### Table 1: Subreducts of relativized RRA apos;s.

1998

"... In PAGE 12: ... Some are not varieties, some are, and for some, we don apos;t know the answer. Table1 lists the results we do have. As a contrast, we add the results for subreducts of RRA (recall that RRA = SRlRST RRA) in the fth column.... In PAGE 12: ...Andr eka et al., 1994a). For completeness apos; sake, we add the results for the full language at the bottom. Table1 should be read as follows. In the left column, we list the operators of the subreduct involved.... In PAGE 12: ... Theorem 5.3 Let K be any item occurring in Table1 . Then (i) if it is labelled by V , it is a non- nitely axiomatizable variety; (ii) if it is labelled by V, it is a canonical nitely axiomatizable variety whose axioms are given in Table 2; (iii) if it is labelled by QV, it is a nitely axiomatizable quasi-variety, but not a variety; (iv) if it is labelled by QV?, it is a quasi-variety, and might still be a variety.... In PAGE 14: ... Theorem 5.5 All classes in Table1 which are labelled with V enjoy the strong amalgamation property. Proof.... In PAGE 14: ... (The proof of) Theorem 4.2 gives us positive amalgamation results for all classes in Table1 labelled with V, since all axioms involved, except AL16 , are treated in that theorem. The canonical equation AL16 corresponds to NL16 8xyzv((Cxyz amp;Ix amp;Cyyv amp;Iv) ! Cvzy); whence, by the proof of Theorem 4.... ..."

Cited by 4

### Table 1 : Equivalent relativizations

2006

"... In PAGE 36: ... We have also established a few new cases of validity of the Conjecture. The main results are summarized in Table1 and 2... In PAGE 36: ...Table 1 : Equivalent relativizations Undirected graph classes Directed graph classes Uniformly k-sparse Uniformly k-sparse Line graphs Directed line graphs Quasi-series-parallel partial orders Finite interval graphs Finite partial orders of dimension 2 Interval graphs (for MS-OI) Partial orders of dimension 2 (for MS-OI) Table 2 : Proved relativizations. Table1 shows the equivalent relativizations. One could add the extensions of these classes by vertex and edge labellings.... ..."

Cited by 9

### Table 3: Probabilistic network characteristics

"... In PAGE 18: ...2 Probabilistic networks We continue our computational study with 8 real-life probabilistic networks from the field of medicine. Table3 shows the origin of the instances and the size of the network. The most effi- cient way to compute the inference in a probabilistic network is by the use of the junction-tree propagation algorithm of Lauritzen and Spiegelhalter [24].... In PAGE 18: ... The moralization of a network (or directed graph) D = (V, A) is the undirected graph G = (V, E) obtained from D by adding edges between every pair of non-adjacent vertices that have a common successor, and then dropping arc directions. The size of the edge set E is also reported in Table3 . After the application of pre-processing... In PAGE 18: ...Table 3: Probabilistic network characteristics techniques for computing the treewidth [8], an additional four instances to conduct our heuris- tics on are available. The size of these four instances is reported in Table3 as well. After [8], henceforth we refer to these instances as instancename {3,4}.... ..."

### Table 24 Management involvement

2004

"... In PAGE 9: ...8 100.0 Table24 displays the results of the survey question asking for the level of management involvement in UML usage. Results show a mixed level of management involvement across the sample population.... ..."

### Table 5 Profile Times for the Hierarchical Bounding Box and Probabilistic Culling Algorithms

1997

"... In PAGE 6: ... Table5 shows the comparative timings, of two culling algorithms, the hierarchical bounding box, and the probabilistic culling algorithm. The time is the total time for the scene walkthrough, but excluding actual rendering time (the rendering occurred on an X11 server running as a separate process).... ..."

Cited by 8

### Table 2: Parameters to the Probabilistic Counting Al- gorithm

1996

"... In PAGE 8: ... The algorithm based on probabilistic counting esti- mates the size of the cube to within a theoretically pre- dicted bound. The values of the parameters we used are shown in Table2 . The estimate is accurate under widely varying data distributions, ranging from uniform to highly skewed.... ..."

Cited by 66

### Table 2: Parameters to the Probabilistic Counting Al- gorithm

1996

"... In PAGE 8: ... The algorithm based on probabilistic counting esti- mates the size of the cube to within a theoretically pre- dicted bound. The values of the parameters we used are shown in Table2 . The estimate is accurate under widely varying data distributions, ranging from uniform to highly skewed.... ..."

Cited by 66

### Table 5 Profile Times for the Hierarchical Bounding Box and Probabilistic Culling Algorithms

"... In PAGE 26: ... Table5 shows the comparative timings, of two culling algorithms, the hierarchical bounding box, and the probabilistic culling algorithm. The time is the total time for the scene walkthrough, but excluding actual rendering time (the rendering occurred on an X11... ..."

### Table 1. Applied methods and involved procedures in consecutive time steps Time

"... In PAGE 6: ... As it is shown in Fig. 1, our agent in consequent stages is learning and later simulating some procedures based on NN and algorithmic methods, which are single out in the Table1 . Consistency of applied methods plays a key role.... ..."

### Table 1: Probabilistic Relevance Propagation Algorithms Method k Neighbors is pis

"... In PAGE 3: ... In particular, the framework can recover most existing link-based algorithms. Table1 shows a family of relevance propagation algorithms which are covered by our general framework. As can be seen from the table, PageRank and its extensions are special cases of the framework.... ..."

Cited by 2