Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
25,160
Table 8: Breakdown of questions by error type, in particular, by component responsible. Numbers are
2000
"... In PAGE 7: ... We examined system logs for a 50-question sample, and made a judgment of what caused the error, when there was an error. Table8 gives the breakdown. Each question was assigned to exactly one line of the table.... ..."
Cited by 23
Table 2: Summary of components that address a particular requirement
2005
"... In PAGE 20: ... Usage Tracking 3.2 Requirement Analysis Summary Table2 gives a summary of which components are involved in addressing a particular requirement, as discussed in section 3.1.... ..."
Table 2: Procedure Utilization in Web Applications. The percentages in parenthesis show the fraction of the entire binary covered by the particular component. The data column shows the size of all the non-code sections of the binary including section headers. The unused column shows the potential bene t of not downloading the entire application binary.
1999
"... In PAGE 3: ...e pro led four web applications and ve desktop applications (c.f., Table 1) to determine if there was an opportunity to improve startup time by improving the layout of procedures in a program binary. Table2 shows the statistics derived from pro ling four web applications. We ran these applica- tions to completion with a typical workload to determine how many procedures in the application are actually used.... In PAGE 7: ...f., Table2 ), we are hard pressed to make the binary more e cient. 3Figure 6 in the Appendix shows the raw startup latency numbers for all network conditions.... ..."
Cited by 8
Table 1: Lower bounds for the different components of communication cost. Pay particular attention to the conditions for the lower bound, given in the text.
1999
Cited by 1
Table 1: The number of principal components required to repre- sent a particular fraction AN of the total variance within each feature.
2001
"... In PAGE 5: ... This calculation is extremely simple, since the variances of the principal component are obtained as a by-product of the PCA. Table1 shows the required dimensionality of each feature in order to span various fractions of the total vari- ance. To demonstrate the ability of the model to represent a new face figure 6 shows a face which was not in the training set projected onto the PCA model, using feature dimensions chosen to preserve increasing fractions of the total variance.... ..."
Cited by 29
Table 1 shows the component models supported by VCF, the metadata facil- ities used for each model, and how dynamic calls are built. Although the existing plugins all rely on such built-in metadata facilities other plugins could use data provided by component clients. This can be used to build plugins for legacy systems that do not adhere to a particular component model.
2003
"... In PAGE 4: ... Table1 . VCF plugins 3 Component Composition One of the primary goals of VCF was to allow the use of components built for different component models in the same client application.... ..."
Cited by 4
Table 2 gives a summary of which components are involved in addressing a particular requirement, as discussed in section 3.1.
2005
"... In PAGE 20: ... Table2 : Summary of components that address a particular requirement 3.3 Scenario Analysis Components Scenario 1 Scenario 2 Scenario 3 01 Trust Service Y Y Y 02 UNI Y Y Y 03 Service Producer Y Y Y 04 License Server Y Y Y 05 User Y Y Y 06 Authentication Service M M Y 07 Credentials Service N M Y 08 Distributor N Y M 09 Payment Gateway N Y N 10 Controller M Y Y 11 Logging Medium Low High Table 3: Scenario Analysis of the components Table 3 examines the components required to address the scenarios discussed in section 2.... ..."
Table 1: The number of principal components required to repre- sent a particular fraction a214 of the total variance within each feature.
"... In PAGE 5: ... This calculation is extremely simple, since the variances of the principal component are obtained as a by-product of the PCA. Table1 shows the required dimensionality of each feature in order to span various fractions of the total vari- ance. To demonstrate the ability of the model to represent a new face figure 6 shows a face which was not in the training set projected onto the PCA model, using feature dimensions chosen to preserve increasing fractions of the total variance.... ..."
Table 4. Spectral Parameters for 3D (Discrete) Case: (a) Original Set of Spectral Parameters for Particular Spectral Components of Synthetic 3D Spectrum and Corresponding Group Parameters (b) after GHOST Condensation of Original Distribution and (c) after GHOST Condensation of HEO 4D-Projectionsa
2005
"... In PAGE 7: ... However, the accuracy for the determination of the contributions of the three spectral components reduces to 5-10% (previously 1-2%). The group at the lowest order parameter was found with larger contribution (group index 1, Table4 c) as compared to the original one (comp. index 1, Table 4a).... In PAGE 7: ... The group at the lowest order parameter was found with larger contribution (group index 1, Table 4c) as compared to the original one (comp. index 1, Table4 a). This is again an example of discrete group confirmation during the slicing process.... ..."
Cited by 2
Table 3. Spectral Parameters for 2D (Discrete) Case: (a) Original Set of Spectral Parameters for Particular Spectral Components of Synthetic 2D Spectrum and Corresponding Group Parameters (b) after GHOST Condensation of Original Distribution and (c) after GHOST Condensation of HEO 4D-Projectionsa
2005
"... In PAGE 7: ... The average group parameters for GHOST condensation of original distribution (GHOST orig.) as well as for GHOST condensation of 4D-projections (GHOST 4D) are presented in Table3 c and Table 3b, respectively. It can be seen from comparison of Tables 3a and 3c that the GHOST condensa- tion parameters are in a very good agreement with the original parameters used for composing synthetic EPR spectrum.... ..."
Cited by 2
Results 1 - 10
of
25,160
