### Table 2 with one additional proposition stating explicitly which

1997

Cited by 152

### Table 4: Prior and posterior probabilities of at least one additive outlier Posterior

2005

### Table IV. Total sample size (in number of subjects) needed to de- tect additive genetic, dominance and common environmental influ- ences in univariate ACE-models and ADE-models for designs with including MZ and DZ twins and siblings added to MZ families only, a power of (1 -b ) = .80, and significance level a= .05

2000

Cited by 39

### Table 7 AWM-HB additive preconditioners; one Gauss{Seidel iteration.

in Stabilizing the Hierarchical Basis by Approximate Wavelets II: Implementation and Numerical Results

1997

Cited by 25

### Table 7. AWM{HB Additive Preconditioners; one Gauss{Seidel iteration

in Stabilizing the Hierarchical Basis by Approximate Wavelets II: Implementation and Numerical Results

1997

Cited by 25

### Table one.oldstyle: Multiplicative-additive linear logic

### Table 6: Adjusted Rand Index for Click. Performance of Click on the various data sets. The results in the clusters column give the number of clusters returned by Click, in addition to one class consisting of all the unclustered elements.

2005

"... In PAGE 9: ... They have been computed requiring all algorithms, reported in Tables 1, 2, 3, 4, 5. Table6 refers to Click, used in an unsupervised fashion, and for the adjusted Rand index. Indeed, Click does not lend itself to adaptation with the FOM methodology.... In PAGE 9: ... Since Click leaves elements unclustered, we have grouped all of those singletons together in one class in order to compute the adjusted Rand index. The number of classes in Table6 accounts for that unification. The first striking conclusion is that no algorithm is mark- edly superior to the others on all indexes and all data sets.... ..."

### Table 3. The most frequent words in the Waxholm speech database plus some additional ones. An approximate English translation is given as well as the frequency rank in the KTH text corpus of 150 million words. Waxholm

"... In PAGE 9: ...57 Word statistics Word frequencies The word frequency ranking of the Waxholm corpus is naturally different from that of large corpora collected from written text. In Table3 , it is compared to a written corpus of 150 million Swedish words - the KTH corpus - that has been collected mostly for the purpose of language modelling in the context of speech recognition. It consists mostly of newspaper text, but also of text from novels, educational books and almost Table 3.... ..."

### Table 1: Axioms of ACP(A; ) and additional ones of ACP (A; ), a; b 2 A [ f ; g, H; I A. BKS axioms for binary Kleene star.

"... In PAGE 7: ... ACP(A; ) is further extended to ACP (A; ) (ACP with branching bisimulation) by adding the constant (silent step) and hiding I (renaming actions in I A into ). Process ex- pressions are subject to the axioms of ACP (A; ), displayed in Table1 (x; y; z; ::: ranging over processes). Note that + and are associative, and that + also is commutative and idempotent.... In PAGE 7: ... For a detailed introduction to ACP(A; ), ACP (A; ) and SC we refer to [BW90]. Axioms for the -operation are included in Table1 . In [FZ94], Fokkink and Zantema prove that (A1){(A5) and (BKS1){(BKS3) axiomatize strong bisimilarity for processes de ned with +; and .... In PAGE 18: ...e., Basic Process Algebra de ned by axioms (A1){(A5) of ACP(A; ) (see Table1 ) together with x$ y = x(x$ y)(x$ y) + y and RSP$ is complete with respect to strong bisimilarity over that signature. Transition rules for $ are x a ??! p x$ y a ??! (x$ y)(x$ y) y$ x a ??! p x a ??! x0 x$ y a ??! x0(x$ y)(x$ y) y$ x a ??! x0 and the remaining transition rules and strong bisimulation equivalence are de ned as usual (cf.... ..."

### Table 3. The most frequent words in the WAXHOLM speech database plus some additional ones. An approximate English translation is given as well as the frequency rank in the KTH text corpus of 150 million words. KTH

"... In PAGE 11: ...Word statistics Word frequencies The word frequency ranking of the WAXHOLM corpus is naturally different from that of large corpora collected fiom written text. In Table3 , it is compared to a written corpus of 150 million Swedish words - the KTH corpus - that has been collected mostly for the purpose of language modelling in the context of speech recognition. It Table 3.... ..."