"... In PAGE 11: ..., 1996]. The variables required to integrate the assim- ilation system are listed in Table3 . As noted in section 2.... ..."

"... In PAGE 21: ... In this section, we integrate both image fusion and match score fusion algorithms as described in Section 5 to evalu- ate the face verification performance. The validation results of the integrated multilevel fusion algorithm are summarized in Table4 . The integrated fusion algorithm yields 99.... ..."

"... In PAGE 8: ... In general, we can find three main overlapped mechanisms to perform integration, the use of top-level ontologies, logical inferences and/or matching functions. Table1 shows the more representative and referenced proposals clas- sified by these three types. One particularity of all these proposals is the use of ontologies to represent either top-level information or domain information or both of them.... ..."

"... In PAGE 6: ... Possibly, some of the an- swers returned by the version with named entities are paraphrases of the correct answer that do not match the regular expressions and therefore they are erroneously classi ed as wrong. In fact, note that the results without named entities reported in Table6 are slightly worse than the ones returned by NIST (Table 4) due to inaccuracies in the reg- ular expressions. 7 Conclusions and Further Research The short time available only allowed us to build a baseline system for the passages task of the Ques- tion Answering track.... ..."

"... In PAGE 1: ... These include: PROSITE (2), which uses regular expressions and profiles, PRINTS (3), which uses Position Specific Scoring Matrix-based (PSSM- based) fingerprints, ProDom (4), which uses automatic sequence clustering, and Pfam (5), SMART (6), TIGRFAMs (7), PIRSF (8), SUPERFAMILY (9), Gene3D (10) and PANTHER (11), all of which use hidden Markov models (HMMs). Table1 shows the coverage of each of these member *To whom correspondence should be addressed.... ..."

"... In PAGE 23: ...nalyzed the results reported in Table 6. Specifically, for each correct match (i.e., case 1 match), we check whether it is due to name and value based matching or due to predictive matching metrics based matching. The result of our analysis is shown in Table7 , where #att denotes the number of correctly matched attributes, #pos denotes the number of matched attributes due to name and value based matching, and #pred denotes the number of matched attributes due to predictive matching metrics based matching. Note that only the results when all the interfaces in each domain are used are reported in Table 7.... In PAGE 23: ... The result of our analysis is shown in Table 7, where #att denotes the number of correctly matched attributes, #pos denotes the number of matched attributes due to name and value based matching, and #pred denotes the number of matched attributes due to predictive matching metrics based matching. Note that only the results when all the interfaces in each domain are used are reported in Table7 . The result indicates that overall about 17% of all correct matches are due to the use of the predictive matching metrics.... ..."

"... In PAGE 18: ...where U = [u1 uN]T are the constraints and the real symmetric N N matrix [A] is given by [A]ij = X w Xi(w)Xj(w) PN k=1 kS k(w) + S (w) ! : 5 E cient Template Matching Algorithms Table1 gives the computational complexity of the measures of match described in section 2. The sequential similarity detection algorithms discussed in section 2 are not represented in table 1, since techniques for increasing their e ciency are an integral part of the measure of match.... ..."

"... In PAGE 9: ... Representing a refinement of the matching degree definitions from [8], we therewith obtain a precise means for differentiating the usability of a Web service on the goal template level. Table2 provides a concise compilation of the matchmaking degree definitions. Table 2.... In PAGE 11: ...Integration of Matchmaking Techniques We complete this section with combing the semantic matchmaking techniques for the goal template and the goal instance level in order to attain an integrated matchmaking framework for our two-phase Web service discovery. We therefore extend matchmaking degrees from Table2 with the matchmaking condition for the goal instance level. Due to their definition, we can simplify the matching condition from Definition 4 for the distinct matchmaking degrees as follows.... In PAGE 13: ... Hence, the condition for the inter- sect match is satisfied (cf. Table2 ). Furthermore, we observe that for the input binding fl2 there can not exist such a common interpretation.... ..."

"... In PAGE 4: ... Samples (1 ml) were resolved on denatur- ing polyacrylamide gels (7 M Urea and 20% 40:1 acrylami- de:bisacrylamide) and analyzed with a Molecular Imager FX system (BioRad, Hercules, CA). Primer extension using alternative templates Oligonucleotides ( Table1 ) were synthesized and PAGE purified from Integrated DNA Technologies (Coralville, IA). These various oligonucleotides have 1 nt mismatch at dif- ferent sites where the formyl and amine primers anneal.... In PAGE 6: ...igure 5. Mismatch discrimination using M-MuLVRT. PAGE gel (20%) of primer extension assays with mismatched DNA 38mer templates using M-MuLVRT. (A and B contains the mismatches in the site of binding of the 30-fragment 8mer and 50-fragment 8mer, respectively (see Table1 for details of mismatches). Final concentrations: dNTPs (100 mM each, for A, T, G, C, with 33 nM [a-32P]dCTP) template (30 pmol, 3 mM), 30-terminal 8mer fragment carrying a 50-amino group (20 pmol, 2 mM), and 50-terminal 30-aldehyde- 8mer (20 pmol, 2 mM).... In PAGE 7: ... Figure 5 collects data obtained with M- MuLVRT that shows it does. Here, the primer extension assays examined with DNA 38mer templates containing single mismatches at various positions throughout the binding region (see Table1 for details of mismatches). Perhaps not surprisingly, a mismatch at the 30 end of the composite primer resulted in no detectable elongation.... ..."