### Table 1: Matrix-based solution to a query

### Table 1. Summary monitoring matrix based on the application of technologies to physiological system responses.

"... In PAGE 5: ... This section considers the applicable classes of technologies and their associated processes that can be used to make the required measurements. Table1 provides a summary of the specifi c monitor- ing techniques that satisfy the various physiological responses, given the classes of technology. The table is the super-set of implementations from which designers can select.... In PAGE 6: ... Sensitive magnetometers will measure magneto- grams that result from the signatures of EMG and EKG activity. MONITOR TEST BED Requirements and Trade-Offs It is neither practical nor cost-effective to use all of the monitoring means identifi ed in Table1 . The systems engineer must optimize the system design by conduct- ing a trade-off assessment in anticipation of defi ning a candidate system architecture.... ..."

### Table 2 New transcription factor binding sites predicted by matrix-based screenings annotated to the AthaMap database

2006

"... In PAGE 6: ... Matrix-based searches were performed as described earlier [1]. Table2 lists the factors, the factor families and the references from which the sequences were extracted. The new data presented here increases the number of transcription factors in the database from previously 36 to 88.... ..."

### Table 2: Iterative probability matrix based on 3966 matches

"... In PAGE 4: ... Only best matches that were fully contained in the 20-bp USR were used since those are more apt to be real SD sites. Starting at TAAGGAG and using its M1 prob- ability table as the initial estimate, this process converged on Table2 in a few seconds. This ta- ble is based on 3996 matches and shows good Table 2: Iterative probability matrix based on 3966 matches... ..."

### Table 6: Iterative probability matrix based on best 2002 USR matches

"... In PAGE 7: ... Consequently, the algorithm was modi ed so that only the best 2000 USRs were used in de ning the probability table on each cycle. A number of slightly di erent local maxima, with slightly less variability were produced using this method (eg Table6 ), in this case with 2025 well-localized matches in 2002 USRs using 113 di erent 7-mers and with a signal/background ratio of 3.1.... ..."

### Table 3: Iterative probability matrix based on best 2000 matches

"... In PAGE 4: ... Consequently, the algorithm was modified so that only the best 2000 USRs were used in defining the probabil- ity table on each cycle. A number of slightly different local maxima, with slightly less vari- ability were produced using this method (eg Table3 ), with a signal-to-background ratio of about 2.9.... ..."

### Table 4: Probability matrix based on M1 neighbors of TAAGGAG

"... In PAGE 6: ... With this assumption, plus the assumption that the po- sition probabilities are independent, each position can be varied and the frequency of each base measured. Ap- plying this to TAAGGAG provides an estimate of the probability table over all instances ( Table4 ). For a ran- domly chosen central k-mer, the resulting probability ta- ble will be close to the rst-order distribution of the data set.... ..."

### Table 5: Iterative probability matrix based on 4058 USR matches

"... In PAGE 7: ... Only best matches that were fully con- tained in the 20-bp USR were used since those are more apt to be real SD sites. Starting at TAAGGAG and us- ing its M1 probability table as the initial estimate, this process converged on Table5 in a few seconds. This ta- ble is based on 4058 instances.... ..."

### Table A: KVM one-year transition matrix based on non-overlapping EDF ranges

in or the International Financial Stability Programme of the L.S.E. or the University of Pompeu Fabra.

2002

### Table 2 Example Transition Probability Matrix based on the example multinomial logit coe cients.

1997

"... In PAGE 6: ... [14] from existing historical data stored in the GRASS database. The table of all probabilities generated by applying Equation (1) to all cover types is called the transition probability matrix (TPM), an ex- ample of which can be found in Table2 . If the TPM in Table 2 were used, for example, a random number from the closed interval [0; 1] less than 0.... ..."

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