### Table 1: Table of kernel dependence functionals. Columns show whether the functional is covari- ance or correlation based, and rows indicate whether the dependence measure is the max- imum singular value of the covariance/correlation operator, or a bound on the mutual in- formation.

2005

"... In PAGE 3: ... Columns show whether the functional is covari- ance or correlation based, and rows indicate whether the dependence measure is the max- imum singular value of the covariance/correlation operator, or a bound on the mutual in- formation. correlation operator: in this respect, the KGV and KMI are analogous (see Table1 ). Indeed, we prove here that under certain reasonable and easily enforced conditions, the KGV is an upper bound on the KMI (and hence on the mutual information near independence), which also becomes tight at independence.... In PAGE 36: ...1 Conclusions We have introduced two novel functionals to measure independence: the constrained covariance (COCO), which is the spectral norm of the covariance operator between reproducing kernel Hilbert spaces, and the kernel mutual information (KMI), which is a function of the entire spectrum of the empirical estimate of this covariance operator. The first quantity is analogous to the kernel canonical correlation (KCC), which is the spectral norm of the correlation operator; the second is analogous to the kernel generalised variance (KGV), which is a function of the empirical correlation operator spectrum (see Table1 in the introduction). We prove two main results.... ..."

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### Table 1 Means and standard deviations of dependent measures for all conditions

2002

"... In PAGE 26: ...ubjects factors. For all statistical tests, a significance level of .05 was applied. Table1 shows the average scores on the dependent measures for all four conditions. Training time was not equal for all conditions, with participants in the visual conditions (M = 47.... In PAGE 38: ...05 was applied. Table1 shows the average scores on the dependent measures for both experimental groups (both retention and transfer score are reported as percentages). Table 1 Mean Scores on Dependent Measures for both groups in Experiment 1 Audio Visual-Text M SD M SD Retention Score (%) 63 12 60 11 Transfer Score (%) 34 22 37 19 Mental Effort Instructions 4.... In PAGE 55: ...etween-subjects factors. For all statistical tests, a significance level of .05 was applied. Table1 shows the means on the dependent measures for all six groups. With regard to time on diagrams, only the two learner-paced groups were compared, because in the system-paced and the double system-paced groups the time spent on studying the diagrams was fixed.... In PAGE 69: ...05 was applied. Table1 shows the means and standard deviations for all dependent measures. Table 1 Means and Standard Deviations of Dependent Measures System-Paced Audio System-Paced Visual-Text Learner-Paced Visual-Text M SD M SD M SD Overall: Number of Fixations 509 302 604 340 765 420 Total Fixation Time (s) 158 97 139 82 174 100 Average Fixation Duration (s) 0.... ..."

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### Table 6 Dynamic Dependency Measures

"... In PAGE 21: ... The Conditional Distribution: Dynamic Dependence, Fractional Integration and Scaling Our analysis of one-day volatilities reinforces earlier findings of strong volatility clustering. The Ljung-Box tests that we report in Table6 indicate that the strong serial dependence is preserved under temporal aggregation. Even at the monthly level, or h = 20 , with only 122 observations, all of the test... In PAGE 22: ... Using numerical techniques, Andersen, Bollerslev and Lange (1998) have recently shown that, given the estimates typically obtained at the daily level, from a theoretical perspective the integrated volatility should remain strongly serially correlated, and highly predictable, under temporal aggregation, even at the monthly level. The Ljung-Box statistics for the realized volatilities presented in Table6 provide empirical confirmation. The results in section 4 indicate that realized daily volatilities appear fractionally integrated.... In PAGE 22: ... The class of fractionally integrated models is self-similar, so that the degree of fractional integration should be invariant to the sampling frequency of the process; see, for example, Beran (1994). This strong prediction is borne out by the estimates for d for the different levels of aggregation, which we report in Table6 . All of the estimates are within two asymptotic standard errors of the average estimate of 0.... ..."

### Table 2: Kernel Algorithms in W-CDMA and 802.11a and their performance on a GPP and SODA.

2006

"... In PAGE 3: ... For example, the searcher, the heaviest workload of the W-CDMA protocol, can be represented by 320-wide vectors. (See Table2 in Section 5.3) 8 to 16bit Data Width { Most algorithms operate on vari- ables with small values.... In PAGE 5: ... While a ho- mogeneous processor system can distribute the system work- load among PEs, a heterogeneous processor system must provide enough units for the worst case workloads of each type of PE. As shown in Table2 in Section 5.3, W-CDMA and 802.... In PAGE 9: ...3 Performance and Power Results Performance Results. Table2 provides a characteri- zation of each kernel algorithm in W-CDMA and 802.11a in terms of extent of vectorization, vector width, bit width, etc.... In PAGE 9: ... This characterization was used to de ne the SODA ar- chitecture, as described in Section 2. Table2 also lists the throughput and latency of each kernel algorithm when im- plemented on SODA. The raw computations are measured in terms of number of execution cycles on a general purpose Al- pha processor.... ..."

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### Table 1 Means and Standard Deviations of Dependent Measures

2002

"... In PAGE 26: ...ubjects factors. For all statistical tests, a significance level of .05 was applied. Table1 shows the average scores on the dependent measures for all four conditions. Training time was not equal for all conditions, with participants in the visual conditions (M = 47.... In PAGE 38: ...05 was applied. Table1 shows the average scores on the dependent measures for both experimental groups (both retention and transfer score are reported as percentages). Table 1 Mean Scores on Dependent Measures for both groups in Experiment 1 Audio Visual-Text M SD M SD Retention Score (%) 63 12 60 11 Transfer Score (%) 34 22 37 19 Mental Effort Instructions 4.... In PAGE 55: ...etween-subjects factors. For all statistical tests, a significance level of .05 was applied. Table1 shows the means on the dependent measures for all six groups. With regard to time on diagrams, only the two learner-paced groups were compared, because in the system-paced and the double system-paced groups the time spent on studying the diagrams was fixed.... In PAGE 69: ...05 was applied. Table1 shows the means and standard deviations for all dependent measures. Table 1 Means and Standard Deviations of Dependent Measures ... ..."

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### Table 1. Mean values (standard deviation) of the dependent measures on the three web sites for the five site map conditions

### Table 4: Means and Standard Deviations of Measures by Condition

2003

"... In PAGE 6: ... The dependent measures were knowledge benefits and capture actions. Table4 shows the mean numbers of benefits and actions by condition1 SPSS (Statistical Package for the Social Sciences) software was used to run the multivariate analysis. It reports four multivariate analyses.... In PAGE 6: ...00 (C) 2003 IEEE Tables 4 and 6 clearly demonstrate the importance of having an organizational knowledge management strategy. It can be seen in Table4 that those organizations that had a strategy in Y2K and now (Both) identified significantly more knowledge benefits, followed by those organizations that had a strategy in either of the two years (Either). Last on this criterion were those organizations that did not have a strategy in Y2K or now (Neither).... ..."

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### Table VII. Performance comparison of network RPC and message-passing systems

1993

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### Table 1. Measurements for Livermore kernels

1998

Cited by 4