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Table 4. Quantitative analysis of reasons for errors and rejections
"... In PAGE 11: ... In order to focus on the discussion of item extraction methods and their respective performance, the recogni- tion problems are not analyzed in this paper. The er- rors given by Recognizer 2 in the non-rejection case and 1% error case are analyzed in detail, and the results are shown in Table4 . Compared with the item extraction method previously proposed in [12], problems caused by the extractor have been alleviated.... ..."
Table 1, along with experimental data for Ni. While there are some quantitative discrepancies between the properties of the model EAM Ni and experimental data, the overall agreement is reasonable and we can expect that the model will adequately reproduce the material response to fast laser heating. Moreover, the knowledge of thermodynamic parameters of the model material will allow us to perform a quantitative analysis and physical interpretation of the simulation results.
"... In PAGE 4: ...xperiment 1726 0.45 9.94 17.47 45.27 24.8 25.85 - 46.50 12.8 - 17.5 Table1 . Some of the material parameters determined for the EAM Ni material.... ..."
Table 1: Parameters used for the quantitative assessment
"... In PAGE 16: ...2 Parameters used for the quantitative assessment of the apos;crop rotation scheme apos; Parameter values are based on several sources: ranges of elasticities are taken from the liter a - ture, cost share parameters are based on the SPEL dataset (Eurostat, 1998, and Kniepert, 1998), and shift parameters are derived from official sources and using information from a farm survey. Table1 gives an overview of the parameters that were used. The actual amount of land taken out of cereal production because of the restriction on land through the apos;crop rotation scheme apos; is difficult to derive for two reasons: first, because of the existence of a very similar program (and hence a similar restriction on land) since 1992 and second, because of the manifold exogenous policy changes in 1995 in Austria.... ..."
Table 3: Fits to the mean values and integrals of event shape variables at all available energies. Satisfactory ts are obtained in most cases. Only for h1 ? T i, R (1 ? T ), and especially for R EEC are the 2=ndf values too large. However, Fig. 8 shows that this is largely due to discrepancies between the data of the di erent experiments. It is remarkable that this simple model leads to perturbative and hadronisation contri- butions comparable with those obtained from the fragmentation models (compare Fig. 8 with Figs. 6 and 7). The values of s obtained are reasonable for many ts. However they should not be interpreted quantitatively, given the simplied power dependence assumed in the ts. The t for RJade 3
1996
"... In PAGE 9: ... The comparisons of the models with the energy dependence of the shape observables suggest that the variables M2 h=E2 vis, Bmax, and the jet rates can be calculated most reliably, because the hadronisation corrections are particularly small for these variables at high energy. In order to assess the sizes of the individual contributions, the energy dependence of each event shape mean for which lower energy data are available was tted by : hfi = 1 tot Z f d df df = hfperti + hfpowi ; (1) and similarly for each restricted-range integral, where fpert is the O( 2 s) expression for the event shape distribution: hfperti = s( ) 2 A 1 ? s(Ecm) ! + s( ) 2 !2 A 2 b0 log 2 Ecm + B! (2) where A and B are parameters available from theory [1], b0 = (33 ? 2Nf)=12 , and is the renormalisation scale, fpow is a simpli ed power dependence with free parameters C1 and C2 to account for the fragmentation plus renormalon dependence: hfpowi = C1 Ecm + C2 E2 cm : (3) The results of these ts are presented in Table3... In PAGE 18: ...ith Figs. 6 and 7). The values of s obtained are reasonable for many ts. However they should not be interpreted quantitatively, given the simplied power dependence assumed in the ts. The t for RJade 3 requires terms proportional to 1=E and to 1=E2 as well as a signi cant O( s) term (compare Table3 and Fig. 8a).... In PAGE 18: ... 6), contrary to a theoretical prediction [29] which expects a 1=E term for RJade 3 and only a 1=E2 power term in case of RDurham 3 . The event shape means h1 ? T i and hM2 h=E2 visi require only a 1=E power behaviour, as predicted in [7,29]: xing C2 to zero changes 2 only marginally (see Table3 ). For hM2 d =E2 visi, the overall power correction is smaller, and successful ts can be obtained using either the 1=E or 1=E2 term alone.... ..."
Table 1. Quantitative Comparison of the Approximation Algorithms. average minimum maximum
1997
"... In PAGE 13: ... In addition, the size of their output has to be taken into account to esti- mate the gain in runtime. Table1 summarizes the average, minimum, and maximum numbers of focal elements both in the original data and their approximations for each candidate algorithm after the fth combination. Remarks: 1.... In PAGE 13: ... For technical reasons the algorithms were not run with identical test data. However, the statistical data in Table1 indicate that the aver- age problem size was approximately equal for all candidates.... In PAGE 14: ... This is due to the fact that only relatively few focal elements with extremely low values are removed from the input data. The average size of a mass function approx- imated with this method is 440 focal elements, the maximum is even more than 1800 (see Table1 ). As a consequence the gain in runtime is the least among all candidates|taking into account both the time to compute the combinations and the approximation itself.... ..."
Cited by 14
Table 1: Notification Evaluation. This is a quantitative anal- ysis of the notification methods under the 4 attack scenarios. The +, 0, and - stand for good, acceptable, and poor respec- tively. We evaluate the notification scheme in terms of ef- fectiveness and feasibility. A more detailed explanation of the reasoning can be found in Section 6.1.2.
2004
Cited by 7
Table 1: Notification Evaluation. This is a quantitative anal- ysis of the notification methods under the 4 attack scenarios. The +, 0, and - stand for good, acceptable, and poor respec- tively. We evaluate the notification scheme in terms of ef- fectiveness and feasibility. A more detailed explanation of the reasoning can be found in Section 6.1.2.
2004
Cited by 7
Table 6: Quantitative results for TDT segmentation. The TDT models were trained on 2M words and
1997
"... In PAGE 9: ... We expect Model A to be infe- rior to Model B for two reasons: the lack of Reuters data in it apos;s training set and the di#0Berence of between one and twoyears in the dates of the stories in the training and test sets. The di#0Berence is quanti#0Cied in Table6 , which shows that P #16 =0:82 for Model A while P #16 =0:88 for Model B. 7.... ..."
Cited by 42
Table 3: Quantitative and qualitative measures. Quantitative measures
"... In PAGE 17: ...Table3 so that we can validate the theoretical performance of Striping Broadcast. We also measured the qualitative performance metrics that indicate the playback quality perceived by the users.... In PAGE 17: ... When an unexpected pause occurred during a playback, the user recorded the pause period using a digital clock running on the same client machine. Each metric in Table3 is the average of the cor- responding measures over four complete playbacks of the same video. 4.... ..."
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