting in a complexity bound of O(E/BD + V ) Once again, dfs is put thru test runs, and the data, which can be seen in Table3 , is put into a regression to see how true to the complexity bound it is.... ..."

### Table 3. Performance results for Complex preference model conflguration.

2004

"... In PAGE 10: ...ig. 1. True (Dashed) and Learned (Solid) Preference Utility curves for Simple Pref- erence experiment. Table3 shows the experimental results for the complex preference model conflg- uration, and Figure 2 shows plots of learned and true models for each user from one of the experimental runs. As in the flrst experiment, the performance of the learned preference model is found to be better than that of the random model for all individuals in the organization (i.... ..."

Cited by 2

### Table 3 Formal Complexity Rules

1997

"... In PAGE 7: ... Next, we show how to use these primitive operations to implement the queries in our high level language, which allows us to determine the worst case run-time complexities of these queries on a pointer RAM. We rst give inference rules in Table3 that take the type and subtype system into account to give asymptotic run-time complexities for implementation of primitive set operations (Table 2) on a pointer RAM. The judgment ? ` fAg S; O(p) time can be read as \ In the type environment ?, if the precondition A is satis ed then S can be executed in O(p) worst case time quot;, where S could be either a statement or an expression, in which case we also assign a type to the computed expression.... In PAGE 7: ... If the predicate A is true then it may be elided. In the judgments in Table3 , may be any type except a strong base, and x is unit-space data computable in O(1) time. We shall not formally prove the soundness of the rules in Table 3 but will try to informally establish... In PAGE 7: ... type to the computed expression. If the predicate A is true then it may be elided. In the judgments in Table 3, may be any type except a strong base, and x is unit-space data computable in O(1) time. We shall not formally prove the soundness of the rules in Table3 but will try to informally establish... ..."

Cited by 5

### Table 1. Average true positive and false positive rates for protein interaction validation based on inferred domain-domain interaction information from various data sources.

2003

"... In PAGE 5: ... Table1 shows that an integrative approach that uses multiple data sources for protein interaction validation is advantageous. By introducing an additional data source of protein complexes, the true postive rate was vastly improved without greatly affecting the false positive rate with the additional inferred domain-domain interactions.... ..."

Cited by 18

### Table 3. Performance results for Complex preference model conflguration.

2004

"... In PAGE 10: ... Figure 1 shows the true and learned preference models for each user from one of the simple preference conflguration runs to give a graphical sense of their correspondence. Table3 shows the experimental results for the complex preference model conflg- uration, and Figure 2 shows plots of learned and true models for each user from one of the experimental runs. As in the flrst experiment, the performance of the learned preference model is found to be better than that of the random model for all individuals in the organization (i.... ..."

Cited by 4

### Table 3. Performance results for Complex preference model conflguration.

2004

"... In PAGE 10: ... Figure 1 shows the true and learned preference models for each user from one of the simple preference conflguration runs to give a graphical sense of their correspondence. Table3 shows the experimental results for the complex preference model conflg- uration, and Figure 2 shows plots of learned and true models for each user from one of the experimental runs. As in the flrst experiment, the performance of the learned preference model is found to be better than that of the random model for all individuals in the organization (i.... ..."

Cited by 4