### Table 1. (cont.) Sporadic Examples of Zw of Index 1 I 10 Index w

"... In PAGE 19: ...Table1 . Sporadic Examples of Zw of Index 1 I 10 Index w Monomials of fw d b2 K-E 1 (1,2,3,5) z10 0 ; z5 1; z3 2z1; z2 3; : : : (17) 10 9 ? 1 (1,3,5,7) z15 0 ; z5 1; z3 2; z2 3z0; : : : (19) 15 9 ? 1 (1,3,5,8) z16 0 ; z5 1z0; z3 2z0; z2 3; : : : (20) 16 10 ? 1 (2,3,5,9) z9 0; z6 1; z3 2z1; z2 3; : : : (13) 18 7 Y 1 (3,3,5,5) g5(z0; z1); f3(z2; z3) 15 5 Y 1 (3,5,7,11) z6 0z2; z5 1; z2 2z3; z2 3z0; : : : (8) 25 5 Y 1 (3,5,7,14) z7 0z2; z5 1z0; g2(z2 2; z3); : : : (9) 28 6 Y 1 (3,5,11,18) g2(z6 0; z3); z5 1z2; z3 2z0; : : : (10) 36 6 Y 1 (5,14,17,21) z7 0z3; z4 1; z3 2z0; z2 3z1; z5 0z1z2 56 4 Y 1 (5,19,27,31) z10 0 z3; z4 1z0; z3 2; z2 3z1; z7 0z1z2 81 3 Y 1 (5,19,27,50) z20 0 ; z10 0 z3; z2 3; z5 1z0; z3 2z1; z7 0z2 1z3 100 4 Y 1 (7,11,27,37) z10 0 z1; z4 1z3; z3 2; z3 3z0 81 3 Y 1 (7,11,27,44) z11 0 z1; z3 2z0; z8 1; z4 1z3; z2 3; z4 0z3 1z2 88 4 Y 1 (9,15,17,20) z5 0z1; z4 1; z3 2z0; z3 3 60 3 Y 1 (9,15,23,23) z6 0z1; z4 1z0; z3 2; z2 2z3; z3; z2z2 3; z3 3 69 5 Y 1 (11,29,39,49) z8 0z2; z4 1z0; z3 2; z2 3z1 127 3 Y 1 (11,49,69,128) z17 0 z2; z5 1z0; z4 2; z2 2z3; z2 3 256 2 Y 1 (13,23,35,57) z8 0z1; z4 1z2; z2 2z3; z2 3z0 127 3 Y 1 (13,35,81,128) z17 0 z1; z5 1z2; z3 2z0; z2 3 256 2 Y 2 (2,3,4,5) z6 0; z4 1; z3 2; z2 3z0; : : : (10) 12 5 ? 2 (2,3,4,7) z7 0; z4 1z0; z3 2z0; z2 3; : : : (11) 14 6 ? 2 (3,4,5,10) z5 0z2; z5 1; z4 2; z2 3; : : : (9) 20 5 Y 2 (3,4,6,7) g3(z2 0; z2); z3 1z2; z2 3z1; z0z3z2 1; z2 0z3 1 18 6 ? 2 (3,4,10,15) z10 0 ; z5 1z3; z3 2; z2 3; : : : (10) 30 7 Y 2 (3,7,8,13) z7 0z2; z3 1z2; z2 2z3; z2 3z0; z5 0z1; z3 0z1z3; z2 0z1z2 2 29 5 ? 2 (3,10,11,19) z10 0 z3; z3 1z2; z2 2z3; z2 3z0; z7 0z2 1; z4 0z1z3; z3 0z1z3 2 41 5 ? 2 (5,13,19,22) z7 0z3; z4 1z0; z3 2; z2 3z1; z5 0z1z2 57 3 Y 2 (5,13,19,35) z14 0 ; z7 0z3; z2 3; z5 1z0; z3 2z1; z5 0z2 1z2 70 3 Y 2 (6,9,10,13) z6 0; z4 1; z3 2z0; z2 3z2; z3 0z2 1 36 4 Y 2 (7,8,19,25) z7 0z1; z4 1z3; z3 2; z2 3z0; z2 0z3 1z2 57 3 Y 2 (7,8,19,32) z8 0z1; z8 1; z4 1z3; z2 3; z3 2z0; z0z3 2; z3 0z3 1z2 64 4 Y 2 (9,12,13,16) z4 0z1; z4 1; z3 2z0; z3 3 48 3 Y 2 (9,12,19,19) z5 0z1; z4 1z0; z3 2; z2 2z3; z2z3 3; z3 3 57 5 Y 2 (9,19,24,31) z9 0; z3 1z2; z3 2z0; z2 3z1 81 3 Y 2 (10,19,35,43) z7 0z2; z5 1z0; z3 2; z2 3z1 105 3 Y 2 (11,21,28,47) z7 0z2; z5 1; z3 2z1; z2 3z0 105 3 Y 2 (11,25,32,41) z6 0z3; z3 1z2; z3 2z0; z2 3z1 107 3 Y 2 (11,25,34,43) z10 0 ; z4 1z0; z2 2z3; z2 3z1 111 3 Y 2 (11,43,61,113) z15 0 z2; z5 1z0; z3 2z1; z2 3 226 2 Y 2 (13,18,45,61) z9 0z1; z5 1z2; z3 2; z2 3z0 135 3 Y... In PAGE 22: ... But I I?n D 2 j ? KZwj so this completes the proof of the lemma. The analysis of most of the sporadic examples of Table1 is easily done with help of Corollary 3.7 which can restated for this purpose as: Corollary 5.... In PAGE 31: ...f degree wi. The simplest situation occurs when f1 = f2 = f3 are forced to vanish. Then Gw = (C )3 is the smallest it can possibly be as P(w) is toric. This is, in fact, common to many examples of the log del Pezzo suraces of Table1 . More precisely, we have Lemma 7.... In PAGE 33: ...As mentioned previously for the log del Pezzo surfaces with a Y in the last column of Table1 and the tables of Theorem 4.5, there is a unique homothety class of Kahler-Einstein metrics corresponding to each point of Md w: But the question remains whether two inequivalent Kahler-Einstein structures can share the same Riemannian metric.... ..."

