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Table 1 : R esultats comparatifs de RS et Tabou pour un nombre maximum de 200 000 mouvements La gure 3 donne une vue plus ne sur la di erence de performance entre tabou et RS pour une instance de la classe 100.15.30.20. En abscisse, on donne le nombre de mouvements de 0 a 200 000 avec un pas de 10 000 et en ordonn e, le co^ ut minimal trouv e par chaque al- gorithme a un nombre de mouvements donn e.
Tableau 1: R egles de fusion Le cas le plus int eressant est celui de la derni ere colonne. La nouvelle carte de profondeur apporte une nouvelle probabilit e d apos;occupation pour un voxel visible a partir de ce point de vue. Si le voxel etait auparavant inconnu ou occult e, alors il sera consid er e comme visible et la nouvelle probabilit e lui sera associ ee. Autrement (le voxel etait d ej a visible), la probabilit e est mise a jour en utilisant une version simpli ee du ltre de Kalman : xi+1 = xi + ki[yi+1 ? xi] k?1 i+1 = k?1 i + 1
Table 1: The en-
"... In PAGE 2: ... We illustrate the modelling power of hypernode databases with a running example showing part of a hypernode database, where the labels of hypernodes represent unique identifiers of hy- pernodes in the database, providing the means by which hypernodes can reference each other. The hypernode shown in Table1 , which is labelled EMPS, models an entity set of employees, where each entity in EMPS is represented by an isolated node in the hypernode. Correspondingly, the hypernode shown in Table 2, which is labelled ED2, models the subset of employees in EMPS work- ing in the Maths department.... In PAGE 9: ... For example, the hypernode shown in Table 11 represents and entity in a grouped database. GROUPED-DEPT1 (attribute ! value) dname ! computing emp ! EMP V ALUE head ! jack address ! london Table1 1: The entity GROUPED-DEPT1 The next result follows from Definitions 4.... In PAGE 13: ...evident that with respect to navigation HD2 is more expressive than HD1, since HD1 HD2 and the HAGs of A and B are subgraphs of the HAGs of C and D, respectively, up to an appropriate renaming of labels. A a Table1 2: The hy- pernode labelled A B b Table 13: The hy- pernode labelled B C a D Table 14: The hy- pernode labelled C D b C Table 15: The hy- pernode labelled D Another open problem is to extend our formalism to deal with integrity constraints such as keys and cardinality constraints. Finally, we mention that an important application of our formalism is in software engineering process modelling [CKO92], as it was shown in [LSO97] that the graph- based approach of the hypernode model provides a suitable platform for such process modelling.... In PAGE 13: ...evident that with respect to navigation HD2 is more expressive than HD1, since HD1 HD2 and the HAGs of A and B are subgraphs of the HAGs of C and D, respectively, up to an appropriate renaming of labels. A a Table 12: The hy- pernode labelled A B b Table1 3: The hy- pernode labelled B C a D Table 14: The hy- pernode labelled C D b C Table 15: The hy- pernode labelled D Another open problem is to extend our formalism to deal with integrity constraints such as keys and cardinality constraints. Finally, we mention that an important application of our formalism is in software engineering process modelling [CKO92], as it was shown in [LSO97] that the graph- based approach of the hypernode model provides a suitable platform for such process modelling.... In PAGE 13: ...evident that with respect to navigation HD2 is more expressive than HD1, since HD1 HD2 and the HAGs of A and B are subgraphs of the HAGs of C and D, respectively, up to an appropriate renaming of labels. A a Table 12: The hy- pernode labelled A B b Table 13: The hy- pernode labelled B C a D Table1 4: The hy- pernode labelled C D b C Table 15: The hy- pernode labelled D Another open problem is to extend our formalism to deal with integrity constraints such as keys and cardinality constraints. Finally, we mention that an important application of our formalism is in software engineering process modelling [CKO92], as it was shown in [LSO97] that the graph- based approach of the hypernode model provides a suitable platform for such process modelling.... In PAGE 13: ...evident that with respect to navigation HD2 is more expressive than HD1, since HD1 HD2 and the HAGs of A and B are subgraphs of the HAGs of C and D, respectively, up to an appropriate renaming of labels. A a Table 12: The hy- pernode labelled A B b Table 13: The hy- pernode labelled B C a D Table 14: The hy- pernode labelled C D b C Table1 5: The hy- pernode labelled D Another open problem is to extend our formalism to deal with integrity constraints such as keys and cardinality constraints. Finally, we mention that an important application of our formalism is in software engineering process modelling [CKO92], as it was shown in [LSO97] that the graph- based approach of the hypernode model provides a suitable platform for such process modelling.... ..."
