### Table 15. Completed evaluation template of er08 at 500kbps, 750kbps, and 1000kbps when ROI encoded with both parametric bit allocation methods and both elastic non-parametric bit allocation methods. Elastic non-

2007

"... In PAGE 72: ... (d) Elastic non-parametric bit allocation with updates. Table15 shows the completed evaluation template by an expert of video er08 at 500kbps, 750kbps, and 1000kbps with each of the four adopted bit allocation methods. From the table, it can be noted that any TBR, there is a general decrease in values for ROI DL? from elastic non-parametric bit allocation approaches to parametric bit allocation approaches.... ..."

### Table 1 Results of the parametric testing on sample wafers

2006

"... In PAGE 3: ... The fabrication of the first batch of sensors to be used in the imaging modules has recently been completed at ITC- IRST. Table1 summarizes the main parameters extracted from the electrical measurements performed on test structures. The depletion voltage is ranging between 10 and 30 V, but for most wafers it is close to 20 V.... ..."

### Table 1: Common Parametric Correlation Forms Name (d; )

1997

"... In PAGE 7: ...ry and normally distributed. Then we can write as in, e.g., Diggle, Liang and Zeger (1994, p 87), y N( 1; ( )) (3) where = [ 2 2 ]0 and ( ) = 2I + 2H( ) with (H( ))ij = (dij; ) a valid parametric correlation function depending upon the distance between sites si and sj. Examples of standard parametric forms of (d; ) are given in Table1 where becomes a scalar capturing the rate of correlation decay. For the scallop data described in the introduction, we take the response Y (si) is log(total catch at si + 1) where the constant one is added to address the observed zero catches.... In PAGE 9: ... Note that rV would not exist if 2+ 2 2 gt; 20, but this would be unlikely in practice. For the asymptotically silled variograms given in Table1 , the relationship between the scalar correlation decay parameter and the ranges rC and rV are presented in Table 2. It is obvious that rV rC with equality if 2 = 0.... In PAGE 14: ... Hence, to complete the Bayesian model, speci cation of prior distributions for and is required. For the parametric models of Table1 , we assume the prior ( ; ) takes the form ( ; ) = 1( ) 2( 2) 3( 2) 4( ): Although the parameters ; 2; 2 and are not truly thought to be independent, the alternative, specifying a joint prior incorporating dependence, is arbitrary and di cult to justify. We prefer to let the data modify our independence assumption through the posterior.... In PAGE 21: ... 6.2 Fitted Semivariogram Models All of the parametric models of Table1 and nonparametric Bessel mixtures with di erent combinations of xed and random parameters were t to the 1993 scallop data. Figure 5 shows the posterior mean of each respective semivariogram while Table 3 provides the model choice criteria for each model along with the independence model ( ( ) = ( 2 + 2)I).... ..."

Cited by 17

### Table 3. A Sample of Parametric Complexity Classi ca- tions (References in [DF98])

1999

"... In PAGE 21: ... The \bad quot;, W[1]-hardness, is based on a miniaturization of Cook apos;s Theorem in a way that establishes a strong analogy between NP and W[1]. Proofs of W[1]-hardness are generally more challenging than NP-completeness, but it is obvious by now (see Table3 ) that this is a very applicable complexity measurement. Problems that are hard do not just go away.... ..."

Cited by 9

### Table 3. A Sample of Parametric Complexity Classi cations (References in [DF98])

1999

"... In PAGE 17: ... The \bad quot;, W[1]-hardness, is based on a miniaturization of Cook apos;s Theorem in a way that establishes a strong analogy between NP and W[1]. Proofs of W[1]-hardness are generally more challenging than NP-completeness, but it is obvious by now (see Table3 ) that this is a very applicable complexity measurement. Problems that are hard do not just go away.... ..."

Cited by 9

### Table 2: Views valid for aggregation functions We call sum and max-queries parametric queries, and count and non-aggregate queries non-parametric queries. In a similar way, we talk about parametric and non-parametric views. Parametric queries and views have an aggregate op- erator that takes an argument. Now, for each of the aggregate operators sum and max we are going to de ne two classes of queries that we con- sider as natural candidates for rewritings. Theorem 5.10

1999

"... In PAGE 6: ... In the sequel we will assume that all views used in rewrit- ings are valid. In Table2 we summarize which kinds of views are valid for which kind of aggregate query. Since for every aggregate query there is an analogous non-aggregate query obtained by projecting out the aggregate term, we allow in fact arbitrary aggregate views in max-query rewritings.... In PAGE 9: ... This will complete the proof because then we obtain a conjunctive rewriting of q from rk by replacing the comparisons Ck in rk with the comparisons C of q. Note that according to our de nition of valid views sum- marized in Table2 , pure disjunctive candidates for rewriting count and sum-queries use only count-views. Note as well, that as the following example demonstrates, the preceding theorem does not hold over the integers.... ..."

Cited by 75

### Table 1: Evaluations of cost functionals for complete solution and one-shot method combined with multi-level method. We can see, in Figure 11, the successive shapes for the one-shot method combined with multi-level parametrization with 3=7=15 parameters. There is convergence to the desired shape after 400 \optimization quot; iterations (1000 sec. CPU). Figure 12-a, shows two-grids-ideal method (where we have alternated with 7 and 15 parameters) which consists in making one optimization iteration on the ne level and solve completely the problem on the coarse level. We still use V-cycles. We see on Figure 12-b that the method becomes \ideal quot; when we relax the coarse level with at least 450 iterations. It implies that at the

1993

"... In PAGE 21: ... For one-shot method, we have used V-cycles alternating on the 3 previous levels. There is a gain of 35 as regards the cost functionals evaluation ! In Table1 , we can see the di erence between complete solution and one- shot method combined with multi-level parametrization. -6 -5 -4 -3 -2 -1 0 1 0 100 200 300 400 500 600 700 800 Log (cost functional) WU (=cost functional evaluation) Complete resolution 15 parameters 7 parameters Simultaneous resolution 15 parameters7 parameters Figure 8: Comparison between one level One-Shot and Complete Resolution... ..."

### Table 1. Parametric Elements

2004

Cited by 3

### Table 2: Parametrizing Y.

2008

"... In PAGE 15: ... We tried our algorithm on examples which we constructed from the canonical surface (given by the binomial ideal with 20 generators) by a linear transformation of the projective space. The randomly generated matrix of the transformation has integral entries with the given maximal absolute value (the first column in Table2 ). We see that almost the whole time is spent for finding the Lie algebra of the surface.... ..."

### Table 1: Benchmark Parametrization

2005

"... In PAGE 32: ...86 3.18 Table1 0: Adaptive Expectations. Output, Investment and Consumption Statistics.... In PAGE 32: ...2539 0.1519 Table1 1: Adaptive Expectations. Correlation Structure.... In PAGE 32: ...85 3.61 Table1 2: Micro-Macro Expectations. Output, Investment and Consumption Statistics.... In PAGE 33: ...1009 0.3298 Table1 3: Micro-Macro Expectations. Correlation Structure.... In PAGE 33: ...676 0.547 Table1 4: Robustness of Simulation Results to Alternative Filtering Procedures. First Difierencing vs.... ..."