Cited by 12

### Table 1. Summary of the Quality Index Q of the filtered image after using different filters

2002

"... In PAGE 8: ... This image yields an excellent estimate of figure 1. The quality indexes achieved by the different filters are compared in Table1 . Figure 8 illustrates the trend of quality indexes Q, Q1, Q2, and Q3 versus iteration number.... ..."

### Table 1 Index terms list

2005

"... In PAGE 13: ... The results are shown in Table 11. In Table1 0, the results indicate that the proposed algorithm outperforms the other two algorithms in all situations. Among these three algorithms, ANN outper- forms SW, and SW outperforms GM.... ..."

### Table 4 shows the performance of the MED algorithms in terms of Weighted SNR (WSNR)[12], Linear Distortion Measure (LDM)[13] and Universal Objective Image Quality Index (UQI)[14]. WSNR uses the contrast sensitivity function (CSF)[15] of human visual system to measure the distortion of halftone image while LDM is used to measure the linear distortion. UQI is an index to qualify an image. In terms of all these measurements, the performance of the proposed algorithm is more or less the same as that of MEDc98.

"... In PAGE 24: ...1339 0.1300 Table4 . Quality measurement of halftones produced with different algorithms in terms of (a) WSNR, (b) LDM and (c) UQI ... ..."

### Table 9. Initial regression coefficients and p-values of significant index terms using the obituary training documents.

2003

"... In PAGE 32: ... After the relatively insignificant lexical objects were excluded, the training data used for the logistic regression analysis on the obituary ontology were normalized using the method described in Section 4. Table9 shows the regression coefficients C0 and C1, and the corresponding p-values for the obituary training documents. As shown in the table, the p-values for Deceased Name and Ending Time are 0.... ..."

Cited by 1

### Table 1: Worst Case Analysis of Evaluation Algorithms; n is the number of index components.

1998

"... In PAGE 4: ...Table 1: Worst Case Analysis of Evaluation Algorithms; n is the number of index components. Table1 shows the worst case analysis of the performance of the evaluation algorithms in terms of the number of bitmap operations and scans. For Algorithm RangeEval, since each range pred- icate evaluation entails an equality predicate evaluation (for BEQ bitmap), the number of bitmap operations of a range predicate eval- uation is at least twice that of the = predicate evaluation.... ..."