Table 1: The en-
"... In PAGE 2: ... We illustrate the modelling power of hypernode databases with a running example showing part of a hypernode database, where the labels of hypernodes represent unique identi ers of hy- pernodes in the database, providing the means by which hypernodes can reference each other. The hypernode shown in Table1 , which is labelled EMPS, models an entity set of employees, where each entity in EMPS is represented by an isolated node in the hypernode. Correspondingly, the hypernode shown in Table 2, which is labelled ED2, models the subset of employees in EMPS work- ing in the Maths department.... In PAGE 9: ... For example, the hypernode shown in Table 11 represents and entity in a grouped database. GROUPED-DEPT1 (attribute ! value) dname ! computing emp ! EMP ? V ALUE head ! jack address ! london Table1 1: The entity GROUPED-DEPT1 The next result follows from De nitions 4.... In PAGE 13: ...evident that with respect to navigation HD2 is more expressive than HD1, since HD1 HD2 and the HAGs of A and B are subgraphs of the HAGs of C and D, respectively, up to an appropriate renaming of labels. A a Table1 2: The hy- pernode labelled A B b Table 13: The hy- pernode labelled B C a D Table 14: The hy- pernode labelled C D b C Table 15: The hy- pernode labelled D Another open problem is to extend our formalism to deal with integrity constraints such as keys and cardinality constraints. Finally, we mention that an important application of our formalism is in software engineering process modelling [CKO92], as it was shown in [LSO97] that the graph- based approach of the hypernode model provides a suitable platform for such process modelling.... In PAGE 13: ...evident that with respect to navigation HD2 is more expressive than HD1, since HD1 HD2 and the HAGs of A and B are subgraphs of the HAGs of C and D, respectively, up to an appropriate renaming of labels. A a Table 12: The hy- pernode labelled A B b Table1 3: The hy- pernode labelled B C a D Table 14: The hy- pernode labelled C D b C Table 15: The hy- pernode labelled D Another open problem is to extend our formalism to deal with integrity constraints such as keys and cardinality constraints. Finally, we mention that an important application of our formalism is in software engineering process modelling [CKO92], as it was shown in [LSO97] that the graph- based approach of the hypernode model provides a suitable platform for such process modelling.... In PAGE 13: ...evident that with respect to navigation HD2 is more expressive than HD1, since HD1 HD2 and the HAGs of A and B are subgraphs of the HAGs of C and D, respectively, up to an appropriate renaming of labels. A a Table 12: The hy- pernode labelled A B b Table 13: The hy- pernode labelled B C a D Table1 4: The hy- pernode labelled C D b C Table 15: The hy- pernode labelled D Another open problem is to extend our formalism to deal with integrity constraints such as keys and cardinality constraints. Finally, we mention that an important application of our formalism is in software engineering process modelling [CKO92], as it was shown in [LSO97] that the graph- based approach of the hypernode model provides a suitable platform for such process modelling.... In PAGE 13: ...evident that with respect to navigation HD2 is more expressive than HD1, since HD1 HD2 and the HAGs of A and B are subgraphs of the HAGs of C and D, respectively, up to an appropriate renaming of labels. A a Table 12: The hy- pernode labelled A B b Table 13: The hy- pernode labelled B C a D Table 14: The hy- pernode labelled C D b C Table1 5: The hy- pernode labelled D Another open problem is to extend our formalism to deal with integrity constraints such as keys and cardinality constraints. Finally, we mention that an important application of our formalism is in software engineering process modelling [CKO92], as it was shown in [LSO97] that the graph- based approach of the hypernode model provides a suitable platform for such process modelling.... ..."
Table 1: Number of word alignment by different preprocessings. de-en es-en fr-en en-de en-es en-fr
2006
Cited by 1
Table 2: Number of phrases extracted from differently preprocessed corpora. de-en es-en fr-en en-de en-es en-fr
2006
"... In PAGE 3: ... The associated counts are aggregated to constitute relative count-based feature functions. Table2 summarizes the size of phrase tables in- duced from the corpora. The number of rules ex- tracted for the hierarchical phrase-based model was roughly twice as large as those for the phrase-based model.... ..."
Cited by 1
Table 3: Open test on the 2005/2006 test sets (BLEU [%]). de-en es-en fr-en en-de en-es en-fr
2006
"... In PAGE 3: ... At the same time, dif- ferently biased word alignment annotations suggest alternative phrase translation pairs that is useful for increasing the coverage of translations. 4 Results Table3 shows the open test translation results on 2005 and 2006 test set (the development-test set and the final test set) 2. We used the merged (hierar- chical) phrase tables for decoding.... ..."
Cited by 1
Table 1: Top 15 en-
in ABSTRACT Image Region Entropy: A Measure of “Visualness” of Web Images Associated with One Concept
"... In PAGE 3: ... This indicates the iterative region selection worked well in case of \yellow quot;. Table1 shows the 15 top adjectives and their image en- tropy. In this case, the entropy of \dark quot; is the lowest, so in this sense \dark quot; is the most \visual quot; adjective among the 150 adjectives under the condition we set in this experiment.... In PAGE 3: ... Regarding other highly-ranked adjectives, \senior quot; and \beautiful quot; includes many human faces, and most of \visual quot; are not photos but graphical images such as screen shots of Windows or Visual C. We show the ranking of color adjectives in the lower part of Table1 . They are relatively ranked in the upper ranking, although images from the Web included many irrelevant im- ages.... ..."
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