### Table I. Generic branch-and-prune algorithm. 1 BranchAndPrune(C: set of constraints, x: box, quot; : positive oating-point number) 2 returns Union of boxes

2006

Cited by 1

### TABLE I Three generic pseudo-code algorithm types for penalized maximum-likelihood image reconstruction. All of the algorithms presented in the text are of one of these three types. Within each type, the algorithms differ in form of the functions g() used in the M-step.

in Penalized Maximum-Likelihood Image Reconstruction using Space-Alternating Generalized EM Algorithms

1995

Cited by 63

### Table 2. The Generic Propagation Framework

2005

"... In PAGE 3: ... This is consistent with its name, sitemap-based term propagation model (or ST model in brief). Table2 shows the relationship between the two existing models and the generic framework. Table 2.... In PAGE 3: ...3.3 Two New Propagation Models From Table2 , one can easily think of two new models for relevance propagation (corresponding to the two question marks): hyperlink-based term propagation model (or HT model) and sitemap-based score propagation model (or SS model). Similar to the ST model, the HT model needs to propagate the frequency of query term in a web page before adopting relevance weighting algorithms to rank the documents.... ..."

Cited by 14

### Table 2. The Generic Propagation Framework

2005

"... In PAGE 3: ... This is consistent with its name, sitemap-based term propagation model (or ST model in brief). Table2 shows the relationship between the two existing models and the generic framework. Table 2.... In PAGE 3: ...3.3 Two New Propagation Models From Table2 , one can easily think of two new models for relevance propagation (corresponding to the two question marks): hyperlink-based term propagation model (or HT model) and sitemap-based score propagation model (or SS model). Similar to the ST model, the HT model needs to propagate the frequency of query term in a web page before adopting relevance weighting algorithms to rank the documents.... ..."

Cited by 14

### Table I: Three generic pseudo-code algorithm types for penalized maximum-likelihood image reconstruction. All of the algorithms presented in the text are of one of these three types. Within each type, the algorithms di er in form of the functions g() used in the M-step.

in Space-Alternating Generalized EM Algorithms For Penalized Maximum-Likelihood Image Reconstruction

### Table 1 A generic time series

2006

"... In PAGE 6: ... 2001) for an extensive survey), none of the tech- niques allows a distance measure that lower bounds a distance measure defined on the original time series. For this reason, the generic time series data mining approach illustrated in Table1 is of little utility, since the approximate solution to problem created in main memory may be arbitrarily dissimilar to the true solution that would have been obtained on the original data. If, however, one had a symbolic approach that allowed lower bounding of the true distance, one could take advantage of the generic time series data mining model, and of a host of other algorithms, definitions and data structures which are only defined for discrete data, including hashing, Markov models, and suffix trees.... ..."

### Table 2. Resemblance between IBROW, on the one hand, and the Semantic Enabled Web Services project and the Semantic Web in general, on the other hand. IBROW3 (Esprit FP4); IBROW (IST, FP5) The SWWS project, Semantic Web Comments The Web is changing the nature of software Web services are orchestrated into In IBROW, problem-solving methods development to a distributive plug-and-play process. complex services. (PSMs) and ontologies were the The components concerned are problem-solving components being configured, versus methods (generic algorithms) and ontologies. Web services today.

### Table 1, MAP scores for 15 highest scoring settings. Abbreviations: GN, genename; GSC, gene specific context; GC, generic context fingerprint; GO, go annotation; MA, matching algorithm; AE, abbreviation expansion.

### Table 9. Number of generic association rules

"... In PAGE 13: ...f memory, running Windows XP. The algorithm MGB was coded in JAVA. Table 8 shows characteristics of real and synthetic datasets used in our eval- uation. Table9 shows the experimental results. The last column consists of the number of all strong association rules extracted.... ..."

### Table 5. Generic persistent step graph algorithm To use this algorithm we have to choose an instance, like for PG and CSG, for parameter functions A() and (). That is why from this generic algorithm a lot of exploration algorithm instances can be proposed. Some instances are studied in section 3.3 (PminSG), 3.4 (PSmaxG) and 3.6 (HPSG). But rst we prove that any instance of PSG preserves deadlocks. 3.2 Preservation of deadlocks The proof that the PSG preserves deadlocks of the state graph is similar to the one given for the CSG in [VAM96], it follows from a normalisation lemma. Normalisation operator N: Operator N extracts from all sequence w of en- abled transitions in a state s, a maximal step or a maximal pre x of a such step. N : S Step(T) T T 7! Step(T) T is de ned as follows:

2002

"... In PAGE 8: ... Persistent sets are subsets of transitions whose exploration is su cient to detect potential deadlocks, steps are used to re all these transitions \together quot; when possible. The algorithm skeleton is shown in Table5 , referred to as the PSG (Persis- tent Step Graph) algorithm in the sequel. Its layout is similar to that of algorithm CSG in Table 3.... In PAGE 10: ... PSG generalises PG Proof. Any PG can be seen as an PSG: Taking (P; o) = fftg j t 2 Pg in the PSG algorithm shown in Table5 , the graph generated is exactly that generated by the PG algorithm in Table 2, assuming both use the same function A(). So, clearly, the PSG may produce graphs whose size is smaller than those produced by PG.... ..."

Cited by 4