Cited by 65

### Table 6-1: List of index terms of sofa8, sofa14, and sofa46 as relevance feedback

2007

"... In PAGE 70: ...11 is shown as below: nullnullnullnull1 nullnullnullnull nullnull nullnullnullnullnull null nullnullnullnull nullnullnullnull nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnull nullnullnullnullnull nullnull nullnullnullnullnull nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnull nullnullnull nullnullnullnullnullnullnullnull null6.1null nullnullnull 6 null 3null Meanwhile, Table6 -1 shows the list of index terms of the three sofas chosen by customer null . Parameter frequency means the number of times an index term appears in the whole user relevance feedback set (sofa14, sofa8, and sofa46).... In PAGE 71: ...association rules from Rule Group 1, we consider the index terms from Table6 -1 as input X (see Table 5-6). Table 6-2 shows the results from the previous matching process.... In PAGE 71: ...association rules from Rule Group 1, we consider the index terms from Table 6-1 as input X (see Table 5-6). Table6 -2 shows the results from the previous matching process. Parameter frequency indicates the importance of this rule and will be considered the weight.... In PAGE 71: ... (see Table 5-6). Table 6-2 shows the results from the previous matching process. Parameter frequency indicates the importance of this rule and will be considered the weight. Table6 -2: the association rules applicable for the three relevance feedback X Index Terms of X Y Index Terms of Y support frequency Rule Group 1 Sofa Design - Metal sofa_design_4 Chair Design - Metal chair_design_3 0.405797 1 Sofa Color - Dark Neutral sofa_color_2 Chair Color - Dark Neutral chair_color_1 0.... In PAGE 71: ...521739 2 Sofa Type - Casual sofa_type_1 Chair Design - Medium Wood chair_design_2 0.442029 1 After analysis, a set of relevant index terms of chairs (null ) has been generated, as shown in Table6 -2.... In PAGE 72: ...2. The analysis shows that the three sofas selected by this customer null share the same index term sofa_color_2 , and that index term matches one association rule, which shows that in this demographic profile (Level 1-5), people who prefer sofas with attribute sofa_color_2 as X are more likely to purchase chairs with attribute chair_color_1 as Y (43% probability) (see Table6 -2). In order to observe the improvement, we highlight the chairs with attribute chair_color_1 which is Dark Neutral in field Color.... In PAGE 72: ... nullnullnull nullnullnull nullnullnull nullnullnullnull nullnull nullnullnullnullnull The order of product presentation on the chair page for customer null is remarkably changed after personalization according to the preferences in the sofa category. To exemplify how the rankings are different, the original top 15 ranking orders nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnull (generated solely based on the demographic information of null ) are shown in Table6 -3, with the new null null null nullnullnull nullnullnull nullnull nullnullnullnullnull nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnull shown in Table 6-4. When this customer null selects one or multiple chairs and move on to the next group of product collection (table), this process will be applied the same manner.... In PAGE 72: ... nullnullnull nullnullnull nullnullnull nullnullnullnull nullnull nullnullnullnullnull The order of product presentation on the chair page for customer null is remarkably changed after personalization according to the preferences in the sofa category. To exemplify how the rankings are different, the original top 15 ranking orders nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnull (generated solely based on the demographic information of null ) are shown in Table 6-3, with the new null null null nullnullnull nullnullnull nullnull nullnullnullnullnull nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnull shown in Table6 -4. When this customer null selects one or multiple chairs and move on to the next group of product collection (table), this process will be applied the same manner.... In PAGE 73: ...Table6 -3: The original nullnullnullnullnull nullnull nullnullnullnullnull nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnull Rank ID Name Price Construction Comfort Color Design Score 1 chair19 Casual_C1_M1 799 3 3 Light Neutral Dark Wood 0.699846 2 chair24 Modern_C1_M3 799 2 3 Light Neutral Metal 0.... In PAGE 73: ...59108 15 chair3 Casual_C2_M3 999 3 3 Medium Neutral Medium Wood 0.582712 Table6 -4: The adapted nullnullnullnull nullnull nullnullnullnullnullnull nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnull Rank ID Name Price Construction Comfort Color Design Score 1 chair19 Casual_C1_M1 799 3 3 Light Neutral Dark Wood 0.526992 2 chair30 Modern_C2_M3 999 2 3 Dark Neutral Metal 0.... In PAGE 74: ...aking. Using the same example from Section 6.2, because chair_color_1 was found to be one of the critical product attributes, the system automatically reweights this index term and rearranges the presentation order of the chairs. nullnullnull nullnullnull nullnull nullnullnullnullnull By comparing the original ranking and new ranking, listed in Table6 -3 and Table 6-4 respectively, we notice that chairs with color attributes Dark Neutral advance including chair30 moves from top 8 to top 2; chair1 from 9 to 8; chair13 from14 to 4, which even outranks chair1 .... ..."

### Table 1: Worst Case Analysis of Evaluation Algorithms; n is the number of index components.

"... In PAGE 4: ...Table 1: Worst Case Analysis of Evaluation Algorithms; n is the number of index components. Table1 shows the worst case analysis of the performance of the evaluation algorithms in terms of the number of bitmap operations and scans. For Algorithm RangeEval, since each range pred- icate evaluation entails an equality predicate evaluation (for B EQ bitmap), the number of bitmap operations of a range predicate eval- uation is at least twice that of the = predicate evaluation.... ..."

### Table 1. Terms for Scheduling Analysis Term Definition

"... In PAGE 3: ...Minimum idle time in thread i including minimum idle time between invocations Table1 presents the terms used in this analysis. At time t, thread i is released.... ..."

### Table 1: Bolivian Terms of Trade Index ..

"... In PAGE 34: ...STATISTICAL APPENDIX Table1 : Bolivian Terms of Trade Index, 1980-92 (1987=100) Year Export price index hbport price index Ternn oftrade index 1980 181.0 91.... ..."