### Table 2: Growing and mapping results. Our algorithm generates a number of solutions (20,000) and keeps an ensemble of acceptable solutions with scores near (within 20% of) the best found. Results are averaged over these ensembles. Error rate is summarized by the mean number of wrong edges per cover. Growing alone yields a surprisingly good error rate, due to the requirement of global consistency of a cover. A simple application of MAPPER then filters the set of fragments, keeping only long enough ones that unambiguously map to the primary sequence. The error rate drops, and a large number of the residues are unambiguously assigned.

### Table 1: Ip: rules for PL expression inclusion

2001

"... In PAGE 7: ... Another related result appears in [28] for a slightly more expres- sive language that allows single wildcards. In contrast to their PTIME complexity result for testing inclusion of path expressions, we are able to provide a set of in- ference rules, denoted by Ip, in Table1 , and to develop a quadratic time algorithm for testing inclusion of PL expressions. Proof sketch: The soundness of Ip can be veri ed by induction on the lengths of Ip-proofs.... ..."

Cited by 52

### Table 1: Ip: rules for PL expression inclusion

"... In PAGE 9: ... We investigate inclusion (containment) of path expressions in PL: Given any PL expressions P and Q, is it the case that P Q? As will become clear shortly, this is important to the proof of Theorem 2, and it is decidable with low complexity. We provide in Table1 a set of inference rules, denoted by Ip, and to develop a quadratic time algorithm for testing inclusion of PL expressions. Theorem 3 Given two PL expressions P and Q, Ip is a set of sound and complete inference rules for determining whether P Q.... ..."

### Table 4: Regular expressions for DNA tests.

2004

Cited by 3

### Table 2. String matching and regular expression matching times. The string matching times are total times of matching 10000 patterns. text string matching regular expression matching matches time (s) time (ms)

1995

"... In PAGE 12: ... To test matching performance we implemented string matching and regular expression matching algorithms for all three data structures. The results of our tests are given in Table2 . The execution times include going through the set of matches.... ..."

Cited by 29

### Table 1: Test Performance of Manual Regular Expressions Attribute Precision Recall

"... In PAGE 2: ... Our source data was drawn from Fairfax County, Virginia police incident reports. Based on this training data, we manually generated regular expressions for the seven attributes listed in Table1 . An independent test dataset was used to assess the performance of the regular expressions.... In PAGE 3: ...Based on the results in Table1 we concluded that regular expressions are suitable for the extraction of attributes in police incident reports. In the course of this work, however, we confirmed that it is both time consuming and tedious to create regular expressions for information extraction manually.... ..."

### Table 5. Explicit representation vs. regular expressions in dataset one

2002

"... In PAGE 10: ... In this way we generated eight test sets for each training set for a total of 9*8=72 test results. Table5 depicts example test results using dataset number one as a training set. We applied a one-tailed t-test to the two distributions, one using an explicit representation and the second using regular expressions.... ..."

Cited by 2

### Table 1: Experimental results for regularity extraction algorithm

"... In PAGE 3: ... Similar to [6], we define Regularity Ex- traction Factor (REF) as the ratio between the number of identical connectiontemplates and the total numberof templates required for matching all nodes. Table1 presents the characteristics of the used test-bench and ex- perimental results for our regularity extraction technique. The first 9 networks describe circuits and the last 3 relate to filters.... In PAGE 3: ... This result is evident from the fact that networks with higherRF enable more identical connectiontem- plates to be found during decomposition for regularity extraction. Table1 indicates that REF values are about 50 % of the network RF values. We alsostudiedthe natureof the commonconnectiontemplates.... ..."

### Table 2: Classes for regular expressions

2005

"... In PAGE 3: ... The constant Epsilon represents the empty string and the constant Emptyset represents the empty set. See Table 1 and Table2 , Appendix A, for more details. Equivalence and simplification of regular expressions is a major topic of research in automata theory.... ..."

Cited by 4