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Table 2: The branching ratios for H and W . the 2HDM (Model I) the BRs are independent of tan , while in the 2HDM (Model I0) the constraint of Eq. (5) creates the inequalities in Table 2. In the MHDM the BRs are dependent on the arbitrary parameters X, Y and Z, and thus span the full range 0 ! 100%.

in unknown title
by unknown authors
"... In PAGE 4: ... In order to search for H one needs to consider its decays. The various branching ratios (BRs) are shown in Table2 , with l referring to either an electron or a muon. For... In PAGE 6: ... The rst method is to search in the lepton channel. We see from Table2 that the decays of H violate lepton universality due to a preference to couple to the heaviest lepton ( ). This is in contrast to the lepton decays of W .... In PAGE 9: ... (8 ! 10) to predict the number of hard pions for various values of BR (t ! Hb). We shall take Ntt = 300; from Table2 one has BR (W ! l l) = 22% and BR (W ! ) = 11%.... In PAGE 10: ... Combining the above e ciencies, it is clear that e0 l gt; el . From Table2 we can obtain the value of BR (H ! ) for the various Higgs models. The maximum signal is obtained when BR (H ! ) ! 100%7 and we will use this in our calculation.... ..."

Table 1: The OSI protocol layers. [Tan88]

in A Discussion of Internet Research Focusing on Mobility and Next Generation Protocol Development
by Wayne Mackrell
"... In PAGE 6: ... Each layer expects certain services from the layer below and is expected to provide certain services to the layer above. The layers in the OSI model are presented in Table1 . The IP protocol itself lies within the network layer as it is used to support internetworking.... ..."

Table 18: The execution time for difierent methods for the flrst data set. Decile HEA Logistic regression MDLEP TAN NB

in K.: An efficient data mining method for learning Bayesian Networks using an evolutionary algorithm-based hybrid approach
by Man Leung, Wong Kwong, Sak Leung
"... In PAGE 41: ... logistic regression, TAN, and NB models. The average and the standard deviations of the execution time for difierent methods are summarized in Table18 . Although HEA is slower than logistic regression, TAN, and NB, it is much faster than MDLEP.... ..."

Table 7: Metrics selected in each of the three experi- ments, with short descriptions (from SAR man page).

in Correlating Instrumentation Data to System States: A Building Block for Automated Diagnosis and Control
by Ira Cohen, Moises Goldszmidt, Terence Kelly, Julie Symons 2004
"... In PAGE 12: ... Table 6 summarizes the accuracy of the TAN models for the other three tests. Table7 shows the metrics selected for the TAN models for each test. We see that for the Memory and I/O bottle- neck tests, the TAN algorithm selected metrics that point directly to the bottleneck.... ..."
Cited by 75

Table 7: Metrics selected in each of the three experi- ments, with short descriptions (from SAR man page).

in Correlating Instrumentation Data to System States: A Building Block for Automated Diagnosis and Control
by Ira Cohen, Moises Goldszmidt, Terence Kelly, Julie Symons, Jeffrey S. Chase 2004
"... In PAGE 13: ... Table 6 summarizes the accuracy of the TAN models for the other three tests. Table7 shows the metrics selected for the TAN models for each test. We see that for the Memory and I/O bottle- neck tests, the TAN algorithm selected metrics that point directly to the bottleneck.... ..."

Table 6: Summary of results from the OpenView testbed.

in Correlating Instrumentation Data to System States: A Building Block for Automated Diagnosis and Control
by Ira Cohen, Moises Goldszmidt, Terence Kelly, Julie Symons 2004
"... In PAGE 12: ... We omit detailed results for the CPU test: for this test the in- duced models obtained 100% accuracy using only CPU metrics. Table6 summarizes the accuracy of the TAN models for the other three tests. Table 7 shows the metrics selected for the TAN models for each test.... ..."
Cited by 75

Table 6: Summary of results from the OpenView testbed.

in Correlating Instrumentation Data to System States: A Building Block for Automated Diagnosis and Control
by Ira Cohen, Moises Goldszmidt, Terence Kelly, Julie Symons, Jeffrey S. Chase 2004
"... In PAGE 13: ... We omit detailed results for the CPU test: for this test the in- duced models obtained 100% accuracy using only CPU metrics. Table6 summarizes the accuracy of the TAN models for the other three tests. Table 7 shows the metrics selected for the TAN models for each test.... ..."

Table 3: Trust criteria for sources and objects of trust (based on TAN, THOEN 2001) Sources and objects of trust

in 1 MANAGEMENT OF PERCEIVED RISKS IN E-BUSINESS FOR EFFICIENT FOOD SUPPLY NETWORK MANAGEMENT: THE CASE OF TRUST 1
by Melanie Fritz, Maurizio Canavari, Melanie Fritz, Maurizio Canavari
"... In PAGE 8: ... Building on this basis, the factors for trust generation in food networks emerged from the empirical work and the literature study have been used for the construction of the hierarchic decision model for trust generation in e-business for food networks. TAN and THOEN (2002) define the transaction partner and available control mechanisms as sources for the generation of trust in transactions and at the same time as objects, which need to be trusted (see Table3 ). They distinguish objective and subjective criteria generating trust towards transaction partner or control mechanism.... ..."

Table 7 shows the expected number of events from each individual background contribution as well as the total backgrounds for 1 fb?1. Table 8 shows the expected number of signal events for tan = 10 (standard model case) and for tan = 40 as well as the signal signi cances as a function of the Higgs mass for 1 fb?1. The reconstructed dijet invariant mass for the highest-ET jets in the event (with both jets required to be b-tagged) and with present CDF Run I dijet resolutions is shown in Figure 6 for both signal and background. All rates are normalized to 1 fb?1. Figure 7 shows the 95% CL exclusion limits, 3 discovery thresholds and 5 discovery thresholds in the tan vs M apos; plane of the MSSM for four di erent total integrated luminosity scenarios, L = 91 pb?1, 2 fb?1, 10 fb?1 and 30 fb?1. Figure 8 shows the required luminosity for 95% CL exclusion limits, 3 and 5 discovery thresholds as a function of the Higgs mass for the MSSM scenario with tan = 10 and 40. Finally, Figure 9 shows the enhancement factor R de ned as:

in CDF Run II Discovery Reach for Neutral MSSM Higgs Bosons via
by Juan Valls Rutgers, Juan A. Valls

Table 7 shows the expected number of events from each individual background contribution as well as the total backgrounds for 1 fb?1. Table 8 shows the expected number of signal events for tan = 10 (standard model case) and for tan = 40 as well as the signal signi cances as a function of the Higgs mass for 1 fb?1. The reconstructed dijet invariant mass for the highest-ET jets in the event (with both jets required to be b-tagged) and with present CDF Run I dijet resolutions is shown in Figure 6 for both signal and background. All rates are normalized to 1 fb?1. Figure 7 shows the 95% CL exclusion limits, 3 discovery thresholds and 5 discovery thresholds in the tan vs M apos; plane of the MSSM for four di erent total integrated luminosity scenarios, L = 91 pb?1, 2 fb?1, 10 fb?1 and 30 fb?1. Figure 8 shows the required luminosity for 95% CL exclusion limits, 3 and 5 discovery thresholds as a function of the Higgs mass for the MSSM scenario with tan = 10 and 40. Finally, Figure 9 shows the enhancement factor R de ned as:

in CDF Run II Discovery Reach for Neutral MSSM Higgs Bosons via p p
by Bb Valls For, J. A. Valls
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